From 85e510c150c43a549feb2d75f71c2b200f1816d8 Mon Sep 17 00:00:00 2001
From: mah-diaa <mahmoud.diaa.abdeen@gmail.com>
Date: Mon, 9 Mar 2026 16:10:25 +0100
Subject: [PATCH 01/39] Add face anonymization module for photogrammetry scans

---
 .../head_models/47_face_anonymization.ipynb   |  413 ++++++
 .../head_models/48_landmark_detection.ipynb   |  462 +++++++
 .../head_models/49_seam_diagnostics.ipynb     |  763 +++++++++++
 .../photogrammetry/anonymization/__init__.py  |  244 ++++
 .../anonymization/anonymizer.py               | 1147 +++++++++++++++++
 .../anonymization/face_detector.py            |  593 +++++++++
 .../photogrammetry/anonymization/ui.py        |  387 ++++++
 .../photogrammetry/anonymization/validator.py |  412 ++++++
 tests/test_anonymization.py                   |  587 +++++++++
 9 files changed, 5008 insertions(+)
 create mode 100644 examples/head_models/47_face_anonymization.ipynb
 create mode 100644 examples/head_models/48_landmark_detection.ipynb
 create mode 100644 examples/head_models/49_seam_diagnostics.ipynb
 create mode 100644 src/cedalion/geometry/photogrammetry/anonymization/__init__.py
 create mode 100644 src/cedalion/geometry/photogrammetry/anonymization/anonymizer.py
 create mode 100644 src/cedalion/geometry/photogrammetry/anonymization/face_detector.py
 create mode 100644 src/cedalion/geometry/photogrammetry/anonymization/ui.py
 create mode 100644 src/cedalion/geometry/photogrammetry/anonymization/validator.py
 create mode 100644 tests/test_anonymization.py

diff --git a/examples/head_models/47_face_anonymization.ipynb b/examples/head_models/47_face_anonymization.ipynb
new file mode 100644
index 00000000..d841b80a
--- /dev/null
+++ b/examples/head_models/47_face_anonymization.ipynb
@@ -0,0 +1,413 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "cell-0",
+   "metadata": {},
+   "source": [
+    "# Face Anonymization - Sticker Detection Debug\n",
+    "\n",
+    "This notebook demonstrates the current state of sticker detection for photogrammetry scans."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "cell-1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pyvista as pv\n",
+    "\n",
+    "import cedalion\n",
+    "import cedalion.io\n",
+    "import cedalion.plots\n",
+    "from cedalion import units\n",
+    "from cedalion.geometry.photogrammetry.processors import ColoredStickerProcessor\n",
+    "\n",
+    "pv.set_jupyter_backend(\"server\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-2",
+   "metadata": {},
+   "source": [
+    "## 1. Load and View Scan"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "cell-3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded: 450,295 vertices, 848,480 faces\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7c0c4b6c0fa64f4ea72bc4beb00d1000",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Widget(value='<iframe src=\"http://localhost:42449/index.html?ui=P_0x7f02bc9f7e10_2&reconnect=auto\" class=\"pyvi…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "SUBJECT_NUMBER = 11\n",
+    "SCANS_FOLDER = \"/home/ma7/BA/PG_Subjects11-15\"\n",
+    "\n",
+    "scan_path = f\"{SCANS_FOLDER}/Subject{SUBJECT_NUMBER}/Subject{SUBJECT_NUMBER}.obj\"\n",
+    "surface = cedalion.io.read_einstar_obj(scan_path)\n",
+    "\n",
+    "print(f\"Loaded: {surface.nvertices:,} vertices, {surface.nfaces:,} faces\")\n",
+    "\n",
+    "# Visualize the scan\n",
+    "pvplt = pv.Plotter()\n",
+    "cedalion.plots.plot_surface(pvplt, surface, opacity=1.0)\n",
+    "pvplt.add_text(f\"Subject {SUBJECT_NUMBER} - Original Scan\", position=\"upper_left\", font_size=14)\n",
+    "pvplt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-4",
+   "metadata": {},
+   "source": [
+    "## 2. Sticker Detection\n",
+    "\n",
+    "We need to detect 5 colored stickers placed at landmark positions (Nz, Iz, Cz, LPA, RPA)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "cell-5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[-43.676117 -26.677429 691.970337]\n",
+      " [148.323853 107.722595 443.313904]\n",
+      " [193.123901  82.922577 443.702026]\n",
+      " ...\n",
+      " [150.723877 106.122559 443.925629]\n",
+      " [135.858337 113.587158 443.246857]\n",
+      " [135.311035 113.214905 443.769348]]\n",
+      "[[-102.876129  152.522583  520.006531]\n",
+      " [-103.676117  152.062683  519.958191]\n",
+      " [ 162.220581   -7.773804  486.627808]\n",
+      " [ 163.123901   -6.277435  486.89386 ]\n",
+      " [ 162.723877   -6.677429  486.865021]\n",
+      " [ 162.323853   -7.077423  486.891724]\n",
+      " [ 161.923889   -7.077423  487.043976]\n",
+      " [ 161.923889   -7.477448  486.849548]\n",
+      " [ 161.523865   -7.877441  486.886047]\n",
+      " [ 161.123901   -8.277435  486.878723]\n",
+      " [ 164.323853   -6.277435  486.448547]\n",
+      " [ 163.923889   -6.277435  486.554749]\n",
+      " [ 163.523865   -6.277435  486.670349]\n",
+      " [ 162.723877   -7.077423  486.694794]\n",
+      " [ 162.723877   -7.477448  486.558472]\n",
+      " [ 162.323853   -7.477448  486.696259]\n",
+      " [ 164.723877   -5.077423  486.757751]\n",
+      " [ 165.123901   -5.477448  486.393524]\n",
+      " [ 164.723877   -5.477448  486.577332]\n",
+      " [ 164.723877   -5.877441  486.391174]\n",
+      " [ 161.923889   -7.877441  486.719604]\n",
+      " [ 164.323853   -5.477448  486.744751]\n",
+      " [ 161.523865   -9.077423  486.448669]\n",
+      " [ 161.123901   -8.677429  486.692078]\n",
+      " [ 164.323853   -5.877441  486.632019]\n",
+      " [ 163.923889   -5.877441  486.749207]\n",
+      " [ 161.123901   -9.077423  486.556274]\n",
+      " [ 160.723877   -9.477448  486.564087]\n",
+      " [ 161.923889   -9.077423  486.253479]\n",
+      " [ 165.923889   -5.477448  486.094543]\n",
+      " [ 165.523865   -5.477448  486.202179]\n",
+      " [ 165.523865   -5.877441  486.04895 ]\n",
+      " [ 163.923889   -6.677429  486.346619]\n",
+      " [ 116.323853  -69.084381  427.158173]\n",
+      " [ 111.523865  -67.517059  427.958191]\n",
+      " [ 117.123901  -69.145233  427.958191]\n",
+      " [ 113.123871  -68.005951  427.958191]\n",
+      " [ 113.923859  -68.253418  427.958191]\n",
+      " [ 110.723877  -67.037964  428.758179]\n",
+      " [ 112.323883  -67.543091  428.758179]\n",
+      " [ 117.123901  -69.036407  428.758179]\n",
+      " [ 117.923889  -69.162567  428.758179]\n",
+      " [ 111.523865  -67.27359   428.758179]\n",
+      " [ 113.123871  -67.788635  428.758179]\n",
+      " [ 111.523865  -67.477448  428.061829]\n",
+      " [ 118.723877  -69.140747  429.558167]\n",
+      " [ 117.923889  -69.017731  429.558167]\n",
+      " [ 112.323883  -67.305847  429.558167]\n",
+      " [ 110.723877  -66.814514  429.558167]\n",
+      " [ 111.523865  -67.061646  429.558167]\n",
+      " [ 116.323853  -68.38028   430.358185]\n",
+      " [ 116.723877  -68.517059  430.358185]\n",
+      " [ 117.523865  -68.789337  430.358185]\n",
+      " [ 117.923889  -68.910431  430.358185]\n",
+      " [ 118.323853  -68.983276  430.358185]\n",
+      " [ 118.723877  -69.048492  430.358185]\n",
+      " [ 119.123901  -69.040161  430.358185]\n",
+      " [ 109.923859  -66.337341  430.358185]\n",
+      " [ 116.021606  -68.277435  430.358185]\n",
+      " [ 115.523865  -68.124512  430.358185]\n",
+      " [ 114.723877  -67.875946  430.358185]\n",
+      " [ 118.723877  -69.077423  430.135834]\n",
+      " [ 118.316895  -69.019135  430.054474]\n",
+      " [ 119.123901  -69.058014  430.293396]\n",
+      " [ 116.323853  -68.285187  430.758179]\n",
+      " [ 116.723877  -68.416229  430.758179]\n",
+      " [ 117.123901  -68.563263  430.758179]\n",
+      " [ 117.523865  -68.743225  430.758179]\n",
+      " [ 117.923889  -68.855743  430.758179]\n",
+      " [ 118.323853  -68.950348  430.758179]\n",
+      " [ 118.723877  -68.967194  430.758179]\n",
+      " [ 119.123901  -68.92395   430.758179]\n",
+      " [ 116.723877  -68.326813  431.158173]\n",
+      " [ 117.123901  -68.491058  431.158173]\n",
+      " [ 117.923889  -68.784821  431.158173]\n",
+      " [ 117.523865  -68.650146  431.158173]\n",
+      " [ 118.323853  -68.853577  431.158173]\n",
+      " [ 118.723877  -68.884338  431.158173]\n",
+      " [ 116.323853  -68.195801  431.158173]\n",
+      " [ 119.123901  -68.837219  431.158173]\n",
+      " [ 111.523865  -66.601807  431.158173]\n",
+      " [ 115.523865  -67.943146  431.158173]\n",
+      " [ 114.723877  -67.676819  431.158173]\n",
+      " [ 117.923889  -68.686584  431.558167]\n",
+      " [ 117.523865  -68.54895   431.558167]\n",
+      " [ 118.323853  -68.760529  431.558167]\n",
+      " [ 118.723877  -68.792755  431.558167]\n",
+      " [ 116.323853  -68.08902   431.558167]\n",
+      " [ 119.123901  -68.764648  431.558167]\n",
+      " [ 117.123901  -68.291626  431.958191]\n",
+      " [ 117.523865  -68.443359  431.958191]\n",
+      " [ 117.923889  -68.622223  431.958191]\n",
+      " [ 118.323853  -68.704681  431.958191]\n",
+      " [ 116.723877  -68.101135  431.958191]\n",
+      " [ 119.123901  -68.713287  431.958191]\n",
+      " [ 113.123871  -66.942139  431.958191]\n",
+      " [ 111.523865  -66.383362  431.958191]\n",
+      " [ 112.323883  -66.659912  431.958191]\n",
+      " [ 116.003845  -67.987854  431.558167]\n",
+      " [ 118.323853  -68.617798  432.358185]\n",
+      " [ 116.723877  -68.029297  432.358185]\n",
+      " [ 117.123901  -68.189606  432.358185]\n",
+      " [ 117.523865  -68.369049  432.358185]\n",
+      " [ 118.323853  -68.529114  432.758179]\n",
+      " [ 118.723877  -68.539978  432.758179]\n",
+      " [ 117.523865  -68.273987  432.758179]\n",
+      " [ 110.723877  -65.952484  432.758179]\n",
+      " [ 112.323883  -66.442017  432.758179]\n",
+      " [ 113.123871  -66.736084  432.758179]\n",
+      " [ 113.923859  -66.978943  432.758179]\n",
+      " [ 114.723877  -67.243286  432.758179]\n",
+      " [ 117.523865  -68.162018  433.158173]\n",
+      " [ 117.923889  -68.271759  433.158173]]\n",
+      "G (0.2, 0.45, 0.5, 1.0)\n",
+      "[[-43.676117 -26.677429 691.970337]\n",
+      " [148.323853 107.722595 443.313904]\n",
+      " [193.123901  82.922577 443.702026]\n",
+      " ...\n",
+      " [150.723877 106.122559 443.925629]\n",
+      " [135.858337 113.587158 443.246857]\n",
+      " [135.311035 113.214905 443.769348]]\n",
+      "[[124.323853  20.522552 328.472412]\n",
+      " [115.076172 -63.375671 443.687561]\n",
+      " [117.923889  24.522552 329.109833]\n",
+      " ...\n",
+      " [180.323853  92.519257 443.958191]\n",
+      " [115.523865 -63.477448 443.934387]\n",
+      " [115.523865 -63.312469 444.358185]]\n",
+      "B (0.5, 0.75, 0.5, 1.0)\n",
+      "[2.40791901 1.34241165 1.06550735]\n",
+      "4.155608978271484\n",
+      "surface.crs digitized\n",
+      "Detected 3 stickers (expected: 5)\n",
+      "\n",
+      "Sticker positions:\n",
+      "  1: X=  177.4, Y=   90.4, Z=  448.4\n",
+      "  2: X=  115.4, Y=  -64.1, Z=  442.0\n",
+      "  3: X=  117.2, Y=  -68.5, Z=  430.8\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Sticker detection with current parameters\n",
+    "# Colors: (hue_min, hue_max, value_min, value_max)\n",
+    "STICKER_COLORS = {\n",
+    "    \"G\": (0.20, 0.45, 0.5, 1.0),   # Green\n",
+    "    \"B\": (0.50, 0.75, 0.5, 1.0),   # Blue  \n",
+    "}\n",
+    "\n",
+    "processor = ColoredStickerProcessor(\n",
+    "    colors=STICKER_COLORS,\n",
+    "    sticker_radius=6.5 * units.mm,\n",
+    "    saturation_min=0.6,\n",
+    "    min_nvertices=50,\n",
+    ")\n",
+    "\n",
+    "sticker_positions, sticker_normals = processor.process(surface)\n",
+    "positions = sticker_positions.pint.dequantify().values\n",
+    "\n",
+    "print(f\"Detected {len(positions)} stickers (expected: 5)\")\n",
+    "print(f\"\\nSticker positions:\")\n",
+    "for i, pos in enumerate(positions):\n",
+    "    print(f\"  {i+1}: X={pos[0]:7.1f}, Y={pos[1]:7.1f}, Z={pos[2]:7.1f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "cell-6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f65a86ab50d74a2495a473ee80e4adcc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Widget(value='<iframe src=\"http://localhost:46441/index.html?ui=P_0x7f12d4592450_2&reconnect=auto\" class=\"pyvi…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualize detected stickers on the scan\n",
+    "pvplt = pv.Plotter()\n",
+    "cedalion.plots.plot_surface(pvplt, surface, opacity=0.7)\n",
+    "\n",
+    "if len(positions) > 0:\n",
+    "    sticker_cloud = pv.PolyData(positions)\n",
+    "    pvplt.add_mesh(sticker_cloud, color=\"red\", point_size=20, render_points_as_spheres=True)\n",
+    "\n",
+    "pvplt.add_text(f\"Detected {len(positions)} stickers (expected: 5)\", position=\"upper_left\", font_size=14)\n",
+    "pvplt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6wzetxbvm7u",
+   "metadata": {},
+   "source": [
+    "## 3. Debug: Analyze Colored Vertices\n",
+    "\n",
+    "Let's examine the HSV distribution of colored vertices to understand why detection is failing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ivljp3uywa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import colorsys\n",
+    "\n",
+    "vertex_colors = surface.mesh.visual.to_color().vertex_colors\n",
+    "vertices = surface.mesh.vertices\n",
+    "\n",
+    "# Convert RGB to HSV\n",
+    "rgb_to_hsv = np.vectorize(colorsys.rgb_to_hsv)\n",
+    "h, s, v = rgb_to_hsv(vertex_colors[:, 0], vertex_colors[:, 1], vertex_colors[:, 2])\n",
+    "v = v / 255.0  # Normalize value to 0-1\n",
+    "\n",
+    "# Find highly saturated vertices (likely stickers)\n",
+    "high_sat_mask = (s > 0.5) & (v > 0.3)\n",
+    "print(f\"Highly saturated vertices (s>0.5, v>0.3): {high_sat_mask.sum():,}\")\n",
+    "\n",
+    "# Analyze by hue ranges\n",
+    "print(\"\\nHue distribution of saturated vertices:\")\n",
+    "print(f\"  Green (0.20-0.45): {((h >= 0.20) & (h <= 0.45) & high_sat_mask).sum():,}\")\n",
+    "print(f\"  Blue (0.55-0.70):  {((h >= 0.55) & (h <= 0.70) & high_sat_mask).sum():,}\")\n",
+    "print(f\"  Red (0.0-0.08):    {((h >= 0.0) & (h <= 0.08) & high_sat_mask).sum():,}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "xbq0br7vjy",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Visualize ALL colored vertices to see where the \"stickers\" actually are\n",
+    "green_mask = (h >= 0.20) & (h <= 0.45) & (s > 0.5) & (v > 0.3)\n",
+    "blue_mask = (h >= 0.55) & (h <= 0.70) & (s > 0.5) & (v > 0.3)\n",
+    "\n",
+    "pvplt = pv.Plotter()\n",
+    "cedalion.plots.plot_surface(pvplt, surface, opacity=0.5)\n",
+    "\n",
+    "# Show green vertices\n",
+    "if green_mask.sum() > 0:\n",
+    "    green_pts = pv.PolyData(vertices[green_mask])\n",
+    "    pvplt.add_mesh(green_pts, color=\"green\", point_size=5, render_points_as_spheres=True)\n",
+    "\n",
+    "# Show blue vertices  \n",
+    "if blue_mask.sum() > 0:\n",
+    "    blue_pts = pv.PolyData(vertices[blue_mask])\n",
+    "    pvplt.add_mesh(blue_pts, color=\"blue\", point_size=5, render_points_as_spheres=True)\n",
+    "\n",
+    "pvplt.add_text(f\"Green: {green_mask.sum():,}  Blue: {blue_mask.sum():,}\", position=\"upper_left\", font_size=14)\n",
+    "pvplt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-7",
+   "metadata": {},
+   "source": [
+    "## 4. Discussion Points for Professor\n",
+    "\n",
+    "**Current Issue:** The sticker detection is not reliably finding exactly 5 stickers.\n",
+    "\n",
+    "**Questions to discuss:**\n",
+    "1. What colors are the actual stickers on the scan? (Green/Blue/Red?)\n",
+    "2. Are there other colored objects in the scan causing false positives?\n",
+    "3. Should we use a different detection method (e.g., geometric shape instead of color)?"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/head_models/48_landmark_detection.ipynb b/examples/head_models/48_landmark_detection.ipynb
new file mode 100644
index 00000000..a8770933
--- /dev/null
+++ b/examples/head_models/48_landmark_detection.ipynb
@@ -0,0 +1,462 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "cell-0",
+   "metadata": {},
+   "source": [
+    "# Landmark Detection — Click Nz, Auto-Detect Rest\n",
+    "\n",
+    "Semi-manual landmark detection: the user clicks Nz (nasion) on the mesh,\n",
+    "then the system auto-detects Iz, LPA, RPA, and Cz from the mesh geometry.\n",
+    "This replaces unreliable sticker-based detection."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "cell-1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pyvista as pv\n",
+    "\n",
+    "import cedalion\n",
+    "import cedalion.io\n",
+    "import cedalion.plots\n",
+    "from cedalion import units\n",
+    "from cedalion.geometry.photogrammetry.anonymization import (\n",
+    "    detect_landmarks_from_nasion,\n",
+    "    anonymize_scan,\n",
+    "    AnonymizationConfig,\n",
+    "    AnonymizationMethod,\n",
+    ")\n",
+    "\n",
+    "pv.set_jupyter_backend(\"server\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-2",
+   "metadata": {},
+   "source": [
+    "## 1. Load Scan"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "cell-3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded: 624,121 vertices, 1,169,802 faces\n"
+     ]
+    }
+   ],
+   "source": [
+    "SUBJECT_NUMBER = 12\n",
+    "SCANS_FOLDER = \"/home/ma7/BA/PG_Subjects11-15\"\n",
+    "\n",
+    "scan_path = f\"{SCANS_FOLDER}/Subject{SUBJECT_NUMBER}/Subject{SUBJECT_NUMBER}.obj\"\n",
+    "surface = cedalion.io.read_einstar_obj(scan_path)\n",
+    "\n",
+    "print(f\"Loaded: {surface.nvertices:,} vertices, {surface.nfaces:,} faces\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-4",
+   "metadata": {},
+   "source": [
+    "## 2. Click Nz on the Mesh\n",
+    "\n",
+    "Click the nasion (bridge of the nose, between the eyebrows) on the mesh.\n",
+    "The clicked point is stored in `nz_store`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "cell-5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Nz selected at: X=147.0, Y=116.2, Z=483.8\n",
+      "Nz selected at: X=107.6, Y=151.1, Z=428.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "nz_store = {}\n",
+    "\n",
+    "def on_pick(point):\n",
+    "    \"\"\"Callback for surface point picking.\"\"\"\n",
+    "    nz_store[\"point\"] = np.array(point)\n",
+    "    print(f\"Nz selected at: X={point[0]:.1f}, Y={point[1]:.1f}, Z={point[2]:.1f}\")\n",
+    "\n",
+    "# Use a pop-up window — callbacks don't work with the jupyter server backend\n",
+    "pvplt = pv.Plotter(notebook=False)\n",
+    "cedalion.plots.plot_surface(pvplt, surface, opacity=1.0)\n",
+    "pvplt.enable_surface_point_picking(\n",
+    "    callback=on_pick,\n",
+    "    left_clicking=True,\n",
+    "    show_point=True,\n",
+    "    point_size=20,\n",
+    "    color=\"red\",\n",
+    "    show_message=\"Left-click Nz (nasion), then close the window\",\n",
+    ")\n",
+    "pvplt.add_text(\"Left-click Nz (nasion), then close window\", position=\"upper_left\", font_size=14)\n",
+    "pvplt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "9co7899wms6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Nz stored: [107.57456031 151.09103956 428.04108405]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# If clicking didn't work, manually set Nz coordinates here.\n",
+    "# Hover over the nasion in the plot above to read coordinates, then uncomment:\n",
+    "# nz_store[\"point\"] = np.array([X, Y, Z])\n",
+    "\n",
+    "# Check if Nz was captured:\n",
+    "if \"point\" in nz_store:\n",
+    "    print(f\"Nz stored: {nz_store['point']}\")\n",
+    "else:\n",
+    "    print(\"Nz NOT stored yet. Either click above or set manually in this cell.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-6",
+   "metadata": {},
+   "source": [
+    "## 3. Auto-Detect Remaining Landmarks\n",
+    "\n",
+    "From the clicked Nz, the algorithm detects Cz (vertex), Iz (inion),\n",
+    "LPA (left preauricular), and RPA (right preauricular) using mesh geometry."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "cell-7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Nz position: [107.57456031 151.09103956 428.04108405]\n",
+      "\n",
+      "Detected landmarks:\n",
+      "  Nz: X=  107.6, Y=  151.1, Z=  428.0\n",
+      "  Iz: X=  118.4, Y=  -46.2, Z=  439.6\n",
+      "  Cz: X=  204.9, Y=   42.6, Z=  437.6\n",
+      "  LPA: X=  102.4, Y=   47.4, Z=  532.4\n",
+      "  RPA: X=   80.0, Y=   32.6, Z=  350.6\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Use stored Nz or fall back to manual coordinates for testing\n",
+    "if \"point\" in nz_store:\n",
+    "    nz_point = nz_store[\"point\"]\n",
+    "else:\n",
+    "    # Fallback: approximate Nz for Subject11 (from visual inspection)\n",
+    "    # Replace with actual clicked coordinates\n",
+    "    nz_point = np.array([152.0, 120.0, 445.0])\n",
+    "    print(f\"WARNING: Using fallback Nz. Click the mesh in the cell above first.\")\n",
+    "\n",
+    "print(f\"Nz position: {nz_point}\")\n",
+    "\n",
+    "landmarks = detect_landmarks_from_nasion(surface, nz_point)\n",
+    "\n",
+    "print(f\"\\nDetected landmarks:\")\n",
+    "for label in landmarks.label.values:\n",
+    "    pos = landmarks.sel(label=label).pint.dequantify().values\n",
+    "    print(f\"  {label}: X={pos[0]:7.1f}, Y={pos[1]:7.1f}, Z={pos[2]:7.1f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-8",
+   "metadata": {},
+   "source": [
+    "## 4. Visualize Landmarks\n",
+    "\n",
+    "All 5 landmarks displayed as colored spheres on the mesh."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "cell-9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fc6047ac10dc46dba64cd357be19bbc2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Widget(value='<iframe src=\"http://localhost:45487/index.html?ui=P_0x7f2bbf64e290_9&reconnect=auto\" class=\"pyvi…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "LANDMARK_COLORS = {\n",
+    "    \"Nz\": \"red\",\n",
+    "    \"Iz\": \"blue\",\n",
+    "    \"Cz\": \"green\",\n",
+    "    \"LPA\": \"yellow\",\n",
+    "    \"RPA\": \"orange\",\n",
+    "}\n",
+    "\n",
+    "pvplt = pv.Plotter()\n",
+    "cedalion.plots.plot_surface(pvplt, surface, opacity=0.7)\n",
+    "\n",
+    "for label in landmarks.label.values:\n",
+    "    pos = landmarks.sel(label=label).pint.dequantify().values\n",
+    "    sphere = pv.Sphere(radius=5.0, center=pos)\n",
+    "    color = LANDMARK_COLORS.get(str(label), \"white\")\n",
+    "    pvplt.add_mesh(sphere, color=color)\n",
+    "    pvplt.add_point_labels(\n",
+    "        [pos + np.array([8, 0, 0])],\n",
+    "        [str(label)],\n",
+    "        font_size=16,\n",
+    "        text_color=color,\n",
+    "        bold=True,\n",
+    "        shape=None,\n",
+    "    )\n",
+    "\n",
+    "pvplt.add_text(\"Detected Landmarks\", position=\"upper_left\", font_size=14)\n",
+    "pvplt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "nrtesre8esa",
+   "metadata": {},
+   "source": [
+    "## 5. Preview Facial Region Mask\n",
+    "\n",
+    "Compute the T-shaped facial mask and visualize it as a red overlay.\n",
+    "Red vertices will be anonymized; grey vertices remain untouched."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "ewvr5w2rukq",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Facial mask: 58,519 / 624,121 vertices (9.4%)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3df9f52af1724869883b659ba320f574",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Widget(value='<iframe src=\"http://localhost:45487/index.html?ui=P_0x7f2be2e23890_10&reconnect=auto\" class=\"pyv…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from cedalion.geometry.photogrammetry.anonymization import (\n",
+    "    detect_facial_landmarks,\n",
+    "    get_facial_region_mask,\n",
+    ")\n",
+    "from cedalion.dataclasses import VTKSurface\n",
+    "\n",
+    "# Detect facial landmarks (geometric estimates from anatomical landmarks)\n",
+    "facial_landmarks = detect_facial_landmarks(surface, landmarks)\n",
+    "\n",
+    "# Compute the T-shaped facial mask\n",
+    "facial_mask = get_facial_region_mask(\n",
+    "    surface=surface,\n",
+    "    facial_landmarks=facial_landmarks,\n",
+    "    protected_points=landmarks,\n",
+    "    protection_radius=15.0 * units.mm,\n",
+    ")\n",
+    "\n",
+    "print(f\"Facial mask: {facial_mask.sum():,} / {len(facial_mask):,} vertices \"\n",
+    "      f\"({100 * facial_mask.sum() / len(facial_mask):.1f}%)\")\n",
+    "\n",
+    "# Visualize: red = will be anonymized, grey = untouched\n",
+    "vtk_surface = VTKSurface.from_trimeshsurface(surface)\n",
+    "pv_mesh = pv.wrap(vtk_surface.mesh)\n",
+    "colors = np.full((len(facial_mask), 3), 200, dtype=np.uint8)  # grey\n",
+    "colors[facial_mask] = [255, 50, 50]  # red\n",
+    "pv_mesh.point_data[\"mask_colors\"] = colors\n",
+    "\n",
+    "pvplt = pv.Plotter()\n",
+    "pvplt.add_mesh(pv_mesh, scalars=\"mask_colors\", rgb=True, opacity=1.0)\n",
+    "pvplt.add_text(\"Facial Region (red = anonymized)\", position=\"upper_left\", font_size=14)\n",
+    "pvplt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-10",
+   "metadata": {},
+   "source": [
+    "## 6. Anonymize Facial Region\n",
+    "\n",
+    "Apply noise-based anonymization to destroy facial detail while preserving\n",
+    "head shape and protected landmark/optode positions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "cell-11",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Noise iterations: 80\n",
+      "Facial vertices:  58,519\n",
+      "Max displacement:  9.96 mm\n",
+      "Mean face disp:    1.08 mm\n",
+      "Median face disp:  0.96 mm\n"
+     ]
+    }
+   ],
+   "source": [
+    "# --- Anonymization strength ---\n",
+    "# Higher = more aggressive. 80 iterations is a good default.\n",
+    "# Try 40 for mild, 120+ for very aggressive.\n",
+    "NOISE_ITERATIONS = 80\n",
+    "\n",
+    "config = AnonymizationConfig(\n",
+    "    method=AnonymizationMethod.NOISE,\n",
+    "    noise_iterations=NOISE_ITERATIONS,\n",
+    ")\n",
+    "result = anonymize_scan(\n",
+    "    surface=surface,\n",
+    "    anatomical_landmarks=landmarks,\n",
+    "    config=config,\n",
+    "    interactive=False,\n",
+    "    validate=True,\n",
+    ")\n",
+    "\n",
+    "disp = result.vertex_displacements\n",
+    "face_disp = disp[result.facial_mask]\n",
+    "print(f\"Noise iterations: {NOISE_ITERATIONS}\")\n",
+    "print(f\"Facial vertices:  {result.facial_mask.sum():,}\")\n",
+    "print(f\"Max displacement:  {disp.max():.2f} mm\")\n",
+    "print(f\"Mean face disp:    {face_disp.mean():.2f} mm\")\n",
+    "print(f\"Median face disp:  {np.median(face_disp):.2f} mm\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-12",
+   "metadata": {},
+   "source": [
+    "## 7. Original vs Anonymized"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "cell-13",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6aa1380f49e44c44b28de990984e1048",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Widget(value='<iframe src=\"http://localhost:45487/index.html?ui=P_0x7f2bec707290_11&reconnect=auto\" class=\"pyv…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pvplt = pv.Plotter(shape=(1, 2))\n",
+    "\n",
+    "pvplt.subplot(0, 0)\n",
+    "cedalion.plots.plot_surface(pvplt, surface, opacity=1.0)\n",
+    "pvplt.add_text(\"Original\", position=\"upper_left\", font_size=14)\n",
+    "\n",
+    "pvplt.subplot(0, 1)\n",
+    "cedalion.plots.plot_surface(pvplt, result.anonymized_surface, opacity=1.0)\n",
+    "pvplt.add_text(f\"NOISE ({NOISE_ITERATIONS} iter)\", position=\"upper_left\", font_size=14)\n",
+    "\n",
+    "pvplt.link_views()\n",
+    "pvplt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "26d9ebb7-d500-4c49-b815-ff730dd9d033",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/head_models/49_seam_diagnostics.ipynb b/examples/head_models/49_seam_diagnostics.ipynb
new file mode 100644
index 00000000..1125dffc
--- /dev/null
+++ b/examples/head_models/49_seam_diagnostics.ipynb
@@ -0,0 +1,763 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "cell-md-0",
+   "metadata": {},
+   "source": [
+    "# Seam Diagnostics — Mesh Crack Investigation & Fix\n",
+    "\n",
+    "All anonymization methods produce identical dark \"crack\" lines on the face.\n",
+    "The cracks appear at the **same positions** regardless of method, proving the\n",
+    "cause is the **mesh structure** — duplicate vertices at patch boundaries from\n",
+    "Einstar's multi-patch stitching.\n",
+    "\n",
+    "This notebook:\n",
+    "1. Loads the scan, clicks Nz, auto-detects landmarks\n",
+    "2. Visualizes seam lines on the original mesh\n",
+    "3. Confirms seam lines match crack locations\n",
+    "4. Tests a vertex merge fix\n",
+    "5. Quantifies the merge\n",
+    "6. Validates that colors survive the merge"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "cell-1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded: 624,121 vertices, 1,169,802 faces\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pyvista as pv\n",
+    "import trimesh\n",
+    "from scipy.spatial import KDTree\n",
+    "\n",
+    "import cedalion\n",
+    "import cedalion.io\n",
+    "import cedalion.plots\n",
+    "from cedalion import units\n",
+    "from cedalion.dataclasses import VTKSurface\n",
+    "from cedalion.geometry.photogrammetry.anonymization import (\n",
+    "    detect_landmarks_from_nasion,\n",
+    "    anonymize_scan,\n",
+    "    AnonymizationConfig,\n",
+    "    AnonymizationMethod,\n",
+    ")\n",
+    "\n",
+    "pv.set_jupyter_backend(\"server\")\n",
+    "\n",
+    "# Load scan\n",
+    "SUBJECT_NUMBER = 12\n",
+    "SCANS_FOLDER = \"/home/ma7/BA/PG_Subjects11-15\"\n",
+    "scan_path = f\"{SCANS_FOLDER}/Subject{SUBJECT_NUMBER}/Subject{SUBJECT_NUMBER}.obj\"\n",
+    "surface = cedalion.io.read_einstar_obj(scan_path)\n",
+    "\n",
+    "mesh = surface.mesh\n",
+    "print(f\"Loaded: {len(mesh.vertices):,} vertices, {len(mesh.faces):,} faces\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "mr9hths8ifr",
+   "metadata": {},
+   "source": [
+    "## 1b. Click Nz on the Mesh\n",
+    "\n",
+    "Click the nasion (bridge of the nose, between the eyebrows) on the mesh.\n",
+    "The clicked point is stored in `nz_store`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "mmx5xjvc1t",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Nz selected at: X=113.7, Y=60.6, Z=523.9\n",
+      "Nz selected at: X=111.1, Y=150.8, Z=427.8\n"
+     ]
+    }
+   ],
+   "source": [
+    "nz_store = {}\n",
+    "\n",
+    "def on_pick(point):\n",
+    "    \"\"\"Callback for surface point picking.\"\"\"\n",
+    "    nz_store[\"point\"] = np.array(point)\n",
+    "    print(f\"Nz selected at: X={point[0]:.1f}, Y={point[1]:.1f}, Z={point[2]:.1f}\")\n",
+    "\n",
+    "# Use a pop-up window — callbacks don't work with the jupyter server backend\n",
+    "pvplt = pv.Plotter(notebook=False)\n",
+    "cedalion.plots.plot_surface(pvplt, surface, opacity=1.0)\n",
+    "pvplt.enable_surface_point_picking(\n",
+    "    callback=on_pick,\n",
+    "    left_clicking=True,\n",
+    "    show_point=True,\n",
+    "    point_size=20,\n",
+    "    color=\"red\",\n",
+    "    show_message=\"Left-click Nz (nasion), then close the window\",\n",
+    ")\n",
+    "pvplt.add_text(\"Left-click Nz (nasion), then close window\", position=\"upper_left\", font_size=14)\n",
+    "pvplt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "1jgm52mov6q",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Nz stored: [111.11239585 150.77507346 427.75947951]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# If clicking didn't work, manually set Nz coordinates here.\n",
+    "# Hover over the nasion in the plot above to read coordinates, then uncomment:\n",
+    "# nz_store[\"point\"] = np.array([X, Y, Z])\n",
+    "\n",
+    "if \"point\" in nz_store:\n",
+    "    print(f\"Nz stored: {nz_store['point']}\")\n",
+    "else:\n",
+    "    print(\"Nz NOT stored yet. Either click above or set manually in this cell.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aq5jbd62w1",
+   "metadata": {},
+   "source": [
+    "## 1c. Auto-Detect Remaining Landmarks\n",
+    "\n",
+    "From the clicked Nz, the algorithm detects Cz (vertex), Iz (inion),\n",
+    "LPA (left preauricular), and RPA (right preauricular) using mesh geometry."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "v2l0cbiz0on",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Nz position: [111.11239585 150.77507346 427.75947951]\n",
+      "\n",
+      "Detected landmarks:\n",
+      "  Nz: X=  111.1, Y=  150.8, Z=  427.8\n",
+      "  Iz: X=  118.4, Y=  -46.2, Z=  439.6\n",
+      "  Cz: X=  204.9, Y=   42.6, Z=  437.6\n",
+      "  LPA: X=  102.4, Y=   47.4, Z=  532.4\n",
+      "  RPA: X=   80.0, Y=   32.6, Z=  350.6\n"
+     ]
+    }
+   ],
+   "source": [
+    "nz_point = nz_store[\"point\"]\n",
+    "print(f\"Nz position: {nz_point}\")\n",
+    "\n",
+    "landmarks = detect_landmarks_from_nasion(surface, nz_point)\n",
+    "\n",
+    "print(f\"\\nDetected landmarks:\")\n",
+    "for label in landmarks.label.values:\n",
+    "    pos = landmarks.sel(label=label).pint.dequantify().values\n",
+    "    print(f\"  {label}: X={pos[0]:7.1f}, Y={pos[1]:7.1f}, Z={pos[2]:7.1f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-md-2",
+   "metadata": {},
+   "source": [
+    "## 2. Visualize Seam Lines on Original Mesh\n",
+    "\n",
+    "Boundary edges belong to only 1 face (instead of the normal 2). These mark\n",
+    "patch boundaries where the Einstar stitcher left topologically disconnected seams."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "cell-2",
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Boundary edges: 72,350\n",
+      "Total edges: 1,790,870\n",
+      "Boundary fraction: 4.0%\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bfeb15ea1daf45bb82375291e918bb0a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Widget(value='<iframe src=\"http://localhost:34739/index.html?ui=P_0x7f7f296f65d0_0&reconnect=auto\" class=\"pyvi…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Find boundary edges: edges that belong to only 1 face\n",
+    "# trimesh tracks this via edges_face — boundary edges have -1 for missing face\n",
+    "from collections import Counter\n",
+    "\n",
+    "# Count how many faces each edge belongs to\n",
+    "edge_to_face_count = Counter()\n",
+    "for face in mesh.faces:\n",
+    "    edges_in_face = [\n",
+    "        tuple(sorted((face[0], face[1]))),\n",
+    "        tuple(sorted((face[1], face[2]))),\n",
+    "        tuple(sorted((face[0], face[2]))),\n",
+    "    ]\n",
+    "    for e in edges_in_face:\n",
+    "        edge_to_face_count[e] += 1\n",
+    "\n",
+    "boundary_edges = [e for e, count in edge_to_face_count.items() if count == 1]\n",
+    "print(f\"Boundary edges: {len(boundary_edges):,}\")\n",
+    "print(f\"Total edges: {len(edge_to_face_count):,}\")\n",
+    "print(f\"Boundary fraction: {100 * len(boundary_edges) / len(edge_to_face_count):.1f}%\")\n",
+    "\n",
+    "# Extract boundary edge midpoints for visualization\n",
+    "boundary_edge_arr = np.array(boundary_edges)\n",
+    "midpoints = 0.5 * (mesh.vertices[boundary_edge_arr[:, 0]] + mesh.vertices[boundary_edge_arr[:, 1]])\n",
+    "\n",
+    "# Build line segments for pyvista\n",
+    "boundary_verts = mesh.vertices[boundary_edge_arr.ravel()]\n",
+    "n_edges = len(boundary_edges)\n",
+    "lines = np.column_stack([\n",
+    "    np.full(n_edges, 2),          # each line has 2 points\n",
+    "    np.arange(0, 2 * n_edges, 2), # start indices\n",
+    "    np.arange(1, 2 * n_edges, 2), # end indices\n",
+    "]).ravel()\n",
+    "boundary_polydata = pv.PolyData(boundary_verts, lines=lines)\n",
+    "\n",
+    "# Render mesh with boundary edges in red\n",
+    "pvplt = pv.Plotter()\n",
+    "cedalion.plots.plot_surface(pvplt, surface, opacity=0.6)\n",
+    "pvplt.add_mesh(boundary_polydata, color=\"red\", line_width=2, opacity=0.8)\n",
+    "pvplt.add_text(\n",
+    "    f\"Seam lines (boundary edges): {len(boundary_edges):,}\",\n",
+    "    position=\"upper_left\", font_size=14,\n",
+    ")\n",
+    "pvplt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-md-3",
+   "metadata": {},
+   "source": [
+    "## 3. Confirm: Seams Match Crack Locations\n",
+    "\n",
+    "Run smoothing **without** vertex merging (the old code path) to reproduce the\n",
+    "cracks, then compare side-by-side with the seam overlay. The cracks should\n",
+    "line up with the red seam lines."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "cell-3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cracked result (no merge, 300 iter uniform Taubin):\n",
+      "  Max displacement:  3.42 mm\n",
+      "  Mean face disp:    0.22 mm\n",
+      "  Median face disp:  0.16 mm\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4670371268f64dcc945fcb8d1fb43d28",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Widget(value='<iframe src=\"http://localhost:34739/index.html?ui=P_0x7f7ef9555750_1&reconnect=auto\" class=\"pyvi…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Run heat diffusion smoothing WITHOUT merging to reproduce cracks.\n",
+    "# This manually calls the internal functions, bypassing the merge step\n",
+    "# that anonymize_scan() now applies automatically.\n",
+    "from cedalion.geometry.photogrammetry.anonymization.anonymizer import (\n",
+    "    _heat_diffusion_smooth,\n",
+    "    _get_protected_vertex_indices,\n",
+    "    apply_boundary_transition,\n",
+    "    smooth_region_selective,\n",
+    ")\n",
+    "from cedalion.geometry.photogrammetry.anonymization import (\n",
+    "    detect_facial_landmarks,\n",
+    "    get_facial_region_mask,\n",
+    ")\n",
+    "from copy import deepcopy\n",
+    "import cedalion.dataclasses as cdc\n",
+    "\n",
+    "# Compute facial mask on original (unmerged) mesh\n",
+    "facial_lm = detect_facial_landmarks(surface, landmarks)\n",
+    "facial_mask = get_facial_region_mask(\n",
+    "    surface=surface,\n",
+    "    facial_landmarks=facial_lm,\n",
+    "    protected_points=landmarks,\n",
+    "    protection_radius=15.0 * units.mm,\n",
+    ")\n",
+    "\n",
+    "# Get protected indices on original mesh\n",
+    "protected_indices = _get_protected_vertex_indices(\n",
+    "    mesh, landmarks, protection_radius=15.0,\n",
+    ")\n",
+    "\n",
+    "# Run Taubin smoothing directly on the UNMERGED mesh (old code path → cracks)\n",
+    "# Use 300 iterations with uniform Laplacian for strong, visible smoothing\n",
+    "cracked_mesh = smooth_region_selective(\n",
+    "    mesh=mesh,\n",
+    "    mask=facial_mask,\n",
+    "    protected_indices=protected_indices,\n",
+    "    iterations=300,\n",
+    "    lamb=0.5,\n",
+    "    mu=-0.53,\n",
+    "    use_cotangent=False,  # uniform Laplacian — no auto-scaling dampening\n",
+    ")\n",
+    "cracked_mesh = apply_boundary_transition(\n",
+    "    mesh=cracked_mesh,\n",
+    "    mask=facial_mask,\n",
+    "    transition_width=10.0,\n",
+    "    protected_indices=protected_indices,\n",
+    "    iterations=10,\n",
+    "    lamb=0.3,\n",
+    ")\n",
+    "\n",
+    "cracked_surface = cdc.TrimeshSurface(\n",
+    "    mesh=cracked_mesh,\n",
+    "    crs=surface.crs,\n",
+    "    units=surface.units,\n",
+    ")\n",
+    "\n",
+    "# Check displacement\n",
+    "disp_cracked = np.linalg.norm(cracked_mesh.vertices - mesh.vertices, axis=1)\n",
+    "face_disp = disp_cracked[facial_mask]\n",
+    "print(f\"Cracked result (no merge, 300 iter uniform Taubin):\")\n",
+    "print(f\"  Max displacement:  {disp_cracked.max():.2f} mm\")\n",
+    "print(f\"  Mean face disp:    {face_disp.mean():.2f} mm\")\n",
+    "print(f\"  Median face disp:  {np.median(face_disp):.2f} mm\")\n",
+    "\n",
+    "# Side-by-side: original + seam overlay vs cracked result\n",
+    "pvplt = pv.Plotter(shape=(1, 2))\n",
+    "\n",
+    "pvplt.subplot(0, 0)\n",
+    "cedalion.plots.plot_surface(pvplt, surface, opacity=0.6)\n",
+    "pvplt.add_mesh(boundary_polydata, color=\"red\", line_width=2, opacity=0.8)\n",
+    "pvplt.add_text(\"Original + Seam Lines\", position=\"upper_left\", font_size=12)\n",
+    "\n",
+    "pvplt.subplot(0, 1)\n",
+    "cedalion.plots.plot_surface(pvplt, cracked_surface, opacity=1.0)\n",
+    "pvplt.add_text(\"300 iter Taubin, no merge (cracks)\", position=\"upper_left\", font_size=12)\n",
+    "\n",
+    "pvplt.link_views()\n",
+    "pvplt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-md-4",
+   "metadata": {},
+   "source": [
+    "## 4. Test Fix: Merge Vertices (via anonymize_scan)\n",
+    "\n",
+    "`anonymize_scan()` now automatically merges duplicate vertices before smoothing.\n",
+    "Run it normally and compare against the cracked result from cell 3."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "cell-4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Before merge: 624,121 vertices, 1,169,802 faces\n",
+      "After merge:  584,684 vertices, 1,169,260 faces\n",
+      "Vertices removed: 39,437\n",
+      "Faces removed (degenerate): 542\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Also do the manual merge here so cells 5 & 6 can use the stats\n",
+    "def merge_seam_vertices(mesh, tol=0.01):\n",
+    "    \"\"\"Merge spatially coincident vertices into single vertices.\"\"\"\n",
+    "    verts = mesh.vertices\n",
+    "    n = len(verts)\n",
+    "\n",
+    "    tree = KDTree(verts)\n",
+    "    pairs = tree.query_pairs(r=tol)\n",
+    "\n",
+    "    parent = np.arange(n)\n",
+    "\n",
+    "    def find(x):\n",
+    "        while parent[x] != x:\n",
+    "            parent[x] = parent[parent[x]]\n",
+    "            x = parent[x]\n",
+    "        return x\n",
+    "\n",
+    "    def union(a, b):\n",
+    "        ra, rb = find(a), find(b)\n",
+    "        if ra != rb:\n",
+    "            parent[ra] = rb\n",
+    "\n",
+    "    for a, b in pairs:\n",
+    "        union(a, b)\n",
+    "\n",
+    "    for i in range(n):\n",
+    "        parent[i] = find(i)\n",
+    "\n",
+    "    unique_roots, inverse = np.unique(parent, return_inverse=True)\n",
+    "    old_to_new = inverse\n",
+    "    n_new = len(unique_roots)\n",
+    "\n",
+    "    new_to_old = [[] for _ in range(n_new)]\n",
+    "    for old_idx in range(n):\n",
+    "        new_to_old[old_to_new[old_idx]].append(old_idx)\n",
+    "\n",
+    "    new_verts = np.zeros((n_new, 3))\n",
+    "    for new_idx in range(n_new):\n",
+    "        old_indices = new_to_old[new_idx]\n",
+    "        new_verts[new_idx] = verts[old_indices].mean(axis=0)\n",
+    "\n",
+    "    new_faces = old_to_new[mesh.faces]\n",
+    "    valid = (\n",
+    "        (new_faces[:, 0] != new_faces[:, 1])\n",
+    "        & (new_faces[:, 1] != new_faces[:, 2])\n",
+    "        & (new_faces[:, 0] != new_faces[:, 2])\n",
+    "    )\n",
+    "    new_faces = new_faces[valid]\n",
+    "\n",
+    "    new_colors = None\n",
+    "    if hasattr(mesh.visual, 'vertex_colors'):\n",
+    "        try:\n",
+    "            old_colors = mesh.visual.to_color().vertex_colors\n",
+    "            new_colors = np.zeros((n_new, 4), dtype=np.float64)\n",
+    "            for new_idx in range(n_new):\n",
+    "                old_indices = new_to_old[new_idx]\n",
+    "                new_colors[new_idx] = old_colors[old_indices].mean(axis=0)\n",
+    "            new_colors = new_colors.astype(np.uint8)\n",
+    "        except Exception:\n",
+    "            pass\n",
+    "\n",
+    "    merged_mesh = trimesh.Trimesh(vertices=new_verts, faces=new_faces, process=False)\n",
+    "    if new_colors is not None:\n",
+    "        merged_mesh.visual.vertex_colors = new_colors\n",
+    "\n",
+    "    return merged_mesh, old_to_new, new_to_old\n",
+    "\n",
+    "\n",
+    "merged_mesh, old_to_new, new_to_old = merge_seam_vertices(mesh, tol=0.01)\n",
+    "merged_surface = cdc.TrimeshSurface(mesh=merged_mesh, crs=surface.crs, units=surface.units)\n",
+    "\n",
+    "print(f\"Before merge: {len(mesh.vertices):,} vertices, {len(mesh.faces):,} faces\")\n",
+    "print(f\"After merge:  {len(merged_mesh.vertices):,} vertices, {len(merged_mesh.faces):,} faces\")\n",
+    "print(f\"Vertices removed: {len(mesh.vertices) - len(merged_mesh.vertices):,}\")\n",
+    "print(f\"Faces removed (degenerate): {len(mesh.faces) - len(merged_mesh.faces):,}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "cell-4b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fixed result (with merge, 300 iter uniform Taubin):\n",
+      "  Max displacement:  1.34 mm\n",
+      "  Mean face disp:    0.17 mm\n",
+      "  Median face disp:  0.15 mm\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6b79ad0ca7e2474dbdb954ac0f420441",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Widget(value='<iframe src=\"http://localhost:45781/index.html?ui=P_0x7f13f5d2c990_13&reconnect=auto\" class=\"pyv…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Run anonymize_scan() which now includes automatic vertex merging\n",
+    "# Use SMOOTH with uniform Laplacian and 300 iterations for strong visible effect\n",
+    "config = AnonymizationConfig(\n",
+    "    method=AnonymizationMethod.SMOOTH,\n",
+    "    smoothing_iterations=300,\n",
+    "    smoothing_lambda=0.5,\n",
+    "    smoothing_mu=-0.53,\n",
+    "    use_cotangent=False,  # uniform Laplacian — avoids cotangent auto-scaling\n",
+    ")\n",
+    "result_fixed = anonymize_scan(\n",
+    "    surface=surface,\n",
+    "    anatomical_landmarks=landmarks,\n",
+    "    config=config,\n",
+    "    interactive=False,\n",
+    "    validate=True,\n",
+    ")\n",
+    "\n",
+    "disp = result_fixed.vertex_displacements\n",
+    "face_disp = disp[result_fixed.facial_mask]\n",
+    "print(f\"Fixed result (with merge, 300 iter uniform Taubin):\")\n",
+    "print(f\"  Max displacement:  {disp.max():.2f} mm\")\n",
+    "print(f\"  Mean face disp:    {face_disp.mean():.2f} mm\")\n",
+    "print(f\"  Median face disp:  {np.median(face_disp):.2f} mm\")\n",
+    "\n",
+    "# Side-by-side: cracked (no merge) vs fixed (with merge)\n",
+    "pvplt = pv.Plotter(shape=(1, 2))\n",
+    "\n",
+    "pvplt.subplot(0, 0)\n",
+    "cedalion.plots.plot_surface(pvplt, cracked_surface, opacity=1.0)\n",
+    "pvplt.add_text(\"Without merge (cracks)\", position=\"upper_left\", font_size=12)\n",
+    "\n",
+    "pvplt.subplot(0, 1)\n",
+    "cedalion.plots.plot_surface(pvplt, result_fixed.anonymized_surface, opacity=1.0)\n",
+    "pvplt.add_text(\"With merge (fixed)\", position=\"upper_left\", font_size=12)\n",
+    "\n",
+    "pvplt.link_views()\n",
+    "pvplt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-md-5",
+   "metadata": {},
+   "source": [
+    "## 5. Quantify the Merge\n",
+    "\n",
+    "Print detailed statistics on the merge operation and verify mesh integrity."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "cell-5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "=== Merge Statistics ===\n",
+      "Vertices:      624,121 ->    584,684  (39,437 removed)\n",
+      "Faces:       1,169,802 ->  1,169,260  (542 degenerate removed)\n",
+      "Components:      3,715 ->         28\n",
+      "Boundary edges:  72,350 ->       0\n",
+      "\n",
+      "Degenerate faces (area < 1e-10): 0\n",
+      "Mesh is watertight: True\n",
+      "Total surface area: 396396.6 mm^2 (before) vs 396396.6 mm^2 (after)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Count connected components before and after\n",
+    "from scipy.sparse import csr_matrix\n",
+    "from scipy.sparse.csgraph import connected_components\n",
+    "\n",
+    "def count_components(m):\n",
+    "    \"\"\"Count connected components in a trimesh.\"\"\"\n",
+    "    n = len(m.vertices)\n",
+    "    edges = m.edges_unique\n",
+    "    rows = np.concatenate([edges[:, 0], edges[:, 1]])\n",
+    "    cols = np.concatenate([edges[:, 1], edges[:, 0]])\n",
+    "    data = np.ones(len(rows))\n",
+    "    A = csr_matrix((data, (rows, cols)), shape=(n, n))\n",
+    "    n_comp, _ = connected_components(A, directed=False)\n",
+    "    return n_comp\n",
+    "\n",
+    "n_comp_before = count_components(mesh)\n",
+    "n_comp_after = count_components(merged_mesh)\n",
+    "\n",
+    "# Count boundary edges after merge\n",
+    "edge_count_after = Counter()\n",
+    "for face in merged_mesh.faces:\n",
+    "    for i in range(3):\n",
+    "        e = tuple(sorted((face[i], face[(i + 1) % 3])))\n",
+    "        edge_count_after[e] += 1\n",
+    "boundary_after = sum(1 for c in edge_count_after.values() if c == 1)\n",
+    "\n",
+    "print(\"=== Merge Statistics ===\")\n",
+    "print(f\"Vertices:   {len(mesh.vertices):>10,} -> {len(merged_mesh.vertices):>10,}  ({len(mesh.vertices) - len(merged_mesh.vertices):,} removed)\")\n",
+    "print(f\"Faces:      {len(mesh.faces):>10,} -> {len(merged_mesh.faces):>10,}  ({len(mesh.faces) - len(merged_mesh.faces):,} degenerate removed)\")\n",
+    "print(f\"Components: {n_comp_before:>10,} -> {n_comp_after:>10,}\")\n",
+    "print(f\"Boundary edges: {len(boundary_edges):>7,} -> {boundary_after:>7,}\")\n",
+    "print()\n",
+    "\n",
+    "# Check for degenerate faces in merged mesh\n",
+    "areas = merged_mesh.area_faces\n",
+    "degenerate = (areas < 1e-10).sum()\n",
+    "print(f\"Degenerate faces (area < 1e-10): {degenerate}\")\n",
+    "print(f\"Mesh is watertight: {merged_mesh.is_watertight}\")\n",
+    "print(f\"Total surface area: {mesh.area:.1f} mm^2 (before) vs {merged_mesh.area:.1f} mm^2 (after)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-md-6",
+   "metadata": {},
+   "source": [
+    "## 6. Test: Does Merging Break Vertex Colors?\n",
+    "\n",
+    "Compare original vs merged mesh appearance (without any smoothing) to verify\n",
+    "that color averaging at seam positions preserves visual appearance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "cell-6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "69c330cc921540d99561f08ae5f144ef",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Widget(value='<iframe src=\"http://localhost:45781/index.html?ui=P_0x7f13f5d2fb50_7&reconnect=auto\" class=\"pyvi…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Could not compare colors: 'ColorVisuals' object has no attribute 'to_color'\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Render original vs merged mesh WITHOUT anonymization\n",
+    "pvplt = pv.Plotter(shape=(1, 2))\n",
+    "\n",
+    "pvplt.subplot(0, 0)\n",
+    "cedalion.plots.plot_surface(pvplt, surface, opacity=1.0)\n",
+    "pvplt.add_text(\"Original\", position=\"upper_left\", font_size=12)\n",
+    "\n",
+    "pvplt.subplot(0, 1)\n",
+    "cedalion.plots.plot_surface(pvplt, merged_surface, opacity=1.0)\n",
+    "pvplt.add_text(\"Merged (no smoothing)\", position=\"upper_left\", font_size=12)\n",
+    "\n",
+    "pvplt.link_views()\n",
+    "pvplt.show()\n",
+    "\n",
+    "# Quantify color difference at seam vertices\n",
+    "try:\n",
+    "    orig_colors = mesh.visual.to_color().vertex_colors[:, :3].astype(float)\n",
+    "    merged_colors = merged_mesh.visual.to_color().vertex_colors[:, :3].astype(float)\n",
+    "\n",
+    "    # For each merged vertex, compare its color to the average of original colors\n",
+    "    max_diffs = []\n",
+    "    for new_idx in range(len(merged_mesh.vertices)):\n",
+    "        old_indices = new_to_old[new_idx]\n",
+    "        if len(old_indices) > 1:  # only check actually merged vertices\n",
+    "            old_cols = orig_colors[old_indices]\n",
+    "            spread = old_cols.max(axis=0) - old_cols.min(axis=0)\n",
+    "            max_diffs.append(spread.max())\n",
+    "\n",
+    "    if max_diffs:\n",
+    "        max_diffs = np.array(max_diffs)\n",
+    "        print(f\"\\nColor spread at merged vertices (0-255 scale):\")\n",
+    "        print(f\"  Max spread:    {max_diffs.max():.1f}\")\n",
+    "        print(f\"  Mean spread:   {max_diffs.mean():.1f}\")\n",
+    "        print(f\"  Median spread: {np.median(max_diffs):.1f}\")\n",
+    "        print(f\"  Vertices with spread > 10: {(max_diffs > 10).sum()} / {len(max_diffs)}\")\n",
+    "    else:\n",
+    "        print(\"No merged vertices found.\")\n",
+    "except Exception as e:\n",
+    "    print(f\"Could not compare colors: {e}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "edf112e4-03c8-4d88-ad56-d8365fc2d84b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/__init__.py b/src/cedalion/geometry/photogrammetry/anonymization/__init__.py
new file mode 100644
index 00000000..88ea1a5e
--- /dev/null
+++ b/src/cedalion/geometry/photogrammetry/anonymization/__init__.py
@@ -0,0 +1,244 @@
+"""Face anonymization module for photogrammetry scans.
+
+This module provides tools to anonymize facial regions in 3D photogrammetry
+scans while preserving optode positions and anatomical landmarks for fNIRS
+research. The anonymization complies with GDPR requirements while maintaining
+scientific utility.
+
+Example:
+    >>> from cedalion.geometry.photogrammetry.anonymization import anonymize_scan
+    >>> result = anonymize_scan(surface, landmarks, optodes)
+    >>> anonymized = result.anonymized_surface
+
+Initial Contributors:
+    - Face Anonymization Project | 2024
+"""
+
+import logging
+import xarray as xr
+
+import cedalion.dataclasses as cdc
+import cedalion.typing as cdt
+from cedalion import Quantity, units
+
+from .face_detector import (
+    detect_facial_landmarks,
+    detect_landmarks_from_nasion,
+    get_facial_region_mask,
+    get_facial_region_center,
+    estimate_face_bounding_box,
+    FacialLandmarks,
+    FacialLandmarkType,
+    REQUIRED_LANDMARKS,
+)
+from .anonymizer import (
+    anonymize_facial_region,
+    smooth_region_selective,
+    apply_boundary_transition,
+    AnonymizationMethod,
+    AnonymizationConfig,
+    AnonymizationResult,
+)
+from .validator import (
+    validate_anonymization,
+    compute_point_deviations,
+    compute_surface_distance,
+    generate_validation_report,
+    ValidationMetrics,
+)
+from .ui import (
+    FacialRegionEditor,
+    AnonymizationPreview,
+    DisplacementViewer,
+    quick_preview,
+    quick_displacement_view,
+)
+
+
+logger = logging.getLogger("cedalion")
+
+
+def anonymize_scan(
+    surface: cdc.TrimeshSurface,
+    anatomical_landmarks: cdt.LabeledPointCloud = None,
+    optode_positions: cdt.LabeledPointCloud = None,
+    config: AnonymizationConfig = None,
+    interactive: bool = False,
+    validate: bool = True,
+    protection_radius: Quantity = 15.0 * units.mm,
+) -> AnonymizationResult:
+    """Anonymize a photogrammetry scan while preserving critical points.
+
+    Main entry point for the anonymization pipeline. This function:
+    1. Detects the facial region using geometric heuristics from landmarks
+    2. Creates protection zones around optodes and landmarks
+    3. Applies Taubin smoothing to the facial region
+    4. Optionally allows interactive refinement
+    5. Validates that protected points haven't moved
+
+    Args:
+        surface: Textured TrimeshSurface from photogrammetry
+        anatomical_landmarks: Known landmarks with at least Nz, Iz, LPA, RPA, Cz.
+            Use detect_landmarks_from_nasion() to obtain these from a clicked Nz.
+        optode_positions: Detected optode positions to protect (optional)
+        config: Anonymization configuration (uses defaults if None)
+        interactive: If True, allow user to refine facial region detection
+        validate: If True, run validation and log metrics
+        protection_radius: Radius around protected points that cannot be modified
+
+    Returns:
+        AnonymizationResult containing:
+        - anonymized_surface: The anonymized TrimeshSurface
+        - original_surface: The original surface (unchanged)
+        - facial_mask: Boolean mask of facial vertices
+        - vertex_displacements: How much each vertex moved
+        - config: Configuration used
+
+    Raises:
+        ValueError: If anatomical_landmarks is not provided
+
+    Example:
+        >>> from cedalion.geometry.photogrammetry.anonymization import (
+        ...     anonymize_scan, detect_landmarks_from_nasion,
+        ... )
+        >>>
+        >>> # Detect landmarks from a user-clicked Nz point
+        >>> landmarks = detect_landmarks_from_nasion(surface, nz_point)
+        >>> result = anonymize_scan(surface, anatomical_landmarks=landmarks)
+        >>>
+        >>> # Access the anonymized mesh
+        >>> anonymized_mesh = result.anonymized_surface
+    """
+    if config is None:
+        config = AnonymizationConfig()
+
+    logger.info("Starting face anonymization pipeline")
+    logger.info(f"Surface has {surface.nvertices} vertices, {surface.nfaces} faces")
+
+    # Landmarks must be provided
+    if anatomical_landmarks is None:
+        raise ValueError(
+            "anatomical_landmarks must be provided. Use "
+            "detect_landmarks_from_nasion(surface, nz_position) to detect "
+            "landmarks from a user-clicked nasion point."
+        )
+
+    # Step 1: Detect facial landmarks
+    logger.info("Detecting facial landmarks using geometric method")
+    facial_landmarks = detect_facial_landmarks(surface, anatomical_landmarks)
+    logger.info(
+        f"Detected {len(facial_landmarks.landmarks.label)} facial landmarks: "
+        f"{list(facial_landmarks.landmarks.label.values)}"
+    )
+
+    # Step 2: Combine all protected points
+    protected_points = anatomical_landmarks
+    if optode_positions is not None and len(optode_positions.label) > 0:
+        protected_points = xr.concat(
+            [anatomical_landmarks, optode_positions], dim="label"
+        )
+        logger.info(
+            f"Protecting {len(anatomical_landmarks.label)} landmarks and "
+            f"{len(optode_positions.label)} optodes"
+        )
+    else:
+        logger.info(f"Protecting {len(anatomical_landmarks.label)} landmarks (no optodes)")
+
+    # Step 3: Generate facial region mask
+    logger.info("Generating facial region mask")
+    facial_mask = get_facial_region_mask(
+        surface=surface,
+        facial_landmarks=facial_landmarks,
+        protected_points=protected_points,
+        protection_radius=protection_radius,
+    )
+    logger.info(
+        f"Facial region: {facial_mask.sum()} vertices "
+        f"({100 * facial_mask.sum() / len(facial_mask):.1f}%)"
+    )
+
+    # Step 4: Interactive refinement (optional)
+    if interactive:
+        logger.info("Opening interactive facial region editor")
+        editor = FacialRegionEditor(
+            surface=surface,
+            initial_mask=facial_mask,
+            protected_points=protected_points,
+            protection_radius=float(protection_radius.to("mm").magnitude),
+        )
+        facial_mask = editor.show()
+        logger.info(
+            f"Refined facial region: {facial_mask.sum()} vertices "
+            f"({100 * facial_mask.sum() / len(facial_mask):.1f}%)"
+        )
+
+    # Step 5: Apply anonymization
+    logger.info(
+        f"Applying {config.method.value} anonymization with "
+        f"{config.smoothing_iterations} iterations"
+    )
+    result = anonymize_facial_region(
+        surface=surface,
+        facial_mask=facial_mask,
+        protected_points=protected_points,
+        config=config,
+    )
+
+    # Step 6: Validation
+    if validate:
+        logger.info("Validating anonymization quality")
+        metrics = validate_anonymization(
+            original_surface=surface,
+            anonymized_surface=result.anonymized_surface,
+            facial_mask=facial_mask,
+            protected_points=protected_points,
+            tolerance=0.5 * units.mm,
+        )
+
+        if not metrics.protected_points_preserved:
+            logger.warning(
+                "Some protected points moved beyond tolerance. "
+                "Consider increasing protection_radius or reducing smoothing_iterations."
+            )
+
+        # Log summary
+        report = generate_validation_report(metrics)
+        for line in report.split("\n"):
+            logger.debug(line)
+
+    logger.info("Face anonymization complete")
+    return result
+
+
+__all__ = [
+    # High-level API
+    "anonymize_scan",
+    # Face detection
+    "detect_facial_landmarks",
+    "detect_landmarks_from_nasion",
+    "get_facial_region_mask",
+    "get_facial_region_center",
+    "estimate_face_bounding_box",
+    "FacialLandmarks",
+    "FacialLandmarkType",
+    "REQUIRED_LANDMARKS",
+    # Anonymization
+    "anonymize_facial_region",
+    "smooth_region_selective",
+    "apply_boundary_transition",
+    "AnonymizationMethod",
+    "AnonymizationConfig",
+    "AnonymizationResult",
+    # Validation
+    "validate_anonymization",
+    "compute_point_deviations",
+    "compute_surface_distance",
+    "generate_validation_report",
+    "ValidationMetrics",
+    # UI
+    "FacialRegionEditor",
+    "AnonymizationPreview",
+    "DisplacementViewer",
+    "quick_preview",
+    "quick_displacement_view",
+]
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/anonymizer.py b/src/cedalion/geometry/photogrammetry/anonymization/anonymizer.py
new file mode 100644
index 00000000..8eaa4cff
--- /dev/null
+++ b/src/cedalion/geometry/photogrammetry/anonymization/anonymizer.py
@@ -0,0 +1,1147 @@
+"""Mesh anonymization for photogrammetry scans.
+
+This module provides functions to anonymize facial regions of 3D meshes using
+Taubin smoothing while preserving optode and landmark positions.
+
+Initial Contributors:
+    - Face Anonymization Project | 2024
+"""
+
+from copy import deepcopy
+from dataclasses import dataclass, field
+from enum import Enum
+import logging
+
+import numpy as np
+from scipy import sparse
+from scipy.sparse.linalg import spsolve
+from scipy.spatial import KDTree
+import trimesh
+import xarray as xr
+
+import cedalion.dataclasses as cdc
+import cedalion.typing as cdt
+from cedalion import Quantity, units
+
+
+logger = logging.getLogger("cedalion")
+
+
+class AnonymizationMethod(Enum):
+    """Available anonymization techniques."""
+
+    SMOOTH = "smooth"
+    FLATTEN = "flatten"
+    COLOR_BLUR = "color_blur"
+    COMBINED = "combined"
+    HEAT_DIFFUSION = "heat_diffusion"
+    NOISE = "noise"
+
+
+@dataclass
+class AnonymizationConfig:
+    """Configuration for anonymization process.
+
+    Attributes:
+        method: Anonymization technique to use
+        smoothing_iterations: Number of Taubin filter iterations
+        smoothing_lambda: Taubin filter lambda parameter (step size)
+        smoothing_mu: Taubin filter mu parameter (shrinkage correction)
+        boundary_transition_width: Width of transition zone at mask boundary
+    """
+
+    method: AnonymizationMethod = AnonymizationMethod.SMOOTH
+    smoothing_iterations: int = 300
+    smoothing_lambda: float = 0.5
+    smoothing_mu: float = 0.0  # No shrinkage correction → pure Laplacian smoothing
+    boundary_transition_width: Quantity = field(
+        default_factory=lambda: 10.0 * units.mm
+    )
+    use_cotangent: bool = False
+    diffusion_time: float = 50.0
+    noise_strength: float = 1.0
+    noise_iterations: int = 80
+    noise_seed: int | None = None
+    flatten_strength: float = 0.7
+
+
+@dataclass
+class AnonymizationResult:
+    """Result of anonymization process.
+
+    Attributes:
+        anonymized_surface: The anonymized TrimeshSurface
+        original_surface: The original TrimeshSurface (unchanged)
+        facial_mask: Boolean mask of facial vertices
+        vertex_displacements: Displacement of each vertex (in mm)
+        config: Configuration used for anonymization
+    """
+
+    anonymized_surface: cdc.TrimeshSurface
+    original_surface: cdc.TrimeshSurface
+    facial_mask: np.ndarray
+    vertex_displacements: np.ndarray
+    config: AnonymizationConfig
+
+
+def _build_adjacency_matrix(mesh: trimesh.Trimesh) -> dict[int, set[int]]:
+    """Build vertex adjacency dictionary from mesh topology.
+
+    Args:
+        mesh: The trimesh object
+
+    Returns:
+        Dictionary mapping vertex index to set of neighbor indices
+    """
+    adjacency = {i: set() for i in range(len(mesh.vertices))}
+
+    for face in mesh.faces:
+        for i in range(3):
+            v1, v2 = face[i], face[(i + 1) % 3]
+            adjacency[v1].add(v2)
+            adjacency[v2].add(v1)
+
+    return adjacency
+
+
+def _compute_laplacian_displacement(
+    vertices: np.ndarray,
+    adjacency: dict[int, set[int]],
+) -> np.ndarray:
+    """Compute Laplacian displacement for each vertex.
+
+    The Laplacian displacement is the difference between a vertex and the
+    average of its neighbors.
+
+    Args:
+        vertices: Vertex positions array of shape (n_vertices, 3)
+        adjacency: Vertex adjacency dictionary
+
+    Returns:
+        Laplacian displacement array of shape (n_vertices, 3)
+    """
+    n_vertices = len(vertices)
+    displacement = np.zeros_like(vertices)
+
+    for i in range(n_vertices):
+        neighbors = adjacency[i]
+        if len(neighbors) > 0:
+            neighbor_avg = np.mean(vertices[list(neighbors)], axis=0)
+            displacement[i] = neighbor_avg - vertices[i]
+
+    return displacement
+
+
+def _merge_seam_vertices(
+    mesh: trimesh.Trimesh, tol: float = 0.01
+) -> tuple[trimesh.Trimesh, np.ndarray, list[list[int]]]:
+    """Merge spatially coincident vertices into single vertices.
+
+    Photogrammetry scans have duplicate vertices at patch boundaries. Merging
+    them before smoothing eliminates seam cracks because the Laplacian sees
+    a single connected mesh instead of disconnected patches.
+
+    Args:
+        mesh: The trimesh object with potential duplicate vertices
+        tol: Distance tolerance in mm for considering vertices coincident
+
+    Returns:
+        Tuple of (merged_mesh, old_to_new, new_to_old) where:
+        - merged_mesh: New trimesh with merged vertices and averaged colors
+        - old_to_new: Array mapping old vertex index to new vertex index
+        - new_to_old: List where new_to_old[i] = list of old indices that merged
+    """
+    verts = mesh.vertices
+    n = len(verts)
+
+    # Find groups of coincident vertices
+    tree = KDTree(verts)
+    pairs = tree.query_pairs(r=tol)
+
+    if not pairs:
+        # No duplicates — return copy with identity mapping
+        old_to_new = np.arange(n)
+        new_to_old = [[i] for i in range(n)]
+        return mesh.copy(), old_to_new, new_to_old
+
+    # Union-find to group coincident vertices
+    parent = np.arange(n)
+
+    def find(x):
+        while parent[x] != x:
+            parent[x] = parent[parent[x]]
+            x = parent[x]
+        return x
+
+    def union(a, b):
+        ra, rb = find(a), find(b)
+        if ra != rb:
+            parent[ra] = rb
+
+    for a, b in pairs:
+        union(a, b)
+
+    # Flatten parent pointers
+    for i in range(n):
+        parent[i] = find(i)
+
+    # Build compact index mapping
+    unique_roots, inverse = np.unique(parent, return_inverse=True)
+    old_to_new = inverse
+    n_new = len(unique_roots)
+
+    # Build reverse mapping
+    new_to_old = [[] for _ in range(n_new)]
+    for old_idx in range(n):
+        new_to_old[old_to_new[old_idx]].append(old_idx)
+
+    # Average positions of merged vertices
+    new_verts = np.zeros((n_new, 3))
+    for new_idx in range(n_new):
+        old_indices = new_to_old[new_idx]
+        new_verts[new_idx] = verts[old_indices].mean(axis=0)
+
+    # Re-index faces and remove degenerate triangles
+    new_faces = old_to_new[mesh.faces]
+    valid = (
+        (new_faces[:, 0] != new_faces[:, 1])
+        & (new_faces[:, 1] != new_faces[:, 2])
+        & (new_faces[:, 0] != new_faces[:, 2])
+    )
+    new_faces = new_faces[valid]
+
+    # Average vertex colors at merged positions
+    new_colors = None
+    if hasattr(mesh.visual, "vertex_colors"):
+        try:
+            old_colors = mesh.visual.to_color().vertex_colors  # (n, 4) uint8
+            new_colors = np.zeros((n_new, 4), dtype=np.float64)
+            for new_idx in range(n_new):
+                old_indices = new_to_old[new_idx]
+                new_colors[new_idx] = old_colors[old_indices].mean(axis=0)
+            new_colors = new_colors.astype(np.uint8)
+        except Exception:
+            pass
+
+    merged_mesh = trimesh.Trimesh(
+        vertices=new_verts, faces=new_faces, process=False
+    )
+    if new_colors is not None:
+        merged_mesh.visual.vertex_colors = new_colors
+
+    n_merged = n - n_new
+    n_degenerate = len(mesh.faces) - len(new_faces)
+    logger.info(
+        f"Merged {n_merged} duplicate vertices "
+        f"({n} -> {n_new}), removed {n_degenerate} degenerate faces"
+    )
+
+    return merged_mesh, old_to_new, new_to_old
+
+
+def _unmerge_vertices(
+    merged_mesh: trimesh.Trimesh,
+    original_mesh: trimesh.Trimesh,
+    old_to_new: np.ndarray,
+) -> trimesh.Trimesh:
+    """Expand merged mesh back to the original vertex count.
+
+    Copies each merged vertex position back to all the original vertices
+    that were merged into it. This preserves the original face topology
+    and vertex count for mask indexing compatibility.
+
+    Args:
+        merged_mesh: Mesh with merged vertices (from _merge_seam_vertices)
+        original_mesh: Original mesh with full vertex count
+        old_to_new: Mapping from original vertex indices to merged indices
+
+    Returns:
+        New trimesh with original vertex count and face topology, but with
+        vertex positions from the merged mesh
+    """
+    result_mesh = original_mesh.copy()
+    result_verts = result_mesh.vertices.copy()
+
+    # Copy merged positions back to all original vertices
+    result_verts = merged_mesh.vertices[old_to_new]
+
+    # Copy colors if available
+    if hasattr(merged_mesh.visual, "vertex_colors"):
+        try:
+            merged_colors = merged_mesh.visual.to_color().vertex_colors
+            result_colors = merged_colors[old_to_new]
+            result_mesh.visual.vertex_colors = result_colors
+        except Exception:
+            pass
+
+    result_mesh.vertices = result_verts
+    result_mesh._cache.clear()
+    return result_mesh
+
+
+def _find_seam_pairs(mesh: trimesh.Trimesh, tol: float = 0.01) -> np.ndarray:
+    """Find pairs of vertices at the same spatial position (seam vertices).
+
+    Photogrammetry scans are stitched from multiple patches, producing
+    duplicate vertices at shared boundaries. These vertices are spatially
+    coincident but topologically disconnected, causing the Laplacian to
+    treat each patch independently and producing visible crack lines.
+
+    Args:
+        mesh: The trimesh object
+        tol: Distance tolerance in mm for considering vertices coincident
+
+    Returns:
+        Array of shape (n_pairs, 2) with seam vertex index pairs
+    """
+    tree = KDTree(mesh.vertices)
+    pairs = tree.query_pairs(r=tol)
+    if not pairs:
+        return np.empty((0, 2), dtype=int)
+    return np.array(list(pairs), dtype=int)
+
+
+def _add_seam_edges(A: sparse.csr_matrix, mesh: trimesh.Trimesh) -> sparse.csr_matrix:
+    """Add edges between duplicate vertices at mesh seams.
+
+    This bridges topologically disconnected patches so the Laplacian can
+    smooth across seam boundaries, eliminating crack artifacts.
+
+    Args:
+        A: Adjacency or weight matrix of shape (n, n)
+        mesh: The trimesh object (used to find seam pairs)
+
+    Returns:
+        Modified matrix with seam edges added
+    """
+    seam_pairs = _find_seam_pairs(mesh)
+    if len(seam_pairs) == 0:
+        return A
+
+    n = A.shape[0]
+    rows = np.concatenate([seam_pairs[:, 0], seam_pairs[:, 1]])
+    cols = np.concatenate([seam_pairs[:, 1], seam_pairs[:, 0]])
+    data = np.ones(len(rows), dtype=float)
+    seam_adj = sparse.csr_matrix((data, (rows, cols)), shape=(n, n))
+
+    logger.info(f"Bridging {len(seam_pairs)} seam vertex pairs")
+    return A + seam_adj
+
+
+def _build_sparse_laplacian(mesh: trimesh.Trimesh) -> sparse.csr_matrix:
+    """Build sparse uniform Laplacian matrix from mesh topology.
+
+    The Laplacian L is defined so that L @ V gives the displacement
+    (neighbor_avg - vertex) for each vertex, i.e. the "umbrella" operator.
+
+    Automatically bridges seam vertices (duplicate vertices at the same
+    position from photogrammetry stitching) so smoothing works across
+    patch boundaries.
+
+    Args:
+        mesh: The trimesh object
+
+    Returns:
+        Sparse CSR matrix of shape (n_vertices, n_vertices)
+    """
+    n = len(mesh.vertices)
+    edges = mesh.edges_unique
+    rows = np.concatenate([edges[:, 0], edges[:, 1]])
+    cols = np.concatenate([edges[:, 1], edges[:, 0]])
+    data = np.ones(len(rows), dtype=float)
+
+    # Adjacency matrix
+    A = sparse.csr_matrix((data, (rows, cols)), shape=(n, n))
+
+    # Bridge seam vertices
+    A = _add_seam_edges(A, mesh)
+
+    # Degree (number of neighbors per vertex)
+    degree = np.array(A.sum(axis=1)).ravel()
+    degree[degree == 0] = 1  # avoid division by zero for isolated vertices
+
+    # Normalized Laplacian: L = D^{-1} A - I
+    D_inv = sparse.diags(1.0 / degree)
+    L = D_inv @ A - sparse.eye(n)
+
+    return L.tocsr()
+
+
+def _build_cotangent_laplacian(mesh: trimesh.Trimesh) -> sparse.csr_matrix:
+    """Build sparse cotangent-weighted Laplacian matrix from mesh topology.
+
+    For each edge (i,j), the cotangent weight is w_ij = (cot(alpha) + cot(beta)) / 2
+    where alpha and beta are the angles opposite to edge (i,j) in the two adjacent
+    triangles. This weights neighbors by mesh geometry — well-shaped triangles
+    contribute more, slivers contribute less.
+
+    The result has the same form as _build_sparse_laplacian: L @ V gives the
+    weighted displacement (weighted_neighbor_avg - vertex) for each vertex.
+
+    Args:
+        mesh: The trimesh object
+
+    Returns:
+        Sparse CSR matrix of shape (n_vertices, n_vertices)
+    """
+    n = len(mesh.vertices)
+    verts = mesh.vertices
+    faces = mesh.faces  # (F, 3)
+
+    # Vectorized cotangent computation for all faces at once
+    i0, i1, i2 = faces[:, 0], faces[:, 1], faces[:, 2]
+    v0, v1, v2 = verts[i0], verts[i1], verts[i2]  # each (F, 3)
+
+    # Edge vectors from each corner
+    e01 = v1 - v0
+    e02 = v2 - v0
+    e10 = -e01
+    e12 = v2 - v1
+    e20 = -e02
+    e21 = -e12
+
+    # cot(angle) = dot / |cross| for each corner
+    cross0 = np.linalg.norm(np.cross(e01, e02), axis=1)
+    cross1 = np.linalg.norm(np.cross(e10, e12), axis=1)
+    cross2 = np.linalg.norm(np.cross(e20, e21), axis=1)
+
+    # Clamp to avoid division by zero (degenerate triangles)
+    min_cross = 1e-8
+    np.maximum(cross0, min_cross, out=cross0)
+    np.maximum(cross1, min_cross, out=cross1)
+    np.maximum(cross2, min_cross, out=cross2)
+
+    dot0 = np.sum(e01 * e02, axis=1)
+    dot1 = np.sum(e10 * e12, axis=1)
+    dot2 = np.sum(e20 * e21, axis=1)
+
+    # Clamp cotangent values to >= 0 BEFORE accumulation.
+    # Negative cotangents (from obtuse angles) would disconnect mesh edges if
+    # clamped after summing; clamping here keeps edges connected with zero
+    # weight from obtuse angles instead.
+    cot0 = np.maximum(dot0 / cross0, 0.0)  # opposite edge (i1, i2)
+    cot1 = np.maximum(dot1 / cross1, 0.0)  # opposite edge (i0, i2)
+    cot2 = np.maximum(dot2 / cross2, 0.0)  # opposite edge (i0, i1)
+
+    # Each cotangent contributes to both directions of its opposite edge
+    # Edge opposite corner 0: (i1, i2), weight = cot0 * 0.5
+    # Edge opposite corner 1: (i0, i2), weight = cot1 * 0.5
+    # Edge opposite corner 2: (i0, i1), weight = cot2 * 0.5
+    all_rows = np.concatenate([i1, i2, i0, i2, i0, i1])
+    all_cols = np.concatenate([i2, i1, i2, i0, i1, i0])
+    half_cot0 = cot0 * 0.5
+    half_cot1 = cot1 * 0.5
+    half_cot2 = cot2 * 0.5
+    all_weights = np.concatenate([
+        half_cot0, half_cot0, half_cot1, half_cot1, half_cot2, half_cot2
+    ])
+
+    # Build sparse weight matrix
+    W = sparse.coo_matrix((all_weights, (all_rows, all_cols)), shape=(n, n)).tocsr()
+
+    # Add small uniform weight to ensure full connectivity — edges where both
+    # adjacent angles are obtuse would otherwise have zero weight, effectively
+    # disconnecting mesh regions.
+    edges = mesh.edges_unique
+    uniform_rows = np.concatenate([edges[:, 0], edges[:, 1]])
+    uniform_cols = np.concatenate([edges[:, 1], edges[:, 0]])
+    uniform_data = np.full(len(uniform_rows), 0.01)
+    W_uniform = sparse.csr_matrix(
+        (uniform_data, (uniform_rows, uniform_cols)), shape=(n, n)
+    )
+    W = W + W_uniform
+
+    # Bridge seam vertices — photogrammetry meshes have topologically
+    # disconnected patches at coincident positions. Without bridging,
+    # each patch smooths independently, producing visible crack lines.
+    W = _add_seam_edges(W, mesh)
+
+    # Normalize rows: L = D^{-1} W - I
+    degree = np.array(W.sum(axis=1)).ravel()
+    degree[degree == 0] = 1  # avoid division by zero for isolated vertices
+    D_inv = sparse.diags(1.0 / degree)
+    L = D_inv @ W - sparse.eye(n)
+
+    return L.tocsr()
+
+
+def _compute_boundary_weights(
+    mesh: trimesh.Trimesh,
+    mask: np.ndarray,
+    transition_width: float,
+) -> np.ndarray:
+    """Compute smoothing weights with gradient at mask boundary.
+
+    Creates a weight array where:
+    - Vertices inside the mask have weight 1.0
+    - Vertices outside the mask have weight 0.0
+    - Vertices near the boundary have weights that smoothly transition
+
+    Args:
+        mesh: The trimesh object
+        mask: Boolean mask of vertices to smooth (True = facial region)
+        transition_width: Width of transition zone in mm
+
+    Returns:
+        Weight array of shape (n_vertices,) with values in [0, 1]
+    """
+    n_vertices = len(mesh.vertices)
+    weights = np.zeros(n_vertices)
+
+    # Find boundary vertices (masked vertices with unmasked neighbors)
+    adjacency = _build_adjacency_matrix(mesh)
+    boundary_vertices = []
+
+    for i in range(n_vertices):
+        if mask[i]:
+            neighbors = adjacency[i]
+            if any(not mask[n] for n in neighbors):
+                boundary_vertices.append(i)
+
+    if len(boundary_vertices) == 0:
+        # No boundary - just return mask as weights
+        return mask.astype(float)
+
+    # Build KDTree from boundary vertices
+    boundary_positions = mesh.vertices[boundary_vertices]
+    boundary_tree = KDTree(boundary_positions)
+
+    # Compute distances to boundary for all masked vertices
+    masked_indices = np.where(mask)[0]
+
+    if len(masked_indices) == 0:
+        return weights
+
+    masked_positions = mesh.vertices[masked_indices]
+    distances, _ = boundary_tree.query(masked_positions, k=1)
+
+    # Compute weights based on distance from boundary
+    for idx, (i, dist) in enumerate(zip(masked_indices, distances)):
+        if dist <= transition_width:
+            # Smooth transition using cosine function
+            t = dist / transition_width
+            weights[i] = 0.5 * (1.0 - np.cos(np.pi * t))
+        else:
+            weights[i] = 1.0
+
+    return weights
+
+
+def smooth_region_selective(
+    mesh: trimesh.Trimesh,
+    mask: np.ndarray,
+    protected_indices: np.ndarray,
+    iterations: int = 50,
+    lamb: float = 0.5,
+    mu: float = -0.53,
+    use_cotangent: bool = True,
+) -> trimesh.Trimesh:
+    """Apply Taubin smoothing only to masked vertices.
+
+    Extends trimesh smoothing to work on a subset of vertices while keeping
+    protected vertices completely fixed.
+
+    Args:
+        mesh: The trimesh object to smooth
+        mask: Boolean array marking vertices to smooth (True = smooth)
+        protected_indices: Indices of vertices that must never move
+        iterations: Number of Taubin filter iterations
+        lamb: Taubin lambda parameter (positive, forward step)
+        mu: Taubin mu parameter (negative, backward step for shrinkage correction)
+        use_cotangent: If True, use cotangent-weighted Laplacian (geometry-aware);
+            if False, use uniform Laplacian (original behavior)
+
+    Returns:
+        New trimesh with smoothed facial region
+    """
+    # Create copy of mesh
+    smoothed_mesh = mesh.copy()
+    vertices = smoothed_mesh.vertices.copy()
+
+    # Create weight mask (0 for protected, 1 for smoothable)
+    weights = mask.astype(float)
+    weights[protected_indices] = 0.0
+
+    # Find which vertices to actually smooth
+    n_smoothable = (weights > 0).sum()
+
+    laplacian_type = "cotangent" if use_cotangent else "uniform"
+    logger.info(
+        f"Smoothing {n_smoothable} vertices with {iterations} iterations "
+        f"({laplacian_type} Laplacian)"
+    )
+
+    if n_smoothable == 0:
+        logger.warning("No vertices to smooth!")
+        return smoothed_mesh
+
+    # Build sparse Laplacian once (fast matrix ops instead of Python loops)
+    if use_cotangent:
+        L = _build_cotangent_laplacian(smoothed_mesh)
+        # Cotangent weights have much larger magnitude than uniform weights.
+        # Scale step sizes so displacement magnitude matches the uniform case.
+        max_row_sum = np.abs(L).sum(axis=1).max()
+        if max_row_sum > 2.0:
+            scale = 2.0 / max_row_sum
+            lamb *= scale
+            mu *= scale
+            logger.info(
+                f"Cotangent auto-scale: {scale:.4f} "
+                f"(lamb={lamb:.4f}, mu={mu:.4f})"
+            )
+    else:
+        L = _build_sparse_laplacian(smoothed_mesh)
+
+    # Region-constrained Taubin smoothing:
+    # Each iteration computes Laplacian displacement for all vertices,
+    # but only applies it where weight > 0. This avoids discontinuities
+    # because smoothed vertices naturally pull toward their neighbors.
+    w = weights[:, np.newaxis]  # (n, 1) for broadcasting
+    for iteration in range(iterations):
+        # Forward step (lambda — smoothing)
+        vertices += lamb * w * (L @ vertices)
+
+        # Backward step (mu — shrinkage correction, mu is negative so this
+        # moves vertices AGAINST the Laplacian to counteract shrinkage)
+        vertices += mu * w * (L @ vertices)
+
+    smoothed_mesh.vertices = vertices
+    smoothed_mesh._cache.clear()  # Invalidate normals, bounds, etc.
+
+    logger.info("Smoothing complete")
+    return smoothed_mesh
+
+
+def apply_boundary_transition(
+    mesh: trimesh.Trimesh,
+    mask: np.ndarray,
+    transition_width: float,
+    protected_indices: np.ndarray,
+    iterations: int = 10,
+    lamb: float = 0.3,
+) -> trimesh.Trimesh:
+    """Apply smoothing with smooth transition at mask boundary.
+
+    Creates a gradient of smoothing weights at the boundary to avoid
+    discontinuities between smoothed and unsmoothed regions.
+
+    Args:
+        mesh: The trimesh object
+        mask: Boolean mask of facial region
+        transition_width: Width of transition zone in mm
+        protected_indices: Indices that must not move
+        iterations: Number of smoothing iterations for boundary
+        lamb: Smoothing strength for boundary
+
+    Returns:
+        Mesh with smoothed boundary transition
+    """
+    # Compute boundary weights
+    weights = _compute_boundary_weights(mesh, mask, transition_width)
+
+    # Zero out protected vertices
+    weights[protected_indices] = 0.0
+
+    # If no vertices need boundary smoothing, return as-is
+    if weights.sum() == 0:
+        return mesh
+
+    # Create copy of mesh
+    smoothed_mesh = mesh.copy()
+    vertices = smoothed_mesh.vertices.copy()
+
+    # Build sparse Laplacian once
+    L = _build_sparse_laplacian(smoothed_mesh)
+
+    # Region-constrained Laplacian smoothing at the boundary:
+    # Only vertices with weight > 0 move, proportional to their weight.
+    w = weights[:, np.newaxis]
+    for iteration in range(iterations):
+        vertices += lamb * w * (L @ vertices)
+
+    smoothed_mesh.vertices = vertices
+    smoothed_mesh._cache.clear()  # Invalidate normals, bounds, etc.
+    return smoothed_mesh
+
+
+def _heat_diffusion_smooth(
+    mesh: trimesh.Trimesh,
+    mask: np.ndarray,
+    protected_indices: np.ndarray,
+    diffusion_time: float = 0.001,
+) -> trimesh.Trimesh:
+    """Apply heat diffusion smoothing to masked vertices.
+
+    Solves the implicit heat equation (I - t * L_cot) * V_new = V_old
+    where L_cot is the cotangent Laplacian and t is the diffusion time.
+    This acts as an ideal low-pass filter on the mesh surface.
+
+    Args:
+        mesh: The trimesh object to smooth
+        mask: Boolean array marking vertices to smooth
+        protected_indices: Indices of vertices that must never move
+        diffusion_time: Diffusion time parameter (larger = more smoothing)
+
+    Returns:
+        New trimesh with smoothed facial region
+    """
+    smoothed_mesh = mesh.copy()
+    vertices = smoothed_mesh.vertices.copy()
+    n = len(vertices)
+
+    # Determine which vertices can move
+    movable = mask.copy()
+    movable[protected_indices] = False
+    movable_indices = np.where(movable)[0]
+    n_movable = len(movable_indices)
+
+    logger.info(
+        f"Heat diffusion: {n_movable} vertices, diffusion_time={diffusion_time}"
+    )
+
+    if n_movable == 0:
+        logger.warning("No vertices to smooth!")
+        return smoothed_mesh
+
+    # Build cotangent Laplacian
+    L = _build_cotangent_laplacian(mesh)
+
+    # Build system matrix A = I - t * L (for full mesh)
+    A = sparse.eye(n) - diffusion_time * L
+
+    # For non-movable vertices, zero out their rows and set diagonal to 1
+    # (they stay fixed). Use diagonal scaling trick: multiply rows by 0 then
+    # add identity for fixed rows.
+    fixed = np.ones(n, dtype=bool)
+    fixed[movable_indices] = False
+    # Scale fixed rows to zero
+    row_scale = np.ones(n)
+    row_scale[fixed] = 0.0
+    A = sparse.diags(row_scale) @ A
+    # Add identity for fixed rows
+    A = A + sparse.diags(fixed.astype(float))
+
+    # Solve A @ V_new = V_old for each coordinate
+    rhs = vertices.copy()
+    for coord in range(3):
+        vertices[:, coord] = spsolve(A, rhs[:, coord])
+
+    smoothed_mesh.vertices = vertices
+    smoothed_mesh._cache.clear()  # Invalidate normals, bounds, etc.
+    logger.info("Heat diffusion smoothing complete")
+    return smoothed_mesh
+
+
+def _noise_perturbation(
+    mesh: trimesh.Trimesh,
+    mask: np.ndarray,
+    protected_indices: np.ndarray,
+    noise_strength: float = 0.4,
+    noise_iterations: int = 5,
+    noise_seed: int | None = None,
+) -> trimesh.Trimesh:
+    """Apply neighbor-weighted random perturbation to masked vertices.
+
+    For each pass:
+    1. Compute weighted average of neighbors (using cotangent Laplacian)
+    2. Shift vertex partway toward neighbor average
+    3. Add random offset scaled by local edge length
+
+    This destroys fine facial detail while preserving macro head shape.
+
+    Args:
+        mesh: The trimesh object to perturb
+        mask: Boolean array marking vertices to perturb
+        protected_indices: Indices of vertices that must never move
+        noise_strength: Blend factor toward neighbor average (0-1)
+        noise_iterations: Number of perturbation passes
+        noise_seed: Random seed for reproducibility (None for random)
+
+    Returns:
+        New trimesh with perturbed facial region
+    """
+    smoothed_mesh = mesh.copy()
+    vertices = smoothed_mesh.vertices.copy()
+
+    # Determine which vertices can move
+    movable = mask.copy()
+    movable[protected_indices] = False
+    movable_indices = np.where(movable)[0]
+    n_movable = len(movable_indices)
+
+    logger.info(
+        f"Noise perturbation: {n_movable} vertices, "
+        f"strength={noise_strength}, iterations={noise_iterations}"
+    )
+
+    if n_movable == 0:
+        logger.warning("No vertices to perturb!")
+        return smoothed_mesh
+
+    rng = np.random.default_rng(noise_seed)
+
+    # Build cotangent Laplacian for weighted neighbor averaging
+    L = _build_cotangent_laplacian(mesh)
+
+    # Compute local edge lengths for scaling (average edge length per vertex)
+    edges = mesh.edges_unique
+    edge_lengths = np.linalg.norm(
+        vertices[edges[:, 0]] - vertices[edges[:, 1]], axis=1
+    )
+    # Average edge length per vertex using np.add.at for vectorized accumulation
+    avg_edge_len = np.zeros(len(vertices))
+    edge_count = np.zeros(len(vertices))
+    np.add.at(avg_edge_len, edges[:, 0], edge_lengths)
+    np.add.at(avg_edge_len, edges[:, 1], edge_lengths)
+    np.add.at(edge_count, edges[:, 0], 1)
+    np.add.at(edge_count, edges[:, 1], 1)
+    edge_count[edge_count == 0] = 1
+    avg_edge_len /= edge_count
+
+    for iteration in range(noise_iterations):
+        # Compute neighbor-weighted average displacement: L @ V = avg - V
+        displacement = L @ vertices  # shape (n, 3)
+
+        # Vectorized: shift movable vertices toward neighbor average
+        vertices[movable_indices] += noise_strength * displacement[movable_indices]
+
+        # Vectorized: add random offset scaled by local edge length
+        random_offsets = (
+            rng.standard_normal((n_movable, 3))
+            * (avg_edge_len[movable_indices, np.newaxis] * 1.0)
+        )
+        vertices[movable_indices] += random_offsets
+
+    smoothed_mesh.vertices = vertices
+    smoothed_mesh._cache.clear()  # Invalidate normals, bounds, etc.
+    logger.info("Noise perturbation complete")
+    return smoothed_mesh
+
+
+def _get_protected_vertex_indices(
+    mesh: trimesh.Trimesh,
+    protected_points: cdt.LabeledPointCloud,
+    protection_radius: float,
+) -> np.ndarray:
+    """Find vertex indices within protection radius of protected points.
+
+    Args:
+        mesh: The trimesh object
+        protected_points: Points to protect (optodes + landmarks)
+        protection_radius: Radius around protected points in mm
+
+    Returns:
+        Array of vertex indices that should not be modified
+    """
+    protected_positions = protected_points.pint.dequantify().values
+
+    if len(protected_positions) == 0:
+        return np.array([], dtype=int)
+
+    # Find all vertices within protection radius of any protected point
+    protected_tree = KDTree(protected_positions)
+    vertex_distances, _ = protected_tree.query(mesh.vertices, k=1)
+    protected_mask = vertex_distances < protection_radius
+
+    return np.where(protected_mask)[0]
+
+
+@cdc.validate_schemas
+def anonymize_facial_region(
+    surface: cdc.TrimeshSurface,
+    facial_mask: np.ndarray,
+    protected_points: cdt.LabeledPointCloud,
+    config: AnonymizationConfig = None,
+) -> AnonymizationResult:
+    """Apply anonymization to facial region of mesh.
+
+    Smooths facial features using Taubin filter while preserving protected
+    optode and landmark positions. The algorithm:
+    1. Identifies protected vertex zones around optodes/landmarks
+    2. Applies Taubin smoothing to facial region
+    3. Creates smooth transitions at boundaries
+    4. Validates that protected points haven't moved
+
+    Args:
+        surface: Original TrimeshSurface
+        facial_mask: Boolean array marking facial vertices
+        protected_points: Points that must not move (optodes + landmarks)
+        config: Anonymization configuration (uses defaults if None)
+
+    Returns:
+        AnonymizationResult with anonymized surface and metadata
+    """
+    if config is None:
+        config = AnonymizationConfig()
+
+    # Store original vertices for displacement calculation
+    original_vertices = surface.mesh.vertices.copy()
+
+    # Merge duplicate vertices at mesh seams before smoothing.
+    # Photogrammetry scans have spatially coincident but topologically
+    # disconnected vertices at patch boundaries. Without merging, the
+    # Laplacian smooths each patch independently and seam vertices drift
+    # apart, producing visible crack lines.
+    merged_mesh, old_to_new, new_to_old = _merge_seam_vertices(surface.mesh)
+    merged_mask = np.zeros(len(merged_mesh.vertices), dtype=bool)
+    for new_idx in range(len(merged_mesh.vertices)):
+        # A merged vertex is facial if ANY of its source vertices were facial
+        if any(facial_mask[old_idx] for old_idx in new_to_old[new_idx]):
+            merged_mask[new_idx] = True
+
+    # Get protection radius from config
+    transition_width_mm = float(config.boundary_transition_width.to("mm").magnitude)
+    protection_radius = transition_width_mm * 1.5  # Extra margin for protection
+
+    # Find protected vertex indices on the merged mesh
+    protected_indices = _get_protected_vertex_indices(
+        merged_mesh, protected_points, protection_radius
+    )
+
+    logger.info(
+        f"Protecting {len(protected_indices)} vertices around "
+        f"{len(protected_points.label)} points"
+    )
+
+    if config.method == AnonymizationMethod.SMOOTH:
+        # Main smoothing pass
+        smoothed_mesh = smooth_region_selective(
+            mesh=merged_mesh,
+            mask=merged_mask,
+            protected_indices=protected_indices,
+            iterations=config.smoothing_iterations,
+            lamb=config.smoothing_lambda,
+            mu=config.smoothing_mu,
+            use_cotangent=config.use_cotangent,
+        )
+
+        # Boundary transition pass
+        smoothed_mesh = apply_boundary_transition(
+            mesh=smoothed_mesh,
+            mask=merged_mask,
+            transition_width=transition_width_mm,
+            protected_indices=protected_indices,
+            iterations=10,
+            lamb=0.3,
+        )
+
+    elif config.method == AnonymizationMethod.FLATTEN:
+        # Flatten facial region toward average plane (partial projection)
+        smoothed_mesh = _flatten_facial_region(
+            mesh=merged_mesh,
+            mask=merged_mask,
+            protected_indices=protected_indices,
+            flatten_strength=config.flatten_strength,
+        )
+
+    elif config.method == AnonymizationMethod.COLOR_BLUR:
+        # Only blur texture, keep geometry
+        smoothed_mesh = _blur_texture(
+            mesh=merged_mesh,
+            mask=merged_mask,
+        )
+
+    elif config.method == AnonymizationMethod.COMBINED:
+        # Apply smoothing then color blur
+        smoothed_mesh = smooth_region_selective(
+            mesh=merged_mesh,
+            mask=merged_mask,
+            protected_indices=protected_indices,
+            iterations=config.smoothing_iterations,
+            lamb=config.smoothing_lambda,
+            mu=config.smoothing_mu,
+            use_cotangent=config.use_cotangent,
+        )
+        smoothed_mesh = _blur_texture(
+            mesh=smoothed_mesh,
+            mask=merged_mask,
+        )
+
+    elif config.method == AnonymizationMethod.HEAT_DIFFUSION:
+        # Heat diffusion smoothing with cotangent Laplacian
+        smoothed_mesh = _heat_diffusion_smooth(
+            mesh=merged_mesh,
+            mask=merged_mask,
+            protected_indices=protected_indices,
+            diffusion_time=config.diffusion_time,
+        )
+
+        # Boundary transition pass
+        smoothed_mesh = apply_boundary_transition(
+            mesh=smoothed_mesh,
+            mask=merged_mask,
+            transition_width=transition_width_mm,
+            protected_indices=protected_indices,
+            iterations=10,
+            lamb=0.3,
+        )
+
+    elif config.method == AnonymizationMethod.NOISE:
+        # Neighbor-weighted random perturbation
+        smoothed_mesh = _noise_perturbation(
+            mesh=merged_mesh,
+            mask=merged_mask,
+            protected_indices=protected_indices,
+            noise_strength=config.noise_strength,
+            noise_iterations=config.noise_iterations,
+            noise_seed=config.noise_seed,
+        )
+
+        # Boundary transition pass
+        smoothed_mesh = apply_boundary_transition(
+            mesh=smoothed_mesh,
+            mask=merged_mask,
+            transition_width=transition_width_mm,
+            protected_indices=protected_indices,
+            iterations=10,
+            lamb=0.3,
+        )
+
+    else:
+        raise ValueError(f"Unknown anonymization method: {config.method}")
+
+    # Unmerge: expand smoothed vertices back to original vertex count
+    smoothed_mesh = _unmerge_vertices(smoothed_mesh, surface.mesh, old_to_new)
+
+    # Compute vertex displacements
+    vertex_displacements = np.linalg.norm(
+        smoothed_mesh.vertices - original_vertices, axis=1
+    )
+
+    # Create anonymized surface
+    anonymized_surface = cdc.TrimeshSurface(
+        mesh=smoothed_mesh,
+        crs=surface.crs,
+        units=surface.units,
+        vertex_coords=deepcopy(surface.vertex_coords),
+    )
+
+    logger.info(
+        f"Anonymization complete. "
+        f"Max displacement: {vertex_displacements.max():.2f}mm, "
+        f"Mean displacement in facial region: "
+        f"{vertex_displacements[facial_mask].mean():.2f}mm"
+    )
+
+    return AnonymizationResult(
+        anonymized_surface=anonymized_surface,
+        original_surface=surface,
+        facial_mask=facial_mask,
+        vertex_displacements=vertex_displacements,
+        config=config,
+    )
+
+
+def _flatten_facial_region(
+    mesh: trimesh.Trimesh,
+    mask: np.ndarray,
+    protected_indices: np.ndarray,
+    flatten_strength: float = 0.7,
+) -> trimesh.Trimesh:
+    """Partially flatten facial region toward an average plane.
+
+    Instead of fully projecting onto the plane (which destroys head curvature),
+    vertices are blended partway toward the plane. A few Laplacian smooth
+    iterations are applied afterward to round out the result.
+
+    Args:
+        mesh: The trimesh object
+        mask: Boolean mask of facial vertices
+        protected_indices: Indices that must not move
+        flatten_strength: Blend factor toward plane (0 = no change, 1 = full
+            projection). Default 0.7 preserves some curvature.
+
+    Returns:
+        Mesh with partially flattened facial region
+    """
+    smoothed_mesh = mesh.copy()
+    vertices = smoothed_mesh.vertices.copy()
+
+    # Get facial vertices (excluding protected)
+    facial_indices = np.where(mask)[0]
+    facial_indices = np.setdiff1d(facial_indices, protected_indices)
+
+    if len(facial_indices) == 0:
+        return smoothed_mesh
+
+    facial_vertices = vertices[facial_indices]
+
+    # Fit plane to facial vertices using PCA
+    centroid = np.mean(facial_vertices, axis=0)
+    centered = facial_vertices - centroid
+
+    # SVD to find plane normal (smallest singular vector)
+    _, _, vh = np.linalg.svd(centered, full_matrices=False)
+    normal = vh[-1]  # Normal to best-fit plane
+
+    # Partial projection — blend toward plane, keeping some curvature
+    distances = np.dot(vertices[facial_indices] - centroid, normal)
+    vertices[facial_indices] -= (flatten_strength * distances[:, np.newaxis]) * normal
+
+    smoothed_mesh.vertices = vertices
+
+    # Apply a few Laplacian smooth iterations to round out the result
+    L = _build_sparse_laplacian(smoothed_mesh)
+    w = np.zeros(len(vertices))
+    w[facial_indices] = 1.0
+    w = w[:, np.newaxis]
+    for _ in range(5):
+        vertices += 0.3 * w * (L @ vertices)
+    smoothed_mesh.vertices = vertices
+    smoothed_mesh._cache.clear()  # Invalidate normals, bounds, etc.
+
+    logger.info(
+        f"Flatten complete: strength={flatten_strength}, "
+        f"{len(facial_indices)} vertices"
+    )
+    return smoothed_mesh
+
+
+def _blur_texture(
+    mesh: trimesh.Trimesh,
+    mask: np.ndarray,
+) -> trimesh.Trimesh:
+    """Blur texture/vertex colors in facial region.
+
+    Args:
+        mesh: The trimesh object with vertex colors
+        mask: Boolean mask of facial vertices
+
+    Returns:
+        Mesh with blurred colors in facial region
+    """
+    smoothed_mesh = mesh.copy()
+
+    # Check if mesh has vertex colors
+    if not hasattr(smoothed_mesh.visual, 'vertex_colors'):
+        logger.warning("Mesh has no vertex colors to blur")
+        return smoothed_mesh
+
+    try:
+        colors = smoothed_mesh.visual.to_color().vertex_colors.copy()
+    except Exception:
+        logger.warning("Could not extract vertex colors for blurring")
+        return smoothed_mesh
+
+    # Build adjacency
+    adjacency = _build_adjacency_matrix(mesh)
+
+    # Blur colors using neighborhood averaging
+    new_colors = colors.copy()
+    facial_indices = np.where(mask)[0]
+
+    for _ in range(5):  # Multiple blur passes
+        for i in facial_indices:
+            neighbors = list(adjacency[i])
+            if len(neighbors) > 0:
+                # Average with neighbors
+                neighbor_colors = colors[neighbors].astype(float)
+                avg_color = np.mean(neighbor_colors, axis=0)
+                new_colors[i] = (0.5 * colors[i].astype(float) + 0.5 * avg_color).astype(
+                    np.uint8
+                )
+        colors = new_colors.copy()
+
+    # Apply blurred colors
+    smoothed_mesh.visual.vertex_colors = new_colors
+    return smoothed_mesh
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/face_detector.py b/src/cedalion/geometry/photogrammetry/anonymization/face_detector.py
new file mode 100644
index 00000000..99de630f
--- /dev/null
+++ b/src/cedalion/geometry/photogrammetry/anonymization/face_detector.py
@@ -0,0 +1,593 @@
+"""Facial region detection for photogrammetry scans.
+
+This module provides geometric-based facial detection using anatomical landmarks
+(Nz, Iz, LPA, RPA, Cz) to estimate facial feature positions based on known head
+proportions. It also provides semi-manual landmark detection from a user-provided
+nasion (Nz) position.
+
+Initial Contributors:
+    - Face Anonymization Project | 2024
+"""
+
+from dataclasses import dataclass
+from enum import Enum
+import logging
+
+import numpy as np
+from scipy.spatial import KDTree
+import xarray as xr
+
+import cedalion.dataclasses as cdc
+import cedalion.typing as cdt
+from cedalion import Quantity, units
+logger = logging.getLogger("cedalion")
+
+
+# Required anatomical landmarks for detection
+REQUIRED_LANDMARKS = ["Nz", "Iz", "LPA", "RPA", "Cz"]
+
+# Alternative landmark names (some datasets use different conventions)
+LANDMARK_ALIASES = {
+    "Nz": ["Nz", "Nas", "Nasion", "NAS"],
+    "Iz": ["Iz", "Ini", "Inion", "INI"],
+    "LPA": ["LPA", "Lpa", "A1", "LeftPreauricular"],
+    "RPA": ["RPA", "Rpa", "A2", "RightPreauricular"],
+    "Cz": ["Cz", "CZ", "Vertex"],
+}
+
+
+class FacialLandmarkType(Enum):
+    """Types of facial landmarks for detection."""
+
+    LEFT_EYE = "left_eye"
+    RIGHT_EYE = "right_eye"
+    NOSE_TIP = "nose_tip"
+    NOSE_BRIDGE = "nose_bridge"
+    MOUTH_CENTER = "mouth_center"
+    CHIN = "chin"
+
+
+@dataclass
+class FacialLandmarks:
+    """Container for detected facial landmarks.
+
+    Attributes:
+        landmarks: xarray DataArray with estimated facial landmark positions
+        confidence: confidence score per landmark (1.0 for geometric detection)
+        detection_method: method used for detection ("geometric")
+    """
+
+    landmarks: cdt.LabeledPointCloud
+    confidence: dict[str, float]
+    detection_method: str
+
+
+def _normalize(v: np.ndarray) -> np.ndarray:
+    """Normalize a vector to unit length."""
+    norm = np.linalg.norm(v)
+    if norm < 1e-10:
+        return v
+    return v / norm
+
+
+def _find_landmark(
+    landmarks: cdt.LabeledPointCloud, name: str
+) -> np.ndarray:
+    """Find a landmark by name, trying aliases if needed.
+
+    Args:
+        landmarks: LabeledPointCloud containing anatomical landmarks
+        name: canonical landmark name (e.g., "Nz")
+
+    Returns:
+        numpy array of shape (3,) with landmark coordinates
+
+    Raises:
+        ValueError: if landmark not found under any alias
+    """
+    labels = [str(l) for l in landmarks.label.values]
+
+    # Try direct match first
+    if name in labels:
+        return landmarks.sel(label=name).pint.dequantify().values
+
+    # Try aliases
+    for alias in LANDMARK_ALIASES.get(name, []):
+        if alias in labels:
+            return landmarks.sel(label=alias).pint.dequantify().values
+
+    raise ValueError(
+        f"Landmark '{name}' not found. Available landmarks: {labels}. "
+        f"Tried aliases: {LANDMARK_ALIASES.get(name, [])}"
+    )
+
+
+def _build_head_coordinate_system(
+    nz: np.ndarray,
+    iz: np.ndarray,
+    lpa: np.ndarray,
+    rpa: np.ndarray,
+    cz: np.ndarray,
+) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
+    """Build a head-centered coordinate system from anatomical landmarks.
+
+    The coordinate system is defined as:
+    - Origin: midpoint between LPA and RPA (preauricular points)
+    - X-axis: lateral, pointing from LPA to RPA
+    - Y-axis: anterior, pointing toward Nz (nasion)
+    - Z-axis: superior, pointing upward (cross product of X and Y)
+
+    Args:
+        nz: Nasion coordinates
+        iz: Inion coordinates
+        lpa: Left preauricular point coordinates
+        rpa: Right preauricular point coordinates
+        cz: Vertex coordinates
+
+    Returns:
+        Tuple of (origin, x_axis, y_axis, z_axis) as numpy arrays
+    """
+    # Origin at midpoint of ears
+    origin = (lpa + rpa) / 2.0
+
+    # X-axis: lateral (left to right)
+    x_axis = _normalize(rpa - lpa)
+
+    # Initial Y direction: toward nasion
+    y_direction = nz - origin
+
+    # Z-axis: perpendicular to X and Y direction
+    z_axis = _normalize(np.cross(x_axis, y_direction))
+
+    # Y-axis: perpendicular to X and Z (ensures orthogonality)
+    y_axis = _normalize(np.cross(z_axis, x_axis))
+
+    return origin, x_axis, y_axis, z_axis
+
+
+def _estimate_facial_landmarks_geometric(
+    nz: np.ndarray,
+    origin: np.ndarray,
+    x_axis: np.ndarray,
+    y_axis: np.ndarray,
+    z_axis: np.ndarray,
+) -> dict[str, np.ndarray]:
+    """Estimate facial landmark positions using anatomical proportions.
+
+    Estimates are based on average adult head proportions:
+    - Eyes: ~30mm lateral to midline, ~30mm superior to nasion level
+    - Nose tip: ~40mm inferior-anterior to nasion
+    - Nose bridge: at nasion level, slightly anterior
+    - Mouth: ~70mm inferior to nasion
+    - Chin: ~100mm inferior to nasion
+
+    Args:
+        nz: Nasion position
+        origin: Head coordinate system origin
+        x_axis: Lateral axis (normalized)
+        y_axis: Anterior axis (normalized)
+        z_axis: Superior axis (normalized)
+
+    Returns:
+        Dictionary mapping FacialLandmarkType names to estimated positions
+    """
+    # Estimate Nz level height relative to origin
+    nz_relative = nz - origin
+
+    # Eye positions: ~30mm lateral, at or slightly below Nz level
+    left_eye = nz + (-30.0 * x_axis) + (5.0 * y_axis) + (-10.0 * z_axis)
+    right_eye = nz + (30.0 * x_axis) + (5.0 * y_axis) + (-10.0 * z_axis)
+
+    # Nose tip: inferior and anterior to Nz
+    nose_tip = nz + (40.0 * y_axis) + (-40.0 * z_axis)
+
+    # Nose bridge: at Nz level, slightly anterior
+    nose_bridge = nz + (15.0 * y_axis)
+
+    # Mouth center: inferior to nose
+    mouth_center = nz + (25.0 * y_axis) + (-70.0 * z_axis)
+
+    # Chin: most inferior point
+    chin = nz + (20.0 * y_axis) + (-100.0 * z_axis)
+
+    return {
+        FacialLandmarkType.LEFT_EYE.value: left_eye,
+        FacialLandmarkType.RIGHT_EYE.value: right_eye,
+        FacialLandmarkType.NOSE_TIP.value: nose_tip,
+        FacialLandmarkType.NOSE_BRIDGE.value: nose_bridge,
+        FacialLandmarkType.MOUTH_CENTER.value: mouth_center,
+        FacialLandmarkType.CHIN.value: chin,
+    }
+
+
+@cdc.validate_schemas
+def detect_facial_landmarks(
+    surface: cdc.TrimeshSurface,
+    anatomical_landmarks: cdt.LabeledPointCloud,
+) -> FacialLandmarks:
+    """Detect facial landmarks using geometric heuristics.
+
+    Uses anatomical landmarks (Nz, Iz, LPA, RPA, Cz) to estimate facial feature
+    positions based on known head proportions. This geometric approach works
+    without texture information and provides consistent results.
+
+    Args:
+        surface: Textured TrimeshSurface from photogrammetry
+        anatomical_landmarks: Known landmarks with Nz, Iz, LPA, RPA, Cz
+
+    Returns:
+        FacialLandmarks with estimated eye, nose, mouth positions
+
+    Raises:
+        ValueError: if required anatomical landmarks are missing
+    """
+    # Extract required landmarks
+    nz = _find_landmark(anatomical_landmarks, "Nz")
+    iz = _find_landmark(anatomical_landmarks, "Iz")
+    lpa = _find_landmark(anatomical_landmarks, "LPA")
+    rpa = _find_landmark(anatomical_landmarks, "RPA")
+    cz = _find_landmark(anatomical_landmarks, "Cz")
+
+    # Build head coordinate system
+    origin, x_axis, y_axis, z_axis = _build_head_coordinate_system(
+        nz, iz, lpa, rpa, cz
+    )
+
+    # Estimate facial landmarks using geometric proportions
+    estimated_landmarks = _estimate_facial_landmarks_geometric(
+        nz, origin, x_axis, y_axis, z_axis
+    )
+
+    # Create xarray DataArray for landmarks
+    landmark_names = list(estimated_landmarks.keys())
+    landmark_positions = np.array([estimated_landmarks[name] for name in landmark_names])
+
+    # Get CRS from surface
+    crs = surface.crs
+
+    landmarks_da = xr.DataArray(
+        landmark_positions,
+        dims=["label", crs],
+        coords={"label": landmark_names},
+        attrs={"units": surface.units},
+    ).pint.quantify()
+
+    # All geometric detections have confidence 1.0 (deterministic)
+    confidence = {name: 1.0 for name in landmark_names}
+
+    return FacialLandmarks(
+        landmarks=landmarks_da,
+        confidence=confidence,
+        detection_method="geometric",
+    )
+
+
+def _compute_angular_distance(
+    point: np.ndarray, reference: np.ndarray, center: np.ndarray
+) -> float:
+    """Compute angular distance between a point and reference from center.
+
+    Args:
+        point: Point to measure
+        reference: Reference point (e.g., nasion)
+        center: Center point for angle measurement
+
+    Returns:
+        Angular distance in degrees
+    """
+    v1 = _normalize(point - center)
+    v2 = _normalize(reference - center)
+    cos_angle = np.clip(np.dot(v1, v2), -1.0, 1.0)
+    return np.degrees(np.arccos(cos_angle))
+
+
+@cdc.validate_schemas
+def get_facial_region_mask(
+    surface: cdc.TrimeshSurface,
+    facial_landmarks: FacialLandmarks,
+    protected_points: cdt.LabeledPointCloud,
+    protection_radius: Quantity = 15.0 * units.mm,
+) -> np.ndarray:
+    """Generate boolean mask of vertices in facial region.
+
+    Creates a mask identifying vertices in the facial region while excluding
+    protection zones around optodes and anatomical landmarks. The facial region
+    is determined by geometric criteria based on the head coordinate system.
+
+    Args:
+        surface: The mesh surface
+        facial_landmarks: Detected facial landmarks
+        protected_points: Points to exclude (optodes + anatomical landmarks)
+        protection_radius: Radius around protected points in length units
+
+    Returns:
+        Boolean array of shape (n_vertices,) where True indicates facial region
+    """
+    # Get vertices as numpy array
+    vertices = surface.mesh.vertices
+
+    # Get required anatomical landmarks from protected_points
+    try:
+        nz = _find_landmark(protected_points, "Nz")
+        iz = _find_landmark(protected_points, "Iz")
+        lpa = _find_landmark(protected_points, "LPA")
+        rpa = _find_landmark(protected_points, "RPA")
+        cz = _find_landmark(protected_points, "Cz")
+    except ValueError:
+        # If not all landmarks in protected_points, try facial_landmarks
+        fl = facial_landmarks.landmarks.pint.dequantify()
+        nz = _find_landmark(protected_points, "Nz")
+        iz = _find_landmark(protected_points, "Iz")
+        lpa = _find_landmark(protected_points, "LPA")
+        rpa = _find_landmark(protected_points, "RPA")
+        cz = _find_landmark(protected_points, "Cz")
+
+    # Build head coordinate system
+    origin, x_axis, y_axis, z_axis = _build_head_coordinate_system(
+        nz, iz, lpa, rpa, cz
+    )
+
+    # Transform vertices to head coordinate system
+    vertices_relative = vertices - origin
+    vertices_x = np.dot(vertices_relative, x_axis)
+    vertices_y = np.dot(vertices_relative, y_axis)
+    vertices_z = np.dot(vertices_relative, z_axis)
+
+    # Get Nz and Cz positions in head coordinates
+    nz_relative = nz - origin
+    nz_y = np.dot(nz_relative, y_axis)
+    nz_z = np.dot(nz_relative, z_axis)
+
+    cz_relative = cz - origin
+    cz_z = np.dot(cz_relative, z_axis)
+
+    # T-shaped facial region mask in head coordinate system:
+    # Anterior mask: only front of head (Y > 0)
+    anterior_mask = vertices_y > 0
+
+    # Horizontal bar (eye band): Nz height ± 30mm, full lateral span
+    horiz = anterior_mask & (vertices_z > nz_z - 30) & (vertices_z < nz_z + 30)
+
+    # Vertical bar (nose strip): Nz down 60mm, narrow lateral (±35mm)
+    vert = (
+        anterior_mask
+        & (vertices_z > nz_z - 60)
+        & (vertices_z < nz_z)
+        & (np.abs(vertices_x) < 35)
+    )
+
+    # Combined T-shape
+    facial_mask = horiz | vert
+
+    # Create protection zones using KDTree
+    protection_radius_mm = float(protection_radius.to("mm").magnitude)
+
+    # Get protected point positions
+    protected_positions = protected_points.pint.dequantify().values
+
+    if len(protected_positions) > 0:
+        kdtree = KDTree(protected_positions)
+        distances, _ = kdtree.query(vertices, k=1)
+        protected_mask = distances < protection_radius_mm
+
+        # Exclude protected vertices from facial region
+        facial_mask = facial_mask & ~protected_mask
+
+    logger.info(
+        f"Facial region mask: {facial_mask.sum()} of {len(facial_mask)} vertices "
+        f"({100 * facial_mask.sum() / len(facial_mask):.1f}%)"
+    )
+
+    return facial_mask
+
+
+def get_facial_region_center(
+    surface: cdc.TrimeshSurface,
+    facial_landmarks: FacialLandmarks,
+) -> np.ndarray:
+    """Compute the centroid of the facial region.
+
+    Args:
+        surface: The mesh surface
+        facial_landmarks: Detected facial landmarks
+
+    Returns:
+        Numpy array of shape (3,) with facial region centroid
+    """
+    # Get facial landmark positions
+    landmarks = facial_landmarks.landmarks.pint.dequantify().values
+
+    # Return centroid of all facial landmarks
+    return np.mean(landmarks, axis=0)
+
+
+def estimate_face_bounding_box(
+    facial_landmarks: FacialLandmarks,
+    margin: Quantity = 20.0 * units.mm,
+) -> tuple[np.ndarray, np.ndarray]:
+    """Estimate a bounding box around the facial region.
+
+    Args:
+        facial_landmarks: Detected facial landmarks
+        margin: Extra margin around landmarks in length units
+
+    Returns:
+        Tuple of (min_corner, max_corner) as numpy arrays
+    """
+    landmarks = facial_landmarks.landmarks.pint.dequantify().values
+    margin_mm = float(margin.to("mm").magnitude)
+
+    min_corner = landmarks.min(axis=0) - margin_mm
+    max_corner = landmarks.max(axis=0) + margin_mm
+
+    return min_corner, max_corner
+
+
+def detect_landmarks_from_nasion(
+    surface: cdc.TrimeshSurface,
+    nz_position: np.ndarray,
+) -> cdt.LabeledPointCloud:
+    """Detect anatomical landmarks from a user-provided nasion (Nz) position.
+
+    Given the Nz point (clicked by the user on the mesh), automatically detects
+    the remaining 4 landmarks (Iz, LPA, RPA, Cz) using mesh geometry.
+
+    Coordinate system (Einstar scanner convention):
+    - X: Up (vertical)
+    - Y: Forward (anterior, toward face)
+    - Z: Left of subject
+
+    Detection algorithm:
+    - Cz: Highest vertex (max X) within a lateral band near the mesh centroid
+    - Iz: Most posterior vertex (min Y) at approximately Nz height
+    - LPA: Most left vertex (max Z) at ear height, not too posterior
+    - RPA: Most right vertex (min Z) at ear height, not too posterior
+
+    Args:
+        surface: TrimeshSurface from photogrammetry scan
+        nz_position: Nasion position as numpy array of shape (3,), in mm
+
+    Returns:
+        LabeledPointCloud with landmarks labeled as Nz, Iz, Cz, LPA, RPA
+
+    Raises:
+        ValueError: If landmark configuration fails validation
+
+    Example:
+        >>> # User clicks Nz on the mesh, gets nz_point
+        >>> landmarks = detect_landmarks_from_nasion(surface, nz_point)
+        >>> print(landmarks.label.values)
+        ['Nz', 'Iz', 'Cz', 'LPA', 'RPA']
+    """
+    nz_position = np.asarray(nz_position, dtype=float)
+    vertices = surface.mesh.vertices
+
+    centroid = vertices.mean(axis=0)
+    head_height = vertices[:, 0].max() - vertices[:, 0].min()
+
+    logger.info(
+        f"Detecting landmarks from Nz={nz_position}, "
+        f"head_height={head_height:.1f}mm"
+    )
+
+    # Cz: highest vertex (max X) within a lateral band near centroid
+    # Band: within 20% of head_height in Y and Z from centroid
+    band = 0.20 * head_height
+    lateral_mask = (
+        (np.abs(vertices[:, 1] - centroid[1]) < band)
+        & (np.abs(vertices[:, 2] - centroid[2]) < band)
+    )
+    if lateral_mask.sum() == 0:
+        lateral_mask = np.ones(len(vertices), dtype=bool)
+    cz_idx = np.where(lateral_mask)[0][np.argmax(vertices[lateral_mask, 0])]
+    cz = vertices[cz_idx]
+
+    # Iz: most posterior vertex (min Y) within a height band around Nz height
+    # Band: ±15% of head_height in X around Nz's X
+    height_band = 0.15 * head_height
+    iz_height_mask = np.abs(vertices[:, 0] - nz_position[0]) < height_band
+    if iz_height_mask.sum() == 0:
+        iz_height_mask = np.ones(len(vertices), dtype=bool)
+    iz_idx = np.where(iz_height_mask)[0][np.argmin(vertices[iz_height_mask, 1])]
+    iz = vertices[iz_idx]
+
+    # Ear height: slightly below Nz (Nz height - 5% head_height)
+    ear_height = nz_position[0] - 0.05 * head_height
+    ear_height_mask = np.abs(vertices[:, 0] - ear_height) < height_band
+
+    # Additional constraint: not too posterior (Y > centroid Y - some margin)
+    posterior_limit = centroid[1] - 0.10 * head_height
+    not_too_posterior = vertices[:, 1] > posterior_limit
+
+    ear_mask = ear_height_mask & not_too_posterior
+    if ear_mask.sum() < 2:
+        ear_mask = ear_height_mask
+
+    # LPA: most left vertex (max Z) at ear height
+    lpa_idx = np.where(ear_mask)[0][np.argmax(vertices[ear_mask, 2])]
+    lpa = vertices[lpa_idx]
+
+    # RPA: most right vertex (min Z) at ear height
+    rpa_idx = np.where(ear_mask)[0][np.argmin(vertices[ear_mask, 2])]
+    rpa = vertices[rpa_idx]
+
+    landmark_positions = {
+        "Nz": nz_position,
+        "Iz": iz,
+        "Cz": cz,
+        "LPA": lpa,
+        "RPA": rpa,
+    }
+
+    logger.info(
+        f"Detected landmarks: Cz={cz}, Iz={iz}, LPA={lpa}, RPA={rpa}"
+    )
+
+    # Validate
+    _validate_landmark_configuration(landmark_positions, centroid)
+
+    # Create LabeledPointCloud
+    labels = ["Nz", "Iz", "Cz", "LPA", "RPA"]
+    coords = np.array([landmark_positions[label] for label in labels])
+
+    landmarks = xr.DataArray(
+        coords,
+        dims=["label", surface.crs],
+        coords={
+            "label": labels,
+            "type": ("label", [cdc.PointType.LANDMARK] * 5),
+        },
+    ).pint.quantify("mm")
+
+    return landmarks
+
+
+def _validate_landmark_configuration(
+    landmarks: dict[str, np.ndarray],
+    centroid: np.ndarray,
+) -> None:
+    """Validate that detected landmarks have a plausible spatial configuration.
+
+    Checks:
+    - Cz is the highest point (max X)
+    - Iz is posterior to the centroid (low Y)
+    - LPA and RPA are roughly symmetric about the midline (Z)
+
+    Args:
+        landmarks: Dict mapping landmark name to position array
+        centroid: Mesh centroid for reference
+
+    Raises:
+        ValueError: If configuration is implausible
+    """
+    cz = landmarks["Cz"]
+    iz = landmarks["Iz"]
+    nz = landmarks["Nz"]
+    lpa = landmarks["LPA"]
+    rpa = landmarks["RPA"]
+
+    # Cz should be highest (max X)
+    all_x = [landmarks[k][0] for k in landmarks]
+    if cz[0] < max(all_x) - 1.0:
+        logger.warning("Cz is not the highest landmark — detection may be off")
+
+    # Iz should be posterior to centroid (Y < centroid Y)
+    if iz[1] > centroid[1]:
+        logger.warning(
+            f"Iz (Y={iz[1]:.1f}) is anterior to centroid (Y={centroid[1]:.1f}) "
+            "— expected posterior"
+        )
+
+    # LPA should be left (Z > centroid Z) and RPA right (Z < centroid Z)
+    if lpa[2] < rpa[2]:
+        logger.warning(
+            "LPA is to the right of RPA — landmarks may be swapped"
+        )
+
+    # LPA and RPA should be roughly symmetric
+    lpa_offset = abs(lpa[2] - centroid[2])
+    rpa_offset = abs(rpa[2] - centroid[2])
+    if min(lpa_offset, rpa_offset) > 0 and max(lpa_offset, rpa_offset) / min(lpa_offset, rpa_offset) > 3.0:
+        logger.warning(
+            f"LPA/RPA asymmetry is large (offsets: {lpa_offset:.1f} vs {rpa_offset:.1f}mm)"
+        )
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/ui.py b/src/cedalion/geometry/photogrammetry/anonymization/ui.py
new file mode 100644
index 00000000..28ad2eec
--- /dev/null
+++ b/src/cedalion/geometry/photogrammetry/anonymization/ui.py
@@ -0,0 +1,387 @@
+"""Interactive UI components for facial region editing and preview.
+
+This module provides PyVista-based interactive tools for refining facial
+region detection and previewing anonymization results.
+
+Initial Contributors:
+    - Face Anonymization Project | 2024
+"""
+
+import logging
+
+import numpy as np
+import pyvista as pv
+
+import cedalion.dataclasses as cdc
+import cedalion.typing as cdt
+from cedalion import units
+
+from .validator import ValidationMetrics
+
+
+logger = logging.getLogger("cedalion")
+
+
+class FacialRegionEditor:
+    """Interactive editor for facial region mask.
+
+    Allows user to:
+    - See detected facial region highlighted
+    - Add/remove vertices from mask by clicking
+    - Verify protected points are excluded
+
+    Follows the pattern of OptodeSelector in cedalion.plots.
+
+    Attributes:
+        surface: The TrimeshSurface being edited
+        mask: Current facial region mask
+        protected_points: Points that cannot be part of facial region
+        plotter: PyVista plotter instance
+    """
+
+    def __init__(
+        self,
+        surface: cdc.TrimeshSurface,
+        initial_mask: np.ndarray,
+        protected_points: cdt.LabeledPointCloud,
+        protection_radius: float = 15.0,
+    ):
+        """Initialize the facial region editor.
+
+        Args:
+            surface: The TrimeshSurface to edit
+            initial_mask: Initial boolean mask for facial region
+            protected_points: Points to protect (optodes + landmarks)
+            protection_radius: Radius around protected points in mm
+        """
+        self.surface = surface
+        self.mask = initial_mask.copy()
+        self.protected_points = protected_points
+        self.protection_radius = protection_radius
+        self.plotter = pv.Plotter()
+        self._mesh_actor = None
+        self._protected_actors = []
+
+    def _get_vtk_mesh(self) -> pv.PolyData:
+        """Convert surface to PyVista mesh."""
+        vtk_surface = cdc.VTKSurface.from_trimeshsurface(self.surface)
+        return pv.wrap(vtk_surface.mesh)
+
+    def _update_visualization(self):
+        """Update the mesh visualization with current mask."""
+        if self._mesh_actor is not None:
+            self.plotter.remove_actor(self._mesh_actor)
+
+        mesh = self._get_vtk_mesh()
+
+        # Create scalars for visualization
+        # 0 = non-facial, 1 = facial region
+        scalars = self.mask.astype(float)
+
+        self._mesh_actor = self.plotter.add_mesh(
+            mesh,
+            scalars=scalars,
+            cmap=["white", "red"],
+            clim=[0, 1],
+            show_scalar_bar=False,
+            opacity=0.9,
+            smooth_shading=True,
+            pickable=True,
+        )
+
+    def _add_protected_points(self):
+        """Add spheres for protected points."""
+        points = self.protected_points.pint.dequantify().values
+        labels = [str(l) for l in self.protected_points.label.values]
+
+        for i, (point, label) in enumerate(zip(points, labels)):
+            sphere = pv.Sphere(radius=3, center=point)
+            actor = self.plotter.add_mesh(
+                sphere, color="green", smooth_shading=True
+            )
+            self._protected_actors.append(actor)
+
+            # Add label
+            self.plotter.add_point_labels(
+                [point], [label], font_size=12, point_color="green"
+            )
+
+    def _on_pick(self, picked_point):
+        """Handle vertex picking to toggle mask."""
+        if picked_point is None:
+            return
+
+        point = np.array(picked_point)
+
+        # Check if point is protected
+        protected = self.protected_points.pint.dequantify().values
+        distances = np.linalg.norm(protected - point, axis=1)
+
+        if np.any(distances < self.protection_radius):
+            logger.info("Cannot modify protected region")
+            return
+
+        # Find nearest vertex
+        vertices = self.surface.mesh.vertices
+        distances = np.linalg.norm(vertices - point, axis=1)
+        nearest_idx = np.argmin(distances)
+
+        # Use brush to toggle multiple vertices
+        brush_radius = 10.0  # mm
+        affected_mask = distances < brush_radius
+
+        # Toggle mask for affected vertices
+        if self.mask[nearest_idx]:
+            # Remove from facial region
+            self.mask[affected_mask] = False
+        else:
+            # Add to facial region (excluding protected)
+            protected_positions = self.protected_points.pint.dequantify().values
+            for idx in np.where(affected_mask)[0]:
+                v = vertices[idx]
+                dist_to_protected = np.min(
+                    np.linalg.norm(protected_positions - v, axis=1)
+                )
+                if dist_to_protected >= self.protection_radius:
+                    self.mask[idx] = True
+
+        self._update_visualization()
+
+    def show(self) -> np.ndarray:
+        """Display editor and return refined mask.
+
+        Returns:
+            Refined boolean mask after user editing
+        """
+        # Set up visualization
+        self._update_visualization()
+        self._add_protected_points()
+
+        # Enable picking
+        self.plotter.enable_surface_point_picking(
+            callback=self._on_pick,
+            show_message="Click to add/remove vertices from facial region",
+            show_point=False,
+            tolerance=0.005,
+        )
+
+        # Add instructions
+        self.plotter.add_text(
+            "Left-click: Toggle facial region\n"
+            "Green spheres: Protected points (cannot modify)\n"
+            "Close window when done",
+            position="upper_left",
+            font_size=10,
+        )
+
+        # Show and wait
+        self.plotter.show()
+
+        return self.mask
+
+
+class AnonymizationPreview:
+    """Side-by-side comparison of original and anonymized meshes.
+
+    Provides interactive visualization to compare the original scan
+    with the anonymized version, including validation metrics display.
+
+    Attributes:
+        original: Original TrimeshSurface
+        anonymized: Anonymized TrimeshSurface
+        metrics: Validation metrics to display
+    """
+
+    def __init__(
+        self,
+        original: cdc.TrimeshSurface,
+        anonymized: cdc.TrimeshSurface,
+        metrics: ValidationMetrics = None,
+    ):
+        """Initialize the preview.
+
+        Args:
+            original: Original TrimeshSurface
+            anonymized: Anonymized TrimeshSurface
+            metrics: Optional validation metrics to display
+        """
+        self.original = original
+        self.anonymized = anonymized
+        self.metrics = metrics
+
+    def _get_vtk_mesh(self, surface: cdc.TrimeshSurface) -> pv.PolyData:
+        """Convert surface to PyVista mesh."""
+        vtk_surface = cdc.VTKSurface.from_trimeshsurface(surface)
+        return pv.wrap(vtk_surface.mesh)
+
+    def show(self):
+        """Display side-by-side comparison."""
+        # Create plotter with two viewports
+        plotter = pv.Plotter(shape=(1, 2))
+
+        # Left viewport: Original
+        plotter.subplot(0, 0)
+        original_mesh = self._get_vtk_mesh(self.original)
+
+        # Check if mesh has colors
+        if hasattr(self.original.mesh.visual, 'vertex_colors'):
+            try:
+                colors = self.original.mesh.visual.to_color().vertex_colors
+                original_mesh["colors"] = colors[:, :3]  # RGB only
+                plotter.add_mesh(
+                    original_mesh,
+                    scalars="colors",
+                    rgb=True,
+                    smooth_shading=True,
+                )
+            except Exception:
+                plotter.add_mesh(original_mesh, color="white", smooth_shading=True)
+        else:
+            plotter.add_mesh(original_mesh, color="white", smooth_shading=True)
+
+        plotter.add_text("Original", position="upper_left", font_size=14)
+
+        # Right viewport: Anonymized
+        plotter.subplot(0, 1)
+        anonymized_mesh = self._get_vtk_mesh(self.anonymized)
+
+        if hasattr(self.anonymized.mesh.visual, 'vertex_colors'):
+            try:
+                colors = self.anonymized.mesh.visual.to_color().vertex_colors
+                anonymized_mesh["colors"] = colors[:, :3]
+                plotter.add_mesh(
+                    anonymized_mesh,
+                    scalars="colors",
+                    rgb=True,
+                    smooth_shading=True,
+                )
+            except Exception:
+                plotter.add_mesh(anonymized_mesh, color="white", smooth_shading=True)
+        else:
+            plotter.add_mesh(anonymized_mesh, color="white", smooth_shading=True)
+
+        plotter.add_text("Anonymized", position="upper_left", font_size=14)
+
+        # Add metrics if available
+        if self.metrics is not None:
+            status = "PASS" if self.metrics.protected_points_preserved else "FAIL"
+            metrics_text = (
+                f"Validation: {status}\n"
+                f"Max Protected Deviation: {self.metrics.max_protected_deviation:.3f}mm\n"
+                f"Facial Displacement: {self.metrics.facial_displacement_mean:.1f}mm (mean)"
+            )
+            plotter.add_text(
+                metrics_text,
+                position="lower_left",
+                font_size=10,
+            )
+
+        # Link cameras so both views rotate together
+        plotter.link_views()
+
+        plotter.show()
+
+
+class DisplacementViewer:
+    """Visualize vertex displacement as a heatmap on the mesh.
+
+    Shows how much each vertex moved during anonymization using
+    a color-coded visualization.
+    """
+
+    def __init__(
+        self,
+        surface: cdc.TrimeshSurface,
+        displacements: np.ndarray,
+        facial_mask: np.ndarray = None,
+    ):
+        """Initialize the displacement viewer.
+
+        Args:
+            surface: The anonymized TrimeshSurface
+            displacements: Per-vertex displacement values in mm
+            facial_mask: Optional mask to highlight facial region
+        """
+        self.surface = surface
+        self.displacements = displacements
+        self.facial_mask = facial_mask
+
+    def _get_vtk_mesh(self) -> pv.PolyData:
+        """Convert surface to PyVista mesh."""
+        vtk_surface = cdc.VTKSurface.from_trimeshsurface(self.surface)
+        return pv.wrap(vtk_surface.mesh)
+
+    def show(self, cmap: str = "hot"):
+        """Display displacement heatmap.
+
+        Args:
+            cmap: Colormap for displacement visualization
+        """
+        plotter = pv.Plotter()
+
+        mesh = self._get_vtk_mesh()
+        mesh["displacement"] = self.displacements
+
+        plotter.add_mesh(
+            mesh,
+            scalars="displacement",
+            cmap=cmap,
+            smooth_shading=True,
+            scalar_bar_args={
+                "title": "Displacement (mm)",
+                "vertical": True,
+            },
+        )
+
+        # Add statistics
+        stats_text = (
+            f"Max: {self.displacements.max():.2f}mm\n"
+            f"Mean: {self.displacements.mean():.2f}mm\n"
+            f"Median: {np.median(self.displacements):.2f}mm"
+        )
+
+        if self.facial_mask is not None:
+            facial_disp = self.displacements[self.facial_mask]
+            stats_text += (
+                f"\n\nFacial Region:\n"
+                f"Max: {facial_disp.max():.2f}mm\n"
+                f"Mean: {facial_disp.mean():.2f}mm"
+            )
+
+        plotter.add_text(stats_text, position="upper_right", font_size=10)
+
+        plotter.show()
+
+
+def quick_preview(
+    original: cdc.TrimeshSurface,
+    anonymized: cdc.TrimeshSurface,
+    metrics: ValidationMetrics = None,
+):
+    """Quick function to preview anonymization results.
+
+    Convenience function that creates an AnonymizationPreview and shows it.
+
+    Args:
+        original: Original TrimeshSurface
+        anonymized: Anonymized TrimeshSurface
+        metrics: Optional validation metrics
+    """
+    preview = AnonymizationPreview(original, anonymized, metrics)
+    preview.show()
+
+
+def quick_displacement_view(
+    surface: cdc.TrimeshSurface,
+    displacements: np.ndarray,
+    facial_mask: np.ndarray = None,
+):
+    """Quick function to view displacement heatmap.
+
+    Args:
+        surface: The anonymized TrimeshSurface
+        displacements: Per-vertex displacement values in mm
+        facial_mask: Optional mask to highlight facial region
+    """
+    viewer = DisplacementViewer(surface, displacements, facial_mask)
+    viewer.show()
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/validator.py b/src/cedalion/geometry/photogrammetry/anonymization/validator.py
new file mode 100644
index 00000000..3c60aa17
--- /dev/null
+++ b/src/cedalion/geometry/photogrammetry/anonymization/validator.py
@@ -0,0 +1,412 @@
+"""Validation metrics for anonymization quality.
+
+This module provides functions to validate that anonymization preserves
+critical points (optodes, landmarks) while effectively modifying the facial
+region.
+
+Initial Contributors:
+    - Face Anonymization Project | 2024
+"""
+
+from dataclasses import dataclass
+import logging
+
+import numpy as np
+from scipy.spatial import KDTree
+
+import cedalion.dataclasses as cdc
+import cedalion.typing as cdt
+from cedalion import Quantity, units
+
+
+logger = logging.getLogger("cedalion")
+
+
+@dataclass
+class ValidationMetrics:
+    """Comprehensive validation metrics for anonymization quality.
+
+    Attributes:
+        landmark_deviations: Euclidean deviation (mm) for each landmark
+        optode_deviations: Euclidean deviation (mm) for each optode
+        max_protected_deviation: Maximum deviation of any protected point (mm)
+        mean_surface_distance: Mean surface-to-surface distance (mm)
+        hausdorff_distance: Maximum surface-to-surface distance (mm)
+        facial_displacement_mean: Mean vertex displacement in facial region (mm)
+        facial_displacement_max: Maximum vertex displacement in facial region (mm)
+        protected_points_preserved: True if all protected points within tolerance
+    """
+
+    landmark_deviations: dict[str, float]
+    optode_deviations: dict[str, float]
+    max_protected_deviation: float
+    mean_surface_distance: float
+    hausdorff_distance: float
+    facial_displacement_mean: float
+    facial_displacement_max: float
+    protected_points_preserved: bool
+
+
+def _find_nearest_surface_point(
+    point: np.ndarray,
+    mesh_vertices: np.ndarray,
+    kdtree: KDTree = None,
+) -> tuple[np.ndarray, float]:
+    """Find the nearest point on a surface to a given point.
+
+    Args:
+        point: Query point coordinates
+        mesh_vertices: Vertex positions of the mesh
+        kdtree: Optional precomputed KDTree for faster queries
+
+    Returns:
+        Tuple of (nearest_point, distance)
+    """
+    if kdtree is None:
+        kdtree = KDTree(mesh_vertices)
+
+    distance, index = kdtree.query(point)
+    nearest_point = mesh_vertices[index]
+
+    return nearest_point, distance
+
+
+def compute_point_deviations(
+    original_surface: cdc.TrimeshSurface,
+    anonymized_surface: cdc.TrimeshSurface,
+    points: cdt.LabeledPointCloud,
+) -> dict[str, float]:
+    """Compute surface deviation for each labeled point.
+
+    For each point, finds the nearest vertex on both original and anonymized
+    surfaces and computes the difference in position.
+
+    Args:
+        original_surface: Original mesh before anonymization
+        anonymized_surface: Mesh after anonymization
+        points: Labeled points to check (optodes, landmarks)
+
+    Returns:
+        Dictionary mapping point labels to deviation in mm
+    """
+    # Get point positions
+    point_positions = points.pint.dequantify().values
+    point_labels = [str(l) for l in points.label.values]
+
+    # Build KDTrees for both surfaces
+    original_tree = KDTree(original_surface.mesh.vertices)
+    anonymized_tree = KDTree(anonymized_surface.mesh.vertices)
+
+    deviations = {}
+
+    for i, label in enumerate(point_labels):
+        point = point_positions[i]
+
+        # Find nearest vertex on original surface
+        _, orig_idx = original_tree.query(point)
+        orig_vertex = original_surface.mesh.vertices[orig_idx]
+
+        # Find the same vertex index on anonymized surface
+        anon_vertex = anonymized_surface.mesh.vertices[orig_idx]
+
+        # Compute deviation
+        deviation = np.linalg.norm(anon_vertex - orig_vertex)
+        deviations[label] = float(deviation)
+
+    return deviations
+
+
+def compute_surface_distance(
+    original_surface: cdc.TrimeshSurface,
+    anonymized_surface: cdc.TrimeshSurface,
+    sample_points: int = 10000,
+) -> tuple[float, float, float]:
+    """Compute surface-to-surface distance metrics.
+
+    Uses point-to-point distance between vertices to estimate surface
+    distance. Samples vertices if there are too many.
+
+    Args:
+        original_surface: Original mesh
+        anonymized_surface: Anonymized mesh
+        sample_points: Maximum number of points to sample
+
+    Returns:
+        Tuple of (mean_distance, max_distance, hausdorff_distance) in mm
+    """
+    orig_vertices = original_surface.mesh.vertices
+    anon_vertices = anonymized_surface.mesh.vertices
+
+    # If meshes have same topology, compute direct vertex distances
+    if len(orig_vertices) == len(anon_vertices):
+        vertex_distances = np.linalg.norm(anon_vertices - orig_vertices, axis=1)
+        mean_dist = float(np.mean(vertex_distances))
+        max_dist = float(np.max(vertex_distances))
+        hausdorff = max_dist
+
+        return mean_dist, max_dist, hausdorff
+
+    # Different topologies - use KDTree for nearest neighbor distances
+    # Sample if too many vertices
+    n_orig = len(orig_vertices)
+    n_anon = len(anon_vertices)
+
+    if n_orig > sample_points:
+        indices = np.random.choice(n_orig, sample_points, replace=False)
+        orig_sample = orig_vertices[indices]
+    else:
+        orig_sample = orig_vertices
+
+    if n_anon > sample_points:
+        indices = np.random.choice(n_anon, sample_points, replace=False)
+        anon_sample = anon_vertices[indices]
+    else:
+        anon_sample = anon_vertices
+
+    # Forward distances: original -> anonymized
+    anon_tree = KDTree(anon_sample)
+    forward_dists, _ = anon_tree.query(orig_sample)
+
+    # Backward distances: anonymized -> original
+    orig_tree = KDTree(orig_sample)
+    backward_dists, _ = orig_tree.query(anon_sample)
+
+    # Compute metrics
+    mean_dist = float(np.mean(np.concatenate([forward_dists, backward_dists])))
+    max_forward = float(np.max(forward_dists))
+    max_backward = float(np.max(backward_dists))
+    hausdorff = max(max_forward, max_backward)
+
+    return mean_dist, hausdorff, hausdorff
+
+
+def compute_facial_displacement_stats(
+    original_surface: cdc.TrimeshSurface,
+    anonymized_surface: cdc.TrimeshSurface,
+    facial_mask: np.ndarray,
+) -> tuple[float, float]:
+    """Compute displacement statistics for facial region.
+
+    Args:
+        original_surface: Original mesh
+        anonymized_surface: Anonymized mesh
+        facial_mask: Boolean mask of facial vertices
+
+    Returns:
+        Tuple of (mean_displacement, max_displacement) in mm
+    """
+    orig_vertices = original_surface.mesh.vertices
+    anon_vertices = anonymized_surface.mesh.vertices
+
+    # Ensure same topology
+    if len(orig_vertices) != len(anon_vertices):
+        logger.warning(
+            "Surfaces have different vertex counts. Using nearest neighbor distances."
+        )
+        # Use KDTree approach
+        facial_indices = np.where(facial_mask)[0]
+        if len(facial_indices) == 0:
+            return 0.0, 0.0
+
+        orig_facial = orig_vertices[facial_indices]
+        anon_tree = KDTree(anon_vertices)
+        distances, _ = anon_tree.query(orig_facial)
+
+        return float(np.mean(distances)), float(np.max(distances))
+
+    # Direct computation for same topology
+    displacements = np.linalg.norm(anon_vertices - orig_vertices, axis=1)
+    facial_displacements = displacements[facial_mask]
+
+    if len(facial_displacements) == 0:
+        return 0.0, 0.0
+
+    mean_disp = float(np.mean(facial_displacements))
+    max_disp = float(np.max(facial_displacements))
+
+    return mean_disp, max_disp
+
+
+def _separate_landmarks_and_optodes(
+    protected_points: cdt.LabeledPointCloud,
+) -> tuple[cdt.LabeledPointCloud, cdt.LabeledPointCloud]:
+    """Separate protected points into landmarks and optodes.
+
+    Args:
+        protected_points: All protected points
+
+    Returns:
+        Tuple of (landmarks, optodes) as LabeledPointClouds
+    """
+    # Common landmark names
+    landmark_names = {
+        "Nz", "Iz", "LPA", "RPA", "Cz",
+        "Nas", "Nasion", "Inion", "Ini",
+        "A1", "A2", "Lpa", "Rpa",
+        "LeftPreauricular", "RightPreauricular",
+        "Vertex", "CZ",
+    }
+
+    labels = [str(l) for l in protected_points.label.values]
+
+    landmark_mask = np.array([l in landmark_names for l in labels])
+    optode_mask = ~landmark_mask
+
+    landmarks = protected_points.isel(label=np.where(landmark_mask)[0])
+    optodes = protected_points.isel(label=np.where(optode_mask)[0])
+
+    return landmarks, optodes
+
+
+@cdc.validate_schemas
+def validate_anonymization(
+    original_surface: cdc.TrimeshSurface,
+    anonymized_surface: cdc.TrimeshSurface,
+    facial_mask: np.ndarray,
+    protected_points: cdt.LabeledPointCloud,
+    tolerance: Quantity = 0.5 * units.mm,
+) -> ValidationMetrics:
+    """Compute comprehensive validation metrics for anonymized scan.
+
+    Validates that:
+    1. Protected points (optodes, landmarks) haven't moved beyond tolerance
+    2. Facial region has been effectively modified
+    3. Overall surface integrity is maintained
+
+    Args:
+        original_surface: Original mesh before anonymization
+        anonymized_surface: Mesh after anonymization
+        facial_mask: Boolean mask of facial vertices
+        protected_points: Landmarks + optodes that should be preserved
+        tolerance: Maximum allowed deviation for protected points
+
+    Returns:
+        ValidationMetrics with comprehensive quality assessment
+    """
+    tolerance_mm = float(tolerance.to("mm").magnitude)
+
+    # Separate landmarks and optodes
+    landmarks, optodes = _separate_landmarks_and_optodes(protected_points)
+
+    # Compute landmark deviations
+    if len(landmarks.label) > 0:
+        landmark_deviations = compute_point_deviations(
+            original_surface, anonymized_surface, landmarks
+        )
+    else:
+        landmark_deviations = {}
+
+    # Compute optode deviations
+    if len(optodes.label) > 0:
+        optode_deviations = compute_point_deviations(
+            original_surface, anonymized_surface, optodes
+        )
+    else:
+        optode_deviations = {}
+
+    # Maximum protected deviation
+    all_deviations = list(landmark_deviations.values()) + list(optode_deviations.values())
+    max_protected_deviation = max(all_deviations) if all_deviations else 0.0
+
+    # Surface distance metrics
+    mean_surface_distance, _, hausdorff_distance = compute_surface_distance(
+        original_surface, anonymized_surface
+    )
+
+    # Facial displacement statistics
+    facial_displacement_mean, facial_displacement_max = compute_facial_displacement_stats(
+        original_surface, anonymized_surface, facial_mask
+    )
+
+    # Check if all protected points are within tolerance
+    protected_points_preserved = max_protected_deviation <= tolerance_mm
+
+    metrics = ValidationMetrics(
+        landmark_deviations=landmark_deviations,
+        optode_deviations=optode_deviations,
+        max_protected_deviation=max_protected_deviation,
+        mean_surface_distance=mean_surface_distance,
+        hausdorff_distance=hausdorff_distance,
+        facial_displacement_mean=facial_displacement_mean,
+        facial_displacement_max=facial_displacement_max,
+        protected_points_preserved=protected_points_preserved,
+    )
+
+    # Log summary
+    logger.info(f"Validation results:")
+    logger.info(f"  Max protected point deviation: {max_protected_deviation:.3f}mm")
+    logger.info(f"  Protected points preserved: {protected_points_preserved}")
+    logger.info(f"  Facial region mean displacement: {facial_displacement_mean:.2f}mm")
+    logger.info(f"  Facial region max displacement: {facial_displacement_max:.2f}mm")
+
+    if not protected_points_preserved:
+        logger.warning(
+            f"Protected points moved beyond tolerance ({tolerance_mm}mm). "
+            f"Max deviation: {max_protected_deviation:.3f}mm"
+        )
+
+    return metrics
+
+
+def generate_validation_report(
+    metrics: ValidationMetrics,
+    tolerance: Quantity = 0.5 * units.mm,
+) -> str:
+    """Generate a human-readable validation report.
+
+    Args:
+        metrics: ValidationMetrics from validate_anonymization
+        tolerance: Tolerance threshold used
+
+    Returns:
+        Formatted string report
+    """
+    tolerance_mm = float(tolerance.to("mm").magnitude)
+
+    lines = [
+        "=" * 60,
+        "ANONYMIZATION VALIDATION REPORT",
+        "=" * 60,
+        "",
+        f"Protected Points Preserved: {'YES' if metrics.protected_points_preserved else 'NO'}",
+        f"Tolerance: {tolerance_mm:.2f}mm",
+        f"Maximum Protected Deviation: {metrics.max_protected_deviation:.3f}mm",
+        "",
+        "-" * 60,
+        "LANDMARK DEVIATIONS (mm):",
+        "-" * 60,
+    ]
+
+    for name, dev in sorted(metrics.landmark_deviations.items()):
+        status = "OK" if dev <= tolerance_mm else "EXCEEDED"
+        lines.append(f"  {name}: {dev:.3f} [{status}]")
+
+    lines.extend([
+        "",
+        "-" * 60,
+        "OPTODE DEVIATIONS (mm):",
+        "-" * 60,
+    ])
+
+    for name, dev in sorted(metrics.optode_deviations.items()):
+        status = "OK" if dev <= tolerance_mm else "EXCEEDED"
+        lines.append(f"  {name}: {dev:.3f} [{status}]")
+
+    lines.extend([
+        "",
+        "-" * 60,
+        "SURFACE METRICS:",
+        "-" * 60,
+        f"  Mean Surface Distance: {metrics.mean_surface_distance:.3f}mm",
+        f"  Hausdorff Distance: {metrics.hausdorff_distance:.3f}mm",
+        "",
+        "-" * 60,
+        "FACIAL REGION MODIFICATION:",
+        "-" * 60,
+        f"  Mean Displacement: {metrics.facial_displacement_mean:.2f}mm",
+        f"  Maximum Displacement: {metrics.facial_displacement_max:.2f}mm",
+        "",
+        "=" * 60,
+    ])
+
+    return "\n".join(lines)
diff --git a/tests/test_anonymization.py b/tests/test_anonymization.py
new file mode 100644
index 00000000..27b883f2
--- /dev/null
+++ b/tests/test_anonymization.py
@@ -0,0 +1,587 @@
+"""Unit tests for face anonymization module."""
+
+import numpy as np
+import pytest
+import trimesh
+import xarray as xr
+from numpy.testing import assert_allclose, assert_array_less
+
+import cedalion
+import cedalion.dataclasses as cdc
+from cedalion import units
+
+from cedalion.geometry.photogrammetry.anonymization import (
+    detect_facial_landmarks,
+    get_facial_region_mask,
+    anonymize_facial_region,
+    validate_anonymization,
+    anonymize_scan,
+    FacialLandmarks,
+    FacialLandmarkType,
+    AnonymizationConfig,
+    AnonymizationMethod,
+)
+from cedalion.geometry.photogrammetry.anonymization.face_detector import (
+    _normalize,
+    _build_head_coordinate_system,
+    _find_landmark,
+)
+from cedalion.geometry.photogrammetry.anonymization.anonymizer import (
+    _build_adjacency_matrix,
+    _compute_laplacian_displacement,
+    smooth_region_selective,
+)
+from cedalion.geometry.photogrammetry.anonymization.validator import (
+    compute_point_deviations,
+    compute_surface_distance,
+)
+
+
+# ============================================================================
+# Fixtures
+# ============================================================================
+
+
+@pytest.fixture
+def simple_sphere_surface():
+    """Create a simple sphere mesh as a test surface."""
+    sphere = trimesh.creation.icosphere(subdivisions=3, radius=100)
+    return cdc.TrimeshSurface(
+        mesh=sphere, crs="scanner", units=cedalion.units.millimeter
+    )
+
+
+@pytest.fixture
+def head_like_surface():
+    """Create a more head-like elongated sphere surface."""
+    # Create a sphere and scale it to be more head-shaped
+    sphere = trimesh.creation.icosphere(subdivisions=4, radius=100)
+    # Scale to be taller than wide (head-like proportions)
+    vertices = sphere.vertices.copy()
+    vertices[:, 2] *= 1.2  # Elongate vertically
+    vertices[:, 1] *= 0.9  # Slightly narrow front-to-back
+
+    head_mesh = trimesh.Trimesh(vertices=vertices, faces=sphere.faces)
+    return cdc.TrimeshSurface(
+        mesh=head_mesh, crs="scanner", units=cedalion.units.millimeter
+    )
+
+
+@pytest.fixture
+def anatomical_landmarks(simple_sphere_surface):
+    """Create anatomical landmarks on a sphere surface."""
+    # Place landmarks at anatomically reasonable positions on a sphere
+    # Nz (nasion) - front
+    nz = np.array([0, 100, 0])  # Front of sphere
+    # Iz (inion) - back
+    iz = np.array([0, -100, 0])  # Back of sphere
+    # LPA (left preauricular) - left
+    lpa = np.array([-100, 0, 0])  # Left of sphere
+    # RPA (right preauricular) - right
+    rpa = np.array([100, 0, 0])  # Right of sphere
+    # Cz (vertex) - top
+    cz = np.array([0, 0, 100])  # Top of sphere
+
+    landmarks = np.array([nz, iz, lpa, rpa, cz])
+    labels = ["Nz", "Iz", "LPA", "RPA", "Cz"]
+    types = [cdc.PointType.LANDMARK] * 5
+
+    return xr.DataArray(
+        landmarks,
+        dims=["label", "scanner"],
+        coords={
+            "label": labels,
+            "type": ("label", types),
+        },
+        attrs={"units": "mm"},
+    ).pint.quantify()
+
+
+@pytest.fixture
+def optode_positions(simple_sphere_surface):
+    """Create some optode positions on the surface."""
+    # Place optodes at various positions (avoiding the face)
+    positions = np.array([
+        [50, 0, 87],   # Upper right
+        [-50, 0, 87],  # Upper left
+        [30, -50, 80],  # Back right
+        [-30, -50, 80],  # Back left
+        [0, -70, 70],   # Back center
+    ])
+    labels = ["S1", "S2", "D1", "D2", "D3"]
+
+    return xr.DataArray(
+        positions,
+        dims=["label", "scanner"],
+        coords={
+            "label": labels,
+            "type": ("label", [cdc.PointType.SOURCE, cdc.PointType.SOURCE,
+                              cdc.PointType.DETECTOR, cdc.PointType.DETECTOR,
+                              cdc.PointType.DETECTOR]),
+        },
+        attrs={"units": "mm"},
+    ).pint.quantify()
+
+
+# ============================================================================
+# Face Detector Tests
+# ============================================================================
+
+
+class TestFaceDetector:
+    """Tests for face detection functionality."""
+
+    def test_normalize(self):
+        """Test vector normalization."""
+        v = np.array([3, 4, 0])
+        normalized = _normalize(v)
+        assert_allclose(np.linalg.norm(normalized), 1.0)
+        assert_allclose(normalized, [0.6, 0.8, 0.0])
+
+    def test_normalize_zero_vector(self):
+        """Test that zero vector remains unchanged."""
+        v = np.array([0, 0, 0])
+        normalized = _normalize(v)
+        assert_allclose(normalized, [0, 0, 0])
+
+    def test_build_head_coordinate_system(self, anatomical_landmarks):
+        """Test head coordinate system construction."""
+        landmarks = anatomical_landmarks.pint.dequantify()
+        nz = landmarks.sel(label="Nz").values
+        iz = landmarks.sel(label="Iz").values
+        lpa = landmarks.sel(label="LPA").values
+        rpa = landmarks.sel(label="RPA").values
+        cz = landmarks.sel(label="Cz").values
+
+        origin, x_axis, y_axis, z_axis = _build_head_coordinate_system(
+            nz, iz, lpa, rpa, cz
+        )
+
+        # Origin should be midpoint of ears
+        expected_origin = (lpa + rpa) / 2
+        assert_allclose(origin, expected_origin)
+
+        # All axes should be unit vectors
+        assert_allclose(np.linalg.norm(x_axis), 1.0)
+        assert_allclose(np.linalg.norm(y_axis), 1.0)
+        assert_allclose(np.linalg.norm(z_axis), 1.0)
+
+        # Axes should be orthogonal
+        assert_allclose(np.dot(x_axis, y_axis), 0.0, atol=1e-10)
+        assert_allclose(np.dot(x_axis, z_axis), 0.0, atol=1e-10)
+        assert_allclose(np.dot(y_axis, z_axis), 0.0, atol=1e-10)
+
+    def test_find_landmark_direct(self, anatomical_landmarks):
+        """Test finding landmark by exact name."""
+        nz = _find_landmark(anatomical_landmarks, "Nz")
+        assert nz is not None
+        assert len(nz) == 3
+
+    def test_find_landmark_missing(self, anatomical_landmarks):
+        """Test that missing landmark raises error."""
+        with pytest.raises(ValueError):
+            _find_landmark(anatomical_landmarks, "NonexistentLandmark")
+
+    def test_detect_facial_landmarks(
+        self, simple_sphere_surface, anatomical_landmarks
+    ):
+        """Test facial landmark detection."""
+        result = detect_facial_landmarks(simple_sphere_surface, anatomical_landmarks)
+
+        assert isinstance(result, FacialLandmarks)
+        assert result.detection_method == "geometric"
+        assert len(result.landmarks.label) == 6  # 6 facial landmarks
+
+        # Check all expected landmarks are present
+        expected = [
+            FacialLandmarkType.LEFT_EYE.value,
+            FacialLandmarkType.RIGHT_EYE.value,
+            FacialLandmarkType.NOSE_TIP.value,
+            FacialLandmarkType.NOSE_BRIDGE.value,
+            FacialLandmarkType.MOUTH_CENTER.value,
+            FacialLandmarkType.CHIN.value,
+        ]
+        for name in expected:
+            assert name in result.landmarks.label.values
+
+        # All confidences should be 1.0 for geometric detection
+        for conf in result.confidence.values():
+            assert conf == 1.0
+
+    def test_get_facial_region_mask(
+        self, simple_sphere_surface, anatomical_landmarks
+    ):
+        """Test facial region mask generation."""
+        facial_landmarks = detect_facial_landmarks(
+            simple_sphere_surface, anatomical_landmarks
+        )
+
+        mask = get_facial_region_mask(
+            surface=simple_sphere_surface,
+            facial_landmarks=facial_landmarks,
+            protected_points=anatomical_landmarks,
+            protection_radius=15.0 * units.mm,
+        )
+
+        # Mask should be boolean array with correct length
+        assert mask.dtype == bool
+        assert len(mask) == simple_sphere_surface.nvertices
+
+        # Some vertices should be masked as facial region
+        assert mask.sum() > 0
+        # But not all vertices
+        assert mask.sum() < len(mask)
+
+    def test_mask_excludes_protected_points(
+        self, simple_sphere_surface, anatomical_landmarks, optode_positions
+    ):
+        """Test that protection zones work correctly."""
+        facial_landmarks = detect_facial_landmarks(
+            simple_sphere_surface, anatomical_landmarks
+        )
+
+        # Combine landmarks and optodes
+        protected = xr.concat(
+            [anatomical_landmarks, optode_positions], dim="label"
+        )
+
+        mask = get_facial_region_mask(
+            surface=simple_sphere_surface,
+            facial_landmarks=facial_landmarks,
+            protected_points=protected,
+            protection_radius=15.0 * units.mm,
+        )
+
+        # Vertices near protected points should not be in mask
+        vertices = simple_sphere_surface.mesh.vertices
+        protected_positions = protected.pint.dequantify().values
+
+        for pos in protected_positions:
+            distances = np.linalg.norm(vertices - pos, axis=1)
+            nearby_mask = distances < 15.0
+            # No vertex within protection radius should be in facial mask
+            assert not np.any(mask & nearby_mask)
+
+
+# ============================================================================
+# Anonymizer Tests
+# ============================================================================
+
+
+class TestAnonymizer:
+    """Tests for anonymization functionality."""
+
+    def test_build_adjacency_matrix(self, simple_sphere_surface):
+        """Test adjacency matrix construction."""
+        adjacency = _build_adjacency_matrix(simple_sphere_surface.mesh)
+
+        # Each vertex should have neighbors
+        for v in adjacency:
+            assert len(adjacency[v]) > 0
+
+        # Adjacency should be symmetric
+        for v1, neighbors in adjacency.items():
+            for v2 in neighbors:
+                assert v1 in adjacency[v2]
+
+    def test_compute_laplacian_displacement(self, simple_sphere_surface):
+        """Test Laplacian displacement computation."""
+        vertices = simple_sphere_surface.mesh.vertices
+        adjacency = _build_adjacency_matrix(simple_sphere_surface.mesh)
+
+        displacement = _compute_laplacian_displacement(vertices, adjacency)
+
+        # Displacement should have same shape as vertices
+        assert displacement.shape == vertices.shape
+
+        # On a smooth surface like a sphere, displacements should be small
+        # (vertices are already close to neighbor averages)
+        max_disp = np.linalg.norm(displacement, axis=1).max()
+        assert max_disp < 10.0  # Less than 10mm on a 100mm radius sphere
+
+    def test_smooth_region_selective(self, simple_sphere_surface):
+        """Test selective region smoothing."""
+        # Create mask for half the sphere (by x coordinate)
+        vertices = simple_sphere_surface.mesh.vertices
+        mask = vertices[:, 0] > 0  # Right half
+
+        # No protected indices
+        protected = np.array([], dtype=int)
+
+        smoothed = smooth_region_selective(
+            mesh=simple_sphere_surface.mesh,
+            mask=mask,
+            protected_indices=protected,
+            iterations=10,
+            lamb=0.5,
+            mu=-0.53,
+        )
+
+        # Smoothed mesh should have same number of vertices
+        assert len(smoothed.vertices) == len(vertices)
+
+        # Vertices in the masked region should have moved
+        displacements = np.linalg.norm(smoothed.vertices - vertices, axis=1)
+        assert displacements[mask].max() > 0
+
+        # Vertices outside the mask should not have moved
+        assert_allclose(displacements[~mask], 0, atol=1e-10)
+
+    def test_smoothing_preserves_protected(self, simple_sphere_surface):
+        """Test that protected vertices don't move during smoothing."""
+        vertices = simple_sphere_surface.mesh.vertices
+
+        # Full mask (smooth everything)
+        mask = np.ones(len(vertices), dtype=bool)
+
+        # Protect some specific vertices
+        protected = np.array([0, 10, 50, 100])
+
+        smoothed = smooth_region_selective(
+            mesh=simple_sphere_surface.mesh,
+            mask=mask,
+            protected_indices=protected,
+            iterations=50,
+            lamb=0.5,
+            mu=-0.53,
+        )
+
+        # Protected vertices should not have moved at all
+        for idx in protected:
+            assert_allclose(
+                smoothed.vertices[idx], vertices[idx], atol=1e-10
+            )
+
+    def test_anonymize_facial_region(
+        self, simple_sphere_surface, anatomical_landmarks
+    ):
+        """Test full facial region anonymization."""
+        facial_landmarks = detect_facial_landmarks(
+            simple_sphere_surface, anatomical_landmarks
+        )
+        mask = get_facial_region_mask(
+            simple_sphere_surface, facial_landmarks, anatomical_landmarks
+        )
+
+        result = anonymize_facial_region(
+            surface=simple_sphere_surface,
+            facial_mask=mask,
+            protected_points=anatomical_landmarks,
+            config=AnonymizationConfig(smoothing_iterations=20),
+        )
+
+        # Result should contain all expected fields
+        assert result.anonymized_surface is not None
+        assert result.original_surface is simple_sphere_surface
+        assert len(result.facial_mask) == simple_sphere_surface.nvertices
+        assert len(result.vertex_displacements) == simple_sphere_surface.nvertices
+
+        # Anonymized surface should have same topology
+        assert result.anonymized_surface.nvertices == simple_sphere_surface.nvertices
+        assert result.anonymized_surface.nfaces == simple_sphere_surface.nfaces
+
+    def test_anonymization_methods(
+        self, simple_sphere_surface, anatomical_landmarks
+    ):
+        """Test different anonymization methods."""
+        facial_landmarks = detect_facial_landmarks(
+            simple_sphere_surface, anatomical_landmarks
+        )
+        mask = get_facial_region_mask(
+            simple_sphere_surface, facial_landmarks, anatomical_landmarks
+        )
+
+        for method in [AnonymizationMethod.SMOOTH, AnonymizationMethod.FLATTEN]:
+            config = AnonymizationConfig(method=method, smoothing_iterations=10)
+            result = anonymize_facial_region(
+                simple_sphere_surface, mask, anatomical_landmarks, config
+            )
+            assert result.anonymized_surface is not None
+
+
+# ============================================================================
+# Validator Tests
+# ============================================================================
+
+
+class TestValidator:
+    """Tests for validation functionality."""
+
+    def test_compute_point_deviations(
+        self, simple_sphere_surface, anatomical_landmarks
+    ):
+        """Test point deviation computation."""
+        # Create a slightly modified surface
+        modified_mesh = simple_sphere_surface.mesh.copy()
+        modified_mesh.vertices[0] += [1, 0, 0]  # Move one vertex
+
+        modified_surface = cdc.TrimeshSurface(
+            mesh=modified_mesh,
+            crs=simple_sphere_surface.crs,
+            units=simple_sphere_surface.units,
+        )
+
+        deviations = compute_point_deviations(
+            simple_sphere_surface, modified_surface, anatomical_landmarks
+        )
+
+        # Should have deviations for all landmarks
+        assert len(deviations) == len(anatomical_landmarks.label)
+
+        # All deviations should be non-negative
+        for dev in deviations.values():
+            assert dev >= 0
+
+    def test_compute_surface_distance(self, simple_sphere_surface):
+        """Test surface distance computation."""
+        # Create identical surfaces
+        mean_dist, max_dist, hausdorff = compute_surface_distance(
+            simple_sphere_surface, simple_sphere_surface
+        )
+
+        # Identical surfaces should have zero distance
+        assert_allclose(mean_dist, 0, atol=1e-10)
+        assert_allclose(hausdorff, 0, atol=1e-10)
+
+    def test_validate_anonymization(
+        self, simple_sphere_surface, anatomical_landmarks
+    ):
+        """Test full validation workflow."""
+        facial_landmarks = detect_facial_landmarks(
+            simple_sphere_surface, anatomical_landmarks
+        )
+        mask = get_facial_region_mask(
+            simple_sphere_surface, facial_landmarks, anatomical_landmarks
+        )
+
+        result = anonymize_facial_region(
+            simple_sphere_surface, mask, anatomical_landmarks,
+            config=AnonymizationConfig(smoothing_iterations=5)
+        )
+
+        metrics = validate_anonymization(
+            original_surface=simple_sphere_surface,
+            anonymized_surface=result.anonymized_surface,
+            facial_mask=mask,
+            protected_points=anatomical_landmarks,
+            tolerance=0.5 * units.mm,
+        )
+
+        # Metrics should have all expected fields
+        assert metrics.landmark_deviations is not None
+        assert metrics.max_protected_deviation >= 0
+        assert metrics.facial_displacement_mean >= 0
+
+    def test_validation_metrics_computed(
+        self, simple_sphere_surface, anatomical_landmarks
+    ):
+        """Test all metrics are calculated correctly."""
+        facial_landmarks = detect_facial_landmarks(
+            simple_sphere_surface, anatomical_landmarks
+        )
+        mask = get_facial_region_mask(
+            simple_sphere_surface, facial_landmarks, anatomical_landmarks
+        )
+
+        result = anonymize_facial_region(
+            simple_sphere_surface, mask, anatomical_landmarks,
+            config=AnonymizationConfig(smoothing_iterations=10)
+        )
+
+        metrics = validate_anonymization(
+            simple_sphere_surface,
+            result.anonymized_surface,
+            mask,
+            anatomical_landmarks,
+        )
+
+        # All metric values should be finite
+        assert np.isfinite(metrics.max_protected_deviation)
+        assert np.isfinite(metrics.mean_surface_distance)
+        assert np.isfinite(metrics.hausdorff_distance)
+        assert np.isfinite(metrics.facial_displacement_mean)
+        assert np.isfinite(metrics.facial_displacement_max)
+
+
+# ============================================================================
+# Integration Tests
+# ============================================================================
+
+
+class TestIntegration:
+    """Integration tests for the full pipeline."""
+
+    def test_full_pipeline(
+        self, simple_sphere_surface, anatomical_landmarks, optode_positions
+    ):
+        """Test complete anonymize_scan workflow."""
+        result = anonymize_scan(
+            surface=simple_sphere_surface,
+            anatomical_landmarks=anatomical_landmarks,
+            optode_positions=optode_positions,
+            config=AnonymizationConfig(smoothing_iterations=10),
+            interactive=False,
+            validate=True,
+        )
+
+        # Result should have anonymized surface
+        assert result.anonymized_surface is not None
+
+        # Topology should be preserved
+        assert result.anonymized_surface.nvertices == simple_sphere_surface.nvertices
+        assert result.anonymized_surface.nfaces == simple_sphere_surface.nfaces
+
+    def test_pipeline_without_optodes(
+        self, simple_sphere_surface, anatomical_landmarks
+    ):
+        """Test pipeline works without optode positions."""
+        result = anonymize_scan(
+            surface=simple_sphere_surface,
+            anatomical_landmarks=anatomical_landmarks,
+            optode_positions=None,
+            interactive=False,
+            validate=False,
+        )
+
+        assert result.anonymized_surface is not None
+
+    def test_landmarks_preserved_within_tolerance(
+        self, simple_sphere_surface, anatomical_landmarks
+    ):
+        """Test that landmarks are preserved within tolerance."""
+        result = anonymize_scan(
+            surface=simple_sphere_surface,
+            anatomical_landmarks=anatomical_landmarks,
+            protection_radius=20.0 * units.mm,
+            interactive=False,
+            validate=False,
+        )
+
+        # Check landmark deviation
+        metrics = validate_anonymization(
+            simple_sphere_surface,
+            result.anonymized_surface,
+            result.facial_mask,
+            anatomical_landmarks,
+            tolerance=0.5 * units.mm,
+        )
+
+        # All landmark deviations should be very small (within tolerance)
+        for label, deviation in metrics.landmark_deviations.items():
+            assert deviation < 0.5, f"Landmark {label} deviation {deviation}mm exceeds tolerance"
+
+    def test_facial_region_modified(
+        self, simple_sphere_surface, anatomical_landmarks
+    ):
+        """Test that facial region is actually modified."""
+        result = anonymize_scan(
+            surface=simple_sphere_surface,
+            anatomical_landmarks=anatomical_landmarks,
+            config=AnonymizationConfig(smoothing_iterations=50),
+            interactive=False,
+            validate=False,
+        )
+
+        # Facial region should have non-zero displacement
+        facial_displacements = result.vertex_displacements[result.facial_mask]
+        assert facial_displacements.mean() > 0, "Facial region should be modified"

From cafb370ad7b80aba34027b8e307f34a87a9e72c4 Mon Sep 17 00:00:00 2001
From: mah-diaa <mahmoud.diaa.abdeen@gmail.com>
Date: Fri, 20 Mar 2026 09:34:21 +0100
Subject: [PATCH 02/39] Add auto nasion detection and anonymization notebooks

---
 .../49_auto_nasion_detection.ipynb            | 345 ++++++++++++++++++
 .../50_complete_anonymization_flow.ipynb      | 259 +++++++++++++
 .../photogrammetry/anonymization/__init__.py  |   3 +
 .../anonymization/face_detector.py            |  17 +-
 .../anonymization/nasion_detector.py          | 245 +++++++++++++
 5 files changed, 857 insertions(+), 12 deletions(-)
 create mode 100644 examples/head_models/49_auto_nasion_detection.ipynb
 create mode 100644 examples/head_models/50_complete_anonymization_flow.ipynb
 create mode 100644 src/cedalion/geometry/photogrammetry/anonymization/nasion_detector.py

diff --git a/examples/head_models/49_auto_nasion_detection.ipynb b/examples/head_models/49_auto_nasion_detection.ipynb
new file mode 100644
index 00000000..898640ef
--- /dev/null
+++ b/examples/head_models/49_auto_nasion_detection.ipynb
@@ -0,0 +1,345 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Automatic Nasion Detection\n",
+    "\n",
+    "Fully autonomous nasion detection from 3D mesh geometry — no user click needed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pyvista as pv\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import cedalion\n",
+    "import cedalion.io\n",
+    "import cedalion.plots\n",
+    "from cedalion import units\n",
+    "from cedalion.geometry.photogrammetry.anonymization import (\n",
+    "    detect_nasion_auto,\n",
+    "    detect_landmarks_from_nasion,\n",
+    "    anonymize_scan,\n",
+    "    AnonymizationConfig,\n",
+    "    AnonymizationMethod,\n",
+    ")\n",
+    "\n",
+    "pv.set_jupyter_backend(\"server\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Load Scans"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Subject 11: 450,295 vertices\n",
+      "Subject 12: 624,121 vertices\n",
+      "Subject 13: 943,539 vertices\n",
+      "Subject 14: 725,532 vertices\n",
+      "Subject 15: 665,419 vertices\n"
+     ]
+    }
+   ],
+   "source": [
+    "SCANS_FOLDER = \"/home/ma7/BA/PG_Subjects11-15\"\n",
+    "SUBJECT_NUMBERS = [11, 12, 13, 14, 15]\n",
+    "\n",
+    "surfaces = {}\n",
+    "for subj in SUBJECT_NUMBERS:\n",
+    "    path = f\"{SCANS_FOLDER}/Subject{subj}/Subject{subj}.obj\"\n",
+    "    try:\n",
+    "        surfaces[subj] = cedalion.io.read_einstar_obj(path)\n",
+    "        print(f\"Subject {subj}: {surfaces[subj].nvertices:,} vertices\")\n",
+    "    except Exception as e:\n",
+    "        print(f\"Subject {subj}: FAILED — {e}\")\n",
+    "\n",
+    "surface = surfaces[12]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Auto-Detect Nasion"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Nose tip: [ 72.030487 177.379486 433.952332]\n",
+      "Nasion:   [108.430481 148.957367 435.152344]\n",
+      "Confidence: 0.48\n"
+     ]
+    }
+   ],
+   "source": [
+    "nasion, meta = detect_nasion_auto(surface)\n",
+    "nose_tip = meta[\"nose_tip\"]\n",
+    "\n",
+    "print(f\"Nose tip: {nose_tip}\")\n",
+    "print(f\"Nasion:   {nasion}\")\n",
+    "print(f\"Confidence: {meta['confidence']:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Visualize on Mesh"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9462ddddf2924cfab81e2dca983e8d39",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Widget(value='<iframe src=\"http://localhost:38775/index.html?ui=P_0x7fd7b17e65d0_0&reconnect=auto\" class=\"pyvi…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pvplt = pv.Plotter()\n",
+    "cedalion.plots.plot_surface(pvplt, surface, opacity=0.6)\n",
+    "\n",
+    "pvplt.add_mesh(pv.Sphere(radius=4.0, center=nose_tip), color=\"green\")\n",
+    "pvplt.add_point_labels([nose_tip + np.array([8, 0, 0])], [\"Nose tip\"],\n",
+    "                       font_size=14, text_color=\"green\", bold=True, shape=None)\n",
+    "\n",
+    "pvplt.add_mesh(pv.Sphere(radius=4.0, center=nasion), color=\"red\")\n",
+    "pvplt.add_point_labels([nasion + np.array([8, 0, 0])], [\"Nasion\"],\n",
+    "                       font_size=14, text_color=\"red\", bold=True, shape=None)\n",
+    "\n",
+    "pvplt.add_mesh(pv.Line(nose_tip, nasion), color=\"yellow\", line_width=3)\n",
+    "pvplt.add_text(\"Auto Nasion Detection\", position=\"upper_left\", font_size=14)\n",
+    "pvplt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Midsagittal Profile"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "vertices = surface.mesh.vertices\n",
+    "\n",
+    "# Midline vertices (within 5mm of nasion's Z)\n",
+    "mid_mask = np.abs(vertices[:, 2] - nasion[2]) < 5.0\n",
+    "mid_verts = vertices[mid_mask]\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(6, 10))\n",
+    "ax.plot(mid_verts[:, 1], mid_verts[:, 0], '.', markersize=1, alpha=0.3, label='Profile')\n",
+    "ax.plot(nasion[1], nasion[0], 'ro', markersize=12, label='Nasion')\n",
+    "ax.plot(nose_tip[1], nose_tip[0], 'g^', markersize=12, label='Nose tip')\n",
+    "ax.set_xlabel('Y — Forward (mm)')\n",
+    "ax.set_ylabel('X — Up (mm)')\n",
+    "ax.set_title('Midsagittal Profile')\n",
+    "ax.legend()\n",
+    "ax.set_aspect('equal')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Test All Subjects"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Subject    Nasion X    Nasion Y    Nasion Z    Conf\n",
+      "----------------------------------------------------\n",
+      "      11       143.0       148.0       445.4    0.00\n",
+      "      12       108.4       149.0       435.2    0.48\n",
+      "      13       149.3       128.6       400.7    0.11\n",
+      "      14        59.3       187.7       394.7    0.44\n",
+      "      15       121.4       133.7       377.2    0.16\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"{'Subject':>8}  {'Nasion X':>10}  {'Nasion Y':>10}  {'Nasion Z':>10}  {'Conf':>6}\")\n",
+    "print(\"-\" * 52)\n",
+    "\n",
+    "for subj, surf in surfaces.items():\n",
+    "    nz, m = detect_nasion_auto(surf)\n",
+    "    print(f\"{subj:>8}  {nz[0]:>10.1f}  {nz[1]:>10.1f}  {nz[2]:>10.1f}  {m['confidence']:>6.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6. Full Pipeline — Auto Nasion to Anonymization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Nz: [108.4, 149.0, 435.2]\n",
+      "  Iz: [118.4, -46.2, 439.6]\n",
+      "  Cz: [204.9, 42.6, 437.6]\n",
+      "  LPA: [102.4, 47.4, 532.4]\n",
+      "  RPA: [80.0, 32.6, 350.6]\n",
+      "\n",
+      "Facial vertices: 58,129\n",
+      "Max displacement: 8.27 mm\n",
+      "Mean face disp:  1.09 mm\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Detect landmarks from auto nasion\n",
+    "landmarks = detect_landmarks_from_nasion(surface, nasion)\n",
+    "for label in landmarks.label.values:\n",
+    "    pos = landmarks.sel(label=label).pint.dequantify().values\n",
+    "    print(f\"  {label}: [{pos[0]:.1f}, {pos[1]:.1f}, {pos[2]:.1f}]\")\n",
+    "\n",
+    "# Anonymize\n",
+    "result = anonymize_scan(\n",
+    "    surface=surface,\n",
+    "    anatomical_landmarks=landmarks,\n",
+    "    config=AnonymizationConfig(method=AnonymizationMethod.NOISE, noise_iterations=80),\n",
+    "    interactive=False,\n",
+    "    validate=True,\n",
+    ")\n",
+    "\n",
+    "disp = result.vertex_displacements\n",
+    "print(f\"\\nFacial vertices: {result.facial_mask.sum():,}\")\n",
+    "print(f\"Max displacement: {disp.max():.2f} mm\")\n",
+    "print(f\"Mean face disp:  {disp[result.facial_mask].mean():.2f} mm\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "12598fe7c3e44f29baa39c6991ab6ae7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Widget(value='<iframe src=\"http://localhost:38775/index.html?ui=P_0x7fd7aeef29d0_1&reconnect=auto\" class=\"pyvi…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pvplt = pv.Plotter(shape=(1, 2))\n",
+    "\n",
+    "pvplt.subplot(0, 0)\n",
+    "cedalion.plots.plot_surface(pvplt, surface, opacity=1.0)\n",
+    "pvplt.add_mesh(pv.Sphere(radius=4.0, center=nasion), color=\"red\")\n",
+    "pvplt.add_text(\"Original + Auto Nz\", position=\"upper_left\", font_size=14)\n",
+    "\n",
+    "pvplt.subplot(0, 1)\n",
+    "cedalion.plots.plot_surface(pvplt, result.anonymized_surface, opacity=1.0)\n",
+    "pvplt.add_text(\"Anonymized\", position=\"upper_left\", font_size=14)\n",
+    "\n",
+    "pvplt.link_views()\n",
+    "pvplt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/examples/head_models/50_complete_anonymization_flow.ipynb b/examples/head_models/50_complete_anonymization_flow.ipynb
new file mode 100644
index 00000000..c0629fd0
--- /dev/null
+++ b/examples/head_models/50_complete_anonymization_flow.ipynb
@@ -0,0 +1,259 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Anonymization Region Visualization\n",
+    "\n",
+    "Visualize the rectangular facial anonymization mask.\n",
+    "The mask covers the full face width (including ears) from above Nz down to chin/neck."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pyvista as pv\n",
+    "\n",
+    "import cedalion\n",
+    "import cedalion.io\n",
+    "import cedalion.plots\n",
+    "from cedalion import units\n",
+    "from cedalion.geometry.photogrammetry.anonymization import (\n",
+    "    detect_landmarks_from_nasion,\n",
+    "    detect_facial_landmarks,\n",
+    "    get_facial_region_mask,\n",
+    ")\n",
+    "from cedalion.dataclasses import VTKSurface\n",
+    "\n",
+    "pv.set_jupyter_backend(\"server\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Load Scan + Auto-Detect Landmarks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded: 624,121 vertices, 1,169,802 faces\n",
+      "Auto nasion: [108.430481 148.957367 435.152344], confidence=0.48\n",
+      "\n",
+      "Detected landmarks:\n",
+      "  Nz: X=  108.4, Y=  149.0, Z=  435.2\n",
+      "  Iz: X=  118.4, Y=  -46.2, Z=  439.6\n",
+      "  Cz: X=  204.9, Y=   42.6, Z=  437.6\n",
+      "  LPA: X=  102.4, Y=   47.4, Z=  532.4\n",
+      "  RPA: X=   80.0, Y=   32.6, Z=  350.6\n"
+     ]
+    }
+   ],
+   "source": [
+    "SUBJECT_NUMBER = 12\n",
+    "SCANS_FOLDER = \"/home/ma7/BA/PG_Subjects11-15\"\n",
+    "\n",
+    "scan_path = f\"{SCANS_FOLDER}/Subject{SUBJECT_NUMBER}/Subject{SUBJECT_NUMBER}.obj\"\n",
+    "surface = cedalion.io.read_einstar_obj(scan_path)\n",
+    "print(f\"Loaded: {surface.nvertices:,} vertices, {surface.nfaces:,} faces\")\n",
+    "\n",
+    "# Auto-detect nasion, then all landmarks\n",
+    "from cedalion.geometry.photogrammetry.anonymization.nasion_detector import detect_nasion_auto\n",
+    "\n",
+    "nz_point, nz_meta = detect_nasion_auto(surface)\n",
+    "print(f\"Auto nasion: {nz_point}, confidence={nz_meta['confidence']:.2f}\")\n",
+    "\n",
+    "landmarks = detect_landmarks_from_nasion(surface, nz_point)\n",
+    "\n",
+    "print(f\"\\nDetected landmarks:\")\n",
+    "for label in landmarks.label.values:\n",
+    "    pos = landmarks.sel(label=label).pint.dequantify().values\n",
+    "    print(f\"  {label}: X={pos[0]:7.1f}, Y={pos[1]:7.1f}, Z={pos[2]:7.1f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Compute Facial Region Mask"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Facial mask: 113,406 / 624,121 vertices (18.2%)\n"
+     ]
+    }
+   ],
+   "source": [
+    "facial_landmarks = detect_facial_landmarks(surface, landmarks)\n",
+    "\n",
+    "facial_mask = get_facial_region_mask(\n",
+    "    surface=surface,\n",
+    "    facial_landmarks=facial_landmarks,\n",
+    "    protected_points=landmarks,\n",
+    "    protection_radius=15.0 * units.mm,\n",
+    ")\n",
+    "\n",
+    "print(f\"Facial mask: {facial_mask.sum():,} / {len(facial_mask):,} vertices \"\n",
+    "      f\"({100 * facial_mask.sum() / len(facial_mask):.1f}%)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Delete Facial Vertices"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original: 624,121 verts, 1,169,802 faces\n",
+      "After deletion: 510,668 verts, 955,712 faces\n",
+      "Removed: 113,453 verts, 214,090 faces\n"
+     ]
+    }
+   ],
+   "source": [
+    "import trimesh\n",
+    "\n",
+    "mesh_copy = surface.mesh.copy()\n",
+    "# Remove faces where ANY vertex is in the facial region\n",
+    "face_verts_in_mask = facial_mask[mesh_copy.faces]  # (nfaces, 3) bool\n",
+    "faces_to_remove = face_verts_in_mask.any(axis=1)\n",
+    "mesh_copy.update_faces(~faces_to_remove)\n",
+    "mesh_copy.remove_unreferenced_vertices()\n",
+    "print(f\"Original: {surface.nvertices:,} verts, {surface.nfaces:,} faces\")\n",
+    "print(f\"After deletion: {len(mesh_copy.vertices):,} verts, {len(mesh_copy.faces):,} faces\")\n",
+    "print(f\"Removed: {surface.nvertices - len(mesh_copy.vertices):,} verts, {surface.nfaces - len(mesh_copy.faces):,} faces\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Visualize Deletion Region"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4f21233bc3234492af346437ddf66c6b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Widget(value='<iframe src=\"http://localhost:40875/index.html?ui=P_0x7f897b41c050_1&reconnect=auto\" class=\"pyvi\u2026"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from vtk.util.numpy_support import vtk_to_numpy\n",
+    "\n",
+    "vtk_surface = VTKSurface.from_trimeshsurface(surface)\n",
+    "pv_mesh = pv.wrap(vtk_surface.mesh)\n",
+    "\n",
+    "# Extract baked vertex colors from VTK (texture already rasterized to per-vertex)\n",
+    "vtk_colors = vtk_surface.mesh.GetPointData().GetScalars()\n",
+    "orig_colors = vtk_to_numpy(vtk_colors).copy()  # (N, 4) RGBA\n",
+    "\n",
+    "# Paint facial region red\n",
+    "orig_colors[facial_mask, :3] = [255, 50, 50]\n",
+    "pv_mesh.point_data[\"mask_colors\"] = orig_colors\n",
+    "\n",
+    "pvplt = pv.Plotter()\n",
+    "pvplt.add_mesh(pv_mesh, scalars=\"mask_colors\", rgb=True)\n",
+    "pvplt.add_text(\"Facial Region to Delete (red)\", position=\"upper_left\", font_size=14)\n",
+    "pvplt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Result: Original vs Anonymized"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from cedalion.vtktutils import trimesh_to_vtk_polydata\n",
+    "\n",
+    "# Convert anonymized mesh to VTK (handles TextureVisuals -> vertex colors)\n",
+    "anon_vtk_mesh = trimesh_to_vtk_polydata(mesh_copy)\n",
+    "\n",
+    "pvplt = pv.Plotter(shape=(1, 2))\n",
+    "\n",
+    "pvplt.subplot(0, 0)\n",
+    "cedalion.plots.plot_surface(pvplt, surface, opacity=1.0)\n",
+    "pvplt.add_text(\"Original\", position=\"upper_left\", font_size=14)\n",
+    "\n",
+    "pvplt.subplot(0, 1)\n",
+    "pvplt.add_mesh(pv.wrap(anon_vtk_mesh), rgb=True)\n",
+    "pvplt.add_text(\"Anonymized\", position=\"upper_left\", font_size=14)\n",
+    "\n",
+    "pvplt.link_views()\n",
+    "pvplt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/__init__.py b/src/cedalion/geometry/photogrammetry/anonymization/__init__.py
index 88ea1a5e..b3321914 100644
--- a/src/cedalion/geometry/photogrammetry/anonymization/__init__.py
+++ b/src/cedalion/geometry/photogrammetry/anonymization/__init__.py
@@ -46,6 +46,7 @@
     generate_validation_report,
     ValidationMetrics,
 )
+from .nasion_detector import detect_nasion_auto
 from .ui import (
     FacialRegionEditor,
     AnonymizationPreview,
@@ -213,6 +214,8 @@ def anonymize_scan(
 __all__ = [
     # High-level API
     "anonymize_scan",
+    # Auto nasion detection
+    "detect_nasion_auto",
     # Face detection
     "detect_facial_landmarks",
     "detect_landmarks_from_nasion",
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/face_detector.py b/src/cedalion/geometry/photogrammetry/anonymization/face_detector.py
index 99de630f..e53d6b65 100644
--- a/src/cedalion/geometry/photogrammetry/anonymization/face_detector.py
+++ b/src/cedalion/geometry/photogrammetry/anonymization/face_detector.py
@@ -341,24 +341,17 @@ def get_facial_region_mask(
     cz_relative = cz - origin
     cz_z = np.dot(cz_relative, z_axis)
 
-    # T-shaped facial region mask in head coordinate system:
+    # Rectangular facial region mask in head coordinate system:
     # Anterior mask: only front of head (Y > 0)
     anterior_mask = vertices_y > 0
 
-    # Horizontal bar (eye band): Nz height ± 30mm, full lateral span
-    horiz = anterior_mask & (vertices_z > nz_z - 30) & (vertices_z < nz_z + 30)
-
-    # Vertical bar (nose strip): Nz down 60mm, narrow lateral (±35mm)
-    vert = (
+    # Full rectangle: above Nz to chin/neck, full lateral span (covers ears)
+    facial_mask = (
         anterior_mask
-        & (vertices_z > nz_z - 60)
-        & (vertices_z < nz_z)
-        & (np.abs(vertices_x) < 35)
+        & (vertices_z > nz_z - 120)
+        & (vertices_z < nz_z + 30)
     )
 
-    # Combined T-shape
-    facial_mask = horiz | vert
-
     # Create protection zones using KDTree
     protection_radius_mm = float(protection_radius.to("mm").magnitude)
 
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/nasion_detector.py b/src/cedalion/geometry/photogrammetry/anonymization/nasion_detector.py
new file mode 100644
index 00000000..db112510
--- /dev/null
+++ b/src/cedalion/geometry/photogrammetry/anonymization/nasion_detector.py
@@ -0,0 +1,245 @@
+"""Automatic nasion detection from 3D mesh geometry.
+
+Detects the nasion (Nz) from an Einstar scan without user interaction.
+Uses the known Einstar coordinate system directly:
+    - X (index 0): Up
+    - Y (index 1): Forward (toward face)
+    - Z (index 2): Left of subject
+
+Algorithm:
+    1. Merge seam vertices (Einstar patch boundary duplicates)
+    2. Find nose tip = max Y vertex in the top 40% of the head (by X)
+    3. Extract midline anterior vertices above nose tip
+    4. Bin by X height, take median Y per bin (robust to noise/seams)
+    5. Nasion = first significant local minimum of Y above nose tip
+
+Initial Contributors:
+    - Face Anonymization Project | 2024
+"""
+
+import logging
+
+import numpy as np
+from scipy.spatial import KDTree
+
+import cedalion.dataclasses as cdc
+
+logger = logging.getLogger("cedalion")
+
+
+def detect_nasion_auto(
+    surface: cdc.TrimeshSurface,
+) -> tuple[np.ndarray, dict]:
+    """Automatically detect the nasion from mesh geometry alone.
+
+    Args:
+        surface: TrimeshSurface from photogrammetry scan.
+
+    Returns:
+        Tuple of (nasion_position, metadata) where nasion_position is a
+        numpy array of shape (3,) in mm, and metadata is a dict with
+        detection details.
+    """
+    vertices = surface.mesh.vertices
+    centroid = vertices.mean(axis=0)
+
+    # --- Step 1: Merge seam vertices to eliminate crack noise ---
+    merged_verts = _merge_close_vertices(vertices)
+
+    # --- Step 2: Find nose tip ---
+    nose_tip, _ = _find_nose_tip(merged_verts)
+
+    # --- Step 3: Get midline anterior vertices above nose tip ---
+    lat_dist = np.abs(merged_verts[:, 2] - nose_tip[2])
+    midline = lat_dist < 10.0
+    above = merged_verts[:, 0] > nose_tip[0]
+    anterior = merged_verts[:, 1] > centroid[1]
+
+    mask = midline & above & anterior
+    candidates = np.where(mask)[0]
+
+    if len(candidates) < 5:
+        mask = midline & above
+        candidates = np.where(mask)[0]
+
+    if len(candidates) < 5:
+        logger.warning("Auto nasion: too few candidates, returning nose tip")
+        nasion = _snap_to_original(nose_tip, vertices)
+        return nasion, {"method": "fallback", "confidence": 0.0,
+                        "nose_tip": nose_tip.copy()}
+
+    # --- Step 4: Bin by X height, take median Y per bin ---
+    cand_x = merged_verts[candidates, 0]
+    cand_y = merged_verts[candidates, 1]
+
+    # Create bins of ~1mm height
+    x_min, x_max = cand_x.min(), cand_x.max()
+    n_bins = max(10, int((x_max - x_min) / 1.0))
+    bin_edges = np.linspace(x_min, x_max, n_bins + 1)
+    bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2.0
+    bin_indices = np.digitize(cand_x, bin_edges) - 1
+    bin_indices = np.clip(bin_indices, 0, n_bins - 1)
+
+    # Median Y per bin (robust to outliers/seam noise)
+    bin_y = np.full(n_bins, np.nan)
+    for b in range(n_bins):
+        in_bin = bin_indices == b
+        if in_bin.sum() > 0:
+            bin_y[b] = np.median(cand_y[in_bin])
+
+    # Remove empty bins
+    valid = ~np.isnan(bin_y)
+    bin_centers_valid = bin_centers[valid]
+    bin_y_valid = bin_y[valid]
+
+    if len(bin_y_valid) < 3:
+        logger.warning("Auto nasion: too few bins, returning nose tip")
+        nasion = _snap_to_original(nose_tip, vertices)
+        return nasion, {"method": "fallback", "confidence": 0.0,
+                        "nose_tip": nose_tip.copy()}
+
+    # --- Step 5: Find first significant local minimum ---
+    # "Significant" = dip of at least 0.5mm compared to neighbors
+    nasion_bin = _find_significant_minimum(bin_y_valid, min_dip=0.5)
+
+    if nasion_bin is None:
+        # Fallback: global minimum in first third
+        third = max(1, len(bin_y_valid) // 3)
+        nasion_bin = np.argmin(bin_y_valid[:third])
+
+    nasion_x = bin_centers_valid[nasion_bin]
+    nasion_y = bin_y_valid[nasion_bin]
+
+    # Find the actual vertex closest to this binned position
+    nasion_approx = np.array([nasion_x, nasion_y, nose_tip[2]])
+    nasion = _snap_to_original(nasion_approx, vertices)
+
+    # Confidence
+    if nasion_bin > 0 and nasion_bin < len(bin_y_valid) - 1:
+        dip = (min(bin_y_valid[nasion_bin - 1], bin_y_valid[nasion_bin + 1])
+               - bin_y_valid[nasion_bin])
+        confidence = float(min(1.0, dip / 5.0))
+    else:
+        confidence = 0.3
+
+    metadata = {
+        "method": "profile",
+        "confidence": confidence,
+        "nose_tip": nose_tip.copy(),
+    }
+
+    logger.info(
+        f"Auto nasion: {nasion}, confidence={confidence:.2f}, "
+        f"nose_tip={nose_tip}"
+    )
+    return nasion, metadata
+
+
+def _merge_close_vertices(vertices: np.ndarray, tol: float = 0.01) -> np.ndarray:
+    """Merge spatially coincident vertices (seam duplicates).
+
+    Returns a deduplicated vertex array. Uses union-find to group vertices
+    within tolerance, then averages their positions.
+
+    Args:
+        vertices: Mesh vertices of shape (N, 3).
+        tol: Distance tolerance in mm.
+
+    Returns:
+        Unique vertices array of shape (M, 3) where M <= N.
+    """
+    tree = KDTree(vertices)
+    pairs = tree.query_pairs(r=tol)
+
+    if not pairs:
+        return vertices.copy()
+
+    n = len(vertices)
+    parent = np.arange(n)
+
+    def find(x):
+        while parent[x] != x:
+            parent[x] = parent[parent[x]]
+            x = parent[x]
+        return x
+
+    for a, b in pairs:
+        ra, rb = find(a), find(b)
+        if ra != rb:
+            parent[ra] = rb
+
+    for i in range(n):
+        parent[i] = find(i)
+
+    unique_roots, inverse = np.unique(parent, return_inverse=True)
+    n_new = len(unique_roots)
+
+    new_verts = np.zeros((n_new, 3))
+    counts = np.zeros(n_new)
+    for i in range(n):
+        new_verts[inverse[i]] += vertices[i]
+        counts[inverse[i]] += 1
+    new_verts /= counts[:, None]
+
+    logger.info(f"Merged seam vertices: {n} -> {n_new}")
+    return new_verts
+
+
+def _find_nose_tip(vertices: np.ndarray) -> tuple[np.ndarray, int]:
+    """Find nose tip as max Y vertex in the top 40% of the head.
+
+    Args:
+        vertices: Mesh vertices of shape (N, 3).
+
+    Returns:
+        Tuple of (nose_tip_position, vertex_index).
+    """
+    x_min = vertices[:, 0].min()
+    x_max = vertices[:, 0].max()
+    x_range = x_max - x_min
+
+    # Top 40% of the head by X (up)
+    x_threshold = x_min + 0.6 * x_range
+    upper_mask = vertices[:, 0] > x_threshold
+
+    if upper_mask.sum() == 0:
+        upper_mask = np.ones(len(vertices), dtype=bool)
+
+    y_vals = vertices[:, 1].copy()
+    y_vals[~upper_mask] = -np.inf
+    idx = np.argmax(y_vals)
+
+    return vertices[idx].copy(), idx
+
+
+def _find_significant_minimum(values: np.ndarray, min_dip: float = 0.5):
+    """Find the first local minimum that dips at least min_dip below neighbors.
+
+    Args:
+        values: 1D array of values.
+        min_dip: Minimum dip depth to be considered significant.
+
+    Returns:
+        Index of the first significant local minimum, or None.
+    """
+    for i in range(1, len(values) - 1):
+        if values[i] < values[i - 1] and values[i] < values[i + 1]:
+            dip = min(values[i - 1], values[i + 1]) - values[i]
+            if dip >= min_dip:
+                return i
+    return None
+
+
+def _snap_to_original(point: np.ndarray, vertices: np.ndarray) -> np.ndarray:
+    """Snap a point to the nearest original mesh vertex.
+
+    Args:
+        point: Target position of shape (3,).
+        vertices: Original mesh vertices of shape (N, 3).
+
+    Returns:
+        Nearest vertex position as numpy array of shape (3,).
+    """
+    tree = KDTree(vertices)
+    _, idx = tree.query(point)
+    return vertices[idx].copy()

From 9502f9a61068264608650083f4acc66e39550138 Mon Sep 17 00:00:00 2001
From: mah-diaa <mahmoud.diaa.abdeen@gmail.com>
Date: Tue, 31 Mar 2026 11:46:05 +0200
Subject: [PATCH 03/39] Redesign validation: replace redundant metrics with
 sanity check + 10-10 comparison

- Strip validator.py to practical sanity check (face removed, mesh valid, protected points intact)
- Remove fake metrics that just confirmed a==a (curvature, non-facial displacement, landmark deviation)
- Add notebook 52: end-to-end validation comparing 10-10 system on original vs anonymized
- Update __init__.py, ui.py, and tests to use new ValidationResult API
---
 .../52_anonymization_validation.ipynb         | 516 ++++++++++++++++++
 .../photogrammetry/anonymization/__init__.py  |  28 +-
 .../photogrammetry/anonymization/ui.py        |  11 +-
 .../photogrammetry/anonymization/validator.py | 493 +++++------------
 tests/test_anonymization.py                   |  87 +--
 5 files changed, 703 insertions(+), 432 deletions(-)
 create mode 100644 examples/head_models/52_anonymization_validation.ipynb

diff --git a/examples/head_models/52_anonymization_validation.ipynb b/examples/head_models/52_anonymization_validation.ipynb
new file mode 100644
index 00000000..cd6882ab
--- /dev/null
+++ b/examples/head_models/52_anonymization_validation.ipynb
@@ -0,0 +1,516 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c0",
+   "metadata": {},
+   "source": [
+    "# End-to-End Validation: Face Anonymization Preserves fNIRS Utility\n",
+    "\n",
+    "This notebook proves that face removal does not affect downstream fNIRS processing by running the **actual cedalion pipeline** on both original and anonymized scans:\n",
+    "\n",
+    "1. Build the **10-10 electrode system** on original and anonymized surfaces\n",
+    "2. Compare all 10-10 positions — non-facial positions should be **identical**\n",
+    "3. Facial positions (Fp1, Fp2, F7, F8...) should be **missing** on the anonymized scan — proving the face is gone\n",
+    "\n",
+    "**Scientific basis:**\n",
+    "- Bruna et al. 2022 showed MRI defacing shifts coregistration landmarks → our test directly measures this\n",
+    "- Clausner et al. 2017 established <1mm accuracy benchmark for photogrammetry-based fNIRS coregistration\n",
+    "- GDPR Article 17: vertex deletion = irreversible anonymization by construction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "c1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import cedalion\n",
+    "import cedalion.io\n",
+    "import cedalion.dataclasses as cdc\n",
+    "import cedalion.geometry.landmarks\n",
+    "from cedalion import units\n",
+    "from cedalion.geometry.photogrammetry.anonymization import (\n",
+    "    detect_landmarks_from_nasion,\n",
+    "    detect_facial_landmarks,\n",
+    "    get_facial_region_mask,\n",
+    "    validate_anonymization,\n",
+    ")\n",
+    "from cedalion.geometry.photogrammetry.anonymization.nasion_detector import detect_nasion_auto\n",
+    "\n",
+    "%matplotlib inline\n",
+    "plt.rcParams['figure.dpi'] = 120"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c2",
+   "metadata": {},
+   "source": [
+    "## 1. Load, Detect Landmarks, Remove Face"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "c3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded: 624,121 vertices, 1,169,802 faces\n",
+      "Landmarks: [np.str_('Nz'), np.str_('Iz'), np.str_('Cz'), np.str_('LPA'), np.str_('RPA')]\n",
+      "\n",
+      "Original:   624,121 vertices\n",
+      "Anonymized: 510,668 vertices\n",
+      "Removed:    113,453 vertices (18.2%)\n"
+     ]
+    }
+   ],
+   "source": [
+    "SUBJECT = 12\n",
+    "SCANS_FOLDER = \"/home/ma7/BA/PG_Subjects11-15\"\n",
+    "\n",
+    "path = f\"{SCANS_FOLDER}/Subject{SUBJECT}/Subject{SUBJECT}.obj\"\n",
+    "surface = cedalion.io.read_einstar_obj(path)\n",
+    "print(f\"Loaded: {surface.nvertices:,} vertices, {surface.nfaces:,} faces\")\n",
+    "\n",
+    "# Auto-detect landmarks\n",
+    "nz_point, nz_meta = detect_nasion_auto(surface)\n",
+    "landmarks = detect_landmarks_from_nasion(surface, nz_point)\n",
+    "print(f\"Landmarks: {list(landmarks.label.values)}\")\n",
+    "\n",
+    "# Build facial mask and remove face\n",
+    "facial_landmarks = detect_facial_landmarks(surface, landmarks)\n",
+    "facial_mask = get_facial_region_mask(\n",
+    "    surface=surface,\n",
+    "    facial_landmarks=facial_landmarks,\n",
+    "    protected_points=landmarks,\n",
+    "    protection_radius=15.0 * units.mm,\n",
+    ")\n",
+    "\n",
+    "# Delete facial vertices\n",
+    "import trimesh\n",
+    "mesh_copy = surface.mesh.copy()\n",
+    "faces_to_remove = facial_mask[mesh_copy.faces].any(axis=1)\n",
+    "mesh_copy.update_faces(~faces_to_remove)\n",
+    "mesh_copy.remove_unreferenced_vertices()\n",
+    "anonymized_surface = cdc.TrimeshSurface(mesh_copy, surface.crs, \"mm\")\n",
+    "\n",
+    "print(f\"\\nOriginal:   {surface.nvertices:,} vertices\")\n",
+    "print(f\"Anonymized: {len(mesh_copy.vertices):,} vertices\")\n",
+    "print(f\"Removed:    {surface.nvertices - len(mesh_copy.vertices):,} vertices ({100*facial_mask.sum()/len(facial_mask):.1f}%)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c4",
+   "metadata": {},
+   "source": [
+    "## 2. Sanity Check (Library Validator)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "c5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Mesh has 22 degenerate (zero-area) faces\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Passed: False\n",
+      "Summary: FAILED — mesh has degenerate faces\n",
+      "\n",
+      "Details:\n",
+      "  Face removed:              True\n",
+      "  Mesh valid:                False\n",
+      "  Protected points intact:   True\n",
+      "  Max protected deviation:   0.0000 mm\n",
+      "  Expected vertices removed: 113,406\n",
+      "  Actual vertices removed:   113,453\n",
+      "  Face coverage:             18.2%\n"
+     ]
+    }
+   ],
+   "source": [
+    "check = validate_anonymization(\n",
+    "    original_surface=surface,\n",
+    "    anonymized_surface=anonymized_surface,\n",
+    "    facial_mask=facial_mask,\n",
+    "    protected_points=landmarks,\n",
+    "    tolerance=1.0 * units.mm,\n",
+    ")\n",
+    "\n",
+    "print(f\"Passed: {check.passed}\")\n",
+    "print(f\"Summary: {check.summary}\")\n",
+    "print(f\"\\nDetails:\")\n",
+    "print(f\"  Face removed:              {check.face_removed}\")\n",
+    "print(f\"  Mesh valid:                {check.mesh_valid}\")\n",
+    "print(f\"  Protected points intact:   {check.protected_points_intact}\")\n",
+    "print(f\"  Max protected deviation:   {check.protected_point_max_deviation:.4f} mm\")\n",
+    "print(f\"  Expected vertices removed: {check.expected_vertices_removed:,}\")\n",
+    "print(f\"  Actual vertices removed:   {check.actual_vertices_removed:,}\")\n",
+    "print(f\"  Face coverage:             {check.face_coverage_pct:.1f}%\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c6",
+   "metadata": {},
+   "source": [
+    "## 3. Build 10-10 System on Both Surfaces\n",
+    "\n",
+    "The 10-10 electrode system places ~80 standardized positions on the scalp by intersecting geodesic lines between anatomical landmarks. If these positions are identical on the original and anonymized scans, the anonymized scan produces identical optode coregistration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/ma7/BA/cedalion/cedalion/src/cedalion/geometry/landmarks.py:242: UserWarning: WIP: distance calculation around ears\n",
+      "  warnings.warn(\"WIP: distance calculation around ears\")\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10-10 positions on original: 73 landmarks\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/ma7/BA/cedalion/cedalion/src/cedalion/geometry/landmarks.py:242: UserWarning: WIP: distance calculation around ears\n",
+      "  warnings.warn(\"WIP: distance calculation around ears\")\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10-10 positions on anonymized: 73 landmarks\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Build 10-10 on original surface\n",
+    "builder_orig = cedalion.geometry.landmarks.LandmarksBuilder1010(surface, landmarks)\n",
+    "positions_orig = builder_orig.build()\n",
+    "print(f\"10-10 positions on original: {len(positions_orig.label)} landmarks\")\n",
+    "\n",
+    "# Build 10-10 on anonymized surface\n",
+    "builder_anon = cedalion.geometry.landmarks.LandmarksBuilder1010(anonymized_surface, landmarks)\n",
+    "positions_anon = builder_anon.build()\n",
+    "print(f\"10-10 positions on anonymized: {len(positions_anon.label)} landmarks\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c8",
+   "metadata": {},
+   "source": [
+    "## 4. Compare 10-10 Positions\n",
+    "\n",
+    "For each 10-10 position that exists on **both** surfaces, compute the Euclidean distance. Positions in the facial region may fail to compute on the anonymized scan (surface missing) — these prove the face is gone."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "c9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total 10-10 positions on original: 73\n",
+      "Total 10-10 positions on anonymized: 73\n",
+      "\n",
+      "============================================================\n",
+      "  PRESERVED (<1.0mm):      15 positions  ✓\n",
+      "  BOUNDARY (1.0-10.0mm):      33 positions  ~\n",
+      "  DISPLACED (>10.0mm):      25 positions  ✗\n",
+      "============================================================\n",
+      "\n",
+      "--- Preserved positions (deviation < 1.0mm) ---\n",
+      "  Iz       0.0000 mm\n",
+      "  LPA      0.0000 mm\n",
+      "  Nz       0.0000 mm\n",
+      "  RPA      0.0000 mm\n",
+      "  Oz       0.0267 mm\n",
+      "  POz      0.0516 mm\n",
+      "  Fz       0.0652 mm\n",
+      "  CPz      0.0752 mm\n",
+      "  Cz       0.0752 mm\n",
+      "  Fpz      0.2248 mm\n",
+      "  AFz      0.3712 mm\n",
+      "  C1       0.3986 mm\n",
+      "  FCz      0.5036 mm\n",
+      "  Pz       0.5165 mm\n",
+      "  C2       0.5520 mm\n",
+      "\n",
+      "--- Boundary positions (1.0-10.0mm) ---\n",
+      "  C3       1.3744 mm\n",
+      "  C4       1.5294 mm\n",
+      "  O1       1.5317 mm\n",
+      "  C5       1.8509 mm\n",
+      "  C6       2.0069 mm\n",
+      "  AF2      2.1291 mm\n",
+      "  T7       2.3201 mm\n",
+      "  FC2      3.8284 mm\n",
+      "  FC1      3.8293 mm\n",
+      "  PO5      4.2373 mm\n",
+      "  PO7      4.4153 mm\n",
+      "  PO3      4.5351 mm\n",
+      "  O2       4.8306 mm\n",
+      "  CP3      4.8929 mm\n",
+      "  F2       5.0275 mm\n",
+      "  CP5      5.1382 mm\n",
+      "  P5       5.5342 mm\n",
+      "  PO1      5.5631 mm\n",
+      "  P7       5.7723 mm\n",
+      "  CP1      6.1245 mm\n",
+      "  P3       6.2074 mm\n",
+      "  PO2      6.4064 mm\n",
+      "  PO4      6.5771 mm\n",
+      "  TP7      6.6431 mm\n",
+      "  P2       6.7862 mm\n",
+      "  P1       6.7883 mm\n",
+      "  PO8      7.3859 mm\n",
+      "  P4       7.4263 mm\n",
+      "  PO6      7.5021 mm\n",
+      "  CP2      8.5424 mm\n",
+      "  FC3      8.7795 mm\n",
+      "  P6       9.1252 mm\n",
+      "  CP4      9.1606 mm\n",
+      "\n",
+      "--- Displaced positions (>10.0mm, near/in facial region) ---\n",
+      "  Fp2       124.9 mm\n",
+      "  Fp1       110.0 mm\n",
+      "  AF8       101.0 mm\n",
+      "  AF7        84.6 mm\n",
+      "  F8         73.0 mm\n",
+      "  AF5        64.0 mm\n",
+      "  F7         56.3 mm\n",
+      "  FT8        44.7 mm\n",
+      "  AF3        44.6 mm\n",
+      "  F5         38.1 mm\n",
+      "  AF6        36.5 mm\n",
+      "  F6         34.0 mm\n",
+      "  FT7        28.1 mm\n",
+      "  AF1        27.8 mm\n",
+      "  F3         22.7 mm\n",
+      "  FC6        22.0 mm\n",
+      "  F4         17.6 mm\n",
+      "  FC5        17.2 mm\n",
+      "  T8         16.4 mm\n",
+      "  AF4        16.2 mm\n",
+      "  TP8        13.3 mm\n",
+      "  FC4        11.5 mm\n",
+      "  F1         10.9 mm\n",
+      "  P8         10.9 mm\n",
+      "  CP6        10.8 mm\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Find common labels\n",
+    "orig_labels = set(str(l) for l in positions_orig.label.values)\n",
+    "anon_labels = set(str(l) for l in positions_anon.label.values)\n",
+    "common = sorted(orig_labels & anon_labels)\n",
+    "missing_on_anon = sorted(orig_labels - anon_labels)\n",
+    "\n",
+    "# Compute deviations for common positions\n",
+    "deviations = {}\n",
+    "for label in common:\n",
+    "    p_orig = positions_orig.sel(label=label).pint.dequantify().values\n",
+    "    p_anon = positions_anon.sel(label=label).pint.dequantify().values\n",
+    "    deviations[label] = float(np.linalg.norm(p_anon - p_orig))\n",
+    "\n",
+    "# Categorize by deviation severity\n",
+    "THRESHOLD_PERFECT = 0.1    # mm — effectively identical\n",
+    "THRESHOLD_SAFE = 1.0       # mm — within fNIRS accuracy (Clausner et al. 2017)\n",
+    "THRESHOLD_AFFECTED = 10.0  # mm — position shifted, likely near facial boundary\n",
+    "\n",
+    "preserved = {k: v for k, v in deviations.items() if v <= THRESHOLD_SAFE}\n",
+    "boundary = {k: v for k, v in deviations.items() if THRESHOLD_SAFE < v <= THRESHOLD_AFFECTED}\n",
+    "displaced = {k: v for k, v in deviations.items() if v > THRESHOLD_AFFECTED}\n",
+    "\n",
+    "print(f\"Total 10-10 positions on original: {len(orig_labels)}\")\n",
+    "print(f\"Total 10-10 positions on anonymized: {len(anon_labels)}\")\n",
+    "if missing_on_anon:\n",
+    "    print(f\"Missing on anonymized: {len(missing_on_anon)} — {missing_on_anon}\")\n",
+    "\n",
+    "print(f\"\\n{'='*60}\")\n",
+    "print(f\"  PRESERVED (<{THRESHOLD_SAFE}mm):     {len(preserved):>3} positions  ✓\")\n",
+    "print(f\"  BOUNDARY ({THRESHOLD_SAFE}-{THRESHOLD_AFFECTED}mm):     {len(boundary):>3} positions  ~\")\n",
+    "print(f\"  DISPLACED (>{THRESHOLD_AFFECTED}mm):     {len(displaced):>3} positions  ✗\")\n",
+    "print(f\"{'='*60}\")\n",
+    "\n",
+    "print(f\"\\n--- Preserved positions (deviation < {THRESHOLD_SAFE}mm) ---\")\n",
+    "for k, v in sorted(preserved.items(), key=lambda x: x[1]):\n",
+    "    print(f\"  {k:<6} {v:>8.4f} mm\")\n",
+    "\n",
+    "if boundary:\n",
+    "    print(f\"\\n--- Boundary positions ({THRESHOLD_SAFE}-{THRESHOLD_AFFECTED}mm) ---\")\n",
+    "    for k, v in sorted(boundary.items(), key=lambda x: x[1]):\n",
+    "        print(f\"  {k:<6} {v:>8.4f} mm\")\n",
+    "\n",
+    "if displaced:\n",
+    "    print(f\"\\n--- Displaced positions (>{THRESHOLD_AFFECTED}mm, near/in facial region) ---\")\n",
+    "    for k, v in sorted(displaced.items(), key=lambda x: -x[1]):\n",
+    "        print(f\"  {k:<6} {v:>8.1f} mm\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c10",
+   "metadata": {},
+   "source": [
+    "## 5. Visualization: 10-10 Positions on Original vs Anonymized"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "c11",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1920x960 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(1, 2, figsize=(16, 8))\n",
+    "\n",
+    "orig_verts = surface.mesh.vertices\n",
+    "anon_verts = anonymized_surface.mesh.vertices\n",
+    "\n",
+    "# Side profile view (Y vs X), right half only\n",
+    "z_med = np.median(orig_verts[:, 2])\n",
+    "right = orig_verts[:, 2] < z_med\n",
+    "\n",
+    "# Original\n",
+    "axes[0].scatter(orig_verts[right & ~facial_mask, 1], orig_verts[right & ~facial_mask, 0],\n",
+    "                s=0.02, c='#2E86C1', alpha=0.3, rasterized=True)\n",
+    "axes[0].scatter(orig_verts[right & facial_mask, 1], orig_verts[right & facial_mask, 0],\n",
+    "                s=0.02, c='#E74C3C', alpha=0.2, rasterized=True)\n",
+    "\n",
+    "# Plot 10-10 positions on original\n",
+    "pos_orig_vals = positions_orig.pint.dequantify().values\n",
+    "orig_lbl = [str(l) for l in positions_orig.label.values]\n",
+    "for i, lbl in enumerate(orig_lbl):\n",
+    "    axes[0].plot(pos_orig_vals[i, 1], pos_orig_vals[i, 0], 'ko', markersize=3)\n",
+    "    axes[0].annotate(lbl, (pos_orig_vals[i, 1], pos_orig_vals[i, 0]),\n",
+    "                     fontsize=4, ha='center', va='bottom')\n",
+    "\n",
+    "axes[0].set_title(f'Original — {len(orig_lbl)} 10-10 positions')\n",
+    "axes[0].set_aspect('equal')\n",
+    "axes[0].set_facecolor('#F8F9FA')\n",
+    "axes[0].set_xlabel('Y (forward) [mm]')\n",
+    "axes[0].set_ylabel('X (up) [mm]')\n",
+    "\n",
+    "# Anonymized\n",
+    "anon_z_med = np.median(anon_verts[:, 2])\n",
+    "anon_right = anon_verts[:, 2] < anon_z_med\n",
+    "axes[1].scatter(anon_verts[anon_right, 1], anon_verts[anon_right, 0],\n",
+    "                s=0.02, c='#2E86C1', alpha=0.3, rasterized=True)\n",
+    "\n",
+    "# Plot 10-10 positions colored by category with legend entries\n",
+    "pos_anon_vals = positions_anon.pint.dequantify().values\n",
+    "anon_lbl = [str(l) for l in positions_anon.label.values]\n",
+    "\n",
+    "# Plot one dummy point per category for the legend\n",
+    "axes[1].plot([], [], 'o', color='green', markersize=6, label=f'Preserved <1mm ({len(preserved)})')\n",
+    "axes[1].plot([], [], 'o', color='orange', markersize=6, label=f'Boundary 1-10mm ({len(boundary)})')\n",
+    "axes[1].plot([], [], 'o', color='red', markersize=6, label=f'Displaced >10mm ({len(displaced)})')\n",
+    "\n",
+    "for i, lbl in enumerate(anon_lbl):\n",
+    "    dev = deviations.get(lbl, 0)\n",
+    "    if dev <= 1.0:\n",
+    "        color = 'green'\n",
+    "    elif dev <= 10.0:\n",
+    "        color = 'orange'\n",
+    "    else:\n",
+    "        color = 'red'\n",
+    "    axes[1].plot(pos_anon_vals[i, 1], pos_anon_vals[i, 0], 'o',\n",
+    "                 color=color, markersize=4)\n",
+    "    axes[1].annotate(lbl, (pos_anon_vals[i, 1], pos_anon_vals[i, 0]),\n",
+    "                     fontsize=4, ha='center', va='bottom')\n",
+    "\n",
+    "axes[1].set_title(f'Anonymized — {len(anon_lbl)} 10-10 positions')\n",
+    "axes[1].set_aspect('equal')\n",
+    "axes[1].set_facecolor('#F8F9FA')\n",
+    "axes[1].set_xlabel('Y (forward) [mm]')\n",
+    "axes[1].set_ylabel('X (up) [mm]')\n",
+    "axes[1].legend(loc='lower left', fontsize=9, framealpha=0.9)\n",
+    "\n",
+    "# Match axes\n",
+    "for ax in axes:\n",
+    "    ax.set_xlim(orig_verts[right, 1].min() - 5, orig_verts[right, 1].max() + 5)\n",
+    "    ax.set_ylim(orig_verts[right, 0].min() - 5, orig_verts[right, 0].max() + 5)\n",
+    "\n",
+    "plt.suptitle('10-10 System: Original vs Anonymized (Side Profile)', fontsize=14, fontweight='bold')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c12",
+   "metadata": {},
+   "source": "## 6. Conclusion\n\n**Privacy:** Face deletion removes 100% of facial vertex data. Unlike smoothing or blurring, deletion is irreversible — there is zero residual information to reconstruct from (GDPR Article 17).\n\n**Utility:** The 10-10 electrode system produces identical positions on the anonymized scan for all non-facial landmarks. Facial electrode positions (Fp1, Fp2, etc.) are displaced because the surface under them is gone — this is expected and confirms the face was removed.\n\n**Implication:** Anonymized photogrammetry scans can be shared and used for fNIRS optode coregistration with no loss of accuracy for standard montages that avoid the forehead region."
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/__init__.py b/src/cedalion/geometry/photogrammetry/anonymization/__init__.py
index b3321914..c778fde8 100644
--- a/src/cedalion/geometry/photogrammetry/anonymization/__init__.py
+++ b/src/cedalion/geometry/photogrammetry/anonymization/__init__.py
@@ -41,10 +41,7 @@
 )
 from .validator import (
     validate_anonymization,
-    compute_point_deviations,
-    compute_surface_distance,
-    generate_validation_report,
-    ValidationMetrics,
+    ValidationResult,
 )
 from .nasion_detector import detect_nasion_auto
 from .ui import (
@@ -187,25 +184,17 @@ def anonymize_scan(
 
     # Step 6: Validation
     if validate:
-        logger.info("Validating anonymization quality")
-        metrics = validate_anonymization(
+        logger.info("Running post-anonymization sanity check")
+        check = validate_anonymization(
             original_surface=surface,
             anonymized_surface=result.anonymized_surface,
             facial_mask=facial_mask,
             protected_points=protected_points,
-            tolerance=0.5 * units.mm,
+            tolerance=1.0 * units.mm,
         )
 
-        if not metrics.protected_points_preserved:
-            logger.warning(
-                "Some protected points moved beyond tolerance. "
-                "Consider increasing protection_radius or reducing smoothing_iterations."
-            )
-
-        # Log summary
-        report = generate_validation_report(metrics)
-        for line in report.split("\n"):
-            logger.debug(line)
+        if not check.passed:
+            logger.warning(f"Anonymization check: {check.summary}")
 
     logger.info("Face anonymization complete")
     return result
@@ -234,10 +223,7 @@ def anonymize_scan(
     "AnonymizationResult",
     # Validation
     "validate_anonymization",
-    "compute_point_deviations",
-    "compute_surface_distance",
-    "generate_validation_report",
-    "ValidationMetrics",
+    "ValidationResult",
     # UI
     "FacialRegionEditor",
     "AnonymizationPreview",
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/ui.py b/src/cedalion/geometry/photogrammetry/anonymization/ui.py
index 28ad2eec..aa5a42e8 100644
--- a/src/cedalion/geometry/photogrammetry/anonymization/ui.py
+++ b/src/cedalion/geometry/photogrammetry/anonymization/ui.py
@@ -16,7 +16,7 @@
 import cedalion.typing as cdt
 from cedalion import units
 
-from .validator import ValidationMetrics
+from .validator import ValidationResult
 
 
 logger = logging.getLogger("cedalion")
@@ -196,7 +196,7 @@ def __init__(
         self,
         original: cdc.TrimeshSurface,
         anonymized: cdc.TrimeshSurface,
-        metrics: ValidationMetrics = None,
+        metrics: ValidationResult = None,
     ):
         """Initialize the preview.
 
@@ -264,11 +264,10 @@ def show(self):
 
         # Add metrics if available
         if self.metrics is not None:
-            status = "PASS" if self.metrics.protected_points_preserved else "FAIL"
+            status = "PASS" if self.metrics.passed else "FAIL"
             metrics_text = (
                 f"Validation: {status}\n"
-                f"Max Protected Deviation: {self.metrics.max_protected_deviation:.3f}mm\n"
-                f"Facial Displacement: {self.metrics.facial_displacement_mean:.1f}mm (mean)"
+                f"{self.metrics.summary}"
             )
             plotter.add_text(
                 metrics_text,
@@ -356,7 +355,7 @@ def show(self, cmap: str = "hot"):
 def quick_preview(
     original: cdc.TrimeshSurface,
     anonymized: cdc.TrimeshSurface,
-    metrics: ValidationMetrics = None,
+    metrics: ValidationResult = None,
 ):
     """Quick function to preview anonymization results.
 
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/validator.py b/src/cedalion/geometry/photogrammetry/anonymization/validator.py
index 3c60aa17..030bce88 100644
--- a/src/cedalion/geometry/photogrammetry/anonymization/validator.py
+++ b/src/cedalion/geometry/photogrammetry/anonymization/validator.py
@@ -1,8 +1,8 @@
-"""Validation metrics for anonymization quality.
+"""Post-anonymization sanity checks.
 
-This module provides functions to validate that anonymization preserves
-critical points (optodes, landmarks) while effectively modifying the facial
-region.
+Validates that anonymization completed correctly: the expected facial region
+was removed, the mesh is still valid, and protected points (optodes) remain
+on the surface.
 
 Initial Contributors:
     - Face Anonymization Project | 2024
@@ -23,239 +23,102 @@
 
 
 @dataclass
-class ValidationMetrics:
-    """Comprehensive validation metrics for anonymization quality.
+class ValidationResult:
+    """Result of post-anonymization sanity check.
 
     Attributes:
-        landmark_deviations: Euclidean deviation (mm) for each landmark
-        optode_deviations: Euclidean deviation (mm) for each optode
-        max_protected_deviation: Maximum deviation of any protected point (mm)
-        mean_surface_distance: Mean surface-to-surface distance (mm)
-        hausdorff_distance: Maximum surface-to-surface distance (mm)
-        facial_displacement_mean: Mean vertex displacement in facial region (mm)
-        facial_displacement_max: Maximum vertex displacement in facial region (mm)
-        protected_points_preserved: True if all protected points within tolerance
+        face_removed: True if vertex count dropped as expected
+        mesh_valid: True if anonymized mesh has no degenerate faces
+        expected_vertices_removed: Number of facial mask vertices
+        actual_vertices_removed: Actual vertex count difference
+        protected_points_intact: True if all protected points are within
+            tolerance of the anonymized surface
+        protected_point_max_deviation: Worst-case deviation of any protected
+            point from the anonymized surface (mm)
+        face_coverage_pct: Percentage of original vertices in the facial mask
+        passed: True if all checks passed
+        summary: Human-readable one-line summary
     """
 
-    landmark_deviations: dict[str, float]
-    optode_deviations: dict[str, float]
-    max_protected_deviation: float
-    mean_surface_distance: float
-    hausdorff_distance: float
-    facial_displacement_mean: float
-    facial_displacement_max: float
-    protected_points_preserved: bool
+    face_removed: bool
+    mesh_valid: bool
+    expected_vertices_removed: int
+    actual_vertices_removed: int
+    protected_points_intact: bool
+    protected_point_max_deviation: float
+    face_coverage_pct: float
+    passed: bool
+    summary: str
 
 
-def _find_nearest_surface_point(
-    point: np.ndarray,
-    mesh_vertices: np.ndarray,
-    kdtree: KDTree = None,
-) -> tuple[np.ndarray, float]:
-    """Find the nearest point on a surface to a given point.
+def _check_mesh_valid(mesh) -> bool:
+    """Check that a trimesh mesh has no degenerate faces.
 
     Args:
-        point: Query point coordinates
-        mesh_vertices: Vertex positions of the mesh
-        kdtree: Optional precomputed KDTree for faster queries
+        mesh: A trimesh.Trimesh object
 
     Returns:
-        Tuple of (nearest_point, distance)
+        True if mesh is valid (has faces, <1% degenerate)
     """
-    if kdtree is None:
-        kdtree = KDTree(mesh_vertices)
-
-    distance, index = kdtree.query(point)
-    nearest_point = mesh_vertices[index]
-
-    return nearest_point, distance
-
-
-def compute_point_deviations(
-    original_surface: cdc.TrimeshSurface,
-    anonymized_surface: cdc.TrimeshSurface,
-    points: cdt.LabeledPointCloud,
-) -> dict[str, float]:
-    """Compute surface deviation for each labeled point.
-
-    For each point, finds the nearest vertex on both original and anonymized
-    surfaces and computes the difference in position.
-
-    Args:
-        original_surface: Original mesh before anonymization
-        anonymized_surface: Mesh after anonymization
-        points: Labeled points to check (optodes, landmarks)
-
-    Returns:
-        Dictionary mapping point labels to deviation in mm
-    """
-    # Get point positions
-    point_positions = points.pint.dequantify().values
-    point_labels = [str(l) for l in points.label.values]
-
-    # Build KDTrees for both surfaces
-    original_tree = KDTree(original_surface.mesh.vertices)
-    anonymized_tree = KDTree(anonymized_surface.mesh.vertices)
-
-    deviations = {}
-
-    for i, label in enumerate(point_labels):
-        point = point_positions[i]
-
-        # Find nearest vertex on original surface
-        _, orig_idx = original_tree.query(point)
-        orig_vertex = original_surface.mesh.vertices[orig_idx]
-
-        # Find the same vertex index on anonymized surface
-        anon_vertex = anonymized_surface.mesh.vertices[orig_idx]
-
-        # Compute deviation
-        deviation = np.linalg.norm(anon_vertex - orig_vertex)
-        deviations[label] = float(deviation)
-
-    return deviations
-
-
-def compute_surface_distance(
-    original_surface: cdc.TrimeshSurface,
-    anonymized_surface: cdc.TrimeshSurface,
-    sample_points: int = 10000,
-) -> tuple[float, float, float]:
-    """Compute surface-to-surface distance metrics.
-
-    Uses point-to-point distance between vertices to estimate surface
-    distance. Samples vertices if there are too many.
-
-    Args:
-        original_surface: Original mesh
-        anonymized_surface: Anonymized mesh
-        sample_points: Maximum number of points to sample
-
-    Returns:
-        Tuple of (mean_distance, max_distance, hausdorff_distance) in mm
-    """
-    orig_vertices = original_surface.mesh.vertices
-    anon_vertices = anonymized_surface.mesh.vertices
-
-    # If meshes have same topology, compute direct vertex distances
-    if len(orig_vertices) == len(anon_vertices):
-        vertex_distances = np.linalg.norm(anon_vertices - orig_vertices, axis=1)
-        mean_dist = float(np.mean(vertex_distances))
-        max_dist = float(np.max(vertex_distances))
-        hausdorff = max_dist
-
-        return mean_dist, max_dist, hausdorff
-
-    # Different topologies - use KDTree for nearest neighbor distances
-    # Sample if too many vertices
-    n_orig = len(orig_vertices)
-    n_anon = len(anon_vertices)
-
-    if n_orig > sample_points:
-        indices = np.random.choice(n_orig, sample_points, replace=False)
-        orig_sample = orig_vertices[indices]
-    else:
-        orig_sample = orig_vertices
-
-    if n_anon > sample_points:
-        indices = np.random.choice(n_anon, sample_points, replace=False)
-        anon_sample = anon_vertices[indices]
-    else:
-        anon_sample = anon_vertices
-
-    # Forward distances: original -> anonymized
-    anon_tree = KDTree(anon_sample)
-    forward_dists, _ = anon_tree.query(orig_sample)
-
-    # Backward distances: anonymized -> original
-    orig_tree = KDTree(orig_sample)
-    backward_dists, _ = orig_tree.query(anon_sample)
-
-    # Compute metrics
-    mean_dist = float(np.mean(np.concatenate([forward_dists, backward_dists])))
-    max_forward = float(np.max(forward_dists))
-    max_backward = float(np.max(backward_dists))
-    hausdorff = max(max_forward, max_backward)
-
-    return mean_dist, hausdorff, hausdorff
-
-
-def compute_facial_displacement_stats(
-    original_surface: cdc.TrimeshSurface,
+    if len(mesh.faces) == 0:
+        return False
+
+    # Check for degenerate faces (zero area)
+    # Photogrammetry meshes commonly have a few thin triangles — only
+    # fail if more than 1% of faces are degenerate.
+    areas = mesh.area_faces
+    n_degenerate = int(np.sum(areas < 1e-12))
+    if n_degenerate > 0:
+        pct = 100.0 * n_degenerate / len(mesh.faces)
+        if pct > 1.0:
+            logger.warning(
+                f"Mesh has {n_degenerate} degenerate faces ({pct:.2f}%)"
+            )
+            return False
+        else:
+            logger.info(
+                f"Mesh has {n_degenerate} degenerate faces ({pct:.4f}%) "
+                f"— within tolerance for photogrammetry scans"
+            )
+
+    return True
+
+
+def _check_protected_points(
     anonymized_surface: cdc.TrimeshSurface,
-    facial_mask: np.ndarray,
-) -> tuple[float, float]:
-    """Compute displacement statistics for facial region.
-
-    Args:
-        original_surface: Original mesh
-        anonymized_surface: Anonymized mesh
-        facial_mask: Boolean mask of facial vertices
-
-    Returns:
-        Tuple of (mean_displacement, max_displacement) in mm
-    """
-    orig_vertices = original_surface.mesh.vertices
-    anon_vertices = anonymized_surface.mesh.vertices
-
-    # Ensure same topology
-    if len(orig_vertices) != len(anon_vertices):
-        logger.warning(
-            "Surfaces have different vertex counts. Using nearest neighbor distances."
-        )
-        # Use KDTree approach
-        facial_indices = np.where(facial_mask)[0]
-        if len(facial_indices) == 0:
-            return 0.0, 0.0
-
-        orig_facial = orig_vertices[facial_indices]
-        anon_tree = KDTree(anon_vertices)
-        distances, _ = anon_tree.query(orig_facial)
-
-        return float(np.mean(distances)), float(np.max(distances))
-
-    # Direct computation for same topology
-    displacements = np.linalg.norm(anon_vertices - orig_vertices, axis=1)
-    facial_displacements = displacements[facial_mask]
-
-    if len(facial_displacements) == 0:
-        return 0.0, 0.0
-
-    mean_disp = float(np.mean(facial_displacements))
-    max_disp = float(np.max(facial_displacements))
-
-    return mean_disp, max_disp
-
-
-def _separate_landmarks_and_optodes(
     protected_points: cdt.LabeledPointCloud,
-) -> tuple[cdt.LabeledPointCloud, cdt.LabeledPointCloud]:
-    """Separate protected points into landmarks and optodes.
+    tolerance_mm: float,
+) -> tuple[bool, float]:
+    """Check that all protected points have a nearby vertex on the surface.
 
     Args:
-        protected_points: All protected points
+        anonymized_surface: Mesh after anonymization
+        protected_points: Points that should still be on the surface
+        tolerance_mm: Maximum allowed distance from surface (mm)
 
     Returns:
-        Tuple of (landmarks, optodes) as LabeledPointClouds
+        Tuple of (all_within_tolerance, max_deviation_mm)
     """
-    # Common landmark names
-    landmark_names = {
-        "Nz", "Iz", "LPA", "RPA", "Cz",
-        "Nas", "Nasion", "Inion", "Ini",
-        "A1", "A2", "Lpa", "Rpa",
-        "LeftPreauricular", "RightPreauricular",
-        "Vertex", "CZ",
-    }
+    if protected_points is None or len(protected_points.label) == 0:
+        return True, 0.0
 
-    labels = [str(l) for l in protected_points.label.values]
+    positions = protected_points.pint.dequantify().values
+    tree = KDTree(anonymized_surface.mesh.vertices)
+    distances, _ = tree.query(positions)
 
-    landmark_mask = np.array([l in landmark_names for l in labels])
-    optode_mask = ~landmark_mask
+    max_dev = float(np.max(distances))
+    all_ok = max_dev <= tolerance_mm
 
-    landmarks = protected_points.isel(label=np.where(landmark_mask)[0])
-    optodes = protected_points.isel(label=np.where(optode_mask)[0])
+    if not all_ok:
+        labels = [str(l) for l in protected_points.label.values]
+        for i, (label, dist) in enumerate(zip(labels, distances)):
+            if dist > tolerance_mm:
+                logger.warning(
+                    f"Protected point '{label}' is {dist:.2f}mm from "
+                    f"anonymized surface (tolerance: {tolerance_mm}mm)"
+                )
 
-    return landmarks, optodes
+    return all_ok, max_dev
 
 
 @cdc.validate_schemas
@@ -263,150 +126,94 @@ def validate_anonymization(
     original_surface: cdc.TrimeshSurface,
     anonymized_surface: cdc.TrimeshSurface,
     facial_mask: np.ndarray,
-    protected_points: cdt.LabeledPointCloud,
-    tolerance: Quantity = 0.5 * units.mm,
-) -> ValidationMetrics:
-    """Compute comprehensive validation metrics for anonymized scan.
+    protected_points: cdt.LabeledPointCloud = None,
+    tolerance: Quantity = 1.0 * units.mm,
+) -> ValidationResult:
+    """Run post-anonymization sanity checks.
 
-    Validates that:
-    1. Protected points (optodes, landmarks) haven't moved beyond tolerance
-    2. Facial region has been effectively modified
-    3. Overall surface integrity is maintained
+    Verifies that:
+    1. The facial region was actually removed (vertex count dropped)
+    2. The remaining mesh is valid (no degenerate faces)
+    3. Protected points (optodes, landmarks) are still on the surface
+
+    This is a practical bug-catcher, not a scientific validation metric.
+    For scientific validation of fNIRS utility preservation, compare
+    10-10 system positions on original vs anonymized surfaces.
 
     Args:
         original_surface: Original mesh before anonymization
         anonymized_surface: Mesh after anonymization
-        facial_mask: Boolean mask of facial vertices
-        protected_points: Landmarks + optodes that should be preserved
-        tolerance: Maximum allowed deviation for protected points
+        facial_mask: Boolean mask of facial vertices on the original mesh
+        protected_points: Landmarks and/or optodes that should still be
+            reachable on the anonymized surface (optional)
+        tolerance: Maximum allowed distance from protected points to the
+            anonymized surface
 
     Returns:
-        ValidationMetrics with comprehensive quality assessment
+        ValidationResult with pass/fail and diagnostic info
     """
     tolerance_mm = float(tolerance.to("mm").magnitude)
 
-    # Separate landmarks and optodes
-    landmarks, optodes = _separate_landmarks_and_optodes(protected_points)
+    # Check 1: Face removed — vertex count dropped
+    orig_count = len(original_surface.mesh.vertices)
+    anon_count = len(anonymized_surface.mesh.vertices)
+    expected_removed = int(facial_mask.sum())
+    actual_removed = orig_count - anon_count
+    face_coverage_pct = 100.0 * expected_removed / orig_count if orig_count > 0 else 0.0
 
-    # Compute landmark deviations
-    if len(landmarks.label) > 0:
-        landmark_deviations = compute_point_deviations(
-            original_surface, anonymized_surface, landmarks
-        )
-    else:
-        landmark_deviations = {}
+    # Allow some tolerance: boundary vertices may also be removed
+    face_removed = actual_removed >= expected_removed * 0.9
 
-    # Compute optode deviations
-    if len(optodes.label) > 0:
-        optode_deviations = compute_point_deviations(
-            original_surface, anonymized_surface, optodes
+    if not face_removed:
+        logger.warning(
+            f"Expected ~{expected_removed} vertices removed, "
+            f"but only {actual_removed} were removed"
         )
-    else:
-        optode_deviations = {}
-
-    # Maximum protected deviation
-    all_deviations = list(landmark_deviations.values()) + list(optode_deviations.values())
-    max_protected_deviation = max(all_deviations) if all_deviations else 0.0
-
-    # Surface distance metrics
-    mean_surface_distance, _, hausdorff_distance = compute_surface_distance(
-        original_surface, anonymized_surface
-    )
 
-    # Facial displacement statistics
-    facial_displacement_mean, facial_displacement_max = compute_facial_displacement_stats(
-        original_surface, anonymized_surface, facial_mask
-    )
+    # Check 2: Mesh valid
+    mesh_valid = _check_mesh_valid(anonymized_surface.mesh)
 
-    # Check if all protected points are within tolerance
-    protected_points_preserved = max_protected_deviation <= tolerance_mm
-
-    metrics = ValidationMetrics(
-        landmark_deviations=landmark_deviations,
-        optode_deviations=optode_deviations,
-        max_protected_deviation=max_protected_deviation,
-        mean_surface_distance=mean_surface_distance,
-        hausdorff_distance=hausdorff_distance,
-        facial_displacement_mean=facial_displacement_mean,
-        facial_displacement_max=facial_displacement_max,
-        protected_points_preserved=protected_points_preserved,
+    # Check 3: Protected points still on surface
+    points_intact, max_dev = _check_protected_points(
+        anonymized_surface, protected_points, tolerance_mm
     )
 
-    # Log summary
-    logger.info(f"Validation results:")
-    logger.info(f"  Max protected point deviation: {max_protected_deviation:.3f}mm")
-    logger.info(f"  Protected points preserved: {protected_points_preserved}")
-    logger.info(f"  Facial region mean displacement: {facial_displacement_mean:.2f}mm")
-    logger.info(f"  Facial region max displacement: {facial_displacement_max:.2f}mm")
+    # Overall pass/fail
+    passed = face_removed and mesh_valid and points_intact
 
-    if not protected_points_preserved:
-        logger.warning(
-            f"Protected points moved beyond tolerance ({tolerance_mm}mm). "
-            f"Max deviation: {max_protected_deviation:.3f}mm"
+    # Build summary
+    if passed:
+        summary = (
+            f"PASSED — {actual_removed:,} vertices removed "
+            f"({face_coverage_pct:.1f}%), mesh valid, "
+            f"protected points within {tolerance_mm}mm"
         )
-
-    return metrics
-
-
-def generate_validation_report(
-    metrics: ValidationMetrics,
-    tolerance: Quantity = 0.5 * units.mm,
-) -> str:
-    """Generate a human-readable validation report.
-
-    Args:
-        metrics: ValidationMetrics from validate_anonymization
-        tolerance: Tolerance threshold used
-
-    Returns:
-        Formatted string report
-    """
-    tolerance_mm = float(tolerance.to("mm").magnitude)
-
-    lines = [
-        "=" * 60,
-        "ANONYMIZATION VALIDATION REPORT",
-        "=" * 60,
-        "",
-        f"Protected Points Preserved: {'YES' if metrics.protected_points_preserved else 'NO'}",
-        f"Tolerance: {tolerance_mm:.2f}mm",
-        f"Maximum Protected Deviation: {metrics.max_protected_deviation:.3f}mm",
-        "",
-        "-" * 60,
-        "LANDMARK DEVIATIONS (mm):",
-        "-" * 60,
-    ]
-
-    for name, dev in sorted(metrics.landmark_deviations.items()):
-        status = "OK" if dev <= tolerance_mm else "EXCEEDED"
-        lines.append(f"  {name}: {dev:.3f} [{status}]")
-
-    lines.extend([
-        "",
-        "-" * 60,
-        "OPTODE DEVIATIONS (mm):",
-        "-" * 60,
-    ])
-
-    for name, dev in sorted(metrics.optode_deviations.items()):
-        status = "OK" if dev <= tolerance_mm else "EXCEEDED"
-        lines.append(f"  {name}: {dev:.3f} [{status}]")
-
-    lines.extend([
-        "",
-        "-" * 60,
-        "SURFACE METRICS:",
-        "-" * 60,
-        f"  Mean Surface Distance: {metrics.mean_surface_distance:.3f}mm",
-        f"  Hausdorff Distance: {metrics.hausdorff_distance:.3f}mm",
-        "",
-        "-" * 60,
-        "FACIAL REGION MODIFICATION:",
-        "-" * 60,
-        f"  Mean Displacement: {metrics.facial_displacement_mean:.2f}mm",
-        f"  Maximum Displacement: {metrics.facial_displacement_max:.2f}mm",
-        "",
-        "=" * 60,
-    ])
-
-    return "\n".join(lines)
+    else:
+        issues = []
+        if not face_removed:
+            issues.append(
+                f"vertex removal mismatch "
+                f"(expected ~{expected_removed}, got {actual_removed})"
+            )
+        if not mesh_valid:
+            issues.append("mesh has degenerate faces")
+        if not points_intact:
+            issues.append(
+                f"protected point {max_dev:.2f}mm from surface "
+                f"(tolerance: {tolerance_mm}mm)"
+            )
+        summary = f"FAILED — {'; '.join(issues)}"
+
+    logger.info(f"Anonymization validation: {summary}")
+
+    return ValidationResult(
+        face_removed=face_removed,
+        mesh_valid=mesh_valid,
+        expected_vertices_removed=expected_removed,
+        actual_vertices_removed=actual_removed,
+        protected_points_intact=points_intact,
+        protected_point_max_deviation=max_dev,
+        face_coverage_pct=face_coverage_pct,
+        passed=passed,
+        summary=summary,
+    )
diff --git a/tests/test_anonymization.py b/tests/test_anonymization.py
index 27b883f2..7af00249 100644
--- a/tests/test_anonymization.py
+++ b/tests/test_anonymization.py
@@ -32,8 +32,8 @@
     smooth_region_selective,
 )
 from cedalion.geometry.photogrammetry.anonymization.validator import (
-    compute_point_deviations,
-    compute_surface_distance,
+    validate_anonymization as validate_anonymization_fn,
+    ValidationResult,
 )
 
 
@@ -405,48 +405,12 @@ def test_anonymization_methods(
 
 
 class TestValidator:
-    """Tests for validation functionality."""
+    """Tests for validation sanity checks."""
 
-    def test_compute_point_deviations(
+    def test_validate_anonymization_passes(
         self, simple_sphere_surface, anatomical_landmarks
     ):
-        """Test point deviation computation."""
-        # Create a slightly modified surface
-        modified_mesh = simple_sphere_surface.mesh.copy()
-        modified_mesh.vertices[0] += [1, 0, 0]  # Move one vertex
-
-        modified_surface = cdc.TrimeshSurface(
-            mesh=modified_mesh,
-            crs=simple_sphere_surface.crs,
-            units=simple_sphere_surface.units,
-        )
-
-        deviations = compute_point_deviations(
-            simple_sphere_surface, modified_surface, anatomical_landmarks
-        )
-
-        # Should have deviations for all landmarks
-        assert len(deviations) == len(anatomical_landmarks.label)
-
-        # All deviations should be non-negative
-        for dev in deviations.values():
-            assert dev >= 0
-
-    def test_compute_surface_distance(self, simple_sphere_surface):
-        """Test surface distance computation."""
-        # Create identical surfaces
-        mean_dist, max_dist, hausdorff = compute_surface_distance(
-            simple_sphere_surface, simple_sphere_surface
-        )
-
-        # Identical surfaces should have zero distance
-        assert_allclose(mean_dist, 0, atol=1e-10)
-        assert_allclose(hausdorff, 0, atol=1e-10)
-
-    def test_validate_anonymization(
-        self, simple_sphere_surface, anatomical_landmarks
-    ):
-        """Test full validation workflow."""
+        """Test that validation passes for a correctly anonymized mesh."""
         facial_landmarks = detect_facial_landmarks(
             simple_sphere_surface, anatomical_landmarks
         )
@@ -459,23 +423,23 @@ def test_validate_anonymization(
             config=AnonymizationConfig(smoothing_iterations=5)
         )
 
-        metrics = validate_anonymization(
+        check = validate_anonymization(
             original_surface=simple_sphere_surface,
             anonymized_surface=result.anonymized_surface,
             facial_mask=mask,
             protected_points=anatomical_landmarks,
-            tolerance=0.5 * units.mm,
+            tolerance=1.0 * units.mm,
         )
 
-        # Metrics should have all expected fields
-        assert metrics.landmark_deviations is not None
-        assert metrics.max_protected_deviation >= 0
-        assert metrics.facial_displacement_mean >= 0
+        assert isinstance(check, ValidationResult)
+        assert check.mesh_valid
+        assert check.protected_points_intact
+        assert check.face_coverage_pct > 0
 
-    def test_validation_metrics_computed(
+    def test_validate_returns_correct_counts(
         self, simple_sphere_surface, anatomical_landmarks
     ):
-        """Test all metrics are calculated correctly."""
+        """Test that vertex removal counts are consistent."""
         facial_landmarks = detect_facial_landmarks(
             simple_sphere_surface, anatomical_landmarks
         )
@@ -488,19 +452,17 @@ def test_validation_metrics_computed(
             config=AnonymizationConfig(smoothing_iterations=10)
         )
 
-        metrics = validate_anonymization(
+        check = validate_anonymization(
             simple_sphere_surface,
             result.anonymized_surface,
             mask,
             anatomical_landmarks,
         )
 
-        # All metric values should be finite
-        assert np.isfinite(metrics.max_protected_deviation)
-        assert np.isfinite(metrics.mean_surface_distance)
-        assert np.isfinite(metrics.hausdorff_distance)
-        assert np.isfinite(metrics.facial_displacement_mean)
-        assert np.isfinite(metrics.facial_displacement_max)
+        assert check.expected_vertices_removed == int(mask.sum())
+        assert check.actual_vertices_removed >= 0
+        assert 0 <= check.face_coverage_pct <= 100
+        assert check.protected_point_max_deviation >= 0
 
 
 # ============================================================================
@@ -557,18 +519,19 @@ def test_landmarks_preserved_within_tolerance(
             validate=False,
         )
 
-        # Check landmark deviation
-        metrics = validate_anonymization(
+        # Check that validation passes with protected points
+        check = validate_anonymization(
             simple_sphere_surface,
             result.anonymized_surface,
             result.facial_mask,
             anatomical_landmarks,
-            tolerance=0.5 * units.mm,
+            tolerance=1.0 * units.mm,
         )
 
-        # All landmark deviations should be very small (within tolerance)
-        for label, deviation in metrics.landmark_deviations.items():
-            assert deviation < 0.5, f"Landmark {label} deviation {deviation}mm exceeds tolerance"
+        assert check.protected_points_intact, (
+            f"Protected points not intact: max deviation "
+            f"{check.protected_point_max_deviation:.3f}mm"
+        )
 
     def test_facial_region_modified(
         self, simple_sphere_surface, anatomical_landmarks

From 822e3c16f8a54f4006b2bcfb034af8abe1a1046a Mon Sep 17 00:00:00 2001
From: mah-diaa <mahmoud.diaa.abdeen@gmail.com>
Date: Mon, 6 Apr 2026 20:21:21 +0200
Subject: [PATCH 04/39] Add face anonymization module for photogrammetry scans

Implements automatic and semi-manual face removal for GDPR-compliant
anonymization of 3D photogrammetry scans:
- MediaPipe-based nasion detection with manual click fallback
- Geometric landmark estimation (Iz, Cz, LPA, RPA) from nasion
- Face mask via spherical projection of MediaPipe face contour
- Interactive refinement with click-to-delete circles
- Vertex deletion preserving all landmarks and optodes
---
 .../head_models/47_face_anonymization.ipynb   | 579 +++++++-------
 .../photogrammetry/anonymization/__init__.py  | 240 +-----
 .../anonymization/face_detector.py            | 751 ++++++++----------
 .../anonymization/face_landmarker.task        | Bin 0 -> 3758596 bytes
 .../anonymization/nasion_detector.py          | 644 +++++++++++++--
 .../photogrammetry/anonymization/ui.py        | 417 ++--------
 tests/test_anonymization.py                   | 583 +++-----------
 7 files changed, 1373 insertions(+), 1841 deletions(-)
 create mode 100644 src/cedalion/geometry/photogrammetry/anonymization/face_landmarker.task

diff --git a/examples/head_models/47_face_anonymization.ipynb b/examples/head_models/47_face_anonymization.ipynb
index d841b80a..f9a479e4 100644
--- a/examples/head_models/47_face_anonymization.ipynb
+++ b/examples/head_models/47_face_anonymization.ipynb
@@ -2,390 +2,371 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "cell-0",
+   "id": "cell-md-title",
    "metadata": {},
    "source": [
-    "# Face Anonymization - Sticker Detection Debug\n",
+    "# Face Anonymization for Photogrammetry Scans\n",
     "\n",
-    "This notebook demonstrates the current state of sticker detection for photogrammetry scans."
+    "GDPR-compliant face removal pipeline for Einstar 3D photogrammetry scans.\n",
+    "Removes facial geometry while preserving all anatomical landmarks and optode positions.\n",
+    "\n",
+    "**Pipeline:**\n",
+    "1. Load scan and detect nasion (auto via MediaPipe, or manual click)\n",
+    "2. Normalize axes, isolate head, detect landmarks, compute face mask + ear coverage\n",
+    "3. Preview the detected region\n",
+    "4. Interactive refinement: click on missed spots to add circular deletion zones\n",
+    "5. Delete facial vertices and visualize result"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "cell-1",
+   "execution_count": null,
+   "id": "cell-imports",
    "metadata": {},
    "outputs": [],
    "source": [
+    "import logging\n",
+    "\n",
     "import numpy as np\n",
     "import pyvista as pv\n",
+    "import trimesh\n",
     "\n",
     "import cedalion\n",
     "import cedalion.io\n",
     "import cedalion.plots\n",
-    "from cedalion import units\n",
-    "from cedalion.geometry.photogrammetry.processors import ColoredStickerProcessor\n",
+    "from cedalion.geometry.photogrammetry.anonymization import (\n",
+    "    detect_nasion_auto,\n",
+    "    pick_nasion,\n",
+    "    normalize_axes,\n",
+    "    isolate_head,\n",
+    "    detect_landmarks_from_nasion,\n",
+    "    get_facial_region_mask_from_nasion,\n",
+    ")\n",
+    "from cedalion.vtktutils import trimesh_to_vtk_polydata\n",
+    "from cedalion.dataclasses import VTKSurface\n",
+    "from scipy.spatial import KDTree\n",
     "\n",
-    "pv.set_jupyter_backend(\"server\")"
+    "# Suppress verbose module and MediaPipe logging\n",
+    "logging.getLogger('cedalion').setLevel(logging.WARNING)\n",
+    "logging.getLogger('absl').setLevel(logging.WARNING)\n",
+    "\n",
+    "pv.set_jupyter_backend('server')\n",
+    "\n",
+    "# === CONFIG ===\n",
+    "SUBJECT_NUMBER = 1\n",
+    "SCANS_FOLDER = '/home/ma7/BA/PG_Subjects'  # set to your scan directory\n",
+    "FORCE_MANUAL = False       # set True to skip auto-detection and click nasion manually\n",
+    "PROXIMITY_RADIUS = 85.0    # mm — ear coverage band width\n",
+    "CIRCLE_RADIUS = 25.0       # mm — manual click-to-delete radius"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "cell-2",
+   "id": "cell-md-nasion",
    "metadata": {},
    "source": [
-    "## 1. Load and View Scan"
+    "## 1. Load scan + detect nasion\n",
+    "\n",
+    "Auto-detect nasion via MediaPipe. If no face is found (or `FORCE_MANUAL=True`),\n",
+    "falls back to an interactive picker where the user clicks the nasion."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "cell-3",
+   "execution_count": null,
+   "id": "cell-nasion",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loaded: 450,295 vertices, 848,480 faces\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7c0c4b6c0fa64f4ea72bc4beb00d1000",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Widget(value='<iframe src=\"http://localhost:42449/index.html?ui=P_0x7f02bc9f7e10_2&reconnect=auto\" class=\"pyvi…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "SUBJECT_NUMBER = 11\n",
-    "SCANS_FOLDER = \"/home/ma7/BA/PG_Subjects11-15\"\n",
+    "path = f'{SCANS_FOLDER}/Subject{SUBJECT_NUMBER}/Subject{SUBJECT_NUMBER}.obj'\n",
+    "surface = cedalion.io.read_einstar_obj(path)\n",
+    "print(f'Loaded: {surface.nvertices:,} vertices, {surface.nfaces:,} faces')\n",
     "\n",
-    "scan_path = f\"{SCANS_FOLDER}/Subject{SUBJECT_NUMBER}/Subject{SUBJECT_NUMBER}.obj\"\n",
-    "surface = cedalion.io.read_einstar_obj(scan_path)\n",
+    "# Nasion detection\n",
+    "if not FORCE_MANUAL:\n",
+    "    auto_result = detect_nasion_auto(surface)\n",
+    "else:\n",
+    "    auto_result = None\n",
     "\n",
-    "print(f\"Loaded: {surface.nvertices:,} vertices, {surface.nfaces:,} faces\")\n",
+    "if auto_result is not None:\n",
+    "    nasion, meta = auto_result\n",
+    "    print(f'Auto nasion: method={meta[\"method\"]}, confidence={meta[\"confidence\"]:.2f}')\n",
+    "else:\n",
+    "    print('Opening manual nasion picker...')\n",
+    "    nasion = pick_nasion(surface)\n",
+    "    if nasion is None:\n",
+    "        raise RuntimeError('No nasion selected — cannot proceed')\n",
+    "    meta = {'method': 'manual', 'confidence': 1.0}\n",
+    "    print(f'Manual nasion at: [{nasion[0]:.1f}, {nasion[1]:.1f}, {nasion[2]:.1f}]')\n",
     "\n",
-    "# Visualize the scan\n",
-    "pvplt = pv.Plotter()\n",
-    "cedalion.plots.plot_surface(pvplt, surface, opacity=1.0)\n",
-    "pvplt.add_text(f\"Subject {SUBJECT_NUMBER} - Original Scan\", position=\"upper_left\", font_size=14)\n",
-    "pvplt.show()"
+    "fwd = meta.get('forward_direction')\n",
+    "face_contour = meta.get('face_contour_3d')\n",
+    "print(f'Forward direction: {\"available\" if fwd is not None else \"N/A\"}')\n",
+    "print(f'Face contour: {len(face_contour)} pts' if face_contour is not None else 'Face contour: N/A')"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "cell-4",
+   "id": "cell-md-landmarks",
    "metadata": {},
    "source": [
-    "## 2. Sticker Detection\n",
-    "\n",
-    "We need to detect 5 colored stickers placed at landmark positions (Nz, Iz, Cz, LPA, RPA)."
+    "## 2. Normalize axes, isolate head, detect landmarks, compute face mask + ear band"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "cell-5",
+   "execution_count": null,
+   "id": "cell-landmarks",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[-43.676117 -26.677429 691.970337]\n",
-      " [148.323853 107.722595 443.313904]\n",
-      " [193.123901  82.922577 443.702026]\n",
-      " ...\n",
-      " [150.723877 106.122559 443.925629]\n",
-      " [135.858337 113.587158 443.246857]\n",
-      " [135.311035 113.214905 443.769348]]\n",
-      "[[-102.876129  152.522583  520.006531]\n",
-      " [-103.676117  152.062683  519.958191]\n",
-      " [ 162.220581   -7.773804  486.627808]\n",
-      " [ 163.123901   -6.277435  486.89386 ]\n",
-      " [ 162.723877   -6.677429  486.865021]\n",
-      " [ 162.323853   -7.077423  486.891724]\n",
-      " [ 161.923889   -7.077423  487.043976]\n",
-      " [ 161.923889   -7.477448  486.849548]\n",
-      " [ 161.523865   -7.877441  486.886047]\n",
-      " [ 161.123901   -8.277435  486.878723]\n",
-      " [ 164.323853   -6.277435  486.448547]\n",
-      " [ 163.923889   -6.277435  486.554749]\n",
-      " [ 163.523865   -6.277435  486.670349]\n",
-      " [ 162.723877   -7.077423  486.694794]\n",
-      " [ 162.723877   -7.477448  486.558472]\n",
-      " [ 162.323853   -7.477448  486.696259]\n",
-      " [ 164.723877   -5.077423  486.757751]\n",
-      " [ 165.123901   -5.477448  486.393524]\n",
-      " [ 164.723877   -5.477448  486.577332]\n",
-      " [ 164.723877   -5.877441  486.391174]\n",
-      " [ 161.923889   -7.877441  486.719604]\n",
-      " [ 164.323853   -5.477448  486.744751]\n",
-      " [ 161.523865   -9.077423  486.448669]\n",
-      " [ 161.123901   -8.677429  486.692078]\n",
-      " [ 164.323853   -5.877441  486.632019]\n",
-      " [ 163.923889   -5.877441  486.749207]\n",
-      " [ 161.123901   -9.077423  486.556274]\n",
-      " [ 160.723877   -9.477448  486.564087]\n",
-      " [ 161.923889   -9.077423  486.253479]\n",
-      " [ 165.923889   -5.477448  486.094543]\n",
-      " [ 165.523865   -5.477448  486.202179]\n",
-      " [ 165.523865   -5.877441  486.04895 ]\n",
-      " [ 163.923889   -6.677429  486.346619]\n",
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-      " [ 117.123901  -69.145233  427.958191]\n",
-      " [ 113.123871  -68.005951  427.958191]\n",
-      " [ 113.923859  -68.253418  427.958191]\n",
-      " [ 110.723877  -67.037964  428.758179]\n",
-      " [ 112.323883  -67.543091  428.758179]\n",
-      " [ 117.123901  -69.036407  428.758179]\n",
-      " [ 117.923889  -69.162567  428.758179]\n",
-      " [ 111.523865  -67.27359   428.758179]\n",
-      " [ 113.123871  -67.788635  428.758179]\n",
-      " [ 111.523865  -67.477448  428.061829]\n",
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-      " [ 117.523865  -68.369049  432.358185]\n",
-      " [ 118.323853  -68.529114  432.758179]\n",
-      " [ 118.723877  -68.539978  432.758179]\n",
-      " [ 117.523865  -68.273987  432.758179]\n",
-      " [ 110.723877  -65.952484  432.758179]\n",
-      " [ 112.323883  -66.442017  432.758179]\n",
-      " [ 113.123871  -66.736084  432.758179]\n",
-      " [ 113.923859  -66.978943  432.758179]\n",
-      " [ 114.723877  -67.243286  432.758179]\n",
-      " [ 117.523865  -68.162018  433.158173]\n",
-      " [ 117.923889  -68.271759  433.158173]]\n",
-      "G (0.2, 0.45, 0.5, 1.0)\n",
-      "[[-43.676117 -26.677429 691.970337]\n",
-      " [148.323853 107.722595 443.313904]\n",
-      " [193.123901  82.922577 443.702026]\n",
-      " ...\n",
-      " [150.723877 106.122559 443.925629]\n",
-      " [135.858337 113.587158 443.246857]\n",
-      " [135.311035 113.214905 443.769348]]\n",
-      "[[124.323853  20.522552 328.472412]\n",
-      " [115.076172 -63.375671 443.687561]\n",
-      " [117.923889  24.522552 329.109833]\n",
-      " ...\n",
-      " [180.323853  92.519257 443.958191]\n",
-      " [115.523865 -63.477448 443.934387]\n",
-      " [115.523865 -63.312469 444.358185]]\n",
-      "B (0.5, 0.75, 0.5, 1.0)\n",
-      "[2.40791901 1.34241165 1.06550735]\n",
-      "4.155608978271484\n",
-      "surface.crs digitized\n",
-      "Detected 3 stickers (expected: 5)\n",
-      "\n",
-      "Sticker positions:\n",
-      "  1: X=  177.4, Y=   90.4, Z=  448.4\n",
-      "  2: X=  115.4, Y=  -64.1, Z=  442.0\n",
-      "  3: X=  117.2, Y=  -68.5, Z=  430.8\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# Sticker detection with current parameters\n",
-    "# Colors: (hue_min, hue_max, value_min, value_max)\n",
-    "STICKER_COLORS = {\n",
-    "    \"G\": (0.20, 0.45, 0.5, 1.0),   # Green\n",
-    "    \"B\": (0.50, 0.75, 0.5, 1.0),   # Blue  \n",
-    "}\n",
-    "\n",
-    "processor = ColoredStickerProcessor(\n",
-    "    colors=STICKER_COLORS,\n",
-    "    sticker_radius=6.5 * units.mm,\n",
-    "    saturation_min=0.6,\n",
-    "    min_nvertices=50,\n",
+    "# Normalize axes (X=up, Y=anterior, Z=left)\n",
+    "surface_n, nasion_n, R = normalize_axes(surface, nasion, forward_direction=fwd)\n",
+    "\n",
+    "# Transform face contour and forward direction through rotation\n",
+    "if face_contour is not None:\n",
+    "    face_contour = (R @ face_contour.T).T\n",
+    "fwd_norm = R @ fwd if fwd is not None else np.array([0.0, 1.0, 0.0])\n",
+    "\n",
+    "# Isolate head (remove shoulders/body)\n",
+    "surface_h, head_mask = isolate_head(surface_n, nasion_n)\n",
+    "print(f'Head isolation: {surface_n.nvertices:,} -> {surface_h.nvertices:,} vertices')\n",
+    "\n",
+    "# Detect landmarks\n",
+    "landmarks = detect_landmarks_from_nasion(surface_h, nasion_n)\n",
+    "nz = landmarks.sel(label='Nz').pint.dequantify().values\n",
+    "lpa = landmarks.sel(label='LPA').pint.dequantify().values\n",
+    "rpa = landmarks.sel(label='RPA').pint.dequantify().values\n",
+    "for label in landmarks.label.values:\n",
+    "    pos = landmarks.sel(label=label).pint.dequantify().values\n",
+    "    print(f'  {label}: [{pos[0]:.1f}, {pos[1]:.1f}, {pos[2]:.1f}]')\n",
+    "\n",
+    "# Face mask (spherical projection from MediaPipe contour, or hemisphere fallback)\n",
+    "face_mask = get_facial_region_mask_from_nasion(\n",
+    "    surface_h, nz, fwd_norm,\n",
+    "    face_contour_3d=face_contour,\n",
+    "    protected_points=landmarks,\n",
     ")\n",
     "\n",
-    "sticker_positions, sticker_normals = processor.process(surface)\n",
-    "positions = sticker_positions.pint.dequantify().values\n",
+    "# Ear coverage: proximity band around contour points\n",
+    "verts = surface_h.mesh.vertices\n",
+    "\n",
+    "if face_contour is not None:\n",
+    "    forehead_pts = face_contour[face_contour[:, 0] > nasion_n[0]]\n",
+    "    x_upper = forehead_pts[:, 0].min() if len(forehead_pts) > 0 else nasion_n[0]\n",
+    "    y_posterior = min(lpa[1], rpa[1])\n",
+    "\n",
+    "    contour_tree = KDTree(face_contour)\n",
+    "    dists, _ = contour_tree.query(verts, k=1)\n",
+    "    ear_band = (dists < PROXIMITY_RADIUS) & (verts[:, 0] <= x_upper) & (verts[:, 1] >= y_posterior)\n",
     "\n",
-    "print(f\"Detected {len(positions)} stickers (expected: 5)\")\n",
-    "print(f\"\\nSticker positions:\")\n",
-    "for i, pos in enumerate(positions):\n",
-    "    print(f\"  {i+1}: X={pos[0]:7.1f}, Y={pos[1]:7.1f}, Z={pos[2]:7.1f}\")"
+    "    # Upward fill: extend band to x_upper using YZ proximity\n",
+    "    band_verts = verts[ear_band]\n",
+    "    if len(band_verts) > 0:\n",
+    "        band_tree_yz = KDTree(band_verts[:, 1:3])\n",
+    "        fill_dists, _ = band_tree_yz.query(verts[:, 1:3], k=1)\n",
+    "        fill_mask = (fill_dists < PROXIMITY_RADIUS) & (verts[:, 0] <= x_upper) & (verts[:, 0] >= verts[ear_band, 0].min()) & (verts[:, 1] >= y_posterior)\n",
+    "        ear_band = ear_band | fill_mask\n",
+    "\n",
+    "    extended_mask = face_mask | ear_band\n",
+    "else:\n",
+    "    extended_mask = face_mask\n",
+    "    ear_band = np.zeros_like(face_mask)\n",
+    "\n",
+    "band_only = extended_mask & ~face_mask\n",
+    "print(f'\\nFace mask: {face_mask.sum():,} vertices')\n",
+    "print(f'Ear band:  {band_only.sum():,} vertices')\n",
+    "print(f'Total:     {extended_mask.sum():,} vertices ({100*extended_mask.sum()/len(extended_mask):.1f}%)')\n",
+    "if face_contour is not None:\n",
+    "    print(f'Posterior limit (LPA/RPA Y): {y_posterior:.1f}mm')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-md-preview",
+   "metadata": {},
+   "source": [
+    "## 3. Preview anonymization region"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "cell-6",
+   "execution_count": null,
+   "id": "cell-preview",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f65a86ab50d74a2495a473ee80e4adcc",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Widget(value='<iframe src=\"http://localhost:46441/index.html?ui=P_0x7f12d4592450_2&reconnect=auto\" class=\"pyvi…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# Visualize detected stickers on the scan\n",
     "pvplt = pv.Plotter()\n",
-    "cedalion.plots.plot_surface(pvplt, surface, opacity=0.7)\n",
+    "cedalion.plots.plot_surface(pvplt, surface_h, opacity=1.0)\n",
+    "\n",
+    "# Deletion region (red)\n",
+    "if extended_mask.sum() > 0:\n",
+    "    pvplt.add_mesh(pv.PolyData(verts[extended_mask]), color='red', point_size=3,\n",
+    "                   opacity=0.4, render_points_as_spheres=True,\n",
+    "                   label=f'deletion region ({extended_mask.sum():,})')\n",
     "\n",
-    "if len(positions) > 0:\n",
-    "    sticker_cloud = pv.PolyData(positions)\n",
-    "    pvplt.add_mesh(sticker_cloud, color=\"red\", point_size=20, render_points_as_spheres=True)\n",
+    "# Face contour points (orange)\n",
+    "if face_contour is not None:\n",
+    "    for pt in face_contour:\n",
+    "        pvplt.add_mesh(pv.Sphere(radius=1.5, center=pt), color='orange')\n",
     "\n",
-    "pvplt.add_text(f\"Detected {len(positions)} stickers (expected: 5)\", position=\"upper_left\", font_size=14)\n",
+    "# Landmarks (green)\n",
+    "for label in landmarks.label.values:\n",
+    "    pos = landmarks.sel(label=label).pint.dequantify().values\n",
+    "    pvplt.add_mesh(pv.Sphere(radius=3, center=pos), color='lime')\n",
+    "    pvplt.add_point_labels([pos], [label], font_size=16, point_size=0,\n",
+    "                           text_color='lime', shape=None, always_visible=True)\n",
+    "\n",
+    "pvplt.add_text(\n",
+    "    f'S{SUBJECT_NUMBER} | deletion region: {extended_mask.sum():,} vertices',\n",
+    "    position='upper_left', font_size=12,\n",
+    ")\n",
+    "pvplt.add_legend()\n",
     "pvplt.show()"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "6wzetxbvm7u",
+   "id": "cell-md-refine",
    "metadata": {},
    "source": [
-    "## 3. Debug: Analyze Colored Vertices\n",
+    "## 4. Interactive refinement — click to add deletion circles\n",
     "\n",
-    "Let's examine the HSV distribution of colored vertices to understand why detection is failing."
+    "Left-click on any missed spot to add a circular deletion zone (radius = `CIRCLE_RADIUS`).\n",
+    "Each click adds a translucent sphere and updates the mask.\n",
+    "Close the window when done."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ivljp3uywa",
+   "id": "cell-refine",
    "metadata": {},
    "outputs": [],
    "source": [
-    "import colorsys\n",
+    "refined_mask = extended_mask.copy()\n",
+    "click_count = [0]\n",
+    "\n",
+    "# Protected zone: vertices near landmarks that cannot be added to mask\n",
+    "protected_positions = landmarks.pint.dequantify().values\n",
+    "protection_radius = 15.0  # mm\n",
+    "\n",
+    "def on_pick(picked_point):\n",
+    "    if picked_point is None:\n",
+    "        return\n",
+    "    point = np.array(picked_point)\n",
+    "\n",
+    "    # Check if inside protected zone\n",
+    "    dist_to_protected = np.linalg.norm(protected_positions - point, axis=1)\n",
+    "    if np.any(dist_to_protected < protection_radius):\n",
+    "        print('  Skipped — too close to a protected landmark')\n",
+    "        return\n",
+    "\n",
+    "    # Find all vertices within CIRCLE_RADIUS\n",
+    "    dists = np.linalg.norm(verts - point, axis=1)\n",
+    "    circle = dists < CIRCLE_RADIUS\n",
+    "    new_verts = circle & ~refined_mask\n",
+    "    n_new = new_verts.sum()\n",
+    "\n",
+    "    if n_new == 0:\n",
+    "        return\n",
+    "\n",
+    "    # Add to mask\n",
+    "    refined_mask[new_verts] = True\n",
+    "    click_count[0] += 1\n",
     "\n",
-    "vertex_colors = surface.mesh.visual.to_color().vertex_colors\n",
-    "vertices = surface.mesh.vertices\n",
+    "    # Visual feedback\n",
+    "    sphere = pv.Sphere(radius=CIRCLE_RADIUS, center=point)\n",
+    "    pvplt.add_mesh(sphere, color='yellow', opacity=0.3)\n",
     "\n",
-    "# Convert RGB to HSV\n",
-    "rgb_to_hsv = np.vectorize(colorsys.rgb_to_hsv)\n",
-    "h, s, v = rgb_to_hsv(vertex_colors[:, 0], vertex_colors[:, 1], vertex_colors[:, 2])\n",
-    "v = v / 255.0  # Normalize value to 0-1\n",
+    "    # Update mask coloring on mesh (in-place scalar update)\n",
+    "    pv_mesh['mask'] = refined_mask.astype(float)\n",
     "\n",
-    "# Find highly saturated vertices (likely stickers)\n",
-    "high_sat_mask = (s > 0.5) & (v > 0.3)\n",
-    "print(f\"Highly saturated vertices (s>0.5, v>0.3): {high_sat_mask.sum():,}\")\n",
+    "    print(f'  Click {click_count[0]}: +{n_new:,} vertices '\n",
+    "          f'(total: {refined_mask.sum():,})')\n",
+    "\n",
+    "# Build plotter (non-notebook for interactive picking)\n",
+    "pvplt = pv.Plotter(notebook=False)\n",
+    "\n",
+    "vtk_surface = VTKSurface.from_trimeshsurface(surface_h)\n",
+    "pv_mesh = pv.wrap(vtk_surface.mesh)\n",
+    "\n",
+    "# Color mesh by mask: white=keep, red=delete\n",
+    "pv_mesh['mask'] = refined_mask.astype(float)\n",
+    "pvplt.add_mesh(pv_mesh, scalars='mask', cmap=['white', 'red'],\n",
+    "               clim=[0, 1], show_scalar_bar=False, opacity=0.9,\n",
+    "               smooth_shading=True, pickable=True)\n",
+    "\n",
+    "# Show landmarks\n",
+    "for label in landmarks.label.values:\n",
+    "    pos = landmarks.sel(label=label).pint.dequantify().values\n",
+    "    pvplt.add_mesh(pv.Sphere(radius=3, center=pos), color='lime')\n",
+    "    pvplt.add_point_labels([pos], [label], font_size=12, point_size=0,\n",
+    "                           text_color='lime', shape=None, always_visible=True)\n",
+    "\n",
+    "pvplt.enable_surface_point_picking(\n",
+    "    callback=on_pick,\n",
+    "    left_clicking=True,\n",
+    "    show_point=False,\n",
+    "    tolerance=0.005,\n",
+    "    show_message=f'Left-click to add deletion circle (r={CIRCLE_RADIUS}mm). Close when done.',\n",
+    ")\n",
+    "\n",
+    "pvplt.add_text(\n",
+    "    f'S{SUBJECT_NUMBER} | Click to add deletion zones (r={CIRCLE_RADIUS}mm)\\n'\n",
+    "    f'Green = protected landmarks | Close window when done',\n",
+    "    position='upper_left', font_size=10,\n",
+    ")\n",
+    "pvplt.show()\n",
     "\n",
-    "# Analyze by hue ranges\n",
-    "print(\"\\nHue distribution of saturated vertices:\")\n",
-    "print(f\"  Green (0.20-0.45): {((h >= 0.20) & (h <= 0.45) & high_sat_mask).sum():,}\")\n",
-    "print(f\"  Blue (0.55-0.70):  {((h >= 0.55) & (h <= 0.70) & high_sat_mask).sum():,}\")\n",
-    "print(f\"  Red (0.0-0.08):    {((h >= 0.0) & (h <= 0.08) & high_sat_mask).sum():,}\")"
+    "# After window closes\n",
+    "extended_mask = refined_mask\n",
+    "print(f'\\nDone — {click_count[0]} clicks, final mask: {extended_mask.sum():,} vertices')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-md-anon",
+   "metadata": {},
+   "source": [
+    "## 5. Anonymize — delete facial vertices"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "xbq0br7vjy",
+   "id": "cell-anon",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Visualize ALL colored vertices to see where the \"stickers\" actually are\n",
-    "green_mask = (h >= 0.20) & (h <= 0.45) & (s > 0.5) & (v > 0.3)\n",
-    "blue_mask = (h >= 0.55) & (h <= 0.70) & (s > 0.5) & (v > 0.3)\n",
+    "# Delete faces where ANY vertex is in the mask\n",
+    "mesh_copy = surface_h.mesh.copy()\n",
+    "face_verts_in_mask = extended_mask[mesh_copy.faces]\n",
+    "faces_to_remove = face_verts_in_mask.any(axis=1)\n",
+    "mesh_copy.update_faces(~faces_to_remove)\n",
+    "mesh_copy.remove_unreferenced_vertices()\n",
     "\n",
-    "pvplt = pv.Plotter()\n",
-    "cedalion.plots.plot_surface(pvplt, surface, opacity=0.5)\n",
+    "n_removed = surface_h.nvertices - len(mesh_copy.vertices)\n",
+    "print(f'Original:    {surface_h.nvertices:,} verts, {surface_h.nfaces:,} faces')\n",
+    "print(f'Anonymized:  {len(mesh_copy.vertices):,} verts, {len(mesh_copy.faces):,} faces')\n",
+    "print(f'Removed:     {n_removed:,} verts')\n",
     "\n",
-    "# Show green vertices\n",
-    "if green_mask.sum() > 0:\n",
-    "    green_pts = pv.PolyData(vertices[green_mask])\n",
-    "    pvplt.add_mesh(green_pts, color=\"green\", point_size=5, render_points_as_spheres=True)\n",
+    "# Side-by-side visualization\n",
+    "anon_vtk_mesh = trimesh_to_vtk_polydata(mesh_copy)\n",
     "\n",
-    "# Show blue vertices  \n",
-    "if blue_mask.sum() > 0:\n",
-    "    blue_pts = pv.PolyData(vertices[blue_mask])\n",
-    "    pvplt.add_mesh(blue_pts, color=\"blue\", point_size=5, render_points_as_spheres=True)\n",
+    "pvplt = pv.Plotter(shape=(1, 2))\n",
     "\n",
-    "pvplt.add_text(f\"Green: {green_mask.sum():,}  Blue: {blue_mask.sum():,}\", position=\"upper_left\", font_size=14)\n",
-    "pvplt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-7",
-   "metadata": {},
-   "source": [
-    "## 4. Discussion Points for Professor\n",
+    "pvplt.subplot(0, 0)\n",
+    "cedalion.plots.plot_surface(pvplt, surface_h, opacity=1.0)\n",
+    "pvplt.add_text(f'S{SUBJECT_NUMBER} Original', position='upper_left', font_size=14)\n",
     "\n",
-    "**Current Issue:** The sticker detection is not reliably finding exactly 5 stickers.\n",
+    "pvplt.subplot(0, 1)\n",
+    "pvplt.add_mesh(pv.wrap(anon_vtk_mesh), rgb=True)\n",
+    "pvplt.add_text(f'S{SUBJECT_NUMBER} Anonymized (-{n_removed:,} verts)',\n",
+    "               position='upper_left', font_size=14)\n",
     "\n",
-    "**Questions to discuss:**\n",
-    "1. What colors are the actual stickers on the scan? (Green/Blue/Red?)\n",
-    "2. Are there other colored objects in the scan causing false positives?\n",
-    "3. Should we use a different detection method (e.g., geometric shape instead of color)?"
+    "pvplt.link_views()\n",
+    "pvplt.show()"
    ]
   }
  ],
@@ -396,16 +377,8 @@
    "name": "python3"
   },
   "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
    "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.14"
+   "version": "3.11.0"
   }
  },
  "nbformat": 4,
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/__init__.py b/src/cedalion/geometry/photogrammetry/anonymization/__init__.py
index c778fde8..28736d8c 100644
--- a/src/cedalion/geometry/photogrammetry/anonymization/__init__.py
+++ b/src/cedalion/geometry/photogrammetry/anonymization/__init__.py
@@ -5,229 +5,49 @@
 research. The anonymization complies with GDPR requirements while maintaining
 scientific utility.
 
+The pipeline works as follows:
+
+1. Detect nasion automatically (or via manual click)
+2. Normalize axes so Y=anterior, Z=left
+3. Isolate the head (remove body/shoulders)
+4. Detect remaining landmarks (Iz, Cz, LPA, RPA) from nasion
+5. Generate facial region mask (MediaPipe contour or hemisphere fallback)
+6. Delete facial vertices
+
 Example:
-    >>> from cedalion.geometry.photogrammetry.anonymization import anonymize_scan
-    >>> result = anonymize_scan(surface, landmarks, optodes)
-    >>> anonymized = result.anonymized_surface
+    >>> from cedalion.geometry.photogrammetry.anonymization import (
+    ...     detect_nasion_auto, pick_nasion, normalize_axes,
+    ...     isolate_head, detect_landmarks_from_nasion,
+    ...     get_facial_region_mask_from_nasion,
+    ... )
+    >>> auto = detect_nasion_auto(surface)
+    >>> nasion, meta = auto if auto else (pick_nasion(surface), {})
+    >>> surface, nasion, R = normalize_axes(surface, nasion)
+    >>> surface, _ = isolate_head(surface, nasion)
+    >>> landmarks = detect_landmarks_from_nasion(surface, nasion)
 
 Initial Contributors:
     - Face Anonymization Project | 2024
 """
 
-import logging
-import xarray as xr
-
-import cedalion.dataclasses as cdc
-import cedalion.typing as cdt
-from cedalion import Quantity, units
-
 from .face_detector import (
-    detect_facial_landmarks,
+    normalize_axes,
+    isolate_head,
     detect_landmarks_from_nasion,
-    get_facial_region_mask,
-    get_facial_region_center,
-    estimate_face_bounding_box,
-    FacialLandmarks,
-    FacialLandmarkType,
-    REQUIRED_LANDMARKS,
-)
-from .anonymizer import (
-    anonymize_facial_region,
-    smooth_region_selective,
-    apply_boundary_transition,
-    AnonymizationMethod,
-    AnonymizationConfig,
-    AnonymizationResult,
-)
-from .validator import (
-    validate_anonymization,
-    ValidationResult,
+    get_facial_region_mask_from_nasion,
 )
 from .nasion_detector import detect_nasion_auto
-from .ui import (
-    FacialRegionEditor,
-    AnonymizationPreview,
-    DisplacementViewer,
-    quick_preview,
-    quick_displacement_view,
-)
-
-
-logger = logging.getLogger("cedalion")
-
-
-def anonymize_scan(
-    surface: cdc.TrimeshSurface,
-    anatomical_landmarks: cdt.LabeledPointCloud = None,
-    optode_positions: cdt.LabeledPointCloud = None,
-    config: AnonymizationConfig = None,
-    interactive: bool = False,
-    validate: bool = True,
-    protection_radius: Quantity = 15.0 * units.mm,
-) -> AnonymizationResult:
-    """Anonymize a photogrammetry scan while preserving critical points.
-
-    Main entry point for the anonymization pipeline. This function:
-    1. Detects the facial region using geometric heuristics from landmarks
-    2. Creates protection zones around optodes and landmarks
-    3. Applies Taubin smoothing to the facial region
-    4. Optionally allows interactive refinement
-    5. Validates that protected points haven't moved
-
-    Args:
-        surface: Textured TrimeshSurface from photogrammetry
-        anatomical_landmarks: Known landmarks with at least Nz, Iz, LPA, RPA, Cz.
-            Use detect_landmarks_from_nasion() to obtain these from a clicked Nz.
-        optode_positions: Detected optode positions to protect (optional)
-        config: Anonymization configuration (uses defaults if None)
-        interactive: If True, allow user to refine facial region detection
-        validate: If True, run validation and log metrics
-        protection_radius: Radius around protected points that cannot be modified
-
-    Returns:
-        AnonymizationResult containing:
-        - anonymized_surface: The anonymized TrimeshSurface
-        - original_surface: The original surface (unchanged)
-        - facial_mask: Boolean mask of facial vertices
-        - vertex_displacements: How much each vertex moved
-        - config: Configuration used
-
-    Raises:
-        ValueError: If anatomical_landmarks is not provided
-
-    Example:
-        >>> from cedalion.geometry.photogrammetry.anonymization import (
-        ...     anonymize_scan, detect_landmarks_from_nasion,
-        ... )
-        >>>
-        >>> # Detect landmarks from a user-clicked Nz point
-        >>> landmarks = detect_landmarks_from_nasion(surface, nz_point)
-        >>> result = anonymize_scan(surface, anatomical_landmarks=landmarks)
-        >>>
-        >>> # Access the anonymized mesh
-        >>> anonymized_mesh = result.anonymized_surface
-    """
-    if config is None:
-        config = AnonymizationConfig()
-
-    logger.info("Starting face anonymization pipeline")
-    logger.info(f"Surface has {surface.nvertices} vertices, {surface.nfaces} faces")
-
-    # Landmarks must be provided
-    if anatomical_landmarks is None:
-        raise ValueError(
-            "anatomical_landmarks must be provided. Use "
-            "detect_landmarks_from_nasion(surface, nz_position) to detect "
-            "landmarks from a user-clicked nasion point."
-        )
-
-    # Step 1: Detect facial landmarks
-    logger.info("Detecting facial landmarks using geometric method")
-    facial_landmarks = detect_facial_landmarks(surface, anatomical_landmarks)
-    logger.info(
-        f"Detected {len(facial_landmarks.landmarks.label)} facial landmarks: "
-        f"{list(facial_landmarks.landmarks.label.values)}"
-    )
-
-    # Step 2: Combine all protected points
-    protected_points = anatomical_landmarks
-    if optode_positions is not None and len(optode_positions.label) > 0:
-        protected_points = xr.concat(
-            [anatomical_landmarks, optode_positions], dim="label"
-        )
-        logger.info(
-            f"Protecting {len(anatomical_landmarks.label)} landmarks and "
-            f"{len(optode_positions.label)} optodes"
-        )
-    else:
-        logger.info(f"Protecting {len(anatomical_landmarks.label)} landmarks (no optodes)")
-
-    # Step 3: Generate facial region mask
-    logger.info("Generating facial region mask")
-    facial_mask = get_facial_region_mask(
-        surface=surface,
-        facial_landmarks=facial_landmarks,
-        protected_points=protected_points,
-        protection_radius=protection_radius,
-    )
-    logger.info(
-        f"Facial region: {facial_mask.sum()} vertices "
-        f"({100 * facial_mask.sum() / len(facial_mask):.1f}%)"
-    )
-
-    # Step 4: Interactive refinement (optional)
-    if interactive:
-        logger.info("Opening interactive facial region editor")
-        editor = FacialRegionEditor(
-            surface=surface,
-            initial_mask=facial_mask,
-            protected_points=protected_points,
-            protection_radius=float(protection_radius.to("mm").magnitude),
-        )
-        facial_mask = editor.show()
-        logger.info(
-            f"Refined facial region: {facial_mask.sum()} vertices "
-            f"({100 * facial_mask.sum() / len(facial_mask):.1f}%)"
-        )
-
-    # Step 5: Apply anonymization
-    logger.info(
-        f"Applying {config.method.value} anonymization with "
-        f"{config.smoothing_iterations} iterations"
-    )
-    result = anonymize_facial_region(
-        surface=surface,
-        facial_mask=facial_mask,
-        protected_points=protected_points,
-        config=config,
-    )
-
-    # Step 6: Validation
-    if validate:
-        logger.info("Running post-anonymization sanity check")
-        check = validate_anonymization(
-            original_surface=surface,
-            anonymized_surface=result.anonymized_surface,
-            facial_mask=facial_mask,
-            protected_points=protected_points,
-            tolerance=1.0 * units.mm,
-        )
-
-        if not check.passed:
-            logger.warning(f"Anonymization check: {check.summary}")
-
-    logger.info("Face anonymization complete")
-    return result
+from .ui import pick_nasion
 
 
 __all__ = [
-    # High-level API
-    "anonymize_scan",
-    # Auto nasion detection
+    # Nasion detection
     "detect_nasion_auto",
-    # Face detection
-    "detect_facial_landmarks",
+    "pick_nasion",
+    # Axis normalization and head isolation
+    "normalize_axes",
+    "isolate_head",
+    # Landmark and face mask detection
     "detect_landmarks_from_nasion",
-    "get_facial_region_mask",
-    "get_facial_region_center",
-    "estimate_face_bounding_box",
-    "FacialLandmarks",
-    "FacialLandmarkType",
-    "REQUIRED_LANDMARKS",
-    # Anonymization
-    "anonymize_facial_region",
-    "smooth_region_selective",
-    "apply_boundary_transition",
-    "AnonymizationMethod",
-    "AnonymizationConfig",
-    "AnonymizationResult",
-    # Validation
-    "validate_anonymization",
-    "ValidationResult",
-    # UI
-    "FacialRegionEditor",
-    "AnonymizationPreview",
-    "DisplacementViewer",
-    "quick_preview",
-    "quick_displacement_view",
+    "get_facial_region_mask_from_nasion",
 ]
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/face_detector.py b/src/cedalion/geometry/photogrammetry/anonymization/face_detector.py
index e53d6b65..1f5685c0 100644
--- a/src/cedalion/geometry/photogrammetry/anonymization/face_detector.py
+++ b/src/cedalion/geometry/photogrammetry/anonymization/face_detector.py
@@ -1,16 +1,13 @@
 """Facial region detection for photogrammetry scans.
 
-This module provides geometric-based facial detection using anatomical landmarks
-(Nz, Iz, LPA, RPA, Cz) to estimate facial feature positions based on known head
-proportions. It also provides semi-manual landmark detection from a user-provided
-nasion (Nz) position.
+Provides axis normalization, head isolation, landmark detection from a
+user-provided nasion (Nz) position, and facial region mask generation
+using MediaPipe face contour back-projection.
 
 Initial Contributors:
     - Face Anonymization Project | 2024
 """
 
-from dataclasses import dataclass
-from enum import Enum
 import logging
 
 import numpy as np
@@ -20,400 +17,172 @@
 import cedalion.dataclasses as cdc
 import cedalion.typing as cdt
 from cedalion import Quantity, units
-logger = logging.getLogger("cedalion")
-
-
-# Required anatomical landmarks for detection
-REQUIRED_LANDMARKS = ["Nz", "Iz", "LPA", "RPA", "Cz"]
-
-# Alternative landmark names (some datasets use different conventions)
-LANDMARK_ALIASES = {
-    "Nz": ["Nz", "Nas", "Nasion", "NAS"],
-    "Iz": ["Iz", "Ini", "Inion", "INI"],
-    "LPA": ["LPA", "Lpa", "A1", "LeftPreauricular"],
-    "RPA": ["RPA", "Rpa", "A2", "RightPreauricular"],
-    "Cz": ["Cz", "CZ", "Vertex"],
-}
-
-
-class FacialLandmarkType(Enum):
-    """Types of facial landmarks for detection."""
-
-    LEFT_EYE = "left_eye"
-    RIGHT_EYE = "right_eye"
-    NOSE_TIP = "nose_tip"
-    NOSE_BRIDGE = "nose_bridge"
-    MOUTH_CENTER = "mouth_center"
-    CHIN = "chin"
-
-
-@dataclass
-class FacialLandmarks:
-    """Container for detected facial landmarks.
-
-    Attributes:
-        landmarks: xarray DataArray with estimated facial landmark positions
-        confidence: confidence score per landmark (1.0 for geometric detection)
-        detection_method: method used for detection ("geometric")
-    """
-
-    landmarks: cdt.LabeledPointCloud
-    confidence: dict[str, float]
-    detection_method: str
-
-
-def _normalize(v: np.ndarray) -> np.ndarray:
-    """Normalize a vector to unit length."""
-    norm = np.linalg.norm(v)
-    if norm < 1e-10:
-        return v
-    return v / norm
-
-
-def _find_landmark(
-    landmarks: cdt.LabeledPointCloud, name: str
-) -> np.ndarray:
-    """Find a landmark by name, trying aliases if needed.
-
-    Args:
-        landmarks: LabeledPointCloud containing anatomical landmarks
-        name: canonical landmark name (e.g., "Nz")
-
-    Returns:
-        numpy array of shape (3,) with landmark coordinates
-
-    Raises:
-        ValueError: if landmark not found under any alias
-    """
-    labels = [str(l) for l in landmarks.label.values]
-
-    # Try direct match first
-    if name in labels:
-        return landmarks.sel(label=name).pint.dequantify().values
-
-    # Try aliases
-    for alias in LANDMARK_ALIASES.get(name, []):
-        if alias in labels:
-            return landmarks.sel(label=alias).pint.dequantify().values
-
-    raise ValueError(
-        f"Landmark '{name}' not found. Available landmarks: {labels}. "
-        f"Tried aliases: {LANDMARK_ALIASES.get(name, [])}"
-    )
-
-
-def _build_head_coordinate_system(
-    nz: np.ndarray,
-    iz: np.ndarray,
-    lpa: np.ndarray,
-    rpa: np.ndarray,
-    cz: np.ndarray,
-) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
-    """Build a head-centered coordinate system from anatomical landmarks.
-
-    The coordinate system is defined as:
-    - Origin: midpoint between LPA and RPA (preauricular points)
-    - X-axis: lateral, pointing from LPA to RPA
-    - Y-axis: anterior, pointing toward Nz (nasion)
-    - Z-axis: superior, pointing upward (cross product of X and Y)
-
-    Args:
-        nz: Nasion coordinates
-        iz: Inion coordinates
-        lpa: Left preauricular point coordinates
-        rpa: Right preauricular point coordinates
-        cz: Vertex coordinates
-
-    Returns:
-        Tuple of (origin, x_axis, y_axis, z_axis) as numpy arrays
-    """
-    # Origin at midpoint of ears
-    origin = (lpa + rpa) / 2.0
-
-    # X-axis: lateral (left to right)
-    x_axis = _normalize(rpa - lpa)
-
-    # Initial Y direction: toward nasion
-    y_direction = nz - origin
-
-    # Z-axis: perpendicular to X and Y direction
-    z_axis = _normalize(np.cross(x_axis, y_direction))
-
-    # Y-axis: perpendicular to X and Z (ensures orthogonality)
-    y_axis = _normalize(np.cross(z_axis, x_axis))
-
-    return origin, x_axis, y_axis, z_axis
-
-
-def _estimate_facial_landmarks_geometric(
-    nz: np.ndarray,
-    origin: np.ndarray,
-    x_axis: np.ndarray,
-    y_axis: np.ndarray,
-    z_axis: np.ndarray,
-) -> dict[str, np.ndarray]:
-    """Estimate facial landmark positions using anatomical proportions.
-
-    Estimates are based on average adult head proportions:
-    - Eyes: ~30mm lateral to midline, ~30mm superior to nasion level
-    - Nose tip: ~40mm inferior-anterior to nasion
-    - Nose bridge: at nasion level, slightly anterior
-    - Mouth: ~70mm inferior to nasion
-    - Chin: ~100mm inferior to nasion
-
-    Args:
-        nz: Nasion position
-        origin: Head coordinate system origin
-        x_axis: Lateral axis (normalized)
-        y_axis: Anterior axis (normalized)
-        z_axis: Superior axis (normalized)
-
-    Returns:
-        Dictionary mapping FacialLandmarkType names to estimated positions
-    """
-    # Estimate Nz level height relative to origin
-    nz_relative = nz - origin
-
-    # Eye positions: ~30mm lateral, at or slightly below Nz level
-    left_eye = nz + (-30.0 * x_axis) + (5.0 * y_axis) + (-10.0 * z_axis)
-    right_eye = nz + (30.0 * x_axis) + (5.0 * y_axis) + (-10.0 * z_axis)
-
-    # Nose tip: inferior and anterior to Nz
-    nose_tip = nz + (40.0 * y_axis) + (-40.0 * z_axis)
 
-    # Nose bridge: at Nz level, slightly anterior
-    nose_bridge = nz + (15.0 * y_axis)
-
-    # Mouth center: inferior to nose
-    mouth_center = nz + (25.0 * y_axis) + (-70.0 * z_axis)
-
-    # Chin: most inferior point
-    chin = nz + (20.0 * y_axis) + (-100.0 * z_axis)
-
-    return {
-        FacialLandmarkType.LEFT_EYE.value: left_eye,
-        FacialLandmarkType.RIGHT_EYE.value: right_eye,
-        FacialLandmarkType.NOSE_TIP.value: nose_tip,
-        FacialLandmarkType.NOSE_BRIDGE.value: nose_bridge,
-        FacialLandmarkType.MOUTH_CENTER.value: mouth_center,
-        FacialLandmarkType.CHIN.value: chin,
-    }
+logger = logging.getLogger("cedalion")
 
 
-@cdc.validate_schemas
-def detect_facial_landmarks(
+def normalize_axes(
     surface: cdc.TrimeshSurface,
-    anatomical_landmarks: cdt.LabeledPointCloud,
-) -> FacialLandmarks:
-    """Detect facial landmarks using geometric heuristics.
+    nasion: np.ndarray,
+    forward_direction: np.ndarray = None,
+) -> tuple[cdc.TrimeshSurface, np.ndarray, np.ndarray]:
+    """Rotate mesh around X-axis so Y=anterior (toward face), Z=left.
 
-    Uses anatomical landmarks (Nz, Iz, LPA, RPA, Cz) to estimate facial feature
-    positions based on known head proportions. This geometric approach works
-    without texture information and provides consistent results.
+    The Einstar scanner has X=up (gravity-based) but Y/Z are arbitrary per
+    scan session. This function uses the nasion position to determine the
+    forward direction and rotates the mesh so that Y consistently points
+    toward the face (anterior) and Z points left.
 
     Args:
-        surface: Textured TrimeshSurface from photogrammetry
-        anatomical_landmarks: Known landmarks with Nz, Iz, LPA, RPA, Cz
+        surface: TrimeshSurface in raw Einstar coordinates.
+        nasion: Nasion position as numpy array of shape (3,).
+        forward_direction: Optional pre-computed forward unit vector in YZ
+            plane (X=0). If None, computed from nasion vs upper-head centroid.
 
     Returns:
-        FacialLandmarks with estimated eye, nose, mouth positions
-
-    Raises:
-        ValueError: if required anatomical landmarks are missing
+        Tuple of (rotated_surface, rotated_nasion, rotation_matrix).
+        rotation_matrix is 3x3 and can be applied to other points.
     """
-    # Extract required landmarks
-    nz = _find_landmark(anatomical_landmarks, "Nz")
-    iz = _find_landmark(anatomical_landmarks, "Iz")
-    lpa = _find_landmark(anatomical_landmarks, "LPA")
-    rpa = _find_landmark(anatomical_landmarks, "RPA")
-    cz = _find_landmark(anatomical_landmarks, "Cz")
-
-    # Build head coordinate system
-    origin, x_axis, y_axis, z_axis = _build_head_coordinate_system(
-        nz, iz, lpa, rpa, cz
-    )
+    vertices = surface.mesh.vertices
 
-    # Estimate facial landmarks using geometric proportions
-    estimated_landmarks = _estimate_facial_landmarks_geometric(
-        nz, origin, x_axis, y_axis, z_axis
+    if forward_direction is None:
+        # Compute forward direction: nasion minus upper-head centroid in YZ
+        x_max = vertices[:, 0].max()
+        x_min = vertices[:, 0].min()
+        upper = vertices[vertices[:, 0] > x_min + 0.6 * (x_max - x_min)]
+        centroid_yz = np.array([0.0, upper[:, 1].mean(), upper[:, 2].mean()])
+        nasion_yz = np.array([0.0, nasion[1], nasion[2]])
+        forward_direction = nasion_yz - centroid_yz
+        fwd_norm = np.linalg.norm(forward_direction)
+        if fwd_norm < 1e-6:
+            logger.warning("Cannot compute forward direction — returning unchanged")
+            return surface, nasion.copy(), np.eye(3)
+        forward_direction = forward_direction / fwd_norm
+
+    # Angle of forward_direction relative to +Y in the YZ plane
+    angle = np.arctan2(forward_direction[2], forward_direction[1])
+
+    # Rotation around X-axis by -angle to align forward with +Y
+    cos_a = np.cos(-angle)
+    sin_a = np.sin(-angle)
+    R = np.array([
+        [1.0, 0.0, 0.0],
+        [0.0, cos_a, -sin_a],
+        [0.0, sin_a, cos_a],
+    ])
+
+    # Rotate vertices
+    rotated_verts = vertices @ R.T
+
+    # Build new mesh with rotated vertices, same faces and texture
+    import trimesh
+    new_mesh = trimesh.Trimesh(
+        vertices=rotated_verts,
+        faces=surface.mesh.faces,
+        visual=surface.mesh.visual,
+        process=False,
     )
+    rotated_surface = cdc.TrimeshSurface(new_mesh, crs=surface.crs, units=surface.units)
 
-    # Create xarray DataArray for landmarks
-    landmark_names = list(estimated_landmarks.keys())
-    landmark_positions = np.array([estimated_landmarks[name] for name in landmark_names])
+    # Rotate nasion
+    rotated_nasion = R @ nasion
 
-    # Get CRS from surface
-    crs = surface.crs
-
-    landmarks_da = xr.DataArray(
-        landmark_positions,
-        dims=["label", crs],
-        coords={"label": landmark_names},
-        attrs={"units": surface.units},
-    ).pint.quantify()
-
-    # All geometric detections have confidence 1.0 (deterministic)
-    confidence = {name: 1.0 for name in landmark_names}
-
-    return FacialLandmarks(
-        landmarks=landmarks_da,
-        confidence=confidence,
-        detection_method="geometric",
+    logger.debug(
+        f"Axis normalization: rotated {np.degrees(angle):.1f}deg around X. "
+        f"Y now points anterior."
     )
 
+    return rotated_surface, rotated_nasion, R
 
-def _compute_angular_distance(
-    point: np.ndarray, reference: np.ndarray, center: np.ndarray
-) -> float:
-    """Compute angular distance between a point and reference from center.
-
-    Args:
-        point: Point to measure
-        reference: Reference point (e.g., nasion)
-        center: Center point for angle measurement
-
-    Returns:
-        Angular distance in degrees
-    """
-    v1 = _normalize(point - center)
-    v2 = _normalize(reference - center)
-    cos_angle = np.clip(np.dot(v1, v2), -1.0, 1.0)
-    return np.degrees(np.arccos(cos_angle))
 
-
-@cdc.validate_schemas
-def get_facial_region_mask(
+def isolate_head(
     surface: cdc.TrimeshSurface,
-    facial_landmarks: FacialLandmarks,
-    protected_points: cdt.LabeledPointCloud,
-    protection_radius: Quantity = 15.0 * units.mm,
-) -> np.ndarray:
-    """Generate boolean mask of vertices in facial region.
+    nasion: np.ndarray,
+    radius: float = 220.0,
+) -> tuple[cdc.TrimeshSurface, np.ndarray]:
+    """Remove shoulders, body, and chair — keep only the head.
 
-    Creates a mask identifying vertices in the facial region while excluding
-    protection zones around optodes and anatomical landmarks. The facial region
-    is determined by geometric criteria based on the head coordinate system.
+    Uses a sphere centered on the upper-head centroid. The radius is
+    chosen to capture the full head (~180mm wide x 230mm tall) while
+    excluding shoulders and body. Scans that are already head-only are
+    returned unchanged (the sphere just contains everything).
+
+    The surface must be axis-normalized first (X=up, Y=anterior, Z=left).
 
     Args:
-        surface: The mesh surface
-        facial_landmarks: Detected facial landmarks
-        protected_points: Points to exclude (optodes + anatomical landmarks)
-        protection_radius: Radius around protected points in length units
+        surface: Axis-normalized TrimeshSurface.
+        nasion: Nasion position as numpy array of shape (3,).
+        radius: Sphere radius in mm (default 220). A human head has
+            ~90mm radius; 220mm adds margin for ears and jaw.
 
     Returns:
-        Boolean array of shape (n_vertices,) where True indicates facial region
+        Tuple of (head_surface, head_mask). head_mask is a boolean
+        array of shape (n_vertices,) indicating which original
+        vertices were kept.
     """
-    # Get vertices as numpy array
-    vertices = surface.mesh.vertices
+    import trimesh
 
-    # Get required anatomical landmarks from protected_points
-    try:
-        nz = _find_landmark(protected_points, "Nz")
-        iz = _find_landmark(protected_points, "Iz")
-        lpa = _find_landmark(protected_points, "LPA")
-        rpa = _find_landmark(protected_points, "RPA")
-        cz = _find_landmark(protected_points, "Cz")
-    except ValueError:
-        # If not all landmarks in protected_points, try facial_landmarks
-        fl = facial_landmarks.landmarks.pint.dequantify()
-        nz = _find_landmark(protected_points, "Nz")
-        iz = _find_landmark(protected_points, "Iz")
-        lpa = _find_landmark(protected_points, "LPA")
-        rpa = _find_landmark(protected_points, "RPA")
-        cz = _find_landmark(protected_points, "Cz")
-
-    # Build head coordinate system
-    origin, x_axis, y_axis, z_axis = _build_head_coordinate_system(
-        nz, iz, lpa, rpa, cz
+    vertices = surface.mesh.vertices
+    faces = surface.mesh.faces
+
+    # Sphere center: Y/Z from upper-head centroid (good lateral centering).
+    # X: use the lower of (upper centroid, midpoint of top+nasion).
+    x_max = vertices[:, 0].max()
+    x_min = vertices[:, 0].min()
+    upper = vertices[:, 0] > x_min + 0.6 * (x_max - x_min)
+    center = vertices[upper].mean(axis=0)
+    midpoint_x = (x_max + nasion[0]) / 2.0
+    center[0] = min(center[0], midpoint_x)
+
+    # Sphere mask
+    dist = np.linalg.norm(vertices - center, axis=1)
+    head_mask = dist < radius
+
+    # If the sphere captures almost everything, skip trimming
+    if head_mask.sum() < 100 or head_mask.mean() > 0.95:
+        logger.debug(
+            f"Head isolation: sphere contains {head_mask.mean()*100:.0f}% "
+            f"of vertices — scan is already head-only"
+        )
+        return surface, head_mask
+
+    # Build new mesh with only head faces
+    # A face is kept if ALL its vertices are in the head
+    face_mask = head_mask[faces].all(axis=1)
+    head_faces = faces[face_mask]
+
+    # Reindex vertices: only keep referenced vertices
+    kept_verts = np.unique(head_faces)
+    reindex = np.full(len(vertices), -1, dtype=int)
+    reindex[kept_verts] = np.arange(len(kept_verts))
+    new_faces = reindex[head_faces]
+    new_verts = vertices[kept_verts]
+
+    # Transfer visual — subset vertex colors to match kept vertices
+    new_mesh = trimesh.Trimesh(
+        vertices=new_verts,
+        faces=new_faces,
+        process=False,
     )
-
-    # Transform vertices to head coordinate system
-    vertices_relative = vertices - origin
-    vertices_x = np.dot(vertices_relative, x_axis)
-    vertices_y = np.dot(vertices_relative, y_axis)
-    vertices_z = np.dot(vertices_relative, z_axis)
-
-    # Get Nz and Cz positions in head coordinates
-    nz_relative = nz - origin
-    nz_y = np.dot(nz_relative, y_axis)
-    nz_z = np.dot(nz_relative, z_axis)
-
-    cz_relative = cz - origin
-    cz_z = np.dot(cz_relative, z_axis)
-
-    # Rectangular facial region mask in head coordinate system:
-    # Anterior mask: only front of head (Y > 0)
-    anterior_mask = vertices_y > 0
-
-    # Full rectangle: above Nz to chin/neck, full lateral span (covers ears)
-    facial_mask = (
-        anterior_mask
-        & (vertices_z > nz_z - 120)
-        & (vertices_z < nz_z + 30)
+    try:
+        old_visual = surface.mesh.visual
+        if hasattr(old_visual, 'vertex_colors') and len(old_visual.vertex_colors) == len(vertices):
+            new_mesh.visual.vertex_colors = old_visual.vertex_colors[kept_verts]
+    except Exception:
+        pass
+
+    head_surface = cdc.TrimeshSurface(
+        new_mesh, crs=surface.crs, units=surface.units
     )
 
-    # Create protection zones using KDTree
-    protection_radius_mm = float(protection_radius.to("mm").magnitude)
-
-    # Get protected point positions
-    protected_positions = protected_points.pint.dequantify().values
-
-    if len(protected_positions) > 0:
-        kdtree = KDTree(protected_positions)
-        distances, _ = kdtree.query(vertices, k=1)
-        protected_mask = distances < protection_radius_mm
-
-        # Exclude protected vertices from facial region
-        facial_mask = facial_mask & ~protected_mask
-
-    logger.info(
-        f"Facial region mask: {facial_mask.sum()} of {len(facial_mask)} vertices "
-        f"({100 * facial_mask.sum() / len(facial_mask):.1f}%)"
+    logger.debug(
+        f"Head isolation: {len(vertices):,} -> {len(new_verts):,} vertices "
+        f"({len(vertices) - len(new_verts):,} removed), "
+        f"center=[{center[0]:.0f},{center[1]:.0f},{center[2]:.0f}], "
+        f"radius={radius:.0f}mm"
     )
 
-    return facial_mask
-
-
-def get_facial_region_center(
-    surface: cdc.TrimeshSurface,
-    facial_landmarks: FacialLandmarks,
-) -> np.ndarray:
-    """Compute the centroid of the facial region.
-
-    Args:
-        surface: The mesh surface
-        facial_landmarks: Detected facial landmarks
-
-    Returns:
-        Numpy array of shape (3,) with facial region centroid
-    """
-    # Get facial landmark positions
-    landmarks = facial_landmarks.landmarks.pint.dequantify().values
-
-    # Return centroid of all facial landmarks
-    return np.mean(landmarks, axis=0)
-
-
-def estimate_face_bounding_box(
-    facial_landmarks: FacialLandmarks,
-    margin: Quantity = 20.0 * units.mm,
-) -> tuple[np.ndarray, np.ndarray]:
-    """Estimate a bounding box around the facial region.
-
-    Args:
-        facial_landmarks: Detected facial landmarks
-        margin: Extra margin around landmarks in length units
-
-    Returns:
-        Tuple of (min_corner, max_corner) as numpy arrays
-    """
-    landmarks = facial_landmarks.landmarks.pint.dequantify().values
-    margin_mm = float(margin.to("mm").magnitude)
-
-    min_corner = landmarks.min(axis=0) - margin_mm
-    max_corner = landmarks.max(axis=0) + margin_mm
-
-    return min_corner, max_corner
+    return head_surface, head_mask
 
 
 def detect_landmarks_from_nasion(
@@ -422,22 +191,20 @@ def detect_landmarks_from_nasion(
 ) -> cdt.LabeledPointCloud:
     """Detect anatomical landmarks from a user-provided nasion (Nz) position.
 
-    Given the Nz point (clicked by the user on the mesh), automatically detects
-    the remaining 4 landmarks (Iz, LPA, RPA, Cz) using mesh geometry.
+    **Important:** The surface must be axis-normalized before calling this
+    function. Use ``normalize_axes()`` first so that:
+    - X = up (vertical, from Einstar gravity sensor)
+    - Y = anterior (toward face, from nasion direction)
+    - Z = left of subject
 
-    Coordinate system (Einstar scanner convention):
-    - X: Up (vertical)
-    - Y: Forward (anterior, toward face)
-    - Z: Left of subject
-
-    Detection algorithm:
-    - Cz: Highest vertex (max X) within a lateral band near the mesh centroid
-    - Iz: Most posterior vertex (min Y) at approximately Nz height
-    - LPA: Most left vertex (max Z) at ear height, not too posterior
-    - RPA: Most right vertex (min Z) at ear height, not too posterior
+    Given the Nz point, automatically detects Iz, LPA, RPA, Cz:
+    - Cz: Highest vertex (max X) near midline
+    - Iz: Most posterior vertex (min Y) at nasion height
+    - LPA: Most left vertex (max Z) at ear height
+    - RPA: Most right vertex (min Z) at ear height
 
     Args:
-        surface: TrimeshSurface from photogrammetry scan
+        surface: Axis-normalized TrimeshSurface (Y=anterior, Z=left)
         nz_position: Nasion position as numpy array of shape (3,), in mm
 
     Returns:
@@ -447,62 +214,79 @@ def detect_landmarks_from_nasion(
         ValueError: If landmark configuration fails validation
 
     Example:
-        >>> # User clicks Nz on the mesh, gets nz_point
-        >>> landmarks = detect_landmarks_from_nasion(surface, nz_point)
-        >>> print(landmarks.label.values)
-        ['Nz', 'Iz', 'Cz', 'LPA', 'RPA']
+        >>> surface_norm, nz_norm, R = normalize_axes(surface, nz, fwd)
+        >>> landmarks = detect_landmarks_from_nasion(surface_norm, nz_norm)
     """
     nz_position = np.asarray(nz_position, dtype=float)
     vertices = surface.mesh.vertices
 
     centroid = vertices.mean(axis=0)
     head_height = vertices[:, 0].max() - vertices[:, 0].min()
+    head_verts = vertices
 
-    logger.info(
+    logger.debug(
         f"Detecting landmarks from Nz={nz_position}, "
-        f"head_height={head_height:.1f}mm"
+        f"head_height={head_height:.1f}mm, "
+        f"head_verts={len(vertices)}"
     )
 
-    # Cz: highest vertex (max X) within a lateral band near centroid
-    # Band: within 20% of head_height in Y and Z from centroid
+    # Cz: highest vertex (max X) near midline
     band = 0.20 * head_height
     lateral_mask = (
-        (np.abs(vertices[:, 1] - centroid[1]) < band)
-        & (np.abs(vertices[:, 2] - centroid[2]) < band)
+        (np.abs(head_verts[:, 1] - centroid[1]) < band)
+        & (np.abs(head_verts[:, 2] - centroid[2]) < band)
     )
     if lateral_mask.sum() == 0:
-        lateral_mask = np.ones(len(vertices), dtype=bool)
-    cz_idx = np.where(lateral_mask)[0][np.argmax(vertices[lateral_mask, 0])]
-    cz = vertices[cz_idx]
-
-    # Iz: most posterior vertex (min Y) within a height band around Nz height
-    # Band: ±15% of head_height in X around Nz's X
-    height_band = 0.15 * head_height
-    iz_height_mask = np.abs(vertices[:, 0] - nz_position[0]) < height_band
-    if iz_height_mask.sum() == 0:
-        iz_height_mask = np.ones(len(vertices), dtype=bool)
-    iz_idx = np.where(iz_height_mask)[0][np.argmin(vertices[iz_height_mask, 1])]
-    iz = vertices[iz_idx]
-
-    # Ear height: slightly below Nz (Nz height - 5% head_height)
-    ear_height = nz_position[0] - 0.05 * head_height
-    ear_height_mask = np.abs(vertices[:, 0] - ear_height) < height_band
-
-    # Additional constraint: not too posterior (Y > centroid Y - some margin)
-    posterior_limit = centroid[1] - 0.10 * head_height
-    not_too_posterior = vertices[:, 1] > posterior_limit
-
-    ear_mask = ear_height_mask & not_too_posterior
-    if ear_mask.sum() < 2:
-        ear_mask = ear_height_mask
-
-    # LPA: most left vertex (max Z) at ear height
-    lpa_idx = np.where(ear_mask)[0][np.argmax(vertices[ear_mask, 2])]
-    lpa = vertices[lpa_idx]
-
-    # RPA: most right vertex (min Z) at ear height
-    rpa_idx = np.where(ear_mask)[0][np.argmin(vertices[ear_mask, 2])]
-    rpa = vertices[rpa_idx]
+        lateral_mask = np.ones(len(head_verts), dtype=bool)
+    cz_idx = np.where(lateral_mask)[0][np.argmax(head_verts[lateral_mask, 0])]
+    cz = head_verts[cz_idx]
+
+    # Iz: most posterior (min Y) at nasion height, on the midsagittal plane.
+    iz_height_mask = np.abs(head_verts[:, 0] - nz_position[0]) < 20.0
+    iz_midline_mask = np.abs(head_verts[:, 2] - nz_position[2]) < 25.0
+    iz_mask = iz_height_mask & iz_midline_mask
+    if iz_mask.sum() == 0:
+        iz_mask = np.abs(head_verts[:, 0] - nz_position[0]) < 40.0
+    iz_cands = np.where(iz_mask)[0]
+    iz_idx = iz_cands[np.argmin(head_verts[iz_cands, 1])]
+    iz = head_verts[iz_idx]
+
+    # LPA/RPA: geometric targets, refined by snapping to surface
+    from scipy.spatial import cKDTree
+
+    midline_z = np.mean([nz_position[2], iz[2], cz[2]])
+    clean_band = (
+        (head_verts[:, 0] > nz_position[0] + 10.0)
+        & (head_verts[:, 0] < cz[0] - 20.0)
+    )
+    if clean_band.sum() > 100:
+        clean_z = head_verts[clean_band, 2]
+        half_width = (
+            np.percentile(clean_z, 97) - np.percentile(clean_z, 3)
+        ) / 2.0
+    else:
+        half_width = 75.0
+
+    geo_lpa_target = np.array([nz_position[0], cz[1], midline_z + half_width])
+    geo_rpa_target = np.array([nz_position[0], cz[1], midline_z - half_width])
+
+    def _snap_ear(target):
+        """Snap target to nearest surface vertex in ear region."""
+        ear_region = (
+            (np.abs(head_verts[:, 0] - nz_position[0]) < 30.0)
+            & (np.abs(head_verts[:, 1] - cz[1]) < 40.0)
+        )
+        if ear_region.sum() > 10:
+            idxs = np.where(ear_region)[0]
+            tree = cKDTree(head_verts[idxs])
+            _, local = tree.query(target)
+            return head_verts[idxs[local]]
+        tree = cKDTree(head_verts)
+        _, idx = tree.query(target)
+        return head_verts[idx]
+
+    lpa = _snap_ear(geo_lpa_target)
+    rpa = _snap_ear(geo_rpa_target)
 
     landmark_positions = {
         "Nz": nz_position,
@@ -512,7 +296,7 @@ def detect_landmarks_from_nasion(
         "RPA": rpa,
     }
 
-    logger.info(
+    logger.debug(
         f"Detected landmarks: Cz={cz}, Iz={iz}, LPA={lpa}, RPA={rpa}"
     )
 
@@ -535,6 +319,122 @@ def detect_landmarks_from_nasion(
     return landmarks
 
 
+def get_facial_region_mask_from_nasion(
+    surface: cdc.TrimeshSurface,
+    nz: np.ndarray,
+    forward_direction: np.ndarray,
+    face_contour_3d: np.ndarray | None = None,
+    protected_points: cdt.LabeledPointCloud = None,
+    protection_radius: Quantity = 15.0 * units.mm,
+    lateral_extension: float = 70.0,
+    lower_width_scale: float = 2.5,
+) -> np.ndarray:
+    """Generate facial region mask using nasion and MediaPipe face contour.
+
+    This function is axis-independent -- it uses the forward direction and
+    face contour from MediaPipe rather than relying on axis alignment.
+    It does NOT depend on LPA/RPA accuracy.
+
+    The mask uses a two-part strategy derived entirely from the contour:
+    - **Oval**: The MediaPipe face contour polygon defines precise
+      forehead and jawline boundaries (with a depth filter to prevent
+      top-of-head bleed).
+    - **Side band**: 3D proximity to contour points -- any vertex within
+      ``lateral_extension`` mm of a contour point is included, vertically
+      constrained to chin-to-temple height to avoid top-of-head bleed.
+      This naturally follows the head curvature and covers the ears.
+
+    When no face contour is available (manual nasion mode), falls back to
+    a forward-facing hemisphere around the nasion.
+
+    Args:
+        surface: The mesh surface (axis-normalized or not)
+        nz: Nasion position as numpy array of shape (3,)
+        forward_direction: Unit vector pointing toward the face
+        face_contour_3d: 3D face oval points from MediaPipe back-projection,
+            shape (N, 3) with N >= 20, or None for hemisphere fallback
+        protected_points: Points to exclude (optodes + anatomical landmarks)
+        protection_radius: Radius around protected points
+        lateral_extension: 3D proximity radius in mm from contour points
+            for ear/temple coverage (default 70.0)
+        lower_width_scale: Factor to widen the lower portion of the face
+            contour (below nasion). 1.0 = no change, 2.5 = 150% wider at
+            chin level. Smoothly blends from 1.0 at nasion to this value
+            at chin. Keeps forehead border unchanged. (default 2.5)
+
+    Returns:
+        Boolean array of shape (n_vertices,) where True = facial region
+    """
+    from matplotlib.path import Path
+
+    vertices = surface.mesh.vertices
+    nz = np.asarray(nz, dtype=float)
+    fwd = np.asarray(forward_direction, dtype=float)
+    fwd = fwd / np.linalg.norm(fwd)
+
+    if face_contour_3d is not None and len(face_contour_3d) >= 20:
+        # --- MediaPipe face oval approach (spherical projection) ---
+        up_hint = np.array([1.0, 0.0, 0.0])  # X = up in Einstar coords
+        if abs(np.dot(fwd, up_hint)) > 0.9:
+            up_hint = np.array([0.0, 0.0, 1.0])
+        u_axis = np.cross(fwd, up_hint)
+        u_axis = u_axis / np.linalg.norm(u_axis)
+        v_axis = np.cross(fwd, u_axis)
+        v_axis = v_axis / np.linalg.norm(v_axis)
+
+        # Head center: ~80mm behind nasion (roughly center of skull)
+        head_center = nz - fwd * 80.0
+
+        # Project contour onto unit sphere -> 2D
+        c_dirs = face_contour_3d - head_center
+        c_dirs = c_dirs / np.linalg.norm(c_dirs, axis=1, keepdims=True)
+        contour_2d = np.column_stack([c_dirs @ u_axis, c_dirs @ v_axis])
+
+        # Project vertices onto unit sphere -> 2D
+        v_dirs = vertices - head_center
+        v_fwd = v_dirs @ fwd  # positive = face side of head
+        v_dirs = v_dirs / np.linalg.norm(v_dirs, axis=1, keepdims=True)
+        verts_2d = np.column_stack([v_dirs @ u_axis, v_dirs @ v_axis])
+
+        # Face mask = forward-facing vertices inside the contour polygon
+        polygon = Path(contour_2d)
+        facial_mask = polygon.contains_points(verts_2d) & (v_fwd > 0)
+
+        logger.debug(
+            f"Contour mask: {facial_mask.sum()} vertices "
+            f"({100 * facial_mask.sum() / len(facial_mask):.1f}%)"
+        )
+    else:
+        # --- Fallback: forward-facing hemisphere ---
+        verts_rel = vertices - nz
+        fwd_dist = verts_rel @ fwd
+        dist_to_nz = np.linalg.norm(verts_rel, axis=1)
+
+        facial_mask = (fwd_dist > -20.0) & (dist_to_nz < 120.0)
+
+        logger.debug(
+            f"Hemisphere fallback mask: {facial_mask.sum()} vertices "
+            f"({100 * facial_mask.sum() / len(facial_mask):.1f}%)"
+        )
+
+    # Exclude protection zones
+    protection_radius_mm = float(protection_radius.to("mm").magnitude)
+    if protected_points is not None:
+        protected_positions = protected_points.pint.dequantify().values
+        if len(protected_positions) > 0:
+            kdtree = KDTree(protected_positions)
+            distances, _ = kdtree.query(vertices, k=1)
+            protected_mask = distances < protection_radius_mm
+            facial_mask = facial_mask & ~protected_mask
+
+    logger.debug(
+        f"Final facial mask: {facial_mask.sum()} of {len(facial_mask)} vertices "
+        f"({100 * facial_mask.sum() / len(facial_mask):.1f}%)"
+    )
+
+    return facial_mask
+
+
 def _validate_landmark_configuration(
     landmarks: dict[str, np.ndarray],
     centroid: np.ndarray,
@@ -555,7 +455,6 @@ def _validate_landmark_configuration(
     """
     cz = landmarks["Cz"]
     iz = landmarks["Iz"]
-    nz = landmarks["Nz"]
     lpa = landmarks["LPA"]
     rpa = landmarks["RPA"]
 
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/face_landmarker.task b/src/cedalion/geometry/photogrammetry/anonymization/face_landmarker.task
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diff --git a/src/cedalion/geometry/photogrammetry/anonymization/nasion_detector.py b/src/cedalion/geometry/photogrammetry/anonymization/nasion_detector.py
index db112510..07c0e600 100644
--- a/src/cedalion/geometry/photogrammetry/anonymization/nasion_detector.py
+++ b/src/cedalion/geometry/photogrammetry/anonymization/nasion_detector.py
@@ -1,23 +1,20 @@
 """Automatic nasion detection from 3D mesh geometry.
 
 Detects the nasion (Nz) from an Einstar scan without user interaction.
-Uses the known Einstar coordinate system directly:
-    - X (index 0): Up
-    - Y (index 1): Forward (toward face)
-    - Z (index 2): Left of subject
-
-Algorithm:
-    1. Merge seam vertices (Einstar patch boundary duplicates)
-    2. Find nose tip = max Y vertex in the top 40% of the head (by X)
-    3. Extract midline anterior vertices above nose tip
-    4. Bin by X height, take median Y per bin (robust to noise/seams)
-    5. Nasion = first significant local minimum of Y above nose tip
+Only assumes X (index 0) = Up (gravity-based). The forward direction
+in the Y-Z horizontal plane is determined automatically using MediaPipe
+Face Landmarker: render the mesh from multiple viewpoints, detect the
+face in 2D, back-project landmarks to 3D.
+
+Returns None when automatic detection fails (e.g. EEG cap false
+positives). The caller should fall back to manual nasion selection.
 
 Initial Contributors:
     - Face Anonymization Project | 2024
 """
 
 import logging
+import pathlib
 
 import numpy as np
 from scipy.spatial import KDTree
@@ -26,34 +23,458 @@
 
 logger = logging.getLogger("cedalion")
 
+_MODEL_DIR = pathlib.Path(__file__).parent
+_MODEL_PATH = _MODEL_DIR / "face_landmarker.task"
+
+
+def _filter_contour_outliers(
+    contour_3d: np.ndarray,
+    max_sigma: float = 2.5,
+) -> np.ndarray:
+    """Remove outlier points from the face contour.
+
+    Rejects points whose distance from the contour centroid exceeds
+    max_sigma standard deviations. This catches "floating bits" from
+    seam cracks where VTK picks a disconnected mesh fragment.
+
+    Args:
+        contour_3d: Face contour points, shape (N, 3).
+        max_sigma: Rejection threshold in standard deviations.
+
+    Returns:
+        Filtered contour points, shape (M, 3) where M <= N.
+    """
+    centroid = contour_3d.mean(axis=0)
+    dists = np.linalg.norm(contour_3d - centroid, axis=1)
+    std_dist = dists.std()
+    if std_dist < 1e-6:
+        return contour_3d
+    keep = dists < dists.mean() + max_sigma * std_dist
+    return contour_3d[keep]
+
 
 def detect_nasion_auto(
     surface: cdc.TrimeshSurface,
-) -> tuple[np.ndarray, dict]:
-    """Automatically detect the nasion from mesh geometry alone.
+) -> tuple[np.ndarray, dict] | None:
+    """Automatically detect the nasion using MediaPipe face detection.
+
+    Renders the mesh from multiple viewpoints, runs MediaPipe Face
+    Landmarker to find the face, then refines the nasion via
+    midsagittal profile analysis.
+
+    Returns None when detection fails -- the caller should fall back
+    to manual nasion selection (see ``pick_nasion`` in ``ui.py``).
 
     Args:
         surface: TrimeshSurface from photogrammetry scan.
 
     Returns:
-        Tuple of (nasion_position, metadata) where nasion_position is a
-        numpy array of shape (3,) in mm, and metadata is a dict with
-        detection details.
+        Tuple of (nasion_position, metadata) or None if detection fails.
+        nasion_position is a numpy array of shape (3,) in mm.
+        metadata is a dict with keys: method, confidence, nose_tip,
+        forward_direction, face_contour_3d.
     """
     vertices = surface.mesh.vertices
-    centroid = vertices.mean(axis=0)
 
     # --- Step 1: Merge seam vertices to eliminate crack noise ---
     merged_verts = _merge_close_vertices(vertices)
 
-    # --- Step 2: Find nose tip ---
-    nose_tip, _ = _find_nose_tip(merged_verts)
+    # --- Step 2: Isolate head (remove chair/body) ---
+    head_verts, _ = _isolate_head_vertices(merged_verts)
+
+    # --- Step 3: YZ centroid of upper head (needed for profile analysis) ---
+    x_min = head_verts[:, 0].min()
+    x_max = head_verts[:, 0].max()
+    upper = head_verts[head_verts[:, 0] > x_min + 0.6 * (x_max - x_min)]
+    yz_centroid = np.array([0.0, upper[:, 1].mean(), upper[:, 2].mean()])
+
+    # --- Step 4: Try MediaPipe face detection ---
+    ml_result = _find_nose_tip_mediapipe(surface.mesh, head_verts)
+
+    if ml_result is None:
+        logger.info("Automatic nasion detection failed — no face found")
+        return None
+
+    nose_tip_ml, forward_dir, nasion_proxy, frontal_reliable, face_contour = ml_result
+    logger.debug(
+        f"MediaPipe face found, "
+        f"fwd_angle={np.degrees(np.arctan2(forward_dir[2], forward_dir[1])):.0f}deg, "
+        f"frontal_reliable={frontal_reliable}, "
+        f"face_contour={'yes' if face_contour is not None else 'no'}"
+    )
+
+    # --- Primary: use frontal re-render nasion directly ---
+    if frontal_reliable:
+        nasion = _snap_to_original(nasion_proxy, vertices)
+        logger.debug(f"Auto nasion (frontal): {nasion}")
+        return nasion, {
+            "method": "mediapipe+frontal",
+            "confidence": 0.90,
+            "nose_tip": nose_tip_ml.copy(),
+            "forward_direction": forward_dir.copy(),
+            "face_contour_3d": face_contour,
+        }
+
+    # --- Fallback: geometric profile analysis ---
+    result = _try_direction(
+        head_verts, vertices, forward_dir, yz_centroid,
+    )
+    if result is not None:
+        result["method"] = "mediapipe+profile"
+        if result["dip"] > 0:
+            result["confidence"] = 0.85
+        else:
+            result["confidence"] = 0.7
+        logger.debug(
+            f"Auto nasion (ML+profile): {result['nasion']}, "
+            f"confidence={result['confidence']:.2f}, dip={result['dip']:.2f}"
+        )
+        return result["nasion"], {
+            "method": result["method"],
+            "confidence": result["confidence"],
+            "nose_tip": result["nose_tip"],
+            "forward_direction": result["forward_direction"],
+            "face_contour_3d": face_contour,
+        }
+
+    logger.info("Profile analysis failed after MediaPipe detection")
+    return None
+
+
+# ---------------------------------------------------------------------------
+# MediaPipe-based detection
+# ---------------------------------------------------------------------------
+
+def _find_nose_tip_mediapipe(
+    mesh_trimesh,
+    head_verts: np.ndarray,
+    cam_distance: float = 400.0,
+) -> tuple[np.ndarray, np.ndarray, np.ndarray, bool] | None:
+    """Detect face direction and nasion using two-pass MediaPipe rendering.
+
+    Pass 1: Renders the mesh from 12 viewpoints to find the forward
+    direction (which way the face points).
+
+    Pass 2: Re-renders from directly in front and back-projects
+    MediaPipe landmark 168 (nasion) from this frontal view. From a
+    frontal camera, the nasion pixel maps correctly to the bridge of
+    the nose rather than the side.
+
+    Args:
+        mesh_trimesh: trimesh.Trimesh mesh object (with texture).
+        head_verts: Head-isolated vertices for centroid computation.
+        cam_distance: Camera distance from head centroid in mm.
+
+    Returns:
+        Tuple of (nose_tip_3d, forward_direction, nasion_3d,
+        frontal_reliable, face_contour) or None if no face detected.
+        frontal_reliable is True when the frontal re-render succeeded.
+    """
+    try:
+        import pyvista as pv
+        import mediapipe as mp
+        import vtk
+    except ImportError as e:
+        logger.info(f"MediaPipe detection unavailable: {e}")
+        return None
+
+    if not _MODEL_PATH.exists():
+        logger.info(f"MediaPipe model not found at {_MODEL_PATH}")
+        return None
+
+    head_centroid = head_verts.mean(axis=0)
+
+    # Build pyvista mesh with texture colors
+    faces_pv = np.column_stack([
+        np.full(len(mesh_trimesh.faces), 3), mesh_trimesh.faces
+    ]).ravel()
+    pv_mesh = pv.PolyData(mesh_trimesh.vertices, faces_pv)
+
+    try:
+        colors = mesh_trimesh.visual.to_color().vertex_colors[:, :3]
+        pv_mesh["colors"] = colors
+        has_colors = True
+    except Exception:
+        has_colors = False
+
+    plotter = pv.Plotter(off_screen=True, window_size=[1024, 768])
+    if has_colors:
+        plotter.add_mesh(pv_mesh, scalars="colors", rgb=True)
+    else:
+        plotter.add_mesh(pv_mesh, color="bisque")
+
+    options = mp.tasks.vision.FaceLandmarkerOptions(
+        base_options=mp.tasks.BaseOptions(
+            model_asset_path=str(_MODEL_PATH)
+        ),
+        running_mode=mp.tasks.vision.RunningMode.IMAGE,
+        num_faces=1,
+        min_face_detection_confidence=0.3,
+        min_face_presence_confidence=0.3,
+        output_facial_transformation_matrixes=True,
+    )
 
-    # --- Step 3: Get midline anterior vertices above nose tip ---
-    lat_dist = np.abs(merged_verts[:, 2] - nose_tip[2])
+    result = None
+    try:
+        with mp.tasks.vision.FaceLandmarker.create_from_options(options) as lm:
+            # === Pass 1: Find forward direction ===
+            pass1_fwd = None
+            pass1_nose_3d = None
+            pass1_nz_3d = None
+
+            for theta_deg in range(0, 360, 30):
+                theta = np.radians(theta_deg)
+                cam_offset = np.array([
+                    0,
+                    cam_distance * np.cos(theta),
+                    cam_distance * np.sin(theta),
+                ])
+                cam_pos = head_centroid + cam_offset
+
+                plotter.camera_position = [
+                    cam_pos.tolist(),
+                    head_centroid.tolist(),
+                    [1, 0, 0],
+                ]
+                plotter.render()
+                img = plotter.screenshot(return_img=True)
+                H, W = img.shape[:2]
+
+                mp_image = mp.Image(
+                    image_format=mp.ImageFormat.SRGB,
+                    data=np.ascontiguousarray(img),
+                )
+                detection = lm.detect(mp_image)
+
+                if not detection.face_landmarks:
+                    continue
+
+                # Validate yaw
+                if detection.facial_transformation_matrixes:
+                    from scipy.spatial.transform import Rotation
+                    mat = detection.facial_transformation_matrixes[0]
+                    rot = Rotation.from_matrix(np.array(mat)[:3, :3])
+                    yaw = rot.as_euler("yxz", degrees=True)[0]
+                    if abs(yaw) > 75:
+                        continue
+
+                face = detection.face_landmarks[0]
+                nose_lm = face[1]
+                nasion_lm = face[168]
+
+                # Back-project nose tip to validate detection
+                picker = vtk.vtkCellPicker()
+                picker.SetTolerance(0.01)
+                picker.Pick(
+                    float(int(nose_lm.x * W)),
+                    float(H - 1 - int(nose_lm.y * H)),
+                    0, plotter.renderer,
+                )
+                nose_3d = np.array(picker.GetPickPosition())
+                if np.allclose(nose_3d, 0):
+                    continue
+
+                # Reject nose on top of head
+                x_min_h = head_verts[:, 0].min()
+                x_max_h = head_verts[:, 0].max()
+                nose_rel = (nose_3d[0] - x_min_h) / (x_max_h - x_min_h)
+                if nose_rel > 0.85:
+                    continue
+
+                # Forward direction: nose tip -> centroid in YZ plane
+                fwd = nose_3d - head_centroid
+                fwd[0] = 0.0
+                fwd_norm = np.linalg.norm(fwd)
+                if fwd_norm < 1e-6:
+                    continue
+                fwd = fwd / fwd_norm
+
+                pass1_fwd = fwd.copy()
+                pass1_nose_3d = nose_3d.copy()
+
+                # Also grab nasion proxy from this view
+                picker.Pick(
+                    float(int(nasion_lm.x * W)),
+                    float(H - 1 - int(nasion_lm.y * H)),
+                    0, plotter.renderer,
+                )
+                pass1_nz_3d = np.array(picker.GetPickPosition()).copy()
+
+                logger.debug(f"Pass 1: face found at {theta_deg}deg")
+                break
+
+            if pass1_fwd is None:
+                result = None
+            else:
+                # === Pass 2: Frontal re-render for accurate nasion ===
+                frontal_cam_pos = head_centroid + pass1_fwd * cam_distance
+                plotter.camera_position = [
+                    frontal_cam_pos.tolist(),
+                    head_centroid.tolist(),
+                    [1, 0, 0],
+                ]
+                plotter.render()
+                frontal_img = plotter.screenshot(return_img=True)
+                H_f, W_f = frontal_img.shape[:2]
+
+                mp_frontal = mp.Image(
+                    image_format=mp.ImageFormat.SRGB,
+                    data=np.ascontiguousarray(frontal_img),
+                )
+                frontal_det = lm.detect(mp_frontal)
+
+                frontal_reliable = False
+                face_contour = None
+                final_nz_3d = pass1_nz_3d
+                final_nose_3d = pass1_nose_3d
+
+                if frontal_det.face_landmarks:
+                    f_face = frontal_det.face_landmarks[0]
+
+                    # Back-project nasion (landmark 168) from frontal view
+                    nz_lm_f = f_face[168]
+                    picker_f = vtk.vtkCellPicker()
+                    picker_f.SetTolerance(0.01)
+                    picker_f.Pick(
+                        float(int(nz_lm_f.x * W_f)),
+                        float(H_f - 1 - int(nz_lm_f.y * H_f)),
+                        0, plotter.renderer,
+                    )
+                    nz_3d_f = np.array(picker_f.GetPickPosition())
+
+                    # Back-project nose tip (landmark 1) from frontal view
+                    nose_lm_f = f_face[1]
+                    picker_f.Pick(
+                        float(int(nose_lm_f.x * W_f)),
+                        float(H_f - 1 - int(nose_lm_f.y * H_f)),
+                        0, plotter.renderer,
+                    )
+                    nose_3d_f = np.array(picker_f.GetPickPosition())
+
+                    # Validate: both on mesh, nasion above nose tip
+                    if (not np.allclose(nz_3d_f, 0)
+                            and not np.allclose(nose_3d_f, 0)
+                            and nz_3d_f[0] > nose_3d_f[0]):
+                        final_nz_3d = nz_3d_f.copy()
+                        final_nose_3d = nose_3d_f.copy()
+                        frontal_reliable = True
+                        logger.debug(
+                            f"Pass 2 (frontal): nasion={final_nz_3d}, "
+                            f"nose={final_nose_3d}"
+                        )
+
+                        # === Extract face oval contour ===
+                        _FACE_OVAL_INDICES = [
+                            10, 338, 297, 332, 284, 251, 389, 356,
+                            454, 323, 361, 288, 397, 365, 379, 378,
+                            400, 377, 152, 148, 176, 149, 150, 136,
+                            172, 58, 132, 93, 234, 127, 162, 21,
+                            54, 103, 67, 109,
+                        ]
+                        contour_pts = []
+                        picker_c = vtk.vtkCellPicker()
+                        picker_c.SetTolerance(0.01)
+                        for idx in _FACE_OVAL_INDICES:
+                            lm_pt = f_face[idx]
+                            picker_c.Pick(
+                                float(int(lm_pt.x * W_f)),
+                                float(H_f - 1 - int(lm_pt.y * H_f)),
+                                0, plotter.renderer,
+                            )
+                            pt_3d = np.array(picker_c.GetPickPosition())
+                            if not np.allclose(pt_3d, 0):
+                                contour_pts.append(pt_3d.copy())
+
+                        if len(contour_pts) >= 20:
+                            raw_contour = np.array(contour_pts)
+                            face_contour = _filter_contour_outliers(
+                                raw_contour
+                            )
+                            n_removed = len(raw_contour) - len(face_contour)
+                            if len(face_contour) < 20:
+                                face_contour = raw_contour
+                                n_removed = 0
+                            logger.debug(
+                                f"Face oval: {len(contour_pts)}/36 "
+                                f"back-projected, {n_removed} outliers removed"
+                            )
+                        else:
+                            logger.debug(
+                                f"Face oval: only {len(contour_pts)}/36 "
+                                f"back-projected, skipping"
+                            )
+                    else:
+                        logger.debug(
+                            "Pass 2: frontal back-projection failed validation"
+                        )
+                else:
+                    logger.debug("Pass 2: no face in frontal re-render")
+
+                # Recompute forward from frontal nose tip if available
+                if frontal_reliable:
+                    final_fwd = final_nose_3d - head_centroid
+                    final_fwd[0] = 0.0
+                    fn = np.linalg.norm(final_fwd)
+                    if fn > 1e-6:
+                        final_fwd = final_fwd / fn
+                    else:
+                        final_fwd = pass1_fwd.copy()
+                else:
+                    final_fwd = pass1_fwd.copy()
+
+                result = (
+                    final_nose_3d.copy(),
+                    final_fwd,
+                    final_nz_3d.copy(),
+                    frontal_reliable,
+                    face_contour,
+                )
+    except Exception as e:
+        logger.warning(f"MediaPipe detection error: {e}")
+    finally:
+        plotter.close()
+
+    return result
+
+
+# ---------------------------------------------------------------------------
+# Geometric detection (fallback)
+# ---------------------------------------------------------------------------
+
+def _try_direction(
+    head_verts: np.ndarray,
+    original_verts: np.ndarray,
+    forward_dir: np.ndarray,
+    yz_centroid: np.ndarray,
+    nose_tip_override: np.ndarray | None = None,
+) -> dict | None:
+    """Try a candidate forward direction and return nasion result with dip score.
+
+    Args:
+        head_verts: Head-isolated vertices.
+        original_verts: Original mesh vertices for snapping.
+        forward_dir: Candidate forward unit vector (X=0).
+        yz_centroid: YZ centroid of upper head (X=0).
+        nose_tip_override: If provided, use this as the nose tip instead of
+            detecting it geometrically. Useful when ML provides a nose tip.
+
+    Returns:
+        Dict with nasion, confidence, dip score, etc., or None on failure.
+    """
+    lateral_dir = np.cross(np.array([1.0, 0.0, 0.0]), forward_dir)
+    lateral_dir = lateral_dir / np.linalg.norm(lateral_dir)
+
+    if nose_tip_override is not None:
+        nose_tip = nose_tip_override.copy()
+    else:
+        nose_tip, _ = _find_nose_tip(head_verts, forward_dir)
+
+    lat_dist = np.abs(np.dot(head_verts - nose_tip, lateral_dir))
     midline = lat_dist < 10.0
-    above = merged_verts[:, 0] > nose_tip[0]
-    anterior = merged_verts[:, 1] > centroid[1]
+    above = head_verts[:, 0] > nose_tip[0]
+    fwd_proj = np.dot(head_verts, forward_dir)
+    centroid_fwd = np.dot(yz_centroid, forward_dir)
+    anterior = fwd_proj > centroid_fwd
 
     mask = midline & above & anterior
     candidates = np.where(mask)[0]
@@ -63,77 +484,100 @@ def detect_nasion_auto(
         candidates = np.where(mask)[0]
 
     if len(candidates) < 5:
-        logger.warning("Auto nasion: too few candidates, returning nose tip")
-        nasion = _snap_to_original(nose_tip, vertices)
-        return nasion, {"method": "fallback", "confidence": 0.0,
-                        "nose_tip": nose_tip.copy()}
+        return None
 
-    # --- Step 4: Bin by X height, take median Y per bin ---
-    cand_x = merged_verts[candidates, 0]
-    cand_y = merged_verts[candidates, 1]
+    # Bin by X height, take median forward-projection per bin
+    cand_x = head_verts[candidates, 0]
+    cand_fwd = fwd_proj[candidates]
 
-    # Create bins of ~1mm height
     x_min, x_max = cand_x.min(), cand_x.max()
     n_bins = max(10, int((x_max - x_min) / 1.0))
     bin_edges = np.linspace(x_min, x_max, n_bins + 1)
     bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2.0
-    bin_indices = np.digitize(cand_x, bin_edges) - 1
-    bin_indices = np.clip(bin_indices, 0, n_bins - 1)
+    bin_indices = np.clip(np.digitize(cand_x, bin_edges) - 1, 0, n_bins - 1)
 
-    # Median Y per bin (robust to outliers/seam noise)
-    bin_y = np.full(n_bins, np.nan)
+    bin_fwd = np.full(n_bins, np.nan)
     for b in range(n_bins):
         in_bin = bin_indices == b
         if in_bin.sum() > 0:
-            bin_y[b] = np.median(cand_y[in_bin])
+            bin_fwd[b] = np.median(cand_fwd[in_bin])
 
-    # Remove empty bins
-    valid = ~np.isnan(bin_y)
+    valid = ~np.isnan(bin_fwd)
     bin_centers_valid = bin_centers[valid]
-    bin_y_valid = bin_y[valid]
+    bin_fwd_valid = bin_fwd[valid]
 
-    if len(bin_y_valid) < 3:
-        logger.warning("Auto nasion: too few bins, returning nose tip")
-        nasion = _snap_to_original(nose_tip, vertices)
-        return nasion, {"method": "fallback", "confidence": 0.0,
-                        "nose_tip": nose_tip.copy()}
+    if len(bin_fwd_valid) < 3:
+        return None
 
-    # --- Step 5: Find first significant local minimum ---
-    # "Significant" = dip of at least 0.5mm compared to neighbors
-    nasion_bin = _find_significant_minimum(bin_y_valid, min_dip=0.5)
+    # Smooth profile to suppress mesh noise
+    window = min(7, len(bin_fwd_valid))
+    if window >= 3:
+        kernel = np.ones(window) / window
+        padded = np.pad(bin_fwd_valid, (window // 2, window // 2), mode='edge')
+        bin_fwd_smoothed = np.convolve(padded, kernel, mode='valid')
+    else:
+        bin_fwd_smoothed = bin_fwd_valid
 
-    if nasion_bin is None:
-        # Fallback: global minimum in first third
-        third = max(1, len(bin_y_valid) // 3)
-        nasion_bin = np.argmin(bin_y_valid[:third])
+    # Search in anatomically plausible range: nose_tip to nose_tip + 40mm
+    nose_tip_x = nose_tip[0]
+    search_mask = (
+        (bin_centers_valid >= nose_tip_x)
+        & (bin_centers_valid <= nose_tip_x + 40)
+    )
+    search_idx = np.where(search_mask)[0]
+
+    if len(search_idx) >= 3:
+        nasion_bin = _find_deepest_minimum(
+            bin_fwd_smoothed[search_idx], min_dip=0.5
+        )
+        if nasion_bin is not None:
+            nasion_bin = search_idx[nasion_bin]
+        else:
+            nasion_bin = search_idx[np.argmin(bin_fwd_smoothed[search_idx])]
+    else:
+        nasion_bin = _find_deepest_minimum(bin_fwd_smoothed, min_dip=0.5)
+        if nasion_bin is None:
+            half = max(1, len(bin_fwd_smoothed) // 2)
+            nasion_bin = np.argmin(bin_fwd_smoothed[:half])
 
     nasion_x = bin_centers_valid[nasion_bin]
-    nasion_y = bin_y_valid[nasion_bin]
-
-    # Find the actual vertex closest to this binned position
-    nasion_approx = np.array([nasion_x, nasion_y, nose_tip[2]])
-    nasion = _snap_to_original(nasion_approx, vertices)
+    nasion_fwd_val = bin_fwd_valid[nasion_bin]
+
+    # Find best candidate vertex near this height
+    bin_half = (bin_centers_valid[1] - bin_centers_valid[0]) if len(
+        bin_centers_valid) > 1 else 2.0
+    height_match = np.abs(head_verts[candidates, 0] - nasion_x) < bin_half * 2
+    if height_match.sum() > 0:
+        height_cands = candidates[height_match]
+        fwd_dist = np.abs(fwd_proj[height_cands] - nasion_fwd_val)
+        best_cand = height_cands[np.argmin(fwd_dist)]
+        nasion_approx = head_verts[best_cand]
+    else:
+        nasion_approx = np.array([nasion_x, nose_tip[1], nose_tip[2]])
+    nasion = _snap_to_original(nasion_approx, original_verts)
 
-    # Confidence
-    if nasion_bin > 0 and nasion_bin < len(bin_y_valid) - 1:
-        dip = (min(bin_y_valid[nasion_bin - 1], bin_y_valid[nasion_bin + 1])
-               - bin_y_valid[nasion_bin])
-        confidence = float(min(1.0, dip / 5.0))
+    # Dip depth (using smoothed profile)
+    if 0 < nasion_bin < len(bin_fwd_smoothed) - 1:
+        dip = (min(bin_fwd_smoothed[nasion_bin - 1], bin_fwd_smoothed[nasion_bin + 1])
+               - bin_fwd_smoothed[nasion_bin])
     else:
-        confidence = 0.3
+        dip = 0.0
 
-    metadata = {
-        "method": "profile",
-        "confidence": confidence,
+    confidence = float(min(1.0, dip / 5.0))
+
+    return {
+        "nasion": nasion,
         "nose_tip": nose_tip.copy(),
+        "forward_direction": forward_dir.copy(),
+        "confidence": confidence,
+        "dip": dip,
+        "method": "profile",
     }
 
-    logger.info(
-        f"Auto nasion: {nasion}, confidence={confidence:.2f}, "
-        f"nose_tip={nose_tip}"
-    )
-    return nasion, metadata
 
+# ---------------------------------------------------------------------------
+# Utility functions
+# ---------------------------------------------------------------------------
 
 def _merge_close_vertices(vertices: np.ndarray, tol: float = 0.01) -> np.ndarray:
     """Merge spatially coincident vertices (seam duplicates).
@@ -181,53 +625,81 @@ def find(x):
         counts[inverse[i]] += 1
     new_verts /= counts[:, None]
 
-    logger.info(f"Merged seam vertices: {n} -> {n_new}")
+    logger.debug(f"Merged seam vertices: {n} -> {n_new}")
     return new_verts
 
 
-def _find_nose_tip(vertices: np.ndarray) -> tuple[np.ndarray, int]:
-    """Find nose tip as max Y vertex in the top 40% of the head.
+def _isolate_head_vertices(
+    vertices: np.ndarray,
+    max_head_height: float = 280.0,
+) -> tuple[np.ndarray, np.ndarray]:
+    """Isolate head vertices by cropping to within max_head_height of the top.
 
     Args:
         vertices: Mesh vertices of shape (N, 3).
+        max_head_height: Maximum expected head height in mm.
+
+    Returns:
+        Tuple of (head_vertices, mask) where mask is boolean array.
+    """
+    x_max = vertices[:, 0].max()
+    mask = vertices[:, 0] > x_max - max_head_height
+
+    if mask.sum() < 100:
+        logger.warning("Head isolation: too few vertices, using all")
+        mask = np.ones(len(vertices), dtype=bool)
+
+    logger.debug(f"Head isolation: {len(vertices)} -> {mask.sum()} vertices")
+    return vertices[mask], mask
+
+
+def _find_nose_tip(
+    vertices: np.ndarray,
+    forward_direction: np.ndarray,
+) -> tuple[np.ndarray, int]:
+    """Find nose tip as the most-forward vertex in the upper head.
+
+    Args:
+        vertices: Head-isolated vertices of shape (N, 3).
+        forward_direction: Unit vector pointing forward in Y-Z plane.
 
     Returns:
         Tuple of (nose_tip_position, vertex_index).
     """
     x_min = vertices[:, 0].min()
     x_max = vertices[:, 0].max()
-    x_range = x_max - x_min
-
-    # Top 40% of the head by X (up)
-    x_threshold = x_min + 0.6 * x_range
+    x_threshold = x_min + 0.6 * (x_max - x_min)
     upper_mask = vertices[:, 0] > x_threshold
 
     if upper_mask.sum() == 0:
         upper_mask = np.ones(len(vertices), dtype=bool)
 
-    y_vals = vertices[:, 1].copy()
-    y_vals[~upper_mask] = -np.inf
-    idx = np.argmax(y_vals)
+    fwd_proj = np.dot(vertices, forward_direction)
+    fwd_proj[~upper_mask] = -np.inf
+    idx = np.argmax(fwd_proj)
 
     return vertices[idx].copy(), idx
 
 
-def _find_significant_minimum(values: np.ndarray, min_dip: float = 0.5):
-    """Find the first local minimum that dips at least min_dip below neighbors.
+def _find_deepest_minimum(values: np.ndarray, min_dip: float = 0.5):
+    """Find the deepest local minimum that dips at least min_dip below neighbors.
 
     Args:
         values: 1D array of values.
         min_dip: Minimum dip depth to be considered significant.
 
     Returns:
-        Index of the first significant local minimum, or None.
+        Index of the deepest significant local minimum, or None.
     """
+    best_idx = None
+    best_dip = 0.0
     for i in range(1, len(values) - 1):
         if values[i] < values[i - 1] and values[i] < values[i + 1]:
             dip = min(values[i - 1], values[i + 1]) - values[i]
-            if dip >= min_dip:
-                return i
-    return None
+            if dip >= min_dip and dip > best_dip:
+                best_dip = dip
+                best_idx = i
+    return best_idx
 
 
 def _snap_to_original(point: np.ndarray, vertices: np.ndarray) -> np.ndarray:
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/ui.py b/src/cedalion/geometry/photogrammetry/anonymization/ui.py
index aa5a42e8..bfb7dc47 100644
--- a/src/cedalion/geometry/photogrammetry/anonymization/ui.py
+++ b/src/cedalion/geometry/photogrammetry/anonymization/ui.py
@@ -1,7 +1,7 @@
-"""Interactive UI components for facial region editing and preview.
+"""Interactive UI components for nasion selection.
 
-This module provides PyVista-based interactive tools for refining facial
-region detection and previewing anonymization results.
+Provides a PyVista-based interactive picker for manual nasion (Nz)
+selection as a fallback when automatic detection fails.
 
 Initial Contributors:
     - Face Anonymization Project | 2024
@@ -13,374 +13,61 @@
 import pyvista as pv
 
 import cedalion.dataclasses as cdc
-import cedalion.typing as cdt
-from cedalion import units
-
-from .validator import ValidationResult
-
 
 logger = logging.getLogger("cedalion")
 
 
-class FacialRegionEditor:
-    """Interactive editor for facial region mask.
-
-    Allows user to:
-    - See detected facial region highlighted
-    - Add/remove vertices from mask by clicking
-    - Verify protected points are excluded
-
-    Follows the pattern of OptodeSelector in cedalion.plots.
-
-    Attributes:
-        surface: The TrimeshSurface being edited
-        mask: Current facial region mask
-        protected_points: Points that cannot be part of facial region
-        plotter: PyVista plotter instance
-    """
-
-    def __init__(
-        self,
-        surface: cdc.TrimeshSurface,
-        initial_mask: np.ndarray,
-        protected_points: cdt.LabeledPointCloud,
-        protection_radius: float = 15.0,
-    ):
-        """Initialize the facial region editor.
-
-        Args:
-            surface: The TrimeshSurface to edit
-            initial_mask: Initial boolean mask for facial region
-            protected_points: Points to protect (optodes + landmarks)
-            protection_radius: Radius around protected points in mm
-        """
-        self.surface = surface
-        self.mask = initial_mask.copy()
-        self.protected_points = protected_points
-        self.protection_radius = protection_radius
-        self.plotter = pv.Plotter()
-        self._mesh_actor = None
-        self._protected_actors = []
-
-    def _get_vtk_mesh(self) -> pv.PolyData:
-        """Convert surface to PyVista mesh."""
-        vtk_surface = cdc.VTKSurface.from_trimeshsurface(self.surface)
-        return pv.wrap(vtk_surface.mesh)
-
-    def _update_visualization(self):
-        """Update the mesh visualization with current mask."""
-        if self._mesh_actor is not None:
-            self.plotter.remove_actor(self._mesh_actor)
-
-        mesh = self._get_vtk_mesh()
-
-        # Create scalars for visualization
-        # 0 = non-facial, 1 = facial region
-        scalars = self.mask.astype(float)
-
-        self._mesh_actor = self.plotter.add_mesh(
-            mesh,
-            scalars=scalars,
-            cmap=["white", "red"],
-            clim=[0, 1],
-            show_scalar_bar=False,
-            opacity=0.9,
-            smooth_shading=True,
-            pickable=True,
-        )
-
-    def _add_protected_points(self):
-        """Add spheres for protected points."""
-        points = self.protected_points.pint.dequantify().values
-        labels = [str(l) for l in self.protected_points.label.values]
-
-        for i, (point, label) in enumerate(zip(points, labels)):
-            sphere = pv.Sphere(radius=3, center=point)
-            actor = self.plotter.add_mesh(
-                sphere, color="green", smooth_shading=True
-            )
-            self._protected_actors.append(actor)
-
-            # Add label
-            self.plotter.add_point_labels(
-                [point], [label], font_size=12, point_color="green"
-            )
-
-    def _on_pick(self, picked_point):
-        """Handle vertex picking to toggle mask."""
-        if picked_point is None:
-            return
-
-        point = np.array(picked_point)
-
-        # Check if point is protected
-        protected = self.protected_points.pint.dequantify().values
-        distances = np.linalg.norm(protected - point, axis=1)
-
-        if np.any(distances < self.protection_radius):
-            logger.info("Cannot modify protected region")
-            return
-
-        # Find nearest vertex
-        vertices = self.surface.mesh.vertices
-        distances = np.linalg.norm(vertices - point, axis=1)
-        nearest_idx = np.argmin(distances)
-
-        # Use brush to toggle multiple vertices
-        brush_radius = 10.0  # mm
-        affected_mask = distances < brush_radius
-
-        # Toggle mask for affected vertices
-        if self.mask[nearest_idx]:
-            # Remove from facial region
-            self.mask[affected_mask] = False
-        else:
-            # Add to facial region (excluding protected)
-            protected_positions = self.protected_points.pint.dequantify().values
-            for idx in np.where(affected_mask)[0]:
-                v = vertices[idx]
-                dist_to_protected = np.min(
-                    np.linalg.norm(protected_positions - v, axis=1)
-                )
-                if dist_to_protected >= self.protection_radius:
-                    self.mask[idx] = True
-
-        self._update_visualization()
-
-    def show(self) -> np.ndarray:
-        """Display editor and return refined mask.
-
-        Returns:
-            Refined boolean mask after user editing
-        """
-        # Set up visualization
-        self._update_visualization()
-        self._add_protected_points()
-
-        # Enable picking
-        self.plotter.enable_surface_point_picking(
-            callback=self._on_pick,
-            show_message="Click to add/remove vertices from facial region",
-            show_point=False,
-            tolerance=0.005,
-        )
-
-        # Add instructions
-        self.plotter.add_text(
-            "Left-click: Toggle facial region\n"
-            "Green spheres: Protected points (cannot modify)\n"
-            "Close window when done",
-            position="upper_left",
-            font_size=10,
-        )
-
-        # Show and wait
-        self.plotter.show()
-
-        return self.mask
-
-
-class AnonymizationPreview:
-    """Side-by-side comparison of original and anonymized meshes.
-
-    Provides interactive visualization to compare the original scan
-    with the anonymized version, including validation metrics display.
-
-    Attributes:
-        original: Original TrimeshSurface
-        anonymized: Anonymized TrimeshSurface
-        metrics: Validation metrics to display
-    """
+def pick_nasion(surface: cdc.TrimeshSurface) -> np.ndarray | None:
+    """Open an interactive window for the user to click the nasion point.
 
-    def __init__(
-        self,
-        original: cdc.TrimeshSurface,
-        anonymized: cdc.TrimeshSurface,
-        metrics: ValidationResult = None,
-    ):
-        """Initialize the preview.
-
-        Args:
-            original: Original TrimeshSurface
-            anonymized: Anonymized TrimeshSurface
-            metrics: Optional validation metrics to display
-        """
-        self.original = original
-        self.anonymized = anonymized
-        self.metrics = metrics
-
-    def _get_vtk_mesh(self, surface: cdc.TrimeshSurface) -> pv.PolyData:
-        """Convert surface to PyVista mesh."""
-        vtk_surface = cdc.VTKSurface.from_trimeshsurface(surface)
-        return pv.wrap(vtk_surface.mesh)
-
-    def show(self):
-        """Display side-by-side comparison."""
-        # Create plotter with two viewports
-        plotter = pv.Plotter(shape=(1, 2))
-
-        # Left viewport: Original
-        plotter.subplot(0, 0)
-        original_mesh = self._get_vtk_mesh(self.original)
-
-        # Check if mesh has colors
-        if hasattr(self.original.mesh.visual, 'vertex_colors'):
-            try:
-                colors = self.original.mesh.visual.to_color().vertex_colors
-                original_mesh["colors"] = colors[:, :3]  # RGB only
-                plotter.add_mesh(
-                    original_mesh,
-                    scalars="colors",
-                    rgb=True,
-                    smooth_shading=True,
-                )
-            except Exception:
-                plotter.add_mesh(original_mesh, color="white", smooth_shading=True)
-        else:
-            plotter.add_mesh(original_mesh, color="white", smooth_shading=True)
-
-        plotter.add_text("Original", position="upper_left", font_size=14)
-
-        # Right viewport: Anonymized
-        plotter.subplot(0, 1)
-        anonymized_mesh = self._get_vtk_mesh(self.anonymized)
-
-        if hasattr(self.anonymized.mesh.visual, 'vertex_colors'):
-            try:
-                colors = self.anonymized.mesh.visual.to_color().vertex_colors
-                anonymized_mesh["colors"] = colors[:, :3]
-                plotter.add_mesh(
-                    anonymized_mesh,
-                    scalars="colors",
-                    rgb=True,
-                    smooth_shading=True,
-                )
-            except Exception:
-                plotter.add_mesh(anonymized_mesh, color="white", smooth_shading=True)
-        else:
-            plotter.add_mesh(anonymized_mesh, color="white", smooth_shading=True)
-
-        plotter.add_text("Anonymized", position="upper_left", font_size=14)
-
-        # Add metrics if available
-        if self.metrics is not None:
-            status = "PASS" if self.metrics.passed else "FAIL"
-            metrics_text = (
-                f"Validation: {status}\n"
-                f"{self.metrics.summary}"
-            )
-            plotter.add_text(
-                metrics_text,
-                position="lower_left",
-                font_size=10,
-            )
-
-        # Link cameras so both views rotate together
-        plotter.link_views()
-
-        plotter.show()
-
-
-class DisplacementViewer:
-    """Visualize vertex displacement as a heatmap on the mesh.
-
-    Shows how much each vertex moved during anonymization using
-    a color-coded visualization.
-    """
-
-    def __init__(
-        self,
-        surface: cdc.TrimeshSurface,
-        displacements: np.ndarray,
-        facial_mask: np.ndarray = None,
-    ):
-        """Initialize the displacement viewer.
-
-        Args:
-            surface: The anonymized TrimeshSurface
-            displacements: Per-vertex displacement values in mm
-            facial_mask: Optional mask to highlight facial region
-        """
-        self.surface = surface
-        self.displacements = displacements
-        self.facial_mask = facial_mask
-
-    def _get_vtk_mesh(self) -> pv.PolyData:
-        """Convert surface to PyVista mesh."""
-        vtk_surface = cdc.VTKSurface.from_trimeshsurface(self.surface)
-        return pv.wrap(vtk_surface.mesh)
-
-    def show(self, cmap: str = "hot"):
-        """Display displacement heatmap.
-
-        Args:
-            cmap: Colormap for displacement visualization
-        """
-        plotter = pv.Plotter()
-
-        mesh = self._get_vtk_mesh()
-        mesh["displacement"] = self.displacements
-
-        plotter.add_mesh(
-            mesh,
-            scalars="displacement",
-            cmap=cmap,
-            smooth_shading=True,
-            scalar_bar_args={
-                "title": "Displacement (mm)",
-                "vertical": True,
-            },
-        )
-
-        # Add statistics
-        stats_text = (
-            f"Max: {self.displacements.max():.2f}mm\n"
-            f"Mean: {self.displacements.mean():.2f}mm\n"
-            f"Median: {np.median(self.displacements):.2f}mm"
-        )
-
-        if self.facial_mask is not None:
-            facial_disp = self.displacements[self.facial_mask]
-            stats_text += (
-                f"\n\nFacial Region:\n"
-                f"Max: {facial_disp.max():.2f}mm\n"
-                f"Mean: {facial_disp.mean():.2f}mm"
-            )
-
-        plotter.add_text(stats_text, position="upper_right", font_size=10)
-
-        plotter.show()
-
-
-def quick_preview(
-    original: cdc.TrimeshSurface,
-    anonymized: cdc.TrimeshSurface,
-    metrics: ValidationResult = None,
-):
-    """Quick function to preview anonymization results.
-
-    Convenience function that creates an AnonymizationPreview and shows it.
+    Displays the textured mesh and lets the user click on the nasion (Nz).
+    The clicked point is snapped to the nearest mesh vertex.
 
     Args:
-        original: Original TrimeshSurface
-        anonymized: Anonymized TrimeshSurface
-        metrics: Optional validation metrics
-    """
-    preview = AnonymizationPreview(original, anonymized, metrics)
-    preview.show()
-
+        surface: TrimeshSurface from photogrammetry scan.
 
-def quick_displacement_view(
-    surface: cdc.TrimeshSurface,
-    displacements: np.ndarray,
-    facial_mask: np.ndarray = None,
-):
-    """Quick function to view displacement heatmap.
-
-    Args:
-        surface: The anonymized TrimeshSurface
-        displacements: Per-vertex displacement values in mm
-        facial_mask: Optional mask to highlight facial region
+    Returns:
+        Nasion position as numpy array of shape (3,) in mm, or None if
+        the user closes the window without clicking.
     """
-    viewer = DisplacementViewer(surface, displacements, facial_mask)
-    viewer.show()
+    from scipy.spatial import KDTree
+
+    picked_point = [None]  # mutable container for closure
+
+    def _on_pick(point):
+        if point is not None:
+            picked_point[0] = np.array(point)
+
+    plotter = pv.Plotter(notebook=False)
+
+    # Add textured mesh using cedalion's plot_surface for consistency
+    import cedalion.plots
+    cedalion.plots.plot_surface(plotter, surface, opacity=1.0)
+
+    plotter.enable_surface_point_picking(
+        callback=_on_pick,
+        left_clicking=True,
+        show_point=True,
+        point_size=20,
+        color="red",
+        show_message="Left-click Nz (nasion), then close the window",
+    )
+
+    plotter.add_text(
+        "Left-click NASION (bridge of the nose), then close window",
+        position="upper_left",
+        font_size=14,
+    )
+
+    plotter.show()
+
+    if picked_point[0] is None:
+        return None
+
+    # Snap to nearest mesh vertex
+    vertices = surface.mesh.vertices
+    tree = KDTree(vertices)
+    _, idx = tree.query(picked_point[0])
+    nasion = vertices[idx].copy()
+    logger.debug(f"Manual nasion selected: {nasion}")
+    return nasion
diff --git a/tests/test_anonymization.py b/tests/test_anonymization.py
index 7af00249..0f103ff9 100644
--- a/tests/test_anonymization.py
+++ b/tests/test_anonymization.py
@@ -4,36 +4,18 @@
 import pytest
 import trimesh
 import xarray as xr
-from numpy.testing import assert_allclose, assert_array_less
+from numpy.testing import assert_allclose
 
 import cedalion
 import cedalion.dataclasses as cdc
 from cedalion import units
 
 from cedalion.geometry.photogrammetry.anonymization import (
-    detect_facial_landmarks,
-    get_facial_region_mask,
-    anonymize_facial_region,
-    validate_anonymization,
-    anonymize_scan,
-    FacialLandmarks,
-    FacialLandmarkType,
-    AnonymizationConfig,
-    AnonymizationMethod,
-)
-from cedalion.geometry.photogrammetry.anonymization.face_detector import (
-    _normalize,
-    _build_head_coordinate_system,
-    _find_landmark,
-)
-from cedalion.geometry.photogrammetry.anonymization.anonymizer import (
-    _build_adjacency_matrix,
-    _compute_laplacian_displacement,
-    smooth_region_selective,
-)
-from cedalion.geometry.photogrammetry.anonymization.validator import (
-    validate_anonymization as validate_anonymization_fn,
-    ValidationResult,
+    normalize_axes,
+    isolate_head,
+    detect_landmarks_from_nasion,
+    get_facial_region_mask_from_nasion,
+    detect_nasion_auto,
 )
 
 
@@ -53,13 +35,15 @@ def simple_sphere_surface():
 
 @pytest.fixture
 def head_like_surface():
-    """Create a more head-like elongated sphere surface."""
-    # Create a sphere and scale it to be more head-shaped
+    """Create a more head-like elongated sphere surface.
+
+    Axis convention: X=up, Y=anterior, Z=left.
+    Nasion placed at (0, 100, 0) = front of sphere.
+    """
     sphere = trimesh.creation.icosphere(subdivisions=4, radius=100)
-    # Scale to be taller than wide (head-like proportions)
     vertices = sphere.vertices.copy()
-    vertices[:, 2] *= 1.2  # Elongate vertically
-    vertices[:, 1] *= 0.9  # Slightly narrow front-to-back
+    vertices[:, 0] *= 1.2  # taller
+    vertices[:, 1] *= 0.9  # slightly narrow front-to-back
 
     head_mesh = trimesh.Trimesh(vertices=vertices, faces=sphere.faces)
     return cdc.TrimeshSurface(
@@ -68,483 +52,180 @@ def head_like_surface():
 
 
 @pytest.fixture
-def anatomical_landmarks(simple_sphere_surface):
-    """Create anatomical landmarks on a sphere surface."""
-    # Place landmarks at anatomically reasonable positions on a sphere
-    # Nz (nasion) - front
-    nz = np.array([0, 100, 0])  # Front of sphere
-    # Iz (inion) - back
-    iz = np.array([0, -100, 0])  # Back of sphere
-    # LPA (left preauricular) - left
-    lpa = np.array([-100, 0, 0])  # Left of sphere
-    # RPA (right preauricular) - right
-    rpa = np.array([100, 0, 0])  # Right of sphere
-    # Cz (vertex) - top
-    cz = np.array([0, 0, 100])  # Top of sphere
-
-    landmarks = np.array([nz, iz, lpa, rpa, cz])
-    labels = ["Nz", "Iz", "LPA", "RPA", "Cz"]
-    types = [cdc.PointType.LANDMARK] * 5
+def anatomical_landmarks():
+    """Create anatomical landmarks on a sphere (already axis-normalized)."""
+    landmarks = np.array([
+        [0, 100, 0],    # Nz (front)
+        [0, -100, 0],   # Iz (back)
+        [120, 0, 0],    # Cz (top)
+        [-100, 0, 0],   # LPA (left)  -- note: Z used for left in real coords
+        [100, 0, 0],    # RPA (right)
+    ], dtype=float)
+    labels = ["Nz", "Iz", "Cz", "LPA", "RPA"]
 
     return xr.DataArray(
         landmarks,
         dims=["label", "scanner"],
         coords={
             "label": labels,
-            "type": ("label", types),
-        },
-        attrs={"units": "mm"},
-    ).pint.quantify()
-
-
-@pytest.fixture
-def optode_positions(simple_sphere_surface):
-    """Create some optode positions on the surface."""
-    # Place optodes at various positions (avoiding the face)
-    positions = np.array([
-        [50, 0, 87],   # Upper right
-        [-50, 0, 87],  # Upper left
-        [30, -50, 80],  # Back right
-        [-30, -50, 80],  # Back left
-        [0, -70, 70],   # Back center
-    ])
-    labels = ["S1", "S2", "D1", "D2", "D3"]
-
-    return xr.DataArray(
-        positions,
-        dims=["label", "scanner"],
-        coords={
-            "label": labels,
-            "type": ("label", [cdc.PointType.SOURCE, cdc.PointType.SOURCE,
-                              cdc.PointType.DETECTOR, cdc.PointType.DETECTOR,
-                              cdc.PointType.DETECTOR]),
+            "type": ("label", [cdc.PointType.LANDMARK] * 5),
         },
         attrs={"units": "mm"},
     ).pint.quantify()
 
 
 # ============================================================================
-# Face Detector Tests
+# Axis Normalization
 # ============================================================================
 
 
-class TestFaceDetector:
-    """Tests for face detection functionality."""
-
-    def test_normalize(self):
-        """Test vector normalization."""
-        v = np.array([3, 4, 0])
-        normalized = _normalize(v)
-        assert_allclose(np.linalg.norm(normalized), 1.0)
-        assert_allclose(normalized, [0.6, 0.8, 0.0])
-
-    def test_normalize_zero_vector(self):
-        """Test that zero vector remains unchanged."""
-        v = np.array([0, 0, 0])
-        normalized = _normalize(v)
-        assert_allclose(normalized, [0, 0, 0])
-
-    def test_build_head_coordinate_system(self, anatomical_landmarks):
-        """Test head coordinate system construction."""
-        landmarks = anatomical_landmarks.pint.dequantify()
-        nz = landmarks.sel(label="Nz").values
-        iz = landmarks.sel(label="Iz").values
-        lpa = landmarks.sel(label="LPA").values
-        rpa = landmarks.sel(label="RPA").values
-        cz = landmarks.sel(label="Cz").values
-
-        origin, x_axis, y_axis, z_axis = _build_head_coordinate_system(
-            nz, iz, lpa, rpa, cz
-        )
-
-        # Origin should be midpoint of ears
-        expected_origin = (lpa + rpa) / 2
-        assert_allclose(origin, expected_origin)
-
-        # All axes should be unit vectors
-        assert_allclose(np.linalg.norm(x_axis), 1.0)
-        assert_allclose(np.linalg.norm(y_axis), 1.0)
-        assert_allclose(np.linalg.norm(z_axis), 1.0)
-
-        # Axes should be orthogonal
-        assert_allclose(np.dot(x_axis, y_axis), 0.0, atol=1e-10)
-        assert_allclose(np.dot(x_axis, z_axis), 0.0, atol=1e-10)
-        assert_allclose(np.dot(y_axis, z_axis), 0.0, atol=1e-10)
-
-    def test_find_landmark_direct(self, anatomical_landmarks):
-        """Test finding landmark by exact name."""
-        nz = _find_landmark(anatomical_landmarks, "Nz")
-        assert nz is not None
-        assert len(nz) == 3
-
-    def test_find_landmark_missing(self, anatomical_landmarks):
-        """Test that missing landmark raises error."""
-        with pytest.raises(ValueError):
-            _find_landmark(anatomical_landmarks, "NonexistentLandmark")
-
-    def test_detect_facial_landmarks(
-        self, simple_sphere_surface, anatomical_landmarks
-    ):
-        """Test facial landmark detection."""
-        result = detect_facial_landmarks(simple_sphere_surface, anatomical_landmarks)
-
-        assert isinstance(result, FacialLandmarks)
-        assert result.detection_method == "geometric"
-        assert len(result.landmarks.label) == 6  # 6 facial landmarks
-
-        # Check all expected landmarks are present
-        expected = [
-            FacialLandmarkType.LEFT_EYE.value,
-            FacialLandmarkType.RIGHT_EYE.value,
-            FacialLandmarkType.NOSE_TIP.value,
-            FacialLandmarkType.NOSE_BRIDGE.value,
-            FacialLandmarkType.MOUTH_CENTER.value,
-            FacialLandmarkType.CHIN.value,
-        ]
-        for name in expected:
-            assert name in result.landmarks.label.values
-
-        # All confidences should be 1.0 for geometric detection
-        for conf in result.confidence.values():
-            assert conf == 1.0
-
-    def test_get_facial_region_mask(
-        self, simple_sphere_surface, anatomical_landmarks
-    ):
-        """Test facial region mask generation."""
-        facial_landmarks = detect_facial_landmarks(
-            simple_sphere_surface, anatomical_landmarks
-        )
-
-        mask = get_facial_region_mask(
-            surface=simple_sphere_surface,
-            facial_landmarks=facial_landmarks,
-            protected_points=anatomical_landmarks,
-            protection_radius=15.0 * units.mm,
-        )
-
-        # Mask should be boolean array with correct length
-        assert mask.dtype == bool
-        assert len(mask) == simple_sphere_surface.nvertices
-
-        # Some vertices should be masked as facial region
-        assert mask.sum() > 0
-        # But not all vertices
-        assert mask.sum() < len(mask)
+class TestNormalizeAxes:
+    """Tests for axis normalization."""
 
-    def test_mask_excludes_protected_points(
-        self, simple_sphere_surface, anatomical_landmarks, optode_positions
-    ):
-        """Test that protection zones work correctly."""
-        facial_landmarks = detect_facial_landmarks(
-            simple_sphere_surface, anatomical_landmarks
-        )
+    def test_rotation_matrix_orthogonal(self, simple_sphere_surface):
+        """Rotation matrix should be orthogonal (R @ R.T = I)."""
+        nasion = np.array([0, 50, 50])
+        _, _, R = normalize_axes(simple_sphere_surface, nasion)
+        assert_allclose(R @ R.T, np.eye(3), atol=1e-10)
 
-        # Combine landmarks and optodes
-        protected = xr.concat(
-            [anatomical_landmarks, optode_positions], dim="label"
-        )
+    def test_nasion_rotated_to_positive_y(self, simple_sphere_surface):
+        """After normalization, nasion Y should be positive (anterior)."""
+        nasion = np.array([0, 50, 50])  # forward in YZ diagonal
+        _, rotated_nasion, _ = normalize_axes(simple_sphere_surface, nasion)
+        # Y should now dominate (anterior direction)
+        assert rotated_nasion[1] > 0
 
-        mask = get_facial_region_mask(
-            surface=simple_sphere_surface,
-            facial_landmarks=facial_landmarks,
-            protected_points=protected,
-            protection_radius=15.0 * units.mm,
-        )
-
-        # Vertices near protected points should not be in mask
-        vertices = simple_sphere_surface.mesh.vertices
-        protected_positions = protected.pint.dequantify().values
-
-        for pos in protected_positions:
-            distances = np.linalg.norm(vertices - pos, axis=1)
-            nearby_mask = distances < 15.0
-            # No vertex within protection radius should be in facial mask
-            assert not np.any(mask & nearby_mask)
+    def test_identity_when_already_aligned(self, simple_sphere_surface):
+        """If nasion already on +Y axis, rotation should be ~identity."""
+        nasion = np.array([0, 100, 0])
+        _, rotated_nasion, R = normalize_axes(simple_sphere_surface, nasion)
+        assert_allclose(R, np.eye(3), atol=1e-6)
+        assert_allclose(rotated_nasion, nasion, atol=1e-6)
 
 
 # ============================================================================
-# Anonymizer Tests
+# Head Isolation
 # ============================================================================
 
 
-class TestAnonymizer:
-    """Tests for anonymization functionality."""
-
-    def test_build_adjacency_matrix(self, simple_sphere_surface):
-        """Test adjacency matrix construction."""
-        adjacency = _build_adjacency_matrix(simple_sphere_surface.mesh)
-
-        # Each vertex should have neighbors
-        for v in adjacency:
-            assert len(adjacency[v]) > 0
-
-        # Adjacency should be symmetric
-        for v1, neighbors in adjacency.items():
-            for v2 in neighbors:
-                assert v1 in adjacency[v2]
-
-    def test_compute_laplacian_displacement(self, simple_sphere_surface):
-        """Test Laplacian displacement computation."""
-        vertices = simple_sphere_surface.mesh.vertices
-        adjacency = _build_adjacency_matrix(simple_sphere_surface.mesh)
+class TestIsolateHead:
+    """Tests for head isolation."""
 
-        displacement = _compute_laplacian_displacement(vertices, adjacency)
+    def test_head_only_scan_unchanged(self, simple_sphere_surface):
+        """A sphere (no body) should be returned largely unchanged."""
+        nasion = np.array([0, 100, 0])
+        head_surface, mask = isolate_head(simple_sphere_surface, nasion)
+        # Sphere radius=100, default sphere radius=220 -> everything fits
+        assert mask.mean() > 0.9
 
-        # Displacement should have same shape as vertices
-        assert displacement.shape == vertices.shape
+    def test_reduces_vertex_count_with_body(self):
+        """Surface with body appended should have vertices removed."""
+        # Head sphere
+        sphere = trimesh.creation.icosphere(subdivisions=3, radius=100)
+        # Body box far below
+        body = trimesh.creation.box(extents=[50, 50, 50])
+        body.vertices[:, 0] -= 400  # move far below head
+        combined = trimesh.util.concatenate([sphere, body])
 
-        # On a smooth surface like a sphere, displacements should be small
-        # (vertices are already close to neighbor averages)
-        max_disp = np.linalg.norm(displacement, axis=1).max()
-        assert max_disp < 10.0  # Less than 10mm on a 100mm radius sphere
-
-    def test_smooth_region_selective(self, simple_sphere_surface):
-        """Test selective region smoothing."""
-        # Create mask for half the sphere (by x coordinate)
-        vertices = simple_sphere_surface.mesh.vertices
-        mask = vertices[:, 0] > 0  # Right half
-
-        # No protected indices
-        protected = np.array([], dtype=int)
-
-        smoothed = smooth_region_selective(
-            mesh=simple_sphere_surface.mesh,
-            mask=mask,
-            protected_indices=protected,
-            iterations=10,
-            lamb=0.5,
-            mu=-0.53,
+        surface = cdc.TrimeshSurface(
+            combined, crs="scanner", units=cedalion.units.millimeter
         )
+        nasion = np.array([0, 100, 0])
+        head_surface, mask = isolate_head(surface, nasion)
+        assert head_surface.nvertices < surface.nvertices
 
-        # Smoothed mesh should have same number of vertices
-        assert len(smoothed.vertices) == len(vertices)
 
-        # Vertices in the masked region should have moved
-        displacements = np.linalg.norm(smoothed.vertices - vertices, axis=1)
-        assert displacements[mask].max() > 0
+# ============================================================================
+# Landmark Detection from Nasion
+# ============================================================================
 
-        # Vertices outside the mask should not have moved
-        assert_allclose(displacements[~mask], 0, atol=1e-10)
 
-    def test_smoothing_preserves_protected(self, simple_sphere_surface):
-        """Test that protected vertices don't move during smoothing."""
-        vertices = simple_sphere_surface.mesh.vertices
+class TestDetectLandmarksFromNasion:
+    """Tests for landmark detection from nasion."""
 
-        # Full mask (smooth everything)
-        mask = np.ones(len(vertices), dtype=bool)
+    def test_returns_five_landmarks(self, head_like_surface):
+        """Should detect exactly 5 landmarks."""
+        nasion = np.array([0, 90, 0])  # front of elongated sphere
+        landmarks = detect_landmarks_from_nasion(head_like_surface, nasion)
 
-        # Protect some specific vertices
-        protected = np.array([0, 10, 50, 100])
+        assert len(landmarks.label) == 5
+        expected_labels = {"Nz", "Iz", "Cz", "LPA", "RPA"}
+        assert set(str(l) for l in landmarks.label.values) == expected_labels
 
-        smoothed = smooth_region_selective(
-            mesh=simple_sphere_surface.mesh,
-            mask=mask,
-            protected_indices=protected,
-            iterations=50,
-            lamb=0.5,
-            mu=-0.53,
-        )
+    def test_cz_is_highest(self, head_like_surface):
+        """Cz should have the highest X coordinate."""
+        nasion = np.array([0, 90, 0])
+        landmarks = detect_landmarks_from_nasion(head_like_surface, nasion)
+        lm = landmarks.pint.dequantify()
 
-        # Protected vertices should not have moved at all
-        for idx in protected:
-            assert_allclose(
-                smoothed.vertices[idx], vertices[idx], atol=1e-10
-            )
-
-    def test_anonymize_facial_region(
-        self, simple_sphere_surface, anatomical_landmarks
-    ):
-        """Test full facial region anonymization."""
-        facial_landmarks = detect_facial_landmarks(
-            simple_sphere_surface, anatomical_landmarks
-        )
-        mask = get_facial_region_mask(
-            simple_sphere_surface, facial_landmarks, anatomical_landmarks
-        )
+        cz_x = float(lm.sel(label="Cz").values[0])
+        for label in ["Nz", "Iz", "LPA", "RPA"]:
+            other_x = float(lm.sel(label=label).values[0])
+            assert cz_x >= other_x - 1.0, f"Cz should be highest, but {label} has higher X"
 
-        result = anonymize_facial_region(
-            surface=simple_sphere_surface,
-            facial_mask=mask,
-            protected_points=anatomical_landmarks,
-            config=AnonymizationConfig(smoothing_iterations=20),
-        )
-
-        # Result should contain all expected fields
-        assert result.anonymized_surface is not None
-        assert result.original_surface is simple_sphere_surface
-        assert len(result.facial_mask) == simple_sphere_surface.nvertices
-        assert len(result.vertex_displacements) == simple_sphere_surface.nvertices
-
-        # Anonymized surface should have same topology
-        assert result.anonymized_surface.nvertices == simple_sphere_surface.nvertices
-        assert result.anonymized_surface.nfaces == simple_sphere_surface.nfaces
-
-    def test_anonymization_methods(
-        self, simple_sphere_surface, anatomical_landmarks
-    ):
-        """Test different anonymization methods."""
-        facial_landmarks = detect_facial_landmarks(
-            simple_sphere_surface, anatomical_landmarks
-        )
-        mask = get_facial_region_mask(
-            simple_sphere_surface, facial_landmarks, anatomical_landmarks
-        )
-
-        for method in [AnonymizationMethod.SMOOTH, AnonymizationMethod.FLATTEN]:
-            config = AnonymizationConfig(method=method, smoothing_iterations=10)
-            result = anonymize_facial_region(
-                simple_sphere_surface, mask, anatomical_landmarks, config
-            )
-            assert result.anonymized_surface is not None
+    def test_nz_position_preserved(self, head_like_surface):
+        """Nz output should match the input nasion position."""
+        nasion = np.array([0, 90, 0])
+        landmarks = detect_landmarks_from_nasion(head_like_surface, nasion)
+        nz_out = landmarks.sel(label="Nz").pint.dequantify().values
+        assert_allclose(nz_out, nasion, atol=1e-6)
 
 
 # ============================================================================
-# Validator Tests
+# Facial Region Mask
 # ============================================================================
 
 
-class TestValidator:
-    """Tests for validation sanity checks."""
+class TestFacialRegionMask:
+    """Tests for facial region mask generation."""
 
-    def test_validate_anonymization_passes(
-        self, simple_sphere_surface, anatomical_landmarks
-    ):
-        """Test that validation passes for a correctly anonymized mesh."""
-        facial_landmarks = detect_facial_landmarks(
-            simple_sphere_surface, anatomical_landmarks
-        )
-        mask = get_facial_region_mask(
-            simple_sphere_surface, facial_landmarks, anatomical_landmarks
+    def test_mask_shape_and_type(self, simple_sphere_surface):
+        """Mask should be boolean with correct length."""
+        nz = np.array([0, 100, 0])
+        fwd = np.array([0, 1, 0])
+        mask = get_facial_region_mask_from_nasion(
+            simple_sphere_surface, nz, fwd
         )
+        assert mask.dtype == bool
+        assert len(mask) == simple_sphere_surface.nvertices
 
-        result = anonymize_facial_region(
-            simple_sphere_surface, mask, anatomical_landmarks,
-            config=AnonymizationConfig(smoothing_iterations=5)
+    def test_some_vertices_masked(self, simple_sphere_surface):
+        """Some but not all vertices should be in the facial region."""
+        nz = np.array([0, 100, 0])
+        fwd = np.array([0, 1, 0])
+        mask = get_facial_region_mask_from_nasion(
+            simple_sphere_surface, nz, fwd
         )
+        assert mask.sum() > 0
+        assert mask.sum() < len(mask)
 
-        check = validate_anonymization(
-            original_surface=simple_sphere_surface,
-            anonymized_surface=result.anonymized_surface,
-            facial_mask=mask,
+    def test_protection_zones_excluded(self, simple_sphere_surface, anatomical_landmarks):
+        """Vertices near protected points should not be masked."""
+        nz = np.array([0, 100, 0])
+        fwd = np.array([0, 1, 0])
+        mask = get_facial_region_mask_from_nasion(
+            simple_sphere_surface, nz, fwd,
             protected_points=anatomical_landmarks,
-            tolerance=1.0 * units.mm,
-        )
-
-        assert isinstance(check, ValidationResult)
-        assert check.mesh_valid
-        assert check.protected_points_intact
-        assert check.face_coverage_pct > 0
-
-    def test_validate_returns_correct_counts(
-        self, simple_sphere_surface, anatomical_landmarks
-    ):
-        """Test that vertex removal counts are consistent."""
-        facial_landmarks = detect_facial_landmarks(
-            simple_sphere_surface, anatomical_landmarks
-        )
-        mask = get_facial_region_mask(
-            simple_sphere_surface, facial_landmarks, anatomical_landmarks
-        )
-
-        result = anonymize_facial_region(
-            simple_sphere_surface, mask, anatomical_landmarks,
-            config=AnonymizationConfig(smoothing_iterations=10)
-        )
-
-        check = validate_anonymization(
-            simple_sphere_surface,
-            result.anonymized_surface,
-            mask,
-            anatomical_landmarks,
+            protection_radius=15.0 * units.mm,
         )
 
-        assert check.expected_vertices_removed == int(mask.sum())
-        assert check.actual_vertices_removed >= 0
-        assert 0 <= check.face_coverage_pct <= 100
-        assert check.protected_point_max_deviation >= 0
+        vertices = simple_sphere_surface.mesh.vertices
+        protected_positions = anatomical_landmarks.pint.dequantify().values
+        for pos in protected_positions:
+            distances = np.linalg.norm(vertices - pos, axis=1)
+            nearby = distances < 15.0
+            assert not np.any(mask & nearby), "Protected zone breached"
 
 
 # ============================================================================
-# Integration Tests
+# Auto Nasion Detection
 # ============================================================================
 
 
-class TestIntegration:
-    """Integration tests for the full pipeline."""
-
-    def test_full_pipeline(
-        self, simple_sphere_surface, anatomical_landmarks, optode_positions
-    ):
-        """Test complete anonymize_scan workflow."""
-        result = anonymize_scan(
-            surface=simple_sphere_surface,
-            anatomical_landmarks=anatomical_landmarks,
-            optode_positions=optode_positions,
-            config=AnonymizationConfig(smoothing_iterations=10),
-            interactive=False,
-            validate=True,
-        )
-
-        # Result should have anonymized surface
-        assert result.anonymized_surface is not None
-
-        # Topology should be preserved
-        assert result.anonymized_surface.nvertices == simple_sphere_surface.nvertices
-        assert result.anonymized_surface.nfaces == simple_sphere_surface.nfaces
-
-    def test_pipeline_without_optodes(
-        self, simple_sphere_surface, anatomical_landmarks
-    ):
-        """Test pipeline works without optode positions."""
-        result = anonymize_scan(
-            surface=simple_sphere_surface,
-            anatomical_landmarks=anatomical_landmarks,
-            optode_positions=None,
-            interactive=False,
-            validate=False,
-        )
-
-        assert result.anonymized_surface is not None
-
-    def test_landmarks_preserved_within_tolerance(
-        self, simple_sphere_surface, anatomical_landmarks
-    ):
-        """Test that landmarks are preserved within tolerance."""
-        result = anonymize_scan(
-            surface=simple_sphere_surface,
-            anatomical_landmarks=anatomical_landmarks,
-            protection_radius=20.0 * units.mm,
-            interactive=False,
-            validate=False,
-        )
-
-        # Check that validation passes with protected points
-        check = validate_anonymization(
-            simple_sphere_surface,
-            result.anonymized_surface,
-            result.facial_mask,
-            anatomical_landmarks,
-            tolerance=1.0 * units.mm,
-        )
-
-        assert check.protected_points_intact, (
-            f"Protected points not intact: max deviation "
-            f"{check.protected_point_max_deviation:.3f}mm"
-        )
-
-    def test_facial_region_modified(
-        self, simple_sphere_surface, anatomical_landmarks
-    ):
-        """Test that facial region is actually modified."""
-        result = anonymize_scan(
-            surface=simple_sphere_surface,
-            anatomical_landmarks=anatomical_landmarks,
-            config=AnonymizationConfig(smoothing_iterations=50),
-            interactive=False,
-            validate=False,
-        )
+class TestDetectNasionAuto:
+    """Tests for automatic nasion detection."""
 
-        # Facial region should have non-zero displacement
-        facial_displacements = result.vertex_displacements[result.facial_mask]
-        assert facial_displacements.mean() > 0, "Facial region should be modified"
+    def test_returns_none_on_featureless_mesh(self, simple_sphere_surface):
+        """A featureless sphere has no face — should return None."""
+        result = detect_nasion_auto(simple_sphere_surface)
+        assert result is None

From 35e330b4e61b71e8aaffa9cb21cd4b0100da748b Mon Sep 17 00:00:00 2001
From: mah-diaa <mahmoud.diaa.abdeen@gmail.com>
Date: Mon, 6 Apr 2026 20:28:19 +0200
Subject: [PATCH 05/39] Add interactive add/remove mode and adjustable brush to
 refinement UI

Refinement step now supports:
- T key to toggle between add and remove mode
- +/- keys to adjust brush radius (5-100mm)
- Color-coded feedback (yellow=add, cyan=remove)
- Status text updates live with mode and radius
---
 .../head_models/47_face_anonymization.ipynb   | 180 ++++++++----------
 1 file changed, 83 insertions(+), 97 deletions(-)

diff --git a/examples/head_models/47_face_anonymization.ipynb b/examples/head_models/47_face_anonymization.ipynb
index f9a479e4..68a8b2b6 100644
--- a/examples/head_models/47_face_anonymization.ipynb
+++ b/examples/head_models/47_face_anonymization.ipynb
@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "cell-imports",
    "metadata": {},
    "outputs": [],
@@ -53,7 +53,7 @@
     "pv.set_jupyter_backend('server')\n",
     "\n",
     "# === CONFIG ===\n",
-    "SUBJECT_NUMBER = 1\n",
+    "SUBJECT_NUMBER = 14\n",
     "SCANS_FOLDER = '/home/ma7/BA/PG_Subjects'  # set to your scan directory\n",
     "FORCE_MANUAL = False       # set True to skip auto-detection and click nasion manually\n",
     "PROXIMITY_RADIUS = 85.0    # mm — ear coverage band width\n",
@@ -73,10 +73,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "cell-nasion",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded: 725,532 vertices, 1,370,614 faces\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
+      "W0000 00:00:1775499859.088019  110419 face_landmarker_graph.cc:174] Sets FaceBlendshapesGraph acceleration to xnnpack by default.\n",
+      "I0000 00:00:1775499859.203284  110419 gl_context_egl.cc:85] Successfully initialized EGL. Major : 1 Minor: 5\n",
+      "I0000 00:00:1775499859.219012  110445 gl_context.cc:385] GL version: 3.2 (OpenGL ES 3.2 Mesa 25.3.1-arch1.2), renderer: AMD Radeon Graphics (radeonsi, renoir, ACO, DRM 3.64, 6.17.9-arch1-1)\n",
+      "INFO: Created TensorFlow Lite XNNPACK delegate for CPU.\n",
+      "W0000 00:00:1775499859.228728  110422 inference_feedback_manager.cc:114] Feedback manager requires a model with a single signature inference. Disabling support for feedback tensors.\n",
+      "W0000 00:00:1775499859.239372  110420 inference_feedback_manager.cc:114] Feedback manager requires a model with a single signature inference. Disabling support for feedback tensors.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Auto nasion: method=mediapipe+frontal, confidence=0.90\n",
+      "Forward direction: available\n",
+      "Face contour: 35 pts\n"
+     ]
+    }
+   ],
    "source": [
     "path = f'{SCANS_FOLDER}/Subject{SUBJECT_NUMBER}/Subject{SUBJECT_NUMBER}.obj'\n",
     "surface = cedalion.io.read_einstar_obj(path)\n",
@@ -115,10 +145,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "cell-landmarks",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Head isolation: 725,532 -> 391,330 vertices\n",
+      "  Nz: [78.6, 91.8, 413.9]\n",
+      "  Iz: [97.4, -129.3, 396.7]\n",
+      "  Cz: [187.1, -46.7, 370.6]\n",
+      "  LPA: [82.2, -57.6, 477.9]\n",
+      "  RPA: [78.2, -47.1, 313.8]\n",
+      "\n",
+      "Face mask: 48,881 vertices\n",
+      "Ear band:  116,466 vertices\n",
+      "Total:     165,347 vertices (42.3%)\n",
+      "Posterior limit (LPA/RPA Y): -57.6mm\n"
+     ]
+    }
+   ],
    "source": [
     "# Normalize axes (X=up, Y=anterior, Z=left)\n",
     "surface_n, nasion_n, R = normalize_axes(surface, nasion, forward_direction=fwd)\n",
@@ -191,10 +239,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "cell-preview",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "93872644296b4fcb89975ab613ccee9b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Widget(value='<iframe src=\"http://localhost:43141/index.html?ui=P_0x7f23201304d0_0&reconnect=auto\" class=\"pyvi…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "pvplt = pv.Plotter()\n",
     "cedalion.plots.plot_surface(pvplt, surface_h, opacity=1.0)\n",
@@ -229,13 +292,7 @@
    "cell_type": "markdown",
    "id": "cell-md-refine",
    "metadata": {},
-   "source": [
-    "## 4. Interactive refinement — click to add deletion circles\n",
-    "\n",
-    "Left-click on any missed spot to add a circular deletion zone (radius = `CIRCLE_RADIUS`).\n",
-    "Each click adds a translucent sphere and updates the mask.\n",
-    "Close the window when done."
-   ]
+   "source": "## 4. Interactive refinement — click to add or remove deletion zones\n\n- **Left-click**: Add/remove vertices within the brush radius\n- **T**: Toggle between **add** mode (mark for deletion) and **remove** mode (undo)\n- **+** / **-**: Increase / decrease brush radius\n- Close the window when done."
   },
   {
    "cell_type": "code",
@@ -243,86 +300,7 @@
    "id": "cell-refine",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "refined_mask = extended_mask.copy()\n",
-    "click_count = [0]\n",
-    "\n",
-    "# Protected zone: vertices near landmarks that cannot be added to mask\n",
-    "protected_positions = landmarks.pint.dequantify().values\n",
-    "protection_radius = 15.0  # mm\n",
-    "\n",
-    "def on_pick(picked_point):\n",
-    "    if picked_point is None:\n",
-    "        return\n",
-    "    point = np.array(picked_point)\n",
-    "\n",
-    "    # Check if inside protected zone\n",
-    "    dist_to_protected = np.linalg.norm(protected_positions - point, axis=1)\n",
-    "    if np.any(dist_to_protected < protection_radius):\n",
-    "        print('  Skipped — too close to a protected landmark')\n",
-    "        return\n",
-    "\n",
-    "    # Find all vertices within CIRCLE_RADIUS\n",
-    "    dists = np.linalg.norm(verts - point, axis=1)\n",
-    "    circle = dists < CIRCLE_RADIUS\n",
-    "    new_verts = circle & ~refined_mask\n",
-    "    n_new = new_verts.sum()\n",
-    "\n",
-    "    if n_new == 0:\n",
-    "        return\n",
-    "\n",
-    "    # Add to mask\n",
-    "    refined_mask[new_verts] = True\n",
-    "    click_count[0] += 1\n",
-    "\n",
-    "    # Visual feedback\n",
-    "    sphere = pv.Sphere(radius=CIRCLE_RADIUS, center=point)\n",
-    "    pvplt.add_mesh(sphere, color='yellow', opacity=0.3)\n",
-    "\n",
-    "    # Update mask coloring on mesh (in-place scalar update)\n",
-    "    pv_mesh['mask'] = refined_mask.astype(float)\n",
-    "\n",
-    "    print(f'  Click {click_count[0]}: +{n_new:,} vertices '\n",
-    "          f'(total: {refined_mask.sum():,})')\n",
-    "\n",
-    "# Build plotter (non-notebook for interactive picking)\n",
-    "pvplt = pv.Plotter(notebook=False)\n",
-    "\n",
-    "vtk_surface = VTKSurface.from_trimeshsurface(surface_h)\n",
-    "pv_mesh = pv.wrap(vtk_surface.mesh)\n",
-    "\n",
-    "# Color mesh by mask: white=keep, red=delete\n",
-    "pv_mesh['mask'] = refined_mask.astype(float)\n",
-    "pvplt.add_mesh(pv_mesh, scalars='mask', cmap=['white', 'red'],\n",
-    "               clim=[0, 1], show_scalar_bar=False, opacity=0.9,\n",
-    "               smooth_shading=True, pickable=True)\n",
-    "\n",
-    "# Show landmarks\n",
-    "for label in landmarks.label.values:\n",
-    "    pos = landmarks.sel(label=label).pint.dequantify().values\n",
-    "    pvplt.add_mesh(pv.Sphere(radius=3, center=pos), color='lime')\n",
-    "    pvplt.add_point_labels([pos], [label], font_size=12, point_size=0,\n",
-    "                           text_color='lime', shape=None, always_visible=True)\n",
-    "\n",
-    "pvplt.enable_surface_point_picking(\n",
-    "    callback=on_pick,\n",
-    "    left_clicking=True,\n",
-    "    show_point=False,\n",
-    "    tolerance=0.005,\n",
-    "    show_message=f'Left-click to add deletion circle (r={CIRCLE_RADIUS}mm). Close when done.',\n",
-    ")\n",
-    "\n",
-    "pvplt.add_text(\n",
-    "    f'S{SUBJECT_NUMBER} | Click to add deletion zones (r={CIRCLE_RADIUS}mm)\\n'\n",
-    "    f'Green = protected landmarks | Close window when done',\n",
-    "    position='upper_left', font_size=10,\n",
-    ")\n",
-    "pvplt.show()\n",
-    "\n",
-    "# After window closes\n",
-    "extended_mask = refined_mask\n",
-    "print(f'\\nDone — {click_count[0]} clicks, final mask: {extended_mask.sum():,} vertices')"
-   ]
+   "source": "refined_mask = extended_mask.copy()\nclick_count = [0]\nbrush_radius = [CIRCLE_RADIUS]  # mutable so key events can change it\nmode = ['add']  # 'add' or 'remove'\n\n# Protected zone: vertices near landmarks that cannot be added to mask\nprotected_positions = landmarks.pint.dequantify().values\nprotection_radius = 15.0  # mm\n\ndef update_title():\n    \"\"\"Update the on-screen status text.\"\"\"\n    color = 'red' if mode[0] == 'add' else 'cyan'\n    pvplt.add_text(\n        f'S{SUBJECT_NUMBER} | Mode: {mode[0].upper()} | Brush: {brush_radius[0]:.0f}mm\\n'\n        f'T=toggle mode | +/-=brush size | Close when done',\n        position='upper_left', font_size=10, name='status',\n        color=color,\n    )\n\ndef on_pick(picked_point):\n    if picked_point is None:\n        return\n    point = np.array(picked_point)\n\n    # Find all vertices within brush radius\n    dists = np.linalg.norm(verts - point, axis=1)\n    circle = dists < brush_radius[0]\n\n    if mode[0] == 'add':\n        # Check if inside protected zone\n        dist_to_protected = np.linalg.norm(protected_positions - point, axis=1)\n        if np.any(dist_to_protected < protection_radius):\n            print('  Skipped — too close to a protected landmark')\n            return\n\n        new_verts = circle & ~refined_mask\n        n_changed = new_verts.sum()\n        if n_changed == 0:\n            return\n        refined_mask[new_verts] = True\n\n        # Yellow sphere for added regions\n        sphere = pv.Sphere(radius=brush_radius[0], center=point)\n        pvplt.add_mesh(sphere, color='yellow', opacity=0.2)\n    else:\n        # Remove mode — unmark vertices\n        removed = circle & refined_mask\n        n_changed = removed.sum()\n        if n_changed == 0:\n            return\n        refined_mask[removed] = False\n\n        # Blue sphere for removed regions\n        sphere = pv.Sphere(radius=brush_radius[0], center=point)\n        pvplt.add_mesh(sphere, color='cyan', opacity=0.2)\n\n    click_count[0] += 1\n\n    # Update mask coloring on mesh (in-place)\n    pv_mesh['mask'] = refined_mask.astype(float)\n\n    sign = '+' if mode[0] == 'add' else '-'\n    print(f'  Click {click_count[0]} ({mode[0]}): {sign}{n_changed:,} vertices '\n          f'(total: {refined_mask.sum():,})')\n\ndef toggle_mode():\n    mode[0] = 'remove' if mode[0] == 'add' else 'add'\n    update_title()\n    print(f'  Mode: {mode[0].upper()}')\n\ndef increase_radius():\n    brush_radius[0] = min(brush_radius[0] + 5, 100)\n    update_title()\n    print(f'  Brush radius: {brush_radius[0]:.0f}mm')\n\ndef decrease_radius():\n    brush_radius[0] = max(brush_radius[0] - 5, 5)\n    update_title()\n    print(f'  Brush radius: {brush_radius[0]:.0f}mm')\n\n# Build plotter (non-notebook for interactive picking)\npvplt = pv.Plotter(notebook=False)\n\nvtk_surface = VTKSurface.from_trimeshsurface(surface_h)\npv_mesh = pv.wrap(vtk_surface.mesh)\n\n# Color mesh by mask: white=keep, red=delete\npv_mesh['mask'] = refined_mask.astype(float)\npvplt.add_mesh(pv_mesh, scalars='mask', cmap=['white', 'red'],\n               clim=[0, 1], show_scalar_bar=False, opacity=0.9,\n               smooth_shading=True, pickable=True)\n\n# Show landmarks\nfor label in landmarks.label.values:\n    pos = landmarks.sel(label=label).pint.dequantify().values\n    pvplt.add_mesh(pv.Sphere(radius=3, center=pos), color='lime')\n    pvplt.add_point_labels([pos], [label], font_size=12, point_size=0,\n                           text_color='lime', shape=None, always_visible=True)\n\npvplt.enable_surface_point_picking(\n    callback=on_pick,\n    left_clicking=True,\n    show_point=False,\n    tolerance=0.005,\n)\n\n# Key bindings\npvplt.add_key_event('t', toggle_mode)\npvplt.add_key_event('plus', increase_radius)\npvplt.add_key_event('minus', decrease_radius)\n\nupdate_title()\npvplt.show()\n\n# After window closes\nextended_mask = refined_mask\nprint(f'\\nDone — {click_count[0]} clicks, final mask: {extended_mask.sum():,} vertices')"
   },
   {
    "cell_type": "markdown",
@@ -377,10 +355,18 @@
    "name": "python3"
   },
   "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
    "name": "python",
-   "version": "3.11.0"
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.14"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}
\ No newline at end of file

From df8286821d4458360f6138dc8ad87965ce0d2121 Mon Sep 17 00:00:00 2001
From: mah-diaa <mahmoud.diaa.abdeen@gmail.com>
Date: Mon, 6 Apr 2026 21:09:24 +0200
Subject: [PATCH 06/39] Improve refinement UI controls visibility and clear
 outputs

Move controls text to lower-right, increase font size to 14, set color
to black for better visibility. Add detailed keyboard shortcut
instructions in the markdown cell. Clear all cell outputs.
---
 .../head_models/47_face_anonymization.ipynb   | 213 +++++++++++-------
 1 file changed, 137 insertions(+), 76 deletions(-)

diff --git a/examples/head_models/47_face_anonymization.ipynb b/examples/head_models/47_face_anonymization.ipynb
index 68a8b2b6..5aa1b6a1 100644
--- a/examples/head_models/47_face_anonymization.ipynb
+++ b/examples/head_models/47_face_anonymization.ipynb
@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "cell-imports",
    "metadata": {},
    "outputs": [],
@@ -56,8 +56,8 @@
     "SUBJECT_NUMBER = 14\n",
     "SCANS_FOLDER = '/home/ma7/BA/PG_Subjects'  # set to your scan directory\n",
     "FORCE_MANUAL = False       # set True to skip auto-detection and click nasion manually\n",
-    "PROXIMITY_RADIUS = 85.0    # mm — ear coverage band width\n",
-    "CIRCLE_RADIUS = 25.0       # mm — manual click-to-delete radius"
+    "PROXIMITY_RADIUS = 85.0    # mm \u2014 ear coverage band width\n",
+    "CIRCLE_RADIUS = 25.0       # mm \u2014 manual click-to-delete radius"
    ]
   },
   {
@@ -73,40 +73,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "cell-nasion",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loaded: 725,532 vertices, 1,370,614 faces\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
-      "W0000 00:00:1775499859.088019  110419 face_landmarker_graph.cc:174] Sets FaceBlendshapesGraph acceleration to xnnpack by default.\n",
-      "I0000 00:00:1775499859.203284  110419 gl_context_egl.cc:85] Successfully initialized EGL. Major : 1 Minor: 5\n",
-      "I0000 00:00:1775499859.219012  110445 gl_context.cc:385] GL version: 3.2 (OpenGL ES 3.2 Mesa 25.3.1-arch1.2), renderer: AMD Radeon Graphics (radeonsi, renoir, ACO, DRM 3.64, 6.17.9-arch1-1)\n",
-      "INFO: Created TensorFlow Lite XNNPACK delegate for CPU.\n",
-      "W0000 00:00:1775499859.228728  110422 inference_feedback_manager.cc:114] Feedback manager requires a model with a single signature inference. Disabling support for feedback tensors.\n",
-      "W0000 00:00:1775499859.239372  110420 inference_feedback_manager.cc:114] Feedback manager requires a model with a single signature inference. Disabling support for feedback tensors.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Auto nasion: method=mediapipe+frontal, confidence=0.90\n",
-      "Forward direction: available\n",
-      "Face contour: 35 pts\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "path = f'{SCANS_FOLDER}/Subject{SUBJECT_NUMBER}/Subject{SUBJECT_NUMBER}.obj'\n",
     "surface = cedalion.io.read_einstar_obj(path)\n",
@@ -125,7 +95,7 @@
     "    print('Opening manual nasion picker...')\n",
     "    nasion = pick_nasion(surface)\n",
     "    if nasion is None:\n",
-    "        raise RuntimeError('No nasion selected — cannot proceed')\n",
+    "        raise RuntimeError('No nasion selected \u2014 cannot proceed')\n",
     "    meta = {'method': 'manual', 'confidence': 1.0}\n",
     "    print(f'Manual nasion at: [{nasion[0]:.1f}, {nasion[1]:.1f}, {nasion[2]:.1f}]')\n",
     "\n",
@@ -145,28 +115,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "cell-landmarks",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Head isolation: 725,532 -> 391,330 vertices\n",
-      "  Nz: [78.6, 91.8, 413.9]\n",
-      "  Iz: [97.4, -129.3, 396.7]\n",
-      "  Cz: [187.1, -46.7, 370.6]\n",
-      "  LPA: [82.2, -57.6, 477.9]\n",
-      "  RPA: [78.2, -47.1, 313.8]\n",
-      "\n",
-      "Face mask: 48,881 vertices\n",
-      "Ear band:  116,466 vertices\n",
-      "Total:     165,347 vertices (42.3%)\n",
-      "Posterior limit (LPA/RPA Y): -57.6mm\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Normalize axes (X=up, Y=anterior, Z=left)\n",
     "surface_n, nasion_n, R = normalize_axes(surface, nasion, forward_direction=fwd)\n",
@@ -239,25 +191,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "cell-preview",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "93872644296b4fcb89975ab613ccee9b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Widget(value='<iframe src=\"http://localhost:43141/index.html?ui=P_0x7f23201304d0_0&reconnect=auto\" class=\"pyvi…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "pvplt = pv.Plotter()\n",
     "cedalion.plots.plot_surface(pvplt, surface_h, opacity=1.0)\n",
@@ -292,7 +229,14 @@
    "cell_type": "markdown",
    "id": "cell-md-refine",
    "metadata": {},
-   "source": "## 4. Interactive refinement — click to add or remove deletion zones\n\n- **Left-click**: Add/remove vertices within the brush radius\n- **T**: Toggle between **add** mode (mark for deletion) and **remove** mode (undo)\n- **+** / **-**: Increase / decrease brush radius\n- Close the window when done."
+   "source": [
+    "## 4. Interactive refinement \u2014 click to add or remove deletion zones\n",
+    "\n",
+    "- **Left-click**: Add/remove vertices within the brush radius\n",
+    "- **T**: Toggle between **add** mode (mark for deletion) and **remove** mode (undo)\n",
+    "- **+** / **-**: Increase / decrease brush radius\n",
+    "- Close the window when done."
+   ]
   },
   {
    "cell_type": "code",
@@ -300,14 +244,131 @@
    "id": "cell-refine",
    "metadata": {},
    "outputs": [],
-   "source": "refined_mask = extended_mask.copy()\nclick_count = [0]\nbrush_radius = [CIRCLE_RADIUS]  # mutable so key events can change it\nmode = ['add']  # 'add' or 'remove'\n\n# Protected zone: vertices near landmarks that cannot be added to mask\nprotected_positions = landmarks.pint.dequantify().values\nprotection_radius = 15.0  # mm\n\ndef update_title():\n    \"\"\"Update the on-screen status text.\"\"\"\n    color = 'red' if mode[0] == 'add' else 'cyan'\n    pvplt.add_text(\n        f'S{SUBJECT_NUMBER} | Mode: {mode[0].upper()} | Brush: {brush_radius[0]:.0f}mm\\n'\n        f'T=toggle mode | +/-=brush size | Close when done',\n        position='upper_left', font_size=10, name='status',\n        color=color,\n    )\n\ndef on_pick(picked_point):\n    if picked_point is None:\n        return\n    point = np.array(picked_point)\n\n    # Find all vertices within brush radius\n    dists = np.linalg.norm(verts - point, axis=1)\n    circle = dists < brush_radius[0]\n\n    if mode[0] == 'add':\n        # Check if inside protected zone\n        dist_to_protected = np.linalg.norm(protected_positions - point, axis=1)\n        if np.any(dist_to_protected < protection_radius):\n            print('  Skipped — too close to a protected landmark')\n            return\n\n        new_verts = circle & ~refined_mask\n        n_changed = new_verts.sum()\n        if n_changed == 0:\n            return\n        refined_mask[new_verts] = True\n\n        # Yellow sphere for added regions\n        sphere = pv.Sphere(radius=brush_radius[0], center=point)\n        pvplt.add_mesh(sphere, color='yellow', opacity=0.2)\n    else:\n        # Remove mode — unmark vertices\n        removed = circle & refined_mask\n        n_changed = removed.sum()\n        if n_changed == 0:\n            return\n        refined_mask[removed] = False\n\n        # Blue sphere for removed regions\n        sphere = pv.Sphere(radius=brush_radius[0], center=point)\n        pvplt.add_mesh(sphere, color='cyan', opacity=0.2)\n\n    click_count[0] += 1\n\n    # Update mask coloring on mesh (in-place)\n    pv_mesh['mask'] = refined_mask.astype(float)\n\n    sign = '+' if mode[0] == 'add' else '-'\n    print(f'  Click {click_count[0]} ({mode[0]}): {sign}{n_changed:,} vertices '\n          f'(total: {refined_mask.sum():,})')\n\ndef toggle_mode():\n    mode[0] = 'remove' if mode[0] == 'add' else 'add'\n    update_title()\n    print(f'  Mode: {mode[0].upper()}')\n\ndef increase_radius():\n    brush_radius[0] = min(brush_radius[0] + 5, 100)\n    update_title()\n    print(f'  Brush radius: {brush_radius[0]:.0f}mm')\n\ndef decrease_radius():\n    brush_radius[0] = max(brush_radius[0] - 5, 5)\n    update_title()\n    print(f'  Brush radius: {brush_radius[0]:.0f}mm')\n\n# Build plotter (non-notebook for interactive picking)\npvplt = pv.Plotter(notebook=False)\n\nvtk_surface = VTKSurface.from_trimeshsurface(surface_h)\npv_mesh = pv.wrap(vtk_surface.mesh)\n\n# Color mesh by mask: white=keep, red=delete\npv_mesh['mask'] = refined_mask.astype(float)\npvplt.add_mesh(pv_mesh, scalars='mask', cmap=['white', 'red'],\n               clim=[0, 1], show_scalar_bar=False, opacity=0.9,\n               smooth_shading=True, pickable=True)\n\n# Show landmarks\nfor label in landmarks.label.values:\n    pos = landmarks.sel(label=label).pint.dequantify().values\n    pvplt.add_mesh(pv.Sphere(radius=3, center=pos), color='lime')\n    pvplt.add_point_labels([pos], [label], font_size=12, point_size=0,\n                           text_color='lime', shape=None, always_visible=True)\n\npvplt.enable_surface_point_picking(\n    callback=on_pick,\n    left_clicking=True,\n    show_point=False,\n    tolerance=0.005,\n)\n\n# Key bindings\npvplt.add_key_event('t', toggle_mode)\npvplt.add_key_event('plus', increase_radius)\npvplt.add_key_event('minus', decrease_radius)\n\nupdate_title()\npvplt.show()\n\n# After window closes\nextended_mask = refined_mask\nprint(f'\\nDone — {click_count[0]} clicks, final mask: {extended_mask.sum():,} vertices')"
+   "source": [
+    "refined_mask = extended_mask.copy()\n",
+    "click_count = [0]\n",
+    "brush_radius = [CIRCLE_RADIUS]  # mutable so key events can change it\n",
+    "mode = ['add']  # 'add' or 'remove'\n",
+    "\n",
+    "# Protected zone: vertices near landmarks that cannot be added to mask\n",
+    "protected_positions = landmarks.pint.dequantify().values\n",
+    "protection_radius = 15.0  # mm\n",
+    "\n",
+    "def update_title():\n",
+    "    \"\"\"Update the on-screen status text.\"\"\"\n",
+    "    pvplt.add_text(\n",
+    "        f'Mode: {mode[0].upper()}  |  Brush: {brush_radius[0]:.0f}mm\\n'\n",
+    "        f'T = toggle add/remove  |  +/- = brush size',\n",
+    "        position='lower_right', font_size=14, name='status',\n",
+    "        color='black',\n",
+    "    )\n",
+    "\n",
+    "def on_pick(picked_point):\n",
+    "    if picked_point is None:\n",
+    "        return\n",
+    "    point = np.array(picked_point)\n",
+    "\n",
+    "    # Find all vertices within brush radius\n",
+    "    dists = np.linalg.norm(verts - point, axis=1)\n",
+    "    circle = dists < brush_radius[0]\n",
+    "\n",
+    "    if mode[0] == 'add':\n",
+    "        # Check if inside protected zone\n",
+    "        dist_to_protected = np.linalg.norm(protected_positions - point, axis=1)\n",
+    "        if np.any(dist_to_protected < protection_radius):\n",
+    "            print('  Skipped \u2014 too close to a protected landmark')\n",
+    "            return\n",
+    "\n",
+    "        new_verts = circle & ~refined_mask\n",
+    "        n_changed = new_verts.sum()\n",
+    "        if n_changed == 0:\n",
+    "            return\n",
+    "        refined_mask[new_verts] = True\n",
+    "\n",
+    "        # Yellow sphere for added regions\n",
+    "        sphere = pv.Sphere(radius=brush_radius[0], center=point)\n",
+    "        pvplt.add_mesh(sphere, color='yellow', opacity=0.2)\n",
+    "    else:\n",
+    "        # Remove mode \u2014 unmark vertices\n",
+    "        removed = circle & refined_mask\n",
+    "        n_changed = removed.sum()\n",
+    "        if n_changed == 0:\n",
+    "            return\n",
+    "        refined_mask[removed] = False\n",
+    "\n",
+    "        # Blue sphere for removed regions\n",
+    "        sphere = pv.Sphere(radius=brush_radius[0], center=point)\n",
+    "        pvplt.add_mesh(sphere, color='cyan', opacity=0.2)\n",
+    "\n",
+    "    click_count[0] += 1\n",
+    "\n",
+    "    # Update mask coloring on mesh (in-place)\n",
+    "    pv_mesh['mask'] = refined_mask.astype(float)\n",
+    "\n",
+    "    sign = '+' if mode[0] == 'add' else '-'\n",
+    "    print(f'  Click {click_count[0]} ({mode[0]}): {sign}{n_changed:,} vertices '\n",
+    "          f'(total: {refined_mask.sum():,})')\n",
+    "\n",
+    "def toggle_mode():\n",
+    "    mode[0] = 'remove' if mode[0] == 'add' else 'add'\n",
+    "    update_title()\n",
+    "    print(f'  Mode: {mode[0].upper()}')\n",
+    "\n",
+    "def increase_radius():\n",
+    "    brush_radius[0] = min(brush_radius[0] + 5, 100)\n",
+    "    update_title()\n",
+    "    print(f'  Brush radius: {brush_radius[0]:.0f}mm')\n",
+    "\n",
+    "def decrease_radius():\n",
+    "    brush_radius[0] = max(brush_radius[0] - 5, 5)\n",
+    "    update_title()\n",
+    "    print(f'  Brush radius: {brush_radius[0]:.0f}mm')\n",
+    "\n",
+    "# Build plotter (non-notebook for interactive picking)\n",
+    "pvplt = pv.Plotter(notebook=False)\n",
+    "\n",
+    "vtk_surface = VTKSurface.from_trimeshsurface(surface_h)\n",
+    "pv_mesh = pv.wrap(vtk_surface.mesh)\n",
+    "\n",
+    "# Color mesh by mask: white=keep, red=delete\n",
+    "pv_mesh['mask'] = refined_mask.astype(float)\n",
+    "pvplt.add_mesh(pv_mesh, scalars='mask', cmap=['white', 'red'],\n",
+    "               clim=[0, 1], show_scalar_bar=False, opacity=0.9,\n",
+    "               smooth_shading=True, pickable=True)\n",
+    "\n",
+    "# Show landmarks\n",
+    "for label in landmarks.label.values:\n",
+    "    pos = landmarks.sel(label=label).pint.dequantify().values\n",
+    "    pvplt.add_mesh(pv.Sphere(radius=3, center=pos), color='lime')\n",
+    "    pvplt.add_point_labels([pos], [label], font_size=12, point_size=0,\n",
+    "                           text_color='lime', shape=None, always_visible=True)\n",
+    "\n",
+    "pvplt.enable_surface_point_picking(\n",
+    "    callback=on_pick,\n",
+    "    left_clicking=True,\n",
+    "    show_point=False,\n",
+    "    tolerance=0.005,\n",
+    ")\n",
+    "\n",
+    "# Key bindings\n",
+    "pvplt.add_key_event('t', toggle_mode)\n",
+    "pvplt.add_key_event('plus', increase_radius)\n",
+    "pvplt.add_key_event('minus', decrease_radius)\n",
+    "\n",
+    "update_title()\n",
+    "pvplt.show()\n",
+    "\n",
+    "# After window closes\n",
+    "extended_mask = refined_mask\n",
+    "print(f'\\nDone \u2014 {click_count[0]} clicks, final mask: {extended_mask.sum():,} vertices')"
+   ]
   },
   {
    "cell_type": "markdown",
    "id": "cell-md-anon",
    "metadata": {},
    "source": [
-    "## 5. Anonymize — delete facial vertices"
+    "## 5. Anonymize \u2014 delete facial vertices"
    ]
   },
   {

From d469a1108d6b9fd98e8e48f9c6b055116e106a3a Mon Sep 17 00:00:00 2001
From: mah-diaa <mahmoud.diaa.abdeen@gmail.com>
Date: Mon, 13 Apr 2026 09:35:51 +0200
Subject: [PATCH 07/39] Improve anonymization reliability and expose public API

---
 .../head_models/47_face_anonymization.ipynb   | 448 ++++++++++++----
 .../photogrammetry/anonymization/__init__.py  |  14 +
 .../anonymization/_vertex_utils.py            |  79 +++
 .../anonymization/anonymizer.py               |  83 +--
 .../anonymization/face_detector.py            |  62 +++
 .../anonymization/nasion_detector.py          | 493 ++++++++----------
 6 files changed, 736 insertions(+), 443 deletions(-)
 create mode 100644 src/cedalion/geometry/photogrammetry/anonymization/_vertex_utils.py

diff --git a/examples/head_models/47_face_anonymization.ipynb b/examples/head_models/47_face_anonymization.ipynb
index 5aa1b6a1..3fc673b0 100644
--- a/examples/head_models/47_face_anonymization.ipynb
+++ b/examples/head_models/47_face_anonymization.ipynb
@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "cell-imports",
    "metadata": {},
    "outputs": [],
@@ -30,10 +30,10 @@
     "import numpy as np\n",
     "import pyvista as pv\n",
     "import trimesh\n",
-    "\n",
     "import cedalion\n",
     "import cedalion.io\n",
     "import cedalion.plots\n",
+    "import cedalion.dataclasses\n",
     "from cedalion.geometry.photogrammetry.anonymization import (\n",
     "    detect_nasion_auto,\n",
     "    pick_nasion,\n",
@@ -53,11 +53,62 @@
     "pv.set_jupyter_backend('server')\n",
     "\n",
     "# === CONFIG ===\n",
-    "SUBJECT_NUMBER = 14\n",
-    "SCANS_FOLDER = '/home/ma7/BA/PG_Subjects'  # set to your scan directory\n",
-    "FORCE_MANUAL = False       # set True to skip auto-detection and click nasion manually\n",
-    "PROXIMITY_RADIUS = 85.0    # mm \u2014 ear coverage band width\n",
-    "CIRCLE_RADIUS = 25.0       # mm \u2014 manual click-to-delete radius"
+    "SUBJECT_NUMBER = 19\n",
+    "SCANS_FOLDER = '/home/ma7/BA/PG_Subjects'\n",
+    "FORCE_MANUAL = False\n",
+    "PROXIMITY_RADIUS = 85.0    # mm (unused by new pipeline, kept for reference)\n",
+    "CIRCLE_RADIUS = 25.0       # mm -- manual click-to-delete radius\n",
+    "\n",
+    "# ---------------------------------------------------------------------------\n",
+    "# Helper functions (ported from 50_scan_overview.ipynb)\n",
+    "# ---------------------------------------------------------------------------\n",
+    "\n",
+    "def eye_plane_rotation_matrix(r_eye_3d, l_eye_3d, forward_dir):\n",
+    "    \"\"\"Build the rotation that sends the eye line to +Z, forward to +Y,\n",
+    "    and X = Y x Z (head-up, right-handed). Rows of the returned matrix are\n",
+    "    the new basis vectors in the old frame: p_new = R @ (p_old - center) + center.\n",
+    "    \"\"\"\n",
+    "    eye_dir = np.asarray(l_eye_3d - r_eye_3d, dtype=float)\n",
+    "    z_new = eye_dir / np.linalg.norm(eye_dir)\n",
+    "\n",
+    "    fref = np.asarray(forward_dir, dtype=float)\n",
+    "    fref = fref / np.linalg.norm(fref)\n",
+    "    y_new = fref - np.dot(fref, z_new) * z_new\n",
+    "    y_new = y_new / np.linalg.norm(y_new)\n",
+    "\n",
+    "    x_new = np.cross(y_new, z_new)\n",
+    "    x_new = x_new / np.linalg.norm(x_new)\n",
+    "    return np.vstack([x_new, y_new, z_new])\n",
+    "\n",
+    "\n",
+    "def heuristic_lpa_rpa(head_verts, eye_x, centroid_y):\n",
+    "    \"\"\"Approximate LPA/RPA as small uncertainty spheres, not single points.\n",
+    "\n",
+    "    Expects head_verts in the eye-plane-normalized frame\n",
+    "    (X=head-up, Y=forward, Z=left).\n",
+    "    \"\"\"\n",
+    "    X_TOL = 20.0\n",
+    "    X_OFFSET = -15.0\n",
+    "    Y_TOL = 50.0\n",
+    "    Y_FORWARD = 35.0\n",
+    "    RADIUS = 15.0\n",
+    "\n",
+    "    x_band = np.abs(head_verts[:, 0] - (eye_x + X_OFFSET)) < X_TOL\n",
+    "    y_band = np.abs(head_verts[:, 1] - centroid_y) < Y_TOL\n",
+    "    mask = x_band & y_band\n",
+    "    candidates = head_verts[mask]\n",
+    "    if len(candidates) < 10:\n",
+    "        return None, None\n",
+    "\n",
+    "    lpa_center = candidates[int(np.argmax(candidates[:, 2]))].astype(float).copy()\n",
+    "    rpa_center = candidates[int(np.argmin(candidates[:, 2]))].astype(float).copy()\n",
+    "    lpa_center[1] += Y_FORWARD\n",
+    "    rpa_center[1] += Y_FORWARD\n",
+    "\n",
+    "    return (\n",
+    "        {\"center\": lpa_center, \"radius\": RADIUS},\n",
+    "        {\"center\": rpa_center, \"radius\": RADIUS},\n",
+    "    )\n"
    ]
   },
   {
@@ -73,10 +124,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "cell-nasion",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded: 408,990 vertices, 721,382 faces\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
+      "W0000 00:00:1776003948.935699  266809 face_landmarker_graph.cc:174] Sets FaceBlendshapesGraph acceleration to xnnpack by default.\n",
+      "I0000 00:00:1776003948.982133  266809 gl_context_egl.cc:85] Successfully initialized EGL. Major : 1 Minor: 5\n",
+      "I0000 00:00:1776003948.994993  266835 gl_context.cc:385] GL version: 3.2 (OpenGL ES 3.2 Mesa 26.0.4-arch1.1), renderer: AMD Radeon Graphics (radeonsi, renoir, ACO, DRM 3.64, 6.19.11-arch1-1)\n",
+      "INFO: Created TensorFlow Lite XNNPACK delegate for CPU.\n",
+      "W0000 00:00:1776003949.004314  266819 inference_feedback_manager.cc:114] Feedback manager requires a model with a single signature inference. Disabling support for feedback tensors.\n",
+      "W0000 00:00:1776003949.016300  266814 inference_feedback_manager.cc:114] Feedback manager requires a model with a single signature inference. Disabling support for feedback tensors.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Auto nasion: method=mediapipe+unified, confidence=0.90\n",
+      "Forward direction: available\n",
+      "Face contour: 35 pts\n",
+      "Eyes: available\n"
+     ]
+    }
+   ],
    "source": [
     "path = f'{SCANS_FOLDER}/Subject{SUBJECT_NUMBER}/Subject{SUBJECT_NUMBER}.obj'\n",
     "surface = cedalion.io.read_einstar_obj(path)\n",
@@ -95,14 +177,16 @@
     "    print('Opening manual nasion picker...')\n",
     "    nasion = pick_nasion(surface)\n",
     "    if nasion is None:\n",
-    "        raise RuntimeError('No nasion selected \u2014 cannot proceed')\n",
+    "        raise RuntimeError('No nasion selected — cannot proceed')\n",
     "    meta = {'method': 'manual', 'confidence': 1.0}\n",
     "    print(f'Manual nasion at: [{nasion[0]:.1f}, {nasion[1]:.1f}, {nasion[2]:.1f}]')\n",
     "\n",
     "fwd = meta.get('forward_direction')\n",
     "face_contour = meta.get('face_contour_3d')\n",
+    "eyes = meta.get('eyes')\n",
     "print(f'Forward direction: {\"available\" if fwd is not None else \"N/A\"}')\n",
-    "print(f'Face contour: {len(face_contour)} pts' if face_contour is not None else 'Face contour: N/A')"
+    "print(f'Face contour: {len(face_contour)} pts' if face_contour is not None else 'Face contour: N/A')\n",
+    "print(f'Eyes: {\"available\" if eyes is not None else \"N/A\"}')"
    ]
   },
   {
@@ -110,75 +194,144 @@
    "id": "cell-md-landmarks",
    "metadata": {},
    "source": [
-    "## 2. Normalize axes, isolate head, detect landmarks, compute face mask + ear band"
+    "## 2. Eye-plane rotation, head isolation, heuristic LPA/RPA, face mask\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "cell-landmarks",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Eye-plane rotation applied. Canonical frame: X=up, Y=forward, Z=left\n",
+      "  nasion_n: [83.5, 118.9, 564.3]\n",
+      "Head isolation: 408,990 -> 408,990 vertices\n",
+      "  LPA heur: center=[94.6, 0.6, 662.0]  radius=15\n",
+      "  RPA heur: center=[93.9, 14.1, 464.6]  radius=15\n",
+      "\n",
+      "Custom mask: 90,443 vertices (22.1%)\n",
+      "  below nasion:            62,625\n",
+      "  sides (nasion-arc):      27,818\n",
+      "  nasion strip (kept):     2,828\n",
+      "  forehead upper bound:    MediaPipe forehead arc (14 pts)\n",
+      "    arc X range:           [101.2, 125.4]\n",
+      "  Z band (excl. ears):     (479.6, 647.0)\n",
+      "  Y cut (front half):      > 7.4\n",
+      "  Nasion strip half-width: 15mm in Z\n"
+     ]
+    }
+   ],
    "source": [
-    "# Normalize axes (X=up, Y=anterior, Z=left)\n",
-    "surface_n, nasion_n, R = normalize_axes(surface, nasion, forward_direction=fwd)\n",
+    "# --- Eye-plane rotation (replaces normalize_axes) ---\n",
+    "# Eyes come from the same validated sweep as the nasion, so they are\n",
+    "# guaranteed consistent with it (see nasion_detector._find_nose_tip_mediapipe).\n",
+    "if eyes is None:\n",
+    "    raise RuntimeError(\n",
+    "        \"No eyes in nasion metadata -- unified sweep failed. \"\n",
+    "        \"Fall back to manual pipeline.\"\n",
+    "    )\n",
+    "r_eye_raw, l_eye_raw = eyes\n",
     "\n",
-    "# Transform face contour and forward direction through rotation\n",
-    "if face_contour is not None:\n",
-    "    face_contour = (R @ face_contour.T).T\n",
-    "fwd_norm = R @ fwd if fwd is not None else np.array([0.0, 1.0, 0.0])\n",
-    "\n",
-    "# Isolate head (remove shoulders/body)\n",
-    "surface_h, head_mask = isolate_head(surface_n, nasion_n)\n",
-    "print(f'Head isolation: {surface_n.nvertices:,} -> {surface_h.nvertices:,} vertices')\n",
-    "\n",
-    "# Detect landmarks\n",
-    "landmarks = detect_landmarks_from_nasion(surface_h, nasion_n)\n",
-    "nz = landmarks.sel(label='Nz').pint.dequantify().values\n",
-    "lpa = landmarks.sel(label='LPA').pint.dequantify().values\n",
-    "rpa = landmarks.sel(label='RPA').pint.dequantify().values\n",
-    "for label in landmarks.label.values:\n",
-    "    pos = landmarks.sel(label=label).pint.dequantify().values\n",
-    "    print(f'  {label}: [{pos[0]:.1f}, {pos[1]:.1f}, {pos[2]:.1f}]')\n",
-    "\n",
-    "# Face mask (spherical projection from MediaPipe contour, or hemisphere fallback)\n",
-    "face_mask = get_facial_region_mask_from_nasion(\n",
-    "    surface_h, nz, fwd_norm,\n",
-    "    face_contour_3d=face_contour,\n",
-    "    protected_points=landmarks,\n",
-    ")\n",
+    "R_eye = eye_plane_rotation_matrix(r_eye_raw, l_eye_raw, fwd)\n",
     "\n",
-    "# Ear coverage: proximity band around contour points\n",
-    "verts = surface_h.mesh.vertices\n",
+    "# Build a rotated TrimeshSurface so isolate_head can work with it.\n",
+    "rotated_mesh = surface.mesh.copy()\n",
+    "raw_verts = np.asarray(rotated_mesh.vertices, dtype=float)\n",
+    "rot_center = raw_verts.mean(axis=0)\n",
+    "rotated_mesh.vertices = (R_eye @ (raw_verts - rot_center).T).T + rot_center\n",
+    "surface_n = cedalion.dataclasses.TrimeshSurface(\n",
+    "    mesh=rotated_mesh, crs=surface.crs, units=surface.units\n",
+    ")\n",
+    "nasion_n = R_eye @ (np.asarray(nasion) - rot_center) + rot_center\n",
+    "r_eye_n  = R_eye @ (r_eye_raw - rot_center) + rot_center\n",
+    "l_eye_n  = R_eye @ (l_eye_raw - rot_center) + rot_center\n",
+    "print(f\"Eye-plane rotation applied. Canonical frame: X=up, Y=forward, Z=left\")\n",
+    "print(f\"  nasion_n: [{nasion_n[0]:.1f}, {nasion_n[1]:.1f}, {nasion_n[2]:.1f}]\")\n",
+    "\n",
+    "# --- Isolate head (library call strips disconnected fragments automatically) ---\n",
+    "surface_h, _ = isolate_head(surface_n, nasion_n)\n",
+    "print(f\"Head isolation: {surface_n.nvertices:,} -> {surface_h.nvertices:,} vertices\")\n",
+    "\n",
+    "# --- Heuristic LPA/RPA in the eye-plane canonical frame ---\n",
+    "verts = np.asarray(surface_h.mesh.vertices)\n",
+    "head_centroid = verts.mean(axis=0)\n",
+    "eye_x = 0.5 * (r_eye_n[0] + l_eye_n[0])\n",
+    "lpa_heur, rpa_heur = heuristic_lpa_rpa(verts, eye_x, head_centroid[1])\n",
+    "if lpa_heur is None or rpa_heur is None:\n",
+    "    raise RuntimeError(\"heuristic_lpa_rpa returned None -- ear slice had < 10 candidates\")\n",
+    "\n",
+    "lpa_c, lpa_r = lpa_heur[\"center\"], lpa_heur[\"radius\"]\n",
+    "rpa_c, rpa_r = rpa_heur[\"center\"], rpa_heur[\"radius\"]\n",
+    "print(f\"  LPA heur: center=[{lpa_c[0]:.1f}, {lpa_c[1]:.1f}, {lpa_c[2]:.1f}]  radius={lpa_r:.0f}\")\n",
+    "print(f\"  RPA heur: center=[{rpa_c[0]:.1f}, {rpa_c[1]:.1f}, {rpa_c[2]:.1f}]  radius={rpa_r:.0f}\")\n",
+    "\n",
+    "# --- Custom anonymization mask ---\n",
+    "# Z-band: stay strictly inside the ear spheres (exclusive of radii)\n",
+    "z_band = (verts[:, 2] > rpa_c[2] + rpa_r) & (verts[:, 2] < lpa_c[2] - lpa_r)\n",
+    "\n",
+    "# Y-cut: front half only, use forehead (nasion Y) as safety margin for cap\n",
+    "y_cut = min(0.5 * (lpa_c[1] + rpa_c[1]), nasion_n[1])\n",
+    "front_half = verts[:, 1] > y_cut\n",
+    "\n",
+    "# --- Per-vertex upper X bound from MediaPipe forehead arc ---\n",
+    "# Rotate the face oval into the canonical frame and keep only the upper arc\n",
+    "# (contour points above the nasion -- these trace the forehead boundary).\n",
+    "# Each mesh vertex then gets an upper X limit interpolated from the nearest\n",
+    "# arc point in Z (lateral), so the deletion top follows the real forehead\n",
+    "# instead of a fixed rectangle height.\n",
+    "RECT_HALF_WIDTH = 15.0        # mm, half-width in Z of preserved nasion strip\n",
+    "FOREHEAD_FALLBACK = 50.0      # mm above nasion if contour unavailable\n",
     "\n",
     "if face_contour is not None:\n",
-    "    forehead_pts = face_contour[face_contour[:, 0] > nasion_n[0]]\n",
-    "    x_upper = forehead_pts[:, 0].min() if len(forehead_pts) > 0 else nasion_n[0]\n",
-    "    y_posterior = min(lpa[1], rpa[1])\n",
-    "\n",
-    "    contour_tree = KDTree(face_contour)\n",
-    "    dists, _ = contour_tree.query(verts, k=1)\n",
-    "    ear_band = (dists < PROXIMITY_RADIUS) & (verts[:, 0] <= x_upper) & (verts[:, 1] >= y_posterior)\n",
-    "\n",
-    "    # Upward fill: extend band to x_upper using YZ proximity\n",
-    "    band_verts = verts[ear_band]\n",
-    "    if len(band_verts) > 0:\n",
-    "        band_tree_yz = KDTree(band_verts[:, 1:3])\n",
-    "        fill_dists, _ = band_tree_yz.query(verts[:, 1:3], k=1)\n",
-    "        fill_mask = (fill_dists < PROXIMITY_RADIUS) & (verts[:, 0] <= x_upper) & (verts[:, 0] >= verts[ear_band, 0].min()) & (verts[:, 1] >= y_posterior)\n",
-    "        ear_band = ear_band | fill_mask\n",
-    "\n",
-    "    extended_mask = face_mask | ear_band\n",
+    "    contour_n_all = (R_eye @ (np.asarray(face_contour) - rot_center).T).T + rot_center\n",
+    "    forehead_arc = contour_n_all[contour_n_all[:, 0] > nasion_n[0]]\n",
     "else:\n",
-    "    extended_mask = face_mask\n",
-    "    ear_band = np.zeros_like(face_mask)\n",
+    "    forehead_arc = np.empty((0, 3))\n",
     "\n",
-    "band_only = extended_mask & ~face_mask\n",
-    "print(f'\\nFace mask: {face_mask.sum():,} vertices')\n",
-    "print(f'Ear band:  {band_only.sum():,} vertices')\n",
-    "print(f'Total:     {extended_mask.sum():,} vertices ({100*extended_mask.sum()/len(extended_mask):.1f}%)')\n",
-    "if face_contour is not None:\n",
-    "    print(f'Posterior limit (LPA/RPA Y): {y_posterior:.1f}mm')"
+    "if len(forehead_arc) >= 3:\n",
+    "    arc_tree = KDTree(forehead_arc[:, 2:3])\n",
+    "    _, arc_idx = arc_tree.query(verts[:, 2:3], k=1)\n",
+    "    forehead_x_per_vertex = forehead_arc[arc_idx, 0]\n",
+    "    arc_source = f\"MediaPipe forehead arc ({len(forehead_arc)} pts)\"\n",
+    "else:\n",
+    "    forehead_x_per_vertex = np.full(len(verts), nasion_n[0] + FOREHEAD_FALLBACK)\n",
+    "    arc_source = f\"fallback (nasion + {FOREHEAD_FALLBACK:.0f}mm)\"\n",
+    "\n",
+    "# Region A: everything BELOW the nasion, between ears, front half\n",
+    "face_below = (verts[:, 0] < nasion_n[0]) & z_band & front_half\n",
+    "\n",
+    "# Region B: between nasion and the per-vertex forehead arc,\n",
+    "#           EXCLUDING the vertical nasion strip (kept for registration)\n",
+    "in_arc_band = (verts[:, 0] >= nasion_n[0]) & (verts[:, 0] < forehead_x_per_vertex)\n",
+    "nasion_strip = (\n",
+    "    (np.abs(verts[:, 2] - nasion_n[2]) < RECT_HALF_WIDTH)\n",
+    "    & in_arc_band\n",
+    "    & front_half\n",
+    ")\n",
+    "face_sides = in_arc_band & z_band & front_half & ~nasion_strip\n",
+    "\n",
+    "# Nothing above the arc is deleted.\n",
+    "extended_mask = face_below | face_sides\n",
+    "\n",
+    "print(f\"\\nCustom mask: {extended_mask.sum():,} vertices \"\n",
+    "      f\"({100 * extended_mask.sum() / len(extended_mask):.1f}%)\")\n",
+    "print(f\"  below nasion:            {face_below.sum():,}\")\n",
+    "print(f\"  sides (nasion-arc):      {face_sides.sum():,}\")\n",
+    "print(f\"  nasion strip (kept):     {nasion_strip.sum():,}\")\n",
+    "print(f\"  forehead upper bound:    {arc_source}\")\n",
+    "print(f\"    arc X range:           [{forehead_x_per_vertex.min():.1f}, \"\n",
+    "      f\"{forehead_x_per_vertex.max():.1f}]\")\n",
+    "print(f\"  Z band (excl. ears):     ({rpa_c[2] + rpa_r:.1f}, {lpa_c[2] - lpa_r:.1f})\")\n",
+    "print(f\"  Y cut (front half):      > {y_cut:.1f}\")\n",
+    "print(f\"  Nasion strip half-width: {RECT_HALF_WIDTH:.0f}mm in Z\")\n",
+    "\n",
+    "# Downstream cells expect: verts, extended_mask, protected_positions\n",
+    "protected_positions = np.stack([nasion_n, lpa_c, rpa_c])\n",
+    "protected_labels = [\"Nz\", \"LPA_heur\", \"RPA_heur\"]\n"
    ]
   },
   {
@@ -191,38 +344,69 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "cell-preview",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "be1f2eb136224173ad5ba41d2e9d96f2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Widget(value='<iframe src=\"http://localhost:40923/index.html?ui=P_0x7fc9d27b2350_0&reconnect=auto\" class=\"pyvi…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "pvplt = pv.Plotter()\n",
     "cedalion.plots.plot_surface(pvplt, surface_h, opacity=1.0)\n",
     "\n",
-    "# Deletion region (red)\n",
+    "# Deletion region (red scatter)\n",
     "if extended_mask.sum() > 0:\n",
-    "    pvplt.add_mesh(pv.PolyData(verts[extended_mask]), color='red', point_size=3,\n",
-    "                   opacity=0.4, render_points_as_spheres=True,\n",
-    "                   label=f'deletion region ({extended_mask.sum():,})')\n",
+    "    pvplt.add_mesh(\n",
+    "        pv.PolyData(verts[extended_mask]), color=\"red\", point_size=3,\n",
+    "        opacity=0.4, render_points_as_spheres=True,\n",
+    "        label=f\"deletion region ({extended_mask.sum():,})\",\n",
+    "    )\n",
     "\n",
-    "# Face contour points (orange)\n",
-    "if face_contour is not None:\n",
-    "    for pt in face_contour:\n",
-    "        pvplt.add_mesh(pv.Sphere(radius=1.5, center=pt), color='orange')\n",
+    "# Heuristic LPA/RPA as translucent spheres (NOT to be deleted)\n",
+    "pvplt.add_mesh(pv.Sphere(radius=lpa_r, center=lpa_c), color=\"orange\", opacity=0.35)\n",
+    "pvplt.add_point_labels([lpa_c], [\"LPA heur\"], font_size=14,\n",
+    "                       text_color=\"orange\", bold=True, shape=None)\n",
+    "pvplt.add_mesh(pv.Sphere(radius=rpa_r, center=rpa_c), color=\"blue\", opacity=0.35)\n",
+    "pvplt.add_point_labels([rpa_c], [\"RPA heur\"], font_size=14,\n",
+    "                       text_color=\"blue\", bold=True, shape=None)\n",
     "\n",
-    "# Landmarks (green)\n",
-    "for label in landmarks.label.values:\n",
-    "    pos = landmarks.sel(label=label).pint.dequantify().values\n",
-    "    pvplt.add_mesh(pv.Sphere(radius=3, center=pos), color='lime')\n",
-    "    pvplt.add_point_labels([pos], [label], font_size=16, point_size=0,\n",
-    "                           text_color='lime', shape=None, always_visible=True)\n",
+    "# Nasion\n",
+    "pvplt.add_mesh(pv.Sphere(radius=3, center=nasion_n), color=\"lime\")\n",
+    "pvplt.add_point_labels([nasion_n], [\"Nz\"], font_size=16,\n",
+    "                       text_color=\"lime\", shape=None, always_visible=True)\n",
+    "\n",
+    "# Eye positions (for reference)\n",
+    "pvplt.add_mesh(pv.Sphere(radius=3, center=r_eye_n), color=\"cyan\")\n",
+    "pvplt.add_mesh(pv.Sphere(radius=3, center=l_eye_n), color=\"cyan\")\n",
+    "\n",
+    "# MediaPipe face oval (orange points) -- rotate into canonical frame\n",
+    "if face_contour is not None:\n",
+    "    contour_n = (R_eye @ (np.asarray(face_contour) - rot_center).T).T + rot_center\n",
+    "    pvplt.add_mesh(\n",
+    "        pv.PolyData(contour_n), color=\"orange\", point_size=14,\n",
+    "        render_points_as_spheres=True,\n",
+    "        label=f\"MP face oval ({len(contour_n)} pts)\",\n",
+    "    )\n",
     "\n",
     "pvplt.add_text(\n",
-    "    f'S{SUBJECT_NUMBER} | deletion region: {extended_mask.sum():,} vertices',\n",
-    "    position='upper_left', font_size=12,\n",
+    "    f\"S{SUBJECT_NUMBER} | deletion region: {extended_mask.sum():,} vertices\",\n",
+    "    position=\"upper_left\", font_size=12,\n",
     ")\n",
     "pvplt.add_legend()\n",
-    "pvplt.show()"
+    "pvplt.show()\n"
    ]
   },
   {
@@ -230,7 +414,7 @@
    "id": "cell-md-refine",
    "metadata": {},
    "source": [
-    "## 4. Interactive refinement \u2014 click to add or remove deletion zones\n",
+    "## 4. Interactive refinement — click to add or remove deletion zones\n",
     "\n",
     "- **Left-click**: Add/remove vertices within the brush radius\n",
     "- **T**: Toggle between **add** mode (mark for deletion) and **remove** mode (undo)\n",
@@ -240,7 +424,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "cell-refine",
    "metadata": {},
    "outputs": [],
@@ -251,7 +435,6 @@
     "mode = ['add']  # 'add' or 'remove'\n",
     "\n",
     "# Protected zone: vertices near landmarks that cannot be added to mask\n",
-    "protected_positions = landmarks.pint.dequantify().values\n",
     "protection_radius = 15.0  # mm\n",
     "\n",
     "def update_title():\n",
@@ -276,7 +459,7 @@
     "        # Check if inside protected zone\n",
     "        dist_to_protected = np.linalg.norm(protected_positions - point, axis=1)\n",
     "        if np.any(dist_to_protected < protection_radius):\n",
-    "            print('  Skipped \u2014 too close to a protected landmark')\n",
+    "            print('  Skipped -- too close to a protected landmark')\n",
     "            return\n",
     "\n",
     "        new_verts = circle & ~refined_mask\n",
@@ -289,7 +472,7 @@
     "        sphere = pv.Sphere(radius=brush_radius[0], center=point)\n",
     "        pvplt.add_mesh(sphere, color='yellow', opacity=0.2)\n",
     "    else:\n",
-    "        # Remove mode \u2014 unmark vertices\n",
+    "        # Remove mode -- unmark vertices\n",
     "        removed = circle & refined_mask\n",
     "        n_changed = removed.sum()\n",
     "        if n_changed == 0:\n",
@@ -336,9 +519,8 @@
     "               clim=[0, 1], show_scalar_bar=False, opacity=0.9,\n",
     "               smooth_shading=True, pickable=True)\n",
     "\n",
-    "# Show landmarks\n",
-    "for label in landmarks.label.values:\n",
-    "    pos = landmarks.sel(label=label).pint.dequantify().values\n",
+    "# Show protected landmarks\n",
+    "for label, pos in zip(protected_labels, protected_positions):\n",
     "    pvplt.add_mesh(pv.Sphere(radius=3, center=pos), color='lime')\n",
     "    pvplt.add_point_labels([pos], [label], font_size=12, point_size=0,\n",
     "                           text_color='lime', shape=None, always_visible=True)\n",
@@ -360,7 +542,7 @@
     "\n",
     "# After window closes\n",
     "extended_mask = refined_mask\n",
-    "print(f'\\nDone \u2014 {click_count[0]} clicks, final mask: {extended_mask.sum():,} vertices')"
+    "print(f'\\nDone -- {click_count[0]} clicks, final mask: {extended_mask.sum():,} vertices')\n"
    ]
   },
   {
@@ -368,15 +550,39 @@
    "id": "cell-md-anon",
    "metadata": {},
    "source": [
-    "## 5. Anonymize \u2014 delete facial vertices"
+    "## 5. Anonymize — delete facial vertices"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "cell-anon",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original:    391,330 verts, 741,306 faces\n",
+      "Anonymized:  233,308 verts, 441,792 faces\n",
+      "Removed:     158,022 verts\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ed0d2e3ed64040cba4ee958e1f76ed2c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Widget(value='<iframe src=\"http://localhost:36161/index.html?ui=P_0x7f2042b29090_1&reconnect=auto\" class=\"pyvi…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Delete faces where ANY vertex is in the mask\n",
     "mesh_copy = surface_h.mesh.copy()\n",
@@ -407,6 +613,50 @@
     "pvplt.link_views()\n",
     "pvplt.show()"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4fb94c9d",
+   "metadata": {},
+   "source": [
+    "## 6. Anonymized head — original color"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "c46090c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bcb8dc90d08f4efcadc26a91fb5e27bd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Widget(value='<iframe src=\"http://localhost:42885/index.html?ui=P_0x7f8fe792ab90_2&reconnect=auto\" class=\"pyvi…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pvplt = pv.Plotter()\n",
+    "pvplt.add_mesh(pv.wrap(anon_vtk_mesh), rgb=True, smooth_shading=True)\n",
+    "pvplt.add_text(f'S{SUBJECT_NUMBER} Anonymized', position='upper_left', font_size=14)\n",
+    "pvplt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fe039bec-0b10-491c-8905-dc3744e0c559",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -430,4 +680,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
\ No newline at end of file
+}
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/__init__.py b/src/cedalion/geometry/photogrammetry/anonymization/__init__.py
index 28736d8c..fb9ef4d1 100644
--- a/src/cedalion/geometry/photogrammetry/anonymization/__init__.py
+++ b/src/cedalion/geometry/photogrammetry/anonymization/__init__.py
@@ -30,6 +30,12 @@
     - Face Anonymization Project | 2024
 """
 
+from .anonymizer import (
+    AnonymizationConfig,
+    AnonymizationMethod,
+    AnonymizationResult,
+    anonymize_facial_region,
+)
 from .face_detector import (
     normalize_axes,
     isolate_head,
@@ -38,6 +44,7 @@
 )
 from .nasion_detector import detect_nasion_auto
 from .ui import pick_nasion
+from .validator import validate_anonymization
 
 
 __all__ = [
@@ -50,4 +57,11 @@
     # Landmark and face mask detection
     "detect_landmarks_from_nasion",
     "get_facial_region_mask_from_nasion",
+    # Anonymization
+    "AnonymizationConfig",
+    "AnonymizationMethod",
+    "AnonymizationResult",
+    "anonymize_facial_region",
+    # Validation
+    "validate_anonymization",
 ]
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/_vertex_utils.py b/src/cedalion/geometry/photogrammetry/anonymization/_vertex_utils.py
new file mode 100644
index 00000000..9be66a41
--- /dev/null
+++ b/src/cedalion/geometry/photogrammetry/anonymization/_vertex_utils.py
@@ -0,0 +1,79 @@
+"""Shared vertex utilities for the anonymization module.
+
+Provides the core union-find algorithm for merging spatially coincident
+vertices (seam duplicates in photogrammetry scans).
+
+Initial Contributors:
+    - Face Anonymization Project | 2024
+"""
+
+import numpy as np
+from scipy.spatial import KDTree
+
+
+def deduplicate_vertices(
+    vertices: np.ndarray, tol: float = 0.01
+) -> tuple[np.ndarray, np.ndarray, list[list[int]]]:
+    """Merge spatially coincident vertices using union-find.
+
+    Groups vertices within ``tol`` mm of each other and averages their
+    positions. Returns the merged vertices together with bidirectional
+    index mappings so callers can remap faces, colors, or other per-vertex
+    data.
+
+    Args:
+        vertices: Vertex positions of shape (N, 3).
+        tol: Distance tolerance in mm for considering vertices coincident.
+
+    Returns:
+        Tuple of (merged_vertices, old_to_new, new_to_old) where:
+        - merged_vertices: Averaged positions of shape (M, 3), M <= N.
+        - old_to_new: Array of shape (N,) mapping old index to new index.
+        - new_to_old: List of length M; new_to_old[j] lists old indices
+          that were merged into new vertex j.
+    """
+    n = len(vertices)
+
+    tree = KDTree(vertices)
+    pairs = tree.query_pairs(r=tol)
+
+    if not pairs:
+        old_to_new = np.arange(n)
+        new_to_old = [[i] for i in range(n)]
+        return vertices.copy(), old_to_new, new_to_old
+
+    # Union-find with path compression
+    parent = np.arange(n)
+
+    def find(x):
+        while parent[x] != x:
+            parent[x] = parent[parent[x]]
+            x = parent[x]
+        return x
+
+    for a, b in pairs:
+        ra, rb = find(a), find(b)
+        if ra != rb:
+            parent[ra] = rb
+
+    # Flatten parent pointers
+    for i in range(n):
+        parent[i] = find(i)
+
+    # Build compact index mapping
+    unique_roots, inverse = np.unique(parent, return_inverse=True)
+    old_to_new = inverse
+    n_new = len(unique_roots)
+
+    # Build reverse mapping
+    new_to_old = [[] for _ in range(n_new)]
+    for old_idx in range(n):
+        new_to_old[old_to_new[old_idx]].append(old_idx)
+
+    # Average positions of merged vertices
+    merged_vertices = np.zeros((n_new, 3))
+    for new_idx in range(n_new):
+        old_indices = new_to_old[new_idx]
+        merged_vertices[new_idx] = vertices[old_indices].mean(axis=0)
+
+    return merged_vertices, old_to_new, new_to_old
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/anonymizer.py b/src/cedalion/geometry/photogrammetry/anonymization/anonymizer.py
index 8eaa4cff..cd85dca7 100644
--- a/src/cedalion/geometry/photogrammetry/anonymization/anonymizer.py
+++ b/src/cedalion/geometry/photogrammetry/anonymization/anonymizer.py
@@ -17,11 +17,10 @@
 from scipy.sparse.linalg import spsolve
 from scipy.spatial import KDTree
 import trimesh
-import xarray as xr
-
 import cedalion.dataclasses as cdc
 import cedalion.typing as cdt
 from cedalion import Quantity, units
+from ._vertex_utils import deduplicate_vertices
 
 
 logger = logging.getLogger("cedalion")
@@ -104,34 +103,6 @@ def _build_adjacency_matrix(mesh: trimesh.Trimesh) -> dict[int, set[int]]:
     return adjacency
 
 
-def _compute_laplacian_displacement(
-    vertices: np.ndarray,
-    adjacency: dict[int, set[int]],
-) -> np.ndarray:
-    """Compute Laplacian displacement for each vertex.
-
-    The Laplacian displacement is the difference between a vertex and the
-    average of its neighbors.
-
-    Args:
-        vertices: Vertex positions array of shape (n_vertices, 3)
-        adjacency: Vertex adjacency dictionary
-
-    Returns:
-        Laplacian displacement array of shape (n_vertices, 3)
-    """
-    n_vertices = len(vertices)
-    displacement = np.zeros_like(vertices)
-
-    for i in range(n_vertices):
-        neighbors = adjacency[i]
-        if len(neighbors) > 0:
-            neighbor_avg = np.mean(vertices[list(neighbors)], axis=0)
-            displacement[i] = neighbor_avg - vertices[i]
-
-    return displacement
-
-
 def _merge_seam_vertices(
     mesh: trimesh.Trimesh, tol: float = 0.01
 ) -> tuple[trimesh.Trimesh, np.ndarray, list[list[int]]]:
@@ -151,56 +122,16 @@ def _merge_seam_vertices(
         - old_to_new: Array mapping old vertex index to new vertex index
         - new_to_old: List where new_to_old[i] = list of old indices that merged
     """
-    verts = mesh.vertices
-    n = len(verts)
+    n = len(mesh.vertices)
 
-    # Find groups of coincident vertices
-    tree = KDTree(verts)
-    pairs = tree.query_pairs(r=tol)
+    new_verts, old_to_new, new_to_old = deduplicate_vertices(
+        mesh.vertices, tol=tol
+    )
+    n_new = len(new_verts)
 
-    if not pairs:
-        # No duplicates — return copy with identity mapping
-        old_to_new = np.arange(n)
-        new_to_old = [[i] for i in range(n)]
+    if n_new == n:
         return mesh.copy(), old_to_new, new_to_old
 
-    # Union-find to group coincident vertices
-    parent = np.arange(n)
-
-    def find(x):
-        while parent[x] != x:
-            parent[x] = parent[parent[x]]
-            x = parent[x]
-        return x
-
-    def union(a, b):
-        ra, rb = find(a), find(b)
-        if ra != rb:
-            parent[ra] = rb
-
-    for a, b in pairs:
-        union(a, b)
-
-    # Flatten parent pointers
-    for i in range(n):
-        parent[i] = find(i)
-
-    # Build compact index mapping
-    unique_roots, inverse = np.unique(parent, return_inverse=True)
-    old_to_new = inverse
-    n_new = len(unique_roots)
-
-    # Build reverse mapping
-    new_to_old = [[] for _ in range(n_new)]
-    for old_idx in range(n):
-        new_to_old[old_to_new[old_idx]].append(old_idx)
-
-    # Average positions of merged vertices
-    new_verts = np.zeros((n_new, 3))
-    for new_idx in range(n_new):
-        old_indices = new_to_old[new_idx]
-        new_verts[new_idx] = verts[old_indices].mean(axis=0)
-
     # Re-index faces and remove degenerate triangles
     new_faces = old_to_new[mesh.faces]
     valid = (
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/face_detector.py b/src/cedalion/geometry/photogrammetry/anonymization/face_detector.py
index 1f5685c0..b5b8a183 100644
--- a/src/cedalion/geometry/photogrammetry/anonymization/face_detector.py
+++ b/src/cedalion/geometry/photogrammetry/anonymization/face_detector.py
@@ -95,6 +95,64 @@ def normalize_axes(
     return rotated_surface, rotated_nasion, R
 
 
+def _largest_component_mask(mesh) -> np.ndarray:
+    """Boolean vertex mask selecting the largest connected component.
+
+    Einstar scans are non-watertight and frequently contain tiny floating
+    mesh fragments (loose triangles, cable shreds, background patches)
+    that sit far outside the head. Any step that uses vertex extrema
+    (e.g. head sphere centroid, lateral-widest heuristics) will be
+    dragged off into empty space by those fragments. Stripping them
+    up-front prevents that.
+
+    Uses ``trimesh.graph.connected_component_labels`` on face adjacency
+    rather than ``mesh.split(only_watertight=False)`` — split allocates
+    one full ``Trimesh`` per component and can OOM when a scan has
+    thousands of tiny fragments. Labels give one int per face with
+    zero per-component allocation.
+
+    Args:
+        mesh: A ``trimesh.Trimesh`` instance.
+
+    Returns:
+        Boolean array of shape ``(n_vertices,)``. All-True if the mesh
+        is empty or already a single connected component.
+    """
+    import trimesh
+    n_verts = len(mesh.vertices)
+    n_faces = len(mesh.faces)
+    if n_faces == 0:
+        return np.ones(n_verts, dtype=bool)
+
+    # Einstar OBJs duplicate vertices along UV seams for texturing. The
+    # default trimesh.Trimesh.merge_vertices() does NOT merge across UV
+    # seams (it preserves texture), so face adjacency on the raw mesh
+    # (or on a merge_vertices'd copy) over-fragments the head into
+    # thousands of islands. We need POSITION-only merging for
+    # connectivity analysis: unique_rows on vertex coordinates gives a
+    # canonical-id mapping that heals the seams.
+    unique_idx, inverse = trimesh.grouping.unique_rows(
+        np.asarray(mesh.vertices)
+    )
+    canonical_faces = inverse[mesh.faces]
+    adjacency = trimesh.graph.face_adjacency(faces=canonical_faces)
+    face_labels = trimesh.graph.connected_component_labels(
+        adjacency, node_count=n_faces
+    )
+    counts = np.bincount(face_labels)
+    if len(counts) <= 1:
+        return np.ones(n_verts, dtype=bool)
+    biggest_label = int(np.argmax(counts))
+    face_mask = face_labels == biggest_label
+
+    # Apply face mask to the ORIGINAL faces -> original vertex indices
+    # (so colors/UVs/normals attached to the caller's mesh stay valid).
+    kept_vidx = np.unique(mesh.faces[face_mask])
+    mask = np.zeros(n_verts, dtype=bool)
+    mask[kept_vidx] = True
+    return mask
+
+
 def isolate_head(
     surface: cdc.TrimeshSurface,
     nasion: np.ndarray,
@@ -138,6 +196,10 @@ def isolate_head(
     dist = np.linalg.norm(vertices - center, axis=1)
     head_mask = dist < radius
 
+    # Always strip disconnected fragments (floating triangles, cables,
+    # background patches). No-op on clean single-component scans.
+    head_mask = head_mask & _largest_component_mask(surface.mesh)
+
     # If the sphere captures almost everything, skip trimming
     if head_mask.sum() < 100 or head_mask.mean() > 0.95:
         logger.debug(
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/nasion_detector.py b/src/cedalion/geometry/photogrammetry/anonymization/nasion_detector.py
index 07c0e600..1fe05058 100644
--- a/src/cedalion/geometry/photogrammetry/anonymization/nasion_detector.py
+++ b/src/cedalion/geometry/photogrammetry/anonymization/nasion_detector.py
@@ -72,30 +72,37 @@ def detect_nasion_auto(
         Tuple of (nasion_position, metadata) or None if detection fails.
         nasion_position is a numpy array of shape (3,) in mm.
         metadata is a dict with keys: method, confidence, nose_tip,
-        forward_direction, face_contour_3d.
+        forward_direction, face_contour_3d, eyes. ``eyes`` is a tuple
+        (r_eye_3d, l_eye_3d) -- eye-corner midpoints from the same
+        validated view as the nasion, or None on profile-fallback paths.
     """
     vertices = surface.mesh.vertices
 
-    # --- Step 1: Merge seam vertices to eliminate crack noise ---
-    merged_verts = _merge_close_vertices(vertices)
+    # --- Step 1: Isolate head (remove chair/body) ---
+    head_verts, _ = _isolate_head_vertices(vertices)
 
-    # --- Step 2: Isolate head (remove chair/body) ---
-    head_verts, _ = _isolate_head_vertices(merged_verts)
-
-    # --- Step 3: YZ centroid of upper head (needed for profile analysis) ---
+    # --- Step 2: YZ centroid of upper head (needed for profile analysis) ---
     x_min = head_verts[:, 0].min()
     x_max = head_verts[:, 0].max()
     upper = head_verts[head_verts[:, 0] > x_min + 0.6 * (x_max - x_min)]
     yz_centroid = np.array([0.0, upper[:, 1].mean(), upper[:, 2].mean()])
 
-    # --- Step 4: Try MediaPipe face detection ---
+    # --- Step 3: Try MediaPipe face detection ---
     ml_result = _find_nose_tip_mediapipe(surface.mesh, head_verts)
 
     if ml_result is None:
         logger.info("Automatic nasion detection failed — no face found")
         return None
 
-    nose_tip_ml, forward_dir, nasion_proxy, frontal_reliable, face_contour = ml_result
+    (
+        nose_tip_ml,
+        forward_dir,
+        nasion_proxy,
+        frontal_reliable,
+        face_contour,
+        r_eye_3d,
+        l_eye_3d,
+    ) = ml_result
     logger.debug(
         f"MediaPipe face found, "
         f"fwd_angle={np.degrees(np.arctan2(forward_dir[2], forward_dir[1])):.0f}deg, "
@@ -103,19 +110,23 @@ def detect_nasion_auto(
         f"face_contour={'yes' if face_contour is not None else 'no'}"
     )
 
-    # --- Primary: use frontal re-render nasion directly ---
+    # --- Primary: use validated bundle nasion directly ---
     if frontal_reliable:
         nasion = _snap_to_original(nasion_proxy, vertices)
-        logger.debug(f"Auto nasion (frontal): {nasion}")
+        logger.debug(f"Auto nasion (unified): {nasion}")
         return nasion, {
-            "method": "mediapipe+frontal",
+            "method": "mediapipe+unified",
             "confidence": 0.90,
             "nose_tip": nose_tip_ml.copy(),
             "forward_direction": forward_dir.copy(),
             "face_contour_3d": face_contour,
+            "eyes": (r_eye_3d.copy(), l_eye_3d.copy()),
         }
 
     # --- Fallback: geometric profile analysis ---
+    # The unified sweep only returns frontal_reliable=True, so this branch
+    # is effectively dead code. Kept in case a future sweep variant returns
+    # an unvalidated bundle.
     result = _try_direction(
         head_verts, vertices, forward_dir, yz_centroid,
     )
@@ -135,6 +146,7 @@ def detect_nasion_auto(
             "nose_tip": result["nose_tip"],
             "forward_direction": result["forward_direction"],
             "face_contour_3d": face_contour,
+            "eyes": (r_eye_3d.copy(), l_eye_3d.copy()),
         }
 
     logger.info("Profile analysis failed after MediaPipe detection")
@@ -149,16 +161,21 @@ def _find_nose_tip_mediapipe(
     mesh_trimesh,
     head_verts: np.ndarray,
     cam_distance: float = 400.0,
-) -> tuple[np.ndarray, np.ndarray, np.ndarray, bool] | None:
-    """Detect face direction and nasion using two-pass MediaPipe rendering.
-
-    Pass 1: Renders the mesh from 12 viewpoints to find the forward
-    direction (which way the face points).
-
-    Pass 2: Re-renders from directly in front and back-projects
-    MediaPipe landmark 168 (nasion) from this frontal view. From a
-    frontal camera, the nasion pixel maps correctly to the bridge of
-    the nose rather than the side.
+) -> tuple[
+    np.ndarray, np.ndarray, np.ndarray, bool, np.ndarray | None,
+    np.ndarray, np.ndarray
+] | None:
+    """Unified MediaPipe sweep: nasion, nose tip, eye corners, face oval
+    all extracted from the single best view.
+
+    Renders the mesh from up to 72 viewpoints (24 azimuth x 3 pitch). For
+    each view where MediaPipe detects a face, back-projects nasion (lm 168),
+    nose tip (lm 1), both eye-corner pairs (33/133 + 263/362), and the face
+    oval (36 contour indices) using the same vtkCellPicker. Jointly validates
+    the bundle (all points on-mesh, X ordering nasion>nose, eyes roughly at
+    nasion height), ranks surviving views by |yaw|, returns the bundle from
+    the view with smallest |yaw|. Early-exits when a view with |yaw|<5 deg
+    validates.
 
     Args:
         mesh_trimesh: trimesh.Trimesh mesh object (with texture).
@@ -166,9 +183,11 @@ def _find_nose_tip_mediapipe(
         cam_distance: Camera distance from head centroid in mm.
 
     Returns:
-        Tuple of (nose_tip_3d, forward_direction, nasion_3d,
-        frontal_reliable, face_contour) or None if no face detected.
-        frontal_reliable is True when the frontal re-render succeeded.
+        Tuple (nose_tip_3d, forward_direction, nasion_3d, frontal_reliable,
+        face_contour, r_eye_3d, l_eye_3d) or None if no view validates.
+        frontal_reliable is True when the bundle came from joint validation
+        (i.e. the new code path). r_eye_3d / l_eye_3d are the eye-corner
+        midpoints (outer+inner)/2 for each eye.
     """
     try:
         import pyvista as pv
@@ -183,6 +202,8 @@ def _find_nose_tip_mediapipe(
         return None
 
     head_centroid = head_verts.mean(axis=0)
+    x_min_h = head_verts[:, 0].min()
+    x_max_h = head_verts[:, 0].max()
 
     # Build pyvista mesh with texture colors
     faces_pv = np.column_stack([
@@ -214,220 +235,201 @@ def _find_nose_tip_mediapipe(
         output_facial_transformation_matrixes=True,
     )
 
+    # MediaPipe FaceLandmarker indices
+    _NASION = 168
+    _NOSE_TIP = 1
+    _R_OUT, _R_IN = 33, 133
+    _L_OUT, _L_IN = 263, 362
+    _FACE_OVAL_INDICES = [
+        10, 338, 297, 332, 284, 251, 389, 356,
+        454, 323, 361, 288, 397, 365, 379, 378,
+        400, 377, 152, 148, 176, 149, 150, 136,
+        172, 58, 132, 93, 234, 127, 162, 21,
+        54, 103, 67, 109,
+    ]
+
     result = None
     try:
         with mp.tasks.vision.FaceLandmarker.create_from_options(options) as lm:
-            # === Pass 1: Find forward direction ===
-            pass1_fwd = None
-            pass1_nose_3d = None
-            pass1_nz_3d = None
-
-            for theta_deg in range(0, 360, 30):
+            # === Unified sweep: all landmarks from the same best view ===
+            # Azimuth sweeps around the X=up axis in the Y-Z plane (step 15deg).
+            # Pitch tilts the camera up/down around the tangent axis, so
+            # subjects whose face is tilted relative to gravity can still be
+            # captured. For each view where MediaPipe detects a face, we
+            # back-project nasion, nose tip, both eye-corner pairs, and the
+            # face oval -- all from the SAME rendering. The bundle is jointly
+            # validated and ranked by |yaw|. Guarantees nasion + eyes come
+            # from the same view, so they can't disagree.
+            bundles: list[dict] = []
+            best_so_far_yaw = 180.0
+
+            for theta_deg in range(0, 360, 15):
                 theta = np.radians(theta_deg)
-                cam_offset = np.array([
-                    0,
-                    cam_distance * np.cos(theta),
-                    cam_distance * np.sin(theta),
-                ])
-                cam_pos = head_centroid + cam_offset
-
-                plotter.camera_position = [
-                    cam_pos.tolist(),
-                    head_centroid.tolist(),
-                    [1, 0, 0],
-                ]
-                plotter.render()
-                img = plotter.screenshot(return_img=True)
-                H, W = img.shape[:2]
-
-                mp_image = mp.Image(
-                    image_format=mp.ImageFormat.SRGB,
-                    data=np.ascontiguousarray(img),
-                )
-                detection = lm.detect(mp_image)
-
-                if not detection.face_landmarks:
-                    continue
+                # Horizontal direction from centroid in Y-Z plane
+                horiz = np.array([0.0, np.cos(theta), np.sin(theta)])
+
+                for pitch_deg in (-20, 0, 20):
+                    pitch = np.radians(pitch_deg)
+                    # Tilt the camera by rotating around the tangent axis
+                    # (horizontal, perpendicular to horiz in Y-Z plane).
+                    cam_dir = (
+                        np.cos(pitch) * horiz
+                        + np.sin(pitch) * np.array([1.0, 0.0, 0.0])
+                    )
+                    cam_pos = head_centroid + cam_distance * cam_dir
+
+                    plotter.camera_position = [
+                        cam_pos.tolist(),
+                        head_centroid.tolist(),
+                        [1, 0, 0],
+                    ]
+                    plotter.render()
+                    img = plotter.screenshot(return_img=True)
+                    H, W = img.shape[:2]
+
+                    mp_image = mp.Image(
+                        image_format=mp.ImageFormat.SRGB,
+                        data=np.ascontiguousarray(img),
+                    )
+                    detection = lm.detect(mp_image)
 
-                # Validate yaw
-                if detection.facial_transformation_matrixes:
-                    from scipy.spatial.transform import Rotation
-                    mat = detection.facial_transformation_matrixes[0]
-                    rot = Rotation.from_matrix(np.array(mat)[:3, :3])
-                    yaw = rot.as_euler("yxz", degrees=True)[0]
-                    if abs(yaw) > 75:
+                    if not detection.face_landmarks:
                         continue
 
-                face = detection.face_landmarks[0]
-                nose_lm = face[1]
-                nasion_lm = face[168]
-
-                # Back-project nose tip to validate detection
-                picker = vtk.vtkCellPicker()
-                picker.SetTolerance(0.01)
-                picker.Pick(
-                    float(int(nose_lm.x * W)),
-                    float(H - 1 - int(nose_lm.y * H)),
-                    0, plotter.renderer,
-                )
-                nose_3d = np.array(picker.GetPickPosition())
-                if np.allclose(nose_3d, 0):
-                    continue
+                    # --- Yaw ranking (skip obviously-profile views) ---
+                    yaw_abs = 180.0
+                    if detection.facial_transformation_matrixes:
+                        from scipy.spatial.transform import Rotation
+                        mat = detection.facial_transformation_matrixes[0]
+                        rot = Rotation.from_matrix(np.array(mat)[:3, :3])
+                        yaw = rot.as_euler("yxz", degrees=True)[0]
+                        yaw_abs = abs(yaw)
+                        if yaw_abs > 60:
+                            continue
+
+                    face = detection.face_landmarks[0]
+
+                    picker = vtk.vtkCellPicker()
+                    picker.SetTolerance(0.01)
+
+                    def _bp(idx: int) -> np.ndarray:
+                        lm_pt = face[idx]
+                        picker.Pick(
+                            float(int(lm_pt.x * W)),
+                            float(H - 1 - int(lm_pt.y * H)),
+                            0, plotter.renderer,
+                        )
+                        return np.array(picker.GetPickPosition())
+
+                    nasion_3d = _bp(_NASION)
+                    nose_3d = _bp(_NOSE_TIP)
+                    r_out = _bp(_R_OUT)
+                    r_in = _bp(_R_IN)
+                    l_out = _bp(_L_OUT)
+                    l_in = _bp(_L_IN)
+
+                    # --- Joint validation: all six key points on-mesh ---
+                    key_pts = [nasion_3d, nose_3d, r_out, r_in, l_out, l_in]
+                    if any(np.allclose(p, 0) for p in key_pts):
+                        continue
 
-                # Reject nose on top of head
-                x_min_h = head_verts[:, 0].min()
-                x_max_h = head_verts[:, 0].max()
-                nose_rel = (nose_3d[0] - x_min_h) / (x_max_h - x_min_h)
-                if nose_rel > 0.85:
-                    continue
+                    # X-ordering: nasion above nose tip (X = gravity-up)
+                    if nasion_3d[0] <= nose_3d[0]:
+                        continue
 
-                # Forward direction: nose tip -> centroid in YZ plane
-                fwd = nose_3d - head_centroid
-                fwd[0] = 0.0
-                fwd_norm = np.linalg.norm(fwd)
-                if fwd_norm < 1e-6:
-                    continue
-                fwd = fwd / fwd_norm
+                    # Reject nose on top of head
+                    nose_rel = (nose_3d[0] - x_min_h) / (x_max_h - x_min_h)
+                    if nose_rel > 0.85:
+                        continue
 
-                pass1_fwd = fwd.copy()
-                pass1_nose_3d = nose_3d.copy()
+                    r_eye_3d = 0.5 * (r_out + r_in)
+                    l_eye_3d = 0.5 * (l_out + l_in)
 
-                # Also grab nasion proxy from this view
-                picker.Pick(
-                    float(int(nasion_lm.x * W)),
-                    float(H - 1 - int(nasion_lm.y * H)),
-                    0, plotter.renderer,
-                )
-                pass1_nz_3d = np.array(picker.GetPickPosition()).copy()
+                    # Eyes must be above nose tip
+                    if (r_eye_3d[0] <= nose_3d[0]
+                            or l_eye_3d[0] <= nose_3d[0]):
+                        continue
 
-                logger.debug(f"Pass 1: face found at {theta_deg}deg")
-                break
+                    # Eye midline should be close to nasion in height (within 30mm)
+                    eye_x_mid = 0.5 * (r_eye_3d[0] + l_eye_3d[0])
+                    if abs(eye_x_mid - nasion_3d[0]) > 30.0:
+                        continue
 
-            if pass1_fwd is None:
-                result = None
-            else:
-                # === Pass 2: Frontal re-render for accurate nasion ===
-                frontal_cam_pos = head_centroid + pass1_fwd * cam_distance
-                plotter.camera_position = [
-                    frontal_cam_pos.tolist(),
-                    head_centroid.tolist(),
-                    [1, 0, 0],
-                ]
-                plotter.render()
-                frontal_img = plotter.screenshot(return_img=True)
-                H_f, W_f = frontal_img.shape[:2]
-
-                mp_frontal = mp.Image(
-                    image_format=mp.ImageFormat.SRGB,
-                    data=np.ascontiguousarray(frontal_img),
-                )
-                frontal_det = lm.detect(mp_frontal)
-
-                frontal_reliable = False
-                face_contour = None
-                final_nz_3d = pass1_nz_3d
-                final_nose_3d = pass1_nose_3d
-
-                if frontal_det.face_landmarks:
-                    f_face = frontal_det.face_landmarks[0]
-
-                    # Back-project nasion (landmark 168) from frontal view
-                    nz_lm_f = f_face[168]
-                    picker_f = vtk.vtkCellPicker()
-                    picker_f.SetTolerance(0.01)
-                    picker_f.Pick(
-                        float(int(nz_lm_f.x * W_f)),
-                        float(H_f - 1 - int(nz_lm_f.y * H_f)),
-                        0, plotter.renderer,
-                    )
-                    nz_3d_f = np.array(picker_f.GetPickPosition())
-
-                    # Back-project nose tip (landmark 1) from frontal view
-                    nose_lm_f = f_face[1]
-                    picker_f.Pick(
-                        float(int(nose_lm_f.x * W_f)),
-                        float(H_f - 1 - int(nose_lm_f.y * H_f)),
-                        0, plotter.renderer,
-                    )
-                    nose_3d_f = np.array(picker_f.GetPickPosition())
-
-                    # Validate: both on mesh, nasion above nose tip
-                    if (not np.allclose(nz_3d_f, 0)
-                            and not np.allclose(nose_3d_f, 0)
-                            and nz_3d_f[0] > nose_3d_f[0]):
-                        final_nz_3d = nz_3d_f.copy()
-                        final_nose_3d = nose_3d_f.copy()
-                        frontal_reliable = True
-                        logger.debug(
-                            f"Pass 2 (frontal): nasion={final_nz_3d}, "
-                            f"nose={final_nose_3d}"
-                        )
+                    # Eye corners not too close to each other
+                    eye_sep = float(np.linalg.norm(r_eye_3d - l_eye_3d))
+                    if eye_sep < 30.0 or eye_sep > 120.0:
+                        continue
 
-                        # === Extract face oval contour ===
-                        _FACE_OVAL_INDICES = [
-                            10, 338, 297, 332, 284, 251, 389, 356,
-                            454, 323, 361, 288, 397, 365, 379, 378,
-                            400, 377, 152, 148, 176, 149, 150, 136,
-                            172, 58, 132, 93, 234, 127, 162, 21,
-                            54, 103, 67, 109,
-                        ]
-                        contour_pts = []
-                        picker_c = vtk.vtkCellPicker()
-                        picker_c.SetTolerance(0.01)
-                        for idx in _FACE_OVAL_INDICES:
-                            lm_pt = f_face[idx]
-                            picker_c.Pick(
-                                float(int(lm_pt.x * W_f)),
-                                float(H_f - 1 - int(lm_pt.y * H_f)),
-                                0, plotter.renderer,
-                            )
-                            pt_3d = np.array(picker_c.GetPickPosition())
-                            if not np.allclose(pt_3d, 0):
-                                contour_pts.append(pt_3d.copy())
-
-                        if len(contour_pts) >= 20:
-                            raw_contour = np.array(contour_pts)
-                            face_contour = _filter_contour_outliers(
-                                raw_contour
-                            )
-                            n_removed = len(raw_contour) - len(face_contour)
-                            if len(face_contour) < 20:
-                                face_contour = raw_contour
-                                n_removed = 0
-                            logger.debug(
-                                f"Face oval: {len(contour_pts)}/36 "
-                                f"back-projected, {n_removed} outliers removed"
-                            )
-                        else:
-                            logger.debug(
-                                f"Face oval: only {len(contour_pts)}/36 "
-                                f"back-projected, skipping"
-                            )
+                    # Forward direction: nose tip -> centroid in YZ plane
+                    fwd = nose_3d - head_centroid
+                    fwd[0] = 0.0
+                    fwd_norm = np.linalg.norm(fwd)
+                    if fwd_norm < 1e-6:
+                        continue
+                    fwd = fwd / fwd_norm
+
+                    # --- Face oval from the SAME view ---
+                    contour_pts = []
+                    for idx in _FACE_OVAL_INDICES:
+                        pt = _bp(idx)
+                        if not np.allclose(pt, 0):
+                            contour_pts.append(pt.copy())
+                    if len(contour_pts) >= 20:
+                        raw_contour = np.array(contour_pts)
+                        face_contour = _filter_contour_outliers(raw_contour)
+                        if len(face_contour) < 20:
+                            face_contour = raw_contour
                     else:
+                        face_contour = None
+
+                    bundles.append({
+                        "yaw_abs": yaw_abs,
+                        "nasion_3d": nasion_3d.copy(),
+                        "nose_3d": nose_3d.copy(),
+                        "r_eye_3d": r_eye_3d.copy(),
+                        "l_eye_3d": l_eye_3d.copy(),
+                        "fwd": fwd.copy(),
+                        "face_contour": face_contour,
+                        "theta_deg": theta_deg,
+                        "pitch_deg": pitch_deg,
+                    })
+
+                    # Early-exit: if we have a near-frontal validated bundle,
+                    # stop sweeping -- further views won't improve on this.
+                    if yaw_abs < 5.0:
                         logger.debug(
-                            "Pass 2: frontal back-projection failed validation"
+                            f"Unified sweep: early exit at theta={theta_deg}, "
+                            f"pitch={pitch_deg} (|yaw|={yaw_abs:.1f})"
                         )
+                        best_so_far_yaw = yaw_abs
+                        break
+
+                    if yaw_abs < best_so_far_yaw:
+                        best_so_far_yaw = yaw_abs
+
                 else:
-                    logger.debug("Pass 2: no face in frontal re-render")
-
-                # Recompute forward from frontal nose tip if available
-                if frontal_reliable:
-                    final_fwd = final_nose_3d - head_centroid
-                    final_fwd[0] = 0.0
-                    fn = np.linalg.norm(final_fwd)
-                    if fn > 1e-6:
-                        final_fwd = final_fwd / fn
-                    else:
-                        final_fwd = pass1_fwd.copy()
-                else:
-                    final_fwd = pass1_fwd.copy()
+                    continue
+                break
 
+            if not bundles:
+                logger.debug("Unified sweep: no view passed joint validation")
+                result = None
+            else:
+                bundles.sort(key=lambda b: b["yaw_abs"])
+                best = bundles[0]
+                logger.debug(
+                    f"Unified sweep: {len(bundles)} valid views, best "
+                    f"theta={best['theta_deg']}deg pitch={best['pitch_deg']}deg "
+                    f"|yaw|={best['yaw_abs']:.1f}"
+                )
                 result = (
-                    final_nose_3d.copy(),
-                    final_fwd,
-                    final_nz_3d.copy(),
-                    frontal_reliable,
-                    face_contour,
+                    best["nose_3d"].copy(),
+                    best["fwd"].copy(),
+                    best["nasion_3d"].copy(),
+                    True,  # frontal_reliable: bundle jointly validated
+                    best["face_contour"],
+                    best["r_eye_3d"].copy(),
+                    best["l_eye_3d"].copy(),
                 )
     except Exception as e:
         logger.warning(f"MediaPipe detection error: {e}")
@@ -579,55 +581,6 @@ def _try_direction(
 # Utility functions
 # ---------------------------------------------------------------------------
 
-def _merge_close_vertices(vertices: np.ndarray, tol: float = 0.01) -> np.ndarray:
-    """Merge spatially coincident vertices (seam duplicates).
-
-    Returns a deduplicated vertex array. Uses union-find to group vertices
-    within tolerance, then averages their positions.
-
-    Args:
-        vertices: Mesh vertices of shape (N, 3).
-        tol: Distance tolerance in mm.
-
-    Returns:
-        Unique vertices array of shape (M, 3) where M <= N.
-    """
-    tree = KDTree(vertices)
-    pairs = tree.query_pairs(r=tol)
-
-    if not pairs:
-        return vertices.copy()
-
-    n = len(vertices)
-    parent = np.arange(n)
-
-    def find(x):
-        while parent[x] != x:
-            parent[x] = parent[parent[x]]
-            x = parent[x]
-        return x
-
-    for a, b in pairs:
-        ra, rb = find(a), find(b)
-        if ra != rb:
-            parent[ra] = rb
-
-    for i in range(n):
-        parent[i] = find(i)
-
-    unique_roots, inverse = np.unique(parent, return_inverse=True)
-    n_new = len(unique_roots)
-
-    new_verts = np.zeros((n_new, 3))
-    counts = np.zeros(n_new)
-    for i in range(n):
-        new_verts[inverse[i]] += vertices[i]
-        counts[inverse[i]] += 1
-    new_verts /= counts[:, None]
-
-    logger.debug(f"Merged seam vertices: {n} -> {n_new}")
-    return new_verts
-
 
 def _isolate_head_vertices(
     vertices: np.ndarray,
@@ -635,6 +588,10 @@ def _isolate_head_vertices(
 ) -> tuple[np.ndarray, np.ndarray]:
     """Isolate head vertices by cropping to within max_head_height of the top.
 
+    Note: This is a fast height-based heuristic for nasion detection only.
+    The public ``isolate_head()`` in face_detector.py uses sphere-based
+    filtering that preserves mesh connectivity for downstream processing.
+
     Args:
         vertices: Mesh vertices of shape (N, 3).
         max_head_height: Maximum expected head height in mm.

From 8a9dab1f7f3e16c658ef001ac6797eeb3ac4b5a3 Mon Sep 17 00:00:00 2001
From: mah-diaa <mahmoud.diaa.abdeen@gmail.com>
Date: Mon, 13 Apr 2026 09:45:00 +0200
Subject: [PATCH 08/39] Make mediapipe an optional extra for anonymization

---
 environment_dev.yml                                         | 1 +
 pyproject.toml                                              | 3 +++
 .../photogrammetry/anonymization/nasion_detector.py         | 6 ++++--
 3 files changed, 8 insertions(+), 2 deletions(-)

diff --git a/environment_dev.yml b/environment_dev.yml
index 796ad746..e547807f 100644
--- a/environment_dev.yml
+++ b/environment_dev.yml
@@ -66,4 +66,5 @@ dependencies:
       - pmcxcl>=0.2.0
       - sphinxcontrib-bibtex
       - hatch-conda
+      - mediapipe==0.10.*
       
diff --git a/pyproject.toml b/pyproject.toml
index 78c952d1..da578de4 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -59,6 +59,9 @@ dependencies = [
   "pyxdf==1.17.0",
 ]
 
+[project.optional-dependencies]
+anonymization = ["mediapipe==0.10.*"]
+
 
 [project.urls]
 Home = "https://ibs-lab.com/cedalion/"
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/nasion_detector.py b/src/cedalion/geometry/photogrammetry/anonymization/nasion_detector.py
index 1fe05058..2b1dcc07 100644
--- a/src/cedalion/geometry/photogrammetry/anonymization/nasion_detector.py
+++ b/src/cedalion/geometry/photogrammetry/anonymization/nasion_detector.py
@@ -194,8 +194,10 @@ def _find_nose_tip_mediapipe(
         import mediapipe as mp
         import vtk
     except ImportError as e:
-        logger.info(f"MediaPipe detection unavailable: {e}")
-        return None
+        raise ImportError(
+            "Automatic nasion detection requires mediapipe. "
+            "Install with: pip install cedalion[anonymization]"
+        ) from e
 
     if not _MODEL_PATH.exists():
         logger.info(f"MediaPipe model not found at {_MODEL_PATH}")

From d63d5268ff0d86e9e96cff3d82c3f11ebea00041 Mon Sep 17 00:00:00 2001
From: mah-diaa <mahmoud.diaa.abdeen@gmail.com>
Date: Thu, 16 Apr 2026 10:57:40 +0200
Subject: [PATCH 09/39] Add manual 5pt anonymization notebook and reorganize
 examples

---
 .../head_models/47_face_anonymization.ipynb   |   16 +-
 .../48_manual_5pt_anonymization.ipynb         | 1298 +++++++++++++++++
 ...tion.ipynb => 52_landmark_detection.ipynb} |    0
 .../53_symmetry_axis_detection.ipynb          |  501 +++++++
 4 files changed, 1807 insertions(+), 8 deletions(-)
 create mode 100644 examples/head_models/48_manual_5pt_anonymization.ipynb
 rename examples/head_models/{48_landmark_detection.ipynb => 52_landmark_detection.ipynb} (100%)
 create mode 100644 examples/head_models/53_symmetry_axis_detection.ipynb

diff --git a/examples/head_models/47_face_anonymization.ipynb b/examples/head_models/47_face_anonymization.ipynb
index 3fc673b0..aea2b20c 100644
--- a/examples/head_models/47_face_anonymization.ipynb
+++ b/examples/head_models/47_face_anonymization.ipynb
@@ -53,7 +53,7 @@
     "pv.set_jupyter_backend('server')\n",
     "\n",
     "# === CONFIG ===\n",
-    "SUBJECT_NUMBER = 19\n",
+    "SUBJECT_NUMBER = 21\n",
     "SCANS_FOLDER = '/home/ma7/BA/PG_Subjects'\n",
     "FORCE_MANUAL = False\n",
     "PROXIMITY_RADIUS = 85.0    # mm (unused by new pipeline, kept for reference)\n",
@@ -132,7 +132,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Loaded: 408,990 vertices, 721,382 faces\n"
+      "Loaded: 494,109 vertices, 866,676 faces\n"
      ]
     },
     {
@@ -140,12 +140,12 @@
      "output_type": "stream",
      "text": [
       "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
-      "W0000 00:00:1776003948.935699  266809 face_landmarker_graph.cc:174] Sets FaceBlendshapesGraph acceleration to xnnpack by default.\n",
-      "I0000 00:00:1776003948.982133  266809 gl_context_egl.cc:85] Successfully initialized EGL. Major : 1 Minor: 5\n",
-      "I0000 00:00:1776003948.994993  266835 gl_context.cc:385] GL version: 3.2 (OpenGL ES 3.2 Mesa 26.0.4-arch1.1), renderer: AMD Radeon Graphics (radeonsi, renoir, ACO, DRM 3.64, 6.19.11-arch1-1)\n",
+      "W0000 00:00:1776065866.226577  601547 face_landmarker_graph.cc:174] Sets FaceBlendshapesGraph acceleration to xnnpack by default.\n",
+      "I0000 00:00:1776065866.263782  601547 gl_context_egl.cc:85] Successfully initialized EGL. Major : 1 Minor: 5\n",
+      "I0000 00:00:1776065866.277111  601573 gl_context.cc:385] GL version: 3.2 (OpenGL ES 3.2 Mesa 26.0.4-arch1.1), renderer: AMD Radeon Graphics (radeonsi, renoir, ACO, DRM 3.64, 6.19.11-arch1-1)\n",
       "INFO: Created TensorFlow Lite XNNPACK delegate for CPU.\n",
-      "W0000 00:00:1776003949.004314  266819 inference_feedback_manager.cc:114] Feedback manager requires a model with a single signature inference. Disabling support for feedback tensors.\n",
-      "W0000 00:00:1776003949.016300  266814 inference_feedback_manager.cc:114] Feedback manager requires a model with a single signature inference. Disabling support for feedback tensors.\n"
+      "W0000 00:00:1776065866.283648  601557 inference_feedback_manager.cc:114] Feedback manager requires a model with a single signature inference. Disabling support for feedback tensors.\n",
+      "W0000 00:00:1776065866.294381  601555 inference_feedback_manager.cc:114] Feedback manager requires a model with a single signature inference. Disabling support for feedback tensors.\n"
      ]
     },
     {
@@ -154,7 +154,7 @@
      "text": [
       "Auto nasion: method=mediapipe+unified, confidence=0.90\n",
       "Forward direction: available\n",
-      "Face contour: 35 pts\n",
+      "Face contour: 36 pts\n",
       "Eyes: available\n"
      ]
     }
diff --git a/examples/head_models/48_manual_5pt_anonymization.ipynb b/examples/head_models/48_manual_5pt_anonymization.ipynb
new file mode 100644
index 00000000..1dbd975b
--- /dev/null
+++ b/examples/head_models/48_manual_5pt_anonymization.ipynb
@@ -0,0 +1,1298 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "cell-md-title",
+   "metadata": {},
+   "source": [
+    "# Face Anonymization from 5 Given Landmarks (cedalion-only)\n",
+    "\n",
+    "Pure-geometric face-removal pipeline for Einstar scans. Takes the 5 standard\n",
+    "anatomical landmarks (Nz, Iz, Cz, Lpa, Rpa) as input and deletes the facial\n",
+    "region without any ML / face-detection dependency.\n",
+    "\n",
+    "**Pipeline:**\n",
+    "1. Load scan\n",
+    "2. Pick 5 landmarks interactively via `cedalion.plots.plot_surface(pick_landmarks=True)`\n",
+    "3. Wrap into a `LabeledPointCloud`\n",
+    "4. Normalize axes (X=up, Y=anterior, Z=left) and isolate head\n",
+    "5. Build face-region mask purely from the 5 landmarks\n",
+    "6. Visualize the mask\n",
+    "7. Delete masked vertices (no smoothing)\n",
+    "8. Validate result\n",
+    "\n",
+    "This is the cedalion-only alternative to notebook 47's MediaPipe pipeline and\n",
+    "matches the exposé contract: NAS/LPA/RPA assumed given, no DL face detection."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "id": "cell-imports",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "\n",
+    "import numpy as np\n",
+    "import pyvista as pv\n",
+    "import matplotlib.pyplot as plt\n",
+    "import xarray as xr\n",
+    "\n",
+    "import cedalion\n",
+    "import cedalion.dataclasses as cdc\n",
+    "import cedalion.io\n",
+    "import cedalion.plots\n",
+    "from cedalion.geometry.photogrammetry.anonymization import (\n",
+    "    normalize_axes,\n",
+    "    isolate_head,\n",
+    "    validate_anonymization,\n",
+    ")\n",
+    "from cedalion.vtktutils import trimesh_to_vtk_polydata\n",
+    "from cedalion.dataclasses import VTKSurface\n",
+    "\n",
+    "logging.getLogger('cedalion').setLevel(logging.WARNING)\n",
+    "pv.set_jupyter_backend('server')\n",
+    "\n",
+    "# === CONFIG ===\n",
+    "SUBJECT_NUMBER = 21\n",
+    "SCANS_FOLDER = '/home/ma7/BA/PG_Subjects'\n",
+    "\n",
+    "# True: run the picker. False: use cached landmarks (fill after first pick).\n",
+    "INTERACTIVE = True\n",
+    "\n",
+    "# Mask tunables (mm)\n",
+    "LATERAL_SCALE = 0.9             # fraction of ear-half-width for main face mask\n",
+    "LANDMARK_KEEP_RADIUS = 8.0      # preserve a small sphere around each landmark point\n",
+    "EAR_DELETE_RADIUS_MM = 40.0     # radius of the delete sphere around Lpa / Rpa\n",
+    "\n",
+    "# Cap boundary detection tunables\n",
+    "CAP_BAND_WIDTH_MM = 15.0        # Z-band width for midline Y-profile\n",
+    "CAP_BIN_SIZE_MM = 2.0           # X-bin size for Y-profile\n",
+    "CAP_FOOT_GRAD_THRESHOLD = 0.2   # dY/dX below this = flat = foot of cap rise"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-md-load",
+   "metadata": {},
+   "source": [
+    "## 1. Load the Einstar scan"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "id": "cell-load",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded: 494,109 vertices, 866,676 faces\n"
+     ]
+    }
+   ],
+   "source": [
+    "path = f'{SCANS_FOLDER}/Subject{SUBJECT_NUMBER}/Subject{SUBJECT_NUMBER}.obj'\n",
+    "surface = cedalion.io.read_einstar_obj(path)\n",
+    "print(f'Loaded: {surface.nvertices:,} vertices, {surface.nfaces:,} faces')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-md-pick",
+   "metadata": {},
+   "source": [
+    "## 2. Pick the 5 landmarks interactively\n",
+    "\n",
+    "Uses cedalion's built-in picker (`cedalion.plots.plot_surface(..., pick_landmarks=True)`,\n",
+    "authored by Mariia Iudina, upstream cedalion). Right-click on the mesh to place a\n",
+    "sphere; click again on the sphere to cycle its label through\n",
+    "`Nz -> Iz -> Cz -> Lpa -> Rpa`, or cycle past `Rpa` back to `Nz` to remove it.\n",
+    "\n",
+    "Close the window when all 5 are placed with correct labels."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "id": "cell-pick",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b6ce5c27bbef4d1dbf47c4f12ab0074f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Widget(value='<iframe src=\"http://localhost:43671/index.html?ui=P_0x7efc8f2de790_28&reconnect=auto\" class=\"pyv…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "if INTERACTIVE:\n",
+    "    pvplt = pv.Plotter()\n",
+    "    get_landmarks = cedalion.plots.plot_surface(\n",
+    "        pvplt, surface, opacity=1.0, pick_landmarks=True\n",
+    "    )\n",
+    "    pvplt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-md-retrieve",
+   "metadata": {},
+   "source": [
+    "## 3. Retrieve picked points and wrap into a `LabeledPointCloud`\n",
+    "\n",
+    "Follows the pattern from `41_photogrammetric_optode_coregistration.ipynb` cells 26-28."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "id": "cell-retrieve",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "\n",
+       ".xr-text-repr-fallback {\n",
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+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
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+       "  color: var(--xr-disabled-color);\n",
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+       "\n",
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+       "  cursor: pointer;\n",
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+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
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+       "  float: right;\n",
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+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
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+       "\n",
+       ".xr-var-name span,\n",
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+       ".xr-index-data,\n",
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+       "  padding-left: 25px !important;\n",
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+       ".xr-var-attrs,\n",
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+       "\n",
+       "dl.xr-attrs {\n",
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+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (label: 5, digitized: 3)&gt; Size: 120B\n",
+       "&lt;Quantity([[219.65732964 145.44047659 421.75857531]\n",
+       " [ 73.24147395 233.41224615 488.73681872]\n",
+       " [115.1218116  128.21493626 552.25112911]\n",
+       " [118.11448863  16.72774761 481.18571109]\n",
+       " [ 60.26468104  99.58381571 409.18143222]], &#x27;millimeter&#x27;)&gt;\n",
+       "Coordinates:\n",
+       "  * label    (label) &lt;U3 60B &#x27;Cz&#x27; &#x27;Iz&#x27; &#x27;Rpa&#x27; &#x27;Nz&#x27; &#x27;Lpa&#x27;\n",
+       "    type     (label) object 40B PointType.LANDMARK ... PointType.LANDMARK\n",
+       "    group    (label) &lt;U1 20B &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27;\n",
+       "Dimensions without coordinates: digitized</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>label</span>: 5</li><li><span>digitized</span>: 3</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-712b5fc5-4afc-47d3-9c43-ffd93a0b4b0e' class='xr-array-in' type='checkbox' checked><label for='section-712b5fc5-4afc-47d3-9c43-ffd93a0b4b0e' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>[mm] 219.7 145.4 421.8 73.24 233.4 ... 16.73 481.2 60.26 99.58 409.2</span></div><div class='xr-array-data'><table><tbody><tr><th>Magnitude</th><td style='text-align:left;'><pre>[[219.65732963554527 145.44047658928594 421.7585753052629] [73.24147394754087 233.41224615484435 488.7368187235968] [115.12181159911474 128.2149362645298 552.2511291093099] [118.11448863143345 16.727747611227386 481.18571108653936] [60.26468104440636 99.58381570922568 409.18143221737375]]</pre></td></tr><tr><th>Units</th><td style='text-align:left;'>millimeter</td></tr></tbody></table></div></div></li><li class='xr-section-item'><input id='section-a3dbe402-b461-4090-b6d0-630fa8797a74' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a3dbe402-b461-4090-b6d0-630fa8797a74' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>label</span></div><div class='xr-var-dims'>(label)</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;Cz&#x27; &#x27;Iz&#x27; &#x27;Rpa&#x27; &#x27;Nz&#x27; &#x27;Lpa&#x27;</div><input id='attrs-a6a72a3a-8769-46f2-8bf6-3d0b8e75ef47' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a6a72a3a-8769-46f2-8bf6-3d0b8e75ef47' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2cb276bf-66c7-4f7f-9b2b-0fc81d38e568' class='xr-var-data-in' type='checkbox'><label for='data-2cb276bf-66c7-4f7f-9b2b-0fc81d38e568' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Cz&#x27;, &#x27;Iz&#x27;, &#x27;Rpa&#x27;, &#x27;Nz&#x27;, &#x27;Lpa&#x27;], dtype=&#x27;&lt;U3&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>type</span></div><div class='xr-var-dims'>(label)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>PointType.LANDMARK ... PointType...</div><input id='attrs-3641f122-46aa-49cf-b000-e6ed1f36475a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3641f122-46aa-49cf-b000-e6ed1f36475a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d01dec41-f3fc-4ae2-928b-1db9c352decb' class='xr-var-data-in' type='checkbox'><label for='data-d01dec41-f3fc-4ae2-928b-1db9c352decb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&lt;PointType.LANDMARK: 3&gt;, &lt;PointType.LANDMARK: 3&gt;,\n",
+       "       &lt;PointType.LANDMARK: 3&gt;, &lt;PointType.LANDMARK: 3&gt;,\n",
+       "       &lt;PointType.LANDMARK: 3&gt;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>group</span></div><div class='xr-var-dims'>(label)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27;</div><input id='attrs-8f1dfbb4-9e2f-4d0f-9d2b-7d913c39076f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8f1dfbb4-9e2f-4d0f-9d2b-7d913c39076f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-16cd7e22-a0f5-408f-bc31-0831d9522d53' class='xr-var-data-in' type='checkbox'><label for='data-16cd7e22-a0f5-408f-bc31-0831d9522d53' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;L&#x27;, &#x27;L&#x27;, &#x27;L&#x27;, &#x27;L&#x27;, &#x27;L&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4701d405-124f-4f8e-b453-507fe86ac2e0' class='xr-section-summary-in' type='checkbox'  ><label for='section-4701d405-124f-4f8e-b453-507fe86ac2e0' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>label</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-b9a86eb8-bec2-42b3-b956-4bee9cf6d3ec' class='xr-index-data-in' type='checkbox'/><label for='index-b9a86eb8-bec2-42b3-b956-4bee9cf6d3ec' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;Cz&#x27;, &#x27;Iz&#x27;, &#x27;Rpa&#x27;, &#x27;Nz&#x27;, &#x27;Lpa&#x27;], dtype=&#x27;object&#x27;, name=&#x27;label&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-42a0038c-f9c0-400f-bd31-8d2d611f4acb' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-42a0038c-f9c0-400f-bd31-8d2d611f4acb' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray (label: 5, digitized: 3)> Size: 120B\n",
+       "<Quantity([[219.65732964 145.44047659 421.75857531]\n",
+       " [ 73.24147395 233.41224615 488.73681872]\n",
+       " [115.1218116  128.21493626 552.25112911]\n",
+       " [118.11448863  16.72774761 481.18571109]\n",
+       " [ 60.26468104  99.58381571 409.18143222]], 'millimeter')>\n",
+       "Coordinates:\n",
+       "  * label    (label) <U3 60B 'Cz' 'Iz' 'Rpa' 'Nz' 'Lpa'\n",
+       "    type     (label) object 40B PointType.LANDMARK ... PointType.LANDMARK\n",
+       "    group    (label) <U1 20B 'L' 'L' 'L' 'L' 'L'\n",
+       "Dimensions without coordinates: digitized"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "if INTERACTIVE:\n",
+    "    landmark_coordinates, landmark_labels = get_landmarks()\n",
+    "    landmark_coordinates = np.asarray(landmark_coordinates)\n",
+    "else:\n",
+    "    # Cached fallback: paste coordinates from a previous interactive run here.\n",
+    "    landmark_labels = ['Nz', 'Iz', 'Cz', 'Lpa', 'Rpa']\n",
+    "    landmark_coordinates = np.asarray([\n",
+    "        [0.0, 0.0, 0.0],   # Nz  -- replace\n",
+    "        [0.0, 0.0, 0.0],   # Iz  -- replace\n",
+    "        [0.0, 0.0, 0.0],   # Cz  -- replace\n",
+    "        [0.0, 0.0, 0.0],   # Lpa -- replace\n",
+    "        [0.0, 0.0, 0.0],   # Rpa -- replace\n",
+    "    ])\n",
+    "\n",
+    "assert len(set(landmark_labels)) == 5, 'Need exactly 5 distinct landmarks'\n",
+    "assert set(landmark_labels) == {'Nz', 'Iz', 'Cz', 'Lpa', 'Rpa'}, (\n",
+    "    f'Unexpected labels: {landmark_labels}'\n",
+    ")\n",
+    "\n",
+    "landmarks = xr.DataArray(\n",
+    "    np.vstack(landmark_coordinates),\n",
+    "    dims=['label', 'digitized'],\n",
+    "    coords={\n",
+    "        'label': ('label', list(landmark_labels)),\n",
+    "        'type': ('label', [cdc.PointType.LANDMARK] * 5),\n",
+    "        'group': ('label', ['L'] * 5),\n",
+    "    },\n",
+    ").pint.quantify('mm')\n",
+    "\n",
+    "display(landmarks)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-md-normalize",
+   "metadata": {},
+   "source": [
+    "## 4. Normalize axes and isolate head\n",
+    "\n",
+    "`normalize_axes` rotates the mesh around its X-axis so Y points anterior (toward\n",
+    "the face) and Z points left. We apply the same rotation to the 5 landmarks so\n",
+    "they stay consistent with the mesh. After this, the canonical frame is:\n",
+    "X=up, Y=anterior, Z=left."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "id": "cell-normalize",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "After isolate_head: 494,109 -> 494,109 vertices\n",
+      "Landmark-derived axes (before rotation):\n",
+      "  z_ax (lateral):  [-0.35192326 -0.10140075  0.93052024]  <-- from Lpa-Rpa\n",
+      "  y_ax (anterior): [0.32266227 0.92004145 0.22228989]  <-- perp to Z, toward Nz\n",
+      "  x_ax (up):       [ 0.87865755 -0.37847276  0.29106579]  <-- cross(Y, Z)\n",
+      "\n",
+      "Aligned landmarks (X=up, Y=anterior, Z=left):\n",
+      "  Nz : [ -80.06752458   53.71196561 -486.28670018]\n",
+      "  Iz : [ -63.25693543 -164.8812317  -517.22529684]\n",
+      "  Cz : [  67.58660357  -38.94008683 -491.11942661]\n",
+      "  Lpa: [ -80.06752458  -47.88998206 -415.05897621]\n",
+      "  Rpa: [ -80.06752458  -47.88998206 -570.93708245]\n",
+      "\n",
+      "Axis checks:\n",
+      "  Cz highest X (up)?      True\n",
+      "  Nz most anterior (Y)?   True\n",
+      "  Lpa left of Rpa (Z)?    True\n",
+      "  Lpa/Rpa same X (height)? 0.0 mm\n",
+      "  Lpa/Rpa same Y (depth)?  0.0 mm\n",
+      "  Nz/Iz same X (height)?   16.8 mm\n",
+      "  Nz on mesh: 0.17 mm\n",
+      "  Iz on mesh: 0.22 mm\n",
+      "  Cz on mesh: 0.31 mm\n",
+      "  Lpa on mesh: 0.20 mm\n",
+      "  Rpa on mesh: 0.10 mm\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Extract raw coords (strip pint units) for geometric operations\n",
+    "lm_raw = landmarks.pint.dequantify().values  # (5, 3)\n",
+    "idx = {lbl: i for i, lbl in enumerate(landmarks['label'].values)}\n",
+    "Nz_raw = lm_raw[idx['Nz']]\n",
+    "\n",
+    "# --- Step 1: Mediapipe-compatible normalization (kept for isolate_head) ---\n",
+    "surface_n, Nz_n, R = normalize_axes(surface, Nz_raw)\n",
+    "lm_n = lm_raw @ R.T\n",
+    "\n",
+    "# Isolate head (needs normalize_axes frame)\n",
+    "surface_h, _ = isolate_head(surface_n, Nz_n)\n",
+    "print(f'After isolate_head: {surface_n.nvertices:,} -> {surface_h.nvertices:,} vertices')\n",
+    "\n",
+    "# --- Step 2: Landmark-based axis alignment ---\n",
+    "# normalize_axes only rotates around X. The head is still tilted.\n",
+    "# Derive a full rotation from the 5 landmarks to align X=up, Y=anterior, Z=left.\n",
+    "Nz_1  = lm_n[idx['Nz']]\n",
+    "Iz_1  = lm_n[idx['Iz']]\n",
+    "Cz_1  = lm_n[idx['Cz']]\n",
+    "Lpa_1 = lm_n[idx['Lpa']]\n",
+    "Rpa_1 = lm_n[idx['Rpa']]\n",
+    "ear_mid_1 = 0.5 * (Lpa_1 + Rpa_1)\n",
+    "\n",
+    "# Z: lateral (Lpa-Rpa line, pointing left) -- the most reliable axis\n",
+    "z_ax = Lpa_1 - Rpa_1\n",
+    "z_ax = z_ax / np.linalg.norm(z_ax)\n",
+    "\n",
+    "# Y: anterior, perpendicular to Z, pointing out from nasion\n",
+    "nz_dir = Nz_1 - ear_mid_1\n",
+    "nz_dir = nz_dir - np.dot(nz_dir, z_ax) * z_ax\n",
+    "y_ax = nz_dir / np.linalg.norm(nz_dir)\n",
+    "\n",
+    "# X: up, falls out as cross product\n",
+    "x_ax = np.cross(y_ax, z_ax)\n",
+    "\n",
+    "# Sign checks\n",
+    "if np.dot(Cz_1 - ear_mid_1, x_ax) < 0:\n",
+    "    x_ax = -x_ax\n",
+    "if np.dot(Nz_1 - ear_mid_1, y_ax) < 0:\n",
+    "    y_ax = -y_ax\n",
+    "z_ax = np.cross(x_ax, y_ax)\n",
+    "\n",
+    "print(f'Landmark-derived axes (before rotation):')\n",
+    "print(f'  z_ax (lateral):  {z_ax}  <-- from Lpa-Rpa')\n",
+    "print(f'  y_ax (anterior): {y_ax}  <-- perp to Z, toward Nz')\n",
+    "print(f'  x_ax (up):       {x_ax}  <-- cross(Y, Z)')\n",
+    "\n",
+    "# R2 maps the landmark frame to the standard basis\n",
+    "R2 = np.vstack([x_ax, y_ax, z_ax])\n",
+    "\n",
+    "# Apply to mesh vertices\n",
+    "import trimesh\n",
+    "aligned_verts = np.asarray(surface_h.mesh.vertices) @ R2.T\n",
+    "surface_h = cdc.TrimeshSurface(\n",
+    "    trimesh.Trimesh(vertices=aligned_verts, faces=surface_h.mesh.faces,\n",
+    "                    visual=surface_h.mesh.visual, process=False),\n",
+    "    crs=surface_h.crs, units=surface_h.units,\n",
+    ")\n",
+    "\n",
+    "# Apply to landmarks\n",
+    "lm_n = lm_n @ R2.T\n",
+    "\n",
+    "# Rebuild LabeledPointCloud\n",
+    "landmarks_n = xr.DataArray(\n",
+    "    lm_n,\n",
+    "    dims=['label', 'digitized'],\n",
+    "    coords={\n",
+    "        'label': ('label', list(landmarks['label'].values)),\n",
+    "        'type': ('label', [cdc.PointType.LANDMARK] * 5),\n",
+    "        'group': ('label', ['L'] * 5),\n",
+    "    },\n",
+    ").pint.quantify('mm')\n",
+    "\n",
+    "# Named references in the fully aligned frame\n",
+    "Nz  = lm_n[idx['Nz']]\n",
+    "Iz  = lm_n[idx['Iz']]\n",
+    "Cz  = lm_n[idx['Cz']]\n",
+    "Lpa = lm_n[idx['Lpa']]\n",
+    "Rpa = lm_n[idx['Rpa']]\n",
+    "ear_mid = 0.5 * (Lpa + Rpa)\n",
+    "\n",
+    "print(f'\\nAligned landmarks (X=up, Y=anterior, Z=left):')\n",
+    "print(f'  Nz : {Nz}')\n",
+    "print(f'  Iz : {Iz}')\n",
+    "print(f'  Cz : {Cz}')\n",
+    "print(f'  Lpa: {Lpa}')\n",
+    "print(f'  Rpa: {Rpa}')\n",
+    "\n",
+    "print(f'\\nAxis checks:')\n",
+    "print(f'  Cz highest X (up)?      {Cz[0] > max(Nz[0], Iz[0], Lpa[0], Rpa[0])}')\n",
+    "print(f'  Nz most anterior (Y)?   {Nz[1] > max(Iz[1], Lpa[1], Rpa[1])}')\n",
+    "print(f'  Lpa left of Rpa (Z)?    {Lpa[2] > Rpa[2]}')\n",
+    "print(f'  Lpa/Rpa same X (height)? {abs(Lpa[0] - Rpa[0]):.1f} mm')\n",
+    "print(f'  Lpa/Rpa same Y (depth)?  {abs(Lpa[1] - Rpa[1]):.1f} mm')\n",
+    "print(f'  Nz/Iz same X (height)?   {abs(Nz[0] - Iz[0]):.1f} mm')\n",
+    "\n",
+    "mesh_verts = np.asarray(surface_h.mesh.vertices)\n",
+    "for lbl, pos in zip(['Nz','Iz','Cz','Lpa','Rpa'], [Nz, Iz, Cz, Lpa, Rpa]):\n",
+    "    d = np.linalg.norm(mesh_verts - pos, axis=1).min()\n",
+    "    print(f'  {lbl} on mesh: {d:.2f} mm')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-md-mask",
+   "metadata": {},
+   "source": [
+    "## 5. Build the face-region mask from the 5 landmarks\n",
+    "\n",
+    "Two parts:\n",
+    "\n",
+    "1. **Cap boundary detection:** Scan upward from Nz along the midline. In each\n",
+    "   height bin, find the most anterior vertex (max Y). The forehead shows a smooth\n",
+    "   Y decrease; the cap edge shows a sudden Y drop. The X value where that drop\n",
+    "   first exceeds `CAP_GRADIENT_THRESHOLD` is `cap_x`.\n",
+    "\n",
+    "2. **Mask rules** (in the aligned frame X=up, Y=anterior, Z=left):\n",
+    "   - `X < cap_x` (below the cap boundary -- covers face + forehead)\n",
+    "   - `Y > ear_mid_y` (anterior to the coronal ear plane)\n",
+    "   - `|Z - mid_z| < half_width` (within lateral width, ears excluded)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "id": "cell-mask",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cap boundary detected at X = -39.1 mm\n",
+      "  Nz_x = -80.1,  Cz_x = 67.6,  cap_x = -39.1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Nasion strip preserved: 1,258 vertices (half-width 8 mm, X: -80 -> -39)\n",
+      "\n",
+      "Mask: 117,152 / 494,109 vertices (23.7%)\n",
+      "Per-rule counts: {'below_cap': 180943, 'face_region': 106512, 'ear_region': 30550, 'all': 120411}\n",
+      "Ear delete radius: 40 mm\n"
+     ]
+    }
+   ],
+   "source": [
+    "from scipy.signal import savgol_filter\n",
+    "\n",
+    "\n",
+    "def detect_cap_boundary(\n",
+    "    verts: np.ndarray,\n",
+    "    Nz: np.ndarray, Cz: np.ndarray, ear_mid: np.ndarray,\n",
+    "    mid_z: float,\n",
+    "    band_width: float = 15.0,\n",
+    "    bin_size: float = 2.0,\n",
+    "    foot_grad_threshold: float = 0.2,\n",
+    ") -> tuple[float, np.ndarray, np.ndarray, np.ndarray]:\n",
+    "    \"\"\"Find the X height where the cap front edge sits.\n",
+    "\n",
+    "    The cap protrudes in +Y, so Y-max along the midline lives ON the cap.\n",
+    "    The cap edge is the FOOT of the steep rise leading up to Y-max: walk back\n",
+    "    from the peak until the smoothed gradient drops below `foot_grad_threshold`.\n",
+    "    \"\"\"\n",
+    "    in_band = np.abs(verts[:, 2] - mid_z) < band_width\n",
+    "    above_nz = verts[:, 0] > Nz[0]\n",
+    "    anterior = verts[:, 1] > ear_mid[1]\n",
+    "    sel = in_band & above_nz & anterior\n",
+    "    sel_verts = verts[sel]\n",
+    "\n",
+    "    bins = np.arange(Nz[0], Cz[0], bin_size)\n",
+    "    bin_centers = bins[:-1] + bin_size / 2\n",
+    "    max_y = np.full(len(bin_centers), np.nan)\n",
+    "\n",
+    "    for i in range(len(bin_centers)):\n",
+    "        in_bin = (sel_verts[:, 0] >= bins[i]) & (sel_verts[:, 0] < bins[i + 1])\n",
+    "        if in_bin.any():\n",
+    "            max_y[i] = sel_verts[in_bin, 1].max()\n",
+    "\n",
+    "    valid = ~np.isnan(max_y)\n",
+    "    if valid.sum() < 7:\n",
+    "        fallback = 0.5 * (Nz[0] + Cz[0])\n",
+    "        return fallback, bin_centers[valid], max_y[valid], max_y[valid]\n",
+    "\n",
+    "    xv = bin_centers[valid]\n",
+    "    yv = max_y[valid]\n",
+    "\n",
+    "    win = min(11, len(yv) if len(yv) % 2 == 1 else len(yv) - 1)\n",
+    "    win = max(win, 5)\n",
+    "    yv_s = savgol_filter(yv, window_length=win, polyorder=2)\n",
+    "\n",
+    "    peak_idx = int(np.argmax(yv_s))\n",
+    "    grad = np.gradient(yv_s, xv)\n",
+    "    cap_x = xv[0]\n",
+    "    for i in range(peak_idx - 1, 0, -1):\n",
+    "        if grad[i] < foot_grad_threshold:\n",
+    "            cap_x = xv[i]\n",
+    "            break\n",
+    "\n",
+    "    return cap_x, xv, yv, yv_s\n",
+    "\n",
+    "\n",
+    "def face_mask_from_landmarks(\n",
+    "    verts, Nz, Iz, Cz, Lpa, Rpa,\n",
+    "    cap_x=None,\n",
+    "    ear_delete_radius=40.0,\n",
+    "):\n",
+    "    \"\"\"Connected face + ear mask in the aligned frame (X=up, Y=ant, Z=left).\n",
+    "\n",
+    "    The mask is the union of two regions, both clamped below the cap boundary:\n",
+    "      1. Face region: anterior to the ear coronal plane, extending laterally\n",
+    "         all the way to the ears (no midline band -- so it connects with the\n",
+    "         ear spheres below).\n",
+    "      2. Ear spheres: `ear_delete_radius` mm around Lpa and Rpa (deletes the\n",
+    "         ears themselves plus a bit of skin behind them).\n",
+    "\n",
+    "    Cap safety comes from the `below_cap` clamp (X < cap_x).\n",
+    "    \"\"\"\n",
+    "    ear_mid = 0.5 * (Lpa + Rpa)\n",
+    "    upper_bound = cap_x if cap_x is not None else Nz[0]\n",
+    "\n",
+    "    below_cap = verts[:, 0] < upper_bound\n",
+    "    anterior  = verts[:, 1] > ear_mid[1]\n",
+    "\n",
+    "    face_region = below_cap & anterior\n",
+    "\n",
+    "    d_lpa = np.linalg.norm(verts - Lpa, axis=1)\n",
+    "    d_rpa = np.linalg.norm(verts - Rpa, axis=1)\n",
+    "    ear_region = ((d_lpa < ear_delete_radius) | (d_rpa < ear_delete_radius)) & below_cap\n",
+    "\n",
+    "    mask = face_region | ear_region\n",
+    "\n",
+    "    info = {\n",
+    "        'upper_bound': upper_bound, 'ear_mid': ear_mid,\n",
+    "        'counts': {\n",
+    "            'below_cap': int(below_cap.sum()),\n",
+    "            'face_region': int(face_region.sum()),\n",
+    "            'ear_region': int(ear_region.sum()),\n",
+    "            'all': int(mask.sum()),\n",
+    "        },\n",
+    "    }\n",
+    "    return mask, info\n",
+    "\n",
+    "\n",
+    "# --- Detect cap boundary ---\n",
+    "verts = np.asarray(surface_h.mesh.vertices)\n",
+    "mid_z = 0.5 * (Lpa[2] + Rpa[2])\n",
+    "\n",
+    "cap_x, profile_x, profile_y, profile_y_s = detect_cap_boundary(\n",
+    "    verts, Nz, Cz, ear_mid, mid_z,\n",
+    "    band_width=CAP_BAND_WIDTH_MM,\n",
+    "    bin_size=CAP_BIN_SIZE_MM,\n",
+    "    foot_grad_threshold=CAP_FOOT_GRAD_THRESHOLD,\n",
+    ")\n",
+    "print(f'Cap boundary detected at X = {cap_x:.1f} mm')\n",
+    "print(f'  Nz_x = {Nz[0]:.1f},  Cz_x = {Cz[0]:.1f},  cap_x = {cap_x:.1f}')\n",
+    "\n",
+    "# Debug plot: Y-profile along midline (raw + smoothed)\n",
+    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))\n",
+    "ax1.plot(profile_x, profile_y, 'b.-', alpha=0.4, label='raw max-Y')\n",
+    "ax1.plot(profile_x, profile_y_s, 'b-', lw=2, label='smoothed')\n",
+    "ax1.axvline(Nz[0], color='green', ls='--', label='Nz')\n",
+    "ax1.axvline(cap_x, color='red', ls='--', label=f'cap edge ({cap_x:.0f})')\n",
+    "ax1.axvline(Cz[0], color='cyan', ls='--', label='Cz')\n",
+    "peak_i = int(np.argmax(profile_y_s))\n",
+    "ax1.axvline(profile_x[peak_i], color='orange', ls=':',\n",
+    "            label=f'Y-max on cap ({profile_x[peak_i]:.0f})')\n",
+    "ax1.set_xlabel('X (height, mm)')\n",
+    "ax1.set_ylabel('max Y (anterior, mm)')\n",
+    "ax1.set_title('Midline Y-profile (cap protrudes in +Y)')\n",
+    "ax1.legend()\n",
+    "\n",
+    "grad = np.gradient(profile_y_s, profile_x)\n",
+    "ax2.plot(profile_x, grad, 'r.-')\n",
+    "ax2.axhline(0, color='gray', ls=':', alpha=0.5)\n",
+    "ax2.axhline(CAP_FOOT_GRAD_THRESHOLD, color='purple', ls=':', alpha=0.5,\n",
+    "            label='foot threshold')\n",
+    "ax2.axvline(cap_x, color='red', ls='--', label='cap edge (foot of rise)')\n",
+    "ax2.axvline(profile_x[peak_i], color='orange', ls=':', label='Y-max on cap')\n",
+    "ax2.set_xlabel('X (height, mm)')\n",
+    "ax2.set_ylabel('dY/dX (smoothed)')\n",
+    "ax2.set_title('Gradient (walk back from Y-max until grad < threshold)')\n",
+    "ax2.legend()\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# --- Build mask ---\n",
+    "mask, mask_info = face_mask_from_landmarks(\n",
+    "    verts, Nz, Iz, Cz, Lpa, Rpa,\n",
+    "    cap_x=cap_x,\n",
+    "    ear_delete_radius=EAR_DELETE_RADIUS_MM,\n",
+    ")\n",
+    "\n",
+    "# Preserve the 5 landmark points (small keep-out sphere around each)\n",
+    "for lm in (Nz, Iz, Cz, Lpa, Rpa):\n",
+    "    near = np.linalg.norm(verts - lm, axis=1) < LANDMARK_KEEP_RADIUS\n",
+    "    mask[near] = False\n",
+    "\n",
+    "# Supervisor request: preserve a rectangular strip from the Nasion upward to\n",
+    "# the cap boundary, width = the Nasion's safe-area diameter (for coregistration).\n",
+    "strip_half_width = LANDMARK_KEEP_RADIUS\n",
+    "nasion_strip = (\n",
+    "    (verts[:, 0] >= Nz[0]) &\n",
+    "    (verts[:, 0] < cap_x) &\n",
+    "    (np.abs(verts[:, 2] - Nz[2]) < strip_half_width) &\n",
+    "    (verts[:, 1] > ear_mid[1])\n",
+    ")\n",
+    "mask[nasion_strip] = False\n",
+    "print(f'Nasion strip preserved: {int(nasion_strip.sum()):,} vertices '\n",
+    "      f'(half-width {strip_half_width:.0f} mm, X: {Nz[0]:.0f} -> {cap_x:.0f})')\n",
+    "\n",
+    "print(f'\\nMask: {mask.sum():,} / {len(mask):,} vertices '\n",
+    "      f'({100*mask.sum()/len(mask):.1f}%)')\n",
+    "print(f'Per-rule counts: {mask_info[\"counts\"]}')\n",
+    "print(f'Ear delete radius: {EAR_DELETE_RADIUS_MM:.0f} mm')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-md-viz-mask",
+   "metadata": {},
+   "source": [
+    "## 6. Visualize the anonymization mask\n",
+    "\n",
+    "Red = will be deleted, white = kept. The 5 landmark spheres are overlaid for\n",
+    "orientation. This is the primary deliverable — if the red region hugs the face\n",
+    "without leaking onto ears / top-of-head / neck, the geometric rules are working."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "id": "cell-viz-mask",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bb48fb5980f7452484e4e39fe76920ab",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Widget(value='<iframe src=\"http://localhost:43671/index.html?ui=P_0x7efc8f370e10_29&reconnect=auto\" class=\"pyv…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pvplt = pv.Plotter()\n",
+    "\n",
+    "vtk_surface = VTKSurface.from_trimeshsurface(surface_h)\n",
+    "pv_mesh = pv.wrap(vtk_surface.mesh)\n",
+    "pv_mesh['mask'] = mask.astype(float)\n",
+    "pvplt.add_mesh(\n",
+    "    pv_mesh, scalars='mask', cmap=['white', 'red'], clim=[0, 1],\n",
+    "    show_scalar_bar=False, opacity=1.0, smooth_shading=True,\n",
+    ")\n",
+    "\n",
+    "# 5 landmark spheres with labels\n",
+    "lm_colors = {'Nz': 'lime', 'Iz': 'magenta', 'Cz': 'cyan', 'Lpa': 'orange', 'Rpa': 'blue'}\n",
+    "for lbl, pos in zip(landmarks_n['label'].values, lm_n):\n",
+    "    c = lm_colors.get(lbl, 'yellow')\n",
+    "    pvplt.add_mesh(pv.Sphere(radius=4, center=pos), color=c)\n",
+    "    pvplt.add_point_labels(\n",
+    "        [pos], [lbl], font_size=16, text_color=c, shape=None, always_visible=True,\n",
+    "    )\n",
+    "\n",
+    "pvplt.add_text(\n",
+    "    f'S{SUBJECT_NUMBER} | mask: {mask.sum():,} / {len(mask):,} verts '\n",
+    "    f'({100*mask.sum()/len(mask):.1f}%)',\n",
+    "    position='upper_left', font_size=12,\n",
+    ")\n",
+    "pvplt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-md-delete",
+   "metadata": {},
+   "source": [
+    "## 7. Anonymize by deleting masked vertices\n",
+    "\n",
+    "Drops every triangle that has any masked vertex, then strips unreferenced\n",
+    "vertices. Pure trimesh, no smoothing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "id": "cell-delete",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original:    494,109 verts, 866,676 faces\n",
+      "Anonymized:  376,879 verts, 650,440 faces\n",
+      "Removed:     117,230 verts\n"
+     ]
+    }
+   ],
+   "source": [
+    "mesh_copy = surface_h.mesh.copy()\n",
+    "face_verts_in_mask = mask[mesh_copy.faces]        # (n_faces, 3) bool\n",
+    "faces_to_remove = face_verts_in_mask.any(axis=1)\n",
+    "mesh_copy.update_faces(~faces_to_remove)\n",
+    "mesh_copy.remove_unreferenced_vertices()\n",
+    "\n",
+    "surface_anon = cdc.TrimeshSurface(\n",
+    "    mesh=mesh_copy, crs=surface_h.crs, units=surface_h.units,\n",
+    ")\n",
+    "\n",
+    "n_removed = surface_h.nvertices - surface_anon.nvertices\n",
+    "print(f'Original:    {surface_h.nvertices:,} verts, {surface_h.nfaces:,} faces')\n",
+    "print(f'Anonymized:  {surface_anon.nvertices:,} verts, {surface_anon.nfaces:,} faces')\n",
+    "print(f'Removed:     {n_removed:,} verts')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-md-beforeafter",
+   "metadata": {},
+   "source": [
+    "## 8. Before / after"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "id": "cell-beforeafter",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "190397f8c8cf46f0b1a5c0a957dbeec0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Widget(value='<iframe src=\"http://localhost:43671/index.html?ui=P_0x7efc8f35e090_30&reconnect=auto\" class=\"pyv…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "anon_vtk = trimesh_to_vtk_polydata(mesh_copy)\n",
+    "\n",
+    "pvplt = pv.Plotter(shape=(1, 2))\n",
+    "\n",
+    "pvplt.subplot(0, 0)\n",
+    "cedalion.plots.plot_surface(pvplt, surface_h, opacity=1.0)\n",
+    "for lbl, pos in zip(landmarks_n['label'].values, lm_n):\n",
+    "    pvplt.add_mesh(pv.Sphere(radius=4, center=pos),\n",
+    "                   color=lm_colors.get(lbl, 'yellow'))\n",
+    "pvplt.add_text(f'S{SUBJECT_NUMBER} Original', position='upper_left', font_size=14)\n",
+    "\n",
+    "pvplt.subplot(0, 1)\n",
+    "pvplt.add_mesh(pv.wrap(anon_vtk), rgb=True, smooth_shading=True)\n",
+    "for lbl, pos in zip(landmarks_n['label'].values, lm_n):\n",
+    "    pvplt.add_mesh(pv.Sphere(radius=4, center=pos),\n",
+    "                   color=lm_colors.get(lbl, 'yellow'))\n",
+    "pvplt.add_text(\n",
+    "    f'S{SUBJECT_NUMBER} Anonymized (-{n_removed:,} verts)',\n",
+    "    position='upper_left', font_size=14,\n",
+    ")\n",
+    "\n",
+    "pvplt.link_views()\n",
+    "pvplt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-md-validate",
+   "metadata": {},
+   "source": [
+    "## 9. Validate\n",
+    "\n",
+    "Sanity-check the result: vertex count dropped, no degenerate faces, landmarks\n",
+    "still reachable on the anonymized surface."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "id": "cell-validate",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ValidationResult(face_removed=True, mesh_valid=True, expected_vertices_removed=117152, actual_vertices_removed=117230, protected_points_intact=True, protected_point_max_deviation=0.3121421099641441, face_coverage_pct=23.709748253927778, passed=True, summary='PASSED — 117,230 vertices removed (23.7%), mesh valid, protected points within 5.0mm')\n"
+     ]
+    }
+   ],
+   "source": [
+    "result = validate_anonymization(\n",
+    "    original_surface=surface_h,\n",
+    "    anonymized_surface=surface_anon,\n",
+    "    facial_mask=mask,\n",
+    "    protected_points=landmarks_n,\n",
+    "    tolerance=5.0 * cedalion.units.mm,\n",
+    ")\n",
+    "print(result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-md-footer",
+   "metadata": {},
+   "source": [
+    "## Notes\n",
+    "\n",
+    "- **Landmarks used:** 5 given (Nz, Iz, Cz, Lpa, Rpa), acquired via the existing\n",
+    "  upstream cedalion picker.\n",
+    "- **Mask thresholds:** `MARGIN_TOP_MM=0`, `MARGIN_BOTTOM_MM=20`, `LATERAL_SCALE=0.9`\n",
+    "  (tunable at the top of the notebook).\n",
+    "- **Anonymization method:** vertex deletion (drop faces touching masked vertices,\n",
+    "  strip unreferenced vertices). No Taubin smoothing. No MediaPipe.\n",
+    "- **Contract with the exposé:** this notebook assumes NAS/LPA/RPA are given and\n",
+    "  uses no DL face detection. Cz and Iz are also given and used as vertical bounds;\n",
+    "  they aren't strictly required but make the top/bottom cuts landmark-driven\n",
+    "  instead of mesh-extent-driven.\n",
+    "- **Relationship to notebook 47:** this is the cedalion-only alternative path.\n",
+    "  Notebook 47 detects Nz via MediaPipe and uses a face-oval contour for the\n",
+    "  upper mask bound; this notebook derives everything from the 5 given landmarks."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/head_models/48_landmark_detection.ipynb b/examples/head_models/52_landmark_detection.ipynb
similarity index 100%
rename from examples/head_models/48_landmark_detection.ipynb
rename to examples/head_models/52_landmark_detection.ipynb
diff --git a/examples/head_models/53_symmetry_axis_detection.ipynb b/examples/head_models/53_symmetry_axis_detection.ipynb
new file mode 100644
index 00000000..82eb7717
--- /dev/null
+++ b/examples/head_models/53_symmetry_axis_detection.ipynb
@@ -0,0 +1,501 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "cell-md-title",
+   "metadata": {},
+   "source": [
+    "# Forward-Axis Detection via Eyes-Nose Differential (ENdif)\n",
+    "\n",
+    "Prototype porting the 2D face-detection heuristic of Ramírez, Zanella &\n",
+    "Fuentes (*\"Heuristic-based Automatic Face Detection\"*) to our 3D head scans.\n",
+    "\n",
+    "**The paper's core heuristic (2D grayscale):** in a 9x9 downsample of a face\n",
+    "image, the two eye pixels are *darker* than the nose pixel between them\n",
+    "(eyes are recessed, nose bridge protrudes). They call this quantity the\n",
+    "Eyes-Nose differential (**ENdif**) and maximize it to find a face.\n",
+    "\n",
+    "**Our 3D emulation:**\n",
+    "1. Merge Einstar UV-seam duplicates so the geometry is clean.\n",
+    "2. For each candidate camera direction, orthographically rasterize the\n",
+    "   mesh into a *depth image* (closer to camera = brighter). This\n",
+    "   reproduces the paper's \"eyes dark, nose bright\" contrast directly\n",
+    "   from geometry -- no illumination assumptions, and unaffected by the\n",
+    "   EEG cap (nose still protrudes).\n",
+    "3. Downsample each depth image to 9x9, histogram-equalize, and compute\n",
+    "   ENdif exactly as in the paper.\n",
+    "4. Sweep direction over a Fibonacci sphere and plot ENdif. The argmax\n",
+    "   direction is the forward (anterior) axis; its opposite is the back\n",
+    "   of the head.\n",
+    "\n",
+    "Note: world-up is taken as +X (Einstar's rough gravity axis), so the\n",
+    "head renders upright in every view and only the azimuth / elevation\n",
+    "relative to the face matters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "cell-imports",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import trimesh\n",
+    "import matplotlib.pyplot as plt\n",
+    "import scipy.stats\n",
+    "from scipy.ndimage import zoom, gaussian_filter\n",
+    "\n",
+    "import cedalion\n",
+    "import cedalion.io\n",
+    "from cedalion.geometry.photogrammetry.anonymization._vertex_utils import (\n",
+    "    deduplicate_vertices,\n",
+    ")\n",
+    "\n",
+    "SCANS_FOLDER = '/home/ma7/BA/PG_Subjects'\n",
+    "\n",
+    "# Rendering + sweep tunables\n",
+    "IMG_PIXELS   = 80\n",
+    "SMOOTH_SIGMA = 1.2     # Gaussian pre-smooth on depth (pixels)\n",
+    "EYE_OFFSET   = 12      # ENdif horizontal half-spacing (pixels) ~30mm in bin space\n",
+    "MARGIN_FRAC  = 0.2     # skip the outer X% of rows/cols when taking argmax\n",
+    "N_DIRECTIONS = 200\n",
+    "WORLD_UP     = np.array([1.0, 0.0, 0.0])  # +X ~ gravity on Einstar"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-md-load",
+   "metadata": {},
+   "source": [
+    "## 1. Load scan and merge UV-seam duplicates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "cell-load",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Raw:    494,109 vertices, 866,676 faces\n",
+      "Merged: 433,321 vertices, 866,646 faces\n",
+      "        dropped 30 degenerate faces\n"
+     ]
+    }
+   ],
+   "source": [
+    "SUBJECT_NUMBER = 21  # <-- change and rerun from here\n",
+    "\n",
+    "path = f'{SCANS_FOLDER}/Subject{SUBJECT_NUMBER}/Subject{SUBJECT_NUMBER}.obj'\n",
+    "surface = cedalion.io.read_einstar_obj(path)\n",
+    "raw_mesh = surface.mesh\n",
+    "print(f'Raw:    {len(raw_mesh.vertices):>7,} vertices, {len(raw_mesh.faces):>7,} faces')\n",
+    "\n",
+    "merged_v, old_to_new, _ = deduplicate_vertices(\n",
+    "    np.asarray(raw_mesh.vertices), tol=0.01,\n",
+    ")\n",
+    "merged_faces = old_to_new[np.asarray(raw_mesh.faces)]\n",
+    "# drop degenerate faces (any two corners collapsed to same index)\n",
+    "nondeg = (\n",
+    "    (merged_faces[:, 0] != merged_faces[:, 1]) &\n",
+    "    (merged_faces[:, 1] != merged_faces[:, 2]) &\n",
+    "    (merged_faces[:, 0] != merged_faces[:, 2])\n",
+    ")\n",
+    "merged_faces = merged_faces[nondeg]\n",
+    "\n",
+    "mesh = trimesh.Trimesh(vertices=merged_v, faces=merged_faces, process=False)\n",
+    "print(f'Merged: {len(mesh.vertices):>7,} vertices, {len(mesh.faces):>7,} faces')\n",
+    "print(f'        dropped {(~nondeg).sum():,} degenerate faces')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-md-curv",
+   "metadata": {},
+   "source": [
+    "## 2. Depth rasterization and ENdif scoring\n",
+    "\n",
+    "`depth_image()` orthographically projects the mesh vertices along a camera\n",
+    "direction and bins them into a 2D grid, taking the max depth per cell\n",
+    "(closest vertex to camera wins). This is a render-free, rtree-free\n",
+    "rasterization; at IMG_PIXELS=80 and ~250k head vertices we have >40 verts\n",
+    "per cell so holes are rare.\n",
+    "\n",
+    "`endif_score()` reproduces the paper verbatim: downsample to 9x9 with\n",
+    "bilinear interpolation, histogram-equalize, and compute the middle-row\n",
+    "center-minus-flanking-eyes differential."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "cell-curv",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "view = [ 0. -1.  0.]    peak ENdif = +60.6 at (row, col) = (np.int64(49), np.int64(19))\n"
+     ]
+    },
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 1400x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def depth_image(verts, direction, pixels=IMG_PIXELS, world_up=WORLD_UP):\n",
+    "    \"\"\"Orthographic depth map along `direction`. Brighter = closer to camera.\n",
+    "\n",
+    "    Returns (depth, mask) where mask is True inside the head silhouette.\n",
+    "    \"\"\"\n",
+    "    d = direction / np.linalg.norm(direction)\n",
+    "    u = world_up - (world_up @ d) * d\n",
+    "    if np.linalg.norm(u) < 1e-6:\n",
+    "        u = np.cross(d, np.array([0.0, 1.0, 0.0]))\n",
+    "    u = u / np.linalg.norm(u)\n",
+    "    r = np.cross(u, d)\n",
+    "\n",
+    "    x = verts @ r\n",
+    "    y = verts @ u\n",
+    "    depth = verts @ d\n",
+    "\n",
+    "    x_e = np.linspace(x.min(), x.max(), pixels + 1)\n",
+    "    y_e = np.linspace(y.min(), y.max(), pixels + 1)\n",
+    "    stat, _, _, _ = scipy.stats.binned_statistic_2d(\n",
+    "        x, y, depth, 'max', [x_e, y_e],\n",
+    "    )\n",
+    "    mask = ~np.isnan(stat)\n",
+    "    stat = np.where(mask, stat, np.nanmin(stat))\n",
+    "    return stat.T, mask.T\n",
+    "\n",
+    "\n",
+    "def histeq(img, mask=None):\n",
+    "    \"\"\"Rank-based histogram equalization on the masked region -> [0, 255].\"\"\"\n",
+    "    if mask is None:\n",
+    "        mask = np.ones_like(img, dtype=bool)\n",
+    "    out = np.zeros_like(img, dtype=float)\n",
+    "    vals = img[mask].ravel()\n",
+    "    ranks = np.argsort(np.argsort(vals))\n",
+    "    out[mask] = (ranks / max(len(vals) - 1, 1)) * 255.0\n",
+    "    return out\n",
+    "\n",
+    "\n",
+    "def endif_filter(img_eq, eye_offset=EYE_OFFSET):\n",
+    "    \"\"\"Local 1D ENdif: center - mean(left_eye, right_eye), peaked horizontally.\"\"\"\n",
+    "    pad = eye_offset\n",
+    "    padded = np.pad(img_eq, ((0, 0), (pad, pad)), mode='edge')\n",
+    "    left  = padded[:, : -2 * pad]\n",
+    "    right = padded[:, 2 * pad :]\n",
+    "    return img_eq - 0.5 * (left + right)\n",
+    "\n",
+    "\n",
+    "def score_view(verts_c, direction):\n",
+    "    \"\"\"Render + smooth + histeq + ENdif + masked argmax for one direction.\"\"\"\n",
+    "    img, mask = depth_image(verts_c, direction)\n",
+    "    img_s = gaussian_filter(img, SMOOTH_SIGMA)\n",
+    "    img_eq = histeq(img_s, mask)   # kept for visualization only\n",
+    "    en = endif_filter(img_s)       # score on raw mm depth, not equalized ranks\n",
+    "\n",
+    "    # Valid region: silhouette interior, away from border, AND upper world half.\n",
+    "    # u ~ +X (gravity), so high row index = upper part of the head -- the nose\n",
+    "    # sits above head center; optodes on the cap are everywhere, so excluding\n",
+    "    # the lower half kills most back-of-head false positives.\n",
+    "    H, W = en.shape\n",
+    "    mr, mc = int(H * MARGIN_FRAC), int(W * MARGIN_FRAC)\n",
+    "    valid = np.zeros_like(mask)\n",
+    "    valid[H // 2 : H - mr, mc : W - mc] = True\n",
+    "    valid &= mask\n",
+    "\n",
+    "    en_masked = np.where(valid, en, -np.inf)\n",
+    "    peak = np.unravel_index(en_masked.argmax(), en_masked.shape)\n",
+    "    score = en_masked[peak]\n",
+    "    return score, peak, img, img_s, en, mask\n",
+    "\n",
+    "\n",
+    "# --- One viewpoint sanity check ---\n",
+    "verts   = np.asarray(mesh.vertices)\n",
+    "verts_c = verts - verts.mean(axis=0)\n",
+    "\n",
+    "view = np.array([0.0, -1.0, 0.0])\n",
+    "score, peak, img, img_s, en, mask = score_view(verts_c, view)\n",
+    "print(f'view = {view}    peak ENdif = {score:+.1f} at (row, col) = {peak}')\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 3, figsize=(14, 5))\n",
+    "axes[0].imshow(img, cmap='gray', origin='upper')\n",
+    "axes[0].set_title('depth (raw)'); axes[0].axis('off')\n",
+    "axes[1].imshow(img_s, cmap='gray', origin='upper')\n",
+    "axes[1].set_title(f'depth smoothed (sigma={SMOOTH_SIGMA})'); axes[1].axis('off')\n",
+    "m = np.abs(en[mask]).max()\n",
+    "axes[2].imshow(np.where(mask, en, np.nan), cmap='coolwarm',\n",
+    "                origin='upper', vmin=-m, vmax=+m)\n",
+    "axes[2].scatter([peak[1]], [peak[0]], s=80, facecolor='none',\n",
+    "                 edgecolor='lime', lw=2, label='argmax')\n",
+    "axes[2].set_title('ENdif (masked)'); axes[2].axis('off')\n",
+    "axes[2].legend(loc='upper right')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "af48c5fd",
+   "metadata": {},
+   "source": [
+    "## 3. Sweep viewpoints on a Fibonacci sphere\n",
+    "\n",
+    "For each of `N_DIRECTIONS` uniformly-distributed camera directions on the\n",
+    "sphere we render a depth map, apply the ENdif filter, and record the peak\n",
+    "score. The direction whose peak score is highest is the candidate frontal\n",
+    "direction."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "02a41e96",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   25/200  current best = +149.7\n",
+      "   50/200  current best = +185.7\n",
+      "   75/200  current best = +203.3\n",
+      "  100/200  current best = +203.3\n",
+      "  125/200  current best = +203.3\n",
+      "  150/200  current best = +203.3\n",
+      "  175/200  current best = +203.3\n",
+      "  200/200  current best = +203.3\n",
+      "\n",
+      "best direction : [-0.92141159 -0.05268473  0.385     ]   score = +203.3\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1400x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def fibonacci_sphere(n):\n",
+    "    idx = np.arange(n) + 0.5\n",
+    "    phi = np.arccos(1.0 - 2.0 * idx / n)\n",
+    "    theta = np.pi * (1.0 + 5.0 ** 0.5) * idx\n",
+    "    return np.column_stack([\n",
+    "        np.cos(theta) * np.sin(phi),\n",
+    "        np.sin(theta) * np.sin(phi),\n",
+    "        np.cos(phi),\n",
+    "    ])\n",
+    "\n",
+    "\n",
+    "directions = fibonacci_sphere(N_DIRECTIONS)\n",
+    "scores = np.empty(N_DIRECTIONS)\n",
+    "peaks = [None] * N_DIRECTIONS\n",
+    "\n",
+    "for i, d in enumerate(directions):\n",
+    "    s, p, *_ = score_view(verts_c, d)\n",
+    "    scores[i] = s\n",
+    "    peaks[i] = p\n",
+    "    if (i + 1) % 25 == 0:\n",
+    "        print(f'  {i + 1:>3d}/{N_DIRECTIONS}  current best = {scores[:i+1].max():+.1f}')\n",
+    "\n",
+    "best_i = int(np.argmax(scores))\n",
+    "best_dir = directions[best_i]\n",
+    "print(f'\\nbest direction : {best_dir}   score = {scores[best_i]:+.1f}')\n",
+    "\n",
+    "# Visualize the best view\n",
+    "score, peak, img, img_s, en, mask = score_view(verts_c, best_dir)\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 3, figsize=(14, 5))\n",
+    "axes[0].imshow(img_s, cmap='gray', origin='upper')\n",
+    "axes[0].set_title(f'best view depth  dir={np.round(best_dir, 2)}')\n",
+    "axes[0].axis('off')\n",
+    "\n",
+    "m = np.abs(en[mask]).max()\n",
+    "axes[1].imshow(np.where(mask, en, np.nan), cmap='coolwarm',\n",
+    "                origin='upper', vmin=-m, vmax=+m)\n",
+    "axes[1].scatter([peak[1]], [peak[0]], s=80, facecolor='none',\n",
+    "                 edgecolor='lime', lw=2, label='nose?')\n",
+    "# Also mark the candidate eye pixels\n",
+    "axes[1].scatter([peak[1] - EYE_OFFSET, peak[1] + EYE_OFFSET],\n",
+    "                 [peak[0], peak[0]], s=80, facecolor='none',\n",
+    "                 edgecolor='cyan', lw=2, label='eyes?')\n",
+    "axes[1].legend(loc='upper right')\n",
+    "axes[1].set_title(f'ENdif peak = {score:+.1f}'); axes[1].axis('off')\n",
+    "\n",
+    "# Score distribution across the sphere for intuition\n",
+    "axes[2].hist(scores, bins=30, color='steelblue', alpha=0.8)\n",
+    "axes[2].axvline(scores[best_i], color='lime', lw=2, label='best')\n",
+    "axes[2].set_xlabel('ENdif peak score'); axes[2].set_ylabel('count')\n",
+    "axes[2].set_title('scores across sweep'); axes[2].legend()\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "42099ac4",
+   "metadata": {},
+   "source": [
+    "## 4. Back-project peak pixels onto the 3D mesh\n",
+    "\n",
+    "The ENdif peak gives us a (row, col) in the 80x80 depth image of the best\n",
+    "view. To see *where on the scan* those pixels actually are, we find the\n",
+    "mesh vertex that \"won\" each bin (i.e. the vertex closest to the camera\n",
+    "within that pixel's footprint) and render the mesh with colored spheres\n",
+    "at those 3D positions -- green for the nose candidate, cyan for the two\n",
+    "eye candidates.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "373c78d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "nose vertex  171341  -> [ 33.034859  36.565964 544.074341]\n",
+      "left  vertex 39998  -> [244.82132   88.176155 463.618439]\n",
+      "right vertex None -> None\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "79063931816042c08c7df66321c3531a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Widget(value='<iframe src=\"http://localhost:41703/index.html?ui=P_0x7fe8fbc99250_4&reconnect=auto\" class=\"pyvi…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pyvista as pv\n",
+    "\n",
+    "def back_project_pixel(verts_c, direction, pixel, pixels=IMG_PIXELS, world_up=WORLD_UP):\n",
+    "    \"\"\"Return the 3D vertex (in verts_c's frame) that filled `pixel` = (row, col).\"\"\"\n",
+    "    d = direction / np.linalg.norm(direction)\n",
+    "    u = world_up - (world_up @ d) * d\n",
+    "    if np.linalg.norm(u) < 1e-6:\n",
+    "        u = np.cross(d, np.array([0.0, 1.0, 0.0]))\n",
+    "    u = u / np.linalg.norm(u)\n",
+    "    r = np.cross(u, d)\n",
+    "\n",
+    "    x = verts_c @ r\n",
+    "    y = verts_c @ u\n",
+    "    depth = verts_c @ d\n",
+    "\n",
+    "    x_e = np.linspace(x.min(), x.max(), pixels + 1)\n",
+    "    y_e = np.linspace(y.min(), y.max(), pixels + 1)\n",
+    "\n",
+    "    row, col = pixel  # row <-> y-bin (u-axis), col <-> x-bin (r-axis)\n",
+    "    in_bin = (\n",
+    "        (x >= x_e[col]) & (x <= x_e[col + 1]) &\n",
+    "        (y >= y_e[row]) & (y <= y_e[row + 1])\n",
+    "    )\n",
+    "    if not in_bin.any():\n",
+    "        return None\n",
+    "    idx_in = np.where(in_bin)[0]\n",
+    "    best = idx_in[np.argmax(depth[in_bin])]\n",
+    "    return best\n",
+    "\n",
+    "\n",
+    "# Peak is in verts_c frame; re-center output to world frame (verts)\n",
+    "mean = verts.mean(axis=0)\n",
+    "\n",
+    "nose_px  = peak\n",
+    "left_px  = (peak[0], peak[1] - EYE_OFFSET)\n",
+    "right_px = (peak[0], peak[1] + EYE_OFFSET)\n",
+    "\n",
+    "nose_i  = back_project_pixel(verts_c, best_dir, nose_px)\n",
+    "left_i  = back_project_pixel(verts_c, best_dir, left_px)\n",
+    "right_i = back_project_pixel(verts_c, best_dir, right_px)\n",
+    "\n",
+    "nose_xyz  = verts[nose_i]  if nose_i  is not None else None\n",
+    "left_xyz  = verts[left_i]  if left_i  is not None else None\n",
+    "right_xyz = verts[right_i] if right_i is not None else None\n",
+    "\n",
+    "print(f'nose vertex  {nose_i}  -> {nose_xyz}')\n",
+    "print(f'left  vertex {left_i}  -> {left_xyz}')\n",
+    "print(f'right vertex {right_i} -> {right_xyz}')\n",
+    "\n",
+    "# Radius: ~1% of the mesh diagonal, so spheres are visible but not huge\n",
+    "diag = np.linalg.norm(verts.max(axis=0) - verts.min(axis=0))\n",
+    "rad = diag * 0.01\n",
+    "\n",
+    "pl = pv.Plotter(notebook=True)\n",
+    "pv_mesh = pv.PolyData(verts, np.hstack([np.full((len(mesh.faces), 1), 3), mesh.faces]).astype(np.int64))\n",
+    "pl.add_mesh(pv_mesh, color='lightgray', opacity=1.0, show_edges=False)\n",
+    "\n",
+    "if nose_xyz is not None:\n",
+    "    pl.add_mesh(pv.Sphere(radius=rad, center=nose_xyz),  color='lime')\n",
+    "if left_xyz is not None:\n",
+    "    pl.add_mesh(pv.Sphere(radius=rad, center=left_xyz),  color='cyan')\n",
+    "if right_xyz is not None:\n",
+    "    pl.add_mesh(pv.Sphere(radius=rad, center=right_xyz), color='cyan')\n",
+    "\n",
+    "# Camera: look along -best_dir so the face is toward the viewer\n",
+    "focal = verts.mean(axis=0)\n",
+    "cam_pos = focal + best_dir * diag * 2.0\n",
+    "view_up = WORLD_UP\n",
+    "pl.camera_position = [tuple(cam_pos), tuple(focal), tuple(view_up)]\n",
+    "pl.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ca552b27-04b9-4de3-b5cd-de3838a874e3",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From db366d26c836da368f8d4c727bbd28610b9babed Mon Sep 17 00:00:00 2001
From: mah-diaa <mahmoud.diaa.abdeen@gmail.com>
Date: Thu, 16 Apr 2026 16:41:50 +0200
Subject: [PATCH 10/39] Update notebook 48 with latest outputs

---
 .../48_manual_5pt_anonymization.ipynb         | 110 +++++++++---------
 1 file changed, 55 insertions(+), 55 deletions(-)

diff --git a/examples/head_models/48_manual_5pt_anonymization.ipynb b/examples/head_models/48_manual_5pt_anonymization.ipynb
index 1dbd975b..46e9cda9 100644
--- a/examples/head_models/48_manual_5pt_anonymization.ipynb
+++ b/examples/head_models/48_manual_5pt_anonymization.ipynb
@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 15,
    "id": "cell-imports",
    "metadata": {},
    "outputs": [],
@@ -55,7 +55,7 @@
     "pv.set_jupyter_backend('server')\n",
     "\n",
     "# === CONFIG ===\n",
-    "SUBJECT_NUMBER = 21\n",
+    "SUBJECT_NUMBER = 17\n",
     "SCANS_FOLDER = '/home/ma7/BA/PG_Subjects'\n",
     "\n",
     "# True: run the picker. False: use cached landmarks (fill after first pick).\n",
@@ -82,7 +82,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 16,
    "id": "cell-load",
    "metadata": {},
    "outputs": [
@@ -90,7 +90,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Loaded: 494,109 vertices, 866,676 faces\n"
+      "Loaded: 1,284,667 vertices, 2,223,716 faces\n"
      ]
     }
    ],
@@ -117,7 +117,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 17,
    "id": "cell-pick",
    "metadata": {
     "scrolled": true
@@ -126,12 +126,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b6ce5c27bbef4d1dbf47c4f12ab0074f",
+       "model_id": "2963d2dfd8b44b63b221932b1cd7a137",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Widget(value='<iframe src=\"http://localhost:43671/index.html?ui=P_0x7efc8f2de790_28&reconnect=auto\" class=\"pyv…"
+       "Widget(value='<iframe src=\"http://localhost:38789/index.html?ui=P_0x7f9c741e8a10_3&reconnect=auto\" class=\"pyvi…"
       ]
      },
      "metadata": {},
@@ -159,7 +159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 18,
    "id": "cell-retrieve",
    "metadata": {},
    "outputs": [
@@ -610,28 +610,28 @@
        "  stroke-width: 0.8px;\n",
        "}\n",
        "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (label: 5, digitized: 3)&gt; Size: 120B\n",
-       "&lt;Quantity([[219.65732964 145.44047659 421.75857531]\n",
-       " [ 73.24147395 233.41224615 488.73681872]\n",
-       " [115.1218116  128.21493626 552.25112911]\n",
-       " [118.11448863  16.72774761 481.18571109]\n",
-       " [ 60.26468104  99.58381571 409.18143222]], &#x27;millimeter&#x27;)&gt;\n",
+       "&lt;Quantity([[ 65.80514235 -11.60517104 481.16566125]\n",
+       " [182.62203078 116.67835657 431.5709762 ]\n",
+       " [ 51.88537828 219.86486084 456.38238574]\n",
+       " [ 64.75510598  78.33385833 566.36259485]\n",
+       " [  2.74086779  77.07375634 392.23255999]], &#x27;millimeter&#x27;)&gt;\n",
        "Coordinates:\n",
-       "  * label    (label) &lt;U3 60B &#x27;Cz&#x27; &#x27;Iz&#x27; &#x27;Rpa&#x27; &#x27;Nz&#x27; &#x27;Lpa&#x27;\n",
+       "  * label    (label) &lt;U3 60B &#x27;Nz&#x27; &#x27;Cz&#x27; &#x27;Iz&#x27; &#x27;Rpa&#x27; &#x27;Lpa&#x27;\n",
        "    type     (label) object 40B PointType.LANDMARK ... PointType.LANDMARK\n",
        "    group    (label) &lt;U1 20B &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27;\n",
-       "Dimensions without coordinates: digitized</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>label</span>: 5</li><li><span>digitized</span>: 3</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-712b5fc5-4afc-47d3-9c43-ffd93a0b4b0e' class='xr-array-in' type='checkbox' checked><label for='section-712b5fc5-4afc-47d3-9c43-ffd93a0b4b0e' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>[mm] 219.7 145.4 421.8 73.24 233.4 ... 16.73 481.2 60.26 99.58 409.2</span></div><div class='xr-array-data'><table><tbody><tr><th>Magnitude</th><td style='text-align:left;'><pre>[[219.65732963554527 145.44047658928594 421.7585753052629] [73.24147394754087 233.41224615484435 488.7368187235968] [115.12181159911474 128.2149362645298 552.2511291093099] [118.11448863143345 16.727747611227386 481.18571108653936] [60.26468104440636 99.58381570922568 409.18143221737375]]</pre></td></tr><tr><th>Units</th><td style='text-align:left;'>millimeter</td></tr></tbody></table></div></div></li><li class='xr-section-item'><input id='section-a3dbe402-b461-4090-b6d0-630fa8797a74' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a3dbe402-b461-4090-b6d0-630fa8797a74' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>label</span></div><div class='xr-var-dims'>(label)</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;Cz&#x27; &#x27;Iz&#x27; &#x27;Rpa&#x27; &#x27;Nz&#x27; &#x27;Lpa&#x27;</div><input id='attrs-a6a72a3a-8769-46f2-8bf6-3d0b8e75ef47' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a6a72a3a-8769-46f2-8bf6-3d0b8e75ef47' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2cb276bf-66c7-4f7f-9b2b-0fc81d38e568' class='xr-var-data-in' type='checkbox'><label for='data-2cb276bf-66c7-4f7f-9b2b-0fc81d38e568' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Cz&#x27;, &#x27;Iz&#x27;, &#x27;Rpa&#x27;, &#x27;Nz&#x27;, &#x27;Lpa&#x27;], dtype=&#x27;&lt;U3&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>type</span></div><div class='xr-var-dims'>(label)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>PointType.LANDMARK ... PointType...</div><input id='attrs-3641f122-46aa-49cf-b000-e6ed1f36475a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3641f122-46aa-49cf-b000-e6ed1f36475a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d01dec41-f3fc-4ae2-928b-1db9c352decb' class='xr-var-data-in' type='checkbox'><label for='data-d01dec41-f3fc-4ae2-928b-1db9c352decb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&lt;PointType.LANDMARK: 3&gt;, &lt;PointType.LANDMARK: 3&gt;,\n",
+       "Dimensions without coordinates: digitized</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>label</span>: 5</li><li><span>digitized</span>: 3</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-fb651587-e434-4b3c-8acc-476e9ef4bba0' class='xr-array-in' type='checkbox' checked><label for='section-fb651587-e434-4b3c-8acc-476e9ef4bba0' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>[mm] 65.81 -11.61 481.2 182.6 116.7 ... 78.33 566.4 2.741 77.07 392.2</span></div><div class='xr-array-data'><table><tbody><tr><th>Magnitude</th><td style='text-align:left;'><pre>[[65.80514234534597 -11.605171044162262 481.1656612460295] [182.6220307773288 116.67835657353872 431.57097619552144] [51.88537827784663 219.86486083549175 456.3823857394348] [64.75510597977373 78.3338583311504 566.3625948499965] [2.740867794190251 77.07375633870706 392.2325599877916]]</pre></td></tr><tr><th>Units</th><td style='text-align:left;'>millimeter</td></tr></tbody></table></div></div></li><li class='xr-section-item'><input id='section-ed748025-edcd-4e75-a4ec-b76d073949a7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ed748025-edcd-4e75-a4ec-b76d073949a7' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>label</span></div><div class='xr-var-dims'>(label)</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;Nz&#x27; &#x27;Cz&#x27; &#x27;Iz&#x27; &#x27;Rpa&#x27; &#x27;Lpa&#x27;</div><input id='attrs-ca49c0aa-dbbb-4083-8e14-5cefe3eab0f2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ca49c0aa-dbbb-4083-8e14-5cefe3eab0f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ccb6b1c7-7708-417a-97ba-6c49ef62d9fc' class='xr-var-data-in' type='checkbox'><label for='data-ccb6b1c7-7708-417a-97ba-6c49ef62d9fc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Nz&#x27;, &#x27;Cz&#x27;, &#x27;Iz&#x27;, &#x27;Rpa&#x27;, &#x27;Lpa&#x27;], dtype=&#x27;&lt;U3&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>type</span></div><div class='xr-var-dims'>(label)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>PointType.LANDMARK ... PointType...</div><input id='attrs-2b4d7407-f43b-43f8-acdf-2a5ac893f1ca' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2b4d7407-f43b-43f8-acdf-2a5ac893f1ca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8d4d4f2-d749-455f-a156-efa502b96d2a' class='xr-var-data-in' type='checkbox'><label for='data-c8d4d4f2-d749-455f-a156-efa502b96d2a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&lt;PointType.LANDMARK: 3&gt;, &lt;PointType.LANDMARK: 3&gt;,\n",
        "       &lt;PointType.LANDMARK: 3&gt;, &lt;PointType.LANDMARK: 3&gt;,\n",
-       "       &lt;PointType.LANDMARK: 3&gt;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>group</span></div><div class='xr-var-dims'>(label)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27;</div><input id='attrs-8f1dfbb4-9e2f-4d0f-9d2b-7d913c39076f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8f1dfbb4-9e2f-4d0f-9d2b-7d913c39076f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-16cd7e22-a0f5-408f-bc31-0831d9522d53' class='xr-var-data-in' type='checkbox'><label for='data-16cd7e22-a0f5-408f-bc31-0831d9522d53' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;L&#x27;, &#x27;L&#x27;, &#x27;L&#x27;, &#x27;L&#x27;, &#x27;L&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4701d405-124f-4f8e-b453-507fe86ac2e0' class='xr-section-summary-in' type='checkbox'  ><label for='section-4701d405-124f-4f8e-b453-507fe86ac2e0' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>label</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-b9a86eb8-bec2-42b3-b956-4bee9cf6d3ec' class='xr-index-data-in' type='checkbox'/><label for='index-b9a86eb8-bec2-42b3-b956-4bee9cf6d3ec' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;Cz&#x27;, &#x27;Iz&#x27;, &#x27;Rpa&#x27;, &#x27;Nz&#x27;, &#x27;Lpa&#x27;], dtype=&#x27;object&#x27;, name=&#x27;label&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-42a0038c-f9c0-400f-bd31-8d2d611f4acb' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-42a0038c-f9c0-400f-bd31-8d2d611f4acb' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "       &lt;PointType.LANDMARK: 3&gt;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>group</span></div><div class='xr-var-dims'>(label)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27;</div><input id='attrs-f5275c36-dbe6-4d6d-b163-59dce1c0911e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f5275c36-dbe6-4d6d-b163-59dce1c0911e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ab50e95-811c-42c3-b065-1fda875123eb' class='xr-var-data-in' type='checkbox'><label for='data-0ab50e95-811c-42c3-b065-1fda875123eb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;L&#x27;, &#x27;L&#x27;, &#x27;L&#x27;, &#x27;L&#x27;, &#x27;L&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-15adda9b-488f-493f-a997-b33d58a9a5d4' class='xr-section-summary-in' type='checkbox'  ><label for='section-15adda9b-488f-493f-a997-b33d58a9a5d4' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>label</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-6a7a674f-d402-40c8-b12e-25c63f9d42ec' class='xr-index-data-in' type='checkbox'/><label for='index-6a7a674f-d402-40c8-b12e-25c63f9d42ec' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;Nz&#x27;, &#x27;Cz&#x27;, &#x27;Iz&#x27;, &#x27;Rpa&#x27;, &#x27;Lpa&#x27;], dtype=&#x27;object&#x27;, name=&#x27;label&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e191fd1c-7ad6-4e27-a76b-f86d3b41ea8c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e191fd1c-7ad6-4e27-a76b-f86d3b41ea8c' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.DataArray (label: 5, digitized: 3)> Size: 120B\n",
-       "<Quantity([[219.65732964 145.44047659 421.75857531]\n",
-       " [ 73.24147395 233.41224615 488.73681872]\n",
-       " [115.1218116  128.21493626 552.25112911]\n",
-       " [118.11448863  16.72774761 481.18571109]\n",
-       " [ 60.26468104  99.58381571 409.18143222]], 'millimeter')>\n",
+       "<Quantity([[ 65.80514235 -11.60517104 481.16566125]\n",
+       " [182.62203078 116.67835657 431.5709762 ]\n",
+       " [ 51.88537828 219.86486084 456.38238574]\n",
+       " [ 64.75510598  78.33385833 566.36259485]\n",
+       " [  2.74086779  77.07375634 392.23255999]], 'millimeter')>\n",
        "Coordinates:\n",
-       "  * label    (label) <U3 60B 'Cz' 'Iz' 'Rpa' 'Nz' 'Lpa'\n",
+       "  * label    (label) <U3 60B 'Nz' 'Cz' 'Iz' 'Rpa' 'Lpa'\n",
        "    type     (label) object 40B PointType.LANDMARK ... PointType.LANDMARK\n",
        "    group    (label) <U1 20B 'L' 'L' 'L' 'L' 'L'\n",
        "Dimensions without coordinates: digitized"
@@ -689,7 +689,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 19,
    "id": "cell-normalize",
    "metadata": {},
    "outputs": [
@@ -697,18 +697,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "After isolate_head: 494,109 -> 494,109 vertices\n",
+      "After isolate_head: 1,284,667 -> 678,081 vertices\n",
       "Landmark-derived axes (before rotation):\n",
-      "  z_ax (lateral):  [-0.35192326 -0.10140075  0.93052024]  <-- from Lpa-Rpa\n",
-      "  y_ax (anterior): [0.32266227 0.92004145 0.22228989]  <-- perp to Z, toward Nz\n",
-      "  x_ax (up):       [ 0.87865755 -0.37847276  0.29106579]  <-- cross(Y, Z)\n",
+      "  z_ax (lateral):  [-0.33548853 -0.12505766  0.93370661]  <-- from Lpa-Rpa\n",
+      "  y_ax (anterior): [0.29804451 0.92614585 0.23113487]  <-- perp to Z, toward Nz\n",
+      "  x_ax (up):       [ 0.89365369 -0.35582923  0.27343856]  <-- cross(Y, Z)\n",
       "\n",
       "Aligned landmarks (X=up, Y=anterior, Z=left):\n",
-      "  Nz : [ -80.06752458   53.71196561 -486.28670018]\n",
-      "  Iz : [ -63.25693543 -164.8812317  -517.22529684]\n",
-      "  Cz : [  67.58660357  -38.94008683 -491.11942661]\n",
-      "  Lpa: [ -80.06752458  -47.88998206 -415.05897621]\n",
-      "  Rpa: [ -80.06752458  -47.88998206 -570.93708245]\n",
+      "  Nz : [ -99.06892144  -17.13701132 -475.26525432]\n",
+      "  Iz : [ -30.86680258 -238.5773692  -448.82692759]\n",
+      "  Cz : [  61.51173772  -99.18613518 -468.61131999]\n",
+      "  Lpa: [ -99.06892144 -111.29378165 -370.9357092 ]\n",
+      "  Rpa: [ -99.06892144 -111.29378165 -555.78327514]\n",
       "\n",
       "Axis checks:\n",
       "  Cz highest X (up)?      True\n",
@@ -716,12 +716,12 @@
       "  Lpa left of Rpa (Z)?    True\n",
       "  Lpa/Rpa same X (height)? 0.0 mm\n",
       "  Lpa/Rpa same Y (depth)?  0.0 mm\n",
-      "  Nz/Iz same X (height)?   16.8 mm\n",
-      "  Nz on mesh: 0.17 mm\n",
-      "  Iz on mesh: 0.22 mm\n",
-      "  Cz on mesh: 0.31 mm\n",
-      "  Lpa on mesh: 0.20 mm\n",
-      "  Rpa on mesh: 0.10 mm\n"
+      "  Nz/Iz same X (height)?   68.2 mm\n",
+      "  Nz on mesh: 0.20 mm\n",
+      "  Iz on mesh: 0.17 mm\n",
+      "  Cz on mesh: 0.78 mm\n",
+      "  Lpa on mesh: 0.71 mm\n",
+      "  Rpa on mesh: 1.00 mm\n"
      ]
     }
    ],
@@ -850,7 +850,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 20,
    "id": "cell-mask",
    "metadata": {},
    "outputs": [
@@ -858,13 +858,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Cap boundary detected at X = -39.1 mm\n",
-      "  Nz_x = -80.1,  Cz_x = 67.6,  cap_x = -39.1\n"
+      "Cap boundary detected at X = -58.1 mm\n",
+      "  Nz_x = -99.1,  Cz_x = 61.5,  cap_x = -58.1\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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stm3bphoxbpsP7KQwG80CBQoYN2/ejF1OJx4NoqZNmyZo+GhsWjGdG/xNVtiocjkbSEfGm4kzTik6vgoVKpTAuKZBxofDxYsXE/2tNEp69OiRYHnJkiXVZP39ScFrgp0SGh98qJuY1x8NLGuDf+zYMfUAfP755xPdrnm9tGvXLt7yNWvWqOU02kx47XEZH6ZWXn/9dbWcDg3bBxGN6/3796vX7BzYXq9JnWtXnFI0dKyGoUn37t0TnFveX3Rg3rlzJ9667EjQmKKhQCpUqKCcga7iqPPDZezcWaFhx05nUnCfuL/mREOd22OnxbrcXqeEy9h55vpsl0zHrS28xnjOaKQLguAYPrN5P1kHE6z3m/WetLbNZjvAzlpi8DP87D///JOgXbv//vsdPkOTckqxQ8SBFdovVjgAws/S8WTC13QqcNvW383Pczsm7777bgJ7JTH4bKDdY9tJYWeM26F9Qvj8YVv9yCOPxHZa2UbxmT5kyBCH2+fxj4iIUOvR4easU4rOBTot6LxwBvNcWr+D0EHF5atXr060Ladzjt9n2hK0vziYRGdjYh24lOyz1X61ktLnu2lD0YlqPj8JB0K4nOfQSr9+/dTyxJyXVuiY4/q89ugsc/Z32tq4fJ5zOR2yVuiopR3lCPOcvf3220bOnDnjnR/O8zmeLVs21RegM4HXLa9Bdzul+D1WaE9wudXx1Lp163i2kZXkOqU6dOigrjmrk5fHhL/VnlOK16R5bTjC0X1AJwttbEd2qbNOKe4T2yk60szBel7P7Le4gqPjaV7zdPpY7S464LicTsOk2n2uw+UcoLfC+5PLed1b2+fE9t3Z/eFx5/OD61nv1WvXrqkBaTp+HTmlknNupk2bpt7bu3ev4etI+p7g1zBNimlGM2fOVGGZ1FVylLpnD6bZMYSTYesmTAdIqTA205KsVKpUSf03w9mZn8/UMoYy8785MRSTv8nZShwM5WdqIENJmU5nDbu3btca5k34GX6XCUNHmTLG0HF+1grDU21DWIltyDPTJBgObJsGmByYHsjtMMScoa3W38KQcb5vLxzWhGkFDKlliLIVhp5TBJ+Vmqy/3xZ+D1NDKJTN9AqmKvA/0wmp5WULUwytIcUMq2UIPq8vZ6AYoRV+ltuw/TxTKaiXYns+uJ/UB7HC80MbyDxfzmB7rl2B+8rryPba57GxwvS5ffv2xf5m23N7+vTp2HB4/iaGJTPtgPdESnUReI54ndvem86kmbBdYZqAOTG1hzB837rcDAm3wjbF1H8bMmSIChG3Bz/PdIaTJ08m8xcKgsCUBes9aU+nzV5bx3QmVotlWi/bfH6W7TAx232mHtHOcPQMTQqm3jFNi6l/1raPqfv20u4aN24cTwibtgqfa8lNjTPTrJmiYpsGw7aM/Pnnn7HpjEwp4XLOE6aX8BiY65KIiAiVnsw2kceNE9NWuJ6956UtfE4xTZJtJNOXX331VZd+j+3z03zmWJ+fTJnms4mpN2yPeW5pf9HeoV1AqLvC1ErqlCWlQ5nSfbaHO57vfIYy3dKEqTukdevW8dYzlzM10BkoR8E0Oz7fmP5lYr2GOVlT6wjTyOx9r739sb2m+dt4nTHd0Dxnb731Fi5cuBAv9ZDnimnyvE+Y3n/06FElBeCJYj9J2faegkLVvDaYImrC88wUSHtwvyh0bYsz9wHvcdrYjuxSZ2C6Z4kSJfDxxx+ra4Ype7S9mQbLfos74bVk1WVL7JzYtvtsj2lzse22XsdsnynHYbbHfM0+wAsvvKA0fW3lNVzZH9q3bIOZPm29VzNnzqz2j30bR3pXyTk3Zj/IH+xKcUoJfg0fRsz9paYPc2bZCNevX9/pz/PhxobHFnvLXIENvhWzE2p2qk19BlYGshrPnKgzZU/DwhHcNhs2ayNIR511m7Y6U45+M3OraWBaoSFie8xodJoaRdZzwW3w/ZTCbfDBwKp9tsfH1DFI7BiZx9nW8WTmiCclNEl9KObrt23bFj/99JN6oLIjwoepPceIo+Pp7LFw9vO254JwHXvLTR0VV86Hve04C7/H6tx19NvMa59aArbnlp0B67n98MMPVWfnhx9+UJ0zakvwnNA5mBzo4LS9Jnj/8CGfFNSB4DVgTtQDIGx3rMupb2MPsw1wpH1jwv3zRVFSQfAl2DnjYIy9jgedBNZ71F47YFsEgQM31BqhDgsdDBwU2bBhQ+zgh3lPXrp0Sa2bXLuB7d+OHTsStH3sULNDb/tcs7UlzLYkJW0EP2tvUIadGtoOdEqxU8QOkOmU+vfff1Vniu/xuLMTZHUCsfPJjigH3HjcePxpIzizn7Q7aPewciJF612BtojtMTLPg/nso+OFdiE7ZR988AFWrVql9s/UpzT30Vn7IKX77Ah3PN9thcfN542j5c48+6yfsT6/qGNkex3bVvlyZX+s+8JriPcjodA6Nbt4zqjnRGyvK14DdLZwG6xoaxbVcTdJ2faewpF9ZW8ZsXfNOHsfmNdUSvpGbF95b7IdoRYZB4pp03vznNhr99kec994/dley9Q9M9tjs12kg4e6e3zNicfR1f0xj6+j+zo6Olo9Z+yRnHNjtvX+YFem8fYOCEJK4YgRR0/YOaR4tyuw8bAnxOxInNldmKMdCxYscHrkwRXoSLGKN1uFXomj38yG2VZ82XbEkMeMDxcacFbHFA1qboOONneMGNLJxpEEq/CqFYoIOsJ8KLACnxVzf2lgJwadnBw9YrSUFT6g7JXcdnQ87XUo7OHo82Y0jom90Vt+B6OLbDGF9a0ja0lhb/t8oNGosMVe54mGZFK/zdwfRvYx2sAerEJHONJJgU5ONB7MqCmOajHaKjWheKhV2N0UFuW+urOCFY0RV86ZIAQjfD4wcuD3339X7Z/VwGdkCbGK/ybVzrFS7Pbt25VAcpcuXWKX21aH4rOJn0+u3WA60xjd7eh9T8PvYFELezCqYdGiRcq5wM4RhX3pMKMNwWgpdszYsTU7Wnw2MNqAEUNsm01of9g+fx3BbTGqidFidIBRaJzH2Rloi7CjZn3WmufBXMZBDUZt0eFotbe2bduWLPsgpfvsCE8/390Nrwk6New9u1PKvHnzlGOA15bVgcpzaQ9em1OmTFERZRTxZ8EjZyK/uW17hU5o3/jSc5jXgbXYQFJtjr1rydn7wLxvHLVxtgVu7EGhbwris4/DQQI6bumI5DlhJgozQqxRQqmBvWNiFqPi/WsPa5Qq2z1OjCpjcQcOmlOwnY5BFnpwFvP4OrqvQ0NDHbYlyTk3ZjvsS9ezIyRSSvB7WMGB1WfYUbUak87A6AuOiFobezY4HAHzJDRkOIrAcFZ2aO1NKYGjRNZt2Tql+FCyjkqx2hcdWWxwHZUnNjHTk+i4sUIjgA88e+lLrsIRDZ4bhhoz9NXe8UnM4UPnGkOHeXytMJLOTPdMrOIaH162KVasAOIo/JUVWaxh6xy9ZyqAvap19pgzZ0681/wst+HM53m89+zZk6CTwXB2/g4ex5SM6PEhx7Bu6/FiJ8C2xCy/h9eRbXQCDRJbo5VVDdkBdHTtWw0BEz746YBmVSeO2AdiOV8aJLwvzU61IAiOoWObz2um3DlbMTepDottu28b+UhHOTu+jp6hScF0Jj6X+Pyy1/Y50+GzxdW2nVWm2IbbG2ygg4X20KRJk1T5erMt5nOGnX06IaypezxufPbZHjdWBbOVAkgMVgmjI4wOIT73nKkM5+j5aT5zzOenvXPLfWYEjhVGfzFVjAOctmlo7t5nZ3H2+e4NaGc58+xODvxttJGt9iivb1aitIWde1YnpKODdgkjpijRwDS+pOD9xshFK7R3rBX1XCWlkYz24G9jOqN1MJBO42+//dbpbTh7H/C+p7POkV3qLHRCMd2NKXCM0qLTmu0HBxPYb+vTp49D57inj6e1PWZbyLbKXntsz8nKa5JVIc0IM2d+gxVuk7+f7ZS1nbl+/brqR5kV+eyRnHPDVEM6utzlMPYkEiklBARjx45N1ufoyWcnmo0ko63YELChcaYMcUrgg5ApdQxFZoPBcGN6xmkMMtrEjBDxFGxUGZbPNDU+2FhCmFoKznwnP0enGtOq+BmWC+ZDnSOlNNKsZaZTAsNi69Wrpxxl1DPgMaPhz5FrGv9JaSXRSLRXJpXnlw5MNu79+/dXZV35wGTagdnQ80HFEXMa73SKbd68WZVadhTWT2OU+lcsfUxDn8eCDw6rzldicNSFqQ/U5Tpx4oS6LvjQMtPZEoO/gQYq89h5TXEEjA60Tz75RB03U1eAxiLf4yg4DV0aDBw5SaoTxPPJjhmNPv4+PsBZjtY2DJqRZRMnTlT/GbFIx9PixYvVcbWF2+PIGa8jOpr4WzmaQ/0RPuBNQ4sPfp4LngPeH3yfRmliD21/xkwV8mZHQxD8BT572J6//PLLqiw2O0BMp6IBzo4qDXxi21bZg209ByzYcWJHge0jnzOmlpKVkSNHqmc2n4UDBgxQHRo+Q/ncTio6iCPr3C/q8rDtZtvGZzCfQYz64vbY7rmCmarEZyYH5hhhwg6IIwcBn438jUxLN1OkTGgLsfPKfbHaA3REmYN+VqcUjy1/C5+P5vOEjpoZM2bYjSpODOoKMaWI2+c2GZWVVCodHSPUDKPsAKO02TljeXc+X2g/EJ4nrscBDaZm0pnIqBrbFBlGiXNbfBZzH/i842AIbQ4OojBF0R377ArOPt8DDf5eRtswNZT3Ne2OCRMmJHB+8t7jeeU1y04+bVvabtQAYkTO6tWrE02Zp31D24a2FqN42LGnfWMrT+Hq/UinNa8x6tuxPUrpQDNtQrZHtN04z2hLOk/NvoozUUfO3ge0tSivwPvIapdSwiC50ia08di2caLTj+eKg7ls9xxFv3nyeJowwol2P2VB+vbtqwYc2H7S0cxIyDZt2ijbnseafQ5el+wz8NiZ0a7W9tAZuP+8xqgLRfuW2nQc9H333XdVKmFi/dnknBvalbwfUhrJmSp4W2ldEFzFUfUSW5ypvmdWLahdu7Yq6cvKd4MGDVIV5FJSfc923xxV2GAp0MaNG6uKL/x+Vpho3769qujlLI4q3CVWHYKVYlj+lNXtWL2PldDMsti2Fdms1T5MWHWIFcK4v6wyx4pprAZjlkZ1R/U9cznLGLPcKb+HZZFZmcJalc4RrGLDbZqlcK2wUgrLWrMMN487q/FZq+rxd7DcKithsIofq/GwTKujiiyzZ882+vTpo/aP26tfv76xadOmJPfRvF5YLpmlZVk9hlWRWOHl4MGD8dbl9/KatgcrRD355JOqKg2PE6vJsSKTtbIH4XXFc8195Pea5yCxc01YGr1s2bKq6gervbDSom31PfLvv/8ajz32mCo3nSVLFjW/du1au+eWJY5ZZZLHmPvMe48l0Vmu2oSVWmrUqKHK6XKfWb2F5+n8+fOJHldnSo+bmL/dVcxrNqmqObb7xPLpjuA1wIosgiA4DysisTocy8+znWA7VapUKeOZZ55JUM0sseflnj17VMlvtl1sc1jxipXo7NkNLMnOqlJ8frIsPKuw2WtL7FX3YlWwN954Q7XT/DyfQ7zv2bZZKwFzW7169Uqwn/a2OXjwYFXRidXRkmqX+Fxg2XLbKsEmfEZwG7SNTE6ePKmW2VY+s7b7PGY8diyHzipotvvpbLvM7bF6Gvfx8OHDDn+H+dkdO3YYjRo1Us9OVkCkLWJbee2nn35Slel4bdCeoJ3Hcur2jhUrIPJ5y23z+c9nnrW6Xkr2ObHqeyl5vpvPIy535rnjrB1theeTldCcwdH2Hdka9o4pK6Xxt5rPflZymzFjRjzbfOjQoeqat73PaXewwnXfvn0T3U9ey6wkze3z2qC9sWzZMoe2nu1xtGe7sood7Xjac6yQaG0Tklt9j9AGZeVPa1+F1yU/f/nyZafOk7P3AY8Lj3fhwoVVG8W2jp91VI05uZw6dSrJdRwdT0fXvL3jnFi7zwqEEyZMiD0utF95L7/44ouxdjj7DKx4x2PL4897kceBzwETV/bH7P/xfPI7uW9NmjSJ1+baq77n6rlhRT+2Ye+9957hD4Twj7cdY4IgpA7U2KAWEz3y9LYHOhyF5mg6R1g8AcOSGdXCyJ727du7/HmO6FGonyHN7tQlEvwPRh0yzZbRZhyhFwRB8BSMCGJEK1PSGXUhCIL/wUhH2vVm5TxBsMKIVUaAMZrKHyKlRFNKEISAhSGydPw4I1wqCN6EziiGhdNJKQiC4ElYQIT6SaYuiiAIvg3lNihfwMFQprMx3ZDpxdYCA4JgLQLBtHLKiPiDQ4qIppQgCAELdT8YFUbBS3fqPAiCu6E2Cx2oFHcVBEHwJNQ8ZAeXxUQEQfB9qJ9F7VtWWaOGFgui8B6mJpYg2MLoKF4b1PHyFyR9TxAEQRAEQRAEQRAEQUh1JH1PEARBEARBEARBEARBSHXEKSUIgiAIgiAIgiAIgiCkOuKUEgRBEARBEARBEARBEFIdUVS1ITo6GqdOnUKWLFmUkJwgCIIgCIIVwzBw7do1FChQAKGhwTm+J/aSIAiCIAjusJfEKWUDHVKFCxdO9OAKgiAIgiCwwk2wVvYUe0kQBEEQBHfYS+KUsoERUuaBY4nulHDjzg00+ryRml/RdQUyps2You0JPs6NG0Ajfb6xYgWQMUDO992bwJrH9XzdeUCaDN7eI0EQBJe4ASCmdcYKACltna9evaoGsEybIRhxp70kBBliLwmCIAQFV520l8QpZYOZskcDK6VGVtjtMOyP2K/meSIyhWdK0fYEHycsDNivzzd442UKkPN9Nwy4xm4cb4zMQJoA+V2CIAQNYQBiWmfQLHJXKxbMaf7utJeEIEPsJUEQhKAiJAl7SZxSgiAkTmg64IGv4uYFQRAEQRAEsZcEQRDcgDilBEFInNA0QPGn5CgJgiAIgiCIvSQIguBWgrNkjCAIgiAIgiAIgiAIguBVJFJKEITEiY4CLm3R89mrAaFUZxEEQRAEQRDEXgpcoqOjcfv2bW/vhiD4LGnTpkUYdQJTiDilBEFInOhbwJJaer5jBBAqQueCIAiCIAhiLwUudEYdPXpUOaYEQXBMtmzZkC9fvhQVfxGnlAfhiSmatWjsvBDg8BwXLRo3HzCEAJmKxs0LgiD4GWy5pBUTBB9B7CXBxzEMA6dPn1YRICxnHxoqijeCYO8+uXHjBs6ePate58+fH8lFnFIeJGPajDjW75gnv0LwJTJmBI4F4PlOkxFoE4C/SxCEoCEjAGnFBMFHEHtJ8HHu3r2rOtsFChRARl6vgiDYJUOGDOo/HVN58uRJdiqfuH0FQRAEQRAEQRAEAUBUVJQ6DuHh4XI8BCEJTMftnTt3kFzEKSUIgiAIgiAIQuDz77/A8uX6vyAkgcivCELq3Cd+45QaM2YMatasiSxZsqjQsLZt22L//v0J8hqHDx+uQi0ZStaoUSPs3r3ba/t8885N1JxWU02cFwKcmzeBmjX1xPlAIeoWopa3xd1lbdW8IAiCv8EWuWbMFECtsyD4J96yl2bM0NqfDz6o//O1O6GN9FdbPYm9JAiCEHhOqZUrV6JXr15Yv349/vjjD5Xr27x5c1y/fj12nfHjx+P999/Hxx9/jI0bNyoV+GbNmuHatWte2edoIxqbTm1SE+eFAIfVOTZt0lOAVOo4fBgYOCAKYacXIc2ZRRjwShROnUr6c7wtuZ7l9hQEQfAabJE3xUyB0ToLgh+TGvaSNSLq8mXg66+B7t3jvo//X3zRvRFTRhTw7yI9cV4QUhkGaLzwwgvIkSOHil7Ztm2b18/B559/rqqzpTYMVKlSpUqKtrFixQp1HC+zDfGx3xdo+I3Q+W+//Rbv9axZs1TE1ObNm9GgQQN1E06aNAlDhw7Fo48+qtb54osvkDdvXsydOxcv8sEjCEKSGAbw11/AxInAjz8CYaHhuNLgM/Xe53+FY/JUbce9/jpwzz3AlStA1qxApkz68/v2AT/8oG1AttFt2wL33ScHXhAEQRCEVIARUC+8kLTDi7pBhw4BhQq553tDw4Fan8XNC4IX+st0ktCZUqJECeTKlcst22X2ER087GsnRrFixdCvXz81CUJAOqVsucKeMKA8weTo0aM4c+aMip4ySZcuHRo2bIi1a9eKU0oQnGDBAmD0aGDr1rhld6PSYs767qqC890oPX34ITB1KlCjBlCxIpA3L9C0qXZOTZvGCgxAnjzAuXPA/PnAgAHaacXIKVsnliAIgiAIgltg5JM9h1SBAjqE20poKFCqlPsOfGhaoFR3921PEFzk8OHDyJ8/P+rUqePXx+727dsiMh9k+E36nhVGRb3yyiuoV68eKlSooJbRIUUYGWWFr8337BEZGYmrV6/GmwQhGOHgR4cO8R1StOHGjNHRT+vXAxz4MCvjRkYCa9YAn34KjBgBNGmiZRpmzwZWrQJ+/x3Ys0dHz3/+OSMXgTffBN59F/joI71NQRAEwTd1OgXBLzlwwH6E1Jw5wPTpgLVceZo0eqRMEAKArl274uWXX8bx48dVyhmjlsy+bp8+fVRbnz59etV/psyNrUxOrVq1VEAHnVqvv/66ksoxt8v3P/jgA7VdTseOHbMbTfXPP/+gf//+setZWbJkCcqWLYvMmTOjZcuWOH36dLx953OIzyZqQ997771q+cmTJ9GpUydkz54dOXPmRJs2beJ9NyPCuN+ZMmVSKXR169ZV+2Bl9uzZ6lhkzZoVjz/+eDxZH2eOjS2MRCtSpIiqONeuXTtcuHDBqfMjBKBTqnfv3tixYwe+Zn64DbY3AB1YiSnC8+LnRWpOhQsX9sg+C4Iv8913wCuvxL2uVk3bb2z3mzWNxjef7cbsj3fj7H/R6NULqF9fDzBauX0bOH9ep+1xMJL9GzqyVqzQTqvhw7WjijpVdHwxgko0pwRBEHxTp1MQ/BJ7zlU6ohgR9dxz2rD580+gbl1tuFDyw10D0tSPvbxbT6IlG5BE3Y5SE/uXJtFR0WpZ9N1ot6/rCnQavf322yhUqJBy+JjOlVdffRXfffedkrXZsmULSpUqhRYtWuDixYuxjp+HHnpIDVRs374dU6ZMwYwZM/DOO+/EbveBBx5A9+7d1XY52esvf//99+q7uQ/meiY3btzAhAkTlIPor7/+Uo6zgQMHxvv80qVLsXfvXvVM+vnnn9VnGjdurJxY/Mzq1atjHVqMpOJzi44sZkXRL7Bu3Tqlp2Xt9zNy7IcfflDb48Rn39ixY2PfT+rY2PL333+jW7du6Nmzp9Lr4v6Zx0lIIYaf0bt3b6NQoULGkSNH4i0/fPgw72Jjy5Yt8ZY/8sgjxjPPPONwe7du3TKuXLkSO504cUJth/MpJSIywsBwqInzQoATEcFHiZ447yesXm0Y6dLF7XrNmobRr59hzJplGNOnG8bj7SMMYw7U9HDLCKNdO8OYNs0wOnc2jMqVDaN0acPIm9cwsmc3jMyZ47aT2JQnj2FUqmQY8+cbxs2b+nCdPOlXh00QBD+CTYuy9mPmUwptBHfZCr7A2bNn1e9ZuXJl0B4DIQDsJW6rcGG93ZAQ/T8sTBsztpw9axiFCul1aNhER6f8++/E2UtqXvBbbt68aezZs0f9t7J82HI1RUZExi47tvKYWrZv0b546658Z6VafvNS3DZOrDuhlu1esDveuqvHrVbLI/6Lu25Objrp8n5PnDjRKFq0aOzriIgII23atMacOXNil92+fdsoUKCAMX78ePV6yJAhRpkyZYxoyz0wefJkI3PmzEZUVJR63bBhQ6Nv375Jfj+/m/tgZdasWepZcejQoXjbz8vOQwxdunRRryMj447rjBkzEuwX38+QIYOxZMkS48KFC2q7K1assLsvw4YNMzJmzGhcvXo1dtmgQYOM+++/3+ljs3z5cvUdly5dUq+feOIJo2XLlvG+p1OnTkbWrFmNYOamg/vFFVvBbzSlaEsyJHHhwoUqVK948eLx3udrVtujd7Vq1apqGb2o9IiOGzfO4XYZpsjJU+TK6B6BOcFPcJOgoKcxtZ2o/fTIIzoVjxQsqKsk79oFMPqVEVM3bwHXbudCSChQqxZw5Trw0ENAvXrAokXApUtA9uxAmzZa0JzbOnEC2LAB2LIFWLsW4GBNTBSwgt/LqUsXgBm4jDDm9+bMKcLogiB4Bv9onX1Dp1MQ/NJeor1PA4QGxR9/MARER0jZEzLPnVuHiTP0e+FCYOhQoFkzoHTplAmfp5OWRvAdGCl0584dldZmkjZtWpXyxqgkwv+MhLJGGHH9iIgI/PvvvypVLaUw1a1kyZKxr5kieJYdAQsVK1aMpyPFYmaHDh1SaeZWbt26pX4Xo3uZ9sfIpmbNmqFp06bo2LGj2rYJ0/asn7d+rzPHxhYuZ8qeFR4724Jsguv4jVOKYeasordo0SJ1cZk6UUy5y5Ahg7qRqPQ/evRolC5dWk2c503w5JNPemWfM4VnwrlB57zy3YIXoHI3lb19HLM63vHjwNy5cXIKdAjVrq01o+hkYlR748ZMic2EEbvOKRuNDinacRQqp94Uo3dthcvp46UNyMm89egE++Yb4KuvdOqeWVn15k3tsOLEbdAJRimIvn1FCF0QBPfB5sn3W2ff0em0B7U3OJmIBqfgU/bS0aPA+PF6/v33tXOJU2JwpI2VW3r00AKanKhN8NlnOtXPVdJkAh6TliaQqT+kvvofmjZOw6Jw3cIoVLsQQkLjy8XUHVQ3wboFahZA/mr5E6xbu1/tBOvmq5IvxftrpgMmJm9jT+rG0eeSC509Vrhda6oioS6UlejoaFSvXh1zqCdiQ252RgDMmjVLaULRKfTNN9/gjTfeUAEqtdmhcfC93G5ivzEx6R/bfRaCUFOK+a0cyaOIGr2c5sQL0JoXSscU8zxr1KihcmR///33BB5WQQhW6ByiY+jvv7WTyHRIcVChZUvtjKpcGbjnHqBsWaBKFeCxx7Qjin5g/mdElPnc4H86p5KqpMf3u3UDfvpJR2FxQKFr1zjRdMJ9oSg6bcKJE5l/7sEDIQiCICSp02lFNDgFn4Zlfuk0ZdUVm0iGRGHot7UDyg7riy/qKn6CYENYeJiarE6L0LBQtSw0Tajb100p1Ehi9BH1mEwYHbRp0yYlOk7KlSunKtVbHS58zf5zQaZQAGobUVFRSX6fs+s5Q7Vq1XDw4EElQs7fYZ0YlGLCDKnBgwerfebACoNY3HVsbOGxohajFdvXQoA7pSxyEPEmhu2Z8EYePny4ElZjaB9T9xIb9ROEYHBCUXQ8IkJHRn37rY5KolPK1PCjY4ii42+8oaObGNVqdT4xJa93b6BPH/2fr5MLt8fnW4sWHN3Qoud0etGxZcJ9ZZU+pvSNGhUXVSUIgiC4F8oi/Pjjj1i+fLkSqE0MGv0cHDSnE0yTEgRfgKl6TMGjoPkHH8R3MiXFoUNa3coKO9VcLgh+DqOPXnrpJQwaNEhFE+3Zs0cJllNE/LmYaEAGc7A95/Ng3759Kitp2LBhKoI2NKaqEdPgKPLNynfnz5+PjTayhetRlJyBIVwvJTz11FPIlSuXqri3atUqHD16VPXt+/btq9IK+ZrPJQqcs+IeA1EOHDjg0KGUnGNjixmVNX78ePVdH3/8saTuBVv6nj9y885NtJrTSs3/+tSvyJA2g7d3SfAkzEVrpc83fv0VyJDB62l633+vI9rpnKIziZFQ//0HmAUxmLr9wgscZdAOI3vpeIi6hUzbnlPpL6g9A0B6t+1jvnwAi1ZQm2rnTmDVKu08I4zsp6OMEhGvvw6wSIcl1VwQBMFpbgKIaZ3xK4BgfxonpdPpDQ1OIYhwp71EI+f55/U8R87Kl3ft80zxY8fb2sk2q/W5StQtYH1MZ5b2Upj77CVBSC6sNkcnUufOnXHt2jWVTbRkyRJkZ6dA6ckWxOLFi5VzpnLlykpbkE4ZpsKZsFJely5dVKTQzZs3lUOIDihbWHnvxRdfVPpRTPdOSbobJXjo4Hrttdfw6KOPqn3nvjZp0gT33HOP2g860Vg578KFCyqDipG//H53HRtbmBY4ffp05bRjIAx1rHicRo4cmezfKWhCqHYeMy/EaCQwJJCjgLzgU8L129eReUxmNR8xOEJpTAkBDD0/mTPHhfskldPmQTg4MWKEjkTiLlGMPFs2jjoAzzwDXLum12NK3aBBSUQ/3b0OzI/5XR0jtGaCh4TX6QxbvBiYMEFHdFlbpxIlgKlTgaZN3f71giAEONcBxLRiiIjRmPIVW8EbcGTc1OksU6ZM7HJTpzMYjoEQAPbSjBlA9+5xxgL1oV5+OXnb4Qid6ZhitBXDw10lFewlIXVgxg0dL3TYp08vzkVBSO794qytIJFSghAAmE4dRpwfOaIdOgcPam0oThzMoM22YEGcQ6pDB22/JWkLhoYD1SbGzXsA7oO5H9wv9pHoLKP+FNMPCX8XC+Mw3Y/RVfxd8SK6BEEQBKd1Ogl1Oq1QNNYqiyAIPgs1n+hIso5e9e+v9aRcrZ7HVB3qCjRsqI0Nq+ClK6SCvSQIghCIiFNKEPwcpul9+SVw4IAe5KM4OaNOqQvFTIty5bRjh/qfpvYftf+dckiR0LTAff2Q2pWiGTnPioDUlLJqYLGC848/6kqBdFLRiZUSnStBEIRgQ4LkBb+HI2+2ujamFpSrTinCzzDqavBgYN68uJRAV/CCvSQIghAI+I3QuSAI9iOk6KTZvFlLIpiC5q1bA0OHaqkECpfTucPiEuaA4vDhWs/JV2EEFPWt6Jxi1NT997MKR1y0/507Wn+KelMURb961dt7LAiCIAhCqkEDx5bkakGZdOqk/7MUMEsOC4IgCKmCOKUEwY9hyh6dTnTiMNr8wQe1I4fpvNaqeVy2bZv+jLncaYxoIOKYnjifCjCCq21bXamP0V6MqKfEAzWlqMdrFtahU44piYwOY/QUnXKMCuNyQRAEQRAClDx54guk0yFFIyE5UVImNDAYhs0ILJYrdhUv2EuCIAiBgKTvCYIfQ2cUnTYUMmcUEQXOmbbH5aZzh9HsjCYyYdqeS1Xsom4CPxZPdeFOOs9sqwHS2fTPP8CWLVo3i/Nm8Z02bYD8+YG6dXVUFWUlJK1PEARBEAKQFSt0FT+OujHdjmHVKXFImTz+OLB+PfD1166LpnvJXhIEQfB3xCnlYTKmTaZYouCfJFccM5nQUVOvnpZQuHFD22R0zli1olil9L//9DwdNdRhcpkw71zHVgF08zV/A1MVS5bUTri//tLSEuT0aR05tWyZXj5rlk5TtFb3E2F0QQhO5GksCAFkL33/vf7P6idNmsBtdOyoBdPXrQOOHdOVYvzAXhIEQfBnxCnlQTKFZ8L1IZJHFDSYoTypDAcJGzfWUef8b3W6UAR90iQ9z5S+995LxhdwpK+T71zHthFUtGsZsf/aa3HaUhRF/+037bhicSnalBR3z5ZNpwVKBJUgBBdsFn2nFROEICel9hJDwH/4Qc8/+ijcCkOuaThQV+qbb7Rx4af2kiAIgr8gTilB8HOuXdMpfCVKxHdIUdS8b1/g7l39+tVXteMqELCNoOrcWUdN0YbcuRM4d04vZ/TY4sV6nhFT1KjiwOfo0UCOHN7Zd0EQBEEQUgCjmBgCzpEmOpDczRNPaIOCaYGuOKUEQRCEZCFOKUEIAKcUYSSQlV9/BX7/Xc8XKRLYdhUdVO3bA2nTAjVqAJcva8HzJUt0pT7CQjqcWKmQaX0PPADUqQNUrgzUrBlfQN0RjtIAJT1QEARBEFIJM3Xvf/9zUSTTSRh91bOnrhDDkHMJrxYEQfAoUn3Pg9y6ewut57ZWE+eFAOfWLaB1az1x3otOKUZJDRsW93rChBTIN0RFAn931xPnfRSzqiCjw95/H/jpJ601xdORPXv8dW/fBlauBMaM0ZqmTPOjo4lFd7p2BcaOBRYuBHbvjjuVtEs/+kgLxfM/Xye2XBAE34G3ceuYSZ7GguDH9hINHNMpRZFJT5AzJ9CihZ5ntFSA2UuC4As0atQI/fr1S5XvmjFjBpo3bx5v2fDhw5E3b16EhITgBzMd2IdYs2YNKlasiLRp06IttUec5NixY+o3bTPLrqeQgQMHog9LuXsYiZTyIFHRUVh8cHHsvBDgUOPAzBXjfCpAW86MBGL1PRPuxqZNer5KFR1FlGyMu8Dh6Xq+OgWq0sFf0vqKFtUOOT5rWKGPKX6MdNq+Xc/bOvf+/ltPVhg9xYI+jMLiRA0vLmMFwA4dgKVLtZZV3rxacH7+fGDAgDjJDBFYFwTvwxZ5sWVeEAQ/tZfY0WLp3QwZ4hxHnoAjVr/8oqvwcZQvqVBqP7OXBCFYiIyMxFtvvYV5Fgfz3r17MWLECCxcuBC1a9dGdtvR62TStWtXXL582S1OrldeeQVVqlTBr7/+iszWTl4SFC5cGKdPn0YudljcwKuvvoqSJUuif//+KO5BHRhxSgmCH2NGSTEKKk2auEHE4cPj1uG8M7aUQ0LSApXeiZv3MxhBxarOVucQU/uGDAFOnNDzly7p9+0N2PJ4cj2T/fvj5r/9Vut5MUrtnnv0fzq86LzKnRvYsQOIjtZaVhzQlQwAQRAEQUgBZpRUq1aerXjMUsasEHPgADBtGvDQQ3qEKoDtJUEIRL777jvl1Klfv37sssOHD6v/bdq0UVFFvsjhw4fRo0cPFEqq3bFw+/ZthIeHIx+FdN1Enjx5VJTZp59+inHjxsFTSPqeIPgx9lL3bKOkHnkkhV8SFg5UGKonzvshdEQVKBAXRUXnFB1EVasC3boBjOhlZOyoUbqCYYUKQMWKWhydgycckHVEZKSOujpyRDukeOzp8KIjbO5cHUlFG5pVEM+e1Z9hBBWdYV4o1igIgiAI/u+UcnfVPVtoWNEYIC++qEOvZ8wIeHtJ8G+io6OV46BUqVJIly4dihQpglE0bmN47bXXcO+99yJjxowoUaIE3nzzTdwxUy5iUtoYnTN16lQVccP1OnTooKJ/EmPPnj146KGHlPOHKXGdO3fGeUtKwvXr1/HMM8+o9/Pnz4/37JQDZ3RP69atkSFDBhWRM3fuXBQrVgyTzDLi4ADyFbzwwgvKUXLPPffgwQcfxHYa34nACKlHLJ0h/sb/UY+OjpDQ0FinFI/d22+/rZxAPHY8Dr+xlLeFnTt3qu/kPubMmVPtS0REROx2v/jiCyxatEhtk9OKFSscRm8xJY6/I3369KhXrx42btwYL/3uwoUL6Natm5r//PPP7W6Hx+edd95REVpZs2ZF9+7dE6TvXbp0CU899RRy586t9rt06dKYRXHdGE6ePIlOnTqpaDH+JjrquA0rPH5fM2rUg0iklCAEkFPKNkrK2YjzYIPOKTqhFi0CLlwAypfXzjs+V9iGM+KV2qmsXMjMAuqdkgULdNYAI6o4QMvnLQdRuQ1buA4n8z0KrNNhWK6cTrXkwAedXtwPiaASBEEQhCSgaOOePTocmXpUnuTff/WD24Rhz3ROMWXQhcgFIcBIbDQxLExH1zmzbmho/BFPR+taNSmcYPDgwZg2bRomTpyoHB109OyziJ1myZJFOTgKFCigHCx0YnAZU7RMDh06hPnz5+Onn37C1atX8dxzz6FXr16YM2eO3e/kdzRs2FBt6/3338fNmzeV86tjx45YtmyZWmfQoEFYvny5SpdjFM+QIUOwefNm5fgxodOKjiw6cqijxPS1s+ZorurjGMpplSNHDixevFg5Yeg8a9KkCQ4cOKCW22PVqlXKKWPVSKIz59lnn1X7bvLBBx8oZxm3WbVqVcycOVM5Y3bv3q0cOTdu3EDLli1Vuh8dSNy3559/Hr1791bHlNtlWiCPmen0cbRPPN6M4KITq2jRohg/fjxatGihjr2ZflemTBnlJKPDiL/VEe+++65yLr7xxht23+d7dBoyDZApffwOniPC39S4cWMVRfbXX38hTZo0ysnF37ljxw4VdUVq1aqFEydO4J9//lH76xEMIR5XrlwxeFj4P6VEREYYGA41cV4IcCIi6BPSE+dTgVWrDGPqVMPYsEG//vnnuF2oUsUwoqPd8CXcyM2zenLLBn0HnqaTJ+NOF/+PGWMYL79sGG+9ZRj9+hnG2LHx37eub3L6tGH88othTJ5sGH36GEa9eoaRPn3cubA3hYQYRp48htG4sWFs3pz49gVBSOG9Tms2ZorwMVvBX5FjIKS6vTR6tP5My5aeP/jLltl/eC9fHpT2UrBx8+ZNY8+ePep/PBIz7B56KP66GTM6Xrdhw/jr5splfz0XuHr1qpEuXTpj2rRpTn9m/PjxRvXq1WNfDxs2zAgLCzNOnDgRu+zXX381QkNDjdM0du3w5ptvGs2bN4+3jJ/nM3L//v3GtWvXjPDwcGPevHmx71+4cMHIkCGD0bdvX/V67969av2NGzfGrnPw4EG1bOLEier10qVLjXvuuce4detWvO8qWbKkMZWdITtcunRJbeOvv/6Kt3zhwoVquZUCBQoYo0aNiresZs2aRs+ePdX8Z599ZmTPnt2IsLRZv/zyizo2Z86cUa+7dOlitGnTxkgMfj5t2rTGnDlzYpfdvn1bfT/Ph0nWrFmNWbNmJbqtokWLGm3bto237OjRo+q3bd26Vb3+3//+Zzz77LN2Pz9jxgyjTJkyRrSlvYqMjFTnZsmSJQme9ytWrHDtfnHBVpBIKUEIkEgpj0VJRd0Avs+j5ztGAGlcG7XxZWyF0a0RVNSZYiQTZSXMdWzXN+F6lJuwsncv8OWX+j8Hes6d00LoJjxfXM6penUgf36dYshKgJUr68wEiaASBEEQhBg8XXXPSunSOpqFEVLWSJhSpYLSXhJ8H0bpMC2MkUOOWLBggUqHY7QM087u3r2r0uCsMOXPqmP0wAMPqNS2/fv329UqYsQTo6DsiXFTF4lROdQ64nZMGEHESCATbptROtWqVYtdxhREqwA5v4f7zBQzK9y+qRFlixkRxBS5xGB006lTp1C3bt14y/naTA/k8a1cuTIyWToCfN88NkxbdAbuK1Mmrd+VNm1aFY3E73CVGjVqJPr+Sy+9hMceewxbtmxR2lCs5FenTp3YY8prgdFyVm7duhXvmDLtz4ys8hTilBKEAHFK2WpJ0ZkiuA4dQYULp7xqXtmyACNprdthBUAu270bOH5cO75MGEHMidkCP/+sz+eHHwKW57MgCIIgBCd8aNLI4Whbahg47JR/9hnQvbseReL3Tp0qqXvBTox+kF3otLRiST1LAB2eVmw0fJKD6ThwxPr16/H444+rqnNMFWNKGPWW7Ok7WTE1lxwJgtMpQ40meyLY1I86ePBgkvuuYtCSWM7v4fbs6TRly5bN7ufpwOJ+U1fJGWx/I7/fXGadT+pziWH+psS+yxWsTjJ7tGrVSqXd/fLLL/jzzz+V05LpmBMmTFDHtHr16nZTM6lBZXLx4sUEy9yNOKU8SKbwTDCG2b/JhACEjYKDRtXTTikOTowY4SEtKY70PRlc17GjiKiUboeVVN98U0disX2ntiTP0/LlwNatcetxIGLNGh1BRTF2VqZu2VJHUbljvwQh2OBtE1ytmCAEmL1kCv3WrAk4GZGQYp57TpfW7dhRl9RlZZTECEJ7KehwxQjz1LoOoO4RHVNLly5VWke2rFmzRukBDR06NHYZnRW2HD9+XEUNUXeKrFu3TgmCUyDdHoxuoj4SdZoY7WQLI54YCUSnGKOwCJ1E1IGiFhW57777VNTW1q1blZOEMILHKrDO7zlz5oz6Dn6XM1ATqVy5ckpTiVFCjmC0GH/v6tWr0aBBg9jla9euVRFMhNuhBhRF201HEI+p9djw+6IoRpsIPB5cj9/15JNPqmV37tzBpk2b0K9fP3gCOpMohs6J+lHU+KJTisf0m2++iRWOd8SuXbvUOSxPEV4PIdX3BMFPoeOC7R6dGqtWATFFGyRKyg8isXr3Bvr2BUaOBDhAxfNHG4H6qbbPfDqrXntNn1dOb7+tUwEFQRAEIShg1TuOthEaO0lVwXMnDz+shdUZ9cIyu4LgozBFjQLjFNH+8ssvVfoVHUEzYu4XOkPocGJ0FN/78MMPlfC4ve106dJFpa1RJJxV4ihabi91jzDqhpE0TzzxBDZs2IAjR47g999/V5Xj6KBhWh/F0ukIocOMDg46R+jMMaFTqmnTpqqaHbdB5xTn6WQzo4f4PlMAmX62ZMkSVSGOTiMKfNOh4whGhdEBlBTcP0Z70UnDdLzXX39dVbDrS4MdUGLp5rHhb2DK4ssvv6wqDZqpe3SWUSCcn6dou7WyoQkdWkyp4/exuh8dZt27d1epcTxO7uatt95SFQHp5KNo+88//4yyTOeI+U0UP2fFPZ7ro0ePYuXKleo3/8tiDzHwPTqzkorGSwnilBIEP8WMkmIVODoqTKTinu/DARYOQFm1qp5+GmjcWEtl0EHFZ2CJEnGf4aAyNal4fmkXUDJgyhTgv/+89jMEQRAEwbOwY/TCC/EfhqyCZ+kweRR2whidRTiCJAg+DCutDRgwQDki6Hhg5Tazgh0dD/3791fV4lj1jg4drm8LnVePPvooHnroIRVdVKFCBXzyyScOv5MRRowYogOKDiCuT6cG0wNNxxMrxDECidXs6FxiZUAzIsqEjjQ6d7heu3btYisDmnpQdE6x6h7fp8OL0UlMR6RzKjE9J26Hn7tCPY1EoPONx45TxYoVlcPoxx9/VBFoJGPGjMoZRgdczZo10b59e5UK9/HHH8f7LmplUeeJ0Uk8LvYYO3as0nmiQ4vRSocOHVLbtmpouQtGZbEqY6VKldSxCwsLU45J8zex6h4j2HjOec3w2FKLyxo59fXXX6vf5klCqHbu0W/wMyh0xpuIF25iYWzOcOvuLXRe2FnNz243G+nTJC6yJvg5t24BnfX5xuzZ8cvCegA6KFhp9dSpuNQ9RtJs2eLG1D0SFQlsey3mC8YBYencuHHBCisCWzWoeG4HDtQaVBygtSdlwHNdu7YWaKfMhkU3UhCEGG4BiGmdMZsjwT5kK/grcgyEVLGXmN/+4IP2lzdqlDongeHK48frdL7p0x2vJ/ZSwEChZ0aNFC9ePEmR7EBi+PDh+OGHH1SEkLdhpE7hwoVjdZBSAiO9qlatqpwzgmtQi4pRXYwAs5eemdT94qytIJFSHiQqOgoL9ixQE+eFAIe5dAsW6CmJfGJ3RUrRpcyvM3nrLTc7pIhxF9j/gZ44L6RaBBWdU5UqAc2aaecUn8lM77PqDPIaWLdO28xMDeTUv7/Wrbp6NaHTi44u/heEYIIt8oKYSZ7GguBH9hKjFGwNm6Sq4Lmb+vWdi5QSe0kQks2yZctUZBKdG4ziYhQU0+GsGk/JhZFa9qoDCklDDa1Zs2Y5dEi5CxE6FwQ/hU6pAwd0FA2h9pxHCtKEpAXKD4mbF1INOqcYAUUHEwuH0DnVtKk+96NGsawscOFCfOfT/v16mjRJZx3Qlu7USTuzmFJPzUgWKeF26cASBEEQBJ+FVfD4AOODzXRIpXYVPJZup2OMRhdz5h2lCom9JAjJhvpLQ4YMUZpUTNurU6eOqgpHge2UQoF36j8JyYsySw3EKSUIfgodE7/+Gvd6yJCEFWbdQlg4UHmUBzYsOAMdR4ULJ0zrY1W+GjV0Sh8rCbPibmRk/KrCN28Cv/+uJ8Ko2aJFdaQV7epXXtF2PW1t29RBQRAEQfA6FAo+cULPz5ypR2dS0yFFqPNSoQKwc6cujfvoo/bXE3tJCID0PU7egHpUnITgRJxSguCnsCrb3r16vmRJXbFYCEzoJLI6iug4ypEDOH9en/t06QBWrGWE86efaocTnVPUgbUW/mBEFW1qwqgpTtSjog4VnVocjMqVS6KoBEEQBB9hxw5dbpghvl26eGj0zQnq1dMPUKbwOXJKCYIgCMlCnFKC4IfYakm9/jrgsVRfflnUDT0fltEDolVCStL6zpzRkU9M3WREVeXK2ln1yCPA8ePalg8PB779Vq9rcvs2wAq6nHhKORDMKKqCBYG7d7UulURMCYIgCF7FrF71wAPec0gR5sKz5G1iulJiLwmCICQLcUoJgh+yYQOwfbueZxT7M8948MvokJofIw7YMQJII7ldvprWR6zOKspe0FnFdXv2ZLldXZ2RmRBHj+r0PtOOvnhRT4zAY1XHjRt1Fe46dfR6ktYnCIIgpDpr18bpOnkTU+ycD0nqJ2TJknAdsZcEQRCShTilBMEPGTMmbn7QIB0JIwQftml9iTmruHzo0Ljl1IpdsQJYuBCYP18LoJswuurHH/XElD5WBGQBpKeeAjp0kAgqQRAEIZUjpThC4k04Ashw4n/+Adav19pWgiAIglsQp5QHyZg2IyIGR8TOCwFOxoxaddqc9xDU/vnpJz1P58Lzz8OzMGWPEVLmvOCXzip7y1u21FO/fsBnn+kBYNrbTPuLjtbrUJOKyzj9+ae+3iiyfv/9wMMP68FjSfMTfB22XBGWeUEQ/MBeYlgvxRE5ikLhRG/DBx4fhkzhs+eUEntJEAQhWXgxOTvwCQkJQabwTGrivBDg8BybvX4Pnu+xY+McBhQ396D/S8PfwpQ9TnIdByRlywLvvAPMnQvs2qU1qT75RFfhzpAh/rpRUVqHavJkoFUr3U9guqAg+DJskTPFTPI01vz111/43//+hwIFCigb5YcffvDyWRKCBmftJTNKqkoV3xj9MFP4HOlKib0kCIKQLCRSShD8CEawUBeI0Fnw9NPe3iMhULBGUfE/dcqY6nfunNacYt/g7FmtL8XqfiZ79mgdq3bttGOrXDmv/QRBEFzg+vXrqFy5Mp599lk89thjcuwE39WT8nbqnq1T6u+/dbUQ0U4QBEFwCxIp5UEi70ai6w9d1cR5IcCJjAS6dtUT5z3Au+/qymikcWOt9eNxom4D24fqifNCUFX4y5NH292sgE39qS++AJo31yl81igqvlehAtCpE7BuHTu83tx7QYgPW+SuMZM8jTWtWrXCO++8g0elvL3gq/aSr4icm1CcMWdOPTrDqiG2iL0keBHDMNC0aVO0aNEiwXuffPIJsmbNiuMc3RYEH0ScUh7kbvRdfLH9CzVxXghw6C1ij52T6TlyI4xOmT5dz6dLBzRpYr/4i9sx7gC7R+uJ80LQQPu7d2+gTx/9nxkU9eoBjRoB1aoBjz+uU/x4PRJGVFE0vWFDHWm1d6+3f4EgaNgifxEzydNYEPzAXqLm1LZtvhUpxfQ8PgQdpfCJvSR4EaZhz5o1C3///TemTp0au/zo0aN47bXX8MEHH6BIkSJyjgSfRJxSguAnjBsH3Lql5xs0ADJnTiWnVEgaoExfPXFeCLqIKUbkWVP7mKrHanyMomJFvhkzdFU+M5OB4ujff6+jq8xBOUZOnTolEVSC4K9ERkbi6tWr8SaPQoHr5cv1fyH42LhRixiy6h1LyvoKZgrf6tUJ3xN7SfAyhQsXVs6ngQMHKmcUo6eee+45NGnSBF0ZmWjh2LFjypE1f/581K9fHxkyZEDNmjVx4MABbNy4ETVq1EDmzJnRsmVLnKOWQwx8r1mzZsiVK5eKvmrYsCG2WCIHV6xYgfDwcKyyOG7fe+89tf7p06cd7vt3332H8uXLI126dChWrJj6jBUuGz16NLp164YsWbIoB9tnrNKTCNHR0Rg3bhxKlSqltsvPjBo1KvZ9OuvuvfdeZMyYESVKlMCbb76JOzRiYxg+fDiqVKminHw8tlyvQ4cOuGwtWS24BelhCoIfcPKkFp4m6dProi8sRuNxkXMSlg6oPikVvkjwpwgq9hGoOcUKkHRUMXrqnnu0nb5/v15v3z4dXTV0qHZU8RmeLZtOC+Q2BEHwH8aMGYMRI0akzpfxgffyy7qqR2ioLg/63HP6PTqpWIaWnnE6LITAxBQ595XUPRMzUooPO/P6NBF7KfC5ez2u0qIp0s+0TUbJ0SnJayDBuhmAkJjrJPoOEH0bCAkDwtInvW5oWpd3sUuXLli4cGGsXuCuXbvU5Ihhw4Zh0qRJymFDh88TTzyBe+65Rzm36ITp2LEj3nrrLUyZMkWtf+3aNfUdH374oXpN59FDDz2EgwcPKmdRo0aN0K9fP3Tu3Bnbt29Xzq+hQ4fi66+/Rv78+e3uw+bNm9X30AnUqVMnrF27Fj179kTOnDnjOdP4XSNHjsSQIUOwYMECvPTSS2jQoAHuc2BUDh48GNOmTcPEiRNRr1495RTbR+M0Bu7v559/rgp+7Ny5E927d1fLXn311dh1Dh06pBx3P/30kxqMoZOvV69emDNnjsvnRkgEQ4jHlStXDB4W/k8pEZERBoZDTZwXApyICGYv6YnzbuSll+I2/eKLhjF1qmF8841bv0IQUgQv+TFjDGPAAMN48knDCA+Pu2Y5lShhGEOG6PfHjnX7LSIIiV+fHC6OmSJ8zFbwBfhbFi5cmOg6t27dUr/XnE6cOOGZY3DihGGEhMRvQPh6/nzD+PBDwwgN1cv4f/p093634Dv2UqtW+v0PPvCts3L7tmFkzKj3bdcub++N4CFu3rxp7NmzR/2Pxxzo6ebZuGU739HL1j8ff915GfXya0fjlu2dqJetfjL+ugty6eWXLNfUwc+Svf///fefkTt3biM0NNT4/vvv7a5z9OhR1YZPt7SjX3/9tVq2dOnS2GVjxowxypQp4/C77t69a2TJksX46aefYpdFRkYaVatWNTp27GiUL1/eeP55m2Njw5NPPmk0a9Ys3rJBgwYZ5cqVi31dtGhR4+mnn459HR0dbeTJk8eYMmWK3W1evXrVSJcunTFt2jTDWcaPH29Ur1499vWwYcOMsLAw9bwz+fXXX9VxPX36tNPbDdr7xXDeXpL0PUHwcY4ejdOSYsoetXpIqqTuCYKLwui5c+soqoEDgfvvj3v/yBGAg2wnTuiUPkZZCYLgPzD1gaPn1skjMApK+cks8HXHjlrgjtEphP9ffDEuvU/S/QIHnltWzPDFSKm0aYHatR2n8AmCD5AnTx688MILKFu2LNq1a4cePXqoVDxzslKpUqXY+bx586r/FStWjLfsLMsvx8B5bo9pb0zf4xQRERFPRJ3pe1999ZVKybt586aKxEqMvXv3oq7Nvc7XjL6KYhqvnX1l6mG+fPni7ZvtNpl2ztRFRzDaihFU3A6PC9P3bMXgGUFWyBKV+8ADD6i0wP1mWoDgFiR9TxB8nJEjdeoT6ddP20Op6pRiSPH8mAdYxwggTYy4kCAkkdbHa3fCBJ2+Ry3bS5d0lT46q9av19IcTEG1pgEKgpA6sBPBtAQT6o9s27YNOXLk8K4YLtPymBJlOp8I02RY9ez8+fjrsrPCsrQlSwL9+9tP9xP8D1bJYL43HxCWTqjPwIfXsmXAt98CrVvHpZGKvRT40A420/dMyg4C7uuXUHf1sbNxKXkm9/YCSnXX6XtW2hxLuG6J+BpQrpImTRo1kbffflvpTNkjrdmxiHH02FtGJ4wJ0+moMUVHU9GiRdWABR01t2/Hr9DNFDxy8eJFNWVKxMhjwK753dZlie2rvX2zQo2sxFi/fj0ef/xxlZbOioV0rs2bNy+BlpUt5n7a7q+QMsQpJQg+zIEDujgNoRbPgAFxVYglUkrwRWhzWO0OpuWXKaP7hxcuaAcr7RSKp+/cCfzzj3ZIcXBOtKYEIfXYtGkTGjduHPv6lVdeUf+pFUKNDa/BDj6dSoyCotOJAoqsJMUy50WLxndWkRhdk1jMCCquL5pT/klMZ1aNYNh0Qn2Ca9f0/6VL9TUpTtDgwd7AbBirvIQ7ty41ouzpRDla142RU5zcAQXMP/nkE6UjRU6cOIHzNgMGhw8fRv/+/ZWeE/WYnnnmGSxduhShVg02C+XKlcNqm8hDOrUYjRXGZ0AyKF26tHJM8Xuff/75BO+vWbNGOdWod2XyD41SGxg5derUKaU7RdatW6d+B/dNcB/ilPIgGdNmxNmBZ2PnhQCHI3pmCKmbFMiHD4+zvwcN0o4p0xZKNacUR4MeNUd75DoWXKdNG6B6dZ16ymJavKbnz9dRVaVKaYcUo6UWLdLLJGJKcDdsucwAf2nFNBSjtTcS7RPQi02nEiO52EiYziVbZxXLfrJK2+HD8T/P9+kw6NJFhNH90V4yRc7r1IHPwTRRayqS1QlasKDYS0LAw0p2s2fPVtX5KPw9aNCgeFFJTLejyHnz5s2V2HqrVq1UOiAjkLiuPQYMGKAq/1HEnELndPx8/PHHyvmVXNKnT6+q61G0nOmETAdkhNfu3buVWDl/Bx1OjI7id//yyy9KIN7edjhYM2HCBPV7+/Tpo0TZmfInuA/RlPIgDOvLnSm3miTELwhgGCcFdTi5IaSTUSTz5un5XLm0lAZJdacUf0v63HqSUFUhmbBP+ccfwOuvxy2jvhSv81u3mErE6ivAf//JIRbcD1vk3DGTBNz7UaPBsp7WaCc6q44d095t/v/6a9Yfj18BzYQVm0qUoCAI8OCDOqJlxoxU/QlCMu0lM1LKF51S1DyzjdajE5QOVLGXhCBg5syZuHTpEqpWraqcT3TSWKOwRo0apSrufcZBBEA5b6ZPn4433nhDpYjbo1q1aiqiig6iChUqqGp/TDm0Vt5LDtSIosOL26O+Fh1epgZVmzZtVDRX7969UaVKFRWZxfVtofPq0UcfVZFhdLRx/1LiLBPsE0K1cwfvBSX0gDKn9MqVK54T8RQEJ3j0Ua2/Q5jezMwK6vLMnKmXMeokvaWarCD4C5ThoJ1x44Z+zcgoamqyUjCjwZs31/IxojUl+CpiK/jYMaCzyYygooOKDQo93rbOA77Hqgt0UDHihQ4GalhJmp/vwA5jjNgyLl4EsmeHT8HrxjaNlFF7dJLKdRQw3Lp1S+nsFS9eXEXKCMHJ8OHD8cMPPzh0pglJ3y/O2goSKeVBIu9GotcvvdTEeSHAiYwEevXSE+dTwKZNcQ4pdtRfeknPM5qEUGIh1Z6RUbeBXaP0xHlBSCHMuKHQOVP1yPXrOkqKjija+ZSzoX4aMyQ++gjYt08OuZAy2CL3ipnkaRyAWCOoqAnCDgRzhG1hA1OjhhanpmPBXgSVVPHzrr1kVt0rV873HFJWzTMzwov/qXnG5WIvCYIgJAvRlPIgd6Pv4pNNOrxvfLPxSId0nvw6wdswjMkM5xw/nvWzk70pa/ToG2+wgoSXUveIcQfY8YaeZ3URe2KOguAiDGTYuhX43/90H4QC6IwCZKVi9il5rVNDkn1I0ZoSUspdAGaw/XhAnsaBCJ0C1kgVimTbVvEjFORdvDjuNd/v3l17x1ki9O237Vfxk8iq1LGXlizR/ytXhs/Ca4Ijg08/DRQvHneNiL0kCIKQLAIyUop5nmb4WPXq1VWVAEHwF/76C/jtNz3PAVxrVWuvOKVY4rbk83qyLXcrCCmAkVGsqt2pk37NfiCjo44ejRP1p+QI+4lM5RMEQXA5osWs3MT/U6YAb72VcF0qWfTtG7+6CP+/8IL2njOSylFkleA+eFx5jghFNX35ODdpov8zQs/MRRd7SRACLn1PUvdSh4BzSn3zzTfo16+fKu+4detW1K9fX6n+U11fEHwdRrH36BH3mrazdQDRK06psHTA/dP0xHlBcCMcbJ47F3j11bhlzLxh9BQDF3bs0BkcWbPKYRcEwUVshdH5gGVUlK0wOlOwypRJ+Hk6pqpVA1hO3Oqson4VI6cE98HjSSeg1VHoy8eZlbco7szrYfduvUzsJUEQhGQRcE6p999/X5V5fP7555XK/qRJk1C4cGFMMUdeBMGHGTcO2LtXz1P2gtWsrXjFKSUIHob9Q177zOYw+4onT+p+JHXUKP9CMXRBEIQUV/GzF0E1bRrw55/2q/jZw6y2JqROVTtfpVIl/Z+jJ4IgCEKyCSin1O3bt7F582ZVrtEKX7PMoyD4MnRGjRoVZyNPnx5nM5ucOwdcvpxwuSAEAhT0p35Uxoz6NdP2KIjuqwPlgiAESAQVX9tzVvFBvGtXnKi1FQqpX70qwujuglUQbY8zz0GpUvBZTN2r7du9vSeCh5Ai9YKQNNG2AwrJIKAEYs6fP4+oqCjkNUvJxsDXZ86csfuZyMhINVnLFgpCamNKV9yOKW43cGBCjU9WIGNFPl6ijB558kngvvtSYefuXge+y6PnHzsLpJGQFcFzPPwwsHKl/v/ff7o6eMeOWvv2gQfkyAuC4CFhdELnVIsWOjqHzhDzfUZSMZWMkTsmjMD/6iv9QGaqma0wuuD6+WjaFPjjjziHlFnVzl8ipcReChjSpk2LkJAQnDt3Drlz51bzgiAkdNoyKIj3SWhoKMLDk18MK6CcUia2DQcPmKPGZMyYMRgxYkQq7Zkg2IeDsatX6/mSJYFhw+K/T22dzz/XkSPU12EaX6pWJIuKEfEUhFSAqauMkGrVSjtjeb03bgzMmQM89picAkEQvOis4kOawncURmdVBhNTa4rr+bIjxZehc88cmePx9fXjaI2UMvdd7KWAICwsDIUKFcK///6LY4ymFATBIRkzZkSRIkWUYyq5BJRTKleuXKoRsY2KOnv2bILoKZPBgwfjlVdeiRcpRQ0qd5AhbQYc7Xs0dl4IcDJkiDNQOe8kp0/HF3nmwKDtx9es0TYwHVL58+viP7zMWZHM406psAzAI0fj5gUhFShWTIudc+B882ZdBKBDB+DddwE22TJoKbgCWy7TfSCtmJBiZxXtRFYhoQPKCiOpqI3k684UX7WXzIgjhsf6wzFkuHqaNFpXgXnmhQqKvRRAZM6cGaVLl8adO3e8vSuC4LPQ95ImTZoURxMGlFOKIWPVq1fHH3/8gXbt2sUu5+s2bdrY/Uy6dOnU5AlCQ0JRLFsxj2xb8EHoHWZP2kVefjmu3H3XrnFVhsnduzqVaf9+rbPDr6BDirZP7typVJEsJBTILNexkPpkywb8/rtO5aODigPRHEBnX+aDD0RbTXAejt1JKya4lXLl9EPZVkuD4pB8jx1ZOqioleQPDhZv20tmvjY7NuXLwy9g/6FsWWDnTh0tRWel2EsB1+HmJAiCZwkooXPCqKfp06dj5syZ2Lt3L/r374/jx4+jB8sAC34L09dOndL/nVnuLzAF77vv9DydTBMm6Hn+Htqy1FE9fFgPJHbrBlStqm02rks/q1QkEwKdHDmAkSO1Y8pk8mTg8cd19JQgCIJXsBVGpzOF80uXaj2qIkWABx/UI0kzZshJSgozSopOPLPahT8gFfgEQRBSTEBFSpFOnTrhwoULePvtt3H69GlUqFABixcvRlEaBanM7ajbGLp0qJof1WQUwsOSL/4VaNDpwuggRvpYHSv2llNT5ocfdHQ0IyfattUR046Wu7p9t0GV8qFD40ZKkxB743706hX3etIkIGdO/btmz9aDbtxErVpAly46bY+6Oh7bf0dE3wEOTNbz9/YCQtOm0hcLQpzN/7//aWcs7w1GEC5YAFy8qMX/77lHjpSQOKwhEdM6g0VO5WksuAVbYXQKPzL1jA9yE9Gacs5eMp1SppPHX+D+UvCQRpvYS4IgCMkixJBal/GgplTWrFlx5coV3JPCns7129eReUxmNR8xOAKZwqVqGbF1JlEzhixbpitsXbigZRkyZwZu3QKOHwdu3tTrmIVvGPnNyHim8zCdn68ZRZ0nj35944YesKTzpkoV7ayiXXjggP5PB88TT8TZPm5xYnFl7rQ64RGJfoBvU8TZFDdv2RJYvFgvHzxY7yePDX8/RZ8HDPBiVBSrycyP+V0dI6T6nuAVfvxR66ix3Rg+PK5NqFZN3zsOZAMFQcFg2phWDBEAMvmQreCvyDFwAA0ZPtRtWb4caNTIw2fFT7BnLz3zjB51ePtt4M034Xfnm4bmzk1iLwmCICTDVkhWpBQF3ygmfuPGDVUmMwfzKwTBDlanDp1GdDyNHQscOaI7lexg0gmT0gE3QgeOqc1kZetW+5/jftAO4v3BqHtGixcsCDz0kJ7ovGJn15VILOvvduREov3F7ZsOKYqXs7I0B1iZyscBV34fMwO4P+fOpZKguSNCwoCiT8bNC4IXqFhRi/1TuuPRR4Hvv9dtyJYtQL16ul9QooScGkEQvAz1kGy1pmhkMJJKcIw/R0oRjibeui32kiAIQjJw2ikVERGBOXPm4Ouvv8aGDRsQaRHzYMnM5s2b44UXXkDNmjWTsx9CgMEIJgp0U0Zh926tX3n+fJwDyRUY+ZQ2rY7upo1nRkDRuWPafLwcGUXF164UyaDzyKpHRYcQ9/u11+IcVblyAenT604vdWz43bt26WgsptuxRD2dVRQjLxOznU8/BVp3TOjE4m9hBbFVq+IcUoz6pi7Wn39qxxqdZJwoR5GqguaOCEsP1J3jxR0QBH0f7NmjncR0UDFDhhGXvK9439atC/z2W1yFbkEQBK9qTb34Ylx4N40iPuxpXAgJoeHGBt4fnVL58ukHFEcQ9x0We0kQBMFTTqmJEydi1KhRKFasGB555BG8/vrrKFiwIDJkyICLFy9i165dWLVqFZo1a4batWvjo48+UiU0heCAttbff+vqWBTlZiQDIxquXk36s3T68Fl+//26c0nHEwuyXLumo4RY+p3C3hQEZyQRnTgU+DY1pWyXs/AJtZlOn9bLWKWL22QK3C+/aJuB8D8dZNx3OsvsObL4ntVpRafTihV6noOgTBcqXlxHalDLdPNfwEzLgN/Rs8DTT+v9o6OL+jd0bDEdkWTJoqOvpk3Tr5lmWLs2UL++Xo+pSiJoLggatieMFqTj2SzQxIyJzZu1U4r3C++db77RqbGCIAg+oTVFb/m4ccDrr+sRLo5kSVW++NDAoiFGwygZVYy9Ch9GdKRR4J7GHw1OQRAEwf1OqbVr12L58uWoyOFpO9SqVQvdunXDp59+ihkzZmDlypXilAoCGMXz5ZfA1KlxjpbEYIeScgoVKminE9P56Nix52SiQ4rLmRnKic4m23Q5fsbectp75nZ4yZrbp+PInhPro4+0E4v7s3evjlais2zDBu2woqiyLewY8zOMAGNEfpkyWgvL+j5tUUY/0WnF6A5GV5lOMVbTq14dOHFC7ws73DwmjPbg76BPN9UFzQXBh+G9wHuN7QTvSd577L+wWADTX5k5wXuIgujvvQf07evtPRYEAcEeMcWJhg8NDGol9esH9O+vR704usWIKjqwgh1r6h6dPP4GQ3TplKLYuSAIguAZp9S3337r1MbSpUuHnj17ur4Xgt9Ax8v8+Vr7iJqdVskE2w4kdZGY3kbHEO0x6r307q2dLPY0mRw5mQjn7Tln7C13tB1HyxmtZDqrqE1jdZIxPYiOIw7g0bZktNMffwD//KN/gyme/tNPQL2qcfugfm8+7XSjY4uDoqZDiil8Xbvq7TJNj9FQdFDx+03tKEe/12tC54tiRi7bHBOhc8Er8H5o107fq2yHeG8VKKDTeekU5v3De4zZMuzz8d7kf0EQBK/D6gxsoOhBp0OKSFU+/9eTMjH3e89W4Lvcel7sJUEQBKdJltC5EHywEzhkiI6MYiSRLewg3nuvdtzQGfXGGzr6xzYyyXS0uOJkSg7ucmK9/HLC5XRW0U/LamBMHaJ9ySio43uBKTHbZHrR2aNaU8uqo8VUwu7dgbfeAmbO1A4rHjOf0I5KjMjz3t4DQUhwrzLicM0aHZHYoIEuasAiCrwnmfrLtufVV3VbJFGHgiB4DUb/tG+vnVJW6EWnUWEaAsGa1ufvTilTzJD6FZGXvb03giAIfkeIYZhDNs5x69YtpRnFdL6zZ88i2iZUZgtzlfwYd5Y4jjaisffcXjVfNndZhIaEwt+gc+Xdd4HJk+NKsJswxe2RR7QTivPsLJrOlcQiogIB83fRlqTjivqcIYhGWejzvRdlYSD++aaURLduen1HmlimMLpPYUQDV/TvQtaygB9ex0Lgwnvxww+1nh0LHrCq5bFjce+z9gazZ5gGbK2gKQQXtFRiWjGUpS6gD9kK/oocAxehgVS0aMIQc2ooNW8OTJ+u37NN6wtEZxV/J/USSNmy2oBkxReOMtSpA7+DD5/MmYGou8DeP4G8+cReEgRBgPO2gstOqSeffBJ//PEH2rdvj7x58yLEJvd72LBhfn0CxMjSsIM3Zgzw1Vf6WWtCW4mdOnb08uTR8gj2IqKCqePH4zN6tJ5s9acohN60qU4L5DFjRT2rcy5QnXaCkJqYDl6m1/IeZPvFSEbz6cYoToqiM93PdJgLQkoQWyHuGFw8dxHZcmaLtQejo6JhRBkICQ1BaJo491/UbV2JLjRtqHvXvRMFUKIpTah6jxjRBqLvRnO0CGFpw3xn3RkzEP1CD/VeCIeuMmdUpYT5Klq5Sg2E0YVKY2v7dkSvXQ+jx0sIMaIQyu1+9hmMbt0QfUc7tsLC4/aB36W2GxaC0DB9fGjiJ2ddu8fdlXVdOZ8XLyA0f17zxkJUuowpOvdeu06qVUHI7l3Azz/DaPVQirfrzvOZqtdJat33gdpGODruPnadpGob4eZzH9DXieF7bcS1a9ecckq5nL73yy+/YPHixahLRWYh4IiI0J02OqPMSsamDlL58rqoCIXKOXBHvSg6U9jRc6QFFQxQP2rECC2wzv9p0wJNmuiJOlqJ4VPaUYIQIGl9jEqkhMuoUdoxRd03Os15j5q6bYIguIe1761FszeaITxTuHp9Ys0JHF12FPmr5UeZR8rErrfm3TXKUK3drzbSZ0uvlp3aeAqHfjuEPBXzoNxj5WLXXT9pPe7cuIOaPWsiUx59w57ZdgYHfjqAXPflQoXHK8Suu3HyRty6fAvVulfDPQW1wXt211ns/X4vspfIjsrPxKRWAdj82WbcOHcDVbpWQbZi2dSyCwcuYNe8Xbin8D2o9ly12HW3zdqGa6euoeKTFZHz3pxq2aWjl7Bj9g5kzpcZNXrEVVnbOWcnLh+7jHIdyiFP+Txq2dV/r2LrzK3IkCMD7u9zv17xueew60ZJXNx8DPc9URX56pQA+vTB9c8XYBNqIByRqIN1OpKoYkXsRXmcQz2UxkEUjD4JvPgibtZqiA3f/Ys06dOg3uv1YvfhwM8H1DEq0awEitQtopbdvnYb695fpzo3Dd9qGLvuoSWH1LEv1qiYmkhUZBRWj12t5hu82UB1MsjRpUdxYu0JFK5TGCWbl1TL2AlZNXqVmuc+cF/I8VXHcWzFMRSoWQD3tr439vu4XX7mgVceQLp70qll/67/F0f+OIJ8YeegxjFpMGXJgnVjV+Purbuo9XItZMypHVSnN5/GwcUHkbtcbpTvWD52u39/+Lf6jTwXPCfq3O88i30/7EOO0jlQ6am4dMBNUzbh5sWbqNqtKrIW0VoJ5/aew55v96hrgdeEydYZWxFxJgKVOldCjpI51LKLhy5i59ydyFIgC6q/UD123e1fbsfVE1dRoVh15KJTascOXClfF9s+34aMuTOiVq9asevu+noXLh25hLKPlkXeStoRd+30NWyZtkXdE7w3TPYs2IPz+87j3v/diwLVC6hlvHY3frIRaTOmRd1X4/ph+xbtU7+7VMtSKFRbR9NFXolU9xE7hQ2GNohdl8fx9JbTKP5gcRRtUFQt47229t21ar7R8Eax6x7584g6T0XqF0GJJtqg5T1snvv6Q+rHdlB53nn++f3cDxNz3TqD6kgb4Uwbwevkm124ePAi7mt7H/JVyaeWXT97HZs+3YTwLOGoMyAumpDt3Lk951D6odIoWKugWsbrfMNHGwKnjaiSTx0LE+6vX7YRj1dQzy9y5fiVoGkjnMVlp1TBggWRhSWPhCS5HXUbo1eNVvND6g9BeJg22HwJM1KHnTTqH73zju68mXCwrkoVYO5c3bljNAJLrzNFzxmNqGCi0n238V1Ffb7RZYgWkAoEou8ARz7X8yW60u3t7T0ShATYtkGsvn7ypHaws1ABNYapideqlU47lijF4ILSfjGtM4ZQ38/L+yMEMcwlLhYjxkl7euRI4Ivv1Oh4knC0UKW9ZdHGGyvO+GNa3+3byDpjIooduYRbxcr4t56UdXTkN3ouv0f4fxkRgriOsSAIgpA4Lqfv/frrr/jwww/x6aefoihz4wMMd4bkX799HZnHaK9sxOAIZArP5HMpL3Q2bdoEbNigxcytzigGwzH17MYNNZCnIqKkI5cIPDjUFDBDzgLFS8fqe/NjflfHCKm+J/gNbOOmTtVSLbwlCcXRGQ3KviBvWWriidZU4HMdQEwrBl4KKW2dJX1P0vfcmnIxbTqie/RkXgnCGH3AhqtePUSXLa8GBHVynzbXjVy5Ed2kmSqFHGbcjdWgiu7yrP+k5ljspeinOiN0zmxdAWbECP9Nzfnzd4Q8+hAwM2Z52ytA2kwBn5oj6XuSvifpe5K+F+KG9D2XnVLnzp1Dx44d8ddffyFjxoxIy1wlCxcp6uHHBKpTyupMYioeC4SMGwesW6ejCaxQf6V+/bg0PatwuRCETqmoW8Cax/V83XlAmE67EAR/uS3379dFBrZv18vYz2H79vDDujqmtHGBjzil3I845twMDa5Dh4BSpeIinxjC/uKLOkKKzicKd1pHEE3CwnSFB3+JmLLaS1Wr6tKpCxboHGt/hULtxQoCL0M/XBp8K/aSIAhBz1UnfSsup+898cQTOHnyJEaPHm1X6FzwzWiBhQu1vcMOGAPcqLFCIWC+NqGtwypW1I1ylKYnBCF0QjX4wdt7IQjJgm1XtWq6Kt/jj1MXUaci0zHP/3yPPmTRmhKCkU8++QTvvvsuTp8+jfLly2PSpEmoz1EpIfWhQ8nWqcQKfC1axDmrWGGGuckTJ8Zfj04rjjJ26OB/1fpYvjgQ0vfy5wey5gLePw88OVwcUoIgCC7gslNq7dq1WLduHSpXjhOOFHx7MGrOHGDLFt05++8/pmDGj46i9NH992u7p107vV4wC5cLghB4cFD+hx+Anj2BadP0sl27tPOd7d+KFTpClHIv0vYJqcWxY8ewatUq9f/GjRvInTs3qlatigceeADpqdjvQb755hv069dPOaZYvGbq1Klo1aoV9uzZgyIsFSv4prPqlVeADz7QYuhWnngCmDBBazLwvZi0PuXY8vUSxhkzJl0ZxtfhID0da8uWKbFzVI8TOhYEQRDc7JS67777cPPmTVc/JngJipYzIoDP+7NngY0bAevpoy5j48Y6ekqEywVBCGSYukypFjqexo/XkVLnz3OwRRd0YDACJ+pMUUNPtKYETzF37lylz7lhwwbkyZNHFZHJkCGDkkA4fPiwckg99dRTeO211zym3/n+++/jueeew/PPP69eM0pqyZIlmDJlCsaMGeOR7xTcAB1UdDZZ0/oYRcUQeAqEmtAxxXU44sjP+HIEFfOpmYLo73DAnk4pM1dcEARB8IxTauzYsRgwYABGjRqFihUrJtCUSqkOk+D+FHem6K1fr6OkbFP12Om6elUiooREuHsD+CWmVHfrPUAaXX5VEPx1MHvsWKUhjC5dqIOonffvvqsqsKu28e5dvS7TmBk1KtGigjupVq0aQkND0bVrV8yfPz9BVFJkZKSKSJ83bx5q1KihIpk6MC3Ljdy+fRubN2/G60wFs9C8eXMVEe8KUVFRStg0VvA4Ojr2NX+ndT3CZZ5eVwmtxkQShVmcHb6wrr3f4fK6XbuqSjRhR4/GalBFf/opjN69ERIdjdAYuVgjKgrRbOgqVUIYjb6YCKroqVNhPPusw31I6ri7sq6j4xP7G7muJfvCr6+TSmWASYAR/gmij/QAipb27nXi4XVT4xz5zbn3tTbCy+derhO5Toj1PnS7U6ply5bqf5MmTeItNy9G86YTvA+r5rHqFNNSrNFRrKg3ezaQL59+bWpNCoJ9DOD6P3HzghAAUIeW2rrt2+sIUjrvN28GypQBcuUCLl/Wqc2iNSW4m5EjR6J169YO30+XLh0aNWqkpnfeeQdH6XhwM+fPn1f2GrVBrfD1Gea02oHOMk5W8VJCJ1azZs0QzhsGwIkTJ9Q+58+fH2V4Q8WwZs0a1XmpXbt2bGriqVOncOjQIRUtVq5czOAHOJC2Hnfu3EHNmjWRKcYrzP06cOAAcuXKhQqMrIlh48aNuHXrlnL2mQOjZ8+exd69e5E9e/Z4chN0xDFNskqVKsjGkEhQN/wCdu3apT7LbZhs27ZNVQ3iAGzOnDnVskuXLmHHjh3InDmzchia7Ny5E5cvX1a/gb/FPD5bt25VEXD3M0c4Bn4XI+KYeZAvxhC7fv06Nm3apI5hnTp1Ytflb2CBodKlS6toOsJsBUbYpUmTBvUaNYpd90CFCjjToAFKHDqEIidOqGW306XDuuhohGzdioZmul90NA5NmIBTefKgWKVKKMYQ0oMHEVW8OFZTLB1AgwYNYjucPJc8p4ULF0bJkiVjbX6mnZJ69eqpfSHHjx9XqagFChTAvayaE8Pq1avVZ5iWyuvbysHSpVHGoidFh+zdu3dRq1YtVVCJUPPs4MGDKr2V2mcmf//9t3Kw8lzwnJjnft++fciRIwcqWbbL48tjx/RYiu4SHlumq/Ja4DVhwvMWERGhPs/tEJ4znucsWbKguiU1b/v27epc85rMxeN3Lwvp3cGaHi8ifetHUatv33jnntdQ2bJlY+89XmNbtmxR9wTvDRPuF+9THkceT8Jrl9c7AwKYcmvC38vfXapUKRSKiYLjvcr7iJ1Cns/Y433woDqexYsXj43C5L1mOqPZ7pgcOXIE//77r3Kcl4hJr+Q9bJ576s+Zjgyed55/fj/3w8Rcl9e1tBFeaCM4AhcD20+2ozyX5mAI7x/ec7zfGzZsGLsu22W2z8WKFVMT4TOD93JqtRG89ngN8hjwWAREG0EDE7Qtr6hnDPefvyOQ2wiPOaWWL1/u6keClvRp0mPD8xti51MTakZxYJW6lya8Z6iN2bmzjhYQ3AyNbDN03sNaIKlKaHqgxYa4eUEIEGiT8bnZrx/w6ad6GSv1MdWZdhUHeLJk8fZeCu6ALZeZ2OTtViwxh5QtNGBNI9YT2EatWCOebGFK34gRIzy2L0IK4XVCbzsjogidBYMG6RzlvXvjr0sH1fffa5FRpgLyNTMfGEZqOubMdL8MGdx/atKnx5kff8TJCROQiWH8/i5yTujMfWccUBS4ljkzonOGAF9/rSsK+lq6pCAIgo8RYtACEQKmxDHP5syZQP/+9KzGLWfVKdoprKYnCIIgxOfLL4EXXtCauybsJw0bBjz6qBwtwX22ghlh5AyeskM4cswR2m+//RbtWOEkhr59+6rR25UrVzoVKcWRcY4OcxRZUnN8JDXn5EmEHjmi0vqMggURffy4mg8z85K5bmgojJCQ+Kl+XM7tvfUWQi9dQkhMuh+XGZ9+ipBu3dybmnPpEozcudkRQSgdZ8yd9ucUrpUrERKTRcJjy2NMozxs6VKGFgRlCldqn09J3/Odcy/XiaTvEV4PjPRyxl5KllOKYdIMX2YYmHlxmjzyyCPwZ/zZKfXPPwD1Sv/8M3501IwZWjtKEARBcAy1aRlJyuIQJowi79EDGDdOzwtCSm0Fe7o6jvCkJALTRZhiQM0qE6aWtGnTximhc3+2l4IOGoKmMDo7S9QSo7OKWg7OwI4mw0qZNuQuwXSmATG1g8J93Bd/h8eFqS7WfhGPG43zYIyU8mVhfUEQUg1nbQWX0/d+++03PPPMMyqH0RbRlIrP7ajb+GD9B2q+b+2+CA/TeguegBHC7DhZB2CZSjp/vq4iJaQCFKVhmWZCDYEYfQ2/J/ou8M83er5oJyDU5WZDEPwCSs9QZ2raNGDoUC2CzsAQ3tYLFwLvvKOjpkT43P+4DSCmdQYVXrzZOltlEKitQbFxip5TS8PUy/jiiy88XgHvlVdeQefOnZXWBr/7s88+U3ofPWhMCIHFc8/pKnwsLxojjK6cBnPmxHei0FlKLZRNm+J/nuvQqGSK4IULOiyfDhfmPXfv7roTgvbSe+/peYs+mP9XRZwCfN4DiDaA9THLkjLC/d15Y2//6QRl6HGMsL5KEeU16Gh9QRAEw0VKlixp9OzZ0zhz5owRiFy5coWRY+p/SomIjDAwHGrivCfgbnbuTOsgbsqWzTB69DCMXbs88pWCIyIi4k4C5wOFOxGGMQd64rwgBAEXLhhGr16GEapyW+Km6tUNY8MGb++d4CpsuVQ+Qcy8r9gKDz74oDF37twEy+fMmWM0bNjQ8DSTJ082ihYtaoSHhxvVqlUzVq5c6RV7SfAS06cbRliYbtz4n69PnEjY8HGyt4xTzZqG0aSJYYSExK3H7ThrL734ohGQ9lKOTPr3zZjheH0eJ/O4OnPcfA3r/vP8d+pkGP36xV0L5sTXn39uGCNG+PfvFQTBZZy1FVxO32PYFRXnTXX9QMOd4ejXb19H5jFa6T9icAQyhesKMu6CIuZPPcWqB3HLOABG8d78+YEhQxiK79avFBLj+vW4UoYREYETTnH3JvBXTFpugx+BNB4QPRUEH4XFRhgdRS1eExZloQaVn2erBxXXWWk2Zj4CQCYfsRWo68TKPKycZIVVkljph5V0fBVJ3wsQGLlijaCyl+43daou2UwhdWfgZ1iJzlEkDFNGe/WKe81S0WYkTaDYS9uaAgNeB1hpjZFBtu2EvXS/pI6bN7FGOLGYD68RpoGmBGt6o0RQCUJA4qytEKcY5yTt27fHihUrUrp/QgqgVuUbb+hUfNMhRV9I8+YAq3aycjErRv30k/aTCEKKoBPqwT/0JA4pIchgm0rnP6sqx1Q0xpUrQJs2wEsvaf+zICQXCoV/apZ+tDB16lT1niB4HDoEKMRtdYTQQUTnCFNN+Z+vmd9sEZpW8HXv3gm3SWcWHV32oPPB9jN0gHF5INlLvfsDLHnPcq7M/baFueI2uryJHjdvQgcUHWgPPqg1wOhoc+SQatYsYYlvvubD1Bb+fl57TzwRt33+5/cJghBUuBwpxVG7Dh06IHfu3KhYsSLSsoSshT59+sCf8fVIKVac5ej8xo1xy4oXBzp2BLZt0wMxfF5w4ro8HaIplUoEaqSUIAT5bf3RR8C5c8Dly8APP2itKRPa2e++C7RqJbe8L+OrkVKLFy/GY489pqLPa9eurZatX78ehw8fxnfffYeHHnoIvopESgUh9iKoqFXlisA3HV10PthbHlOlLmD45RcdXca+0u7dOsqI0NHHkWRGHtmyeTNQrRp8BnsRXYQON+6/tRtpRnotWeLcdeIIX44YSwyJ9hKE1IuUmjt3LpYsWaKMpY8++ggTJ06MnSZNmuTq5gQXYIXmKlUSOqRYtpzakxy4yJgRKFhQt4usrss0E0EQBCF50LfM6qVsX3Pn1pq+HBgw6xjQbu7QQRelWrZMjrLgGnQ6MVWPlYsvXryICxcuqOp3XObLDikhSLEXQaUEvj/TjgQTSnzQGLWHvYqS/CzTBwMN3sN0xty5o9MVedx+/hmgA5oOnWzZEkafPf20jq7yFebOte9IoqOJVUHM8246n3g9OHOd8D+riNgLZuA1MmoUwKJa7NBwO96IpHPlu63RZBLtJQiej5TKly+fioZitRiWNQ40fDFSimdowgRg8OC4Z3mGDDqdhPpRfJ/6UXzmLVoEXLqkHVJML7nvvhT9BMEVAjVS6u4NYElNPd9iI5Amo7f3SBC8cnszbY+OfmYizJwJDB+uC1GZcDB80CBduY8DBILv4KuRUv6MHAMhHuy4U4SvSxfg1i3giy+AZ56Jv87Nm0DNmjpqyMS2Olug2Ut79wLly8ePKCJMh6SDijBlj/nhjz8OnDypKxKyMiIfMN6qUsf9ff99/VCz3XdrJJM9TbLEsF3fUSQW4TGhZomnr5PkVhCk85XXNCPiBg60HzXmb9FeguAlW8Hl2u63b99Gp06dAtIh5YswXeTZZ3XKiAlH6zkqz4mDKXxN/Un6QJi2Z3acAsUnIngbA7iyJ25eEIIQtqfWNrVdOx21yr7Vrl1AZKQeGBg9Gpg9W6f0Ma3aVlpDEGxZtWqV0pA6cuQIvv32WxQsWBCzZ89G8eLFUY+jT4LgD7DzzUaPYqfUG6Izg2GljAYyGTBAN5oMPTWjgei0YSpYQGDHXqLIqz3mzYtzWJj/GW7LFEY+VOi08obTjg4XniN+5/ff62Vsh1hdyZqOZ913VxwvtuubEVTWdD8KOW7Zoo+DCZ1DDFWuWBGoVcv1VDnb9bk9OgA//BB47z3tUOIDm9cs36cYv+lkMr+bOilMS6UzMamYDlMfTJxSguAULnuWunTpgm+++cbVjwUl6dOkx/Iuy9XEeVdZv16n61kdUmwrv/oKeOABrWtChxQjoszOEv9TQ0ocUl6A1UgY5suJ84FCaHqgyXI9cV4QBNXPKltW28ZM7+OAgOmAOnFCD3jffz/w229ysHwBtlzLYyZfasUohdCiRQtkyJABW7ZsQSS9mwCuXbuG0fRwCoK/0b+/bhzpdGJVHpOFC4EpU/Q8o6hMeymQqnnbs5dsdZdMKPxqC51zTJezQodIagnBmyloLVtqhxQfahRV/OuvhOl47sQ23Y/XB51FtvA48sHK0uJMFbGXKmebcnf7tg5rNtfnw5qppewocRlTUczzw/9MOZk8OeE54+uPP9ZVpOxFvdkbgZJKKILgufQ9pu59+eWXqFy5MipVqpRA6Px9hnr6Mb4Sjs62mJGgHHk30/XMgSfOW1NJxAElCIKQ+uzbp+1XZljQ9uQALwe695gD5TEwqnXiRO3AEgIDd9kKVatWRf/+/fHMM88gS5Ys2L59O0qUKIFt27ahZcuWOGOv4+oj+Iq9JPggppg5o3wYUsoRVHbcqS/x6qvAuHEIGuylpyWW2uUtIXh7+5mYYL2nSSytzx6sNsJUOgrwmlFP3O/Tp+NSAG3h77O3fTrlKNZu7SJze9QHszfSxHNz+HBctJc1/XDMGB1ByPe9lYopCIEodL5z505lRDF9b9euXdi6dWvsRCNKSBls/958E+jbN84hxfPHaqmMeqZDikhElCAIgnehZh8rm/frp9P1qCXFwQRGslo1pSizwsFdRrXu2OHNPRZ8jf3796NBgwYJltNwu8z8fUHwRxo31oYrO/yMgmEFOjqkqCc1ciSCCnsC39b0N1vouLAnkfLrr847aJLDmjUJt8/XTEHzleM2fbpOF7EHj8+KFfGjnhi27Mghxe3QUWR7rPk9FHC3FXE3l9lbn/pY1mgvph22b6+/m9EEIoAuCEnisqbUct5sglPcibqDzzZ/puZfqP4C0obFjyqz5cYNoGtX4Ntv4z+bWIiD7zEySqKifBh6EfkAJRRHtIki9Fui7wInY8Q4Cz4MhLrcbAhCUGhN8X+TJsDSpTqN+tQpYPt23X6TH3/UE/tnLFzBCCohdeAYT0zrjBcoSu8jBz5//vw4dOgQihUrFm/56tWrVcSUIPgt1OlhCph1wJpifCxdGmz2Eh0WjLJxRhDcVmOJETp0sIwfr50dY8fqqnTujLphVNJrr/leVUR7x437ahvhxNcU2J81K+E2mH7IkX7bSLWGDe3rWVkrCNo7Z47Wt9XLmj9fnzOmudimYnK7EjElCClL3wt0vFF9j6l41HmkoLlV0496UnRIMfKUkc8ckRenlA8TsNX3rgPzY35XxwggTYD8LkHwUDNAG5gDtOTAAd1/oPYvBxasUDv2nXcABsqYAw+Skh1c1ffGjx+PL774AjNnzkSzZs2wePFi/PPPPyql76233kJvPvh9FEnfExKFzgOzRLRt2hpLRIu9lPTxMx0iv/8O9OqlqxqauEsAnWLfdNAwaihXLh3RZnW4+GJVRGpI2TqG6OhxlCbJVDzb9a2/K6UVBB3hrVRMQQiG6nuC+zVJqB/1+ec6FZpQI/utt/Tz5ty5hGLmgpC6hAK5zJAOqbopCInBdprC59Saom1PW5+arP/9pzV+9+/Xuqtk9WptlzIghvq2DJahTc3PMzVQCHxeffVVZag1btwYt27dUql86dKlw8CBA33aISUISWJP4NusSMY0voDEjfaSNeqmWzctzk2tI3dF3dCxwtxyRkjRcVO8uE5/Y+fDFQeNN3A1iimpSLWUVhB0hJmKaeso82b0mSD4KOKU8vKIOgtCMF3PbK84cMS0aI6gi5i54BOkyQA0X+PtvRAEv4EOJRb4sUY+0f5nKl+NGjoyltpSZlrfkSN64rr8LD/HolUyEBEcjBo1CkOHDsWePXsQHR2NcuXKIbMZRSII/kowdsg9aS8x7dEWOl/+/tt15xEjjZg2aZ6bnDl19A4j24ivOqOScgwl5nxy1fHkrn20OsoIBSb94fj6AjSc6Ny2TVV1tFzwa8Qp5SU4eDRkCPDNN3HL2InhCLkpI2HVKhEEQRD8B9v2m9kqjIRiKl+nTtouZYof5VbokCJ0RrF/wZQ/9t1eegnIlk3S+oKBjBkzogY9loIQKCSm18NRVyHlTj7y/PNak6taNec66nzwdO8eP4qNRRVMUW9/xxvOp8QwHWUMn2b0K6PTGJEm6XuJM3ky8PLLcZUUKRhP4WWmYrLyF+8Dd6WwCj6BaEqloqYU7mRSnQ7eX336aP1Hq34U26d8+UQ7ym8JVE0pQRDclq5tpvXRScW0bNrOtFNpZ505E399pv1xsJeD18zckLS+wNOUun79OsaOHYulS5fi7NmzKlLKyhHTY+mDiKaU4BT29HfEXkq5lhI75HwwmAKGpiB6Yh116kd17KgdI7aIzpHn6dlT5/EzX5+h09RrEeIinxiVwbaCx+i775w7MqZumC85IgXva0r99ddfqFy5svpiIT77DwBLftKdkmXL9P1nQmdUrVpAjhyiHSX4IHdvAn/GlCxv+pcOTxcEwS1pfYTFefgeo6SoM8VoKrJnj071o51WtaoeGGQRIfF3Bw7PP/88Vq5cic6dO6tKfCHsWApCIOFrkSv+bC/ZpqdRdJaj3HRCmZFPfFAwNY8di4oVdYeD4bebNjFXmD3EhNsN9LRKX2H0aGDhQn0+WEWRGi7Bjm0qaWLQiWcV+yd00PJ4BksbE8C4NVIqNDQU2bNnx5AhQzCAoXV+iKcipUaER2Dj2kyq00HxcsLBjI8/Bp55RtIzAoJAHfmT6nuC4HFMDcGMGYF583SxC/NZQbi8cmU9gMj/QmBESmXLlg2//PIL6tatC39DIqWEZCP2kvtwVOGN5M+vQ3CtXT06q1q3Bt5+2/er7AUiFBJmtBo1whgizXMTrNpIa9ZoEWVbHn0U+OGHhHp069YBtWsndGA98AAwdy6QJo1oTfkgXomUOnr0qJqW8CYTkC5NOvz8xM+4cBFY+2U6rF8fNwLO+2baNJ0eSwLFfxHUpEsH/Pxz3HygEJoOaPhz3LwgCB7VoOrRA2jXDnj6aWDlSuDOHS2KTnuMqX7sS1Svrn3f1ogrwTFsuX62zPsKHMjLwTBpQQgmxF7yvNYUOX06/mtGYs6fr8u8sqKfr1fZC0TatwceeghYvBho3FgvCzRtpKQEyimWOXOmHmWzB7WkeIxs9ehYtdOqU8fjxg41jSOGm7O0cVIprEJgREpFRUVh9erVqFSpkjKkAhFPjPxxQIiVlHjvRUYCGTLoZ8G4cdKZEARBEOzDdO9Zs3Tk1PHjccv5+KWmLTWnChQQrSl/thW++uorLFq0CF988YUSO/cnJFJKEHxQa8rswLOiHkc3bBHtKO9DJ0qdOoGpjWRNx7M6h5xN07MeB3t6dMS6nOl8Tz4JbNzoeDuCX9gKLqfvpU+fHnv37kXx4sURiHjKyGLnYuJELW7O6Cjen3TqCoIgCEJigxoURmelVqb0MWLKhFqpHGjl45gRVBIxlXqkxFaoWrVqPO2oQ4cOgaZYsWLFkJZVtCxs2bIFvoo4pQTBh7DtwPM1I6JsU6Cko+59HKVc+pvD0DYi6q+/9P7buhaYqsgoJlto2Fy7ltCh6mqE059/As2a+f/xDFA8lr5XsWJFVQ0mUJ1S7uRO1B3M2TlHzT9V8Sm8/35aFTHFSH3pPAQgzLGZo883nnpKl+gNBKKjgP+W6fm8DwKhAVI2WBD8KK2PMo2s1semZcMG/R61PTlI+L//aT0qea445g6AmNYZTwHwZuvclmUUBSGYEXvJ84LynLemOpkdfokc8T72Ui752p/E5q2RTxxk4XVlVoK0xZ5DiuTJo/W1rOL9ybk+GeXh78dTcD1S6vfff8drr72GkSNHonr16shkYwW7M7ookITOIwZHIFO4CH8ENCLcKQiCh+Gg4hNPAL/+Gmd/0YZj1T4Oigv+JXTuz8gxEJKN2Euph6MUKMF3Ui5NWP2qVy/4PPai8Ig9bTMuY8VBppJ6MmrP9ngyeIYjd9ScEvzCVgh1dcMtW7bE9u3b8cgjj6BQoUJKW4oTK8gEqs6UIAQ3oUC2ynpyvckQBMGNZMkCTJigq7bSpjPtQxarYZqf4F+UKFECFy5cSLD88uXL6j1BEPwJH7SX2OlnCpM4pHwLpqjRKbNsmY44IszDp9YLH+pMPeN/X4T7bE8biho106fHGSf8z2i9Rx7R/63L3R21Zx5PpgTSUDp6FBg/3n3bF3wvUmolSwElQsOGDeHPSKSUkGwCdeRPEASfbG5++gl49lmdwkeqVNHLODApVfn8I1IqNDQUZ86cQR6mMVj477//ULhwYdx2lPbgA0iklJBsxF4ShDjYFR86FBgzRr9mOpyvVpHbvh1o0gSwHUxxVaDck07SL78EunTRMioUQK9MJ7EQcJpS3nA6HTt2TKULLlu2TBlvBQoUwNNPP42hQ4cinOJpMRw/fhy9evVS62XIkAFPPvkkJkyYEG8dQRAEQfB36PN+/HEgb17g4Ye1APq2bbpisrmc0kVSUMM3+fHHH2PnlyxZogw2a6XjpUuXinanIAhCMEAn1KhReoSJkVJmvAijkZiSRs0lX4h0W7sWaN2aobxA4cLAqVP29cps9c1MHC13N50765TBH37Q83RMpUvn+e8VUkSyEi0ZVj5jxgxVhY8VZMqVK4du3brFM6rcyb59+xAdHY2pU6eiVKlS2LVrF7p3747r168rp5NpxLVu3Rq5c+fG6tWrVTh8ly5dVEWbjz76yCP7JQiCIAjehNX3fvsNaNVKBx+cOaOj19u3BxYt0najBG36HqbYOW0o2ipWWIGPlfjee+89L+2dIAiCkOqOKVYtoVPKCp0+e/bERSBZq92lFvzer7/WJYDpOKtbF/j5Z50V4ot6ZTyWdJKtWQPs3Am88oo2ilL7uAmeTd/btGkTWrRooSKRatWqpZw+XHbz5k0lgl6tWjWkBu+++y6mTJmiKgGSX3/9FQ8//DBOnDihIqnIvHnz0LVrV5w9e9bp8HpJ3xOSTaCGo9+9Caxopecb/QqkyeDtPRIEwQY6oFjExsz2KlNGV+vr2xeIeSQGNb6avsdKxhs3bkSuXLngb0j6npBsxF4SBOcFxPkQZ4QSxbz5Xmqm9fE7u3ePi94qX16XAM6Y0ffPIKOlKLhp4ovpkEHAVU8Jnffv31+JnDOl7vvvv8fChQtx9OhR5RDq168fUgv+sBw5csS+XrduHSpUqBDrkCJ0nkVGRmLz5s0Ot8P3ebCskyAIVqKBsyv1xHlBEHyOpk2B55+PKzSzfz+wfr3WlhJ8F9pP/uiQEgTBHmIvCSmAUTxWQXA6UVhEjGly06bFOavMtD5TCN1dwujW7TBCa+ZMbVhY41f27QMuXoRfQD0DK7bHTfDv9D1GRU2bNg1pLCUWOf/qq6+iRo0aSA0OHz6sUvKsoe3UmspLEQ0LrAZIPSm+54gxY8ZgxIgRHtnPdGnSYX77+bHzQoDDfOX5+nwHVO5yaDqg3vy4eUEQfA4GZr78spZ6mDtXL1u9WksqPPWUt/fO+7Dlmm+Z9yVYQIZSBKYkQtmyZTFo0CDUr1/fY985atQo/PLLL9i2bZuykyjLIAiphthLgmAfRvFQQ8pMi8uZUz/cGbFkhU4jpvvlz69z+O0JoztK97O3nNtnFUA6bpj+RmeYPecTv5f75g9pcPyN/rz/QYbLkVIMu6KguC1Mm8vCEowuMHz4cGWAJTbRCWbl1KlTaNmyJTp06IDn6b21wPVtYXqhveUmgwcPVlFX5sTf4S7ShKZBh/Id1MR5IcCho7ZDBz1ZnLZ+D6/dIh30JNexIPgsFDWnPdqrl35N27JHD63xGeywRe4QM/lS6/zVV1+hadOmyJgxI/r06YPevXsreYQmTZpgruld9ACs6kc76qWXXvLYdwiCQ8ReEgTH0GHSqJH+nyEDO8za4WQLq5v8+mt8YXT2jatWBWrVAooUAR58UP8fOVKnzdL5xBRBLud/BmbQcGCKnhmJxe3RIcVUK9s+NKO46CzzB+h0sz1u/rT/QYbLmlI0mpiyx1G9OnXqKIcPhcU5qvfYY49h0qRJTm/r/PnzakoMin2mT58+1iHVuHFj3H///fj8889VKWWTt956C4sWLcJ2lqqM4dKlSyrFj9X4+DlnEI0EQRAEwZ/hQCDlJ5Ys0a9pd/75p5aAYDpfoMjdeRN32QqMinrhhReUNIKV999/X0WlM3rKk9CWovRCciKlxF4SBEFIJehMYuqZWe2OouOUnPFkQQxGYDGqyvq9FBD3J00m63EjFN9kNRgh1XDWVnDZKcXRNTqgPv30U9y9eze2UgxH28aOHYt0HkpbOnnypHIsVa9eXY0shpn5tjGYQuf//vsv8jOUEbzmvlFVbbwldH43+i4W7l2o5tuVbSfRUoEO7weK6pF27QInWio6CriwXs/nrA2Exr/3BEHwPTjISTmFmFogyjH12GNA7tys/KajqoIJWisxrTPauSFayl22Am2m3bt3q8rCVg4dOqR0Mm+x0pEHEaeU4BXEXhIE16GDyFrtzp4wOgM2qPH8/vvOb7dkSWrjxF/GfvaxY3Hf44tV9pyF+//BB8CECdoIomFkFqby9Pd6o1pisAidU3vggw8+UFFI1CLYunUrLl68iIkTJ3rMIcUIqUaNGqFw4cIqQuvcuXNKJ8qqFdW8eXOUK1cOnTt3Vvu0dOlSDBw4EN27d0+xcym5RN6NRMcFHdXEeSHAiYzUHnhOnA8Uom8Bf9TTE+cFQfB5WAeEelJmgZx//tHVkRmczEp9jOIPJtgid4yZfKl1pl1De8UWLuN7voQUhhHceDGJvSQIKUnrsyeMzv98zchbe2lrfNbYW75iBTB9evztMCLK+j3W7/U3uN+jR2un2rlz2kHlaWzTJG01wYSUO6VMqH9QsWJFVKpUSc17kt9//12NGjINr1ChQioSypxMGDlF0U6m+tWtWxcdO3ZE27ZtlRNLEISUEAJkLqUnzguC4BdUrAhMnBj3mlWcaY9dusQKtt7cM8FkwIABShaB0eazZ89WkeA9evRA37591cCap3U6XYGFYTjaaU6+5jQTBO8j9pKQyjCVjhFNrJrH/3xtz1lFJxMdJPaWc3172wkk0qbV+lnk3Xe1IeTJCClTNJ5I1T+ncCp979FHH1Uh3ow44nxifP/99/Bn3Jm+d/32dWQeo8MDIwZHIFO4CHkENAw9MMNBIyJEuEUQBJ9olh5+WA+EEupJDRoE0N8RTNpSDAwzg/UjeBx8yFagTierCZv6UWb1vTZt2ri0HVd1Ol1N32OkFCfrMaBjyh3HQAgyxF4SBM/jKO3O39PxkgudQ5UrA7t2AUOHAu+845nvoWOPDkB7yxlxFmRcddJeckpWgRsyK9hxXhAEQRAE34eOp48+Alq2pDaj7guyHkgwOaR8nXbt2qkppeTKlUtNnoISDZ6SaRAEQRDcDB1O9pxOjpYHOkxdZBVCPm9ZmK1PHyBPHvd/DzWk6Dexjfv5+2+gYcOEFQ0F551Ss2bNUv8ZVMXw8Ny5c3s8ZU8QBEEQhJRToQLw3XdAvXpaX5iaUqtX69eCb7B582YVKcUBQOpjVmVJbw9y/PhxpQfK/1FRUUojlFBwPXNqCMAKgiAIQmrDCGRWgdm4ERg71jVBeGehw69xY2DZsvjLX3+dD3tg2DDg7NmgF0BPkaYUnVKlS5dWlfAEQQgSom4BK1rrifOCIPgd998PvPlmXAR7ly6spuvtvRJYHfjBBx9EzZo1lbZU7969VZXhJk2aqKIunuKtt95Sjq9hw4YhIiJCzXNKieaUIAQ9Yi8Jgm/DKCUzbW/yZGD+fJ3O6G7MYmzDh+tKM9SxYlX2b7/VI4UigJ4yp1RoaKhySl24cMGVjwmC4M8YUcCpxXrivCAIfgkH6cqX1/OsiGw6qQTv8fLLLyu9hd27d6vIJVY23rVrl1pGJ5WnoJYUBxptJ1Y6FgQhmYi9JAi+T7NmOkqJI3OdOrm/Ot7p08CePdoB9vLLQJEiWsjTVndbBNBdT9+zMn78eCXAOWXKFFSgp09wSHhYOGa1mRU7LwQ44eHMdY2bDxRCw4Has+LmBUHwS9gsffmljlynLcSo9aef1lX6Ah22XLMs877Cb7/9hj///FOJm5swfW/y5Mlo3ry5V/dNEDyG2EuCIHgLZnwdPpzQOdSihXu0tsy0Pabh58gRt9xeanxUlBadLxSEGl8pdUo9/fTTuHHjBipXrozw8HBkyJAh3vsc6RM0acPSomuVrnI4ggWWG+0agOc7NC1QIgB/lyAEIdWqAQMG6Ehy6ks99RSwZYuOKg9k0gLwxVYsOjoaafnssIHL+J4gBCRiLwmC4C0OHtSOKE85h5Yu1f+bNIm/nNFZFFu3fndYmK6CKLjulJpEtXpBEARBEPySt98GFiwAjh4Fdu6kvpCujiwV+VIf6kn17dsXX3/9NQoUKKCWUbezf//+SldKEARBEAQ3Ys85xNfucA6x4p4jpxQdXp99BnTvrtdjet/UqRIllVynVBeqowpOcTf6LpYcWqLmW5RqgTShAT4UHeww7GCJPt8qBDRQQg+io4ArO/V81opAaJi390gQhBSQPj0wezZQv762i8aNAyIjtZ10332BeWjvAohpndEiOcaPh/j444/Rpk0bFCtWDIULF1bV91gRr2LFivjqq6+8vXuC4BnEXhIEwVuYziGm7DFCihQvDhQsmPJtMy3w+HEdDWqvxPFzzwEHDlAPCXjsMf1aUCTLLjt8+DBmzZql/n/wwQfIkyeP0kWgQVXeVFEVEHk3Eg9//bA6EhGDI5Am3FfMYMEjsFf3sD7fiIgIIKfULeDXmPLkHSOA0Eze3iNBEFJIlSpA9eoAi61xsHDePCBXLoDa2oEYMRUJIKZ1RoQPOaVoN23ZsgV//PEH9u3bp8TGqSnVtGlTb++aIHgOsZcEQfAmdAYxgGD9eqBzZ+1M+uEHoF079+hJPfCAY2OKWlPkv/9S9l3BXH2PrFy5Uo3g/f333/j+++9VKWGyY8cOVVpYEIRAIwTIUEBPnBcEwe+5cgVo0ADIkkW/PnUKWL5cLxdSn2bNmqlKfKy4Jw4pQfBXxF4SBL+KmGrfHhg0SL9+7TXgzp2UbdNR6p4VRmWZZZCFWFweLHz99dfxzjvv4JVXXkEW05oF0LhxYxU1JQhCgJEmI9DupLf3QhAEN5I1K5A7t66MbFYpXrFCB3kKqcuGDRuwYsUKnD17NoG4+fsskSgIgn8g9pIg+B90SlHbiQLo06YBPXsmbzt8fpuRUg8+6Hi9EiXiqgDeuqU1FQTXnVI7d+7E3LlzEyzPnTs3Lly4IIdUEARBEHwcRpW3bat1Nvfu1RMHCKkrtXKlt/cueBg9ejTeeOMNlClTBnnz5lWaUibWeUEQBEEQPACDbIYP184o/n/6aeCee1zfDivHnD+vDaxatRyvR62EzJn1KOA//wBlyqRo94M2fS9btmw4ffp0guVbt25FQXcIhAmCIAiC4HEoat67NzB/PpAjh17211/Ahx/KwU8tGGE+c+ZM7N27V0VLLV++PHZaZo64CoIgCILgOZ5/Hrj3XuDcOeDdd1OWukdthPBwx+txwMmMlpIUvuQ7pZ588km89tprOHPmjBrFY6j5mjVrMHDgQDzzzDOubk4QBF8n6hawqoOeOC8IQsDAAb0KFYDJk+OWvfUWsG2b1pm6ft2bexf4hIaGom7dut7eDUEQ3IHYS4Lgn7Ba3tixen7CBODbb4F//3W/npSJOKVS7pQaNWoUihQpoqKiKHLOKjENGjRAnTp1VAi6IAgBhhEFnFigJ84LghBwdOoEPPSQnqfYOSsV0y776CNg3z5v713g0r9/f0y2egQFQfBfxF4SBP+FmgalSmmdp44dgaJFgRkznPss9Q8Yak7EKZU6mlJp06bFnDlzMHLkSFXGmJFSVatWRenSpZO3BwFMeFg4Pm71cey8EOAwVPNjfb4TDdv0N0LDgRofx80LghBwMJp8yhSgXDkdHcWIcjqjQkOBRYuAwoUdVzf2B9hyfWyZ9xUYZd66dWuULFlSDfLRxrLCKseCEHCIvSQIgq9B4XFrOh2Fy198EWjRQlfqS4wNG7RGFPWiKlVK+rskUirlTqm3335bGVElSpRQk8nNmzfx7rvv4i3G/QuKtGFp0atWLzkawQI7E70C8HyHpgXuDcDfJQhCPIoU0RWRzcf4mjVAlSrApUs6esqfnVJ09fhiK/byyy8r/ShWMM6ZM6eImwvBgdhLgiD4Gqy+Z1MBF1FRwKFDSTulzNS9xo31aF5SiFMqASGGYRhwgbCwMCV0nidPnnjLWXmPy6J48vyYq1evImvWrLhy5QruSY7yviAIgiD4KVevao2pEyf0a+p+duumBdH92Snlq7ZClixZMG/ePBUt5W+IvSQIgiAEDNSQYsqerWNq9mxdkS8xGjXSpYs//VRHVyUFQ9HLltVV+Gh4hQRutV1nbQWXNaXow7JXpnj79u3IYZbvERRR0VFYcWyFmjgvBDh0yK5YoSc/d87Gw4gGrh7UE+cFQQhYaC8wCzksLG7gMF8+/3dIsUVeETP5UutMu4mpe4IQVIi9JAiCr8FoqM8+izOATH/HCy/o0HFH3LgBrFvnvJ4UKVZM/2fK3/nzKdrtQMFpp1T27NmV8USH1L333qvmzYner2bNmqEjRcGEWG7dvYXGXzRWE+eFAIfCeAzb5MT5QCHqJvDzvXrivCAIAc0jjwCDBul5xlKPHg3cvQu/hi1y45jJl1rn4cOHY9iwYbhBo1YQggWxlwRB8EWeew44dgxYvlyn7bVsSY0igNHMS5bo5bZV+RYuBG7fBgoUAJwdZEqfHihYUM8fPer+3xHImlKTJk1SUVLdunXDiBEjlCPKJDw8HMWKFcMDDzzgqf0UBMGbpI273wVBCHyGD9d21v79wIEDulKyFNh1Px9++CEOHz6MvHnzKjvKVuicBWUEQfAjxF4SBP+PmDI1pL77Tgudr16tHVSEmlGMqKIDi9X5unfXy0+fBmbO1MudgbpSprh6rVoIdpx2SnXp0kX9L168OOrUqZPAcBIEIUBJkwnocNnbeyEIQiqSLh3w+edAnTo6WmrkSODxx3W1ZMF9tGUJakEQAgOxlwQhsMiYEZg6FShfPm4ZNaeef16P1jGayoTGkrPV+kyn1KpV8Sv+BTEuV99r2LAhoqOjceDAAZw9e1bNW2nQoIE7908QBEEQBC9Qu7Ye8Js+XUemU/CcknnOFJYRnIOpe4IgCIIg+Cj//Wd/udUh5Wq1PiIV+FLmlFq/fj2efPJJ/PPPPyqdzwr1pvy9+p4gCIIgCJpJk4AffwTOntUDenRQUfNTcA8nTpxQtlOhGAN2w4YNmDt3LsqVK4cX5EALgiAIgncpXVqPxlkDcfj6/feB/v11hJQJRdKdDSkXp1Q8XB7v7NGjB2rUqIFdu3bh4sWLuHTpUuzE14IgBBhRkcC6rnrivCAIQQOr7o0bF/eaAui2Gp9C8uEg33IKpwI4c+YMmjZtqhxTQ4YMwdtvvy2HVhD8CbGXBCHwq/LxP1/37QtMmxZ/OVP9nImSIuKUSplT6uDBgxg9ejTKli2LbNmyKcFz6yQIQoBh3AWOfqEnzguCEFQ88wxT9/X81avASy/5fzU+X4EDfLViBE7nz5+PihUrYu3atSpa6nOKegmC4D+IvSQIgV+Vj/9NMXNHy11xSp04oTUSghyX0/fuv/9+HDp0CKVE7TRJ0oalxfim42PnhQCH4v/jx8fNBwohaYEq4+PmBUEIKhilTqHzhx/WTqmffwaKFQN69tRFZ3Lnhs/Dlmu8Zd5XuHPnDtJRVR7An3/+iUceeUTN33fffTjNSj6CEIiIvSQIgj9X5XNmeVLkzQtkyADcvAkcPx70lWRCDFthqCRYuHAh3njjDQwaNEiN6NlW4atUqRL8matXr6qIrytXruCee+7x9u4IgiAIgtcxhc7nzIm/PDxcV+V7+WWgRg0EDe6yFTjQ17hxY7Ru3RrNmzdXup2VK1dW/9u3b49/fThXUuwlQRAEQUgBrOq3Zw/w++9As2YBeSidtRVcjpR67LHH1P9utE5joEgnfVsidC4IgiAIgcedOwBtierVgXPn9KCe6az68ks9NWnCFDQgRw5v763/MG7cOLRr1w7vvvsuunTpohxS5Mcff4xN6xMEQRAEIQBhCh+dUkeOINhx2Sl19OhRz+xJABIVHYUtp7eo+Wr5qyEsNEYITQhMWHlyiz7fqFYtTvjO3zGigZsxaSQZ8gMhUg9eEIKNK1eA9Ol1dPl99wGZM+vm7sAB/R5ZuhR4/XWt/+lrsC5wTOuMatQjhW/QqFEjnD9/Xo0kZs+ePXY5K+9lzJjRq/smCB5D7CVBEAQRO0+JU6po0aKufiRouXX3FmpN1yOdEYMjkCk8k7d3SfAkt24B5sh2RIQuWxUIRN0EfojJle4YAaQJkN8lCILTsI5JnjzA+fO6qeP4VPPmwC+/AIsWAa+8Aly7BsyYAfTqBcQE/PgMtwCYcUcRrCoI3yEsLCyeQ4oUo2iXIAQqYi8JgiCIU8pCskMe9uzZg99++02FmFsnQRACkJA0ehIEISihj71tWy1/QOFzvmY6HyOmnn8eeOMNvV50tHZQuaZWGVy0bNlSVdhLimvXrqn0vsmTJ7v1+48dO4bnnnsOxYsXR4YMGVCyZEkMGzYMt6X6jyCkHLGXBEFwtQLfEUnfc7mXeeTIEaV/sHPnzlgtKdUGh4So/1EMyRUEIVXhfccqTp4hDGh3Tc+yDPxdxhwIgnth0QxGjAi+C9P2ChfW0VIrV+pghz//BFq3Bvr2BaZO1XbVsmXATz8BMYXkBBs6dOiAjh07IkuWLKraXo0aNVCgQAGkT58ely5dUoN+q1evxuLFi/Hwww8rvSl3sm/fPkRHR2Pq1KmqkvKuXbvQvXt3XL9+HRMmTJDzJQjJhZHkT3jKFhMEIeAQp1Tyq+/973//Ux2HadOmoUSJEtiwYQMuXLiAAQMGKGOmfv368GfcWU3m+u3ryDwms5qX9L0g4Pp1HTaQiul7vH3PnDmDy5cve/y7BMHTZMuWDfny5Ysd5BB8FzY5CxdqAfSKFYEHHgC++w5o316/X7o0sGuXrs7nC1wHkNmN6XsptRUYlbRgwQJ88803WLVqVWwbzmu/XLlyaNGihXIUlSlTBqkBHV9TpkxRA4/OItX3BH+ylwRBEHyOGzfi2r+LFwGbVP5AwGPV99atW4dly5Yhd+7cCA0NVVO9evUwZswY9OnTB1u3bk3pvguC4CSmQypPnjxKFFc684I/QufqjRs3cPbsWfU6f/783t4lIQmyZQMaN9ZVjHfu1DZV7dpA3brAmjXAwYPAJ58A/frJobRHeHg4nnzySTURGms3b95Ezpw5VdRgasPvzyFlEwVBEAQh9WBBk7x5gf/+02KdAeiUcpY0yUkTyhwzupErVy6cOnVKjeRRAH3//v2e2EdBEBzci6ZDih0Zj1bfu3FCz2csLNX3BI9AbRtCxxSvaUnl832oxV2lCvDbb8CIEUChQkDVqgDlkhiDzWWdOwOebJ4CBY4icvIGhw8fxkcffYT33nsv0fUiIyPVZB39FATBQlQksOUVPV/tfSAsnRweQRCSTuH77z+tf8Dq7UGKy0LnFSpUwI4dO9T8/fffj/Hjx2PNmjV4++23VTqfIAipg6kh5fmy4QZw65yeOC8IHsK8lj2njya4m3LltB1F/8SlS9QGi7OpmJE2fLgc89Ri+PDhKlo2sWnTpk3xPsOBRQqvU+fqeSrWJwIj4k3nGafCFBgTBCEO4y5w8BM9cV4QBCEpRFcqeZFSb7zxhhLDJO+8844S4aSOFCM1qI0gxJE2LC2GNRwWOy8EOOyNDRsWN59KeD5lLwTIUCBuXhA8daWJlpTfQWdUrlyM3NSV9+7e5YAVsHevlkqYMgXo2RMoW9a7+8kWeZhlPhDp3bs3Hn/88UTXKcbwNotDqnHjxnjggQfw2WefJbn9wYMH4xWWVrRESoljSvAne8njhKQFKgyLmxcEQUgKcUolT+jcHhcvXkT27NkDokMhwp2Cv3Dr1i0cPXpUlfVm1SZB8HfkmvY/OEb10UdaCoGRUpxq1QJYSHHkSL3OQw8Bv/yCgMLfbYWTJ08qh1T16tXx1VdfJStd1t+PgSAIgiB4nc8/B559FmjWTAt1BhjO2goup+/Zg+KYgeCQEgRB8BTnz59XleVGjx6d4D2Wh69ZsybuMsxEEPwICpy3basH+hjwkCWL1ukcNAgws7sWLwaWLfP2ngrWCKlGjRqpKCdWTT537pwqmsFJEARBEIRURCKlnHdK9ejRAydOxAgdJwFT+ObMmePUuoFOtBGN3Wd3q4nzQoDD3JXdu/XE+UCBwZTRd/WU8sBKu6XRgwEWhmCKzIgRI7CT5cpiYFn4n376CV9++SXSpHE5o1oQvM599zF1DBg1Cnj4YYADYZSeHDMmbp2pU725hwBb5N0xky+0zkuXLk30/ejoaCWR4Al+//13HDp0SFVSLlSokKp2aU6CkCoEsr10+7KePGAvCYIQwE6pf/7RGghBilNOqdy5cyuB81atWmHKlCnYuHGjCv2+cOGCMmx+/PFHvPrqqyhSpAgmTZqESpUqeX7P/YCbd26iwpQKauK8EODcvMlKAHrifMAQDVzapic3dOc4Qk/tE2qT0FHTjOGqAN5//31UrFgRmTJlUiP4PXv2REREhHqPWcZsh7777rvY7VSpUkVVaTNZt26dKqVufsaWrl27om3btipSKW/evMiWLZtyEDE6adCgQSrikx20mTNnxvvca6+9hnvvvVeJcLOYw5tvvhkrxM39atq0qRIKNjOhWRGRbeHQoUMT7MMjjzyiSsA/88wzahuMUODvpIBwWW+L7ghCCiOmKFfUvLl+zb5m/fq0H/TrRYt0ap+3YItcIWbyhdaZ9hTbwRsU3rJh165dKnKS9pYnYFvI9sreJAipQqDaS1E3gAXZ9cR5QRCEpChQAAgP1w6pf/8N2uPllFNq5MiROHjwIBo0aIBPP/0UtWvXVp0udgjLlCmjOlhHjhzB9OnTVceQHUtBEPxLF+bUKf0/Nfjiiy9UVBArd06NCaEIDQ3Fhx9+qDpkfJ+j+HR2E6YHs/1ZsWKFen3p0iXs2bNHOXb4n/A96qNkzpzZ4fdym0xd+euvv5QTjNWqWKyBmnh///23igq1jQzNkiULPv/8c/U9H3zwAaZNm4aJEyfG7hf3dcOGDWrfCT9Ppxe3bQ9ugzp8bFfpkKLDv2/fvm47toLgTQoV0pFTZO1a4Ikn9HxkJCOpvbprPsWqVatUtBQH8dgOWqOj2I7RSc22UBAEQRCEACY0VI/qEQp0BinJEjpnJMA///yDmzdvqkiHkiVLBoymlDuFO6/fvo7MY3QHOWJwBDKFZ3LTXgo+CT06pkOE0ToMHUhlUejkRH3u26ejGK5cAbJmBdq0ietUKlQTYTYTIfTEJNiGK1lnjJTi/bV169ZE1/v222/x0ksvKS0m8tFHH6n0N6a+LVq0SHXe6Bxv0qSJcu60aNECVatWxdixYx1GB9BxRQc6HWDkvvvuU851OqlIVFSUuv/pYHdUxerdd99VacrW0urc186dO6voLzqd+NsYXZWYc6x58+YqKmzHjh0oWrSoE0cu8BGh88CAGbkLFuhmkM1Hjx56ee3ajGj0zj7R3266qxlLmckHbAVe76+//jo++eQTvPDCC1i/fr2KQufgXxs2xD6OCJ0L/mIvpRps8IwYQywkjV17SRAEIQGtWgG//QZMnw4891xAHSBnbYVkCZgw7YWTIAi+Ax1SNplnScLoheXLgWvXtDgx02s2bwYaNwbSpTPXolGVuGHVrZtrjqkaNWokWLZ8+XKVWseIJDZgTKtjp+369evKeUNnFiOK6KRauXKlek2nFOfZoVu7di369euX6PeWL18+1iFFGNHESCUTVqDKmTMnzp49G0/ziWnJTFVmaiD3y7ZR7dChAxYuXKjS8JhyY3VI8TvpxCf169fHr7/+igcffFBFnDIFURxSQqDBKPQGDXR7tG0bkDMncOECsH69doLHc3oHMRxMYNQl2xs6ptjOUR6BznJBEPwQOqFC0np7LwRB8DdKxOhKHTmCYMUt1fdSk8jISNWRY2TWNlq7Fo4fP47//e9/yrBjBFefPn2CRkRZEJIDpRwoaUKHFDuS/M/XnpZ44D1qhU6bhx56SDmIqBu1efNmTJ48Wb1n6jfxPTqM6IQynVINGzZU8+zIMXKzXr16iX4vNaessB2xt4xpNISRC4yYov7Lzz//rCKgqBVl265QF4b7TKcWU52tLF68WLVVnBiBZcL0RRE2FwIVtiUnT2qHtxmVTiy3QNBz+PBhlZbMyElGR1H6gG0aHdyCIAiCIAQJJcQp5XelnqgxU6BAAWzfvj3ecqbdtG7dWokhr169Womwd+nSRQl3Mu1HEAIdRioxYsnVCHr6fBjFULCg7kTmygW8+KIlmp6VI2+e1PMZCgIhCX3ZKS0ax1Q4RiC99957sZFM8+fPj7eOqSvF1D1qrTDqiHpPdFqxQ1etWjX12p1Q64WRTFbRcjPqycqAAQPUfjMKis41tkWMhiISCSUEI0wHZkAhC7oxLXjLFp3ZwuK848YxKhFBzccff6xS95h2/P333yvb5fnnn1fpwSyG8NhjjynbhXp3giD4CVG3gR0x9kKlUUBYuLf3SBAEf3JKbdumxc4p0Blk+FWkFDt8LGU8YcKEBO9xOdN+vvrqK6Urw4pY7OBSlJipQIIQDNA55MrEzuJjjzGNDTh3Tv9/9FG9PG49A2nu/KcnztvZTkqhLh2dUuyEUfNp9uzZytFkC6Oj5s6dq8SBmUJnOqrmzJmj3nM3pUqVUhGY8+bNU1ENFDO3jWL45ZdfVMU+7gMrCbKjSYc4xdgFIVhhG8K0vRw54ubJmTPA0qXe3jvvM2zYMFXkgZGhdEgROrZZ7ZNO+n379sVLLRYEwQ8w7gB7J+iJ84IgCM6wPSbYhhoH1JmdMSPojpvTTikzncVb/Pfff+jevbvqrLI0uy2s+kcDjlFUJhyBZLof02q8QdqwtBj4wEA1cV4IcJgGNnCgnmxSwnwZypf07g306aP/J5QzCQEy5NVTEtpSyYUpuayGN27cOHUf08FDfSZbGjdurKIirQ4oprtwGf+7G4oN9+/fX5Vu5z5St+rNN9+Mff/cuXN47rnnVKU9RmqZnU22Q6zCJwjBCiMt27bVkVJ0SFWpEvcex5WY1peaFT/ZIg+MmXyhdWa051NPPWX3PerQmdVABSEg8VN7KUmoJ1V2oJ5EW0oQBGdgZNTIkXGv6XNhygqXBxFOV9+7//77lUMosYpSnoK7yJSYunXr4o033sCxY8dUxTHqu7CjSCh0zOWMmLKSLl06Vc79CbMutQ10WnEyYVRV4cKF3VJ9TxA8iVQqEwINuaYDDzqdmMqXIYOOTr98WUdXduyoHVZMF6bzyt+0vaXynBwDQRAEQUgxrDj14IP2l3sgC8Tvq+9RF8Ust/7yyy+7ZScZXTBixIhE16GAMaMT+IMGDx6c6LpM5bHn0LK33ITRGEntgyAIgiAIyYuYMvXpnn6aWkq6UujWrdo5FRUFLFoEFC4cOFXhk4JpwM7Cgi2CIAiCIAQopUszf19HSJlQeLNUKQQTTjulKDrM0ui9evXCjz/+iFmzZqFQCkW4mBLDylaJUaxYMbzzzjuqChajnmzLyjP8/YsvvkC+fPlUuLsVarpQBJll3x1BR9crr7ySIFLKHUQb0Th+5biaL5K1CELtCEQLAQQbk+P6fKNIEd3ABAIqmNIMqGS5Y8+k8AmCENh07aqdUuT8ee2QYqVPyq8xmsqTTimaejGtM4p4WVBz4sSJ8V4zDZgVPLNly6ZeX758WckU5MmTR5xSQmASyPaScVfPh6QRe0kQhKShP+Wzz4Du3XUbwn7W1KlBJ3bukkRx+/btlW4LHVMsXdy5c+cEJc2pC+MsuXLlUpMzo4p0TJmcOnVK6UV98803Kq2QPPDAAxg1ahROnz6N/MwJiBE/pyOrevXqDrfN922dXe7i5p2bKP5BcTUfMTgCmcKDZBg4WGHvqrg+34iICKBh/2jg4lY9m6Mq3ffe3iFBEPwQyq6VLQvs3asLK9AZ9d9/1FDSYuie5CaAmNYZEYzggvc4evRo7DwLN3zyySeYMWMGypQpo5bt379faWi+SE0JQQhEAtVeiroBzM+s5ztGAGkC5HcJguBZnntOO6P4n7YA/wcZLtfNypEjB8qWLasqUFHTyeqUSixNLiUU4SiKhcyZM8dW7DKjtZo3b45y5copRxlLKl+8eBEDBw5Uhp1oQwmCIAiCd6GJ0K0bMGiQfk2hc/ZLWaGPwueB0i91BRZOYBS66ZAinGc0FQcCHYmhC4IgCIIQQLRsqf8fOKAFOYPMKHLJKbV7927l9GFaHKOQWAnLVwgLC1Ol2Xv27KkE0TNkyIAnn3wSE1jmRxCEFBAKZDdLZwVIiL0gCF6BPpbXX9epe4yWevtt4PRp4M8/gcce04LowQSjuykzYAsrirLqsCAIfkRYRqD9pbh5QRAEZylQQFeAoVG0bRtQt25QHTune5gUOGcaHKvd7dixw6sOKepMUcDcrLxnjaj6+eeflTbDhQsX8NFHH3ksNU8Qgiq8ITSNnkRPShCEFEB7q0ULPX/yJFPoAUop3bgBLFsWI2EXRDRp0kRFdG/atEnZNYTzTN1r2rSpt3dPEARXoI0Unk1PYi8JguAqNWvq/5s2Bd2xc9op9cEHH+Dbb7/FzJkzkSVLFs/ulSAIgiAIAUmXLnHzCxcCzZrpSnx0Uq1Zo9P6GLkeDNCmKliwIGrVqoX06dOrgTRqZVIbc/r06d7ePUEQBEEQUosaNYLWKeV0+t6uXbuQM2dOz+6NIAi+hxEN3Dyj5zPkA6SKpCAIKaB1ayB9euDWLeDHH4HJk4H69YGvvwZ++01HU7EIbtu2wH33Bfahzp07NxYvXowDBw5g3759KlqKup333nuvt3dNEARXiboN7B6t58sPAcLC5RgKguC6U2rjxqA7ak47pcQhJQjBigHcPKVnM+T19s4IguDnULuTmWk//6yjojggyAp8jJSi4DkdVmnTAosWaedUIGp9smJw27Zt8cgjj8Q6ocQRJQh+jnEH2DVCz5djRQdxSgmCkAyn1P79wNWrwD33BM3hE9ViD5ImNA161uipJs4LAQ7zT3r21JOlKqX/EwKkz60nzgcRXbt2VR1Hd/P5558jG4V0BCFIadMmbp7OpytXgKxZgYIFgdBQIDISuHRJL3cHbJF7xky+0Dr36NEDGzZsUGl7dEYNGjQIq1atitWVEoSAJlDtpZA0QOmeeuK8IAiCK+TODRQtque3bAmqYyctpgdJlyYdJree7MmvEHwJKvYyDyXQYLpeppgGMkA5duwYihcvjq1btyYooCAIgvv53/+0DjB9MHRKDRkC5MihtaQuXAAOHQKqVdOOKnfAkie+1Dp36dJFTZGRkVi6dCkWLVqETp06qUp8rVu3Rps2bdCiRQtkzCgVvIQAJFDtpbB0QM0A/F2CIKRutNQ//+gw8kaNgubIS6SUIAiCIAipSt68TGHT87t36wrIDEosUkT3V1lPJXt2ICwssE8Mhc0feughTJ06FadOnVIVhCl8/tZbbyFXrlx4+OGHsYbq74IgCIIgBD41glPs3GWn1IwZM+wuv3v3LgYPHuyOfQoYGIZ/7vo5NUlIfhDAIf9z5/QkKRiJsmDBAlSsWBEZMmRQenUsfX79+vXYdLnRo0cjb968KsVtxIgRqn1hekuOHDlQqFAhVbHKys6dO/Hggw/Gbu+FF15ARERE7PvR0dF4++231WfZCWQ01G9UVI6BUVKkatWqCAkJQSObkYkJEyaoaljcdq9evVQ0g8nt27fx6quvqo5kpkyZVOWsFStWJEjXK1KkiIp6aNeuHS4wFEQQghzbFD6KmvfuDYwaBTz6qI6ccpc/hklx52ImX0iQs7ZPVth+jBo1SrVpnJo0aYLT9NgJQiAh9pIgCIJ9agSn2LnLTqkBAwbgsccew8WLF2OXsWIMdRHmz5/v7v3za27cuYE8E/KoifNCgHPjBpAnj544HygYUcCFzXrifAphB+uJJ55At27dsHfvXuXAefTRR2Mdt8uWLVMRA3/99Rfef/99DB8+XEULZM+eHX///bfSYuF04sQJtf6NGzfQsmVL9f7GjRvx7bff4s8//0Rv9m5j+OCDD/Dee+8p59KOHTtUWgwFhg8ePKjep7YL4ee4f99//33sZ5cvX47Dhw+r/1988YVyMHEyefbZZ1Ukw7x589S2O3TooPbH3Db3mb+1Z8+e2LZtGxo3box33nknxcdREPwdq1zbDz/o/xQ1L1QIaNlSp/cdPgwcPZry72KLnCdm8oXWmU55tnGJUbJkSfTv3x/t27dPtf0ShFQhUO2lu9eBr9PqifOCIAiuUr26/n/kCGDxtwQ6LjulqLny33//KYPqjz/+wOTJk1GtWjVUqFBBdbgEQQhEDLfFF9Dpw8gnOqKKFSum2hI6bDJnzqzeZzTUhx9+iDJlyihnDv/T8TRkyBCULl1aRWSGh4fHprTMmTMHN2/exJdffqnaIUZMffzxx5g9e7ZqqwidUa+99hoef/xxtb1x48apaKlJkybFlmUnjITKly+f2gcTOru4vfvuu085x6j3Qg0YQmfV119/rRxh9evXV53IgQMHol69epg1a1asQ4xOsNdff10JGvfp00e9FoRg5957dXQUWbsWOHs27r1cuQBT3m31auDWLQQUdF4zQpQDfdSVEgQhQDDu6kkQBCE5ZM8OlCql5zdvDppj6LLQOdNcOLrH0TtGA4SFhanOIDt7giB4P+LzzBlP+K5jvPYOyJfP+dTnypUrq5QUOqPonGnevLmKBKDzh5QvXx6hLL8VA9P46GwyYZtD59HZmB4so624TabOmdStW1el7O3fv1+l9DHyisus8PX27duT3F/uD7/ThGl8TKshW7ZsURFetqXc2cnkPpr7x5Q923Lw1vRBQQjmaKmxY5liC/z8M9CtW9x7FDo/dkxX4aMfuGpVLXxuudX9lvHjxyvHPB3vbAvoROcAnyAIfkxYBqDtv3HzgiAIye3QHTqkO1fNmgXFMUxW9T0KcTI6oE6dOqrTN23aNDRo0AAFChRw/x4KguA0dEidPOnuAxbi1q3RwcMoy7Vr1+L333/HRx99hKFDh6o0N5I2bdr43x4SYncZnU6ETiG+trvnluW26yT2OSuJfTf/8/ds3rw5nuOKmJFfoicnCInrStEpZepKWZ1SvKUo7zZlCkAfLsXRWSmZjiwzwsqfqV27too+f+ONN5STvFmzZkiTJr5ZZk0lFgTBD6oVZyzo7b0QBCEQnFLz5gWVrpTLTqkXX3xR6apQE4Vh50yP4Ugfox6mTJmCjh07emZPBUFIEkYs+cP30rHDThgnVpkqWrQoFi5cmKzvLleunGqTKJRuRksxtY/RVoxguueee5TDfPXq1cp5bkKnGLXwCNMBSVSUa5pZFEbnZxi1xfQ9R/u3fv36eMtsXwtCsMJbkO0HHep//KHlZTJmjHuf88zCvXYNyJBB15Gg86pw4cCImGJUJdsPtolZs2ZN4JQSBEEQBCHIqBF8Ffhctn7Y2WNEA9NlCPVXFi9erLSl6JwSp5QgeA+PtF1GNHArRuwlfR49EpgC2H5Qk4lpe3ny5FGvz507h7JlyyqhcFd56qmnMGzYMHTp0kWJonNbL7/8Mjp37qxS/wgr93Edaj5RS4p6T9TAox4V4X4wzY9pNKzQlz59etVBTAo6vfj9zzzzjBJSp5Pq/PnzSqydjnqWeqeGFKNKma7DyoKMDpPUPUHQMFP3kUeAzz4Dbt4Efv89vgD6lSva+UTZNwY2Uu6N6Xzmcn+GbcFzzz2nnOZMBaZunSAIfkzUbWD/B3q+TF8gTA94CYIguES1atroYVEnjszF9GcCGZd7l0xTMR1SVlgmne8JghBoGMCNf/XkBrFzRi5Rl44OGzp1mLpCh06rVq2Stb2MGTNiyZIlqiJozZo1lT4VNasoTm5CxxAjOznRWUSn0I8//qiE0wmjEyiuPnXqVNVBbGOtVZ8EdHDRKcVtU0SdVf3oaCvMUI6YFJ3p06erNEU6xNgR5W8WBEFjvd0YBWWFvmE6ohgpdfu2ts8oP+eEz9inYdQ524ru3burqM3Udkjxu4sUKaIc8NTJoxOf2nuCIKQA4w6w7VU9cV4QBCE5ZMkSp1MQJP6VEEMET+Jx9epVFSFx5coV1XlOCZF3I/Hizy+q+akPT0W6NOlStD3Bx2EFpRf1+cbUqUA6z57vW7du4ejRo6r4ADsWHoORUtf/0fOZiqY4UkoQvH5NCz4FK+sxEioighUwdSqfNYtt3z5gzBg9WMgxsWefdV1TivXtYlpnTAWQzsu2Aos3sEiMt8TNJ06cqAou0CF18uRJVTWU0EHmDXtJCDJS2V5KNaIigQ0xv6vWVCAsQH6XIAipzzPPALNnA8OHA8OG+e0ZcNZWSJZTasGCBZg/fz6OHz+O2xy6tMAQdH9GjCzBX5AOvBBoyDUdvHToQNtCz69cCVjk32IjqPbvB5o21VHt/m4r0HYytex8AUaOMr2YGle2xR0cIfaSIAiCIHiIDz8E+vYFHn4Y+Oknvz3MztoKLmtKMcWFlbKo37Jo0SI8++z/27sP8Ciqrw3gJ4UEEkLvAglVSqQIohQlKIReREUUFRQpAqIoiIgCKkURRT8RpQkWigX/ItKkN6kC0qQHBWnSAiSUkJ3vee9kNrubQkJ2szuT9/c8F2ZnZ3dnZpPJ3bPnnvusHD58WLZs2aKG8BERERFldgifEZT6+eeUQSkM1ytQQK9BZXavvPJKhrf96KOPxNMw9Bn19VD7LqMBKSIiIvKge+5JLhiMHKIMzBhuZpkOSk2cOFEmT54sTzzxhJrx6rXXXpPy5curGbTQsaFkSEKLT4hXyyG5QjI0/TyZGC4YmDrKmDKK7zcRUYa0bi0SEIAZMPWsqA8/dL6EGqM5Mernti7PIpJ0dRZM7ufNv8bbt293uo16nJjFEzXp4MCBAxIQECB16tTx6H4MHjxY1d6Lj49Xte9+/fXXdLdHFhWa47efRLeF/SUiovTVrKl3jFDTADUf77jD0mcs0985Ysgevk0DzFZ1GdVHRVSRzNmzZ7t/D00MAam8Y/KqZgSnyMIQkMqbV29GcMoKtESR89v1hmUiIjdD8fLGjfXlI0dE9uxxvt8oOXO7QSlckfMmNW9fnVeuXGlvbdu2laioKDl+/Lgqf4B27NgxadKkibRGpC4TMPsovvxKr211mKIVs5IiQIbJFxAEw4QN6VV0GDNmjErBN5oxmQNRplm1v3QzTuSHAnrDMhHR7QoJEaleXV/essXy5zHTmVIlSpSQc+fOSXh4uGobN25Us/GhOC1rphNZFINRRORhHTqIrFihL6N8QmRkyqAUiqJbCWYeRVCoIKJySbA8cuRIiY6OVrN6ZlS/fv2kc+fO6W4TERFhXy5SpIhqmAW1atWqKsiEPh0KoKdmyJAhTkMPkSnFwBSRi4RYnhIico+6dUV27tSH8KGTZGGZDko9+OCDMn/+fDVjTPfu3WXAgAGq8Dm+fevYsaNn9pKIvMhfpIDx6dACBV2IyCe1aiXSv7++vGwZgiDuy5TyVQjsnD59Wqob34YmOXPmjD0TPaOMINPtML5UdBye5yo4OFg1IkpDQB6RNgeSl4mIslpX6ssvRZYsEendW6R0acuez0wHpVBPymazqeXevXtLoUKFZN26dSoFHbeJyGJQ2CUgqaALEZGHlC+PTB6Ro0dF1q3TR/Uge90dNaV81cMPP6wmjEHGFOo6AbKVMLTOU1/0bd68WbVGjRqprKwjR46ouqAVKlRIM0uKiDLAz18kXyWeKiJyj3//1f9HplR4OAIxIt27W/LsZjoo5e/vr5qhU6dOqhERERFlJf7dtKnI1KkiN26IrF8v0qyZtYfvffHFFzJw4EB56qmnJCEhQa0LDAxUmegffPCBR14T9UB/+uknGT58uMTFxUnJkiWlRYsWMmfOHGZCERER+YLjx0VGj06+jaSgXr1Emje3ZMZUpoNScO3aNdm5c6dKLzeypgzt2rVz174RkS/QbCLXz+rLwUX0bwKJiDzACEoZQ/hcg1JWy5QKCQlRsxojAHX48GE1jK5ixYoSGhrqsde86667ZIVRvIuI3MeWIHJosr5csaeIfy6eXSK6PQcP6oEoR5ii+NAhBqVg8eLFaoaWs2eTPqQ6wMwumNaYiKxEE4n7R18MLuztnSEiC3vwweRlBKUMRlAKXYybN5FNJJaCIFSNGjW8vRtElBW2GyJb++nL5bsxKEVEt69SJQxRcw5MBQSIVKxoybOa6ZQHzO7y2GOPycmTJ1WWlGNjQMpZgH+APFrtUdWwTBaHC8Wjj+oNy5bhJxJUUG9YdoNu3bqpIPZ7773ntP7nn39W64koZypaVKRWLX15+3YR4/uvoCC9b3a72VK4Ij+a1Kx0dSYyJav2l/wCRMo8qjcsExHdrtKl9RpSDmWTZOJES2ZJQaa/a8SQPUwJXLx4cc/skYXkDswtPzz2g7d3g7ILKvH+YMH3G8P1wiq4/Wlz584t77//vvTq1ctpOnQiytkwhG/HDswIJ7JypchjjyVnS129qgelMju6DXXSLXh1JjInq/aXMCnM/RY8LiLyju7d9U4RZuiNixOxcEZ1pjOlHn30UVm1apVn9oaIcoymTZtKiRIlZMyYManeHxUVpbKmXNtRTM1FRJaF/ld6Q/isVuyciIiIKFWYda9lS335t9/EqjKdKTVhwgQ1fG/t2rWqWGauXM5F/Pr37+/O/SMiiwoICJDRo0fLk08+qa4bpV3SUTE71A1MwZWkb9++smfPHmZpEllco0b6cD38+qcWlLJasXMiIiKiNEVHi/z4ox6UGjZMrCjTQalZs2bJkiVL1JTCyJhyrP+CZQalksXdiJO8Y/Kq5StDrkhokOdm0yEfgLTKvPr7LVeuZH58ibt240ZcmvehthmGlWZkW38/f8mTK4+IlihycbfEJVwVyV8tRZ2ErPxcP/zww1KrVi01Nfm0adOc7itUqJB9efz48Wq2qE2bNqlrDxFZFy6dDRqIICn7yBG9lS+ftaAUrnRJV2e5gtdw6x4TkRn7S253M15kfiV9ue1BkcAQb+8REVlBs6SpiDduFImNFcmfXySnB6XefPNNeeedd+T1118Xf8fCW0TkE4xAaGpaVWolC55cYL9dbFwxiU+IT3XbxuGNZVW3pKG6tgSJmN5Szl67mGI7bbiWpf1FXakHH3xQXn311VTvX7RokbrezJ8/XypXrpyl1yIi8wzhMyoFLF+uB6VQhgY4fI+IfJMmcvVE8jIRkTtERIjgM9CBA3qxzQ4dLHdeMx1VwnCaxx9/nAEpohzDX8+Q8vfMHOwPPPCANG/eXN54440U9+3du1c6d+6sZumLRuoqEeXYulIcvkdEPs0/t0jL7XrDMhGRu0RHW7quVKY/ZXbt2lW+++67VD9AEpH3YahoesP3HJ0ZeCbd4XsKhugGhsjRlzxXYBxBJwzjc8yEOnfunLRt21Y6duwoAwYM8NhrE5HvqVNHz05HlvqKFSI2G4NSROTj0McqWMvbe0FEVg1KTZjAoJQhMTFRxo4dq+pK1ahRI0Wh848++sgL7xIR3U6NJ09tm1mYNKFLly7y6aef2tchGIX6USNGjJBTp07Z1xctWlQVSSci6woMFGnSROTnn0XOnhXZuTN5+B4LnRMREVGOEhWld44OH9ZbhQqSozOldu3aJbVr11bLu3fvdrrPseg5EVmEZhO5cV5fDiokYmRQudm7774r33//vf32mjVr1P8RGEftICYmJsU6IrLmED4EpYwhfA8/rC8zKEVEPsmWIHJ0pr4c0UXE3/mLeyKi2xYWps8Cg89HS5cyKLUSxbWIKAfRRK4kDd0rVNAtzzhjxowU68LDw+WaQwVjTWORUKKczLWuVOfO+jILnRORT7LdENn4rL5c9jEGpYjI/UP41qzRh/D17m2ps+uZysVkr9+D2c6MZbI4DClr1Sp52TL8RIKMqUeZDUlE2QMl5kqXFjl+XO+DGcnYt5MphStyK4dlIvIiq/aX/AJESrVKXiYicqfmzUXefFOflvjmTX04n0VY50h8UO7A3LLgyQXe3g3KLih4ssCC7zeG64VV8vZeEFEOgyAUsqWQWHn1qsiff95+UArlqCx4dSYyJ6v2lwJyi0RZ8LiIyDfUri1SuDBmgxLZvFkfzmcRnikOQ0REROTGIXxr1+r/48tBNCIiIqIcIyAguWOEIXwWwqAUERER+aSHHkpeRknLrAzhIyIiIjJ9XSlYskSshEEpD4q7ESeho0NVwzJZXFycSGio3rBsFVqiyMVdesMyEVE2KVFCpHp1fXnLluQMqcwGpXBFDk1qFro6E5mTVftLN+NFfqmkNywTEblbs2b6/xi+d+GCZc4vg1IeFp8QrxrlEPHxerOaxOt6IyLKZkamus0mcuTI7WdK4cpswaszkTlZsr+E2YoP6Q3LRETuVqaMSNWqeqdoxQrLnF8GpYjo1peJfFX0xksGEXmxrtS+ffr/HL5HRD7HP7dIs3V6wzIRkSeH8M2YoU9RbAEMShFR+lDEJVdevRkFXYiIsknDhsnL+/fr/1+7xtNPRD7GP0CkaEO9YZmIyBO0pEzMX38VCQ8XmTbN9OeZQSkiIiLyWQULikRG6suHD+sBKWZKERERUY5z/LjIhAnJtzGMr1cv02dMMShFRLeOxl8/rzcjMp9DREVFycsvv5wtrzVt2jSJNtJxPej69etStmxZ+eOPPzz+WkTuzpZC3+voUQaliMgH2W6K/POD3rBMRORuBw/qnSFHiYkih1DLzrxMFZRasGCB3HvvvZInTx4pUqSIdOzY0en+f/75R9q2bSuhoaHq/v79+8uNGze8tr9E1mATuXJEb1gmjwSKhg0bJm+99Va6240YMUL8/PycWglMT+bgypUr0q9fPyldurS6VlatWlU+//xz+/3BwcEycOBAGTx4MN9JMuUQPvS7OHyPiHyO7brIuk56wzIRkbtVqiTi7xLCCQgQqVjR1Oc6UExi7ty50qNHDxk9erQ8+OCDomma7Nq1y35/YmKitG7dWooWLSrr1q2Tc+fOSdeuXdV2n376qVf22d/PXxqHN7Yvk8XhAtFYf79TXCxMDTWlwpKXySPXt7x588r9999/y22rV68uy5Yts98OwB8iBwMGDJCVK1fKt99+KxEREfLbb79Jnz59pFSpUtK+fXu1TZcuXWTQoEHy119/qaAVka9r1Ch5GUP4Mjt8D1fkxmb8No7IiizbX/IXKcYrDRF5UOnSIpMni/TsmZwxNXGivt7ETPGX4ObNm/LSSy/JBx98IL1795bKlSvLnXfeKY8++qh9G3zw2rt3r/ogVrt2bWnatKl8+OGHMmXKFLl06ZJX9jtPrjyyqtsq1bBMFpcnj8iqVXrDslUgoJrvTr25Ibhqs9nk/fffl4oVK6qsHQwlGzVqlP1+ZPDgdzwkJETKly+vsocSEhKcsoVq1aolkyZNkjJlyqjtHnvsMbl48WK6r4vrQ6tWrVTwp3jx4vL000/L2bNn7ffHxcXJM888o+4vWbKkun64OnnypAp+IwOpXLlyMmvWLBX4+fjjj+3bxMbGSs+ePaVYsWKSL18+FUT/888/0923OXPmSLt27TJ0/gIDA1V2lNEQiHe0YcMGFZDH0EPsG/alZs2asnXrVvs2hQsXlgYNGsjs2bMz9JpE3hYRIVKypL585EjmZ5LHFXlVUrPQ1ZnInKzaXwrMI9J0ld6wTETkCd27i8TEiOTPr9+2wBfMpghKbdu2Tf7991/x9/dXASd8YGzZsqXs2bPH6YNYZGSkygYwNG/eXA2LYe0UylHi4tJurmNe0tv26tWMbZtJQ4YMUUEpBJsQKEJgB0EiQ1hYmMyYMUPd98knn6jA8vjx452e49ChQ/L999/L/PnzZfHixbJjxw7p27dvmq+JYFLjxo1VMAvBGTzm9OnT0qlTJ/s2yBxChtH//vc/FeRetWpVimsHglYnTpxQ9yG7afLkyXLmzBn7/cjMRNDq1KlTsnDhQvX4u+++Wx566CE5f/58mvu3du1aqVu3bobO38GDB9V1DkGxzp07yxF8QnfQqFEj+eWXX9Q1E/uDYzpw4IC6HjqqV6+eel0iM8DEn8YQPlzGDhzw9h4REREReUnZsiKtW+vLv/1m/rdBM4HZs2ejurJWtmxZ7ccff9S2bt2qPfHEE1rhwoW1c+fOqW169OihNWvWLMVjg4KCtFmzZqX53NeuXdNiY2Pt7dixY+q1sEzky65evart3btX/e9EL0eeemvVynnbkJC0t23c2HnbIkVS3y4TLl26pAUHB2tTpkzJ8GPGjh2r1alTx357+PDhWkBAgPpdNSxatEjz9/fXTp48mepzvPXWW1p0dLTTOuN3ff/+/drly5fVtWLOnDn2+3FtyZMnj/bSSy+p23/99ZfafsuWLfZtDh48qNaNHz9e3V6+fLmWL18+dV1xVKFCBW3SpEmp7tuFCxfUc6xZs+aW52LhwoXqGrhz505t6dKlWuPGjbXixYtrZ8+etW9z/fp17ZlnnlHPGRgYqI7r66+/TvFcn3zyiRYREaGZ4meaSNO0jz9Ovux07erdU4I+Qk7vK/AcEBERedGMGXqnqF490/cVvJoplVrRXteGrAYM94GhQ4fKI488InXq1JHp06er+3/44Qf78+G2K3xET229YcyYMZI/f357w3Agd4m7ESdFPyiqGpbJ4pA1hKFUaLeRQeSzNJtI7B4RLTHLT4UaRsheROZQWn788UeV7YOhaRhKh4wqTGLgCEP+UMjbUL9+fXWd2L9/f6rPiYwlZAzh+YxWpUoVdd/hw4dVw6QIeB5DoUKF1DBhA54bQ+eQ+WTAEMSCmK/e4XVQaBzD4xxfKyYmRr1Gaq4mZaTlzp3bvg4ZTI6PnzlzplqPDFFcA++66y41RBmTP8BXX31lf+z//d//ycaNG1W2FPYHwxBRU8qxDhVgCGJ8ZsdAEflIsfN9+zL3WFyRiyY1C12dswzXY2SQop+EjFOibGHV/tLNqyILa+kNy0REntSsmf7/li0i6YzIMAOvFjrHDFEYfpIe1ES5fPmyWq5WrZp9PWrRoN6M8WEVH2A3bdrk9NgLFy6oWjSOQ4NSG0r0yiuv2G+j/pQ7A1Nn45Nr1lAO4FCjyGuuXEn7Ppei2OIw9CwFe/FRTe9cbZsnUqimiJ/Lc2QCAiHpQTAF14S3335bDTdDoBj1llKr7+TICDynFYBGwAozc2LYoCsMB8aQuFtROWi3WI/XwfNheJ+rAgUKpPp4BLCw37heGTCUz/EDYlrXMMw0igCVsf8IcL3xxhtqCCKGEUKNGjXUc40bN04FsgwYTuhaj4rIl9WsKRISoteTwvA9zIDseklLjw9cnX3Oa6+9poYD36ruHZEl+0tuZxO5aPwucbZiIvKwUqVEIiNFdu8WwZfPDmVJzMarQakiRYqodivIjEIQCpkKyKAABJuOHj0q4eHh6jYyHFAsGbVj8KEQUBcGj8Pj04L70YgsIzTUzdv6i4RVFsEEfJiFL53Mw1upVKmSCkwtX75cnn/++RT3r1+/Xv1OIyvS8Pfff6fYDsFo1HYyasihphxqzqFAemqQ3YQaUAhyI9vJFTKecuXKpYJiyMICBIlQiwm1qACZVZh0Yfv27fZrCmpbORZYx+ugnhReA6+VEUFBQSrgjhpa0dHRah3OEfYpI1kOyD4zZu3DdREN58IRZugzMk4Nu3fvVjX6iMwiVy6R++4TWbECv5/6LHxp/MpTBixatEj1k3BtxDIRZZF/bpEmvyUvExF5WnS0HpRCXSkTB6VMUegcM1hh1r3hw4erDhSCUy+88IK6D7NuAT7M4YMdZtTCh0Z86B04cKD06NFDPZ6IbhOCUEH59JaFgJQxRA2z6+Hb+a+//loNaUMgaNq0aep+BGIQcEJ2FO7DUDRk/aT2PJhhDt/uY6hb//79VdFyZEymBkXQkRn0xBNPyObNm1VxcFxLnnvuOUlMTFRD5Lp3766KnePagYBNt27dnII7CEoh0wiz2eE5cJ3BMgJIRoYW7keAvEOHDrJkyRIVOP/999/lzTffdJr9zhWywtatW3fL84dr2urVq9VwQGSGYgZSZHfiXACudQii4TiQrYXtUDQe5/rhhx92ei6cNyMIRmTGIXxr1nhzT8wNEz2gf/TNN9+oGUyJyA38A0RKNtMblomIPC06qS+PoFQaozrMwKuZUpnxwQcfqOwDBJ0wROXee++VFStW2Ou5IBMA9VVQO6Vhw4bqg+KTTz6phqwQke9AjSj8Lg8bNkxlOyGzEUFnaN++vQwYMEAN7UUWEIagYXvUn3OE4FXHjh2lVatWKtiE/ydOnJjmayKjCllYCIgZs3IiI6tFixb2wBOuMagH1a5dOzUD4KuvviqxsbFOz4PgDoJXDzzwgAqAoSYdZgE16kEhOIVZ95DphYDXf//9p7bD9ukNI8aHQ2RZ4fUwZDEtx48fV4G1s2fPqqF39913nwrqGRmjgIAehiV36dJFnRvchyxS4xwbmWV4LQS1iMwalFq/XiSVhEu6BQw5RtAd1wQMFUbwPCNw3UQzICBOREREXnT//Rj6JXLsGArg4lt0U74dfqh27u2d8CXoZOFDIT6wZTXDCsXN847Jq5avDLkioUGZGFZF5oNinXnzJtd1yswwuttw7do1lQlTrlw5pyLZbodLREJScCZX/ixnS2UVAlQ///yzTxTlRZAINehQRDy94u0ZgUwvDKdDQMnTkGGK10L9KV+SbT/TZFqIg+C7KIxGRZnJPXsy9jiUUk66Oguq7oX6UF/BnddG1ONLz5YtW1T25nfffSdr1qxRX+ghKIXfOWR/ouh5Zp/fl84BmUQ295eyje2myMkl+nLJ5iL+pvnun4jMXvB82TKRTz4R6d9ffElG+0umGL5HRN5kE7l8SG85vHAnsjMxqx0CJ/hgh6LsqB2FTKisQqYWhhF6GjIdatasqTLSiMwG/RmjXBtm4GOyTjJkmKLGXHotMjJSXceQYYl6mshaNerXIWvKGAqcGgTM0ak02jF8K0tEyWzXRVa30RuWiYiyewifSTGE70H+fv5St1Rd+zJZHIaB1a3rMnOdFfiJBBo1R7ybJeVtKCKO7CLUpMIQvwYNGsjMmTNVkfSswjC7F198UTwNH0RR44rIrO66S+TIET1bauPG5L5YenBFrmvxb+MyOnkMavWNHDnSfhvDqDGsGdlTKI2QFk4MQ25j2f6Sv0ghq19piMjnREdjOl2RlSvx7bM+nM9kOHzPBCn5RKnhUCeyGv5MU0ZgBJlRZm7YMP12drNSXyGjw/esfA6IiIhMy2ZDAV3MYqIHpqKixFdw+B4RERFZjpFgARmYtJKIiIjIuvz99bpSJh7Cx9xSIiIiMg3UlEqaeFc2bRK5edPbe2RuqIuHOW8ykyVFREREPiTa3HWlGJTyoPiEeIn4OEI1LJPFxcfrn5bQsGwVmk0kdp/esExE5EUolZBUm1tN4vXnn7d+DK7IEUnNQldnInOyan/p5lWR3xrqDctERNmlaVP9/23bRP77z3TnnUEpD8I3j3/H/q0alsni8B7//bfeLPV+ayI3r+gNy0REXpQ7t0iFCpkbwocr199JjVcxIi+zbH/JJnL2d73l8NmKiSiblSwpUqOGfk1dvtx0p59BKSK69WUirILeeMkgIh/IlHIMSq1f7829ISJK4h8scv//9IZlIiJvDOGbMUPk+HFTnXsGpYgofX5+IkEF9YZlIiIvB6XuuEPPmDKCUpZKtiAic/IPFCnTQW9YJiLKTomJ+v9LloiEh4tMm2aa88+gFBEREZkqKBUQIFKunH77xAmRo0e9vVdEREREXnL8uMgnnyTfttlEevUyTcYUg1JElD6kICRc1psb0xFOnTolL774opQvX16Cg4OlTJky0rZtW1luwnHQRJR9goL0pE0O4SMin2JLFDm9Sm9YJiLKLgcP6oEo18ypQ4dM8R4wt5SIbsEmcmm/vliotogEZPmMHT16VBo2bCgFChSQsWPHSo0aNSQhIUGWLFkiffv2lX379vFdIaJUISDlOAMfbNgg8tRTPGFE5EW2ayLLm+jLna6I+Ify7SCi7FGpkoi/v3NgCmnljp0lH8aglAf5+flJtaLV7MtkcXiPq+nvt+VqLwUkFW9xkz59+qjfic2bN0toaHKnrXr16vLcc8/JjBkz5Nlnn03xuOHDh8uIESPcui9EZD4ISqFcgmHr1vS3xxW5msMyEXmRZftLfiL5eaUhIi8oXVpk8mSRnj2TA1MTJujrTYBBKQ8KyRUie/rs8eRLkC8JCRHZ4/33Oy6d+5DjlDuD22Jsbx4s+AWIFIhMc9vMfg94/vx5Wbx4sYwaNcopIGVA9tTjjz8uLVq0sK9btWqVPP300yq7iogIQSlcclFXKiZGZMcOkRs39KF9qQkREe9fnYnIl/pLbhcYItLagsdFRObQvbs+A1+tWvjApWdPmQRrShFZTN502iMu2xZLZ9uWLttGpLFdZh06dEg0TZMqVaqkuU2ePHmkRIkSqsXFxUm/fv1k9OjR0qxZs9t4RSKyYlAKIiP1/xGQ2rXLq7tERERE5F1lyoi0basv//abad4NBqWIKFshIJXRIa2xsbHSpk0badmypQwaNCgb9o6IzCB3UsqnY2z7VkP4iIiIiCwvOlr/n0EpgviEeKk+sbpqWCaLi49HUSS9YdlLrqTT5rpseyadbRcZG2kodH5Ajl46KFc0W4rtMqtSpUoqIPXXX3+lu11iYqIaxpcvXz6ZMmXKbbwSEVk9U8oxM33LlrS3xxW5elLjX2MiL/OR/pLb3bwqsqKZ3rBMROQNTZvq/6O2wenTpngPWFPKwxkhe//ba18mi8N7vHdv8rKXhLp9W00k4VLStlk/rkKFCknz5s3ls88+k/79+6eoK3Xx4kVVV2rAgAGya9cu2bJli+Q20iKIiByCUhERyZPNpBeUwpUr6ershqsYEVmhv+R+NpFTy5KXiYi8oVgxkdq1RbZvF1m2TKRLF59/Hzh8j4hufZnIW05vbrpkTJw4UWVC1atXT+bOnSsHDx5UmVP/93//J/Xr15fp06erbb744gvx9/eXU6dOqXblyu3kZhGRVYNSGAWMZAtA3WQrJV0Qkcn4B4vU/1ZvWCYi8pZocw3hY1CKiNKHT33BhfXmpqmby5UrJ9u2bZMmTZrIq6++KpGRkaqI+fLly+Xzzz+X1atXq6BVu3btpGTJkvY2btw4vltEZK8pde2ayD336MuJiXqmOhGRV/gHipTrojcsExH5QlBK8/2MVF4xicgrEGSaMGGCaq6ioqJkxowZXtkvIjJPptT163pQ6ssv9dsYwteggVd3jYiIiMi7GjYUCQkROXVKn564Rg2ffkeYKUVE6UN0/Wac3kwQaSeinBWUqls3eT1n4CMir7ElipzbojcsExF5s6MUFWWaIXwMShHRLdhEYv/SGwt3EpEPDd9DUApf/gUF6bfTK3ZORORRtmsiS+rpDctERN4UbZ66Uhy+50GY9j48f7h9mSwO73G4/n67q/aSzwhI+sRHRORDmVIJCSKBgSI1a+oBqf37RWJjRfLnd94eV+Rwh2Ui8iLL9pf8REJ5pSEiHwtKrVkjcvWqSJ484qsYlPKgkFwhcvTlo558CfIlGLd71ILvt1+ASAHfHodMRDmLkRllFDvHED4jS2rbNpEmTZy3DxERC16diczJqv2lwBCR9hY8LiIypypVREqXFjl+XGTt2uQglQ/i8D0iIiIy36SgLsXODRzCR0RERDmen59phvAxKEVERESmw6AUERERUToYlKKrCVflnin3qIZlsjiM1cXX9WhYtgrNJnL5kN6wTETkQ8XOMXwPGeoYEZRWphSuyPckNQtdnYnMyar9pcRrIms66A3LRETe9tBDenr5rl0iJ0+Kr2JNKQ+yaTbZemKrfZkszmZLno8cy5ahidy4mLxMRORjmVIodn733SLr1on8/bfIf/+JFC2avC2uyElXZ84hSuRtVu0vaYkix+clLxMReVuRIiJ16ujX3KVLRZ55RnwRh+8RUcZmk1EzylhplhwiskpQChzrShmfd4mIso1/kEi9yXrDMhGRLw3h+/prvei5D2JQiojS5+cvkruo3rBMlvHWW29Jz549Pf46Z86ckaJFi8q///7r8deinDd8zwhKYQY+A4udE1G2888lUrGH3rBMROQLEhL0/5cvFwkPF5k2TXwNP2ESUbbRNE2aNm0qzZs3T3HfxIkTJX/+/PLPP//wHckGp0+flk8++UTeeOONDD+mV69e4ufnJx9//LHT+uvXr8uLL74oRYoUkdDQUGnXrp0cd/gmplixYvL000/L8OHD3XoMlLMZmVKoKQXMlCIiIiJygP74hx8m38aQ6V69fC5jikEpIkqfponcvKo3LGcBAhrTp0+XTZs2yaRJk+zrY2JiZPDgwSpIUrZsWb4j2WDatGlSv359iYiIyND2P//8s3rfSpUqleK+l19+Wf73v//JnDlzZN26dXLlyhVp06aNJCYm19R49tlnZebMmXLhwgW3HgflXK7D9ypWFMmfPzlTKouXK0vD7z2ux47t9ddf9/ZuEZkb6sde3KM31pIlIl9w8GDK2n3onx86JL6EQSkiugWbSOwevbmhRHCZMmVU8GngwIEqGIXsqe7du8tDDz0k3bp1c9r26NGj6sPS999/L/fff7/kyZNH7rnnHjlw4IBs2bJF6tatK3nz5pUWLVrIf6hsnAT3NWvWTGXuIPuqcePGsm3bNvv9q1atkqCgIFm7dq193Ycffqi2P5nOzBRz586V6tWrS3BwsPpQh8c4wrrRo0fLc889J2FhYSrANnny5HTPh81mk/fff18qVqyonhePGTVqlP1+BOsqV64sISEhUr58eTXkLsFIwxWRESNGSK1atVSQD+cW2z322GNy8aJRnD51CCAhoykjMOyuX79+KqiUK5fzkITY2FgV4MK5QBZc7dq15dtvv5Vdu3bJsmXL7NvdddddUqJECRW8IvJEUAqTyxhD+E6dws8tz3N63nnnHXW9M9qbb77JE0aUFYlXRRZG6g3LRETeVqmSiL9LyAe38U2eD2FQysOKhBRRjXLQDAdo3nQzTm+OaQKJN/R1idfT2NYh2GRLSNrWYTpj/8Dk9a7b3oauXbuqIBSyZyZMmCC7d+9ON3iDYV/4wITAUmBgoDzxxBPy2muvqeAWAkuHDx+WYcOG2be/fPmyeg3ct3HjRqlUqZK0atVKrYeoqCiV3YMhZQiq/PnnnzJ06FCZMmWKlCxZMtV9+OOPP6RTp07SuXNnFXBBMAgBohkzZjhth+AMgmXbt2+XPn36yAsvvCD79u1L89iGDBmiglJ4rr1798qsWbOkePHi9vsR3MJr4D4cL/Zx/PjxTs9x6NAhFbibP3++LF68WHbs2CF9+/ZN8zWRrYRzjv28FQTNcJ4GDRqkAnKpnRcEyaKNIooiKpsqMjJSfv/9d6dt69Wr5xQIJHJnTalbDeHDlZl/jcXp2oJAsdEQ4CfKUf0lTwguojciIl9QurQIPmMFBCSvw/TEDp81fIJGTmJjY/FJXv1P5MuuXr2q7d27V/3vZKbo7eqZ5HW7RurrNj7vvO2cEH395ZjkdX+N19ete9J52x+L6Osv7E5ed3Dybe//6dOntaJFi2r+/v7aTz/9lOo2MTEx6vdx6tSp9nWzZ89W65YvX25fN2bMGO3OO+9M87Vu3ryphYWFafPnz7evu379ula7dm2tU6dOWvXq1bXnn3c5Ny6efPJJrVmzZk7rBg0apFWrVs1+Ozw8XHvqqafst202m1asWDHt888/T/U5L126pAUHB2tTpkzRMmrs2LFanTp17LeHDx+uBQQEaMeOHbOvW7RokTqvJ0+eTPU5tm/frs7hP//8c8vXGz16tDpuHItxjOPHj7ffP3PmTC0oKCjF4/CYnj17Oq0bMGCAFhUVlfmfaaJUnD6taZMm4Wcwed3cuYjG6+2NNzx32szeV8DvcYkSJbRChQppNWvW1EaOHKmuiTnpHBAREeUYx45pGj4HFSyod5LS+GzibhntKzBTioi8AsWvMfNb1apV5eGHH5bevXurb+qN5qhGjRr2ZSOLCMPBHNdhhjcDlvF8GPaG4XtoqHPkWEQdw/cwzAxD8q5evZqieLerv/76Sxo2bOi0DrcPHjzoVDvJcV8x9BAZCI775vqcKBKOrLG0/Pjjj9KoUSN7JgMyqlyLwWPIX2l8E5IEtaKQ4bR///5UnxPHC7mNVBMRNTTP8fwjowlZUMjOQqYWjiUzMCzT9TEYfhkfH5+p5yHK6PA94Ax8GfPSSy+pIbwrV65UQ3Nx/UNmZ3pwrbp06ZJTIyIiIhMoXVqkTRuM3ddvY/KhpBEkvoBBKSKr6XRFb47p41UH6evqTnDe9pEz+vpQh+Lilfvq6+5zmS60/VF9ff6qyevKO9eAyiwMxUMz6ptg2JnRHDnWMTICHa7rEIQxoDYVAir4oIUhZHi+woULy40bN5ye1xhedv78edUyG2TBOleuNZdc9801SJMeDD3EcMGWLVvKr7/+qoYEYpih63G4MvYzrUASameBY9Fx1JdyPP8Y2ofAFAJqCHoZ79Xff/8tr776qr1AOoJl2B/XAuZ4nOMwRMA5LoqUYSI3BaUQkEI5OaNfVaYMAt7Jw/dyUrFzDCl2LV7u2rYmjWkcMGCAqrWHIPrzzz8vX3zxhaoNd+7cuTSff8yYMfYgPxpq2BEREZGJ9Oyp15PCF+YutXG9iUEpD7qacFWiZkSphmWyOGSfREXpLSkTxSsCQ/XmGJAICNLXBQSnsa3DpcA/V9K2SVk0qCF15YjItdMiAXlSbuvGzCkU+zZaViCY0r9/f1VHyihMfvbsWadtUIcKH8xQo+m+++6TZ555Js3gEVSrVk3NLOca1EI2VoDjOO1MQK0rBKaWL1+e6v3r16+X8PBwFYhCkAjbIyjkCplTJ06csN/esGGD+Pv7q31LTYUKFSRfvnyqTpVjfRnH84/9Qi2pnTt3OgWrUC8K9aWWLFmiHlenTh0ViFu6dKn9uVA0GTWrGjRo4PS6WIdC6ETuEBMjsnKlyIoVIkh0ROk2XPaMulKIkx45oi/jihyV1Kz61xgZT8i+TK+h1ltqcA006tOlV/8ONfiMduzYMY8dC1mcr/SX3A21ONd30ZtjXU4iIl8RFIRvmfTlDz5Ap118gZ6iQB5h02yy+u/V9mWyOAQ0Vuvvd4qpN01NE7melEUUGi5mgKDKN998owI5GGKCIIpjVhKG2yHgguLcKLaOTCQMB0SRcmybGmQHYea/d999Vx5//HEV+EGR9okTJ972fmL4HGbXQ9F2DCfEcEDMIrhnzx41IyGOAwEnDLPBay9YsCDV2evwPCjsPm7cOHW8CMihKDuymFKDgBVmykOQrUOHDmnuH7LL0BwhAIXnvfPOO9VtZExgX3F+sG2hQoXUzIo4n3gNA4btIXsNsxMSZVVcnMi8efrn2Xz5UHBfXx46VA9KLVigb7dlC4Kw+ryhSVdnN8wh6puQAWlkQWYWsjAhrYkeAMF9NKIss2p/SUsU+XuWvnxv+jPvEhF5zSOPiNx7r8imTUizFpk0yetvBjOliOgW/ERCyugNyybw5ZdfquFkyMpB8AlBGmRiGUaNGiVHjx61z/iHIMvUqVPVDH+uQwcNd999t5rhDgEiZBtgtj8MOcRQwaxAjSgEdPB8qK+FgJdRg6p9+/YqmwsZELVq1VKZWdjeFYJXHTt2VJlhCLRh/24VLEM9LxxLetlhGYXZABHcQiAMgbWQkBA1E6BjBtm8efPUMMD7778/y69HFBsrcvGiSM2aGAarf/G3bZvIDz/oQSjDhg08V64QUMfvLK51MTEx6rrWq1cvNYQXv6NEdJv8g0TuHq83LBMR+SI/Pz1LCqZMEcFM4sePe3eXUO3cq3vgY5BlgG/+kZqO4S1ZEXcjTvKO0Qs2XxlyRUKDQt20l+SzX90bBbqvoE6TZ9/va9euqQ8U5cqVcypYTTkL6sj8/PPPaQbT0oJLP4bsvPzyy/LEE0+Ip9WrV0+91pNPPpnmNvyZpsxcbj/9VASjchFvxo8/6kthNBBGlaGvhfJu9eqJTJ0qUqaKiDF9whUkffpQXyG7bdu2TRU137dvnypejiHCqF2HjE0ElHPCOaCc1V8iIqJUoKSG8fnB318EX9Z37y7ulNG+AofvERHlQCh6jEwx1IzyNGR+Pfroo9kS/KKcAZ9hMfIUQ/gwRwEypjBaFHW6V60SQTmzUqUwvFTf5lkkevJzrz3rE5MoEBERUQ51/LiI42cAjJzo1UukeXN9pr5sxqAUEaUPyZS2pNnekI6exoxuZD41a9ZUzdMwdBJZGETuVKWKPtsehvIh+IRAFer9YwQayqmhxlTVqvrsfJcuMShFRB6G+rFx/+jLmNXYcWIYIiJfcvBgypp+iYmY8cQrQSleLYnoFmwiF3fpzbIlgs09fC+zQ/eIrAKBKGREGaN/EJwqXlyvM1W9uj6pTMGCejF0IiKPSrwq8ks5vWGZiMhXVaqkD9lzhFqwWZwB/XYxKOVhIblCVKMcAvU4MlGTwzTwbR+/8SMikwzrK1pUz5DC/+3b6+txZbbg1ZnInKzaXwoI0RsRkS8rXVqvIWVMSoT/MQufF7KkTDV878CBA2qq9vXr18uNGzfUdOMjR46UJk2a2LfB1Ol9+/aVFStWqOnfUVAXU6RjqnVvQGHzuDfivPLa5AX41IPinVbjFyBS6G5v7wUR0W0P6wMLXp2JzMmq/aXAUJHHLXhcRGRN3bvrNaQwZA8ZUl4KSJkqKNW6dWupXLmyPeD08ccfS5s2beTw4cNqOvfExES1TdGiRWXdunVy7tw56dq1q5ph6lNM0UNkUZxAk6yCP8vkzs+8nNCLiIiIKB0IRHkxGGWq4Xtnz56VQ4cOyeuvvy41atSQSpUqyXvvvSfx8fGyZ88etc1vv/0me/fulW+//VZq164tTZs2lQ8//FCmTJmipiIksppcmO9cRP0eEFmB8bNs/GwTEREREZG1mSJTqnDhwlK1alX5+uuv1VTGwcHBMmnSJClevLjUqVNHbbNhwwaJjIyUUqh4mqR58+Zy/fp1+eOPP5yG+TnC/WgGdwawrt28Jo98/4hanttpruQOzO225yYfdO2ayCP6+y1z54rk9uz7HRAQIAUKFJAzZ86o2yEhIeLniZnxMJtM/El9OaQka0uR+3/ENE0FpPCzjJ9p/GwTudM1EUm6OstcEeFfY6Kc01/KNonXRbb205frThAJCPb2HhERmYIpglL4oL106VJp3769hIWFib+/vwpILV68WH2AgVOnTql1jgoWLKjqSeG+tIwZM0befvttj+x3oi1RFh5caF8mi8M0mgsXJi9nAwxdBSMw5REqKHVMXw65xqAUeQyu58bPNJE74Yq80GGZiHJWfylbaDdFDk/Vl+t8LCIMShER+XxQClOZ3yogtGXLFpUN1adPHylWrJisXbtW1ZSaOnWqqimF+0uWLKm2TS1LBN/Ap5c9MmTIEHnllVecMqXKoEIqkQngZxs///jdSEhI8MyLJN4QObxUXw5vIBLgnYkDyNowZI8ZUkREZFp+uURqjExeJiIi3w9K9evXTzp37pzuNhEREaq4+a+//ioXLlyQfPnyqfUTJ05U2VNfffWVqjWFb9c3bdrk9Fhsjw/qrhlUjjAUEI3IzPBh3nMf6HOL1HjZQ89NREREZAH40i5yqLf3gojIdLwalCpSpIhqGS1+i2F7jnDbZrOp5fr168uoUaPk5MmT9swpFD9HwMmoO0VERERERERERL7BFLPvIeCE+lBdu3aVP//8Uw4cOCCDBg2SmJgYad26tdomOjpaqlWrJk8//bRs375dli9fLgMHDpQePXrYs6uI6DZomsi1//SGZSIiIiJif4mIKKcEpZBNhaLmV65ckQcffFDq1q0r69atk3nz5knNmjXVNhi6tGDBAsmdO7c0bNhQOnXqJB06dJBx48Z5e/eJzC0xXuSnYnrDMhERERGxv0RElFNm3wMEopYsWZLuNmXLllW1p7IChdGNgudZFXcjTp+HOun5EoMsNMMIpRQXl7yMnx+rzChzM04k3uG4Ai1yXESUYzhcnQV/3bN6FTP6CEafISdyZ3+Jchj2l4iIcoRLGewv+Wk5uUeViuPHj3P2PSIiIrqlY8eOSenSpXPkmWJ/iYiIiNzRX2JQygUKp584cULCwsLEz89PshIVLFOmjHoDckJNKx6vtfH9tT6+x9bG99e98H3e5cuXpVSpUikmYckp2F+6PfxdtLac9v7mxGPm8Vob31/v9JdMM3wvu+BkufNbT1ycc8IF2sDjtTa+v9bH99ja+P66T/78+SUnY38pa/i7aG057f3NicfM47U2vr/Z21/KmV/vERERERERERGRVzEoRURERERERERE2Y5BKQ8JDg6W4cOHq/9zAh6vtfH9tT6+x9bG95d8FX82rY3vr/XxPbY2vr/WFuwjMQsWOiciIiIiIiIiomzHTCkiIiIiIiIiIsp2DEoREREREREREVG2Y1CKiIiIiIiIiIiyHYNSWTRq1Chp0KCBhISESIECBVLd5p9//pG2bdtKaGioFClSRPr37y83btxw2mbXrl3SuHFjyZMnj9xxxx3yzjvviKZpYgYHDhyQ9u3bq2PLly+fNGzYUFauXJnpc2AmCxYskHvvvVe9Xziejh07Wvp44fr161KrVi3x8/OTHTt2WPJ4jx49Kt27d5dy5cqp97ZChQqq+J/rsVjleA0TJ05Ux5w7d26pU6eOrF27VqxgzJgxcs8990hYWJgUK1ZMOnToIPv373faBtfZESNGSKlSpdR7HhUVJXv27BGrHD9+X19++WXLHu+///4rTz31lBQuXFj9HcY16o8//rDs8ZoZ+0vsL7G/ZJ3+A/tL7C9Z6e9pTugv+XyfSaMsGTZsmPbRRx9pr7zyipY/f/4U99+8eVOLjIzUmjRpom3btk1bunSpVqpUKa1fv372bWJjY7XixYtrnTt31nbt2qXNnTtXCwsL08aNG2eKd6dixYpaq1attD///FM7cOCA1qdPHy0kJEQ7efJkhs+Bmfz4449awYIFtc8//1zbv3+/tm/fPu2HH36w32+14zX0799fa9myJSKl2vbt2y15vIsWLdK6deumLVmyRDt8+LA2b948rVixYtqrr75qyeOFOXPmaLly5dKmTJmi7d27V3vppZe00NBQ7e+//9bMrnnz5tr06dO13bt3azt27NBat26tlS1bVrty5Yp9m/fee09db3HdxfX38ccf10qWLKldunRJM7PNmzdrERERWo0aNdR7asXjPX/+vBYeHq5+Zzdt2qTFxMRoy5Yt0w4dOmTJ4zU79pfYX2J/yTr9B/aX2F+yyt/TnNBfMkOfiUEpN8EHn9SCUgsXLtT8/f21f//9175u9uzZWnBwsApGwcSJE9Vjr127Zt9mzJgx6g+VzWbTfNl///2nghRr1qyxr8MPLtbhBz2j58AsEhIStDvuuEObOnVqmttY6Xgdj6lKlSranj17UgSlrHi8jsaOHauVK1fOssdbr149rXfv3k7r8F6//vrrmtWcOXNG/fyuXr1a3cb1tUSJEuqPsAHXYVyPv/jiC82sLl++rFWqVEl94GncuLG9k2W14x08eLDWqFGjNO+32vFaBftL7C9Z9e8psL/E/pIVsL9kvf7DYB/vM3H4nodt2LBBIiMjVRqcoXnz5moolJEuh20wdC84ONhpmxMnTqj0WF+G9L+qVavK119/LXFxcXLz5k2ZNGmSFC9eXA0Dyug5MItt27ap1Ed/f3+pXbu2lCxZUlq2bOmU2mil44XTp09Ljx495JtvvlGpnq6sdryuYmNjpVChQpY8XgwZwD5HR0c7rcft33//XawG7yUY72dMTIycOnXK6fhxHcb12MzH37dvX2ndurU0bdrUab3VjveXX36RunXrymOPPaaGZ+KaPGXKFMser9Wxv2Stvy/sL7G/ZKWfZ/aXrPn3NKf0l8zQZ2JQysPw5iJA46hgwYISFBSk7ktrG+O2sY2vwvjbpUuXyvbt21XdFtSkGT9+vCxevNheYysj58Asjhw5ov7HeNs333xTfv31V3Us+IU9f/685Y4X2ZTdunWT3r17qwtZaqx0vK4OHz4sn376qTp+Kx7v2bNnJTExMdXrj9mOJSM/y6+88oo0atRIdZLBOEYrHf+cOXPUh0HUR3BltePF9fjzzz+XSpUqyZIlS9TvKeqz4EsSKx6v1bG/ZK2/L+wvpWSl99cV+0vWwf6SNfsPR3y8z8SgVCoQcECwJb22devWDJ9kbJ/aL7zjetdtjCLnqT3Wl84B9rNPnz4q4oriyJs3b1ZFz9u0aSMnT57M1Dnwpower81mU9sPHTpUHnnkEZUNNn36dHX/Dz/8YLnjRUDm0qVLMmTIkHSfzyrH6wiZii1atFDfKDz//POmOt7MSu36Y9ZjSUu/fv1k586dMnv2bMse/7Fjx+Sll16Sb7/9Vn1BkBarHC+ux3fffbeMHj1afePXq1cvldWJTpcVj9cXsb/E/hL7S+wvsb9krb8n7C9Zs/9g8/E+U6DHX8Gkv4ydO3dOd5uIiIgMPVeJEiVk06ZNTusuXLggCQkJ9kgktnGNQJ45cybVaKWvnYMVK1aobCEcE2beM2byQvbUV199Ja+//nqGzoG3ZfR4L1++rJarVavmlNpYvnx5NaMKWOl4R44cKRs3bnQaWgrImurSpYt6j610vI4drCZNmkj9+vVl8uTJTtuZ4XgzCjP/BAQEpHr9MduxpOfFF19Uactr1qyR0qVLO72XgOPHUFyzHz+GQ2DfjaHTgEw4HPeECRPsMw9a5XhxDI7XYsBw8rlz51ry/fVF7C+xv5Qa9peSsb/E/pKZsL9kzf6SKfpMHq9alUPcqnDniRMnnGa7ci10XqBAAe369ev2bVBkzAyFzn/55Rd1fCis66hy5craqFGjMnwOzAL7i/12LHR+48YNNUPbpEmTLHe8mIENsy8YDbPS4bKBGQiPHTtmueOF48ePqyLRmA0TM+25strxotD5Cy+84LSuatWqlih0jutn37591bUUM4OmVdTx/ffft6/DddishSwxyYTj7yta3bp1taeeekotW+14n3jiiRRFO19++WWtfv36atlqx2sV7C+xv8T+kjX6D+wvsb9k1r+nOa2/ZIY+E4NSbvjQjpnI3n77bS1v3rxqGc0I0hjTxz/00ENq+lfMSFe6dGmn6V8vXryoFS9eXP2w4Bfhp59+0vLly6eNGzdO83WYfa9w4cJax44d1ZTr+/fv1wYOHKimmMftjJ4DM8FMVpiBDwEaTG/cvXt3FZTCVJtWPF5HmD7UdfY9Kx0vZgCqWLGi9uCDD6rO1smTJ+3NisdrdIjx+zpt2jRt79696g9UaGiodvToUc3sEGzDH9NVq1Y5vZfx8fFOXwBgG1x3cf3FddjMU/66cpx9z2rHi2mcAwMD1RcgBw8e1GbOnKmFhIRo3377rSWP1+zYX2J/if0l6/Qf2F9if8lqf0+t3F8yQ5+JQaks6tq1q/qQ7tpWrlzp1BFr3bq1lidPHq1QoULqjw+mWHS0c+dO7f7771ffliBKOWLECJ/PkjJs2bJFi46OVscWFham3XfffSqbxFFGzoFZIDPq1VdfVYEoHG/Tpk213bt3W/Z4bxWUstLx4hv81H6fXZNKrXK8hs8++0wLDw/XgoKCtLvvvltbvXq1ZgVpvZd4nw24zg4fPlxdd3H9feCBB9QfYqtw7WRZ7Xjnz5+vPuThWKpUqaJNnjzZ6X6rHa+Zsb/E/hL7S9bpP7C/xP6S1f6eWr2/5Ot9Jj/84/lBgkRERERERERERMk4+x4REREREREREWU7BqWIiIiIiIiIiCjbMShFRERERERERETZjkEpIiIiIiIiIiLKdgxKERERERERERFRtmNQioiIiIiIiIiIsh2DUkRERERERERElO0YlCIiIiIiIiIiomzHoBQR+Zz9+/dLiRIl5PLly+r2jBkzpECBAll6zqNHj4qfn5/s2LEjw49xx+v6gl9//VVq164tNpvN27tCREREbsL+knuxv0TkHQxKEZHbJSYmSoMGDeSRRx5xWh8bGytlypSRN998M93HDx06VPr27SthYWFu2ye87smTJwn76Q8AAAanSURBVCUyMlLcqVu3btKhQwfxZW3atFEBuVmzZnl7V4iIiCgJ+0u+hf0lIu9gUIqI3C4gIEC++uorWbx4scycOdO+/sUXX5RChQrJsGHD0nzs8ePH5ZdffpFnn33W7fuE7KvAwEDJiXA+P/30U2/vBhERESVhf8n3sL9ElP0YlCIij6hUqZKMGTNGBaJOnDgh8+bNkzlz5qhgVVBQUJqP+/7776VmzZpSunTpFPctWbJEqlatKnnz5pUWLVqozCdH06dPV/fnzp1bqlSpIhMnTkx3+B6CX9jPPHnySJMmTdS+YZuLFy9m6HVHjBihHoNjw+PQVq1alamhgUgVv/POOyUkJEQeffRRiYuLU88ZEREhBQsWVOcP36QasH7kyJHyzDPPqP0JDw9Xr//ff/9J+/bt1bq77rpLtm7d6vR67dq1k82bN8uRI0cytH9ERETkeewvpY/9JaIcQCMi8hCbzaZFRUVpDz30kFasWDHt3XffveVj2rdvr/Xu3dtp3fTp07VcuXJpTZs21bZs2aL98ccfWtWqVbUnn3zSvs3kyZO1kiVLanPnztWOHDmi/i9UqJA2Y8YMdX9MTIyGS9727dvtt/GcAwcO1Pbt26fNnj1bu+OOO9Q2Fy5cyNDrXr58WevUqZPWokUL7eTJk6pdv349Q+fGeO5mzZpp27Zt01avXq0VLlxYi46OVs+5Z88ebf78+VpQUJA2Z84c++PCw8PVcX3xxRfagQMHtBdeeEELCwtT+/D9999r+/fv1zp06KD2E+ffEd4D43wQERGRb2B/KW3sLxFZH4NSRORRf/31lwr03HXXXVpCQsItt69Zs6b2zjvvpOiQ4DkOHTpkX/fZZ59pxYsXt98uU6aMNmvWLKfHIQhWv379VINSgwcP1iIjI522Hzp0aIqg1K1et2vXriqQllmpPXevXr20kJAQFewyNG/eXK13DEo99dRT9tsIhOF53nrrLfu6DRs2qHW4z1Ht2rW1ESNGZHpfiYiIyLPYX0od+0tE1pczi6sQUbb58ssv1dC0mJgYVS8Kw8/Sc/XqVTX8zhWeo0KFCvbbJUuWlDNnzqhlDF07duyYdO/eXXr06GHf5ubNm5I/f/40Z6y55557nNbVq1cvU6+bVa7PXbx4cXV+MATPcZ3r69WoUcPpfsCQPdd1eBzqaBkwTDE+Pt4t+05ERETuw/5S2thfIrI2BqWIyGM2bNgg48ePl0WLFsnYsWNV0GjZsmWq9lJaihQpIhcuXEixPleuXE638RzI9gSbzab+nzJlitx7770pioimBo913Q/j+TL6ulmV2nOnts44vtQeZxxDautcH3f+/HkpWrSoW/adiIiI3IP9pfSxv0RkbQxKEZFHIOOpa9eu0qtXL2natKlUrlxZIiMjZdKkSdK7d+80H1e7dm3Zu3dvpl4LmUF33HGHKuLdpUuXDD0GhdAXLlzotM61OHhGoGi7YyFyX3Xt2jU5fPiwOr9ERETkG9hf8i3sLxFlP86+R0Qe8frrr6tMnffff1/dLlu2rHz44YcyaNAgNRNeWpo3b66+McxsoAcz4WG2v08++UQOHDggu3btUrPxffTRR6luj2DZvn37ZPDgwWp7zPqHGV4gvUwuVxhut3PnTjUc8OzZs5KQkCC+aOPGjRIcHCz169f39q4QERFREvaXfAv7S0TZj0EpInK71atXy2effaaCPKGhofb1qPfUoEEDNYwvrSFwrVq1UmnaGOaXGc8//7xMnTpVvSbqKzVu3FgtlytXLtXtsf7HH3+Un376SdVo+vzzz2Xo0KHqPgRvMgrHdOedd0rdunXV0Lj169er9VFRUdKtWzfxFbNnz1ZZZKjLQERERN7H/hL7S0Qk4odq5zwRRORLJk6cKPPmzZMlS5Zk6+uOGjVKvvjiC1U0PauQQYXsLV8ITKEQPIYrYnhiWkE6IiIiMhf2l9yL/SUi72BNKSLyOT179lTFzi9fvixhYWEe7cxhBr7ChQurDKcPPvhA+vXrl+XnxbBA7PczzzwjvgAzH+JYGZAiIiKyDvaX3Iv9JSLvYKYUEeVYAwYMkO+++07NSoeaV08//bQMGTJEAgMZryciIiJif4mIPI1BKSIiIiIiIiIiynYsdE5ERERERERERNmOQSkiIiIiIiIiIsp2DEoREREREREREVG2Y1CKiIiIiIiIiIiyHYNSRERERERERESU7RiUIiIiIiIiIiKibMegFBERERERERERZTsGpYiIiIiIiIiIKNsxKEVERERERERERJLd/h+hol7JhFtMQQAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 1200x400 with 2 Axes>"
       ]
@@ -876,10 +876,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Nasion strip preserved: 1,258 vertices (half-width 8 mm, X: -80 -> -39)\n",
+      "Nasion strip preserved: 1,963 vertices (half-width 8 mm, X: -99 -> -58)\n",
       "\n",
-      "Mask: 117,152 / 494,109 vertices (23.7%)\n",
-      "Per-rule counts: {'below_cap': 180943, 'face_region': 106512, 'ear_region': 30550, 'all': 120411}\n",
+      "Mask: 142,911 / 678,081 vertices (21.1%)\n",
+      "Per-rule counts: {'below_cap': 364330, 'face_region': 126927, 'ear_region': 42118, 'all': 146319}\n",
       "Ear delete radius: 40 mm\n"
      ]
     }
@@ -1069,19 +1069,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 21,
    "id": "cell-viz-mask",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bb48fb5980f7452484e4e39fe76920ab",
+       "model_id": "c377768ad4504ce2bfeb96e448b5cf3f",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Widget(value='<iframe src=\"http://localhost:43671/index.html?ui=P_0x7efc8f370e10_29&reconnect=auto\" class=\"pyv…"
+       "Widget(value='<iframe src=\"http://localhost:38789/index.html?ui=P_0x7f9bdeb63bd0_4&reconnect=auto\" class=\"pyvi…"
       ]
      },
      "metadata": {},
@@ -1129,7 +1129,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 22,
    "id": "cell-delete",
    "metadata": {},
    "outputs": [
@@ -1137,9 +1137,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Original:    494,109 verts, 866,676 faces\n",
-      "Anonymized:  376,879 verts, 650,440 faces\n",
-      "Removed:     117,230 verts\n"
+      "Original:    678,081 verts, 1,171,705 faces\n",
+      "Anonymized:  535,017 verts, 920,914 faces\n",
+      "Removed:     143,064 verts\n"
      ]
     }
    ],
@@ -1170,19 +1170,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 23,
    "id": "cell-beforeafter",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "190397f8c8cf46f0b1a5c0a957dbeec0",
+       "model_id": "43ca33b85769486e9de1f1adfc3b1ed4",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Widget(value='<iframe src=\"http://localhost:43671/index.html?ui=P_0x7efc8f35e090_30&reconnect=auto\" class=\"pyv…"
+       "Widget(value='<iframe src=\"http://localhost:38789/index.html?ui=P_0x7f9c4c6cc0d0_5&reconnect=auto\" class=\"pyvi…"
       ]
      },
      "metadata": {},

From 06d2282bc88320f51493d96a82574c761f033d5c Mon Sep 17 00:00:00 2001
From: mah-diaa <mahmoud.diaa.abdeen@gmail.com>
Date: Thu, 16 Apr 2026 16:42:11 +0200
Subject: [PATCH 11/39] Remove smoothing-based anonymization code and
 exploratory notebooks

---
 .../49_auto_nasion_detection.ipynb            |  345 ------
 .../head_models/49_seam_diagnostics.ipynb     |  763 ------------
 .../50_complete_anonymization_flow.ipynb      |  259 ----
 .../52_anonymization_validation.ipynb         |  516 --------
 .../head_models/52_landmark_detection.ipynb   |  462 -------
 .../53_symmetry_axis_detection.ipynb          |  501 --------
 .../photogrammetry/anonymization/__init__.py  |   11 -
 .../anonymization/_vertex_utils.py            |   79 --
 .../anonymization/anonymizer.py               | 1078 -----------------
 9 files changed, 4014 deletions(-)
 delete mode 100644 examples/head_models/49_auto_nasion_detection.ipynb
 delete mode 100644 examples/head_models/49_seam_diagnostics.ipynb
 delete mode 100644 examples/head_models/50_complete_anonymization_flow.ipynb
 delete mode 100644 examples/head_models/52_anonymization_validation.ipynb
 delete mode 100644 examples/head_models/52_landmark_detection.ipynb
 delete mode 100644 examples/head_models/53_symmetry_axis_detection.ipynb
 delete mode 100644 src/cedalion/geometry/photogrammetry/anonymization/_vertex_utils.py
 delete mode 100644 src/cedalion/geometry/photogrammetry/anonymization/anonymizer.py

diff --git a/examples/head_models/49_auto_nasion_detection.ipynb b/examples/head_models/49_auto_nasion_detection.ipynb
deleted file mode 100644
index 898640ef..00000000
--- a/examples/head_models/49_auto_nasion_detection.ipynb
+++ /dev/null
@@ -1,345 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Automatic Nasion Detection\n",
-    "\n",
-    "Fully autonomous nasion detection from 3D mesh geometry — no user click needed."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pyvista as pv\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "import cedalion\n",
-    "import cedalion.io\n",
-    "import cedalion.plots\n",
-    "from cedalion import units\n",
-    "from cedalion.geometry.photogrammetry.anonymization import (\n",
-    "    detect_nasion_auto,\n",
-    "    detect_landmarks_from_nasion,\n",
-    "    anonymize_scan,\n",
-    "    AnonymizationConfig,\n",
-    "    AnonymizationMethod,\n",
-    ")\n",
-    "\n",
-    "pv.set_jupyter_backend(\"server\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 1. Load Scans"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Subject 11: 450,295 vertices\n",
-      "Subject 12: 624,121 vertices\n",
-      "Subject 13: 943,539 vertices\n",
-      "Subject 14: 725,532 vertices\n",
-      "Subject 15: 665,419 vertices\n"
-     ]
-    }
-   ],
-   "source": [
-    "SCANS_FOLDER = \"/home/ma7/BA/PG_Subjects11-15\"\n",
-    "SUBJECT_NUMBERS = [11, 12, 13, 14, 15]\n",
-    "\n",
-    "surfaces = {}\n",
-    "for subj in SUBJECT_NUMBERS:\n",
-    "    path = f\"{SCANS_FOLDER}/Subject{subj}/Subject{subj}.obj\"\n",
-    "    try:\n",
-    "        surfaces[subj] = cedalion.io.read_einstar_obj(path)\n",
-    "        print(f\"Subject {subj}: {surfaces[subj].nvertices:,} vertices\")\n",
-    "    except Exception as e:\n",
-    "        print(f\"Subject {subj}: FAILED — {e}\")\n",
-    "\n",
-    "surface = surfaces[12]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 2. Auto-Detect Nasion"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Nose tip: [ 72.030487 177.379486 433.952332]\n",
-      "Nasion:   [108.430481 148.957367 435.152344]\n",
-      "Confidence: 0.48\n"
-     ]
-    }
-   ],
-   "source": [
-    "nasion, meta = detect_nasion_auto(surface)\n",
-    "nose_tip = meta[\"nose_tip\"]\n",
-    "\n",
-    "print(f\"Nose tip: {nose_tip}\")\n",
-    "print(f\"Nasion:   {nasion}\")\n",
-    "print(f\"Confidence: {meta['confidence']:.2f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 3. Visualize on Mesh"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9462ddddf2924cfab81e2dca983e8d39",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Widget(value='<iframe src=\"http://localhost:38775/index.html?ui=P_0x7fd7b17e65d0_0&reconnect=auto\" class=\"pyvi…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pvplt = pv.Plotter()\n",
-    "cedalion.plots.plot_surface(pvplt, surface, opacity=0.6)\n",
-    "\n",
-    "pvplt.add_mesh(pv.Sphere(radius=4.0, center=nose_tip), color=\"green\")\n",
-    "pvplt.add_point_labels([nose_tip + np.array([8, 0, 0])], [\"Nose tip\"],\n",
-    "                       font_size=14, text_color=\"green\", bold=True, shape=None)\n",
-    "\n",
-    "pvplt.add_mesh(pv.Sphere(radius=4.0, center=nasion), color=\"red\")\n",
-    "pvplt.add_point_labels([nasion + np.array([8, 0, 0])], [\"Nasion\"],\n",
-    "                       font_size=14, text_color=\"red\", bold=True, shape=None)\n",
-    "\n",
-    "pvplt.add_mesh(pv.Line(nose_tip, nasion), color=\"yellow\", line_width=3)\n",
-    "pvplt.add_text(\"Auto Nasion Detection\", position=\"upper_left\", font_size=14)\n",
-    "pvplt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 4. Midsagittal Profile"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x1000 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "vertices = surface.mesh.vertices\n",
-    "\n",
-    "# Midline vertices (within 5mm of nasion's Z)\n",
-    "mid_mask = np.abs(vertices[:, 2] - nasion[2]) < 5.0\n",
-    "mid_verts = vertices[mid_mask]\n",
-    "\n",
-    "fig, ax = plt.subplots(figsize=(6, 10))\n",
-    "ax.plot(mid_verts[:, 1], mid_verts[:, 0], '.', markersize=1, alpha=0.3, label='Profile')\n",
-    "ax.plot(nasion[1], nasion[0], 'ro', markersize=12, label='Nasion')\n",
-    "ax.plot(nose_tip[1], nose_tip[0], 'g^', markersize=12, label='Nose tip')\n",
-    "ax.set_xlabel('Y — Forward (mm)')\n",
-    "ax.set_ylabel('X — Up (mm)')\n",
-    "ax.set_title('Midsagittal Profile')\n",
-    "ax.legend()\n",
-    "ax.set_aspect('equal')\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 5. Test All Subjects"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " Subject    Nasion X    Nasion Y    Nasion Z    Conf\n",
-      "----------------------------------------------------\n",
-      "      11       143.0       148.0       445.4    0.00\n",
-      "      12       108.4       149.0       435.2    0.48\n",
-      "      13       149.3       128.6       400.7    0.11\n",
-      "      14        59.3       187.7       394.7    0.44\n",
-      "      15       121.4       133.7       377.2    0.16\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"{'Subject':>8}  {'Nasion X':>10}  {'Nasion Y':>10}  {'Nasion Z':>10}  {'Conf':>6}\")\n",
-    "print(\"-\" * 52)\n",
-    "\n",
-    "for subj, surf in surfaces.items():\n",
-    "    nz, m = detect_nasion_auto(surf)\n",
-    "    print(f\"{subj:>8}  {nz[0]:>10.1f}  {nz[1]:>10.1f}  {nz[2]:>10.1f}  {m['confidence']:>6.2f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 6. Full Pipeline — Auto Nasion to Anonymization"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  Nz: [108.4, 149.0, 435.2]\n",
-      "  Iz: [118.4, -46.2, 439.6]\n",
-      "  Cz: [204.9, 42.6, 437.6]\n",
-      "  LPA: [102.4, 47.4, 532.4]\n",
-      "  RPA: [80.0, 32.6, 350.6]\n",
-      "\n",
-      "Facial vertices: 58,129\n",
-      "Max displacement: 8.27 mm\n",
-      "Mean face disp:  1.09 mm\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Detect landmarks from auto nasion\n",
-    "landmarks = detect_landmarks_from_nasion(surface, nasion)\n",
-    "for label in landmarks.label.values:\n",
-    "    pos = landmarks.sel(label=label).pint.dequantify().values\n",
-    "    print(f\"  {label}: [{pos[0]:.1f}, {pos[1]:.1f}, {pos[2]:.1f}]\")\n",
-    "\n",
-    "# Anonymize\n",
-    "result = anonymize_scan(\n",
-    "    surface=surface,\n",
-    "    anatomical_landmarks=landmarks,\n",
-    "    config=AnonymizationConfig(method=AnonymizationMethod.NOISE, noise_iterations=80),\n",
-    "    interactive=False,\n",
-    "    validate=True,\n",
-    ")\n",
-    "\n",
-    "disp = result.vertex_displacements\n",
-    "print(f\"\\nFacial vertices: {result.facial_mask.sum():,}\")\n",
-    "print(f\"Max displacement: {disp.max():.2f} mm\")\n",
-    "print(f\"Mean face disp:  {disp[result.facial_mask].mean():.2f} mm\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "12598fe7c3e44f29baa39c6991ab6ae7",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Widget(value='<iframe src=\"http://localhost:38775/index.html?ui=P_0x7fd7aeef29d0_1&reconnect=auto\" class=\"pyvi…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pvplt = pv.Plotter(shape=(1, 2))\n",
-    "\n",
-    "pvplt.subplot(0, 0)\n",
-    "cedalion.plots.plot_surface(pvplt, surface, opacity=1.0)\n",
-    "pvplt.add_mesh(pv.Sphere(radius=4.0, center=nasion), color=\"red\")\n",
-    "pvplt.add_text(\"Original + Auto Nz\", position=\"upper_left\", font_size=14)\n",
-    "\n",
-    "pvplt.subplot(0, 1)\n",
-    "cedalion.plots.plot_surface(pvplt, result.anonymized_surface, opacity=1.0)\n",
-    "pvplt.add_text(\"Anonymized\", position=\"upper_left\", font_size=14)\n",
-    "\n",
-    "pvplt.link_views()\n",
-    "pvplt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.14"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/examples/head_models/49_seam_diagnostics.ipynb b/examples/head_models/49_seam_diagnostics.ipynb
deleted file mode 100644
index 1125dffc..00000000
--- a/examples/head_models/49_seam_diagnostics.ipynb
+++ /dev/null
@@ -1,763 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "cell-md-0",
-   "metadata": {},
-   "source": [
-    "# Seam Diagnostics — Mesh Crack Investigation & Fix\n",
-    "\n",
-    "All anonymization methods produce identical dark \"crack\" lines on the face.\n",
-    "The cracks appear at the **same positions** regardless of method, proving the\n",
-    "cause is the **mesh structure** — duplicate vertices at patch boundaries from\n",
-    "Einstar's multi-patch stitching.\n",
-    "\n",
-    "This notebook:\n",
-    "1. Loads the scan, clicks Nz, auto-detects landmarks\n",
-    "2. Visualizes seam lines on the original mesh\n",
-    "3. Confirms seam lines match crack locations\n",
-    "4. Tests a vertex merge fix\n",
-    "5. Quantifies the merge\n",
-    "6. Validates that colors survive the merge"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "cell-1",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loaded: 624,121 vertices, 1,169,802 faces\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "import pyvista as pv\n",
-    "import trimesh\n",
-    "from scipy.spatial import KDTree\n",
-    "\n",
-    "import cedalion\n",
-    "import cedalion.io\n",
-    "import cedalion.plots\n",
-    "from cedalion import units\n",
-    "from cedalion.dataclasses import VTKSurface\n",
-    "from cedalion.geometry.photogrammetry.anonymization import (\n",
-    "    detect_landmarks_from_nasion,\n",
-    "    anonymize_scan,\n",
-    "    AnonymizationConfig,\n",
-    "    AnonymizationMethod,\n",
-    ")\n",
-    "\n",
-    "pv.set_jupyter_backend(\"server\")\n",
-    "\n",
-    "# Load scan\n",
-    "SUBJECT_NUMBER = 12\n",
-    "SCANS_FOLDER = \"/home/ma7/BA/PG_Subjects11-15\"\n",
-    "scan_path = f\"{SCANS_FOLDER}/Subject{SUBJECT_NUMBER}/Subject{SUBJECT_NUMBER}.obj\"\n",
-    "surface = cedalion.io.read_einstar_obj(scan_path)\n",
-    "\n",
-    "mesh = surface.mesh\n",
-    "print(f\"Loaded: {len(mesh.vertices):,} vertices, {len(mesh.faces):,} faces\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "mr9hths8ifr",
-   "metadata": {},
-   "source": [
-    "## 1b. Click Nz on the Mesh\n",
-    "\n",
-    "Click the nasion (bridge of the nose, between the eyebrows) on the mesh.\n",
-    "The clicked point is stored in `nz_store`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "mmx5xjvc1t",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Nz selected at: X=113.7, Y=60.6, Z=523.9\n",
-      "Nz selected at: X=111.1, Y=150.8, Z=427.8\n"
-     ]
-    }
-   ],
-   "source": [
-    "nz_store = {}\n",
-    "\n",
-    "def on_pick(point):\n",
-    "    \"\"\"Callback for surface point picking.\"\"\"\n",
-    "    nz_store[\"point\"] = np.array(point)\n",
-    "    print(f\"Nz selected at: X={point[0]:.1f}, Y={point[1]:.1f}, Z={point[2]:.1f}\")\n",
-    "\n",
-    "# Use a pop-up window — callbacks don't work with the jupyter server backend\n",
-    "pvplt = pv.Plotter(notebook=False)\n",
-    "cedalion.plots.plot_surface(pvplt, surface, opacity=1.0)\n",
-    "pvplt.enable_surface_point_picking(\n",
-    "    callback=on_pick,\n",
-    "    left_clicking=True,\n",
-    "    show_point=True,\n",
-    "    point_size=20,\n",
-    "    color=\"red\",\n",
-    "    show_message=\"Left-click Nz (nasion), then close the window\",\n",
-    ")\n",
-    "pvplt.add_text(\"Left-click Nz (nasion), then close window\", position=\"upper_left\", font_size=14)\n",
-    "pvplt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "1jgm52mov6q",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Nz stored: [111.11239585 150.77507346 427.75947951]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# If clicking didn't work, manually set Nz coordinates here.\n",
-    "# Hover over the nasion in the plot above to read coordinates, then uncomment:\n",
-    "# nz_store[\"point\"] = np.array([X, Y, Z])\n",
-    "\n",
-    "if \"point\" in nz_store:\n",
-    "    print(f\"Nz stored: {nz_store['point']}\")\n",
-    "else:\n",
-    "    print(\"Nz NOT stored yet. Either click above or set manually in this cell.\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "aq5jbd62w1",
-   "metadata": {},
-   "source": [
-    "## 1c. Auto-Detect Remaining Landmarks\n",
-    "\n",
-    "From the clicked Nz, the algorithm detects Cz (vertex), Iz (inion),\n",
-    "LPA (left preauricular), and RPA (right preauricular) using mesh geometry."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "v2l0cbiz0on",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Nz position: [111.11239585 150.77507346 427.75947951]\n",
-      "\n",
-      "Detected landmarks:\n",
-      "  Nz: X=  111.1, Y=  150.8, Z=  427.8\n",
-      "  Iz: X=  118.4, Y=  -46.2, Z=  439.6\n",
-      "  Cz: X=  204.9, Y=   42.6, Z=  437.6\n",
-      "  LPA: X=  102.4, Y=   47.4, Z=  532.4\n",
-      "  RPA: X=   80.0, Y=   32.6, Z=  350.6\n"
-     ]
-    }
-   ],
-   "source": [
-    "nz_point = nz_store[\"point\"]\n",
-    "print(f\"Nz position: {nz_point}\")\n",
-    "\n",
-    "landmarks = detect_landmarks_from_nasion(surface, nz_point)\n",
-    "\n",
-    "print(f\"\\nDetected landmarks:\")\n",
-    "for label in landmarks.label.values:\n",
-    "    pos = landmarks.sel(label=label).pint.dequantify().values\n",
-    "    print(f\"  {label}: X={pos[0]:7.1f}, Y={pos[1]:7.1f}, Z={pos[2]:7.1f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-md-2",
-   "metadata": {},
-   "source": [
-    "## 2. Visualize Seam Lines on Original Mesh\n",
-    "\n",
-    "Boundary edges belong to only 1 face (instead of the normal 2). These mark\n",
-    "patch boundaries where the Einstar stitcher left topologically disconnected seams."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "cell-2",
-   "metadata": {
-    "jupyter": {
-     "source_hidden": true
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Boundary edges: 72,350\n",
-      "Total edges: 1,790,870\n",
-      "Boundary fraction: 4.0%\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bfeb15ea1daf45bb82375291e918bb0a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Widget(value='<iframe src=\"http://localhost:34739/index.html?ui=P_0x7f7f296f65d0_0&reconnect=auto\" class=\"pyvi…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Find boundary edges: edges that belong to only 1 face\n",
-    "# trimesh tracks this via edges_face — boundary edges have -1 for missing face\n",
-    "from collections import Counter\n",
-    "\n",
-    "# Count how many faces each edge belongs to\n",
-    "edge_to_face_count = Counter()\n",
-    "for face in mesh.faces:\n",
-    "    edges_in_face = [\n",
-    "        tuple(sorted((face[0], face[1]))),\n",
-    "        tuple(sorted((face[1], face[2]))),\n",
-    "        tuple(sorted((face[0], face[2]))),\n",
-    "    ]\n",
-    "    for e in edges_in_face:\n",
-    "        edge_to_face_count[e] += 1\n",
-    "\n",
-    "boundary_edges = [e for e, count in edge_to_face_count.items() if count == 1]\n",
-    "print(f\"Boundary edges: {len(boundary_edges):,}\")\n",
-    "print(f\"Total edges: {len(edge_to_face_count):,}\")\n",
-    "print(f\"Boundary fraction: {100 * len(boundary_edges) / len(edge_to_face_count):.1f}%\")\n",
-    "\n",
-    "# Extract boundary edge midpoints for visualization\n",
-    "boundary_edge_arr = np.array(boundary_edges)\n",
-    "midpoints = 0.5 * (mesh.vertices[boundary_edge_arr[:, 0]] + mesh.vertices[boundary_edge_arr[:, 1]])\n",
-    "\n",
-    "# Build line segments for pyvista\n",
-    "boundary_verts = mesh.vertices[boundary_edge_arr.ravel()]\n",
-    "n_edges = len(boundary_edges)\n",
-    "lines = np.column_stack([\n",
-    "    np.full(n_edges, 2),          # each line has 2 points\n",
-    "    np.arange(0, 2 * n_edges, 2), # start indices\n",
-    "    np.arange(1, 2 * n_edges, 2), # end indices\n",
-    "]).ravel()\n",
-    "boundary_polydata = pv.PolyData(boundary_verts, lines=lines)\n",
-    "\n",
-    "# Render mesh with boundary edges in red\n",
-    "pvplt = pv.Plotter()\n",
-    "cedalion.plots.plot_surface(pvplt, surface, opacity=0.6)\n",
-    "pvplt.add_mesh(boundary_polydata, color=\"red\", line_width=2, opacity=0.8)\n",
-    "pvplt.add_text(\n",
-    "    f\"Seam lines (boundary edges): {len(boundary_edges):,}\",\n",
-    "    position=\"upper_left\", font_size=14,\n",
-    ")\n",
-    "pvplt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-md-3",
-   "metadata": {},
-   "source": [
-    "## 3. Confirm: Seams Match Crack Locations\n",
-    "\n",
-    "Run smoothing **without** vertex merging (the old code path) to reproduce the\n",
-    "cracks, then compare side-by-side with the seam overlay. The cracks should\n",
-    "line up with the red seam lines."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "cell-3",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Cracked result (no merge, 300 iter uniform Taubin):\n",
-      "  Max displacement:  3.42 mm\n",
-      "  Mean face disp:    0.22 mm\n",
-      "  Median face disp:  0.16 mm\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4670371268f64dcc945fcb8d1fb43d28",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Widget(value='<iframe src=\"http://localhost:34739/index.html?ui=P_0x7f7ef9555750_1&reconnect=auto\" class=\"pyvi…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Run heat diffusion smoothing WITHOUT merging to reproduce cracks.\n",
-    "# This manually calls the internal functions, bypassing the merge step\n",
-    "# that anonymize_scan() now applies automatically.\n",
-    "from cedalion.geometry.photogrammetry.anonymization.anonymizer import (\n",
-    "    _heat_diffusion_smooth,\n",
-    "    _get_protected_vertex_indices,\n",
-    "    apply_boundary_transition,\n",
-    "    smooth_region_selective,\n",
-    ")\n",
-    "from cedalion.geometry.photogrammetry.anonymization import (\n",
-    "    detect_facial_landmarks,\n",
-    "    get_facial_region_mask,\n",
-    ")\n",
-    "from copy import deepcopy\n",
-    "import cedalion.dataclasses as cdc\n",
-    "\n",
-    "# Compute facial mask on original (unmerged) mesh\n",
-    "facial_lm = detect_facial_landmarks(surface, landmarks)\n",
-    "facial_mask = get_facial_region_mask(\n",
-    "    surface=surface,\n",
-    "    facial_landmarks=facial_lm,\n",
-    "    protected_points=landmarks,\n",
-    "    protection_radius=15.0 * units.mm,\n",
-    ")\n",
-    "\n",
-    "# Get protected indices on original mesh\n",
-    "protected_indices = _get_protected_vertex_indices(\n",
-    "    mesh, landmarks, protection_radius=15.0,\n",
-    ")\n",
-    "\n",
-    "# Run Taubin smoothing directly on the UNMERGED mesh (old code path → cracks)\n",
-    "# Use 300 iterations with uniform Laplacian for strong, visible smoothing\n",
-    "cracked_mesh = smooth_region_selective(\n",
-    "    mesh=mesh,\n",
-    "    mask=facial_mask,\n",
-    "    protected_indices=protected_indices,\n",
-    "    iterations=300,\n",
-    "    lamb=0.5,\n",
-    "    mu=-0.53,\n",
-    "    use_cotangent=False,  # uniform Laplacian — no auto-scaling dampening\n",
-    ")\n",
-    "cracked_mesh = apply_boundary_transition(\n",
-    "    mesh=cracked_mesh,\n",
-    "    mask=facial_mask,\n",
-    "    transition_width=10.0,\n",
-    "    protected_indices=protected_indices,\n",
-    "    iterations=10,\n",
-    "    lamb=0.3,\n",
-    ")\n",
-    "\n",
-    "cracked_surface = cdc.TrimeshSurface(\n",
-    "    mesh=cracked_mesh,\n",
-    "    crs=surface.crs,\n",
-    "    units=surface.units,\n",
-    ")\n",
-    "\n",
-    "# Check displacement\n",
-    "disp_cracked = np.linalg.norm(cracked_mesh.vertices - mesh.vertices, axis=1)\n",
-    "face_disp = disp_cracked[facial_mask]\n",
-    "print(f\"Cracked result (no merge, 300 iter uniform Taubin):\")\n",
-    "print(f\"  Max displacement:  {disp_cracked.max():.2f} mm\")\n",
-    "print(f\"  Mean face disp:    {face_disp.mean():.2f} mm\")\n",
-    "print(f\"  Median face disp:  {np.median(face_disp):.2f} mm\")\n",
-    "\n",
-    "# Side-by-side: original + seam overlay vs cracked result\n",
-    "pvplt = pv.Plotter(shape=(1, 2))\n",
-    "\n",
-    "pvplt.subplot(0, 0)\n",
-    "cedalion.plots.plot_surface(pvplt, surface, opacity=0.6)\n",
-    "pvplt.add_mesh(boundary_polydata, color=\"red\", line_width=2, opacity=0.8)\n",
-    "pvplt.add_text(\"Original + Seam Lines\", position=\"upper_left\", font_size=12)\n",
-    "\n",
-    "pvplt.subplot(0, 1)\n",
-    "cedalion.plots.plot_surface(pvplt, cracked_surface, opacity=1.0)\n",
-    "pvplt.add_text(\"300 iter Taubin, no merge (cracks)\", position=\"upper_left\", font_size=12)\n",
-    "\n",
-    "pvplt.link_views()\n",
-    "pvplt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-md-4",
-   "metadata": {},
-   "source": [
-    "## 4. Test Fix: Merge Vertices (via anonymize_scan)\n",
-    "\n",
-    "`anonymize_scan()` now automatically merges duplicate vertices before smoothing.\n",
-    "Run it normally and compare against the cracked result from cell 3."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "cell-4",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Before merge: 624,121 vertices, 1,169,802 faces\n",
-      "After merge:  584,684 vertices, 1,169,260 faces\n",
-      "Vertices removed: 39,437\n",
-      "Faces removed (degenerate): 542\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Also do the manual merge here so cells 5 & 6 can use the stats\n",
-    "def merge_seam_vertices(mesh, tol=0.01):\n",
-    "    \"\"\"Merge spatially coincident vertices into single vertices.\"\"\"\n",
-    "    verts = mesh.vertices\n",
-    "    n = len(verts)\n",
-    "\n",
-    "    tree = KDTree(verts)\n",
-    "    pairs = tree.query_pairs(r=tol)\n",
-    "\n",
-    "    parent = np.arange(n)\n",
-    "\n",
-    "    def find(x):\n",
-    "        while parent[x] != x:\n",
-    "            parent[x] = parent[parent[x]]\n",
-    "            x = parent[x]\n",
-    "        return x\n",
-    "\n",
-    "    def union(a, b):\n",
-    "        ra, rb = find(a), find(b)\n",
-    "        if ra != rb:\n",
-    "            parent[ra] = rb\n",
-    "\n",
-    "    for a, b in pairs:\n",
-    "        union(a, b)\n",
-    "\n",
-    "    for i in range(n):\n",
-    "        parent[i] = find(i)\n",
-    "\n",
-    "    unique_roots, inverse = np.unique(parent, return_inverse=True)\n",
-    "    old_to_new = inverse\n",
-    "    n_new = len(unique_roots)\n",
-    "\n",
-    "    new_to_old = [[] for _ in range(n_new)]\n",
-    "    for old_idx in range(n):\n",
-    "        new_to_old[old_to_new[old_idx]].append(old_idx)\n",
-    "\n",
-    "    new_verts = np.zeros((n_new, 3))\n",
-    "    for new_idx in range(n_new):\n",
-    "        old_indices = new_to_old[new_idx]\n",
-    "        new_verts[new_idx] = verts[old_indices].mean(axis=0)\n",
-    "\n",
-    "    new_faces = old_to_new[mesh.faces]\n",
-    "    valid = (\n",
-    "        (new_faces[:, 0] != new_faces[:, 1])\n",
-    "        & (new_faces[:, 1] != new_faces[:, 2])\n",
-    "        & (new_faces[:, 0] != new_faces[:, 2])\n",
-    "    )\n",
-    "    new_faces = new_faces[valid]\n",
-    "\n",
-    "    new_colors = None\n",
-    "    if hasattr(mesh.visual, 'vertex_colors'):\n",
-    "        try:\n",
-    "            old_colors = mesh.visual.to_color().vertex_colors\n",
-    "            new_colors = np.zeros((n_new, 4), dtype=np.float64)\n",
-    "            for new_idx in range(n_new):\n",
-    "                old_indices = new_to_old[new_idx]\n",
-    "                new_colors[new_idx] = old_colors[old_indices].mean(axis=0)\n",
-    "            new_colors = new_colors.astype(np.uint8)\n",
-    "        except Exception:\n",
-    "            pass\n",
-    "\n",
-    "    merged_mesh = trimesh.Trimesh(vertices=new_verts, faces=new_faces, process=False)\n",
-    "    if new_colors is not None:\n",
-    "        merged_mesh.visual.vertex_colors = new_colors\n",
-    "\n",
-    "    return merged_mesh, old_to_new, new_to_old\n",
-    "\n",
-    "\n",
-    "merged_mesh, old_to_new, new_to_old = merge_seam_vertices(mesh, tol=0.01)\n",
-    "merged_surface = cdc.TrimeshSurface(mesh=merged_mesh, crs=surface.crs, units=surface.units)\n",
-    "\n",
-    "print(f\"Before merge: {len(mesh.vertices):,} vertices, {len(mesh.faces):,} faces\")\n",
-    "print(f\"After merge:  {len(merged_mesh.vertices):,} vertices, {len(merged_mesh.faces):,} faces\")\n",
-    "print(f\"Vertices removed: {len(mesh.vertices) - len(merged_mesh.vertices):,}\")\n",
-    "print(f\"Faces removed (degenerate): {len(mesh.faces) - len(merged_mesh.faces):,}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "cell-4b",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Fixed result (with merge, 300 iter uniform Taubin):\n",
-      "  Max displacement:  1.34 mm\n",
-      "  Mean face disp:    0.17 mm\n",
-      "  Median face disp:  0.15 mm\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6b79ad0ca7e2474dbdb954ac0f420441",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Widget(value='<iframe src=\"http://localhost:45781/index.html?ui=P_0x7f13f5d2c990_13&reconnect=auto\" class=\"pyv…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Run anonymize_scan() which now includes automatic vertex merging\n",
-    "# Use SMOOTH with uniform Laplacian and 300 iterations for strong visible effect\n",
-    "config = AnonymizationConfig(\n",
-    "    method=AnonymizationMethod.SMOOTH,\n",
-    "    smoothing_iterations=300,\n",
-    "    smoothing_lambda=0.5,\n",
-    "    smoothing_mu=-0.53,\n",
-    "    use_cotangent=False,  # uniform Laplacian — avoids cotangent auto-scaling\n",
-    ")\n",
-    "result_fixed = anonymize_scan(\n",
-    "    surface=surface,\n",
-    "    anatomical_landmarks=landmarks,\n",
-    "    config=config,\n",
-    "    interactive=False,\n",
-    "    validate=True,\n",
-    ")\n",
-    "\n",
-    "disp = result_fixed.vertex_displacements\n",
-    "face_disp = disp[result_fixed.facial_mask]\n",
-    "print(f\"Fixed result (with merge, 300 iter uniform Taubin):\")\n",
-    "print(f\"  Max displacement:  {disp.max():.2f} mm\")\n",
-    "print(f\"  Mean face disp:    {face_disp.mean():.2f} mm\")\n",
-    "print(f\"  Median face disp:  {np.median(face_disp):.2f} mm\")\n",
-    "\n",
-    "# Side-by-side: cracked (no merge) vs fixed (with merge)\n",
-    "pvplt = pv.Plotter(shape=(1, 2))\n",
-    "\n",
-    "pvplt.subplot(0, 0)\n",
-    "cedalion.plots.plot_surface(pvplt, cracked_surface, opacity=1.0)\n",
-    "pvplt.add_text(\"Without merge (cracks)\", position=\"upper_left\", font_size=12)\n",
-    "\n",
-    "pvplt.subplot(0, 1)\n",
-    "cedalion.plots.plot_surface(pvplt, result_fixed.anonymized_surface, opacity=1.0)\n",
-    "pvplt.add_text(\"With merge (fixed)\", position=\"upper_left\", font_size=12)\n",
-    "\n",
-    "pvplt.link_views()\n",
-    "pvplt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-md-5",
-   "metadata": {},
-   "source": [
-    "## 5. Quantify the Merge\n",
-    "\n",
-    "Print detailed statistics on the merge operation and verify mesh integrity."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "cell-5",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "=== Merge Statistics ===\n",
-      "Vertices:      624,121 ->    584,684  (39,437 removed)\n",
-      "Faces:       1,169,802 ->  1,169,260  (542 degenerate removed)\n",
-      "Components:      3,715 ->         28\n",
-      "Boundary edges:  72,350 ->       0\n",
-      "\n",
-      "Degenerate faces (area < 1e-10): 0\n",
-      "Mesh is watertight: True\n",
-      "Total surface area: 396396.6 mm^2 (before) vs 396396.6 mm^2 (after)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Count connected components before and after\n",
-    "from scipy.sparse import csr_matrix\n",
-    "from scipy.sparse.csgraph import connected_components\n",
-    "\n",
-    "def count_components(m):\n",
-    "    \"\"\"Count connected components in a trimesh.\"\"\"\n",
-    "    n = len(m.vertices)\n",
-    "    edges = m.edges_unique\n",
-    "    rows = np.concatenate([edges[:, 0], edges[:, 1]])\n",
-    "    cols = np.concatenate([edges[:, 1], edges[:, 0]])\n",
-    "    data = np.ones(len(rows))\n",
-    "    A = csr_matrix((data, (rows, cols)), shape=(n, n))\n",
-    "    n_comp, _ = connected_components(A, directed=False)\n",
-    "    return n_comp\n",
-    "\n",
-    "n_comp_before = count_components(mesh)\n",
-    "n_comp_after = count_components(merged_mesh)\n",
-    "\n",
-    "# Count boundary edges after merge\n",
-    "edge_count_after = Counter()\n",
-    "for face in merged_mesh.faces:\n",
-    "    for i in range(3):\n",
-    "        e = tuple(sorted((face[i], face[(i + 1) % 3])))\n",
-    "        edge_count_after[e] += 1\n",
-    "boundary_after = sum(1 for c in edge_count_after.values() if c == 1)\n",
-    "\n",
-    "print(\"=== Merge Statistics ===\")\n",
-    "print(f\"Vertices:   {len(mesh.vertices):>10,} -> {len(merged_mesh.vertices):>10,}  ({len(mesh.vertices) - len(merged_mesh.vertices):,} removed)\")\n",
-    "print(f\"Faces:      {len(mesh.faces):>10,} -> {len(merged_mesh.faces):>10,}  ({len(mesh.faces) - len(merged_mesh.faces):,} degenerate removed)\")\n",
-    "print(f\"Components: {n_comp_before:>10,} -> {n_comp_after:>10,}\")\n",
-    "print(f\"Boundary edges: {len(boundary_edges):>7,} -> {boundary_after:>7,}\")\n",
-    "print()\n",
-    "\n",
-    "# Check for degenerate faces in merged mesh\n",
-    "areas = merged_mesh.area_faces\n",
-    "degenerate = (areas < 1e-10).sum()\n",
-    "print(f\"Degenerate faces (area < 1e-10): {degenerate}\")\n",
-    "print(f\"Mesh is watertight: {merged_mesh.is_watertight}\")\n",
-    "print(f\"Total surface area: {mesh.area:.1f} mm^2 (before) vs {merged_mesh.area:.1f} mm^2 (after)\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-md-6",
-   "metadata": {},
-   "source": [
-    "## 6. Test: Does Merging Break Vertex Colors?\n",
-    "\n",
-    "Compare original vs merged mesh appearance (without any smoothing) to verify\n",
-    "that color averaging at seam positions preserves visual appearance."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "cell-6",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "69c330cc921540d99561f08ae5f144ef",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Widget(value='<iframe src=\"http://localhost:45781/index.html?ui=P_0x7f13f5d2fb50_7&reconnect=auto\" class=\"pyvi…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Could not compare colors: 'ColorVisuals' object has no attribute 'to_color'\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Render original vs merged mesh WITHOUT anonymization\n",
-    "pvplt = pv.Plotter(shape=(1, 2))\n",
-    "\n",
-    "pvplt.subplot(0, 0)\n",
-    "cedalion.plots.plot_surface(pvplt, surface, opacity=1.0)\n",
-    "pvplt.add_text(\"Original\", position=\"upper_left\", font_size=12)\n",
-    "\n",
-    "pvplt.subplot(0, 1)\n",
-    "cedalion.plots.plot_surface(pvplt, merged_surface, opacity=1.0)\n",
-    "pvplt.add_text(\"Merged (no smoothing)\", position=\"upper_left\", font_size=12)\n",
-    "\n",
-    "pvplt.link_views()\n",
-    "pvplt.show()\n",
-    "\n",
-    "# Quantify color difference at seam vertices\n",
-    "try:\n",
-    "    orig_colors = mesh.visual.to_color().vertex_colors[:, :3].astype(float)\n",
-    "    merged_colors = merged_mesh.visual.to_color().vertex_colors[:, :3].astype(float)\n",
-    "\n",
-    "    # For each merged vertex, compare its color to the average of original colors\n",
-    "    max_diffs = []\n",
-    "    for new_idx in range(len(merged_mesh.vertices)):\n",
-    "        old_indices = new_to_old[new_idx]\n",
-    "        if len(old_indices) > 1:  # only check actually merged vertices\n",
-    "            old_cols = orig_colors[old_indices]\n",
-    "            spread = old_cols.max(axis=0) - old_cols.min(axis=0)\n",
-    "            max_diffs.append(spread.max())\n",
-    "\n",
-    "    if max_diffs:\n",
-    "        max_diffs = np.array(max_diffs)\n",
-    "        print(f\"\\nColor spread at merged vertices (0-255 scale):\")\n",
-    "        print(f\"  Max spread:    {max_diffs.max():.1f}\")\n",
-    "        print(f\"  Mean spread:   {max_diffs.mean():.1f}\")\n",
-    "        print(f\"  Median spread: {np.median(max_diffs):.1f}\")\n",
-    "        print(f\"  Vertices with spread > 10: {(max_diffs > 10).sum()} / {len(max_diffs)}\")\n",
-    "    else:\n",
-    "        print(\"No merged vertices found.\")\n",
-    "except Exception as e:\n",
-    "    print(f\"Could not compare colors: {e}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "edf112e4-03c8-4d88-ad56-d8365fc2d84b",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.14"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/examples/head_models/50_complete_anonymization_flow.ipynb b/examples/head_models/50_complete_anonymization_flow.ipynb
deleted file mode 100644
index c0629fd0..00000000
--- a/examples/head_models/50_complete_anonymization_flow.ipynb
+++ /dev/null
@@ -1,259 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Anonymization Region Visualization\n",
-    "\n",
-    "Visualize the rectangular facial anonymization mask.\n",
-    "The mask covers the full face width (including ears) from above Nz down to chin/neck."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pyvista as pv\n",
-    "\n",
-    "import cedalion\n",
-    "import cedalion.io\n",
-    "import cedalion.plots\n",
-    "from cedalion import units\n",
-    "from cedalion.geometry.photogrammetry.anonymization import (\n",
-    "    detect_landmarks_from_nasion,\n",
-    "    detect_facial_landmarks,\n",
-    "    get_facial_region_mask,\n",
-    ")\n",
-    "from cedalion.dataclasses import VTKSurface\n",
-    "\n",
-    "pv.set_jupyter_backend(\"server\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 1. Load Scan + Auto-Detect Landmarks"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loaded: 624,121 vertices, 1,169,802 faces\n",
-      "Auto nasion: [108.430481 148.957367 435.152344], confidence=0.48\n",
-      "\n",
-      "Detected landmarks:\n",
-      "  Nz: X=  108.4, Y=  149.0, Z=  435.2\n",
-      "  Iz: X=  118.4, Y=  -46.2, Z=  439.6\n",
-      "  Cz: X=  204.9, Y=   42.6, Z=  437.6\n",
-      "  LPA: X=  102.4, Y=   47.4, Z=  532.4\n",
-      "  RPA: X=   80.0, Y=   32.6, Z=  350.6\n"
-     ]
-    }
-   ],
-   "source": [
-    "SUBJECT_NUMBER = 12\n",
-    "SCANS_FOLDER = \"/home/ma7/BA/PG_Subjects11-15\"\n",
-    "\n",
-    "scan_path = f\"{SCANS_FOLDER}/Subject{SUBJECT_NUMBER}/Subject{SUBJECT_NUMBER}.obj\"\n",
-    "surface = cedalion.io.read_einstar_obj(scan_path)\n",
-    "print(f\"Loaded: {surface.nvertices:,} vertices, {surface.nfaces:,} faces\")\n",
-    "\n",
-    "# Auto-detect nasion, then all landmarks\n",
-    "from cedalion.geometry.photogrammetry.anonymization.nasion_detector import detect_nasion_auto\n",
-    "\n",
-    "nz_point, nz_meta = detect_nasion_auto(surface)\n",
-    "print(f\"Auto nasion: {nz_point}, confidence={nz_meta['confidence']:.2f}\")\n",
-    "\n",
-    "landmarks = detect_landmarks_from_nasion(surface, nz_point)\n",
-    "\n",
-    "print(f\"\\nDetected landmarks:\")\n",
-    "for label in landmarks.label.values:\n",
-    "    pos = landmarks.sel(label=label).pint.dequantify().values\n",
-    "    print(f\"  {label}: X={pos[0]:7.1f}, Y={pos[1]:7.1f}, Z={pos[2]:7.1f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 2. Compute Facial Region Mask"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Facial mask: 113,406 / 624,121 vertices (18.2%)\n"
-     ]
-    }
-   ],
-   "source": [
-    "facial_landmarks = detect_facial_landmarks(surface, landmarks)\n",
-    "\n",
-    "facial_mask = get_facial_region_mask(\n",
-    "    surface=surface,\n",
-    "    facial_landmarks=facial_landmarks,\n",
-    "    protected_points=landmarks,\n",
-    "    protection_radius=15.0 * units.mm,\n",
-    ")\n",
-    "\n",
-    "print(f\"Facial mask: {facial_mask.sum():,} / {len(facial_mask):,} vertices \"\n",
-    "      f\"({100 * facial_mask.sum() / len(facial_mask):.1f}%)\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 3. Delete Facial Vertices"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Original: 624,121 verts, 1,169,802 faces\n",
-      "After deletion: 510,668 verts, 955,712 faces\n",
-      "Removed: 113,453 verts, 214,090 faces\n"
-     ]
-    }
-   ],
-   "source": [
-    "import trimesh\n",
-    "\n",
-    "mesh_copy = surface.mesh.copy()\n",
-    "# Remove faces where ANY vertex is in the facial region\n",
-    "face_verts_in_mask = facial_mask[mesh_copy.faces]  # (nfaces, 3) bool\n",
-    "faces_to_remove = face_verts_in_mask.any(axis=1)\n",
-    "mesh_copy.update_faces(~faces_to_remove)\n",
-    "mesh_copy.remove_unreferenced_vertices()\n",
-    "print(f\"Original: {surface.nvertices:,} verts, {surface.nfaces:,} faces\")\n",
-    "print(f\"After deletion: {len(mesh_copy.vertices):,} verts, {len(mesh_copy.faces):,} faces\")\n",
-    "print(f\"Removed: {surface.nvertices - len(mesh_copy.vertices):,} verts, {surface.nfaces - len(mesh_copy.faces):,} faces\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 4. Visualize Deletion Region"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4f21233bc3234492af346437ddf66c6b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Widget(value='<iframe src=\"http://localhost:40875/index.html?ui=P_0x7f897b41c050_1&reconnect=auto\" class=\"pyvi\u2026"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from vtk.util.numpy_support import vtk_to_numpy\n",
-    "\n",
-    "vtk_surface = VTKSurface.from_trimeshsurface(surface)\n",
-    "pv_mesh = pv.wrap(vtk_surface.mesh)\n",
-    "\n",
-    "# Extract baked vertex colors from VTK (texture already rasterized to per-vertex)\n",
-    "vtk_colors = vtk_surface.mesh.GetPointData().GetScalars()\n",
-    "orig_colors = vtk_to_numpy(vtk_colors).copy()  # (N, 4) RGBA\n",
-    "\n",
-    "# Paint facial region red\n",
-    "orig_colors[facial_mask, :3] = [255, 50, 50]\n",
-    "pv_mesh.point_data[\"mask_colors\"] = orig_colors\n",
-    "\n",
-    "pvplt = pv.Plotter()\n",
-    "pvplt.add_mesh(pv_mesh, scalars=\"mask_colors\", rgb=True)\n",
-    "pvplt.add_text(\"Facial Region to Delete (red)\", position=\"upper_left\", font_size=14)\n",
-    "pvplt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 5. Result: Original vs Anonymized"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from cedalion.vtktutils import trimesh_to_vtk_polydata\n",
-    "\n",
-    "# Convert anonymized mesh to VTK (handles TextureVisuals -> vertex colors)\n",
-    "anon_vtk_mesh = trimesh_to_vtk_polydata(mesh_copy)\n",
-    "\n",
-    "pvplt = pv.Plotter(shape=(1, 2))\n",
-    "\n",
-    "pvplt.subplot(0, 0)\n",
-    "cedalion.plots.plot_surface(pvplt, surface, opacity=1.0)\n",
-    "pvplt.add_text(\"Original\", position=\"upper_left\", font_size=14)\n",
-    "\n",
-    "pvplt.subplot(0, 1)\n",
-    "pvplt.add_mesh(pv.wrap(anon_vtk_mesh), rgb=True)\n",
-    "pvplt.add_text(\"Anonymized\", position=\"upper_left\", font_size=14)\n",
-    "\n",
-    "pvplt.link_views()\n",
-    "pvplt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.14"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
\ No newline at end of file
diff --git a/examples/head_models/52_anonymization_validation.ipynb b/examples/head_models/52_anonymization_validation.ipynb
deleted file mode 100644
index cd6882ab..00000000
--- a/examples/head_models/52_anonymization_validation.ipynb
+++ /dev/null
@@ -1,516 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "c0",
-   "metadata": {},
-   "source": [
-    "# End-to-End Validation: Face Anonymization Preserves fNIRS Utility\n",
-    "\n",
-    "This notebook proves that face removal does not affect downstream fNIRS processing by running the **actual cedalion pipeline** on both original and anonymized scans:\n",
-    "\n",
-    "1. Build the **10-10 electrode system** on original and anonymized surfaces\n",
-    "2. Compare all 10-10 positions — non-facial positions should be **identical**\n",
-    "3. Facial positions (Fp1, Fp2, F7, F8...) should be **missing** on the anonymized scan — proving the face is gone\n",
-    "\n",
-    "**Scientific basis:**\n",
-    "- Bruna et al. 2022 showed MRI defacing shifts coregistration landmarks → our test directly measures this\n",
-    "- Clausner et al. 2017 established <1mm accuracy benchmark for photogrammetry-based fNIRS coregistration\n",
-    "- GDPR Article 17: vertex deletion = irreversible anonymization by construction"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "c1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import cedalion\n",
-    "import cedalion.io\n",
-    "import cedalion.dataclasses as cdc\n",
-    "import cedalion.geometry.landmarks\n",
-    "from cedalion import units\n",
-    "from cedalion.geometry.photogrammetry.anonymization import (\n",
-    "    detect_landmarks_from_nasion,\n",
-    "    detect_facial_landmarks,\n",
-    "    get_facial_region_mask,\n",
-    "    validate_anonymization,\n",
-    ")\n",
-    "from cedalion.geometry.photogrammetry.anonymization.nasion_detector import detect_nasion_auto\n",
-    "\n",
-    "%matplotlib inline\n",
-    "plt.rcParams['figure.dpi'] = 120"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c2",
-   "metadata": {},
-   "source": [
-    "## 1. Load, Detect Landmarks, Remove Face"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "c3",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loaded: 624,121 vertices, 1,169,802 faces\n",
-      "Landmarks: [np.str_('Nz'), np.str_('Iz'), np.str_('Cz'), np.str_('LPA'), np.str_('RPA')]\n",
-      "\n",
-      "Original:   624,121 vertices\n",
-      "Anonymized: 510,668 vertices\n",
-      "Removed:    113,453 vertices (18.2%)\n"
-     ]
-    }
-   ],
-   "source": [
-    "SUBJECT = 12\n",
-    "SCANS_FOLDER = \"/home/ma7/BA/PG_Subjects11-15\"\n",
-    "\n",
-    "path = f\"{SCANS_FOLDER}/Subject{SUBJECT}/Subject{SUBJECT}.obj\"\n",
-    "surface = cedalion.io.read_einstar_obj(path)\n",
-    "print(f\"Loaded: {surface.nvertices:,} vertices, {surface.nfaces:,} faces\")\n",
-    "\n",
-    "# Auto-detect landmarks\n",
-    "nz_point, nz_meta = detect_nasion_auto(surface)\n",
-    "landmarks = detect_landmarks_from_nasion(surface, nz_point)\n",
-    "print(f\"Landmarks: {list(landmarks.label.values)}\")\n",
-    "\n",
-    "# Build facial mask and remove face\n",
-    "facial_landmarks = detect_facial_landmarks(surface, landmarks)\n",
-    "facial_mask = get_facial_region_mask(\n",
-    "    surface=surface,\n",
-    "    facial_landmarks=facial_landmarks,\n",
-    "    protected_points=landmarks,\n",
-    "    protection_radius=15.0 * units.mm,\n",
-    ")\n",
-    "\n",
-    "# Delete facial vertices\n",
-    "import trimesh\n",
-    "mesh_copy = surface.mesh.copy()\n",
-    "faces_to_remove = facial_mask[mesh_copy.faces].any(axis=1)\n",
-    "mesh_copy.update_faces(~faces_to_remove)\n",
-    "mesh_copy.remove_unreferenced_vertices()\n",
-    "anonymized_surface = cdc.TrimeshSurface(mesh_copy, surface.crs, \"mm\")\n",
-    "\n",
-    "print(f\"\\nOriginal:   {surface.nvertices:,} vertices\")\n",
-    "print(f\"Anonymized: {len(mesh_copy.vertices):,} vertices\")\n",
-    "print(f\"Removed:    {surface.nvertices - len(mesh_copy.vertices):,} vertices ({100*facial_mask.sum()/len(facial_mask):.1f}%)\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c4",
-   "metadata": {},
-   "source": [
-    "## 2. Sanity Check (Library Validator)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "c5",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Mesh has 22 degenerate (zero-area) faces\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Passed: False\n",
-      "Summary: FAILED — mesh has degenerate faces\n",
-      "\n",
-      "Details:\n",
-      "  Face removed:              True\n",
-      "  Mesh valid:                False\n",
-      "  Protected points intact:   True\n",
-      "  Max protected deviation:   0.0000 mm\n",
-      "  Expected vertices removed: 113,406\n",
-      "  Actual vertices removed:   113,453\n",
-      "  Face coverage:             18.2%\n"
-     ]
-    }
-   ],
-   "source": [
-    "check = validate_anonymization(\n",
-    "    original_surface=surface,\n",
-    "    anonymized_surface=anonymized_surface,\n",
-    "    facial_mask=facial_mask,\n",
-    "    protected_points=landmarks,\n",
-    "    tolerance=1.0 * units.mm,\n",
-    ")\n",
-    "\n",
-    "print(f\"Passed: {check.passed}\")\n",
-    "print(f\"Summary: {check.summary}\")\n",
-    "print(f\"\\nDetails:\")\n",
-    "print(f\"  Face removed:              {check.face_removed}\")\n",
-    "print(f\"  Mesh valid:                {check.mesh_valid}\")\n",
-    "print(f\"  Protected points intact:   {check.protected_points_intact}\")\n",
-    "print(f\"  Max protected deviation:   {check.protected_point_max_deviation:.4f} mm\")\n",
-    "print(f\"  Expected vertices removed: {check.expected_vertices_removed:,}\")\n",
-    "print(f\"  Actual vertices removed:   {check.actual_vertices_removed:,}\")\n",
-    "print(f\"  Face coverage:             {check.face_coverage_pct:.1f}%\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c6",
-   "metadata": {},
-   "source": [
-    "## 3. Build 10-10 System on Both Surfaces\n",
-    "\n",
-    "The 10-10 electrode system places ~80 standardized positions on the scalp by intersecting geodesic lines between anatomical landmarks. If these positions are identical on the original and anonymized scans, the anonymized scan produces identical optode coregistration."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "c7",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/ma7/BA/cedalion/cedalion/src/cedalion/geometry/landmarks.py:242: UserWarning: WIP: distance calculation around ears\n",
-      "  warnings.warn(\"WIP: distance calculation around ears\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "10-10 positions on original: 73 landmarks\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/ma7/BA/cedalion/cedalion/src/cedalion/geometry/landmarks.py:242: UserWarning: WIP: distance calculation around ears\n",
-      "  warnings.warn(\"WIP: distance calculation around ears\")\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "10-10 positions on anonymized: 73 landmarks\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Build 10-10 on original surface\n",
-    "builder_orig = cedalion.geometry.landmarks.LandmarksBuilder1010(surface, landmarks)\n",
-    "positions_orig = builder_orig.build()\n",
-    "print(f\"10-10 positions on original: {len(positions_orig.label)} landmarks\")\n",
-    "\n",
-    "# Build 10-10 on anonymized surface\n",
-    "builder_anon = cedalion.geometry.landmarks.LandmarksBuilder1010(anonymized_surface, landmarks)\n",
-    "positions_anon = builder_anon.build()\n",
-    "print(f\"10-10 positions on anonymized: {len(positions_anon.label)} landmarks\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c8",
-   "metadata": {},
-   "source": [
-    "## 4. Compare 10-10 Positions\n",
-    "\n",
-    "For each 10-10 position that exists on **both** surfaces, compute the Euclidean distance. Positions in the facial region may fail to compute on the anonymized scan (surface missing) — these prove the face is gone."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "c9",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Total 10-10 positions on original: 73\n",
-      "Total 10-10 positions on anonymized: 73\n",
-      "\n",
-      "============================================================\n",
-      "  PRESERVED (<1.0mm):      15 positions  ✓\n",
-      "  BOUNDARY (1.0-10.0mm):      33 positions  ~\n",
-      "  DISPLACED (>10.0mm):      25 positions  ✗\n",
-      "============================================================\n",
-      "\n",
-      "--- Preserved positions (deviation < 1.0mm) ---\n",
-      "  Iz       0.0000 mm\n",
-      "  LPA      0.0000 mm\n",
-      "  Nz       0.0000 mm\n",
-      "  RPA      0.0000 mm\n",
-      "  Oz       0.0267 mm\n",
-      "  POz      0.0516 mm\n",
-      "  Fz       0.0652 mm\n",
-      "  CPz      0.0752 mm\n",
-      "  Cz       0.0752 mm\n",
-      "  Fpz      0.2248 mm\n",
-      "  AFz      0.3712 mm\n",
-      "  C1       0.3986 mm\n",
-      "  FCz      0.5036 mm\n",
-      "  Pz       0.5165 mm\n",
-      "  C2       0.5520 mm\n",
-      "\n",
-      "--- Boundary positions (1.0-10.0mm) ---\n",
-      "  C3       1.3744 mm\n",
-      "  C4       1.5294 mm\n",
-      "  O1       1.5317 mm\n",
-      "  C5       1.8509 mm\n",
-      "  C6       2.0069 mm\n",
-      "  AF2      2.1291 mm\n",
-      "  T7       2.3201 mm\n",
-      "  FC2      3.8284 mm\n",
-      "  FC1      3.8293 mm\n",
-      "  PO5      4.2373 mm\n",
-      "  PO7      4.4153 mm\n",
-      "  PO3      4.5351 mm\n",
-      "  O2       4.8306 mm\n",
-      "  CP3      4.8929 mm\n",
-      "  F2       5.0275 mm\n",
-      "  CP5      5.1382 mm\n",
-      "  P5       5.5342 mm\n",
-      "  PO1      5.5631 mm\n",
-      "  P7       5.7723 mm\n",
-      "  CP1      6.1245 mm\n",
-      "  P3       6.2074 mm\n",
-      "  PO2      6.4064 mm\n",
-      "  PO4      6.5771 mm\n",
-      "  TP7      6.6431 mm\n",
-      "  P2       6.7862 mm\n",
-      "  P1       6.7883 mm\n",
-      "  PO8      7.3859 mm\n",
-      "  P4       7.4263 mm\n",
-      "  PO6      7.5021 mm\n",
-      "  CP2      8.5424 mm\n",
-      "  FC3      8.7795 mm\n",
-      "  P6       9.1252 mm\n",
-      "  CP4      9.1606 mm\n",
-      "\n",
-      "--- Displaced positions (>10.0mm, near/in facial region) ---\n",
-      "  Fp2       124.9 mm\n",
-      "  Fp1       110.0 mm\n",
-      "  AF8       101.0 mm\n",
-      "  AF7        84.6 mm\n",
-      "  F8         73.0 mm\n",
-      "  AF5        64.0 mm\n",
-      "  F7         56.3 mm\n",
-      "  FT8        44.7 mm\n",
-      "  AF3        44.6 mm\n",
-      "  F5         38.1 mm\n",
-      "  AF6        36.5 mm\n",
-      "  F6         34.0 mm\n",
-      "  FT7        28.1 mm\n",
-      "  AF1        27.8 mm\n",
-      "  F3         22.7 mm\n",
-      "  FC6        22.0 mm\n",
-      "  F4         17.6 mm\n",
-      "  FC5        17.2 mm\n",
-      "  T8         16.4 mm\n",
-      "  AF4        16.2 mm\n",
-      "  TP8        13.3 mm\n",
-      "  FC4        11.5 mm\n",
-      "  F1         10.9 mm\n",
-      "  P8         10.9 mm\n",
-      "  CP6        10.8 mm\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Find common labels\n",
-    "orig_labels = set(str(l) for l in positions_orig.label.values)\n",
-    "anon_labels = set(str(l) for l in positions_anon.label.values)\n",
-    "common = sorted(orig_labels & anon_labels)\n",
-    "missing_on_anon = sorted(orig_labels - anon_labels)\n",
-    "\n",
-    "# Compute deviations for common positions\n",
-    "deviations = {}\n",
-    "for label in common:\n",
-    "    p_orig = positions_orig.sel(label=label).pint.dequantify().values\n",
-    "    p_anon = positions_anon.sel(label=label).pint.dequantify().values\n",
-    "    deviations[label] = float(np.linalg.norm(p_anon - p_orig))\n",
-    "\n",
-    "# Categorize by deviation severity\n",
-    "THRESHOLD_PERFECT = 0.1    # mm — effectively identical\n",
-    "THRESHOLD_SAFE = 1.0       # mm — within fNIRS accuracy (Clausner et al. 2017)\n",
-    "THRESHOLD_AFFECTED = 10.0  # mm — position shifted, likely near facial boundary\n",
-    "\n",
-    "preserved = {k: v for k, v in deviations.items() if v <= THRESHOLD_SAFE}\n",
-    "boundary = {k: v for k, v in deviations.items() if THRESHOLD_SAFE < v <= THRESHOLD_AFFECTED}\n",
-    "displaced = {k: v for k, v in deviations.items() if v > THRESHOLD_AFFECTED}\n",
-    "\n",
-    "print(f\"Total 10-10 positions on original: {len(orig_labels)}\")\n",
-    "print(f\"Total 10-10 positions on anonymized: {len(anon_labels)}\")\n",
-    "if missing_on_anon:\n",
-    "    print(f\"Missing on anonymized: {len(missing_on_anon)} — {missing_on_anon}\")\n",
-    "\n",
-    "print(f\"\\n{'='*60}\")\n",
-    "print(f\"  PRESERVED (<{THRESHOLD_SAFE}mm):     {len(preserved):>3} positions  ✓\")\n",
-    "print(f\"  BOUNDARY ({THRESHOLD_SAFE}-{THRESHOLD_AFFECTED}mm):     {len(boundary):>3} positions  ~\")\n",
-    "print(f\"  DISPLACED (>{THRESHOLD_AFFECTED}mm):     {len(displaced):>3} positions  ✗\")\n",
-    "print(f\"{'='*60}\")\n",
-    "\n",
-    "print(f\"\\n--- Preserved positions (deviation < {THRESHOLD_SAFE}mm) ---\")\n",
-    "for k, v in sorted(preserved.items(), key=lambda x: x[1]):\n",
-    "    print(f\"  {k:<6} {v:>8.4f} mm\")\n",
-    "\n",
-    "if boundary:\n",
-    "    print(f\"\\n--- Boundary positions ({THRESHOLD_SAFE}-{THRESHOLD_AFFECTED}mm) ---\")\n",
-    "    for k, v in sorted(boundary.items(), key=lambda x: x[1]):\n",
-    "        print(f\"  {k:<6} {v:>8.4f} mm\")\n",
-    "\n",
-    "if displaced:\n",
-    "    print(f\"\\n--- Displaced positions (>{THRESHOLD_AFFECTED}mm, near/in facial region) ---\")\n",
-    "    for k, v in sorted(displaced.items(), key=lambda x: -x[1]):\n",
-    "        print(f\"  {k:<6} {v:>8.1f} mm\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c10",
-   "metadata": {},
-   "source": [
-    "## 5. Visualization: 10-10 Positions on Original vs Anonymized"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "c11",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1920x960 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, axes = plt.subplots(1, 2, figsize=(16, 8))\n",
-    "\n",
-    "orig_verts = surface.mesh.vertices\n",
-    "anon_verts = anonymized_surface.mesh.vertices\n",
-    "\n",
-    "# Side profile view (Y vs X), right half only\n",
-    "z_med = np.median(orig_verts[:, 2])\n",
-    "right = orig_verts[:, 2] < z_med\n",
-    "\n",
-    "# Original\n",
-    "axes[0].scatter(orig_verts[right & ~facial_mask, 1], orig_verts[right & ~facial_mask, 0],\n",
-    "                s=0.02, c='#2E86C1', alpha=0.3, rasterized=True)\n",
-    "axes[0].scatter(orig_verts[right & facial_mask, 1], orig_verts[right & facial_mask, 0],\n",
-    "                s=0.02, c='#E74C3C', alpha=0.2, rasterized=True)\n",
-    "\n",
-    "# Plot 10-10 positions on original\n",
-    "pos_orig_vals = positions_orig.pint.dequantify().values\n",
-    "orig_lbl = [str(l) for l in positions_orig.label.values]\n",
-    "for i, lbl in enumerate(orig_lbl):\n",
-    "    axes[0].plot(pos_orig_vals[i, 1], pos_orig_vals[i, 0], 'ko', markersize=3)\n",
-    "    axes[0].annotate(lbl, (pos_orig_vals[i, 1], pos_orig_vals[i, 0]),\n",
-    "                     fontsize=4, ha='center', va='bottom')\n",
-    "\n",
-    "axes[0].set_title(f'Original — {len(orig_lbl)} 10-10 positions')\n",
-    "axes[0].set_aspect('equal')\n",
-    "axes[0].set_facecolor('#F8F9FA')\n",
-    "axes[0].set_xlabel('Y (forward) [mm]')\n",
-    "axes[0].set_ylabel('X (up) [mm]')\n",
-    "\n",
-    "# Anonymized\n",
-    "anon_z_med = np.median(anon_verts[:, 2])\n",
-    "anon_right = anon_verts[:, 2] < anon_z_med\n",
-    "axes[1].scatter(anon_verts[anon_right, 1], anon_verts[anon_right, 0],\n",
-    "                s=0.02, c='#2E86C1', alpha=0.3, rasterized=True)\n",
-    "\n",
-    "# Plot 10-10 positions colored by category with legend entries\n",
-    "pos_anon_vals = positions_anon.pint.dequantify().values\n",
-    "anon_lbl = [str(l) for l in positions_anon.label.values]\n",
-    "\n",
-    "# Plot one dummy point per category for the legend\n",
-    "axes[1].plot([], [], 'o', color='green', markersize=6, label=f'Preserved <1mm ({len(preserved)})')\n",
-    "axes[1].plot([], [], 'o', color='orange', markersize=6, label=f'Boundary 1-10mm ({len(boundary)})')\n",
-    "axes[1].plot([], [], 'o', color='red', markersize=6, label=f'Displaced >10mm ({len(displaced)})')\n",
-    "\n",
-    "for i, lbl in enumerate(anon_lbl):\n",
-    "    dev = deviations.get(lbl, 0)\n",
-    "    if dev <= 1.0:\n",
-    "        color = 'green'\n",
-    "    elif dev <= 10.0:\n",
-    "        color = 'orange'\n",
-    "    else:\n",
-    "        color = 'red'\n",
-    "    axes[1].plot(pos_anon_vals[i, 1], pos_anon_vals[i, 0], 'o',\n",
-    "                 color=color, markersize=4)\n",
-    "    axes[1].annotate(lbl, (pos_anon_vals[i, 1], pos_anon_vals[i, 0]),\n",
-    "                     fontsize=4, ha='center', va='bottom')\n",
-    "\n",
-    "axes[1].set_title(f'Anonymized — {len(anon_lbl)} 10-10 positions')\n",
-    "axes[1].set_aspect('equal')\n",
-    "axes[1].set_facecolor('#F8F9FA')\n",
-    "axes[1].set_xlabel('Y (forward) [mm]')\n",
-    "axes[1].set_ylabel('X (up) [mm]')\n",
-    "axes[1].legend(loc='lower left', fontsize=9, framealpha=0.9)\n",
-    "\n",
-    "# Match axes\n",
-    "for ax in axes:\n",
-    "    ax.set_xlim(orig_verts[right, 1].min() - 5, orig_verts[right, 1].max() + 5)\n",
-    "    ax.set_ylim(orig_verts[right, 0].min() - 5, orig_verts[right, 0].max() + 5)\n",
-    "\n",
-    "plt.suptitle('10-10 System: Original vs Anonymized (Side Profile)', fontsize=14, fontweight='bold')\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c12",
-   "metadata": {},
-   "source": "## 6. Conclusion\n\n**Privacy:** Face deletion removes 100% of facial vertex data. Unlike smoothing or blurring, deletion is irreversible — there is zero residual information to reconstruct from (GDPR Article 17).\n\n**Utility:** The 10-10 electrode system produces identical positions on the anonymized scan for all non-facial landmarks. Facial electrode positions (Fp1, Fp2, etc.) are displaced because the surface under them is gone — this is expected and confirms the face was removed.\n\n**Implication:** Anonymized photogrammetry scans can be shared and used for fNIRS optode coregistration with no loss of accuracy for standard montages that avoid the forehead region."
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.14"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
\ No newline at end of file
diff --git a/examples/head_models/52_landmark_detection.ipynb b/examples/head_models/52_landmark_detection.ipynb
deleted file mode 100644
index a8770933..00000000
--- a/examples/head_models/52_landmark_detection.ipynb
+++ /dev/null
@@ -1,462 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "cell-0",
-   "metadata": {},
-   "source": [
-    "# Landmark Detection — Click Nz, Auto-Detect Rest\n",
-    "\n",
-    "Semi-manual landmark detection: the user clicks Nz (nasion) on the mesh,\n",
-    "then the system auto-detects Iz, LPA, RPA, and Cz from the mesh geometry.\n",
-    "This replaces unreliable sticker-based detection."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "cell-1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pyvista as pv\n",
-    "\n",
-    "import cedalion\n",
-    "import cedalion.io\n",
-    "import cedalion.plots\n",
-    "from cedalion import units\n",
-    "from cedalion.geometry.photogrammetry.anonymization import (\n",
-    "    detect_landmarks_from_nasion,\n",
-    "    anonymize_scan,\n",
-    "    AnonymizationConfig,\n",
-    "    AnonymizationMethod,\n",
-    ")\n",
-    "\n",
-    "pv.set_jupyter_backend(\"server\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-2",
-   "metadata": {},
-   "source": [
-    "## 1. Load Scan"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "cell-3",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loaded: 624,121 vertices, 1,169,802 faces\n"
-     ]
-    }
-   ],
-   "source": [
-    "SUBJECT_NUMBER = 12\n",
-    "SCANS_FOLDER = \"/home/ma7/BA/PG_Subjects11-15\"\n",
-    "\n",
-    "scan_path = f\"{SCANS_FOLDER}/Subject{SUBJECT_NUMBER}/Subject{SUBJECT_NUMBER}.obj\"\n",
-    "surface = cedalion.io.read_einstar_obj(scan_path)\n",
-    "\n",
-    "print(f\"Loaded: {surface.nvertices:,} vertices, {surface.nfaces:,} faces\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-4",
-   "metadata": {},
-   "source": [
-    "## 2. Click Nz on the Mesh\n",
-    "\n",
-    "Click the nasion (bridge of the nose, between the eyebrows) on the mesh.\n",
-    "The clicked point is stored in `nz_store`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "cell-5",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Nz selected at: X=147.0, Y=116.2, Z=483.8\n",
-      "Nz selected at: X=107.6, Y=151.1, Z=428.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "nz_store = {}\n",
-    "\n",
-    "def on_pick(point):\n",
-    "    \"\"\"Callback for surface point picking.\"\"\"\n",
-    "    nz_store[\"point\"] = np.array(point)\n",
-    "    print(f\"Nz selected at: X={point[0]:.1f}, Y={point[1]:.1f}, Z={point[2]:.1f}\")\n",
-    "\n",
-    "# Use a pop-up window — callbacks don't work with the jupyter server backend\n",
-    "pvplt = pv.Plotter(notebook=False)\n",
-    "cedalion.plots.plot_surface(pvplt, surface, opacity=1.0)\n",
-    "pvplt.enable_surface_point_picking(\n",
-    "    callback=on_pick,\n",
-    "    left_clicking=True,\n",
-    "    show_point=True,\n",
-    "    point_size=20,\n",
-    "    color=\"red\",\n",
-    "    show_message=\"Left-click Nz (nasion), then close the window\",\n",
-    ")\n",
-    "pvplt.add_text(\"Left-click Nz (nasion), then close window\", position=\"upper_left\", font_size=14)\n",
-    "pvplt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "9co7899wms6",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Nz stored: [107.57456031 151.09103956 428.04108405]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# If clicking didn't work, manually set Nz coordinates here.\n",
-    "# Hover over the nasion in the plot above to read coordinates, then uncomment:\n",
-    "# nz_store[\"point\"] = np.array([X, Y, Z])\n",
-    "\n",
-    "# Check if Nz was captured:\n",
-    "if \"point\" in nz_store:\n",
-    "    print(f\"Nz stored: {nz_store['point']}\")\n",
-    "else:\n",
-    "    print(\"Nz NOT stored yet. Either click above or set manually in this cell.\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-6",
-   "metadata": {},
-   "source": [
-    "## 3. Auto-Detect Remaining Landmarks\n",
-    "\n",
-    "From the clicked Nz, the algorithm detects Cz (vertex), Iz (inion),\n",
-    "LPA (left preauricular), and RPA (right preauricular) using mesh geometry."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "cell-7",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Nz position: [107.57456031 151.09103956 428.04108405]\n",
-      "\n",
-      "Detected landmarks:\n",
-      "  Nz: X=  107.6, Y=  151.1, Z=  428.0\n",
-      "  Iz: X=  118.4, Y=  -46.2, Z=  439.6\n",
-      "  Cz: X=  204.9, Y=   42.6, Z=  437.6\n",
-      "  LPA: X=  102.4, Y=   47.4, Z=  532.4\n",
-      "  RPA: X=   80.0, Y=   32.6, Z=  350.6\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Use stored Nz or fall back to manual coordinates for testing\n",
-    "if \"point\" in nz_store:\n",
-    "    nz_point = nz_store[\"point\"]\n",
-    "else:\n",
-    "    # Fallback: approximate Nz for Subject11 (from visual inspection)\n",
-    "    # Replace with actual clicked coordinates\n",
-    "    nz_point = np.array([152.0, 120.0, 445.0])\n",
-    "    print(f\"WARNING: Using fallback Nz. Click the mesh in the cell above first.\")\n",
-    "\n",
-    "print(f\"Nz position: {nz_point}\")\n",
-    "\n",
-    "landmarks = detect_landmarks_from_nasion(surface, nz_point)\n",
-    "\n",
-    "print(f\"\\nDetected landmarks:\")\n",
-    "for label in landmarks.label.values:\n",
-    "    pos = landmarks.sel(label=label).pint.dequantify().values\n",
-    "    print(f\"  {label}: X={pos[0]:7.1f}, Y={pos[1]:7.1f}, Z={pos[2]:7.1f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-8",
-   "metadata": {},
-   "source": [
-    "## 4. Visualize Landmarks\n",
-    "\n",
-    "All 5 landmarks displayed as colored spheres on the mesh."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "cell-9",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fc6047ac10dc46dba64cd357be19bbc2",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Widget(value='<iframe src=\"http://localhost:45487/index.html?ui=P_0x7f2bbf64e290_9&reconnect=auto\" class=\"pyvi…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "LANDMARK_COLORS = {\n",
-    "    \"Nz\": \"red\",\n",
-    "    \"Iz\": \"blue\",\n",
-    "    \"Cz\": \"green\",\n",
-    "    \"LPA\": \"yellow\",\n",
-    "    \"RPA\": \"orange\",\n",
-    "}\n",
-    "\n",
-    "pvplt = pv.Plotter()\n",
-    "cedalion.plots.plot_surface(pvplt, surface, opacity=0.7)\n",
-    "\n",
-    "for label in landmarks.label.values:\n",
-    "    pos = landmarks.sel(label=label).pint.dequantify().values\n",
-    "    sphere = pv.Sphere(radius=5.0, center=pos)\n",
-    "    color = LANDMARK_COLORS.get(str(label), \"white\")\n",
-    "    pvplt.add_mesh(sphere, color=color)\n",
-    "    pvplt.add_point_labels(\n",
-    "        [pos + np.array([8, 0, 0])],\n",
-    "        [str(label)],\n",
-    "        font_size=16,\n",
-    "        text_color=color,\n",
-    "        bold=True,\n",
-    "        shape=None,\n",
-    "    )\n",
-    "\n",
-    "pvplt.add_text(\"Detected Landmarks\", position=\"upper_left\", font_size=14)\n",
-    "pvplt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "nrtesre8esa",
-   "metadata": {},
-   "source": [
-    "## 5. Preview Facial Region Mask\n",
-    "\n",
-    "Compute the T-shaped facial mask and visualize it as a red overlay.\n",
-    "Red vertices will be anonymized; grey vertices remain untouched."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "ewvr5w2rukq",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Facial mask: 58,519 / 624,121 vertices (9.4%)\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3df9f52af1724869883b659ba320f574",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Widget(value='<iframe src=\"http://localhost:45487/index.html?ui=P_0x7f2be2e23890_10&reconnect=auto\" class=\"pyv…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from cedalion.geometry.photogrammetry.anonymization import (\n",
-    "    detect_facial_landmarks,\n",
-    "    get_facial_region_mask,\n",
-    ")\n",
-    "from cedalion.dataclasses import VTKSurface\n",
-    "\n",
-    "# Detect facial landmarks (geometric estimates from anatomical landmarks)\n",
-    "facial_landmarks = detect_facial_landmarks(surface, landmarks)\n",
-    "\n",
-    "# Compute the T-shaped facial mask\n",
-    "facial_mask = get_facial_region_mask(\n",
-    "    surface=surface,\n",
-    "    facial_landmarks=facial_landmarks,\n",
-    "    protected_points=landmarks,\n",
-    "    protection_radius=15.0 * units.mm,\n",
-    ")\n",
-    "\n",
-    "print(f\"Facial mask: {facial_mask.sum():,} / {len(facial_mask):,} vertices \"\n",
-    "      f\"({100 * facial_mask.sum() / len(facial_mask):.1f}%)\")\n",
-    "\n",
-    "# Visualize: red = will be anonymized, grey = untouched\n",
-    "vtk_surface = VTKSurface.from_trimeshsurface(surface)\n",
-    "pv_mesh = pv.wrap(vtk_surface.mesh)\n",
-    "colors = np.full((len(facial_mask), 3), 200, dtype=np.uint8)  # grey\n",
-    "colors[facial_mask] = [255, 50, 50]  # red\n",
-    "pv_mesh.point_data[\"mask_colors\"] = colors\n",
-    "\n",
-    "pvplt = pv.Plotter()\n",
-    "pvplt.add_mesh(pv_mesh, scalars=\"mask_colors\", rgb=True, opacity=1.0)\n",
-    "pvplt.add_text(\"Facial Region (red = anonymized)\", position=\"upper_left\", font_size=14)\n",
-    "pvplt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-10",
-   "metadata": {},
-   "source": [
-    "## 6. Anonymize Facial Region\n",
-    "\n",
-    "Apply noise-based anonymization to destroy facial detail while preserving\n",
-    "head shape and protected landmark/optode positions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "cell-11",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Noise iterations: 80\n",
-      "Facial vertices:  58,519\n",
-      "Max displacement:  9.96 mm\n",
-      "Mean face disp:    1.08 mm\n",
-      "Median face disp:  0.96 mm\n"
-     ]
-    }
-   ],
-   "source": [
-    "# --- Anonymization strength ---\n",
-    "# Higher = more aggressive. 80 iterations is a good default.\n",
-    "# Try 40 for mild, 120+ for very aggressive.\n",
-    "NOISE_ITERATIONS = 80\n",
-    "\n",
-    "config = AnonymizationConfig(\n",
-    "    method=AnonymizationMethod.NOISE,\n",
-    "    noise_iterations=NOISE_ITERATIONS,\n",
-    ")\n",
-    "result = anonymize_scan(\n",
-    "    surface=surface,\n",
-    "    anatomical_landmarks=landmarks,\n",
-    "    config=config,\n",
-    "    interactive=False,\n",
-    "    validate=True,\n",
-    ")\n",
-    "\n",
-    "disp = result.vertex_displacements\n",
-    "face_disp = disp[result.facial_mask]\n",
-    "print(f\"Noise iterations: {NOISE_ITERATIONS}\")\n",
-    "print(f\"Facial vertices:  {result.facial_mask.sum():,}\")\n",
-    "print(f\"Max displacement:  {disp.max():.2f} mm\")\n",
-    "print(f\"Mean face disp:    {face_disp.mean():.2f} mm\")\n",
-    "print(f\"Median face disp:  {np.median(face_disp):.2f} mm\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-12",
-   "metadata": {},
-   "source": [
-    "## 7. Original vs Anonymized"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "cell-13",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6aa1380f49e44c44b28de990984e1048",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Widget(value='<iframe src=\"http://localhost:45487/index.html?ui=P_0x7f2bec707290_11&reconnect=auto\" class=\"pyv…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pvplt = pv.Plotter(shape=(1, 2))\n",
-    "\n",
-    "pvplt.subplot(0, 0)\n",
-    "cedalion.plots.plot_surface(pvplt, surface, opacity=1.0)\n",
-    "pvplt.add_text(\"Original\", position=\"upper_left\", font_size=14)\n",
-    "\n",
-    "pvplt.subplot(0, 1)\n",
-    "cedalion.plots.plot_surface(pvplt, result.anonymized_surface, opacity=1.0)\n",
-    "pvplt.add_text(f\"NOISE ({NOISE_ITERATIONS} iter)\", position=\"upper_left\", font_size=14)\n",
-    "\n",
-    "pvplt.link_views()\n",
-    "pvplt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "26d9ebb7-d500-4c49-b815-ff730dd9d033",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.14"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/examples/head_models/53_symmetry_axis_detection.ipynb b/examples/head_models/53_symmetry_axis_detection.ipynb
deleted file mode 100644
index 82eb7717..00000000
--- a/examples/head_models/53_symmetry_axis_detection.ipynb
+++ /dev/null
@@ -1,501 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "cell-md-title",
-   "metadata": {},
-   "source": [
-    "# Forward-Axis Detection via Eyes-Nose Differential (ENdif)\n",
-    "\n",
-    "Prototype porting the 2D face-detection heuristic of Ramírez, Zanella &\n",
-    "Fuentes (*\"Heuristic-based Automatic Face Detection\"*) to our 3D head scans.\n",
-    "\n",
-    "**The paper's core heuristic (2D grayscale):** in a 9x9 downsample of a face\n",
-    "image, the two eye pixels are *darker* than the nose pixel between them\n",
-    "(eyes are recessed, nose bridge protrudes). They call this quantity the\n",
-    "Eyes-Nose differential (**ENdif**) and maximize it to find a face.\n",
-    "\n",
-    "**Our 3D emulation:**\n",
-    "1. Merge Einstar UV-seam duplicates so the geometry is clean.\n",
-    "2. For each candidate camera direction, orthographically rasterize the\n",
-    "   mesh into a *depth image* (closer to camera = brighter). This\n",
-    "   reproduces the paper's \"eyes dark, nose bright\" contrast directly\n",
-    "   from geometry -- no illumination assumptions, and unaffected by the\n",
-    "   EEG cap (nose still protrudes).\n",
-    "3. Downsample each depth image to 9x9, histogram-equalize, and compute\n",
-    "   ENdif exactly as in the paper.\n",
-    "4. Sweep direction over a Fibonacci sphere and plot ENdif. The argmax\n",
-    "   direction is the forward (anterior) axis; its opposite is the back\n",
-    "   of the head.\n",
-    "\n",
-    "Note: world-up is taken as +X (Einstar's rough gravity axis), so the\n",
-    "head renders upright in every view and only the azimuth / elevation\n",
-    "relative to the face matters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "cell-imports",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import trimesh\n",
-    "import matplotlib.pyplot as plt\n",
-    "import scipy.stats\n",
-    "from scipy.ndimage import zoom, gaussian_filter\n",
-    "\n",
-    "import cedalion\n",
-    "import cedalion.io\n",
-    "from cedalion.geometry.photogrammetry.anonymization._vertex_utils import (\n",
-    "    deduplicate_vertices,\n",
-    ")\n",
-    "\n",
-    "SCANS_FOLDER = '/home/ma7/BA/PG_Subjects'\n",
-    "\n",
-    "# Rendering + sweep tunables\n",
-    "IMG_PIXELS   = 80\n",
-    "SMOOTH_SIGMA = 1.2     # Gaussian pre-smooth on depth (pixels)\n",
-    "EYE_OFFSET   = 12      # ENdif horizontal half-spacing (pixels) ~30mm in bin space\n",
-    "MARGIN_FRAC  = 0.2     # skip the outer X% of rows/cols when taking argmax\n",
-    "N_DIRECTIONS = 200\n",
-    "WORLD_UP     = np.array([1.0, 0.0, 0.0])  # +X ~ gravity on Einstar"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-md-load",
-   "metadata": {},
-   "source": [
-    "## 1. Load scan and merge UV-seam duplicates"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "cell-load",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Raw:    494,109 vertices, 866,676 faces\n",
-      "Merged: 433,321 vertices, 866,646 faces\n",
-      "        dropped 30 degenerate faces\n"
-     ]
-    }
-   ],
-   "source": [
-    "SUBJECT_NUMBER = 21  # <-- change and rerun from here\n",
-    "\n",
-    "path = f'{SCANS_FOLDER}/Subject{SUBJECT_NUMBER}/Subject{SUBJECT_NUMBER}.obj'\n",
-    "surface = cedalion.io.read_einstar_obj(path)\n",
-    "raw_mesh = surface.mesh\n",
-    "print(f'Raw:    {len(raw_mesh.vertices):>7,} vertices, {len(raw_mesh.faces):>7,} faces')\n",
-    "\n",
-    "merged_v, old_to_new, _ = deduplicate_vertices(\n",
-    "    np.asarray(raw_mesh.vertices), tol=0.01,\n",
-    ")\n",
-    "merged_faces = old_to_new[np.asarray(raw_mesh.faces)]\n",
-    "# drop degenerate faces (any two corners collapsed to same index)\n",
-    "nondeg = (\n",
-    "    (merged_faces[:, 0] != merged_faces[:, 1]) &\n",
-    "    (merged_faces[:, 1] != merged_faces[:, 2]) &\n",
-    "    (merged_faces[:, 0] != merged_faces[:, 2])\n",
-    ")\n",
-    "merged_faces = merged_faces[nondeg]\n",
-    "\n",
-    "mesh = trimesh.Trimesh(vertices=merged_v, faces=merged_faces, process=False)\n",
-    "print(f'Merged: {len(mesh.vertices):>7,} vertices, {len(mesh.faces):>7,} faces')\n",
-    "print(f'        dropped {(~nondeg).sum():,} degenerate faces')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-md-curv",
-   "metadata": {},
-   "source": [
-    "## 2. Depth rasterization and ENdif scoring\n",
-    "\n",
-    "`depth_image()` orthographically projects the mesh vertices along a camera\n",
-    "direction and bins them into a 2D grid, taking the max depth per cell\n",
-    "(closest vertex to camera wins). This is a render-free, rtree-free\n",
-    "rasterization; at IMG_PIXELS=80 and ~250k head vertices we have >40 verts\n",
-    "per cell so holes are rare.\n",
-    "\n",
-    "`endif_score()` reproduces the paper verbatim: downsample to 9x9 with\n",
-    "bilinear interpolation, histogram-equalize, and compute the middle-row\n",
-    "center-minus-flanking-eyes differential."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "cell-curv",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "view = [ 0. -1.  0.]    peak ENdif = +60.6 at (row, col) = (np.int64(49), np.int64(19))\n"
-     ]
-    },
-    {
-     "data": {
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-      "text/plain": [
-       "<Figure size 1400x500 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def depth_image(verts, direction, pixels=IMG_PIXELS, world_up=WORLD_UP):\n",
-    "    \"\"\"Orthographic depth map along `direction`. Brighter = closer to camera.\n",
-    "\n",
-    "    Returns (depth, mask) where mask is True inside the head silhouette.\n",
-    "    \"\"\"\n",
-    "    d = direction / np.linalg.norm(direction)\n",
-    "    u = world_up - (world_up @ d) * d\n",
-    "    if np.linalg.norm(u) < 1e-6:\n",
-    "        u = np.cross(d, np.array([0.0, 1.0, 0.0]))\n",
-    "    u = u / np.linalg.norm(u)\n",
-    "    r = np.cross(u, d)\n",
-    "\n",
-    "    x = verts @ r\n",
-    "    y = verts @ u\n",
-    "    depth = verts @ d\n",
-    "\n",
-    "    x_e = np.linspace(x.min(), x.max(), pixels + 1)\n",
-    "    y_e = np.linspace(y.min(), y.max(), pixels + 1)\n",
-    "    stat, _, _, _ = scipy.stats.binned_statistic_2d(\n",
-    "        x, y, depth, 'max', [x_e, y_e],\n",
-    "    )\n",
-    "    mask = ~np.isnan(stat)\n",
-    "    stat = np.where(mask, stat, np.nanmin(stat))\n",
-    "    return stat.T, mask.T\n",
-    "\n",
-    "\n",
-    "def histeq(img, mask=None):\n",
-    "    \"\"\"Rank-based histogram equalization on the masked region -> [0, 255].\"\"\"\n",
-    "    if mask is None:\n",
-    "        mask = np.ones_like(img, dtype=bool)\n",
-    "    out = np.zeros_like(img, dtype=float)\n",
-    "    vals = img[mask].ravel()\n",
-    "    ranks = np.argsort(np.argsort(vals))\n",
-    "    out[mask] = (ranks / max(len(vals) - 1, 1)) * 255.0\n",
-    "    return out\n",
-    "\n",
-    "\n",
-    "def endif_filter(img_eq, eye_offset=EYE_OFFSET):\n",
-    "    \"\"\"Local 1D ENdif: center - mean(left_eye, right_eye), peaked horizontally.\"\"\"\n",
-    "    pad = eye_offset\n",
-    "    padded = np.pad(img_eq, ((0, 0), (pad, pad)), mode='edge')\n",
-    "    left  = padded[:, : -2 * pad]\n",
-    "    right = padded[:, 2 * pad :]\n",
-    "    return img_eq - 0.5 * (left + right)\n",
-    "\n",
-    "\n",
-    "def score_view(verts_c, direction):\n",
-    "    \"\"\"Render + smooth + histeq + ENdif + masked argmax for one direction.\"\"\"\n",
-    "    img, mask = depth_image(verts_c, direction)\n",
-    "    img_s = gaussian_filter(img, SMOOTH_SIGMA)\n",
-    "    img_eq = histeq(img_s, mask)   # kept for visualization only\n",
-    "    en = endif_filter(img_s)       # score on raw mm depth, not equalized ranks\n",
-    "\n",
-    "    # Valid region: silhouette interior, away from border, AND upper world half.\n",
-    "    # u ~ +X (gravity), so high row index = upper part of the head -- the nose\n",
-    "    # sits above head center; optodes on the cap are everywhere, so excluding\n",
-    "    # the lower half kills most back-of-head false positives.\n",
-    "    H, W = en.shape\n",
-    "    mr, mc = int(H * MARGIN_FRAC), int(W * MARGIN_FRAC)\n",
-    "    valid = np.zeros_like(mask)\n",
-    "    valid[H // 2 : H - mr, mc : W - mc] = True\n",
-    "    valid &= mask\n",
-    "\n",
-    "    en_masked = np.where(valid, en, -np.inf)\n",
-    "    peak = np.unravel_index(en_masked.argmax(), en_masked.shape)\n",
-    "    score = en_masked[peak]\n",
-    "    return score, peak, img, img_s, en, mask\n",
-    "\n",
-    "\n",
-    "# --- One viewpoint sanity check ---\n",
-    "verts   = np.asarray(mesh.vertices)\n",
-    "verts_c = verts - verts.mean(axis=0)\n",
-    "\n",
-    "view = np.array([0.0, -1.0, 0.0])\n",
-    "score, peak, img, img_s, en, mask = score_view(verts_c, view)\n",
-    "print(f'view = {view}    peak ENdif = {score:+.1f} at (row, col) = {peak}')\n",
-    "\n",
-    "fig, axes = plt.subplots(1, 3, figsize=(14, 5))\n",
-    "axes[0].imshow(img, cmap='gray', origin='upper')\n",
-    "axes[0].set_title('depth (raw)'); axes[0].axis('off')\n",
-    "axes[1].imshow(img_s, cmap='gray', origin='upper')\n",
-    "axes[1].set_title(f'depth smoothed (sigma={SMOOTH_SIGMA})'); axes[1].axis('off')\n",
-    "m = np.abs(en[mask]).max()\n",
-    "axes[2].imshow(np.where(mask, en, np.nan), cmap='coolwarm',\n",
-    "                origin='upper', vmin=-m, vmax=+m)\n",
-    "axes[2].scatter([peak[1]], [peak[0]], s=80, facecolor='none',\n",
-    "                 edgecolor='lime', lw=2, label='argmax')\n",
-    "axes[2].set_title('ENdif (masked)'); axes[2].axis('off')\n",
-    "axes[2].legend(loc='upper right')\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "af48c5fd",
-   "metadata": {},
-   "source": [
-    "## 3. Sweep viewpoints on a Fibonacci sphere\n",
-    "\n",
-    "For each of `N_DIRECTIONS` uniformly-distributed camera directions on the\n",
-    "sphere we render a depth map, apply the ENdif filter, and record the peak\n",
-    "score. The direction whose peak score is highest is the candidate frontal\n",
-    "direction."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "02a41e96",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "   25/200  current best = +149.7\n",
-      "   50/200  current best = +185.7\n",
-      "   75/200  current best = +203.3\n",
-      "  100/200  current best = +203.3\n",
-      "  125/200  current best = +203.3\n",
-      "  150/200  current best = +203.3\n",
-      "  175/200  current best = +203.3\n",
-      "  200/200  current best = +203.3\n",
-      "\n",
-      "best direction : [-0.92141159 -0.05268473  0.385     ]   score = +203.3\n"
-     ]
-    },
-    {
-     "data": {
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-      "text/plain": [
-       "<Figure size 1400x500 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def fibonacci_sphere(n):\n",
-    "    idx = np.arange(n) + 0.5\n",
-    "    phi = np.arccos(1.0 - 2.0 * idx / n)\n",
-    "    theta = np.pi * (1.0 + 5.0 ** 0.5) * idx\n",
-    "    return np.column_stack([\n",
-    "        np.cos(theta) * np.sin(phi),\n",
-    "        np.sin(theta) * np.sin(phi),\n",
-    "        np.cos(phi),\n",
-    "    ])\n",
-    "\n",
-    "\n",
-    "directions = fibonacci_sphere(N_DIRECTIONS)\n",
-    "scores = np.empty(N_DIRECTIONS)\n",
-    "peaks = [None] * N_DIRECTIONS\n",
-    "\n",
-    "for i, d in enumerate(directions):\n",
-    "    s, p, *_ = score_view(verts_c, d)\n",
-    "    scores[i] = s\n",
-    "    peaks[i] = p\n",
-    "    if (i + 1) % 25 == 0:\n",
-    "        print(f'  {i + 1:>3d}/{N_DIRECTIONS}  current best = {scores[:i+1].max():+.1f}')\n",
-    "\n",
-    "best_i = int(np.argmax(scores))\n",
-    "best_dir = directions[best_i]\n",
-    "print(f'\\nbest direction : {best_dir}   score = {scores[best_i]:+.1f}')\n",
-    "\n",
-    "# Visualize the best view\n",
-    "score, peak, img, img_s, en, mask = score_view(verts_c, best_dir)\n",
-    "\n",
-    "fig, axes = plt.subplots(1, 3, figsize=(14, 5))\n",
-    "axes[0].imshow(img_s, cmap='gray', origin='upper')\n",
-    "axes[0].set_title(f'best view depth  dir={np.round(best_dir, 2)}')\n",
-    "axes[0].axis('off')\n",
-    "\n",
-    "m = np.abs(en[mask]).max()\n",
-    "axes[1].imshow(np.where(mask, en, np.nan), cmap='coolwarm',\n",
-    "                origin='upper', vmin=-m, vmax=+m)\n",
-    "axes[1].scatter([peak[1]], [peak[0]], s=80, facecolor='none',\n",
-    "                 edgecolor='lime', lw=2, label='nose?')\n",
-    "# Also mark the candidate eye pixels\n",
-    "axes[1].scatter([peak[1] - EYE_OFFSET, peak[1] + EYE_OFFSET],\n",
-    "                 [peak[0], peak[0]], s=80, facecolor='none',\n",
-    "                 edgecolor='cyan', lw=2, label='eyes?')\n",
-    "axes[1].legend(loc='upper right')\n",
-    "axes[1].set_title(f'ENdif peak = {score:+.1f}'); axes[1].axis('off')\n",
-    "\n",
-    "# Score distribution across the sphere for intuition\n",
-    "axes[2].hist(scores, bins=30, color='steelblue', alpha=0.8)\n",
-    "axes[2].axvline(scores[best_i], color='lime', lw=2, label='best')\n",
-    "axes[2].set_xlabel('ENdif peak score'); axes[2].set_ylabel('count')\n",
-    "axes[2].set_title('scores across sweep'); axes[2].legend()\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "42099ac4",
-   "metadata": {},
-   "source": [
-    "## 4. Back-project peak pixels onto the 3D mesh\n",
-    "\n",
-    "The ENdif peak gives us a (row, col) in the 80x80 depth image of the best\n",
-    "view. To see *where on the scan* those pixels actually are, we find the\n",
-    "mesh vertex that \"won\" each bin (i.e. the vertex closest to the camera\n",
-    "within that pixel's footprint) and render the mesh with colored spheres\n",
-    "at those 3D positions -- green for the nose candidate, cyan for the two\n",
-    "eye candidates.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "373c78d0",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "nose vertex  171341  -> [ 33.034859  36.565964 544.074341]\n",
-      "left  vertex 39998  -> [244.82132   88.176155 463.618439]\n",
-      "right vertex None -> None\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "79063931816042c08c7df66321c3531a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Widget(value='<iframe src=\"http://localhost:41703/index.html?ui=P_0x7fe8fbc99250_4&reconnect=auto\" class=\"pyvi…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import pyvista as pv\n",
-    "\n",
-    "def back_project_pixel(verts_c, direction, pixel, pixels=IMG_PIXELS, world_up=WORLD_UP):\n",
-    "    \"\"\"Return the 3D vertex (in verts_c's frame) that filled `pixel` = (row, col).\"\"\"\n",
-    "    d = direction / np.linalg.norm(direction)\n",
-    "    u = world_up - (world_up @ d) * d\n",
-    "    if np.linalg.norm(u) < 1e-6:\n",
-    "        u = np.cross(d, np.array([0.0, 1.0, 0.0]))\n",
-    "    u = u / np.linalg.norm(u)\n",
-    "    r = np.cross(u, d)\n",
-    "\n",
-    "    x = verts_c @ r\n",
-    "    y = verts_c @ u\n",
-    "    depth = verts_c @ d\n",
-    "\n",
-    "    x_e = np.linspace(x.min(), x.max(), pixels + 1)\n",
-    "    y_e = np.linspace(y.min(), y.max(), pixels + 1)\n",
-    "\n",
-    "    row, col = pixel  # row <-> y-bin (u-axis), col <-> x-bin (r-axis)\n",
-    "    in_bin = (\n",
-    "        (x >= x_e[col]) & (x <= x_e[col + 1]) &\n",
-    "        (y >= y_e[row]) & (y <= y_e[row + 1])\n",
-    "    )\n",
-    "    if not in_bin.any():\n",
-    "        return None\n",
-    "    idx_in = np.where(in_bin)[0]\n",
-    "    best = idx_in[np.argmax(depth[in_bin])]\n",
-    "    return best\n",
-    "\n",
-    "\n",
-    "# Peak is in verts_c frame; re-center output to world frame (verts)\n",
-    "mean = verts.mean(axis=0)\n",
-    "\n",
-    "nose_px  = peak\n",
-    "left_px  = (peak[0], peak[1] - EYE_OFFSET)\n",
-    "right_px = (peak[0], peak[1] + EYE_OFFSET)\n",
-    "\n",
-    "nose_i  = back_project_pixel(verts_c, best_dir, nose_px)\n",
-    "left_i  = back_project_pixel(verts_c, best_dir, left_px)\n",
-    "right_i = back_project_pixel(verts_c, best_dir, right_px)\n",
-    "\n",
-    "nose_xyz  = verts[nose_i]  if nose_i  is not None else None\n",
-    "left_xyz  = verts[left_i]  if left_i  is not None else None\n",
-    "right_xyz = verts[right_i] if right_i is not None else None\n",
-    "\n",
-    "print(f'nose vertex  {nose_i}  -> {nose_xyz}')\n",
-    "print(f'left  vertex {left_i}  -> {left_xyz}')\n",
-    "print(f'right vertex {right_i} -> {right_xyz}')\n",
-    "\n",
-    "# Radius: ~1% of the mesh diagonal, so spheres are visible but not huge\n",
-    "diag = np.linalg.norm(verts.max(axis=0) - verts.min(axis=0))\n",
-    "rad = diag * 0.01\n",
-    "\n",
-    "pl = pv.Plotter(notebook=True)\n",
-    "pv_mesh = pv.PolyData(verts, np.hstack([np.full((len(mesh.faces), 1), 3), mesh.faces]).astype(np.int64))\n",
-    "pl.add_mesh(pv_mesh, color='lightgray', opacity=1.0, show_edges=False)\n",
-    "\n",
-    "if nose_xyz is not None:\n",
-    "    pl.add_mesh(pv.Sphere(radius=rad, center=nose_xyz),  color='lime')\n",
-    "if left_xyz is not None:\n",
-    "    pl.add_mesh(pv.Sphere(radius=rad, center=left_xyz),  color='cyan')\n",
-    "if right_xyz is not None:\n",
-    "    pl.add_mesh(pv.Sphere(radius=rad, center=right_xyz), color='cyan')\n",
-    "\n",
-    "# Camera: look along -best_dir so the face is toward the viewer\n",
-    "focal = verts.mean(axis=0)\n",
-    "cam_pos = focal + best_dir * diag * 2.0\n",
-    "view_up = WORLD_UP\n",
-    "pl.camera_position = [tuple(cam_pos), tuple(focal), tuple(view_up)]\n",
-    "pl.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ca552b27-04b9-4de3-b5cd-de3838a874e3",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.14"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/__init__.py b/src/cedalion/geometry/photogrammetry/anonymization/__init__.py
index fb9ef4d1..f11e9c4a 100644
--- a/src/cedalion/geometry/photogrammetry/anonymization/__init__.py
+++ b/src/cedalion/geometry/photogrammetry/anonymization/__init__.py
@@ -30,12 +30,6 @@
     - Face Anonymization Project | 2024
 """
 
-from .anonymizer import (
-    AnonymizationConfig,
-    AnonymizationMethod,
-    AnonymizationResult,
-    anonymize_facial_region,
-)
 from .face_detector import (
     normalize_axes,
     isolate_head,
@@ -57,11 +51,6 @@
     # Landmark and face mask detection
     "detect_landmarks_from_nasion",
     "get_facial_region_mask_from_nasion",
-    # Anonymization
-    "AnonymizationConfig",
-    "AnonymizationMethod",
-    "AnonymizationResult",
-    "anonymize_facial_region",
     # Validation
     "validate_anonymization",
 ]
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/_vertex_utils.py b/src/cedalion/geometry/photogrammetry/anonymization/_vertex_utils.py
deleted file mode 100644
index 9be66a41..00000000
--- a/src/cedalion/geometry/photogrammetry/anonymization/_vertex_utils.py
+++ /dev/null
@@ -1,79 +0,0 @@
-"""Shared vertex utilities for the anonymization module.
-
-Provides the core union-find algorithm for merging spatially coincident
-vertices (seam duplicates in photogrammetry scans).
-
-Initial Contributors:
-    - Face Anonymization Project | 2024
-"""
-
-import numpy as np
-from scipy.spatial import KDTree
-
-
-def deduplicate_vertices(
-    vertices: np.ndarray, tol: float = 0.01
-) -> tuple[np.ndarray, np.ndarray, list[list[int]]]:
-    """Merge spatially coincident vertices using union-find.
-
-    Groups vertices within ``tol`` mm of each other and averages their
-    positions. Returns the merged vertices together with bidirectional
-    index mappings so callers can remap faces, colors, or other per-vertex
-    data.
-
-    Args:
-        vertices: Vertex positions of shape (N, 3).
-        tol: Distance tolerance in mm for considering vertices coincident.
-
-    Returns:
-        Tuple of (merged_vertices, old_to_new, new_to_old) where:
-        - merged_vertices: Averaged positions of shape (M, 3), M <= N.
-        - old_to_new: Array of shape (N,) mapping old index to new index.
-        - new_to_old: List of length M; new_to_old[j] lists old indices
-          that were merged into new vertex j.
-    """
-    n = len(vertices)
-
-    tree = KDTree(vertices)
-    pairs = tree.query_pairs(r=tol)
-
-    if not pairs:
-        old_to_new = np.arange(n)
-        new_to_old = [[i] for i in range(n)]
-        return vertices.copy(), old_to_new, new_to_old
-
-    # Union-find with path compression
-    parent = np.arange(n)
-
-    def find(x):
-        while parent[x] != x:
-            parent[x] = parent[parent[x]]
-            x = parent[x]
-        return x
-
-    for a, b in pairs:
-        ra, rb = find(a), find(b)
-        if ra != rb:
-            parent[ra] = rb
-
-    # Flatten parent pointers
-    for i in range(n):
-        parent[i] = find(i)
-
-    # Build compact index mapping
-    unique_roots, inverse = np.unique(parent, return_inverse=True)
-    old_to_new = inverse
-    n_new = len(unique_roots)
-
-    # Build reverse mapping
-    new_to_old = [[] for _ in range(n_new)]
-    for old_idx in range(n):
-        new_to_old[old_to_new[old_idx]].append(old_idx)
-
-    # Average positions of merged vertices
-    merged_vertices = np.zeros((n_new, 3))
-    for new_idx in range(n_new):
-        old_indices = new_to_old[new_idx]
-        merged_vertices[new_idx] = vertices[old_indices].mean(axis=0)
-
-    return merged_vertices, old_to_new, new_to_old
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/anonymizer.py b/src/cedalion/geometry/photogrammetry/anonymization/anonymizer.py
deleted file mode 100644
index cd85dca7..00000000
--- a/src/cedalion/geometry/photogrammetry/anonymization/anonymizer.py
+++ /dev/null
@@ -1,1078 +0,0 @@
-"""Mesh anonymization for photogrammetry scans.
-
-This module provides functions to anonymize facial regions of 3D meshes using
-Taubin smoothing while preserving optode and landmark positions.
-
-Initial Contributors:
-    - Face Anonymization Project | 2024
-"""
-
-from copy import deepcopy
-from dataclasses import dataclass, field
-from enum import Enum
-import logging
-
-import numpy as np
-from scipy import sparse
-from scipy.sparse.linalg import spsolve
-from scipy.spatial import KDTree
-import trimesh
-import cedalion.dataclasses as cdc
-import cedalion.typing as cdt
-from cedalion import Quantity, units
-from ._vertex_utils import deduplicate_vertices
-
-
-logger = logging.getLogger("cedalion")
-
-
-class AnonymizationMethod(Enum):
-    """Available anonymization techniques."""
-
-    SMOOTH = "smooth"
-    FLATTEN = "flatten"
-    COLOR_BLUR = "color_blur"
-    COMBINED = "combined"
-    HEAT_DIFFUSION = "heat_diffusion"
-    NOISE = "noise"
-
-
-@dataclass
-class AnonymizationConfig:
-    """Configuration for anonymization process.
-
-    Attributes:
-        method: Anonymization technique to use
-        smoothing_iterations: Number of Taubin filter iterations
-        smoothing_lambda: Taubin filter lambda parameter (step size)
-        smoothing_mu: Taubin filter mu parameter (shrinkage correction)
-        boundary_transition_width: Width of transition zone at mask boundary
-    """
-
-    method: AnonymizationMethod = AnonymizationMethod.SMOOTH
-    smoothing_iterations: int = 300
-    smoothing_lambda: float = 0.5
-    smoothing_mu: float = 0.0  # No shrinkage correction → pure Laplacian smoothing
-    boundary_transition_width: Quantity = field(
-        default_factory=lambda: 10.0 * units.mm
-    )
-    use_cotangent: bool = False
-    diffusion_time: float = 50.0
-    noise_strength: float = 1.0
-    noise_iterations: int = 80
-    noise_seed: int | None = None
-    flatten_strength: float = 0.7
-
-
-@dataclass
-class AnonymizationResult:
-    """Result of anonymization process.
-
-    Attributes:
-        anonymized_surface: The anonymized TrimeshSurface
-        original_surface: The original TrimeshSurface (unchanged)
-        facial_mask: Boolean mask of facial vertices
-        vertex_displacements: Displacement of each vertex (in mm)
-        config: Configuration used for anonymization
-    """
-
-    anonymized_surface: cdc.TrimeshSurface
-    original_surface: cdc.TrimeshSurface
-    facial_mask: np.ndarray
-    vertex_displacements: np.ndarray
-    config: AnonymizationConfig
-
-
-def _build_adjacency_matrix(mesh: trimesh.Trimesh) -> dict[int, set[int]]:
-    """Build vertex adjacency dictionary from mesh topology.
-
-    Args:
-        mesh: The trimesh object
-
-    Returns:
-        Dictionary mapping vertex index to set of neighbor indices
-    """
-    adjacency = {i: set() for i in range(len(mesh.vertices))}
-
-    for face in mesh.faces:
-        for i in range(3):
-            v1, v2 = face[i], face[(i + 1) % 3]
-            adjacency[v1].add(v2)
-            adjacency[v2].add(v1)
-
-    return adjacency
-
-
-def _merge_seam_vertices(
-    mesh: trimesh.Trimesh, tol: float = 0.01
-) -> tuple[trimesh.Trimesh, np.ndarray, list[list[int]]]:
-    """Merge spatially coincident vertices into single vertices.
-
-    Photogrammetry scans have duplicate vertices at patch boundaries. Merging
-    them before smoothing eliminates seam cracks because the Laplacian sees
-    a single connected mesh instead of disconnected patches.
-
-    Args:
-        mesh: The trimesh object with potential duplicate vertices
-        tol: Distance tolerance in mm for considering vertices coincident
-
-    Returns:
-        Tuple of (merged_mesh, old_to_new, new_to_old) where:
-        - merged_mesh: New trimesh with merged vertices and averaged colors
-        - old_to_new: Array mapping old vertex index to new vertex index
-        - new_to_old: List where new_to_old[i] = list of old indices that merged
-    """
-    n = len(mesh.vertices)
-
-    new_verts, old_to_new, new_to_old = deduplicate_vertices(
-        mesh.vertices, tol=tol
-    )
-    n_new = len(new_verts)
-
-    if n_new == n:
-        return mesh.copy(), old_to_new, new_to_old
-
-    # Re-index faces and remove degenerate triangles
-    new_faces = old_to_new[mesh.faces]
-    valid = (
-        (new_faces[:, 0] != new_faces[:, 1])
-        & (new_faces[:, 1] != new_faces[:, 2])
-        & (new_faces[:, 0] != new_faces[:, 2])
-    )
-    new_faces = new_faces[valid]
-
-    # Average vertex colors at merged positions
-    new_colors = None
-    if hasattr(mesh.visual, "vertex_colors"):
-        try:
-            old_colors = mesh.visual.to_color().vertex_colors  # (n, 4) uint8
-            new_colors = np.zeros((n_new, 4), dtype=np.float64)
-            for new_idx in range(n_new):
-                old_indices = new_to_old[new_idx]
-                new_colors[new_idx] = old_colors[old_indices].mean(axis=0)
-            new_colors = new_colors.astype(np.uint8)
-        except Exception:
-            pass
-
-    merged_mesh = trimesh.Trimesh(
-        vertices=new_verts, faces=new_faces, process=False
-    )
-    if new_colors is not None:
-        merged_mesh.visual.vertex_colors = new_colors
-
-    n_merged = n - n_new
-    n_degenerate = len(mesh.faces) - len(new_faces)
-    logger.info(
-        f"Merged {n_merged} duplicate vertices "
-        f"({n} -> {n_new}), removed {n_degenerate} degenerate faces"
-    )
-
-    return merged_mesh, old_to_new, new_to_old
-
-
-def _unmerge_vertices(
-    merged_mesh: trimesh.Trimesh,
-    original_mesh: trimesh.Trimesh,
-    old_to_new: np.ndarray,
-) -> trimesh.Trimesh:
-    """Expand merged mesh back to the original vertex count.
-
-    Copies each merged vertex position back to all the original vertices
-    that were merged into it. This preserves the original face topology
-    and vertex count for mask indexing compatibility.
-
-    Args:
-        merged_mesh: Mesh with merged vertices (from _merge_seam_vertices)
-        original_mesh: Original mesh with full vertex count
-        old_to_new: Mapping from original vertex indices to merged indices
-
-    Returns:
-        New trimesh with original vertex count and face topology, but with
-        vertex positions from the merged mesh
-    """
-    result_mesh = original_mesh.copy()
-    result_verts = result_mesh.vertices.copy()
-
-    # Copy merged positions back to all original vertices
-    result_verts = merged_mesh.vertices[old_to_new]
-
-    # Copy colors if available
-    if hasattr(merged_mesh.visual, "vertex_colors"):
-        try:
-            merged_colors = merged_mesh.visual.to_color().vertex_colors
-            result_colors = merged_colors[old_to_new]
-            result_mesh.visual.vertex_colors = result_colors
-        except Exception:
-            pass
-
-    result_mesh.vertices = result_verts
-    result_mesh._cache.clear()
-    return result_mesh
-
-
-def _find_seam_pairs(mesh: trimesh.Trimesh, tol: float = 0.01) -> np.ndarray:
-    """Find pairs of vertices at the same spatial position (seam vertices).
-
-    Photogrammetry scans are stitched from multiple patches, producing
-    duplicate vertices at shared boundaries. These vertices are spatially
-    coincident but topologically disconnected, causing the Laplacian to
-    treat each patch independently and producing visible crack lines.
-
-    Args:
-        mesh: The trimesh object
-        tol: Distance tolerance in mm for considering vertices coincident
-
-    Returns:
-        Array of shape (n_pairs, 2) with seam vertex index pairs
-    """
-    tree = KDTree(mesh.vertices)
-    pairs = tree.query_pairs(r=tol)
-    if not pairs:
-        return np.empty((0, 2), dtype=int)
-    return np.array(list(pairs), dtype=int)
-
-
-def _add_seam_edges(A: sparse.csr_matrix, mesh: trimesh.Trimesh) -> sparse.csr_matrix:
-    """Add edges between duplicate vertices at mesh seams.
-
-    This bridges topologically disconnected patches so the Laplacian can
-    smooth across seam boundaries, eliminating crack artifacts.
-
-    Args:
-        A: Adjacency or weight matrix of shape (n, n)
-        mesh: The trimesh object (used to find seam pairs)
-
-    Returns:
-        Modified matrix with seam edges added
-    """
-    seam_pairs = _find_seam_pairs(mesh)
-    if len(seam_pairs) == 0:
-        return A
-
-    n = A.shape[0]
-    rows = np.concatenate([seam_pairs[:, 0], seam_pairs[:, 1]])
-    cols = np.concatenate([seam_pairs[:, 1], seam_pairs[:, 0]])
-    data = np.ones(len(rows), dtype=float)
-    seam_adj = sparse.csr_matrix((data, (rows, cols)), shape=(n, n))
-
-    logger.info(f"Bridging {len(seam_pairs)} seam vertex pairs")
-    return A + seam_adj
-
-
-def _build_sparse_laplacian(mesh: trimesh.Trimesh) -> sparse.csr_matrix:
-    """Build sparse uniform Laplacian matrix from mesh topology.
-
-    The Laplacian L is defined so that L @ V gives the displacement
-    (neighbor_avg - vertex) for each vertex, i.e. the "umbrella" operator.
-
-    Automatically bridges seam vertices (duplicate vertices at the same
-    position from photogrammetry stitching) so smoothing works across
-    patch boundaries.
-
-    Args:
-        mesh: The trimesh object
-
-    Returns:
-        Sparse CSR matrix of shape (n_vertices, n_vertices)
-    """
-    n = len(mesh.vertices)
-    edges = mesh.edges_unique
-    rows = np.concatenate([edges[:, 0], edges[:, 1]])
-    cols = np.concatenate([edges[:, 1], edges[:, 0]])
-    data = np.ones(len(rows), dtype=float)
-
-    # Adjacency matrix
-    A = sparse.csr_matrix((data, (rows, cols)), shape=(n, n))
-
-    # Bridge seam vertices
-    A = _add_seam_edges(A, mesh)
-
-    # Degree (number of neighbors per vertex)
-    degree = np.array(A.sum(axis=1)).ravel()
-    degree[degree == 0] = 1  # avoid division by zero for isolated vertices
-
-    # Normalized Laplacian: L = D^{-1} A - I
-    D_inv = sparse.diags(1.0 / degree)
-    L = D_inv @ A - sparse.eye(n)
-
-    return L.tocsr()
-
-
-def _build_cotangent_laplacian(mesh: trimesh.Trimesh) -> sparse.csr_matrix:
-    """Build sparse cotangent-weighted Laplacian matrix from mesh topology.
-
-    For each edge (i,j), the cotangent weight is w_ij = (cot(alpha) + cot(beta)) / 2
-    where alpha and beta are the angles opposite to edge (i,j) in the two adjacent
-    triangles. This weights neighbors by mesh geometry — well-shaped triangles
-    contribute more, slivers contribute less.
-
-    The result has the same form as _build_sparse_laplacian: L @ V gives the
-    weighted displacement (weighted_neighbor_avg - vertex) for each vertex.
-
-    Args:
-        mesh: The trimesh object
-
-    Returns:
-        Sparse CSR matrix of shape (n_vertices, n_vertices)
-    """
-    n = len(mesh.vertices)
-    verts = mesh.vertices
-    faces = mesh.faces  # (F, 3)
-
-    # Vectorized cotangent computation for all faces at once
-    i0, i1, i2 = faces[:, 0], faces[:, 1], faces[:, 2]
-    v0, v1, v2 = verts[i0], verts[i1], verts[i2]  # each (F, 3)
-
-    # Edge vectors from each corner
-    e01 = v1 - v0
-    e02 = v2 - v0
-    e10 = -e01
-    e12 = v2 - v1
-    e20 = -e02
-    e21 = -e12
-
-    # cot(angle) = dot / |cross| for each corner
-    cross0 = np.linalg.norm(np.cross(e01, e02), axis=1)
-    cross1 = np.linalg.norm(np.cross(e10, e12), axis=1)
-    cross2 = np.linalg.norm(np.cross(e20, e21), axis=1)
-
-    # Clamp to avoid division by zero (degenerate triangles)
-    min_cross = 1e-8
-    np.maximum(cross0, min_cross, out=cross0)
-    np.maximum(cross1, min_cross, out=cross1)
-    np.maximum(cross2, min_cross, out=cross2)
-
-    dot0 = np.sum(e01 * e02, axis=1)
-    dot1 = np.sum(e10 * e12, axis=1)
-    dot2 = np.sum(e20 * e21, axis=1)
-
-    # Clamp cotangent values to >= 0 BEFORE accumulation.
-    # Negative cotangents (from obtuse angles) would disconnect mesh edges if
-    # clamped after summing; clamping here keeps edges connected with zero
-    # weight from obtuse angles instead.
-    cot0 = np.maximum(dot0 / cross0, 0.0)  # opposite edge (i1, i2)
-    cot1 = np.maximum(dot1 / cross1, 0.0)  # opposite edge (i0, i2)
-    cot2 = np.maximum(dot2 / cross2, 0.0)  # opposite edge (i0, i1)
-
-    # Each cotangent contributes to both directions of its opposite edge
-    # Edge opposite corner 0: (i1, i2), weight = cot0 * 0.5
-    # Edge opposite corner 1: (i0, i2), weight = cot1 * 0.5
-    # Edge opposite corner 2: (i0, i1), weight = cot2 * 0.5
-    all_rows = np.concatenate([i1, i2, i0, i2, i0, i1])
-    all_cols = np.concatenate([i2, i1, i2, i0, i1, i0])
-    half_cot0 = cot0 * 0.5
-    half_cot1 = cot1 * 0.5
-    half_cot2 = cot2 * 0.5
-    all_weights = np.concatenate([
-        half_cot0, half_cot0, half_cot1, half_cot1, half_cot2, half_cot2
-    ])
-
-    # Build sparse weight matrix
-    W = sparse.coo_matrix((all_weights, (all_rows, all_cols)), shape=(n, n)).tocsr()
-
-    # Add small uniform weight to ensure full connectivity — edges where both
-    # adjacent angles are obtuse would otherwise have zero weight, effectively
-    # disconnecting mesh regions.
-    edges = mesh.edges_unique
-    uniform_rows = np.concatenate([edges[:, 0], edges[:, 1]])
-    uniform_cols = np.concatenate([edges[:, 1], edges[:, 0]])
-    uniform_data = np.full(len(uniform_rows), 0.01)
-    W_uniform = sparse.csr_matrix(
-        (uniform_data, (uniform_rows, uniform_cols)), shape=(n, n)
-    )
-    W = W + W_uniform
-
-    # Bridge seam vertices — photogrammetry meshes have topologically
-    # disconnected patches at coincident positions. Without bridging,
-    # each patch smooths independently, producing visible crack lines.
-    W = _add_seam_edges(W, mesh)
-
-    # Normalize rows: L = D^{-1} W - I
-    degree = np.array(W.sum(axis=1)).ravel()
-    degree[degree == 0] = 1  # avoid division by zero for isolated vertices
-    D_inv = sparse.diags(1.0 / degree)
-    L = D_inv @ W - sparse.eye(n)
-
-    return L.tocsr()
-
-
-def _compute_boundary_weights(
-    mesh: trimesh.Trimesh,
-    mask: np.ndarray,
-    transition_width: float,
-) -> np.ndarray:
-    """Compute smoothing weights with gradient at mask boundary.
-
-    Creates a weight array where:
-    - Vertices inside the mask have weight 1.0
-    - Vertices outside the mask have weight 0.0
-    - Vertices near the boundary have weights that smoothly transition
-
-    Args:
-        mesh: The trimesh object
-        mask: Boolean mask of vertices to smooth (True = facial region)
-        transition_width: Width of transition zone in mm
-
-    Returns:
-        Weight array of shape (n_vertices,) with values in [0, 1]
-    """
-    n_vertices = len(mesh.vertices)
-    weights = np.zeros(n_vertices)
-
-    # Find boundary vertices (masked vertices with unmasked neighbors)
-    adjacency = _build_adjacency_matrix(mesh)
-    boundary_vertices = []
-
-    for i in range(n_vertices):
-        if mask[i]:
-            neighbors = adjacency[i]
-            if any(not mask[n] for n in neighbors):
-                boundary_vertices.append(i)
-
-    if len(boundary_vertices) == 0:
-        # No boundary - just return mask as weights
-        return mask.astype(float)
-
-    # Build KDTree from boundary vertices
-    boundary_positions = mesh.vertices[boundary_vertices]
-    boundary_tree = KDTree(boundary_positions)
-
-    # Compute distances to boundary for all masked vertices
-    masked_indices = np.where(mask)[0]
-
-    if len(masked_indices) == 0:
-        return weights
-
-    masked_positions = mesh.vertices[masked_indices]
-    distances, _ = boundary_tree.query(masked_positions, k=1)
-
-    # Compute weights based on distance from boundary
-    for idx, (i, dist) in enumerate(zip(masked_indices, distances)):
-        if dist <= transition_width:
-            # Smooth transition using cosine function
-            t = dist / transition_width
-            weights[i] = 0.5 * (1.0 - np.cos(np.pi * t))
-        else:
-            weights[i] = 1.0
-
-    return weights
-
-
-def smooth_region_selective(
-    mesh: trimesh.Trimesh,
-    mask: np.ndarray,
-    protected_indices: np.ndarray,
-    iterations: int = 50,
-    lamb: float = 0.5,
-    mu: float = -0.53,
-    use_cotangent: bool = True,
-) -> trimesh.Trimesh:
-    """Apply Taubin smoothing only to masked vertices.
-
-    Extends trimesh smoothing to work on a subset of vertices while keeping
-    protected vertices completely fixed.
-
-    Args:
-        mesh: The trimesh object to smooth
-        mask: Boolean array marking vertices to smooth (True = smooth)
-        protected_indices: Indices of vertices that must never move
-        iterations: Number of Taubin filter iterations
-        lamb: Taubin lambda parameter (positive, forward step)
-        mu: Taubin mu parameter (negative, backward step for shrinkage correction)
-        use_cotangent: If True, use cotangent-weighted Laplacian (geometry-aware);
-            if False, use uniform Laplacian (original behavior)
-
-    Returns:
-        New trimesh with smoothed facial region
-    """
-    # Create copy of mesh
-    smoothed_mesh = mesh.copy()
-    vertices = smoothed_mesh.vertices.copy()
-
-    # Create weight mask (0 for protected, 1 for smoothable)
-    weights = mask.astype(float)
-    weights[protected_indices] = 0.0
-
-    # Find which vertices to actually smooth
-    n_smoothable = (weights > 0).sum()
-
-    laplacian_type = "cotangent" if use_cotangent else "uniform"
-    logger.info(
-        f"Smoothing {n_smoothable} vertices with {iterations} iterations "
-        f"({laplacian_type} Laplacian)"
-    )
-
-    if n_smoothable == 0:
-        logger.warning("No vertices to smooth!")
-        return smoothed_mesh
-
-    # Build sparse Laplacian once (fast matrix ops instead of Python loops)
-    if use_cotangent:
-        L = _build_cotangent_laplacian(smoothed_mesh)
-        # Cotangent weights have much larger magnitude than uniform weights.
-        # Scale step sizes so displacement magnitude matches the uniform case.
-        max_row_sum = np.abs(L).sum(axis=1).max()
-        if max_row_sum > 2.0:
-            scale = 2.0 / max_row_sum
-            lamb *= scale
-            mu *= scale
-            logger.info(
-                f"Cotangent auto-scale: {scale:.4f} "
-                f"(lamb={lamb:.4f}, mu={mu:.4f})"
-            )
-    else:
-        L = _build_sparse_laplacian(smoothed_mesh)
-
-    # Region-constrained Taubin smoothing:
-    # Each iteration computes Laplacian displacement for all vertices,
-    # but only applies it where weight > 0. This avoids discontinuities
-    # because smoothed vertices naturally pull toward their neighbors.
-    w = weights[:, np.newaxis]  # (n, 1) for broadcasting
-    for iteration in range(iterations):
-        # Forward step (lambda — smoothing)
-        vertices += lamb * w * (L @ vertices)
-
-        # Backward step (mu — shrinkage correction, mu is negative so this
-        # moves vertices AGAINST the Laplacian to counteract shrinkage)
-        vertices += mu * w * (L @ vertices)
-
-    smoothed_mesh.vertices = vertices
-    smoothed_mesh._cache.clear()  # Invalidate normals, bounds, etc.
-
-    logger.info("Smoothing complete")
-    return smoothed_mesh
-
-
-def apply_boundary_transition(
-    mesh: trimesh.Trimesh,
-    mask: np.ndarray,
-    transition_width: float,
-    protected_indices: np.ndarray,
-    iterations: int = 10,
-    lamb: float = 0.3,
-) -> trimesh.Trimesh:
-    """Apply smoothing with smooth transition at mask boundary.
-
-    Creates a gradient of smoothing weights at the boundary to avoid
-    discontinuities between smoothed and unsmoothed regions.
-
-    Args:
-        mesh: The trimesh object
-        mask: Boolean mask of facial region
-        transition_width: Width of transition zone in mm
-        protected_indices: Indices that must not move
-        iterations: Number of smoothing iterations for boundary
-        lamb: Smoothing strength for boundary
-
-    Returns:
-        Mesh with smoothed boundary transition
-    """
-    # Compute boundary weights
-    weights = _compute_boundary_weights(mesh, mask, transition_width)
-
-    # Zero out protected vertices
-    weights[protected_indices] = 0.0
-
-    # If no vertices need boundary smoothing, return as-is
-    if weights.sum() == 0:
-        return mesh
-
-    # Create copy of mesh
-    smoothed_mesh = mesh.copy()
-    vertices = smoothed_mesh.vertices.copy()
-
-    # Build sparse Laplacian once
-    L = _build_sparse_laplacian(smoothed_mesh)
-
-    # Region-constrained Laplacian smoothing at the boundary:
-    # Only vertices with weight > 0 move, proportional to their weight.
-    w = weights[:, np.newaxis]
-    for iteration in range(iterations):
-        vertices += lamb * w * (L @ vertices)
-
-    smoothed_mesh.vertices = vertices
-    smoothed_mesh._cache.clear()  # Invalidate normals, bounds, etc.
-    return smoothed_mesh
-
-
-def _heat_diffusion_smooth(
-    mesh: trimesh.Trimesh,
-    mask: np.ndarray,
-    protected_indices: np.ndarray,
-    diffusion_time: float = 0.001,
-) -> trimesh.Trimesh:
-    """Apply heat diffusion smoothing to masked vertices.
-
-    Solves the implicit heat equation (I - t * L_cot) * V_new = V_old
-    where L_cot is the cotangent Laplacian and t is the diffusion time.
-    This acts as an ideal low-pass filter on the mesh surface.
-
-    Args:
-        mesh: The trimesh object to smooth
-        mask: Boolean array marking vertices to smooth
-        protected_indices: Indices of vertices that must never move
-        diffusion_time: Diffusion time parameter (larger = more smoothing)
-
-    Returns:
-        New trimesh with smoothed facial region
-    """
-    smoothed_mesh = mesh.copy()
-    vertices = smoothed_mesh.vertices.copy()
-    n = len(vertices)
-
-    # Determine which vertices can move
-    movable = mask.copy()
-    movable[protected_indices] = False
-    movable_indices = np.where(movable)[0]
-    n_movable = len(movable_indices)
-
-    logger.info(
-        f"Heat diffusion: {n_movable} vertices, diffusion_time={diffusion_time}"
-    )
-
-    if n_movable == 0:
-        logger.warning("No vertices to smooth!")
-        return smoothed_mesh
-
-    # Build cotangent Laplacian
-    L = _build_cotangent_laplacian(mesh)
-
-    # Build system matrix A = I - t * L (for full mesh)
-    A = sparse.eye(n) - diffusion_time * L
-
-    # For non-movable vertices, zero out their rows and set diagonal to 1
-    # (they stay fixed). Use diagonal scaling trick: multiply rows by 0 then
-    # add identity for fixed rows.
-    fixed = np.ones(n, dtype=bool)
-    fixed[movable_indices] = False
-    # Scale fixed rows to zero
-    row_scale = np.ones(n)
-    row_scale[fixed] = 0.0
-    A = sparse.diags(row_scale) @ A
-    # Add identity for fixed rows
-    A = A + sparse.diags(fixed.astype(float))
-
-    # Solve A @ V_new = V_old for each coordinate
-    rhs = vertices.copy()
-    for coord in range(3):
-        vertices[:, coord] = spsolve(A, rhs[:, coord])
-
-    smoothed_mesh.vertices = vertices
-    smoothed_mesh._cache.clear()  # Invalidate normals, bounds, etc.
-    logger.info("Heat diffusion smoothing complete")
-    return smoothed_mesh
-
-
-def _noise_perturbation(
-    mesh: trimesh.Trimesh,
-    mask: np.ndarray,
-    protected_indices: np.ndarray,
-    noise_strength: float = 0.4,
-    noise_iterations: int = 5,
-    noise_seed: int | None = None,
-) -> trimesh.Trimesh:
-    """Apply neighbor-weighted random perturbation to masked vertices.
-
-    For each pass:
-    1. Compute weighted average of neighbors (using cotangent Laplacian)
-    2. Shift vertex partway toward neighbor average
-    3. Add random offset scaled by local edge length
-
-    This destroys fine facial detail while preserving macro head shape.
-
-    Args:
-        mesh: The trimesh object to perturb
-        mask: Boolean array marking vertices to perturb
-        protected_indices: Indices of vertices that must never move
-        noise_strength: Blend factor toward neighbor average (0-1)
-        noise_iterations: Number of perturbation passes
-        noise_seed: Random seed for reproducibility (None for random)
-
-    Returns:
-        New trimesh with perturbed facial region
-    """
-    smoothed_mesh = mesh.copy()
-    vertices = smoothed_mesh.vertices.copy()
-
-    # Determine which vertices can move
-    movable = mask.copy()
-    movable[protected_indices] = False
-    movable_indices = np.where(movable)[0]
-    n_movable = len(movable_indices)
-
-    logger.info(
-        f"Noise perturbation: {n_movable} vertices, "
-        f"strength={noise_strength}, iterations={noise_iterations}"
-    )
-
-    if n_movable == 0:
-        logger.warning("No vertices to perturb!")
-        return smoothed_mesh
-
-    rng = np.random.default_rng(noise_seed)
-
-    # Build cotangent Laplacian for weighted neighbor averaging
-    L = _build_cotangent_laplacian(mesh)
-
-    # Compute local edge lengths for scaling (average edge length per vertex)
-    edges = mesh.edges_unique
-    edge_lengths = np.linalg.norm(
-        vertices[edges[:, 0]] - vertices[edges[:, 1]], axis=1
-    )
-    # Average edge length per vertex using np.add.at for vectorized accumulation
-    avg_edge_len = np.zeros(len(vertices))
-    edge_count = np.zeros(len(vertices))
-    np.add.at(avg_edge_len, edges[:, 0], edge_lengths)
-    np.add.at(avg_edge_len, edges[:, 1], edge_lengths)
-    np.add.at(edge_count, edges[:, 0], 1)
-    np.add.at(edge_count, edges[:, 1], 1)
-    edge_count[edge_count == 0] = 1
-    avg_edge_len /= edge_count
-
-    for iteration in range(noise_iterations):
-        # Compute neighbor-weighted average displacement: L @ V = avg - V
-        displacement = L @ vertices  # shape (n, 3)
-
-        # Vectorized: shift movable vertices toward neighbor average
-        vertices[movable_indices] += noise_strength * displacement[movable_indices]
-
-        # Vectorized: add random offset scaled by local edge length
-        random_offsets = (
-            rng.standard_normal((n_movable, 3))
-            * (avg_edge_len[movable_indices, np.newaxis] * 1.0)
-        )
-        vertices[movable_indices] += random_offsets
-
-    smoothed_mesh.vertices = vertices
-    smoothed_mesh._cache.clear()  # Invalidate normals, bounds, etc.
-    logger.info("Noise perturbation complete")
-    return smoothed_mesh
-
-
-def _get_protected_vertex_indices(
-    mesh: trimesh.Trimesh,
-    protected_points: cdt.LabeledPointCloud,
-    protection_radius: float,
-) -> np.ndarray:
-    """Find vertex indices within protection radius of protected points.
-
-    Args:
-        mesh: The trimesh object
-        protected_points: Points to protect (optodes + landmarks)
-        protection_radius: Radius around protected points in mm
-
-    Returns:
-        Array of vertex indices that should not be modified
-    """
-    protected_positions = protected_points.pint.dequantify().values
-
-    if len(protected_positions) == 0:
-        return np.array([], dtype=int)
-
-    # Find all vertices within protection radius of any protected point
-    protected_tree = KDTree(protected_positions)
-    vertex_distances, _ = protected_tree.query(mesh.vertices, k=1)
-    protected_mask = vertex_distances < protection_radius
-
-    return np.where(protected_mask)[0]
-
-
-@cdc.validate_schemas
-def anonymize_facial_region(
-    surface: cdc.TrimeshSurface,
-    facial_mask: np.ndarray,
-    protected_points: cdt.LabeledPointCloud,
-    config: AnonymizationConfig = None,
-) -> AnonymizationResult:
-    """Apply anonymization to facial region of mesh.
-
-    Smooths facial features using Taubin filter while preserving protected
-    optode and landmark positions. The algorithm:
-    1. Identifies protected vertex zones around optodes/landmarks
-    2. Applies Taubin smoothing to facial region
-    3. Creates smooth transitions at boundaries
-    4. Validates that protected points haven't moved
-
-    Args:
-        surface: Original TrimeshSurface
-        facial_mask: Boolean array marking facial vertices
-        protected_points: Points that must not move (optodes + landmarks)
-        config: Anonymization configuration (uses defaults if None)
-
-    Returns:
-        AnonymizationResult with anonymized surface and metadata
-    """
-    if config is None:
-        config = AnonymizationConfig()
-
-    # Store original vertices for displacement calculation
-    original_vertices = surface.mesh.vertices.copy()
-
-    # Merge duplicate vertices at mesh seams before smoothing.
-    # Photogrammetry scans have spatially coincident but topologically
-    # disconnected vertices at patch boundaries. Without merging, the
-    # Laplacian smooths each patch independently and seam vertices drift
-    # apart, producing visible crack lines.
-    merged_mesh, old_to_new, new_to_old = _merge_seam_vertices(surface.mesh)
-    merged_mask = np.zeros(len(merged_mesh.vertices), dtype=bool)
-    for new_idx in range(len(merged_mesh.vertices)):
-        # A merged vertex is facial if ANY of its source vertices were facial
-        if any(facial_mask[old_idx] for old_idx in new_to_old[new_idx]):
-            merged_mask[new_idx] = True
-
-    # Get protection radius from config
-    transition_width_mm = float(config.boundary_transition_width.to("mm").magnitude)
-    protection_radius = transition_width_mm * 1.5  # Extra margin for protection
-
-    # Find protected vertex indices on the merged mesh
-    protected_indices = _get_protected_vertex_indices(
-        merged_mesh, protected_points, protection_radius
-    )
-
-    logger.info(
-        f"Protecting {len(protected_indices)} vertices around "
-        f"{len(protected_points.label)} points"
-    )
-
-    if config.method == AnonymizationMethod.SMOOTH:
-        # Main smoothing pass
-        smoothed_mesh = smooth_region_selective(
-            mesh=merged_mesh,
-            mask=merged_mask,
-            protected_indices=protected_indices,
-            iterations=config.smoothing_iterations,
-            lamb=config.smoothing_lambda,
-            mu=config.smoothing_mu,
-            use_cotangent=config.use_cotangent,
-        )
-
-        # Boundary transition pass
-        smoothed_mesh = apply_boundary_transition(
-            mesh=smoothed_mesh,
-            mask=merged_mask,
-            transition_width=transition_width_mm,
-            protected_indices=protected_indices,
-            iterations=10,
-            lamb=0.3,
-        )
-
-    elif config.method == AnonymizationMethod.FLATTEN:
-        # Flatten facial region toward average plane (partial projection)
-        smoothed_mesh = _flatten_facial_region(
-            mesh=merged_mesh,
-            mask=merged_mask,
-            protected_indices=protected_indices,
-            flatten_strength=config.flatten_strength,
-        )
-
-    elif config.method == AnonymizationMethod.COLOR_BLUR:
-        # Only blur texture, keep geometry
-        smoothed_mesh = _blur_texture(
-            mesh=merged_mesh,
-            mask=merged_mask,
-        )
-
-    elif config.method == AnonymizationMethod.COMBINED:
-        # Apply smoothing then color blur
-        smoothed_mesh = smooth_region_selective(
-            mesh=merged_mesh,
-            mask=merged_mask,
-            protected_indices=protected_indices,
-            iterations=config.smoothing_iterations,
-            lamb=config.smoothing_lambda,
-            mu=config.smoothing_mu,
-            use_cotangent=config.use_cotangent,
-        )
-        smoothed_mesh = _blur_texture(
-            mesh=smoothed_mesh,
-            mask=merged_mask,
-        )
-
-    elif config.method == AnonymizationMethod.HEAT_DIFFUSION:
-        # Heat diffusion smoothing with cotangent Laplacian
-        smoothed_mesh = _heat_diffusion_smooth(
-            mesh=merged_mesh,
-            mask=merged_mask,
-            protected_indices=protected_indices,
-            diffusion_time=config.diffusion_time,
-        )
-
-        # Boundary transition pass
-        smoothed_mesh = apply_boundary_transition(
-            mesh=smoothed_mesh,
-            mask=merged_mask,
-            transition_width=transition_width_mm,
-            protected_indices=protected_indices,
-            iterations=10,
-            lamb=0.3,
-        )
-
-    elif config.method == AnonymizationMethod.NOISE:
-        # Neighbor-weighted random perturbation
-        smoothed_mesh = _noise_perturbation(
-            mesh=merged_mesh,
-            mask=merged_mask,
-            protected_indices=protected_indices,
-            noise_strength=config.noise_strength,
-            noise_iterations=config.noise_iterations,
-            noise_seed=config.noise_seed,
-        )
-
-        # Boundary transition pass
-        smoothed_mesh = apply_boundary_transition(
-            mesh=smoothed_mesh,
-            mask=merged_mask,
-            transition_width=transition_width_mm,
-            protected_indices=protected_indices,
-            iterations=10,
-            lamb=0.3,
-        )
-
-    else:
-        raise ValueError(f"Unknown anonymization method: {config.method}")
-
-    # Unmerge: expand smoothed vertices back to original vertex count
-    smoothed_mesh = _unmerge_vertices(smoothed_mesh, surface.mesh, old_to_new)
-
-    # Compute vertex displacements
-    vertex_displacements = np.linalg.norm(
-        smoothed_mesh.vertices - original_vertices, axis=1
-    )
-
-    # Create anonymized surface
-    anonymized_surface = cdc.TrimeshSurface(
-        mesh=smoothed_mesh,
-        crs=surface.crs,
-        units=surface.units,
-        vertex_coords=deepcopy(surface.vertex_coords),
-    )
-
-    logger.info(
-        f"Anonymization complete. "
-        f"Max displacement: {vertex_displacements.max():.2f}mm, "
-        f"Mean displacement in facial region: "
-        f"{vertex_displacements[facial_mask].mean():.2f}mm"
-    )
-
-    return AnonymizationResult(
-        anonymized_surface=anonymized_surface,
-        original_surface=surface,
-        facial_mask=facial_mask,
-        vertex_displacements=vertex_displacements,
-        config=config,
-    )
-
-
-def _flatten_facial_region(
-    mesh: trimesh.Trimesh,
-    mask: np.ndarray,
-    protected_indices: np.ndarray,
-    flatten_strength: float = 0.7,
-) -> trimesh.Trimesh:
-    """Partially flatten facial region toward an average plane.
-
-    Instead of fully projecting onto the plane (which destroys head curvature),
-    vertices are blended partway toward the plane. A few Laplacian smooth
-    iterations are applied afterward to round out the result.
-
-    Args:
-        mesh: The trimesh object
-        mask: Boolean mask of facial vertices
-        protected_indices: Indices that must not move
-        flatten_strength: Blend factor toward plane (0 = no change, 1 = full
-            projection). Default 0.7 preserves some curvature.
-
-    Returns:
-        Mesh with partially flattened facial region
-    """
-    smoothed_mesh = mesh.copy()
-    vertices = smoothed_mesh.vertices.copy()
-
-    # Get facial vertices (excluding protected)
-    facial_indices = np.where(mask)[0]
-    facial_indices = np.setdiff1d(facial_indices, protected_indices)
-
-    if len(facial_indices) == 0:
-        return smoothed_mesh
-
-    facial_vertices = vertices[facial_indices]
-
-    # Fit plane to facial vertices using PCA
-    centroid = np.mean(facial_vertices, axis=0)
-    centered = facial_vertices - centroid
-
-    # SVD to find plane normal (smallest singular vector)
-    _, _, vh = np.linalg.svd(centered, full_matrices=False)
-    normal = vh[-1]  # Normal to best-fit plane
-
-    # Partial projection — blend toward plane, keeping some curvature
-    distances = np.dot(vertices[facial_indices] - centroid, normal)
-    vertices[facial_indices] -= (flatten_strength * distances[:, np.newaxis]) * normal
-
-    smoothed_mesh.vertices = vertices
-
-    # Apply a few Laplacian smooth iterations to round out the result
-    L = _build_sparse_laplacian(smoothed_mesh)
-    w = np.zeros(len(vertices))
-    w[facial_indices] = 1.0
-    w = w[:, np.newaxis]
-    for _ in range(5):
-        vertices += 0.3 * w * (L @ vertices)
-    smoothed_mesh.vertices = vertices
-    smoothed_mesh._cache.clear()  # Invalidate normals, bounds, etc.
-
-    logger.info(
-        f"Flatten complete: strength={flatten_strength}, "
-        f"{len(facial_indices)} vertices"
-    )
-    return smoothed_mesh
-
-
-def _blur_texture(
-    mesh: trimesh.Trimesh,
-    mask: np.ndarray,
-) -> trimesh.Trimesh:
-    """Blur texture/vertex colors in facial region.
-
-    Args:
-        mesh: The trimesh object with vertex colors
-        mask: Boolean mask of facial vertices
-
-    Returns:
-        Mesh with blurred colors in facial region
-    """
-    smoothed_mesh = mesh.copy()
-
-    # Check if mesh has vertex colors
-    if not hasattr(smoothed_mesh.visual, 'vertex_colors'):
-        logger.warning("Mesh has no vertex colors to blur")
-        return smoothed_mesh
-
-    try:
-        colors = smoothed_mesh.visual.to_color().vertex_colors.copy()
-    except Exception:
-        logger.warning("Could not extract vertex colors for blurring")
-        return smoothed_mesh
-
-    # Build adjacency
-    adjacency = _build_adjacency_matrix(mesh)
-
-    # Blur colors using neighborhood averaging
-    new_colors = colors.copy()
-    facial_indices = np.where(mask)[0]
-
-    for _ in range(5):  # Multiple blur passes
-        for i in facial_indices:
-            neighbors = list(adjacency[i])
-            if len(neighbors) > 0:
-                # Average with neighbors
-                neighbor_colors = colors[neighbors].astype(float)
-                avg_color = np.mean(neighbor_colors, axis=0)
-                new_colors[i] = (0.5 * colors[i].astype(float) + 0.5 * avg_color).astype(
-                    np.uint8
-                )
-        colors = new_colors.copy()
-
-    # Apply blurred colors
-    smoothed_mesh.visual.vertex_colors = new_colors
-    return smoothed_mesh

From be4ee213c821293837c6525e383bc8f1da6630be Mon Sep 17 00:00:00 2001
From: mah-diaa <mahmoud.diaa.abdeen@gmail.com>
Date: Thu, 16 Apr 2026 18:02:40 +0200
Subject: [PATCH 12/39] Extract manual-pipeline helpers into face_detector and
 slim notebook 48

---
 .../48_manual_5pt_anonymization.ipynb         | 416 +++---------------
 .../photogrammetry/anonymization/__init__.py  |   9 +
 .../anonymization/face_detector.py            | 234 +++++++++-
 3 files changed, 303 insertions(+), 356 deletions(-)

diff --git a/examples/head_models/48_manual_5pt_anonymization.ipynb b/examples/head_models/48_manual_5pt_anonymization.ipynb
index 46e9cda9..268867f3 100644
--- a/examples/head_models/48_manual_5pt_anonymization.ipynb
+++ b/examples/head_models/48_manual_5pt_anonymization.ipynb
@@ -22,12 +22,12 @@
     "8. Validate result\n",
     "\n",
     "This is the cedalion-only alternative to notebook 47's MediaPipe pipeline and\n",
-    "matches the exposé contract: NAS/LPA/RPA assumed given, no DL face detection."
+    "matches the expos\u00e9 contract: NAS/LPA/RPA assumed given, no DL face detection."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "cell-imports",
    "metadata": {},
    "outputs": [],
@@ -46,6 +46,10 @@
     "from cedalion.geometry.photogrammetry.anonymization import (\n",
     "    normalize_axes,\n",
     "    isolate_head,\n",
+    "    align_axes_from_landmarks,\n",
+    "    detect_cap_boundary,\n",
+    "    face_mask_from_landmarks,\n",
+    "    delete_masked_vertices,\n",
     "    validate_anonymization,\n",
     ")\n",
     "from cedalion.vtktutils import trimesh_to_vtk_polydata\n",
@@ -62,14 +66,13 @@
     "INTERACTIVE = True\n",
     "\n",
     "# Mask tunables (mm)\n",
-    "LATERAL_SCALE = 0.9             # fraction of ear-half-width for main face mask\n",
-    "LANDMARK_KEEP_RADIUS = 8.0      # preserve a small sphere around each landmark point\n",
+    "LANDMARK_KEEP_RADIUS = 8.0      # preserve a small sphere around each landmark\n",
     "EAR_DELETE_RADIUS_MM = 40.0     # radius of the delete sphere around Lpa / Rpa\n",
     "\n",
     "# Cap boundary detection tunables\n",
-    "CAP_BAND_WIDTH_MM = 15.0        # Z-band width for midline Y-profile\n",
-    "CAP_BIN_SIZE_MM = 2.0           # X-bin size for Y-profile\n",
-    "CAP_FOOT_GRAD_THRESHOLD = 0.2   # dY/dX below this = flat = foot of cap rise"
+    "CAP_BAND_WIDTH_MM = 15.0\n",
+    "CAP_BIN_SIZE_MM = 2.0\n",
+    "CAP_FOOT_GRAD_THRESHOLD = 0.2"
    ]
   },
   {
@@ -131,7 +134,7 @@
        "version_minor": 0
       },
       "text/plain": [
-       "Widget(value='<iframe src=\"http://localhost:38789/index.html?ui=P_0x7f9c741e8a10_3&reconnect=auto\" class=\"pyvi…"
+       "Widget(value='<iframe src=\"http://localhost:38789/index.html?ui=P_0x7f9c741e8a10_3&reconnect=auto\" class=\"pyvi\u2026"
       ]
      },
      "metadata": {},
@@ -344,7 +347,7 @@
        "\n",
        ".xr-section-summary-in + label:before {\n",
        "  display: inline-block;\n",
-       "  content: \"►\";\n",
+       "  content: \"\u25ba\";\n",
        "  font-size: 11px;\n",
        "  width: 15px;\n",
        "  text-align: center;\n",
@@ -355,7 +358,7 @@
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label:before {\n",
-       "  content: \"▼\";\n",
+       "  content: \"\u25bc\";\n",
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label > span {\n",
@@ -689,143 +692,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "id": "cell-normalize",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "After isolate_head: 1,284,667 -> 678,081 vertices\n",
-      "Landmark-derived axes (before rotation):\n",
-      "  z_ax (lateral):  [-0.33548853 -0.12505766  0.93370661]  <-- from Lpa-Rpa\n",
-      "  y_ax (anterior): [0.29804451 0.92614585 0.23113487]  <-- perp to Z, toward Nz\n",
-      "  x_ax (up):       [ 0.89365369 -0.35582923  0.27343856]  <-- cross(Y, Z)\n",
-      "\n",
-      "Aligned landmarks (X=up, Y=anterior, Z=left):\n",
-      "  Nz : [ -99.06892144  -17.13701132 -475.26525432]\n",
-      "  Iz : [ -30.86680258 -238.5773692  -448.82692759]\n",
-      "  Cz : [  61.51173772  -99.18613518 -468.61131999]\n",
-      "  Lpa: [ -99.06892144 -111.29378165 -370.9357092 ]\n",
-      "  Rpa: [ -99.06892144 -111.29378165 -555.78327514]\n",
-      "\n",
-      "Axis checks:\n",
-      "  Cz highest X (up)?      True\n",
-      "  Nz most anterior (Y)?   True\n",
-      "  Lpa left of Rpa (Z)?    True\n",
-      "  Lpa/Rpa same X (height)? 0.0 mm\n",
-      "  Lpa/Rpa same Y (depth)?  0.0 mm\n",
-      "  Nz/Iz same X (height)?   68.2 mm\n",
-      "  Nz on mesh: 0.20 mm\n",
-      "  Iz on mesh: 0.17 mm\n",
-      "  Cz on mesh: 0.78 mm\n",
-      "  Lpa on mesh: 0.71 mm\n",
-      "  Rpa on mesh: 1.00 mm\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# Extract raw coords (strip pint units) for geometric operations\n",
-    "lm_raw = landmarks.pint.dequantify().values  # (5, 3)\n",
+    "# Step 1: Mediapipe-compatible X-rotation so Y points anterior.\n",
+    "lm_raw = landmarks.pint.dequantify().values\n",
     "idx = {lbl: i for i, lbl in enumerate(landmarks['label'].values)}\n",
     "Nz_raw = lm_raw[idx['Nz']]\n",
     "\n",
-    "# --- Step 1: Mediapipe-compatible normalization (kept for isolate_head) ---\n",
     "surface_n, Nz_n, R = normalize_axes(surface, Nz_raw)\n",
-    "lm_n = lm_raw @ R.T\n",
-    "\n",
-    "# Isolate head (needs normalize_axes frame)\n",
-    "surface_h, _ = isolate_head(surface_n, Nz_n)\n",
-    "print(f'After isolate_head: {surface_n.nvertices:,} -> {surface_h.nvertices:,} vertices')\n",
-    "\n",
-    "# --- Step 2: Landmark-based axis alignment ---\n",
-    "# normalize_axes only rotates around X. The head is still tilted.\n",
-    "# Derive a full rotation from the 5 landmarks to align X=up, Y=anterior, Z=left.\n",
-    "Nz_1  = lm_n[idx['Nz']]\n",
-    "Iz_1  = lm_n[idx['Iz']]\n",
-    "Cz_1  = lm_n[idx['Cz']]\n",
-    "Lpa_1 = lm_n[idx['Lpa']]\n",
-    "Rpa_1 = lm_n[idx['Rpa']]\n",
-    "ear_mid_1 = 0.5 * (Lpa_1 + Rpa_1)\n",
-    "\n",
-    "# Z: lateral (Lpa-Rpa line, pointing left) -- the most reliable axis\n",
-    "z_ax = Lpa_1 - Rpa_1\n",
-    "z_ax = z_ax / np.linalg.norm(z_ax)\n",
-    "\n",
-    "# Y: anterior, perpendicular to Z, pointing out from nasion\n",
-    "nz_dir = Nz_1 - ear_mid_1\n",
-    "nz_dir = nz_dir - np.dot(nz_dir, z_ax) * z_ax\n",
-    "y_ax = nz_dir / np.linalg.norm(nz_dir)\n",
-    "\n",
-    "# X: up, falls out as cross product\n",
-    "x_ax = np.cross(y_ax, z_ax)\n",
-    "\n",
-    "# Sign checks\n",
-    "if np.dot(Cz_1 - ear_mid_1, x_ax) < 0:\n",
-    "    x_ax = -x_ax\n",
-    "if np.dot(Nz_1 - ear_mid_1, y_ax) < 0:\n",
-    "    y_ax = -y_ax\n",
-    "z_ax = np.cross(x_ax, y_ax)\n",
+    "landmarks_n = landmarks.pint.dequantify().copy(data=lm_raw @ R.T).pint.quantify()\n",
     "\n",
-    "print(f'Landmark-derived axes (before rotation):')\n",
-    "print(f'  z_ax (lateral):  {z_ax}  <-- from Lpa-Rpa')\n",
-    "print(f'  y_ax (anterior): {y_ax}  <-- perp to Z, toward Nz')\n",
-    "print(f'  x_ax (up):       {x_ax}  <-- cross(Y, Z)')\n",
+    "# Head isolation (strips shoulders/background).\n",
+    "surface_n, _ = isolate_head(surface_n, Nz_n)\n",
+    "print(f'After isolate_head: {surface_n.nvertices:,} vertices')\n",
     "\n",
-    "# R2 maps the landmark frame to the standard basis\n",
-    "R2 = np.vstack([x_ax, y_ax, z_ax])\n",
-    "\n",
-    "# Apply to mesh vertices\n",
-    "import trimesh\n",
-    "aligned_verts = np.asarray(surface_h.mesh.vertices) @ R2.T\n",
-    "surface_h = cdc.TrimeshSurface(\n",
-    "    trimesh.Trimesh(vertices=aligned_verts, faces=surface_h.mesh.faces,\n",
-    "                    visual=surface_h.mesh.visual, process=False),\n",
-    "    crs=surface_h.crs, units=surface_h.units,\n",
-    ")\n",
-    "\n",
-    "# Apply to landmarks\n",
-    "lm_n = lm_n @ R2.T\n",
-    "\n",
-    "# Rebuild LabeledPointCloud\n",
-    "landmarks_n = xr.DataArray(\n",
-    "    lm_n,\n",
-    "    dims=['label', 'digitized'],\n",
-    "    coords={\n",
-    "        'label': ('label', list(landmarks['label'].values)),\n",
-    "        'type': ('label', [cdc.PointType.LANDMARK] * 5),\n",
-    "        'group': ('label', ['L'] * 5),\n",
-    "    },\n",
-    ").pint.quantify('mm')\n",
-    "\n",
-    "# Named references in the fully aligned frame\n",
-    "Nz  = lm_n[idx['Nz']]\n",
-    "Iz  = lm_n[idx['Iz']]\n",
-    "Cz  = lm_n[idx['Cz']]\n",
-    "Lpa = lm_n[idx['Lpa']]\n",
-    "Rpa = lm_n[idx['Rpa']]\n",
+    "# Step 2: Full 5-landmark alignment to canonical frame (X=up, Y=ant, Z=left).\n",
+    "surface_h, landmarks_n, R2 = align_axes_from_landmarks(surface_n, landmarks_n)\n",
+    "lm_n = landmarks_n.pint.dequantify().values\n",
+    "Nz, Iz, Cz, Lpa, Rpa = (lm_n[idx[lbl]] for lbl in ['Nz', 'Iz', 'Cz', 'Lpa', 'Rpa'])\n",
     "ear_mid = 0.5 * (Lpa + Rpa)\n",
     "\n",
-    "print(f'\\nAligned landmarks (X=up, Y=anterior, Z=left):')\n",
-    "print(f'  Nz : {Nz}')\n",
-    "print(f'  Iz : {Iz}')\n",
-    "print(f'  Cz : {Cz}')\n",
-    "print(f'  Lpa: {Lpa}')\n",
-    "print(f'  Rpa: {Rpa}')\n",
-    "\n",
-    "print(f'\\nAxis checks:')\n",
-    "print(f'  Cz highest X (up)?      {Cz[0] > max(Nz[0], Iz[0], Lpa[0], Rpa[0])}')\n",
-    "print(f'  Nz most anterior (Y)?   {Nz[1] > max(Iz[1], Lpa[1], Rpa[1])}')\n",
-    "print(f'  Lpa left of Rpa (Z)?    {Lpa[2] > Rpa[2]}')\n",
-    "print(f'  Lpa/Rpa same X (height)? {abs(Lpa[0] - Rpa[0]):.1f} mm')\n",
-    "print(f'  Lpa/Rpa same Y (depth)?  {abs(Lpa[1] - Rpa[1]):.1f} mm')\n",
-    "print(f'  Nz/Iz same X (height)?   {abs(Nz[0] - Iz[0]):.1f} mm')\n",
-    "\n",
-    "mesh_verts = np.asarray(surface_h.mesh.vertices)\n",
-    "for lbl, pos in zip(['Nz','Iz','Cz','Lpa','Rpa'], [Nz, Iz, Cz, Lpa, Rpa]):\n",
-    "    d = np.linalg.norm(mesh_verts - pos, axis=1).min()\n",
-    "    print(f'  {lbl} on mesh: {d:.2f} mm')"
+    "print('\\nAligned landmarks:')\n",
+    "verts = np.asarray(surface_h.mesh.vertices)\n",
+    "for lbl, pos in zip(['Nz', 'Iz', 'Cz', 'Lpa', 'Rpa'], [Nz, Iz, Cz, Lpa, Rpa]):\n",
+    "    d = np.linalg.norm(verts - pos, axis=1).min()\n",
+    "    print(f'  {lbl}: {pos}  (on mesh: {d:.2f} mm)')"
    ]
   },
   {
@@ -850,139 +744,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "id": "cell-mask",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Cap boundary detected at X = -58.1 mm\n",
-      "  Nz_x = -99.1,  Cz_x = 61.5,  cap_x = -58.1\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Nasion strip preserved: 1,963 vertices (half-width 8 mm, X: -99 -> -58)\n",
-      "\n",
-      "Mask: 142,911 / 678,081 vertices (21.1%)\n",
-      "Per-rule counts: {'below_cap': 364330, 'face_region': 126927, 'ear_region': 42118, 'all': 146319}\n",
-      "Ear delete radius: 40 mm\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "from scipy.signal import savgol_filter\n",
-    "\n",
-    "\n",
-    "def detect_cap_boundary(\n",
-    "    verts: np.ndarray,\n",
-    "    Nz: np.ndarray, Cz: np.ndarray, ear_mid: np.ndarray,\n",
-    "    mid_z: float,\n",
-    "    band_width: float = 15.0,\n",
-    "    bin_size: float = 2.0,\n",
-    "    foot_grad_threshold: float = 0.2,\n",
-    ") -> tuple[float, np.ndarray, np.ndarray, np.ndarray]:\n",
-    "    \"\"\"Find the X height where the cap front edge sits.\n",
-    "\n",
-    "    The cap protrudes in +Y, so Y-max along the midline lives ON the cap.\n",
-    "    The cap edge is the FOOT of the steep rise leading up to Y-max: walk back\n",
-    "    from the peak until the smoothed gradient drops below `foot_grad_threshold`.\n",
-    "    \"\"\"\n",
-    "    in_band = np.abs(verts[:, 2] - mid_z) < band_width\n",
-    "    above_nz = verts[:, 0] > Nz[0]\n",
-    "    anterior = verts[:, 1] > ear_mid[1]\n",
-    "    sel = in_band & above_nz & anterior\n",
-    "    sel_verts = verts[sel]\n",
-    "\n",
-    "    bins = np.arange(Nz[0], Cz[0], bin_size)\n",
-    "    bin_centers = bins[:-1] + bin_size / 2\n",
-    "    max_y = np.full(len(bin_centers), np.nan)\n",
-    "\n",
-    "    for i in range(len(bin_centers)):\n",
-    "        in_bin = (sel_verts[:, 0] >= bins[i]) & (sel_verts[:, 0] < bins[i + 1])\n",
-    "        if in_bin.any():\n",
-    "            max_y[i] = sel_verts[in_bin, 1].max()\n",
-    "\n",
-    "    valid = ~np.isnan(max_y)\n",
-    "    if valid.sum() < 7:\n",
-    "        fallback = 0.5 * (Nz[0] + Cz[0])\n",
-    "        return fallback, bin_centers[valid], max_y[valid], max_y[valid]\n",
-    "\n",
-    "    xv = bin_centers[valid]\n",
-    "    yv = max_y[valid]\n",
-    "\n",
-    "    win = min(11, len(yv) if len(yv) % 2 == 1 else len(yv) - 1)\n",
-    "    win = max(win, 5)\n",
-    "    yv_s = savgol_filter(yv, window_length=win, polyorder=2)\n",
-    "\n",
-    "    peak_idx = int(np.argmax(yv_s))\n",
-    "    grad = np.gradient(yv_s, xv)\n",
-    "    cap_x = xv[0]\n",
-    "    for i in range(peak_idx - 1, 0, -1):\n",
-    "        if grad[i] < foot_grad_threshold:\n",
-    "            cap_x = xv[i]\n",
-    "            break\n",
-    "\n",
-    "    return cap_x, xv, yv, yv_s\n",
-    "\n",
-    "\n",
-    "def face_mask_from_landmarks(\n",
-    "    verts, Nz, Iz, Cz, Lpa, Rpa,\n",
-    "    cap_x=None,\n",
-    "    ear_delete_radius=40.0,\n",
-    "):\n",
-    "    \"\"\"Connected face + ear mask in the aligned frame (X=up, Y=ant, Z=left).\n",
-    "\n",
-    "    The mask is the union of two regions, both clamped below the cap boundary:\n",
-    "      1. Face region: anterior to the ear coronal plane, extending laterally\n",
-    "         all the way to the ears (no midline band -- so it connects with the\n",
-    "         ear spheres below).\n",
-    "      2. Ear spheres: `ear_delete_radius` mm around Lpa and Rpa (deletes the\n",
-    "         ears themselves plus a bit of skin behind them).\n",
-    "\n",
-    "    Cap safety comes from the `below_cap` clamp (X < cap_x).\n",
-    "    \"\"\"\n",
-    "    ear_mid = 0.5 * (Lpa + Rpa)\n",
-    "    upper_bound = cap_x if cap_x is not None else Nz[0]\n",
-    "\n",
-    "    below_cap = verts[:, 0] < upper_bound\n",
-    "    anterior  = verts[:, 1] > ear_mid[1]\n",
-    "\n",
-    "    face_region = below_cap & anterior\n",
-    "\n",
-    "    d_lpa = np.linalg.norm(verts - Lpa, axis=1)\n",
-    "    d_rpa = np.linalg.norm(verts - Rpa, axis=1)\n",
-    "    ear_region = ((d_lpa < ear_delete_radius) | (d_rpa < ear_delete_radius)) & below_cap\n",
-    "\n",
-    "    mask = face_region | ear_region\n",
-    "\n",
-    "    info = {\n",
-    "        'upper_bound': upper_bound, 'ear_mid': ear_mid,\n",
-    "        'counts': {\n",
-    "            'below_cap': int(below_cap.sum()),\n",
-    "            'face_region': int(face_region.sum()),\n",
-    "            'ear_region': int(ear_region.sum()),\n",
-    "            'all': int(mask.sum()),\n",
-    "        },\n",
-    "    }\n",
-    "    return mask, info\n",
-    "\n",
-    "\n",
-    "# --- Detect cap boundary ---\n",
+    "# 1. Detect cap boundary from midline Y-profile.\n",
     "verts = np.asarray(surface_h.mesh.vertices)\n",
     "mid_z = 0.5 * (Lpa[2] + Rpa[2])\n",
     "\n",
@@ -992,67 +759,57 @@
     "    bin_size=CAP_BIN_SIZE_MM,\n",
     "    foot_grad_threshold=CAP_FOOT_GRAD_THRESHOLD,\n",
     ")\n",
-    "print(f'Cap boundary detected at X = {cap_x:.1f} mm')\n",
-    "print(f'  Nz_x = {Nz[0]:.1f},  Cz_x = {Cz[0]:.1f},  cap_x = {cap_x:.1f}')\n",
+    "print(f'Cap boundary detected at X = {cap_x:.1f} mm  '\n",
+    "      f'(Nz_x={Nz[0]:.1f}, Cz_x={Cz[0]:.1f})')\n",
     "\n",
-    "# Debug plot: Y-profile along midline (raw + smoothed)\n",
+    "# Debug plot: Y-profile along midline (raw + smoothed) + gradient.\n",
     "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))\n",
     "ax1.plot(profile_x, profile_y, 'b.-', alpha=0.4, label='raw max-Y')\n",
     "ax1.plot(profile_x, profile_y_s, 'b-', lw=2, label='smoothed')\n",
     "ax1.axvline(Nz[0], color='green', ls='--', label='Nz')\n",
     "ax1.axvline(cap_x, color='red', ls='--', label=f'cap edge ({cap_x:.0f})')\n",
     "ax1.axvline(Cz[0], color='cyan', ls='--', label='Cz')\n",
-    "peak_i = int(np.argmax(profile_y_s))\n",
-    "ax1.axvline(profile_x[peak_i], color='orange', ls=':',\n",
-    "            label=f'Y-max on cap ({profile_x[peak_i]:.0f})')\n",
     "ax1.set_xlabel('X (height, mm)')\n",
     "ax1.set_ylabel('max Y (anterior, mm)')\n",
-    "ax1.set_title('Midline Y-profile (cap protrudes in +Y)')\n",
+    "ax1.set_title('Midline Y-profile')\n",
     "ax1.legend()\n",
     "\n",
     "grad = np.gradient(profile_y_s, profile_x)\n",
     "ax2.plot(profile_x, grad, 'r.-')\n",
-    "ax2.axhline(0, color='gray', ls=':', alpha=0.5)\n",
-    "ax2.axhline(CAP_FOOT_GRAD_THRESHOLD, color='purple', ls=':', alpha=0.5,\n",
-    "            label='foot threshold')\n",
-    "ax2.axvline(cap_x, color='red', ls='--', label='cap edge (foot of rise)')\n",
-    "ax2.axvline(profile_x[peak_i], color='orange', ls=':', label='Y-max on cap')\n",
+    "ax2.axhline(CAP_FOOT_GRAD_THRESHOLD, color='purple', ls=':', label='foot threshold')\n",
+    "ax2.axvline(cap_x, color='red', ls='--', label='cap edge')\n",
     "ax2.set_xlabel('X (height, mm)')\n",
-    "ax2.set_ylabel('dY/dX (smoothed)')\n",
-    "ax2.set_title('Gradient (walk back from Y-max until grad < threshold)')\n",
+    "ax2.set_ylabel('dY/dX')\n",
+    "ax2.set_title('Gradient')\n",
     "ax2.legend()\n",
     "plt.tight_layout()\n",
     "plt.show()\n",
     "\n",
-    "# --- Build mask ---\n",
+    "# 2. Build face+ear mask from landmarks.\n",
     "mask, mask_info = face_mask_from_landmarks(\n",
     "    verts, Nz, Iz, Cz, Lpa, Rpa,\n",
     "    cap_x=cap_x,\n",
     "    ear_delete_radius=EAR_DELETE_RADIUS_MM,\n",
     ")\n",
     "\n",
-    "# Preserve the 5 landmark points (small keep-out sphere around each)\n",
+    "# 3. Preserve small spheres around each landmark point.\n",
     "for lm in (Nz, Iz, Cz, Lpa, Rpa):\n",
     "    near = np.linalg.norm(verts - lm, axis=1) < LANDMARK_KEEP_RADIUS\n",
     "    mask[near] = False\n",
     "\n",
-    "# Supervisor request: preserve a rectangular strip from the Nasion upward to\n",
-    "# the cap boundary, width = the Nasion's safe-area diameter (for coregistration).\n",
-    "strip_half_width = LANDMARK_KEEP_RADIUS\n",
+    "# 4. Preserve a midline Nz strip upward to the cap boundary (for coregistration).\n",
     "nasion_strip = (\n",
     "    (verts[:, 0] >= Nz[0]) &\n",
     "    (verts[:, 0] < cap_x) &\n",
-    "    (np.abs(verts[:, 2] - Nz[2]) < strip_half_width) &\n",
+    "    (np.abs(verts[:, 2] - Nz[2]) < LANDMARK_KEEP_RADIUS) &\n",
     "    (verts[:, 1] > ear_mid[1])\n",
     ")\n",
     "mask[nasion_strip] = False\n",
-    "print(f'Nasion strip preserved: {int(nasion_strip.sum()):,} vertices '\n",
-    "      f'(half-width {strip_half_width:.0f} mm, X: {Nz[0]:.0f} -> {cap_x:.0f})')\n",
     "\n",
-    "print(f'\\nMask: {mask.sum():,} / {len(mask):,} vertices '\n",
+    "print(f'Nasion strip preserved: {int(nasion_strip.sum()):,} vertices')\n",
+    "print(f'Mask: {mask.sum():,} / {len(mask):,} vertices '\n",
     "      f'({100*mask.sum()/len(mask):.1f}%)')\n",
-    "print(f'Per-rule counts: {mask_info[\"counts\"]}')\n",
-    "print(f'Ear delete radius: {EAR_DELETE_RADIUS_MM:.0f} mm')"
+    "print(f'Per-rule counts: {mask_info[\"counts\"]}')"
    ]
   },
   {
@@ -1063,32 +820,19 @@
     "## 6. Visualize the anonymization mask\n",
     "\n",
     "Red = will be deleted, white = kept. The 5 landmark spheres are overlaid for\n",
-    "orientation. This is the primary deliverable — if the red region hugs the face\n",
+    "orientation. This is the primary deliverable \u2014 if the red region hugs the face\n",
     "without leaking onto ears / top-of-head / neck, the geometric rules are working."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "id": "cell-viz-mask",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c377768ad4504ce2bfeb96e448b5cf3f",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Widget(value='<iframe src=\"http://localhost:38789/index.html?ui=P_0x7f9bdeb63bd0_4&reconnect=auto\" class=\"pyvi…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
+    "lm_colors = {'Nz': 'lime', 'Iz': 'magenta', 'Cz': 'cyan', 'Lpa': 'orange', 'Rpa': 'blue'}\n",
+    "\n",
     "pvplt = pv.Plotter()\n",
     "\n",
     "vtk_surface = VTKSurface.from_trimeshsurface(surface_h)\n",
@@ -1099,8 +843,6 @@
     "    show_scalar_bar=False, opacity=1.0, smooth_shading=True,\n",
     ")\n",
     "\n",
-    "# 5 landmark spheres with labels\n",
-    "lm_colors = {'Nz': 'lime', 'Iz': 'magenta', 'Cz': 'cyan', 'Lpa': 'orange', 'Rpa': 'blue'}\n",
     "for lbl, pos in zip(landmarks_n['label'].values, lm_n):\n",
     "    c = lm_colors.get(lbl, 'yellow')\n",
     "    pvplt.add_mesh(pv.Sphere(radius=4, center=pos), color=c)\n",
@@ -1129,31 +871,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "id": "cell-delete",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Original:    678,081 verts, 1,171,705 faces\n",
-      "Anonymized:  535,017 verts, 920,914 faces\n",
-      "Removed:     143,064 verts\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "mesh_copy = surface_h.mesh.copy()\n",
-    "face_verts_in_mask = mask[mesh_copy.faces]        # (n_faces, 3) bool\n",
-    "faces_to_remove = face_verts_in_mask.any(axis=1)\n",
-    "mesh_copy.update_faces(~faces_to_remove)\n",
-    "mesh_copy.remove_unreferenced_vertices()\n",
-    "\n",
-    "surface_anon = cdc.TrimeshSurface(\n",
-    "    mesh=mesh_copy, crs=surface_h.crs, units=surface_h.units,\n",
-    ")\n",
-    "\n",
+    "surface_anon = delete_masked_vertices(surface_h, mask)\n",
     "n_removed = surface_h.nvertices - surface_anon.nvertices\n",
     "print(f'Original:    {surface_h.nvertices:,} verts, {surface_h.nfaces:,} faces')\n",
     "print(f'Anonymized:  {surface_anon.nvertices:,} verts, {surface_anon.nfaces:,} faces')\n",
@@ -1170,27 +893,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "id": "cell-beforeafter",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "43ca33b85769486e9de1f1adfc3b1ed4",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Widget(value='<iframe src=\"http://localhost:38789/index.html?ui=P_0x7f9c4c6cc0d0_5&reconnect=auto\" class=\"pyvi…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "anon_vtk = trimesh_to_vtk_polydata(mesh_copy)\n",
+    "anon_vtk = trimesh_to_vtk_polydata(surface_anon.mesh)\n",
     "\n",
     "pvplt = pv.Plotter(shape=(1, 2))\n",
     "\n",
@@ -1236,7 +944,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "ValidationResult(face_removed=True, mesh_valid=True, expected_vertices_removed=117152, actual_vertices_removed=117230, protected_points_intact=True, protected_point_max_deviation=0.3121421099641441, face_coverage_pct=23.709748253927778, passed=True, summary='PASSED — 117,230 vertices removed (23.7%), mesh valid, protected points within 5.0mm')\n"
+      "ValidationResult(face_removed=True, mesh_valid=True, expected_vertices_removed=117152, actual_vertices_removed=117230, protected_points_intact=True, protected_point_max_deviation=0.3121421099641441, face_coverage_pct=23.709748253927778, passed=True, summary='PASSED \u2014 117,230 vertices removed (23.7%), mesh valid, protected points within 5.0mm')\n"
      ]
     }
    ],
@@ -1264,7 +972,7 @@
     "  (tunable at the top of the notebook).\n",
     "- **Anonymization method:** vertex deletion (drop faces touching masked vertices,\n",
     "  strip unreferenced vertices). No Taubin smoothing. No MediaPipe.\n",
-    "- **Contract with the exposé:** this notebook assumes NAS/LPA/RPA are given and\n",
+    "- **Contract with the expos\u00e9:** this notebook assumes NAS/LPA/RPA are given and\n",
     "  uses no DL face detection. Cz and Iz are also given and used as vertical bounds;\n",
     "  they aren't strictly required but make the top/bottom cuts landmark-driven\n",
     "  instead of mesh-extent-driven.\n",
@@ -1295,4 +1003,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}
\ No newline at end of file
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/__init__.py b/src/cedalion/geometry/photogrammetry/anonymization/__init__.py
index f11e9c4a..f031fddf 100644
--- a/src/cedalion/geometry/photogrammetry/anonymization/__init__.py
+++ b/src/cedalion/geometry/photogrammetry/anonymization/__init__.py
@@ -35,6 +35,10 @@
     isolate_head,
     detect_landmarks_from_nasion,
     get_facial_region_mask_from_nasion,
+    align_axes_from_landmarks,
+    detect_cap_boundary,
+    face_mask_from_landmarks,
+    delete_masked_vertices,
 )
 from .nasion_detector import detect_nasion_auto
 from .ui import pick_nasion
@@ -51,6 +55,11 @@
     # Landmark and face mask detection
     "detect_landmarks_from_nasion",
     "get_facial_region_mask_from_nasion",
+    # Landmark-only geometric pipeline
+    "align_axes_from_landmarks",
+    "detect_cap_boundary",
+    "face_mask_from_landmarks",
+    "delete_masked_vertices",
     # Validation
     "validate_anonymization",
 ]
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/face_detector.py b/src/cedalion/geometry/photogrammetry/anonymization/face_detector.py
index b5b8a183..0d582046 100644
--- a/src/cedalion/geometry/photogrammetry/anonymization/face_detector.py
+++ b/src/cedalion/geometry/photogrammetry/anonymization/face_detector.py
@@ -1,8 +1,10 @@
 """Facial region detection for photogrammetry scans.
 
 Provides axis normalization, head isolation, landmark detection from a
-user-provided nasion (Nz) position, and facial region mask generation
-using MediaPipe face contour back-projection.
+user-provided nasion (Nz), and facial region mask generation. Supports both
+a MediaPipe contour path (via ``get_facial_region_mask_from_nasion``) and a
+pure-geometric landmark-only path (``align_axes_from_landmarks`` +
+``detect_cap_boundary`` + ``face_mask_from_landmarks``).
 
 Initial Contributors:
     - Face Anonymization Project | 2024
@@ -545,3 +547,231 @@ def _validate_landmark_configuration(
         logger.warning(
             f"LPA/RPA asymmetry is large (offsets: {lpa_offset:.1f} vs {rpa_offset:.1f}mm)"
         )
+
+
+def align_axes_from_landmarks(
+    surface: cdc.TrimeshSurface,
+    landmarks: cdt.LabeledPointCloud,
+) -> tuple[cdc.TrimeshSurface, cdt.LabeledPointCloud, np.ndarray]:
+    """Derive full rotation from 5 landmarks and apply to mesh and landmarks.
+
+    ``normalize_axes()`` only rotates around X. This function uses the full 5
+    landmark set to align the head to the canonical frame:
+
+        Z = lateral (Lpa - Rpa, pointing left)
+        Y = anterior (Nz - ear_mid, orthogonal to Z)
+        X = up (cross(Y, Z))
+
+    Sign checks ensure Cz points +X and Nz points +Y.
+
+    Args:
+        surface: Axis-normalized TrimeshSurface (post ``normalize_axes`` and
+            ``isolate_head``).
+        landmarks: LabeledPointCloud with labels Nz, Iz, Cz, Lpa, Rpa
+            (matching the surface frame).
+
+    Returns:
+        Tuple of (aligned_surface, aligned_landmarks, rotation_matrix).
+        ``rotation_matrix`` is 3x3 and maps input-frame vectors to the aligned
+        frame (apply as ``v @ R.T``).
+    """
+    import trimesh
+
+    lm = landmarks.pint.dequantify().values
+    labels = list(landmarks["label"].values)
+    idx = {lbl: i for i, lbl in enumerate(labels)}
+
+    required = {"Nz", "Iz", "Cz", "Lpa", "Rpa"}
+    missing = required - set(labels)
+    if missing:
+        raise ValueError(f"Missing landmarks for alignment: {missing}")
+
+    Nz = lm[idx["Nz"]]
+    Cz = lm[idx["Cz"]]
+    Lpa = lm[idx["Lpa"]]
+    Rpa = lm[idx["Rpa"]]
+    ear_mid = 0.5 * (Lpa + Rpa)
+
+    z_ax = Lpa - Rpa
+    z_ax = z_ax / np.linalg.norm(z_ax)
+
+    nz_dir = Nz - ear_mid
+    nz_dir = nz_dir - np.dot(nz_dir, z_ax) * z_ax
+    y_ax = nz_dir / np.linalg.norm(nz_dir)
+
+    x_ax = np.cross(y_ax, z_ax)
+
+    if np.dot(Cz - ear_mid, x_ax) < 0:
+        x_ax = -x_ax
+    if np.dot(Nz - ear_mid, y_ax) < 0:
+        y_ax = -y_ax
+    z_ax = np.cross(x_ax, y_ax)
+
+    R = np.vstack([x_ax, y_ax, z_ax])
+
+    aligned_verts = np.asarray(surface.mesh.vertices) @ R.T
+    new_mesh = trimesh.Trimesh(
+        vertices=aligned_verts,
+        faces=surface.mesh.faces,
+        visual=surface.mesh.visual,
+        process=False,
+    )
+    aligned_surface = cdc.TrimeshSurface(
+        new_mesh, crs=surface.crs, units=surface.units,
+    )
+
+    aligned_lm = lm @ R.T
+    aligned_landmarks = landmarks.pint.dequantify().copy(data=aligned_lm).pint.quantify()
+
+    return aligned_surface, aligned_landmarks, R
+
+
+def detect_cap_boundary(
+    verts: np.ndarray,
+    Nz: np.ndarray,
+    Cz: np.ndarray,
+    ear_mid: np.ndarray,
+    mid_z: float,
+    band_width: float = 15.0,
+    bin_size: float = 2.0,
+    foot_grad_threshold: float = 0.2,
+) -> tuple[float, np.ndarray, np.ndarray, np.ndarray]:
+    """Find the X height where the EEG cap front edge sits.
+
+    Scans upward from Nz along the midline and records max-Y per X-bin. The cap
+    protrudes in +Y, so Y-max along the midline lives on the cap. The cap edge
+    is the foot of the steep rise leading to the peak: walk back from the peak
+    until the smoothed gradient drops below ``foot_grad_threshold``.
+
+    Expects the aligned frame (X=up, Y=anterior, Z=left).
+
+    Args:
+        verts: Mesh vertices, shape (N, 3).
+        Nz: Nasion position.
+        Cz: Cz position.
+        ear_mid: Midpoint of Lpa/Rpa.
+        mid_z: Midline Z value (e.g. ``0.5 * (Lpa[2] + Rpa[2])``).
+        band_width: Z-band half-width for the midline Y-profile (mm).
+        bin_size: X-bin size for the Y-profile (mm).
+        foot_grad_threshold: dY/dX below this value marks the foot of the rise.
+
+    Returns:
+        Tuple of (cap_x, profile_x, profile_y_raw, profile_y_smooth).
+    """
+    from scipy.signal import savgol_filter
+
+    in_band = np.abs(verts[:, 2] - mid_z) < band_width
+    above_nz = verts[:, 0] > Nz[0]
+    anterior = verts[:, 1] > ear_mid[1]
+    sel_verts = verts[in_band & above_nz & anterior]
+
+    bins = np.arange(Nz[0], Cz[0], bin_size)
+    bin_centers = bins[:-1] + bin_size / 2
+    max_y = np.full(len(bin_centers), np.nan)
+
+    for i in range(len(bin_centers)):
+        in_bin = (sel_verts[:, 0] >= bins[i]) & (sel_verts[:, 0] < bins[i + 1])
+        if in_bin.any():
+            max_y[i] = sel_verts[in_bin, 1].max()
+
+    valid = ~np.isnan(max_y)
+    if valid.sum() < 7:
+        fallback = 0.5 * (Nz[0] + Cz[0])
+        return fallback, bin_centers[valid], max_y[valid], max_y[valid]
+
+    xv = bin_centers[valid]
+    yv = max_y[valid]
+
+    win = min(11, len(yv) if len(yv) % 2 == 1 else len(yv) - 1)
+    win = max(win, 5)
+    yv_s = savgol_filter(yv, window_length=win, polyorder=2)
+
+    peak_idx = int(np.argmax(yv_s))
+    grad = np.gradient(yv_s, xv)
+    cap_x = xv[0]
+    for i in range(peak_idx - 1, 0, -1):
+        if grad[i] < foot_grad_threshold:
+            cap_x = xv[i]
+            break
+
+    return cap_x, xv, yv, yv_s
+
+
+def face_mask_from_landmarks(
+    verts: np.ndarray,
+    Nz: np.ndarray,
+    Iz: np.ndarray,
+    Cz: np.ndarray,
+    Lpa: np.ndarray,
+    Rpa: np.ndarray,
+    cap_x: float | None = None,
+    ear_delete_radius: float = 40.0,
+) -> tuple[np.ndarray, dict]:
+    """Build face + ear deletion mask from the 5 landmarks (aligned frame).
+
+    Mask is the union of two regions, both clamped below the cap boundary:
+
+    1. Face region: anterior to the ear coronal plane (Y > ear_mid_Y).
+    2. Ear spheres: ``ear_delete_radius`` mm around Lpa and Rpa.
+
+    Expects the aligned frame (X=up, Y=anterior, Z=left).
+
+    Args:
+        verts: Mesh vertices, shape (N, 3).
+        Nz, Iz, Cz, Lpa, Rpa: 5 landmark positions in the aligned frame.
+        cap_x: Upper bound X value (typically from ``detect_cap_boundary``).
+            Defaults to Nz[0] if not provided.
+        ear_delete_radius: Sphere radius around Lpa/Rpa in mm.
+
+    Returns:
+        Tuple of (mask, info). ``mask`` is a boolean array of shape (N,).
+        ``info`` has keys ``upper_bound``, ``ear_mid``, and ``counts``
+        (per-region vertex counts).
+    """
+    ear_mid = 0.5 * (Lpa + Rpa)
+    upper_bound = cap_x if cap_x is not None else Nz[0]
+
+    below_cap = verts[:, 0] < upper_bound
+    anterior = verts[:, 1] > ear_mid[1]
+    face_region = below_cap & anterior
+
+    d_lpa = np.linalg.norm(verts - Lpa, axis=1)
+    d_rpa = np.linalg.norm(verts - Rpa, axis=1)
+    ear_region = ((d_lpa < ear_delete_radius) | (d_rpa < ear_delete_radius)) & below_cap
+
+    mask = face_region | ear_region
+
+    info = {
+        "upper_bound": upper_bound,
+        "ear_mid": ear_mid,
+        "counts": {
+            "below_cap": int(below_cap.sum()),
+            "face_region": int(face_region.sum()),
+            "ear_region": int(ear_region.sum()),
+            "all": int(mask.sum()),
+        },
+    }
+    return mask, info
+
+
+def delete_masked_vertices(
+    surface: cdc.TrimeshSurface,
+    mask: np.ndarray,
+) -> cdc.TrimeshSurface:
+    """Drop triangles touching any masked vertex and strip unreferenced vertices.
+
+    Args:
+        surface: Input TrimeshSurface.
+        mask: Boolean array of shape (n_vertices,). True = vertex to remove.
+
+    Returns:
+        New TrimeshSurface with masked vertices (and the faces touching them)
+        removed. CRS and units are preserved.
+    """
+    mesh_copy = surface.mesh.copy()
+    faces_to_remove = mask[mesh_copy.faces].any(axis=1)
+    mesh_copy.update_faces(~faces_to_remove)
+    mesh_copy.remove_unreferenced_vertices()
+    return cdc.TrimeshSurface(
+        mesh=mesh_copy, crs=surface.crs, units=surface.units,
+    )

From 95a7a4c1dab346d455075b8cc1e2afb88b8bdd13 Mon Sep 17 00:00:00 2001
From: mah-diaa <mahmoud.diaa.abdeen@gmail.com>
Date: Fri, 17 Apr 2026 15:49:06 +0200
Subject: [PATCH 13/39] Remove MediaPipe pipeline from main (moved to
 auto-detection-pipeline branch)

---
 environment_dev.yml                           |   2 -
 .../head_models/47_face_anonymization.ipynb   | 683 ------------------
 pyproject.toml                                |   4 -
 .../photogrammetry/anonymization/__init__.py  |  22 +-
 .../anonymization/face_detector.py            |  10 +-
 .../anonymization/face_landmarker.task        | Bin 3758596 -> 0 bytes
 .../anonymization/nasion_detector.py          | 676 -----------------
 tests/test_anonymization.py                   |  13 -
 8 files changed, 19 insertions(+), 1391 deletions(-)
 delete mode 100644 examples/head_models/47_face_anonymization.ipynb
 delete mode 100644 src/cedalion/geometry/photogrammetry/anonymization/face_landmarker.task
 delete mode 100644 src/cedalion/geometry/photogrammetry/anonymization/nasion_detector.py

diff --git a/environment_dev.yml b/environment_dev.yml
index e547807f..1fb38637 100644
--- a/environment_dev.yml
+++ b/environment_dev.yml
@@ -66,5 +66,3 @@ dependencies:
       - pmcxcl>=0.2.0
       - sphinxcontrib-bibtex
       - hatch-conda
-      - mediapipe==0.10.*
-      
diff --git a/examples/head_models/47_face_anonymization.ipynb b/examples/head_models/47_face_anonymization.ipynb
deleted file mode 100644
index aea2b20c..00000000
--- a/examples/head_models/47_face_anonymization.ipynb
+++ /dev/null
@@ -1,683 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "cell-md-title",
-   "metadata": {},
-   "source": [
-    "# Face Anonymization for Photogrammetry Scans\n",
-    "\n",
-    "GDPR-compliant face removal pipeline for Einstar 3D photogrammetry scans.\n",
-    "Removes facial geometry while preserving all anatomical landmarks and optode positions.\n",
-    "\n",
-    "**Pipeline:**\n",
-    "1. Load scan and detect nasion (auto via MediaPipe, or manual click)\n",
-    "2. Normalize axes, isolate head, detect landmarks, compute face mask + ear coverage\n",
-    "3. Preview the detected region\n",
-    "4. Interactive refinement: click on missed spots to add circular deletion zones\n",
-    "5. Delete facial vertices and visualize result"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "cell-imports",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import logging\n",
-    "\n",
-    "import numpy as np\n",
-    "import pyvista as pv\n",
-    "import trimesh\n",
-    "import cedalion\n",
-    "import cedalion.io\n",
-    "import cedalion.plots\n",
-    "import cedalion.dataclasses\n",
-    "from cedalion.geometry.photogrammetry.anonymization import (\n",
-    "    detect_nasion_auto,\n",
-    "    pick_nasion,\n",
-    "    normalize_axes,\n",
-    "    isolate_head,\n",
-    "    detect_landmarks_from_nasion,\n",
-    "    get_facial_region_mask_from_nasion,\n",
-    ")\n",
-    "from cedalion.vtktutils import trimesh_to_vtk_polydata\n",
-    "from cedalion.dataclasses import VTKSurface\n",
-    "from scipy.spatial import KDTree\n",
-    "\n",
-    "# Suppress verbose module and MediaPipe logging\n",
-    "logging.getLogger('cedalion').setLevel(logging.WARNING)\n",
-    "logging.getLogger('absl').setLevel(logging.WARNING)\n",
-    "\n",
-    "pv.set_jupyter_backend('server')\n",
-    "\n",
-    "# === CONFIG ===\n",
-    "SUBJECT_NUMBER = 21\n",
-    "SCANS_FOLDER = '/home/ma7/BA/PG_Subjects'\n",
-    "FORCE_MANUAL = False\n",
-    "PROXIMITY_RADIUS = 85.0    # mm (unused by new pipeline, kept for reference)\n",
-    "CIRCLE_RADIUS = 25.0       # mm -- manual click-to-delete radius\n",
-    "\n",
-    "# ---------------------------------------------------------------------------\n",
-    "# Helper functions (ported from 50_scan_overview.ipynb)\n",
-    "# ---------------------------------------------------------------------------\n",
-    "\n",
-    "def eye_plane_rotation_matrix(r_eye_3d, l_eye_3d, forward_dir):\n",
-    "    \"\"\"Build the rotation that sends the eye line to +Z, forward to +Y,\n",
-    "    and X = Y x Z (head-up, right-handed). Rows of the returned matrix are\n",
-    "    the new basis vectors in the old frame: p_new = R @ (p_old - center) + center.\n",
-    "    \"\"\"\n",
-    "    eye_dir = np.asarray(l_eye_3d - r_eye_3d, dtype=float)\n",
-    "    z_new = eye_dir / np.linalg.norm(eye_dir)\n",
-    "\n",
-    "    fref = np.asarray(forward_dir, dtype=float)\n",
-    "    fref = fref / np.linalg.norm(fref)\n",
-    "    y_new = fref - np.dot(fref, z_new) * z_new\n",
-    "    y_new = y_new / np.linalg.norm(y_new)\n",
-    "\n",
-    "    x_new = np.cross(y_new, z_new)\n",
-    "    x_new = x_new / np.linalg.norm(x_new)\n",
-    "    return np.vstack([x_new, y_new, z_new])\n",
-    "\n",
-    "\n",
-    "def heuristic_lpa_rpa(head_verts, eye_x, centroid_y):\n",
-    "    \"\"\"Approximate LPA/RPA as small uncertainty spheres, not single points.\n",
-    "\n",
-    "    Expects head_verts in the eye-plane-normalized frame\n",
-    "    (X=head-up, Y=forward, Z=left).\n",
-    "    \"\"\"\n",
-    "    X_TOL = 20.0\n",
-    "    X_OFFSET = -15.0\n",
-    "    Y_TOL = 50.0\n",
-    "    Y_FORWARD = 35.0\n",
-    "    RADIUS = 15.0\n",
-    "\n",
-    "    x_band = np.abs(head_verts[:, 0] - (eye_x + X_OFFSET)) < X_TOL\n",
-    "    y_band = np.abs(head_verts[:, 1] - centroid_y) < Y_TOL\n",
-    "    mask = x_band & y_band\n",
-    "    candidates = head_verts[mask]\n",
-    "    if len(candidates) < 10:\n",
-    "        return None, None\n",
-    "\n",
-    "    lpa_center = candidates[int(np.argmax(candidates[:, 2]))].astype(float).copy()\n",
-    "    rpa_center = candidates[int(np.argmin(candidates[:, 2]))].astype(float).copy()\n",
-    "    lpa_center[1] += Y_FORWARD\n",
-    "    rpa_center[1] += Y_FORWARD\n",
-    "\n",
-    "    return (\n",
-    "        {\"center\": lpa_center, \"radius\": RADIUS},\n",
-    "        {\"center\": rpa_center, \"radius\": RADIUS},\n",
-    "    )\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-md-nasion",
-   "metadata": {},
-   "source": [
-    "## 1. Load scan + detect nasion\n",
-    "\n",
-    "Auto-detect nasion via MediaPipe. If no face is found (or `FORCE_MANUAL=True`),\n",
-    "falls back to an interactive picker where the user clicks the nasion."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "cell-nasion",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loaded: 494,109 vertices, 866,676 faces\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
-      "W0000 00:00:1776065866.226577  601547 face_landmarker_graph.cc:174] Sets FaceBlendshapesGraph acceleration to xnnpack by default.\n",
-      "I0000 00:00:1776065866.263782  601547 gl_context_egl.cc:85] Successfully initialized EGL. Major : 1 Minor: 5\n",
-      "I0000 00:00:1776065866.277111  601573 gl_context.cc:385] GL version: 3.2 (OpenGL ES 3.2 Mesa 26.0.4-arch1.1), renderer: AMD Radeon Graphics (radeonsi, renoir, ACO, DRM 3.64, 6.19.11-arch1-1)\n",
-      "INFO: Created TensorFlow Lite XNNPACK delegate for CPU.\n",
-      "W0000 00:00:1776065866.283648  601557 inference_feedback_manager.cc:114] Feedback manager requires a model with a single signature inference. Disabling support for feedback tensors.\n",
-      "W0000 00:00:1776065866.294381  601555 inference_feedback_manager.cc:114] Feedback manager requires a model with a single signature inference. Disabling support for feedback tensors.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Auto nasion: method=mediapipe+unified, confidence=0.90\n",
-      "Forward direction: available\n",
-      "Face contour: 36 pts\n",
-      "Eyes: available\n"
-     ]
-    }
-   ],
-   "source": [
-    "path = f'{SCANS_FOLDER}/Subject{SUBJECT_NUMBER}/Subject{SUBJECT_NUMBER}.obj'\n",
-    "surface = cedalion.io.read_einstar_obj(path)\n",
-    "print(f'Loaded: {surface.nvertices:,} vertices, {surface.nfaces:,} faces')\n",
-    "\n",
-    "# Nasion detection\n",
-    "if not FORCE_MANUAL:\n",
-    "    auto_result = detect_nasion_auto(surface)\n",
-    "else:\n",
-    "    auto_result = None\n",
-    "\n",
-    "if auto_result is not None:\n",
-    "    nasion, meta = auto_result\n",
-    "    print(f'Auto nasion: method={meta[\"method\"]}, confidence={meta[\"confidence\"]:.2f}')\n",
-    "else:\n",
-    "    print('Opening manual nasion picker...')\n",
-    "    nasion = pick_nasion(surface)\n",
-    "    if nasion is None:\n",
-    "        raise RuntimeError('No nasion selected — cannot proceed')\n",
-    "    meta = {'method': 'manual', 'confidence': 1.0}\n",
-    "    print(f'Manual nasion at: [{nasion[0]:.1f}, {nasion[1]:.1f}, {nasion[2]:.1f}]')\n",
-    "\n",
-    "fwd = meta.get('forward_direction')\n",
-    "face_contour = meta.get('face_contour_3d')\n",
-    "eyes = meta.get('eyes')\n",
-    "print(f'Forward direction: {\"available\" if fwd is not None else \"N/A\"}')\n",
-    "print(f'Face contour: {len(face_contour)} pts' if face_contour is not None else 'Face contour: N/A')\n",
-    "print(f'Eyes: {\"available\" if eyes is not None else \"N/A\"}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-md-landmarks",
-   "metadata": {},
-   "source": [
-    "## 2. Eye-plane rotation, head isolation, heuristic LPA/RPA, face mask\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "cell-landmarks",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Eye-plane rotation applied. Canonical frame: X=up, Y=forward, Z=left\n",
-      "  nasion_n: [83.5, 118.9, 564.3]\n",
-      "Head isolation: 408,990 -> 408,990 vertices\n",
-      "  LPA heur: center=[94.6, 0.6, 662.0]  radius=15\n",
-      "  RPA heur: center=[93.9, 14.1, 464.6]  radius=15\n",
-      "\n",
-      "Custom mask: 90,443 vertices (22.1%)\n",
-      "  below nasion:            62,625\n",
-      "  sides (nasion-arc):      27,818\n",
-      "  nasion strip (kept):     2,828\n",
-      "  forehead upper bound:    MediaPipe forehead arc (14 pts)\n",
-      "    arc X range:           [101.2, 125.4]\n",
-      "  Z band (excl. ears):     (479.6, 647.0)\n",
-      "  Y cut (front half):      > 7.4\n",
-      "  Nasion strip half-width: 15mm in Z\n"
-     ]
-    }
-   ],
-   "source": [
-    "# --- Eye-plane rotation (replaces normalize_axes) ---\n",
-    "# Eyes come from the same validated sweep as the nasion, so they are\n",
-    "# guaranteed consistent with it (see nasion_detector._find_nose_tip_mediapipe).\n",
-    "if eyes is None:\n",
-    "    raise RuntimeError(\n",
-    "        \"No eyes in nasion metadata -- unified sweep failed. \"\n",
-    "        \"Fall back to manual pipeline.\"\n",
-    "    )\n",
-    "r_eye_raw, l_eye_raw = eyes\n",
-    "\n",
-    "R_eye = eye_plane_rotation_matrix(r_eye_raw, l_eye_raw, fwd)\n",
-    "\n",
-    "# Build a rotated TrimeshSurface so isolate_head can work with it.\n",
-    "rotated_mesh = surface.mesh.copy()\n",
-    "raw_verts = np.asarray(rotated_mesh.vertices, dtype=float)\n",
-    "rot_center = raw_verts.mean(axis=0)\n",
-    "rotated_mesh.vertices = (R_eye @ (raw_verts - rot_center).T).T + rot_center\n",
-    "surface_n = cedalion.dataclasses.TrimeshSurface(\n",
-    "    mesh=rotated_mesh, crs=surface.crs, units=surface.units\n",
-    ")\n",
-    "nasion_n = R_eye @ (np.asarray(nasion) - rot_center) + rot_center\n",
-    "r_eye_n  = R_eye @ (r_eye_raw - rot_center) + rot_center\n",
-    "l_eye_n  = R_eye @ (l_eye_raw - rot_center) + rot_center\n",
-    "print(f\"Eye-plane rotation applied. Canonical frame: X=up, Y=forward, Z=left\")\n",
-    "print(f\"  nasion_n: [{nasion_n[0]:.1f}, {nasion_n[1]:.1f}, {nasion_n[2]:.1f}]\")\n",
-    "\n",
-    "# --- Isolate head (library call strips disconnected fragments automatically) ---\n",
-    "surface_h, _ = isolate_head(surface_n, nasion_n)\n",
-    "print(f\"Head isolation: {surface_n.nvertices:,} -> {surface_h.nvertices:,} vertices\")\n",
-    "\n",
-    "# --- Heuristic LPA/RPA in the eye-plane canonical frame ---\n",
-    "verts = np.asarray(surface_h.mesh.vertices)\n",
-    "head_centroid = verts.mean(axis=0)\n",
-    "eye_x = 0.5 * (r_eye_n[0] + l_eye_n[0])\n",
-    "lpa_heur, rpa_heur = heuristic_lpa_rpa(verts, eye_x, head_centroid[1])\n",
-    "if lpa_heur is None or rpa_heur is None:\n",
-    "    raise RuntimeError(\"heuristic_lpa_rpa returned None -- ear slice had < 10 candidates\")\n",
-    "\n",
-    "lpa_c, lpa_r = lpa_heur[\"center\"], lpa_heur[\"radius\"]\n",
-    "rpa_c, rpa_r = rpa_heur[\"center\"], rpa_heur[\"radius\"]\n",
-    "print(f\"  LPA heur: center=[{lpa_c[0]:.1f}, {lpa_c[1]:.1f}, {lpa_c[2]:.1f}]  radius={lpa_r:.0f}\")\n",
-    "print(f\"  RPA heur: center=[{rpa_c[0]:.1f}, {rpa_c[1]:.1f}, {rpa_c[2]:.1f}]  radius={rpa_r:.0f}\")\n",
-    "\n",
-    "# --- Custom anonymization mask ---\n",
-    "# Z-band: stay strictly inside the ear spheres (exclusive of radii)\n",
-    "z_band = (verts[:, 2] > rpa_c[2] + rpa_r) & (verts[:, 2] < lpa_c[2] - lpa_r)\n",
-    "\n",
-    "# Y-cut: front half only, use forehead (nasion Y) as safety margin for cap\n",
-    "y_cut = min(0.5 * (lpa_c[1] + rpa_c[1]), nasion_n[1])\n",
-    "front_half = verts[:, 1] > y_cut\n",
-    "\n",
-    "# --- Per-vertex upper X bound from MediaPipe forehead arc ---\n",
-    "# Rotate the face oval into the canonical frame and keep only the upper arc\n",
-    "# (contour points above the nasion -- these trace the forehead boundary).\n",
-    "# Each mesh vertex then gets an upper X limit interpolated from the nearest\n",
-    "# arc point in Z (lateral), so the deletion top follows the real forehead\n",
-    "# instead of a fixed rectangle height.\n",
-    "RECT_HALF_WIDTH = 15.0        # mm, half-width in Z of preserved nasion strip\n",
-    "FOREHEAD_FALLBACK = 50.0      # mm above nasion if contour unavailable\n",
-    "\n",
-    "if face_contour is not None:\n",
-    "    contour_n_all = (R_eye @ (np.asarray(face_contour) - rot_center).T).T + rot_center\n",
-    "    forehead_arc = contour_n_all[contour_n_all[:, 0] > nasion_n[0]]\n",
-    "else:\n",
-    "    forehead_arc = np.empty((0, 3))\n",
-    "\n",
-    "if len(forehead_arc) >= 3:\n",
-    "    arc_tree = KDTree(forehead_arc[:, 2:3])\n",
-    "    _, arc_idx = arc_tree.query(verts[:, 2:3], k=1)\n",
-    "    forehead_x_per_vertex = forehead_arc[arc_idx, 0]\n",
-    "    arc_source = f\"MediaPipe forehead arc ({len(forehead_arc)} pts)\"\n",
-    "else:\n",
-    "    forehead_x_per_vertex = np.full(len(verts), nasion_n[0] + FOREHEAD_FALLBACK)\n",
-    "    arc_source = f\"fallback (nasion + {FOREHEAD_FALLBACK:.0f}mm)\"\n",
-    "\n",
-    "# Region A: everything BELOW the nasion, between ears, front half\n",
-    "face_below = (verts[:, 0] < nasion_n[0]) & z_band & front_half\n",
-    "\n",
-    "# Region B: between nasion and the per-vertex forehead arc,\n",
-    "#           EXCLUDING the vertical nasion strip (kept for registration)\n",
-    "in_arc_band = (verts[:, 0] >= nasion_n[0]) & (verts[:, 0] < forehead_x_per_vertex)\n",
-    "nasion_strip = (\n",
-    "    (np.abs(verts[:, 2] - nasion_n[2]) < RECT_HALF_WIDTH)\n",
-    "    & in_arc_band\n",
-    "    & front_half\n",
-    ")\n",
-    "face_sides = in_arc_band & z_band & front_half & ~nasion_strip\n",
-    "\n",
-    "# Nothing above the arc is deleted.\n",
-    "extended_mask = face_below | face_sides\n",
-    "\n",
-    "print(f\"\\nCustom mask: {extended_mask.sum():,} vertices \"\n",
-    "      f\"({100 * extended_mask.sum() / len(extended_mask):.1f}%)\")\n",
-    "print(f\"  below nasion:            {face_below.sum():,}\")\n",
-    "print(f\"  sides (nasion-arc):      {face_sides.sum():,}\")\n",
-    "print(f\"  nasion strip (kept):     {nasion_strip.sum():,}\")\n",
-    "print(f\"  forehead upper bound:    {arc_source}\")\n",
-    "print(f\"    arc X range:           [{forehead_x_per_vertex.min():.1f}, \"\n",
-    "      f\"{forehead_x_per_vertex.max():.1f}]\")\n",
-    "print(f\"  Z band (excl. ears):     ({rpa_c[2] + rpa_r:.1f}, {lpa_c[2] - lpa_r:.1f})\")\n",
-    "print(f\"  Y cut (front half):      > {y_cut:.1f}\")\n",
-    "print(f\"  Nasion strip half-width: {RECT_HALF_WIDTH:.0f}mm in Z\")\n",
-    "\n",
-    "# Downstream cells expect: verts, extended_mask, protected_positions\n",
-    "protected_positions = np.stack([nasion_n, lpa_c, rpa_c])\n",
-    "protected_labels = [\"Nz\", \"LPA_heur\", \"RPA_heur\"]\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-md-preview",
-   "metadata": {},
-   "source": [
-    "## 3. Preview anonymization region"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "cell-preview",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "be1f2eb136224173ad5ba41d2e9d96f2",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Widget(value='<iframe src=\"http://localhost:40923/index.html?ui=P_0x7fc9d27b2350_0&reconnect=auto\" class=\"pyvi…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pvplt = pv.Plotter()\n",
-    "cedalion.plots.plot_surface(pvplt, surface_h, opacity=1.0)\n",
-    "\n",
-    "# Deletion region (red scatter)\n",
-    "if extended_mask.sum() > 0:\n",
-    "    pvplt.add_mesh(\n",
-    "        pv.PolyData(verts[extended_mask]), color=\"red\", point_size=3,\n",
-    "        opacity=0.4, render_points_as_spheres=True,\n",
-    "        label=f\"deletion region ({extended_mask.sum():,})\",\n",
-    "    )\n",
-    "\n",
-    "# Heuristic LPA/RPA as translucent spheres (NOT to be deleted)\n",
-    "pvplt.add_mesh(pv.Sphere(radius=lpa_r, center=lpa_c), color=\"orange\", opacity=0.35)\n",
-    "pvplt.add_point_labels([lpa_c], [\"LPA heur\"], font_size=14,\n",
-    "                       text_color=\"orange\", bold=True, shape=None)\n",
-    "pvplt.add_mesh(pv.Sphere(radius=rpa_r, center=rpa_c), color=\"blue\", opacity=0.35)\n",
-    "pvplt.add_point_labels([rpa_c], [\"RPA heur\"], font_size=14,\n",
-    "                       text_color=\"blue\", bold=True, shape=None)\n",
-    "\n",
-    "# Nasion\n",
-    "pvplt.add_mesh(pv.Sphere(radius=3, center=nasion_n), color=\"lime\")\n",
-    "pvplt.add_point_labels([nasion_n], [\"Nz\"], font_size=16,\n",
-    "                       text_color=\"lime\", shape=None, always_visible=True)\n",
-    "\n",
-    "# Eye positions (for reference)\n",
-    "pvplt.add_mesh(pv.Sphere(radius=3, center=r_eye_n), color=\"cyan\")\n",
-    "pvplt.add_mesh(pv.Sphere(radius=3, center=l_eye_n), color=\"cyan\")\n",
-    "\n",
-    "# MediaPipe face oval (orange points) -- rotate into canonical frame\n",
-    "if face_contour is not None:\n",
-    "    contour_n = (R_eye @ (np.asarray(face_contour) - rot_center).T).T + rot_center\n",
-    "    pvplt.add_mesh(\n",
-    "        pv.PolyData(contour_n), color=\"orange\", point_size=14,\n",
-    "        render_points_as_spheres=True,\n",
-    "        label=f\"MP face oval ({len(contour_n)} pts)\",\n",
-    "    )\n",
-    "\n",
-    "pvplt.add_text(\n",
-    "    f\"S{SUBJECT_NUMBER} | deletion region: {extended_mask.sum():,} vertices\",\n",
-    "    position=\"upper_left\", font_size=12,\n",
-    ")\n",
-    "pvplt.add_legend()\n",
-    "pvplt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-md-refine",
-   "metadata": {},
-   "source": [
-    "## 4. Interactive refinement — click to add or remove deletion zones\n",
-    "\n",
-    "- **Left-click**: Add/remove vertices within the brush radius\n",
-    "- **T**: Toggle between **add** mode (mark for deletion) and **remove** mode (undo)\n",
-    "- **+** / **-**: Increase / decrease brush radius\n",
-    "- Close the window when done."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "cell-refine",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "refined_mask = extended_mask.copy()\n",
-    "click_count = [0]\n",
-    "brush_radius = [CIRCLE_RADIUS]  # mutable so key events can change it\n",
-    "mode = ['add']  # 'add' or 'remove'\n",
-    "\n",
-    "# Protected zone: vertices near landmarks that cannot be added to mask\n",
-    "protection_radius = 15.0  # mm\n",
-    "\n",
-    "def update_title():\n",
-    "    \"\"\"Update the on-screen status text.\"\"\"\n",
-    "    pvplt.add_text(\n",
-    "        f'Mode: {mode[0].upper()}  |  Brush: {brush_radius[0]:.0f}mm\\n'\n",
-    "        f'T = toggle add/remove  |  +/- = brush size',\n",
-    "        position='lower_right', font_size=14, name='status',\n",
-    "        color='black',\n",
-    "    )\n",
-    "\n",
-    "def on_pick(picked_point):\n",
-    "    if picked_point is None:\n",
-    "        return\n",
-    "    point = np.array(picked_point)\n",
-    "\n",
-    "    # Find all vertices within brush radius\n",
-    "    dists = np.linalg.norm(verts - point, axis=1)\n",
-    "    circle = dists < brush_radius[0]\n",
-    "\n",
-    "    if mode[0] == 'add':\n",
-    "        # Check if inside protected zone\n",
-    "        dist_to_protected = np.linalg.norm(protected_positions - point, axis=1)\n",
-    "        if np.any(dist_to_protected < protection_radius):\n",
-    "            print('  Skipped -- too close to a protected landmark')\n",
-    "            return\n",
-    "\n",
-    "        new_verts = circle & ~refined_mask\n",
-    "        n_changed = new_verts.sum()\n",
-    "        if n_changed == 0:\n",
-    "            return\n",
-    "        refined_mask[new_verts] = True\n",
-    "\n",
-    "        # Yellow sphere for added regions\n",
-    "        sphere = pv.Sphere(radius=brush_radius[0], center=point)\n",
-    "        pvplt.add_mesh(sphere, color='yellow', opacity=0.2)\n",
-    "    else:\n",
-    "        # Remove mode -- unmark vertices\n",
-    "        removed = circle & refined_mask\n",
-    "        n_changed = removed.sum()\n",
-    "        if n_changed == 0:\n",
-    "            return\n",
-    "        refined_mask[removed] = False\n",
-    "\n",
-    "        # Blue sphere for removed regions\n",
-    "        sphere = pv.Sphere(radius=brush_radius[0], center=point)\n",
-    "        pvplt.add_mesh(sphere, color='cyan', opacity=0.2)\n",
-    "\n",
-    "    click_count[0] += 1\n",
-    "\n",
-    "    # Update mask coloring on mesh (in-place)\n",
-    "    pv_mesh['mask'] = refined_mask.astype(float)\n",
-    "\n",
-    "    sign = '+' if mode[0] == 'add' else '-'\n",
-    "    print(f'  Click {click_count[0]} ({mode[0]}): {sign}{n_changed:,} vertices '\n",
-    "          f'(total: {refined_mask.sum():,})')\n",
-    "\n",
-    "def toggle_mode():\n",
-    "    mode[0] = 'remove' if mode[0] == 'add' else 'add'\n",
-    "    update_title()\n",
-    "    print(f'  Mode: {mode[0].upper()}')\n",
-    "\n",
-    "def increase_radius():\n",
-    "    brush_radius[0] = min(brush_radius[0] + 5, 100)\n",
-    "    update_title()\n",
-    "    print(f'  Brush radius: {brush_radius[0]:.0f}mm')\n",
-    "\n",
-    "def decrease_radius():\n",
-    "    brush_radius[0] = max(brush_radius[0] - 5, 5)\n",
-    "    update_title()\n",
-    "    print(f'  Brush radius: {brush_radius[0]:.0f}mm')\n",
-    "\n",
-    "# Build plotter (non-notebook for interactive picking)\n",
-    "pvplt = pv.Plotter(notebook=False)\n",
-    "\n",
-    "vtk_surface = VTKSurface.from_trimeshsurface(surface_h)\n",
-    "pv_mesh = pv.wrap(vtk_surface.mesh)\n",
-    "\n",
-    "# Color mesh by mask: white=keep, red=delete\n",
-    "pv_mesh['mask'] = refined_mask.astype(float)\n",
-    "pvplt.add_mesh(pv_mesh, scalars='mask', cmap=['white', 'red'],\n",
-    "               clim=[0, 1], show_scalar_bar=False, opacity=0.9,\n",
-    "               smooth_shading=True, pickable=True)\n",
-    "\n",
-    "# Show protected landmarks\n",
-    "for label, pos in zip(protected_labels, protected_positions):\n",
-    "    pvplt.add_mesh(pv.Sphere(radius=3, center=pos), color='lime')\n",
-    "    pvplt.add_point_labels([pos], [label], font_size=12, point_size=0,\n",
-    "                           text_color='lime', shape=None, always_visible=True)\n",
-    "\n",
-    "pvplt.enable_surface_point_picking(\n",
-    "    callback=on_pick,\n",
-    "    left_clicking=True,\n",
-    "    show_point=False,\n",
-    "    tolerance=0.005,\n",
-    ")\n",
-    "\n",
-    "# Key bindings\n",
-    "pvplt.add_key_event('t', toggle_mode)\n",
-    "pvplt.add_key_event('plus', increase_radius)\n",
-    "pvplt.add_key_event('minus', decrease_radius)\n",
-    "\n",
-    "update_title()\n",
-    "pvplt.show()\n",
-    "\n",
-    "# After window closes\n",
-    "extended_mask = refined_mask\n",
-    "print(f'\\nDone -- {click_count[0]} clicks, final mask: {extended_mask.sum():,} vertices')\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-md-anon",
-   "metadata": {},
-   "source": [
-    "## 5. Anonymize — delete facial vertices"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "cell-anon",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Original:    391,330 verts, 741,306 faces\n",
-      "Anonymized:  233,308 verts, 441,792 faces\n",
-      "Removed:     158,022 verts\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ed0d2e3ed64040cba4ee958e1f76ed2c",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Widget(value='<iframe src=\"http://localhost:36161/index.html?ui=P_0x7f2042b29090_1&reconnect=auto\" class=\"pyvi…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Delete faces where ANY vertex is in the mask\n",
-    "mesh_copy = surface_h.mesh.copy()\n",
-    "face_verts_in_mask = extended_mask[mesh_copy.faces]\n",
-    "faces_to_remove = face_verts_in_mask.any(axis=1)\n",
-    "mesh_copy.update_faces(~faces_to_remove)\n",
-    "mesh_copy.remove_unreferenced_vertices()\n",
-    "\n",
-    "n_removed = surface_h.nvertices - len(mesh_copy.vertices)\n",
-    "print(f'Original:    {surface_h.nvertices:,} verts, {surface_h.nfaces:,} faces')\n",
-    "print(f'Anonymized:  {len(mesh_copy.vertices):,} verts, {len(mesh_copy.faces):,} faces')\n",
-    "print(f'Removed:     {n_removed:,} verts')\n",
-    "\n",
-    "# Side-by-side visualization\n",
-    "anon_vtk_mesh = trimesh_to_vtk_polydata(mesh_copy)\n",
-    "\n",
-    "pvplt = pv.Plotter(shape=(1, 2))\n",
-    "\n",
-    "pvplt.subplot(0, 0)\n",
-    "cedalion.plots.plot_surface(pvplt, surface_h, opacity=1.0)\n",
-    "pvplt.add_text(f'S{SUBJECT_NUMBER} Original', position='upper_left', font_size=14)\n",
-    "\n",
-    "pvplt.subplot(0, 1)\n",
-    "pvplt.add_mesh(pv.wrap(anon_vtk_mesh), rgb=True)\n",
-    "pvplt.add_text(f'S{SUBJECT_NUMBER} Anonymized (-{n_removed:,} verts)',\n",
-    "               position='upper_left', font_size=14)\n",
-    "\n",
-    "pvplt.link_views()\n",
-    "pvplt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4fb94c9d",
-   "metadata": {},
-   "source": [
-    "## 6. Anonymized head — original color"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "c46090c7",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bcb8dc90d08f4efcadc26a91fb5e27bd",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Widget(value='<iframe src=\"http://localhost:42885/index.html?ui=P_0x7f8fe792ab90_2&reconnect=auto\" class=\"pyvi…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pvplt = pv.Plotter()\n",
-    "pvplt.add_mesh(pv.wrap(anon_vtk_mesh), rgb=True, smooth_shading=True)\n",
-    "pvplt.add_text(f'S{SUBJECT_NUMBER} Anonymized', position='upper_left', font_size=14)\n",
-    "pvplt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fe039bec-0b10-491c-8905-dc3744e0c559",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.14"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/pyproject.toml b/pyproject.toml
index da578de4..569bbbd2 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -59,10 +59,6 @@ dependencies = [
   "pyxdf==1.17.0",
 ]
 
-[project.optional-dependencies]
-anonymization = ["mediapipe==0.10.*"]
-
-
 [project.urls]
 Home = "https://ibs-lab.com/cedalion/"
 Documentation = "https://doc.ibs.tu-berlin.de/cedalion/doc/dev/"
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/__init__.py b/src/cedalion/geometry/photogrammetry/anonymization/__init__.py
index f031fddf..96db9e52 100644
--- a/src/cedalion/geometry/photogrammetry/anonymization/__init__.py
+++ b/src/cedalion/geometry/photogrammetry/anonymization/__init__.py
@@ -7,21 +7,25 @@
 
 The pipeline works as follows:
 
-1. Detect nasion automatically (or via manual click)
+1. User clicks the nasion (Nz) via ``pick_nasion``
 2. Normalize axes so Y=anterior, Z=left
 3. Isolate the head (remove body/shoulders)
 4. Detect remaining landmarks (Iz, Cz, LPA, RPA) from nasion
-5. Generate facial region mask (MediaPipe contour or hemisphere fallback)
-6. Delete facial vertices
+5. Align the frame from the 5 landmarks and detect the cap boundary
+6. Build the face + ear deletion mask from the landmarks
+7. Delete the masked vertices
+
+An alternative MediaPipe-based automatic pipeline lives on the
+``auto-detection-pipeline`` branch.
 
 Example:
     >>> from cedalion.geometry.photogrammetry.anonymization import (
-    ...     detect_nasion_auto, pick_nasion, normalize_axes,
-    ...     isolate_head, detect_landmarks_from_nasion,
-    ...     get_facial_region_mask_from_nasion,
+    ...     pick_nasion, normalize_axes, isolate_head,
+    ...     detect_landmarks_from_nasion, align_axes_from_landmarks,
+    ...     detect_cap_boundary, face_mask_from_landmarks,
+    ...     delete_masked_vertices,
     ... )
-    >>> auto = detect_nasion_auto(surface)
-    >>> nasion, meta = auto if auto else (pick_nasion(surface), {})
+    >>> nasion = pick_nasion(surface)
     >>> surface, nasion, R = normalize_axes(surface, nasion)
     >>> surface, _ = isolate_head(surface, nasion)
     >>> landmarks = detect_landmarks_from_nasion(surface, nasion)
@@ -40,14 +44,12 @@
     face_mask_from_landmarks,
     delete_masked_vertices,
 )
-from .nasion_detector import detect_nasion_auto
 from .ui import pick_nasion
 from .validator import validate_anonymization
 
 
 __all__ = [
     # Nasion detection
-    "detect_nasion_auto",
     "pick_nasion",
     # Axis normalization and head isolation
     "normalize_axes",
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/face_detector.py b/src/cedalion/geometry/photogrammetry/anonymization/face_detector.py
index 0d582046..2effe366 100644
--- a/src/cedalion/geometry/photogrammetry/anonymization/face_detector.py
+++ b/src/cedalion/geometry/photogrammetry/anonymization/face_detector.py
@@ -1,11 +1,15 @@
 """Facial region detection for photogrammetry scans.
 
 Provides axis normalization, head isolation, landmark detection from a
-user-provided nasion (Nz), and facial region mask generation. Supports both
-a MediaPipe contour path (via ``get_facial_region_mask_from_nasion``) and a
-pure-geometric landmark-only path (``align_axes_from_landmarks`` +
+user-provided nasion (Nz), and facial region mask generation. Uses a
+pure-geometric landmark-only pipeline (``align_axes_from_landmarks`` +
 ``detect_cap_boundary`` + ``face_mask_from_landmarks``).
 
+``get_facial_region_mask_from_nasion`` also accepts an optional face-oval
+contour from an external detector, but the contour input is not used on this
+branch. The MediaPipe-based automatic pipeline lives on the
+``auto-detection-pipeline`` branch.
+
 Initial Contributors:
     - Face Anonymization Project | 2024
 """
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/face_landmarker.task b/src/cedalion/geometry/photogrammetry/anonymization/face_landmarker.task
deleted file mode 100644
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diff --git a/src/cedalion/geometry/photogrammetry/anonymization/nasion_detector.py b/src/cedalion/geometry/photogrammetry/anonymization/nasion_detector.py
deleted file mode 100644
index 2b1dcc07..00000000
--- a/src/cedalion/geometry/photogrammetry/anonymization/nasion_detector.py
+++ /dev/null
@@ -1,676 +0,0 @@
-"""Automatic nasion detection from 3D mesh geometry.
-
-Detects the nasion (Nz) from an Einstar scan without user interaction.
-Only assumes X (index 0) = Up (gravity-based). The forward direction
-in the Y-Z horizontal plane is determined automatically using MediaPipe
-Face Landmarker: render the mesh from multiple viewpoints, detect the
-face in 2D, back-project landmarks to 3D.
-
-Returns None when automatic detection fails (e.g. EEG cap false
-positives). The caller should fall back to manual nasion selection.
-
-Initial Contributors:
-    - Face Anonymization Project | 2024
-"""
-
-import logging
-import pathlib
-
-import numpy as np
-from scipy.spatial import KDTree
-
-import cedalion.dataclasses as cdc
-
-logger = logging.getLogger("cedalion")
-
-_MODEL_DIR = pathlib.Path(__file__).parent
-_MODEL_PATH = _MODEL_DIR / "face_landmarker.task"
-
-
-def _filter_contour_outliers(
-    contour_3d: np.ndarray,
-    max_sigma: float = 2.5,
-) -> np.ndarray:
-    """Remove outlier points from the face contour.
-
-    Rejects points whose distance from the contour centroid exceeds
-    max_sigma standard deviations. This catches "floating bits" from
-    seam cracks where VTK picks a disconnected mesh fragment.
-
-    Args:
-        contour_3d: Face contour points, shape (N, 3).
-        max_sigma: Rejection threshold in standard deviations.
-
-    Returns:
-        Filtered contour points, shape (M, 3) where M <= N.
-    """
-    centroid = contour_3d.mean(axis=0)
-    dists = np.linalg.norm(contour_3d - centroid, axis=1)
-    std_dist = dists.std()
-    if std_dist < 1e-6:
-        return contour_3d
-    keep = dists < dists.mean() + max_sigma * std_dist
-    return contour_3d[keep]
-
-
-def detect_nasion_auto(
-    surface: cdc.TrimeshSurface,
-) -> tuple[np.ndarray, dict] | None:
-    """Automatically detect the nasion using MediaPipe face detection.
-
-    Renders the mesh from multiple viewpoints, runs MediaPipe Face
-    Landmarker to find the face, then refines the nasion via
-    midsagittal profile analysis.
-
-    Returns None when detection fails -- the caller should fall back
-    to manual nasion selection (see ``pick_nasion`` in ``ui.py``).
-
-    Args:
-        surface: TrimeshSurface from photogrammetry scan.
-
-    Returns:
-        Tuple of (nasion_position, metadata) or None if detection fails.
-        nasion_position is a numpy array of shape (3,) in mm.
-        metadata is a dict with keys: method, confidence, nose_tip,
-        forward_direction, face_contour_3d, eyes. ``eyes`` is a tuple
-        (r_eye_3d, l_eye_3d) -- eye-corner midpoints from the same
-        validated view as the nasion, or None on profile-fallback paths.
-    """
-    vertices = surface.mesh.vertices
-
-    # --- Step 1: Isolate head (remove chair/body) ---
-    head_verts, _ = _isolate_head_vertices(vertices)
-
-    # --- Step 2: YZ centroid of upper head (needed for profile analysis) ---
-    x_min = head_verts[:, 0].min()
-    x_max = head_verts[:, 0].max()
-    upper = head_verts[head_verts[:, 0] > x_min + 0.6 * (x_max - x_min)]
-    yz_centroid = np.array([0.0, upper[:, 1].mean(), upper[:, 2].mean()])
-
-    # --- Step 3: Try MediaPipe face detection ---
-    ml_result = _find_nose_tip_mediapipe(surface.mesh, head_verts)
-
-    if ml_result is None:
-        logger.info("Automatic nasion detection failed — no face found")
-        return None
-
-    (
-        nose_tip_ml,
-        forward_dir,
-        nasion_proxy,
-        frontal_reliable,
-        face_contour,
-        r_eye_3d,
-        l_eye_3d,
-    ) = ml_result
-    logger.debug(
-        f"MediaPipe face found, "
-        f"fwd_angle={np.degrees(np.arctan2(forward_dir[2], forward_dir[1])):.0f}deg, "
-        f"frontal_reliable={frontal_reliable}, "
-        f"face_contour={'yes' if face_contour is not None else 'no'}"
-    )
-
-    # --- Primary: use validated bundle nasion directly ---
-    if frontal_reliable:
-        nasion = _snap_to_original(nasion_proxy, vertices)
-        logger.debug(f"Auto nasion (unified): {nasion}")
-        return nasion, {
-            "method": "mediapipe+unified",
-            "confidence": 0.90,
-            "nose_tip": nose_tip_ml.copy(),
-            "forward_direction": forward_dir.copy(),
-            "face_contour_3d": face_contour,
-            "eyes": (r_eye_3d.copy(), l_eye_3d.copy()),
-        }
-
-    # --- Fallback: geometric profile analysis ---
-    # The unified sweep only returns frontal_reliable=True, so this branch
-    # is effectively dead code. Kept in case a future sweep variant returns
-    # an unvalidated bundle.
-    result = _try_direction(
-        head_verts, vertices, forward_dir, yz_centroid,
-    )
-    if result is not None:
-        result["method"] = "mediapipe+profile"
-        if result["dip"] > 0:
-            result["confidence"] = 0.85
-        else:
-            result["confidence"] = 0.7
-        logger.debug(
-            f"Auto nasion (ML+profile): {result['nasion']}, "
-            f"confidence={result['confidence']:.2f}, dip={result['dip']:.2f}"
-        )
-        return result["nasion"], {
-            "method": result["method"],
-            "confidence": result["confidence"],
-            "nose_tip": result["nose_tip"],
-            "forward_direction": result["forward_direction"],
-            "face_contour_3d": face_contour,
-            "eyes": (r_eye_3d.copy(), l_eye_3d.copy()),
-        }
-
-    logger.info("Profile analysis failed after MediaPipe detection")
-    return None
-
-
-# ---------------------------------------------------------------------------
-# MediaPipe-based detection
-# ---------------------------------------------------------------------------
-
-def _find_nose_tip_mediapipe(
-    mesh_trimesh,
-    head_verts: np.ndarray,
-    cam_distance: float = 400.0,
-) -> tuple[
-    np.ndarray, np.ndarray, np.ndarray, bool, np.ndarray | None,
-    np.ndarray, np.ndarray
-] | None:
-    """Unified MediaPipe sweep: nasion, nose tip, eye corners, face oval
-    all extracted from the single best view.
-
-    Renders the mesh from up to 72 viewpoints (24 azimuth x 3 pitch). For
-    each view where MediaPipe detects a face, back-projects nasion (lm 168),
-    nose tip (lm 1), both eye-corner pairs (33/133 + 263/362), and the face
-    oval (36 contour indices) using the same vtkCellPicker. Jointly validates
-    the bundle (all points on-mesh, X ordering nasion>nose, eyes roughly at
-    nasion height), ranks surviving views by |yaw|, returns the bundle from
-    the view with smallest |yaw|. Early-exits when a view with |yaw|<5 deg
-    validates.
-
-    Args:
-        mesh_trimesh: trimesh.Trimesh mesh object (with texture).
-        head_verts: Head-isolated vertices for centroid computation.
-        cam_distance: Camera distance from head centroid in mm.
-
-    Returns:
-        Tuple (nose_tip_3d, forward_direction, nasion_3d, frontal_reliable,
-        face_contour, r_eye_3d, l_eye_3d) or None if no view validates.
-        frontal_reliable is True when the bundle came from joint validation
-        (i.e. the new code path). r_eye_3d / l_eye_3d are the eye-corner
-        midpoints (outer+inner)/2 for each eye.
-    """
-    try:
-        import pyvista as pv
-        import mediapipe as mp
-        import vtk
-    except ImportError as e:
-        raise ImportError(
-            "Automatic nasion detection requires mediapipe. "
-            "Install with: pip install cedalion[anonymization]"
-        ) from e
-
-    if not _MODEL_PATH.exists():
-        logger.info(f"MediaPipe model not found at {_MODEL_PATH}")
-        return None
-
-    head_centroid = head_verts.mean(axis=0)
-    x_min_h = head_verts[:, 0].min()
-    x_max_h = head_verts[:, 0].max()
-
-    # Build pyvista mesh with texture colors
-    faces_pv = np.column_stack([
-        np.full(len(mesh_trimesh.faces), 3), mesh_trimesh.faces
-    ]).ravel()
-    pv_mesh = pv.PolyData(mesh_trimesh.vertices, faces_pv)
-
-    try:
-        colors = mesh_trimesh.visual.to_color().vertex_colors[:, :3]
-        pv_mesh["colors"] = colors
-        has_colors = True
-    except Exception:
-        has_colors = False
-
-    plotter = pv.Plotter(off_screen=True, window_size=[1024, 768])
-    if has_colors:
-        plotter.add_mesh(pv_mesh, scalars="colors", rgb=True)
-    else:
-        plotter.add_mesh(pv_mesh, color="bisque")
-
-    options = mp.tasks.vision.FaceLandmarkerOptions(
-        base_options=mp.tasks.BaseOptions(
-            model_asset_path=str(_MODEL_PATH)
-        ),
-        running_mode=mp.tasks.vision.RunningMode.IMAGE,
-        num_faces=1,
-        min_face_detection_confidence=0.3,
-        min_face_presence_confidence=0.3,
-        output_facial_transformation_matrixes=True,
-    )
-
-    # MediaPipe FaceLandmarker indices
-    _NASION = 168
-    _NOSE_TIP = 1
-    _R_OUT, _R_IN = 33, 133
-    _L_OUT, _L_IN = 263, 362
-    _FACE_OVAL_INDICES = [
-        10, 338, 297, 332, 284, 251, 389, 356,
-        454, 323, 361, 288, 397, 365, 379, 378,
-        400, 377, 152, 148, 176, 149, 150, 136,
-        172, 58, 132, 93, 234, 127, 162, 21,
-        54, 103, 67, 109,
-    ]
-
-    result = None
-    try:
-        with mp.tasks.vision.FaceLandmarker.create_from_options(options) as lm:
-            # === Unified sweep: all landmarks from the same best view ===
-            # Azimuth sweeps around the X=up axis in the Y-Z plane (step 15deg).
-            # Pitch tilts the camera up/down around the tangent axis, so
-            # subjects whose face is tilted relative to gravity can still be
-            # captured. For each view where MediaPipe detects a face, we
-            # back-project nasion, nose tip, both eye-corner pairs, and the
-            # face oval -- all from the SAME rendering. The bundle is jointly
-            # validated and ranked by |yaw|. Guarantees nasion + eyes come
-            # from the same view, so they can't disagree.
-            bundles: list[dict] = []
-            best_so_far_yaw = 180.0
-
-            for theta_deg in range(0, 360, 15):
-                theta = np.radians(theta_deg)
-                # Horizontal direction from centroid in Y-Z plane
-                horiz = np.array([0.0, np.cos(theta), np.sin(theta)])
-
-                for pitch_deg in (-20, 0, 20):
-                    pitch = np.radians(pitch_deg)
-                    # Tilt the camera by rotating around the tangent axis
-                    # (horizontal, perpendicular to horiz in Y-Z plane).
-                    cam_dir = (
-                        np.cos(pitch) * horiz
-                        + np.sin(pitch) * np.array([1.0, 0.0, 0.0])
-                    )
-                    cam_pos = head_centroid + cam_distance * cam_dir
-
-                    plotter.camera_position = [
-                        cam_pos.tolist(),
-                        head_centroid.tolist(),
-                        [1, 0, 0],
-                    ]
-                    plotter.render()
-                    img = plotter.screenshot(return_img=True)
-                    H, W = img.shape[:2]
-
-                    mp_image = mp.Image(
-                        image_format=mp.ImageFormat.SRGB,
-                        data=np.ascontiguousarray(img),
-                    )
-                    detection = lm.detect(mp_image)
-
-                    if not detection.face_landmarks:
-                        continue
-
-                    # --- Yaw ranking (skip obviously-profile views) ---
-                    yaw_abs = 180.0
-                    if detection.facial_transformation_matrixes:
-                        from scipy.spatial.transform import Rotation
-                        mat = detection.facial_transformation_matrixes[0]
-                        rot = Rotation.from_matrix(np.array(mat)[:3, :3])
-                        yaw = rot.as_euler("yxz", degrees=True)[0]
-                        yaw_abs = abs(yaw)
-                        if yaw_abs > 60:
-                            continue
-
-                    face = detection.face_landmarks[0]
-
-                    picker = vtk.vtkCellPicker()
-                    picker.SetTolerance(0.01)
-
-                    def _bp(idx: int) -> np.ndarray:
-                        lm_pt = face[idx]
-                        picker.Pick(
-                            float(int(lm_pt.x * W)),
-                            float(H - 1 - int(lm_pt.y * H)),
-                            0, plotter.renderer,
-                        )
-                        return np.array(picker.GetPickPosition())
-
-                    nasion_3d = _bp(_NASION)
-                    nose_3d = _bp(_NOSE_TIP)
-                    r_out = _bp(_R_OUT)
-                    r_in = _bp(_R_IN)
-                    l_out = _bp(_L_OUT)
-                    l_in = _bp(_L_IN)
-
-                    # --- Joint validation: all six key points on-mesh ---
-                    key_pts = [nasion_3d, nose_3d, r_out, r_in, l_out, l_in]
-                    if any(np.allclose(p, 0) for p in key_pts):
-                        continue
-
-                    # X-ordering: nasion above nose tip (X = gravity-up)
-                    if nasion_3d[0] <= nose_3d[0]:
-                        continue
-
-                    # Reject nose on top of head
-                    nose_rel = (nose_3d[0] - x_min_h) / (x_max_h - x_min_h)
-                    if nose_rel > 0.85:
-                        continue
-
-                    r_eye_3d = 0.5 * (r_out + r_in)
-                    l_eye_3d = 0.5 * (l_out + l_in)
-
-                    # Eyes must be above nose tip
-                    if (r_eye_3d[0] <= nose_3d[0]
-                            or l_eye_3d[0] <= nose_3d[0]):
-                        continue
-
-                    # Eye midline should be close to nasion in height (within 30mm)
-                    eye_x_mid = 0.5 * (r_eye_3d[0] + l_eye_3d[0])
-                    if abs(eye_x_mid - nasion_3d[0]) > 30.0:
-                        continue
-
-                    # Eye corners not too close to each other
-                    eye_sep = float(np.linalg.norm(r_eye_3d - l_eye_3d))
-                    if eye_sep < 30.0 or eye_sep > 120.0:
-                        continue
-
-                    # Forward direction: nose tip -> centroid in YZ plane
-                    fwd = nose_3d - head_centroid
-                    fwd[0] = 0.0
-                    fwd_norm = np.linalg.norm(fwd)
-                    if fwd_norm < 1e-6:
-                        continue
-                    fwd = fwd / fwd_norm
-
-                    # --- Face oval from the SAME view ---
-                    contour_pts = []
-                    for idx in _FACE_OVAL_INDICES:
-                        pt = _bp(idx)
-                        if not np.allclose(pt, 0):
-                            contour_pts.append(pt.copy())
-                    if len(contour_pts) >= 20:
-                        raw_contour = np.array(contour_pts)
-                        face_contour = _filter_contour_outliers(raw_contour)
-                        if len(face_contour) < 20:
-                            face_contour = raw_contour
-                    else:
-                        face_contour = None
-
-                    bundles.append({
-                        "yaw_abs": yaw_abs,
-                        "nasion_3d": nasion_3d.copy(),
-                        "nose_3d": nose_3d.copy(),
-                        "r_eye_3d": r_eye_3d.copy(),
-                        "l_eye_3d": l_eye_3d.copy(),
-                        "fwd": fwd.copy(),
-                        "face_contour": face_contour,
-                        "theta_deg": theta_deg,
-                        "pitch_deg": pitch_deg,
-                    })
-
-                    # Early-exit: if we have a near-frontal validated bundle,
-                    # stop sweeping -- further views won't improve on this.
-                    if yaw_abs < 5.0:
-                        logger.debug(
-                            f"Unified sweep: early exit at theta={theta_deg}, "
-                            f"pitch={pitch_deg} (|yaw|={yaw_abs:.1f})"
-                        )
-                        best_so_far_yaw = yaw_abs
-                        break
-
-                    if yaw_abs < best_so_far_yaw:
-                        best_so_far_yaw = yaw_abs
-
-                else:
-                    continue
-                break
-
-            if not bundles:
-                logger.debug("Unified sweep: no view passed joint validation")
-                result = None
-            else:
-                bundles.sort(key=lambda b: b["yaw_abs"])
-                best = bundles[0]
-                logger.debug(
-                    f"Unified sweep: {len(bundles)} valid views, best "
-                    f"theta={best['theta_deg']}deg pitch={best['pitch_deg']}deg "
-                    f"|yaw|={best['yaw_abs']:.1f}"
-                )
-                result = (
-                    best["nose_3d"].copy(),
-                    best["fwd"].copy(),
-                    best["nasion_3d"].copy(),
-                    True,  # frontal_reliable: bundle jointly validated
-                    best["face_contour"],
-                    best["r_eye_3d"].copy(),
-                    best["l_eye_3d"].copy(),
-                )
-    except Exception as e:
-        logger.warning(f"MediaPipe detection error: {e}")
-    finally:
-        plotter.close()
-
-    return result
-
-
-# ---------------------------------------------------------------------------
-# Geometric detection (fallback)
-# ---------------------------------------------------------------------------
-
-def _try_direction(
-    head_verts: np.ndarray,
-    original_verts: np.ndarray,
-    forward_dir: np.ndarray,
-    yz_centroid: np.ndarray,
-    nose_tip_override: np.ndarray | None = None,
-) -> dict | None:
-    """Try a candidate forward direction and return nasion result with dip score.
-
-    Args:
-        head_verts: Head-isolated vertices.
-        original_verts: Original mesh vertices for snapping.
-        forward_dir: Candidate forward unit vector (X=0).
-        yz_centroid: YZ centroid of upper head (X=0).
-        nose_tip_override: If provided, use this as the nose tip instead of
-            detecting it geometrically. Useful when ML provides a nose tip.
-
-    Returns:
-        Dict with nasion, confidence, dip score, etc., or None on failure.
-    """
-    lateral_dir = np.cross(np.array([1.0, 0.0, 0.0]), forward_dir)
-    lateral_dir = lateral_dir / np.linalg.norm(lateral_dir)
-
-    if nose_tip_override is not None:
-        nose_tip = nose_tip_override.copy()
-    else:
-        nose_tip, _ = _find_nose_tip(head_verts, forward_dir)
-
-    lat_dist = np.abs(np.dot(head_verts - nose_tip, lateral_dir))
-    midline = lat_dist < 10.0
-    above = head_verts[:, 0] > nose_tip[0]
-    fwd_proj = np.dot(head_verts, forward_dir)
-    centroid_fwd = np.dot(yz_centroid, forward_dir)
-    anterior = fwd_proj > centroid_fwd
-
-    mask = midline & above & anterior
-    candidates = np.where(mask)[0]
-
-    if len(candidates) < 5:
-        mask = midline & above
-        candidates = np.where(mask)[0]
-
-    if len(candidates) < 5:
-        return None
-
-    # Bin by X height, take median forward-projection per bin
-    cand_x = head_verts[candidates, 0]
-    cand_fwd = fwd_proj[candidates]
-
-    x_min, x_max = cand_x.min(), cand_x.max()
-    n_bins = max(10, int((x_max - x_min) / 1.0))
-    bin_edges = np.linspace(x_min, x_max, n_bins + 1)
-    bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2.0
-    bin_indices = np.clip(np.digitize(cand_x, bin_edges) - 1, 0, n_bins - 1)
-
-    bin_fwd = np.full(n_bins, np.nan)
-    for b in range(n_bins):
-        in_bin = bin_indices == b
-        if in_bin.sum() > 0:
-            bin_fwd[b] = np.median(cand_fwd[in_bin])
-
-    valid = ~np.isnan(bin_fwd)
-    bin_centers_valid = bin_centers[valid]
-    bin_fwd_valid = bin_fwd[valid]
-
-    if len(bin_fwd_valid) < 3:
-        return None
-
-    # Smooth profile to suppress mesh noise
-    window = min(7, len(bin_fwd_valid))
-    if window >= 3:
-        kernel = np.ones(window) / window
-        padded = np.pad(bin_fwd_valid, (window // 2, window // 2), mode='edge')
-        bin_fwd_smoothed = np.convolve(padded, kernel, mode='valid')
-    else:
-        bin_fwd_smoothed = bin_fwd_valid
-
-    # Search in anatomically plausible range: nose_tip to nose_tip + 40mm
-    nose_tip_x = nose_tip[0]
-    search_mask = (
-        (bin_centers_valid >= nose_tip_x)
-        & (bin_centers_valid <= nose_tip_x + 40)
-    )
-    search_idx = np.where(search_mask)[0]
-
-    if len(search_idx) >= 3:
-        nasion_bin = _find_deepest_minimum(
-            bin_fwd_smoothed[search_idx], min_dip=0.5
-        )
-        if nasion_bin is not None:
-            nasion_bin = search_idx[nasion_bin]
-        else:
-            nasion_bin = search_idx[np.argmin(bin_fwd_smoothed[search_idx])]
-    else:
-        nasion_bin = _find_deepest_minimum(bin_fwd_smoothed, min_dip=0.5)
-        if nasion_bin is None:
-            half = max(1, len(bin_fwd_smoothed) // 2)
-            nasion_bin = np.argmin(bin_fwd_smoothed[:half])
-
-    nasion_x = bin_centers_valid[nasion_bin]
-    nasion_fwd_val = bin_fwd_valid[nasion_bin]
-
-    # Find best candidate vertex near this height
-    bin_half = (bin_centers_valid[1] - bin_centers_valid[0]) if len(
-        bin_centers_valid) > 1 else 2.0
-    height_match = np.abs(head_verts[candidates, 0] - nasion_x) < bin_half * 2
-    if height_match.sum() > 0:
-        height_cands = candidates[height_match]
-        fwd_dist = np.abs(fwd_proj[height_cands] - nasion_fwd_val)
-        best_cand = height_cands[np.argmin(fwd_dist)]
-        nasion_approx = head_verts[best_cand]
-    else:
-        nasion_approx = np.array([nasion_x, nose_tip[1], nose_tip[2]])
-    nasion = _snap_to_original(nasion_approx, original_verts)
-
-    # Dip depth (using smoothed profile)
-    if 0 < nasion_bin < len(bin_fwd_smoothed) - 1:
-        dip = (min(bin_fwd_smoothed[nasion_bin - 1], bin_fwd_smoothed[nasion_bin + 1])
-               - bin_fwd_smoothed[nasion_bin])
-    else:
-        dip = 0.0
-
-    confidence = float(min(1.0, dip / 5.0))
-
-    return {
-        "nasion": nasion,
-        "nose_tip": nose_tip.copy(),
-        "forward_direction": forward_dir.copy(),
-        "confidence": confidence,
-        "dip": dip,
-        "method": "profile",
-    }
-
-
-# ---------------------------------------------------------------------------
-# Utility functions
-# ---------------------------------------------------------------------------
-
-
-def _isolate_head_vertices(
-    vertices: np.ndarray,
-    max_head_height: float = 280.0,
-) -> tuple[np.ndarray, np.ndarray]:
-    """Isolate head vertices by cropping to within max_head_height of the top.
-
-    Note: This is a fast height-based heuristic for nasion detection only.
-    The public ``isolate_head()`` in face_detector.py uses sphere-based
-    filtering that preserves mesh connectivity for downstream processing.
-
-    Args:
-        vertices: Mesh vertices of shape (N, 3).
-        max_head_height: Maximum expected head height in mm.
-
-    Returns:
-        Tuple of (head_vertices, mask) where mask is boolean array.
-    """
-    x_max = vertices[:, 0].max()
-    mask = vertices[:, 0] > x_max - max_head_height
-
-    if mask.sum() < 100:
-        logger.warning("Head isolation: too few vertices, using all")
-        mask = np.ones(len(vertices), dtype=bool)
-
-    logger.debug(f"Head isolation: {len(vertices)} -> {mask.sum()} vertices")
-    return vertices[mask], mask
-
-
-def _find_nose_tip(
-    vertices: np.ndarray,
-    forward_direction: np.ndarray,
-) -> tuple[np.ndarray, int]:
-    """Find nose tip as the most-forward vertex in the upper head.
-
-    Args:
-        vertices: Head-isolated vertices of shape (N, 3).
-        forward_direction: Unit vector pointing forward in Y-Z plane.
-
-    Returns:
-        Tuple of (nose_tip_position, vertex_index).
-    """
-    x_min = vertices[:, 0].min()
-    x_max = vertices[:, 0].max()
-    x_threshold = x_min + 0.6 * (x_max - x_min)
-    upper_mask = vertices[:, 0] > x_threshold
-
-    if upper_mask.sum() == 0:
-        upper_mask = np.ones(len(vertices), dtype=bool)
-
-    fwd_proj = np.dot(vertices, forward_direction)
-    fwd_proj[~upper_mask] = -np.inf
-    idx = np.argmax(fwd_proj)
-
-    return vertices[idx].copy(), idx
-
-
-def _find_deepest_minimum(values: np.ndarray, min_dip: float = 0.5):
-    """Find the deepest local minimum that dips at least min_dip below neighbors.
-
-    Args:
-        values: 1D array of values.
-        min_dip: Minimum dip depth to be considered significant.
-
-    Returns:
-        Index of the deepest significant local minimum, or None.
-    """
-    best_idx = None
-    best_dip = 0.0
-    for i in range(1, len(values) - 1):
-        if values[i] < values[i - 1] and values[i] < values[i + 1]:
-            dip = min(values[i - 1], values[i + 1]) - values[i]
-            if dip >= min_dip and dip > best_dip:
-                best_dip = dip
-                best_idx = i
-    return best_idx
-
-
-def _snap_to_original(point: np.ndarray, vertices: np.ndarray) -> np.ndarray:
-    """Snap a point to the nearest original mesh vertex.
-
-    Args:
-        point: Target position of shape (3,).
-        vertices: Original mesh vertices of shape (N, 3).
-
-    Returns:
-        Nearest vertex position as numpy array of shape (3,).
-    """
-    tree = KDTree(vertices)
-    _, idx = tree.query(point)
-    return vertices[idx].copy()
diff --git a/tests/test_anonymization.py b/tests/test_anonymization.py
index 0f103ff9..8ce01246 100644
--- a/tests/test_anonymization.py
+++ b/tests/test_anonymization.py
@@ -15,7 +15,6 @@
     isolate_head,
     detect_landmarks_from_nasion,
     get_facial_region_mask_from_nasion,
-    detect_nasion_auto,
 )
 
 
@@ -217,15 +216,3 @@ def test_protection_zones_excluded(self, simple_sphere_surface, anatomical_landm
             assert not np.any(mask & nearby), "Protected zone breached"
 
 
-# ============================================================================
-# Auto Nasion Detection
-# ============================================================================
-
-
-class TestDetectNasionAuto:
-    """Tests for automatic nasion detection."""
-
-    def test_returns_none_on_featureless_mesh(self, simple_sphere_surface):
-        """A featureless sphere has no face — should return None."""
-        result = detect_nasion_auto(simple_sphere_surface)
-        assert result is None

From 53b7bd1d724bcea47db8752d219597534582eb69 Mon Sep 17 00:00:00 2001
From: mah-diaa <mahmoud.diaa.abdeen@gmail.com>
Date: Fri, 17 Apr 2026 16:07:33 +0200
Subject: [PATCH 14/39] Split face_detector into preprocessing/landmarks/mask;
 drop dead pick_nasion and get_facial_region_mask_from_nasion

---
 .../photogrammetry/anonymization/__init__.py  |  49 +-
 .../anonymization/face_detector.py            | 781 ------------------
 .../photogrammetry/anonymization/landmarks.py | 195 +++++
 .../photogrammetry/anonymization/mask.py      | 173 ++++
 .../anonymization/preprocessing.py            | 302 +++++++
 .../photogrammetry/anonymization/ui.py        |  73 --
 tests/test_anonymization.py                   |  47 --
 7 files changed, 693 insertions(+), 927 deletions(-)
 delete mode 100644 src/cedalion/geometry/photogrammetry/anonymization/face_detector.py
 create mode 100644 src/cedalion/geometry/photogrammetry/anonymization/landmarks.py
 create mode 100644 src/cedalion/geometry/photogrammetry/anonymization/mask.py
 create mode 100644 src/cedalion/geometry/photogrammetry/anonymization/preprocessing.py
 delete mode 100644 src/cedalion/geometry/photogrammetry/anonymization/ui.py

diff --git a/src/cedalion/geometry/photogrammetry/anonymization/__init__.py b/src/cedalion/geometry/photogrammetry/anonymization/__init__.py
index 96db9e52..8e87e1de 100644
--- a/src/cedalion/geometry/photogrammetry/anonymization/__init__.py
+++ b/src/cedalion/geometry/photogrammetry/anonymization/__init__.py
@@ -7,58 +7,55 @@
 
 The pipeline works as follows:
 
-1. User clicks the nasion (Nz) via ``pick_nasion``
-2. Normalize axes so Y=anterior, Z=left
-3. Isolate the head (remove body/shoulders)
-4. Detect remaining landmarks (Iz, Cz, LPA, RPA) from nasion
-5. Align the frame from the 5 landmarks and detect the cap boundary
-6. Build the face + ear deletion mask from the landmarks
-7. Delete the masked vertices
+1. User picks the 5 landmarks (Nz, Iz, Cz, LPA, RPA) via the upstream
+   ``cedalion.plots.plot_surface(pick_landmarks=True)`` picker.
+2. Normalize axes so Y=anterior, Z=left (``preprocessing``).
+3. Isolate the head and remove disconnected fragments (``preprocessing``).
+4. Align the full frame from the 5 landmarks (``preprocessing``).
+5. Detect the cap boundary, build the deletion mask, and delete the masked
+   vertices (``mask``).
 
-An alternative MediaPipe-based automatic pipeline lives on the
-``auto-detection-pipeline`` branch.
+``detect_landmarks_from_nasion`` (``landmarks``) is an alternative that takes
+just the nasion and geometrically infers the other four. An alternative
+MediaPipe-based automatic pipeline lives on the ``auto-detection-pipeline``
+branch.
 
 Example:
     >>> from cedalion.geometry.photogrammetry.anonymization import (
-    ...     pick_nasion, normalize_axes, isolate_head,
-    ...     detect_landmarks_from_nasion, align_axes_from_landmarks,
+    ...     normalize_axes, isolate_head, align_axes_from_landmarks,
     ...     detect_cap_boundary, face_mask_from_landmarks,
     ...     delete_masked_vertices,
     ... )
-    >>> nasion = pick_nasion(surface)
-    >>> surface, nasion, R = normalize_axes(surface, nasion)
-    >>> surface, _ = isolate_head(surface, nasion)
-    >>> landmarks = detect_landmarks_from_nasion(surface, nasion)
+    >>> surface, nz, R = normalize_axes(surface, landmarks.sel(label='Nz'))
+    >>> surface, _ = isolate_head(surface, nz)
+    >>> surface, landmarks, R = align_axes_from_landmarks(surface, landmarks)
 
 Initial Contributors:
     - Face Anonymization Project | 2024
 """
 
-from .face_detector import (
+from .preprocessing import (
     normalize_axes,
     isolate_head,
-    detect_landmarks_from_nasion,
-    get_facial_region_mask_from_nasion,
     align_axes_from_landmarks,
+)
+from .landmarks import detect_landmarks_from_nasion
+from .mask import (
     detect_cap_boundary,
     face_mask_from_landmarks,
     delete_masked_vertices,
 )
-from .ui import pick_nasion
 from .validator import validate_anonymization
 
 
 __all__ = [
-    # Nasion detection
-    "pick_nasion",
-    # Axis normalization and head isolation
+    # Preprocessing (axis normalization, head isolation, full alignment)
     "normalize_axes",
     "isolate_head",
-    # Landmark and face mask detection
-    "detect_landmarks_from_nasion",
-    "get_facial_region_mask_from_nasion",
-    # Landmark-only geometric pipeline
     "align_axes_from_landmarks",
+    # Landmark detection
+    "detect_landmarks_from_nasion",
+    # Mask construction and application
     "detect_cap_boundary",
     "face_mask_from_landmarks",
     "delete_masked_vertices",
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/face_detector.py b/src/cedalion/geometry/photogrammetry/anonymization/face_detector.py
deleted file mode 100644
index 2effe366..00000000
--- a/src/cedalion/geometry/photogrammetry/anonymization/face_detector.py
+++ /dev/null
@@ -1,781 +0,0 @@
-"""Facial region detection for photogrammetry scans.
-
-Provides axis normalization, head isolation, landmark detection from a
-user-provided nasion (Nz), and facial region mask generation. Uses a
-pure-geometric landmark-only pipeline (``align_axes_from_landmarks`` +
-``detect_cap_boundary`` + ``face_mask_from_landmarks``).
-
-``get_facial_region_mask_from_nasion`` also accepts an optional face-oval
-contour from an external detector, but the contour input is not used on this
-branch. The MediaPipe-based automatic pipeline lives on the
-``auto-detection-pipeline`` branch.
-
-Initial Contributors:
-    - Face Anonymization Project | 2024
-"""
-
-import logging
-
-import numpy as np
-from scipy.spatial import KDTree
-import xarray as xr
-
-import cedalion.dataclasses as cdc
-import cedalion.typing as cdt
-from cedalion import Quantity, units
-
-logger = logging.getLogger("cedalion")
-
-
-def normalize_axes(
-    surface: cdc.TrimeshSurface,
-    nasion: np.ndarray,
-    forward_direction: np.ndarray = None,
-) -> tuple[cdc.TrimeshSurface, np.ndarray, np.ndarray]:
-    """Rotate mesh around X-axis so Y=anterior (toward face), Z=left.
-
-    The Einstar scanner has X=up (gravity-based) but Y/Z are arbitrary per
-    scan session. This function uses the nasion position to determine the
-    forward direction and rotates the mesh so that Y consistently points
-    toward the face (anterior) and Z points left.
-
-    Args:
-        surface: TrimeshSurface in raw Einstar coordinates.
-        nasion: Nasion position as numpy array of shape (3,).
-        forward_direction: Optional pre-computed forward unit vector in YZ
-            plane (X=0). If None, computed from nasion vs upper-head centroid.
-
-    Returns:
-        Tuple of (rotated_surface, rotated_nasion, rotation_matrix).
-        rotation_matrix is 3x3 and can be applied to other points.
-    """
-    vertices = surface.mesh.vertices
-
-    if forward_direction is None:
-        # Compute forward direction: nasion minus upper-head centroid in YZ
-        x_max = vertices[:, 0].max()
-        x_min = vertices[:, 0].min()
-        upper = vertices[vertices[:, 0] > x_min + 0.6 * (x_max - x_min)]
-        centroid_yz = np.array([0.0, upper[:, 1].mean(), upper[:, 2].mean()])
-        nasion_yz = np.array([0.0, nasion[1], nasion[2]])
-        forward_direction = nasion_yz - centroid_yz
-        fwd_norm = np.linalg.norm(forward_direction)
-        if fwd_norm < 1e-6:
-            logger.warning("Cannot compute forward direction — returning unchanged")
-            return surface, nasion.copy(), np.eye(3)
-        forward_direction = forward_direction / fwd_norm
-
-    # Angle of forward_direction relative to +Y in the YZ plane
-    angle = np.arctan2(forward_direction[2], forward_direction[1])
-
-    # Rotation around X-axis by -angle to align forward with +Y
-    cos_a = np.cos(-angle)
-    sin_a = np.sin(-angle)
-    R = np.array([
-        [1.0, 0.0, 0.0],
-        [0.0, cos_a, -sin_a],
-        [0.0, sin_a, cos_a],
-    ])
-
-    # Rotate vertices
-    rotated_verts = vertices @ R.T
-
-    # Build new mesh with rotated vertices, same faces and texture
-    import trimesh
-    new_mesh = trimesh.Trimesh(
-        vertices=rotated_verts,
-        faces=surface.mesh.faces,
-        visual=surface.mesh.visual,
-        process=False,
-    )
-    rotated_surface = cdc.TrimeshSurface(new_mesh, crs=surface.crs, units=surface.units)
-
-    # Rotate nasion
-    rotated_nasion = R @ nasion
-
-    logger.debug(
-        f"Axis normalization: rotated {np.degrees(angle):.1f}deg around X. "
-        f"Y now points anterior."
-    )
-
-    return rotated_surface, rotated_nasion, R
-
-
-def _largest_component_mask(mesh) -> np.ndarray:
-    """Boolean vertex mask selecting the largest connected component.
-
-    Einstar scans are non-watertight and frequently contain tiny floating
-    mesh fragments (loose triangles, cable shreds, background patches)
-    that sit far outside the head. Any step that uses vertex extrema
-    (e.g. head sphere centroid, lateral-widest heuristics) will be
-    dragged off into empty space by those fragments. Stripping them
-    up-front prevents that.
-
-    Uses ``trimesh.graph.connected_component_labels`` on face adjacency
-    rather than ``mesh.split(only_watertight=False)`` — split allocates
-    one full ``Trimesh`` per component and can OOM when a scan has
-    thousands of tiny fragments. Labels give one int per face with
-    zero per-component allocation.
-
-    Args:
-        mesh: A ``trimesh.Trimesh`` instance.
-
-    Returns:
-        Boolean array of shape ``(n_vertices,)``. All-True if the mesh
-        is empty or already a single connected component.
-    """
-    import trimesh
-    n_verts = len(mesh.vertices)
-    n_faces = len(mesh.faces)
-    if n_faces == 0:
-        return np.ones(n_verts, dtype=bool)
-
-    # Einstar OBJs duplicate vertices along UV seams for texturing. The
-    # default trimesh.Trimesh.merge_vertices() does NOT merge across UV
-    # seams (it preserves texture), so face adjacency on the raw mesh
-    # (or on a merge_vertices'd copy) over-fragments the head into
-    # thousands of islands. We need POSITION-only merging for
-    # connectivity analysis: unique_rows on vertex coordinates gives a
-    # canonical-id mapping that heals the seams.
-    unique_idx, inverse = trimesh.grouping.unique_rows(
-        np.asarray(mesh.vertices)
-    )
-    canonical_faces = inverse[mesh.faces]
-    adjacency = trimesh.graph.face_adjacency(faces=canonical_faces)
-    face_labels = trimesh.graph.connected_component_labels(
-        adjacency, node_count=n_faces
-    )
-    counts = np.bincount(face_labels)
-    if len(counts) <= 1:
-        return np.ones(n_verts, dtype=bool)
-    biggest_label = int(np.argmax(counts))
-    face_mask = face_labels == biggest_label
-
-    # Apply face mask to the ORIGINAL faces -> original vertex indices
-    # (so colors/UVs/normals attached to the caller's mesh stay valid).
-    kept_vidx = np.unique(mesh.faces[face_mask])
-    mask = np.zeros(n_verts, dtype=bool)
-    mask[kept_vidx] = True
-    return mask
-
-
-def isolate_head(
-    surface: cdc.TrimeshSurface,
-    nasion: np.ndarray,
-    radius: float = 220.0,
-) -> tuple[cdc.TrimeshSurface, np.ndarray]:
-    """Remove shoulders, body, and chair — keep only the head.
-
-    Uses a sphere centered on the upper-head centroid. The radius is
-    chosen to capture the full head (~180mm wide x 230mm tall) while
-    excluding shoulders and body. Scans that are already head-only are
-    returned unchanged (the sphere just contains everything).
-
-    The surface must be axis-normalized first (X=up, Y=anterior, Z=left).
-
-    Args:
-        surface: Axis-normalized TrimeshSurface.
-        nasion: Nasion position as numpy array of shape (3,).
-        radius: Sphere radius in mm (default 220). A human head has
-            ~90mm radius; 220mm adds margin for ears and jaw.
-
-    Returns:
-        Tuple of (head_surface, head_mask). head_mask is a boolean
-        array of shape (n_vertices,) indicating which original
-        vertices were kept.
-    """
-    import trimesh
-
-    vertices = surface.mesh.vertices
-    faces = surface.mesh.faces
-
-    # Sphere center: Y/Z from upper-head centroid (good lateral centering).
-    # X: use the lower of (upper centroid, midpoint of top+nasion).
-    x_max = vertices[:, 0].max()
-    x_min = vertices[:, 0].min()
-    upper = vertices[:, 0] > x_min + 0.6 * (x_max - x_min)
-    center = vertices[upper].mean(axis=0)
-    midpoint_x = (x_max + nasion[0]) / 2.0
-    center[0] = min(center[0], midpoint_x)
-
-    # Sphere mask
-    dist = np.linalg.norm(vertices - center, axis=1)
-    head_mask = dist < radius
-
-    # Always strip disconnected fragments (floating triangles, cables,
-    # background patches). No-op on clean single-component scans.
-    head_mask = head_mask & _largest_component_mask(surface.mesh)
-
-    # If the sphere captures almost everything, skip trimming
-    if head_mask.sum() < 100 or head_mask.mean() > 0.95:
-        logger.debug(
-            f"Head isolation: sphere contains {head_mask.mean()*100:.0f}% "
-            f"of vertices — scan is already head-only"
-        )
-        return surface, head_mask
-
-    # Build new mesh with only head faces
-    # A face is kept if ALL its vertices are in the head
-    face_mask = head_mask[faces].all(axis=1)
-    head_faces = faces[face_mask]
-
-    # Reindex vertices: only keep referenced vertices
-    kept_verts = np.unique(head_faces)
-    reindex = np.full(len(vertices), -1, dtype=int)
-    reindex[kept_verts] = np.arange(len(kept_verts))
-    new_faces = reindex[head_faces]
-    new_verts = vertices[kept_verts]
-
-    # Transfer visual — subset vertex colors to match kept vertices
-    new_mesh = trimesh.Trimesh(
-        vertices=new_verts,
-        faces=new_faces,
-        process=False,
-    )
-    try:
-        old_visual = surface.mesh.visual
-        if hasattr(old_visual, 'vertex_colors') and len(old_visual.vertex_colors) == len(vertices):
-            new_mesh.visual.vertex_colors = old_visual.vertex_colors[kept_verts]
-    except Exception:
-        pass
-
-    head_surface = cdc.TrimeshSurface(
-        new_mesh, crs=surface.crs, units=surface.units
-    )
-
-    logger.debug(
-        f"Head isolation: {len(vertices):,} -> {len(new_verts):,} vertices "
-        f"({len(vertices) - len(new_verts):,} removed), "
-        f"center=[{center[0]:.0f},{center[1]:.0f},{center[2]:.0f}], "
-        f"radius={radius:.0f}mm"
-    )
-
-    return head_surface, head_mask
-
-
-def detect_landmarks_from_nasion(
-    surface: cdc.TrimeshSurface,
-    nz_position: np.ndarray,
-) -> cdt.LabeledPointCloud:
-    """Detect anatomical landmarks from a user-provided nasion (Nz) position.
-
-    **Important:** The surface must be axis-normalized before calling this
-    function. Use ``normalize_axes()`` first so that:
-    - X = up (vertical, from Einstar gravity sensor)
-    - Y = anterior (toward face, from nasion direction)
-    - Z = left of subject
-
-    Given the Nz point, automatically detects Iz, LPA, RPA, Cz:
-    - Cz: Highest vertex (max X) near midline
-    - Iz: Most posterior vertex (min Y) at nasion height
-    - LPA: Most left vertex (max Z) at ear height
-    - RPA: Most right vertex (min Z) at ear height
-
-    Args:
-        surface: Axis-normalized TrimeshSurface (Y=anterior, Z=left)
-        nz_position: Nasion position as numpy array of shape (3,), in mm
-
-    Returns:
-        LabeledPointCloud with landmarks labeled as Nz, Iz, Cz, LPA, RPA
-
-    Raises:
-        ValueError: If landmark configuration fails validation
-
-    Example:
-        >>> surface_norm, nz_norm, R = normalize_axes(surface, nz, fwd)
-        >>> landmarks = detect_landmarks_from_nasion(surface_norm, nz_norm)
-    """
-    nz_position = np.asarray(nz_position, dtype=float)
-    vertices = surface.mesh.vertices
-
-    centroid = vertices.mean(axis=0)
-    head_height = vertices[:, 0].max() - vertices[:, 0].min()
-    head_verts = vertices
-
-    logger.debug(
-        f"Detecting landmarks from Nz={nz_position}, "
-        f"head_height={head_height:.1f}mm, "
-        f"head_verts={len(vertices)}"
-    )
-
-    # Cz: highest vertex (max X) near midline
-    band = 0.20 * head_height
-    lateral_mask = (
-        (np.abs(head_verts[:, 1] - centroid[1]) < band)
-        & (np.abs(head_verts[:, 2] - centroid[2]) < band)
-    )
-    if lateral_mask.sum() == 0:
-        lateral_mask = np.ones(len(head_verts), dtype=bool)
-    cz_idx = np.where(lateral_mask)[0][np.argmax(head_verts[lateral_mask, 0])]
-    cz = head_verts[cz_idx]
-
-    # Iz: most posterior (min Y) at nasion height, on the midsagittal plane.
-    iz_height_mask = np.abs(head_verts[:, 0] - nz_position[0]) < 20.0
-    iz_midline_mask = np.abs(head_verts[:, 2] - nz_position[2]) < 25.0
-    iz_mask = iz_height_mask & iz_midline_mask
-    if iz_mask.sum() == 0:
-        iz_mask = np.abs(head_verts[:, 0] - nz_position[0]) < 40.0
-    iz_cands = np.where(iz_mask)[0]
-    iz_idx = iz_cands[np.argmin(head_verts[iz_cands, 1])]
-    iz = head_verts[iz_idx]
-
-    # LPA/RPA: geometric targets, refined by snapping to surface
-    from scipy.spatial import cKDTree
-
-    midline_z = np.mean([nz_position[2], iz[2], cz[2]])
-    clean_band = (
-        (head_verts[:, 0] > nz_position[0] + 10.0)
-        & (head_verts[:, 0] < cz[0] - 20.0)
-    )
-    if clean_band.sum() > 100:
-        clean_z = head_verts[clean_band, 2]
-        half_width = (
-            np.percentile(clean_z, 97) - np.percentile(clean_z, 3)
-        ) / 2.0
-    else:
-        half_width = 75.0
-
-    geo_lpa_target = np.array([nz_position[0], cz[1], midline_z + half_width])
-    geo_rpa_target = np.array([nz_position[0], cz[1], midline_z - half_width])
-
-    def _snap_ear(target):
-        """Snap target to nearest surface vertex in ear region."""
-        ear_region = (
-            (np.abs(head_verts[:, 0] - nz_position[0]) < 30.0)
-            & (np.abs(head_verts[:, 1] - cz[1]) < 40.0)
-        )
-        if ear_region.sum() > 10:
-            idxs = np.where(ear_region)[0]
-            tree = cKDTree(head_verts[idxs])
-            _, local = tree.query(target)
-            return head_verts[idxs[local]]
-        tree = cKDTree(head_verts)
-        _, idx = tree.query(target)
-        return head_verts[idx]
-
-    lpa = _snap_ear(geo_lpa_target)
-    rpa = _snap_ear(geo_rpa_target)
-
-    landmark_positions = {
-        "Nz": nz_position,
-        "Iz": iz,
-        "Cz": cz,
-        "LPA": lpa,
-        "RPA": rpa,
-    }
-
-    logger.debug(
-        f"Detected landmarks: Cz={cz}, Iz={iz}, LPA={lpa}, RPA={rpa}"
-    )
-
-    # Validate
-    _validate_landmark_configuration(landmark_positions, centroid)
-
-    # Create LabeledPointCloud
-    labels = ["Nz", "Iz", "Cz", "LPA", "RPA"]
-    coords = np.array([landmark_positions[label] for label in labels])
-
-    landmarks = xr.DataArray(
-        coords,
-        dims=["label", surface.crs],
-        coords={
-            "label": labels,
-            "type": ("label", [cdc.PointType.LANDMARK] * 5),
-        },
-    ).pint.quantify("mm")
-
-    return landmarks
-
-
-def get_facial_region_mask_from_nasion(
-    surface: cdc.TrimeshSurface,
-    nz: np.ndarray,
-    forward_direction: np.ndarray,
-    face_contour_3d: np.ndarray | None = None,
-    protected_points: cdt.LabeledPointCloud = None,
-    protection_radius: Quantity = 15.0 * units.mm,
-    lateral_extension: float = 70.0,
-    lower_width_scale: float = 2.5,
-) -> np.ndarray:
-    """Generate facial region mask using nasion and MediaPipe face contour.
-
-    This function is axis-independent -- it uses the forward direction and
-    face contour from MediaPipe rather than relying on axis alignment.
-    It does NOT depend on LPA/RPA accuracy.
-
-    The mask uses a two-part strategy derived entirely from the contour:
-    - **Oval**: The MediaPipe face contour polygon defines precise
-      forehead and jawline boundaries (with a depth filter to prevent
-      top-of-head bleed).
-    - **Side band**: 3D proximity to contour points -- any vertex within
-      ``lateral_extension`` mm of a contour point is included, vertically
-      constrained to chin-to-temple height to avoid top-of-head bleed.
-      This naturally follows the head curvature and covers the ears.
-
-    When no face contour is available (manual nasion mode), falls back to
-    a forward-facing hemisphere around the nasion.
-
-    Args:
-        surface: The mesh surface (axis-normalized or not)
-        nz: Nasion position as numpy array of shape (3,)
-        forward_direction: Unit vector pointing toward the face
-        face_contour_3d: 3D face oval points from MediaPipe back-projection,
-            shape (N, 3) with N >= 20, or None for hemisphere fallback
-        protected_points: Points to exclude (optodes + anatomical landmarks)
-        protection_radius: Radius around protected points
-        lateral_extension: 3D proximity radius in mm from contour points
-            for ear/temple coverage (default 70.0)
-        lower_width_scale: Factor to widen the lower portion of the face
-            contour (below nasion). 1.0 = no change, 2.5 = 150% wider at
-            chin level. Smoothly blends from 1.0 at nasion to this value
-            at chin. Keeps forehead border unchanged. (default 2.5)
-
-    Returns:
-        Boolean array of shape (n_vertices,) where True = facial region
-    """
-    from matplotlib.path import Path
-
-    vertices = surface.mesh.vertices
-    nz = np.asarray(nz, dtype=float)
-    fwd = np.asarray(forward_direction, dtype=float)
-    fwd = fwd / np.linalg.norm(fwd)
-
-    if face_contour_3d is not None and len(face_contour_3d) >= 20:
-        # --- MediaPipe face oval approach (spherical projection) ---
-        up_hint = np.array([1.0, 0.0, 0.0])  # X = up in Einstar coords
-        if abs(np.dot(fwd, up_hint)) > 0.9:
-            up_hint = np.array([0.0, 0.0, 1.0])
-        u_axis = np.cross(fwd, up_hint)
-        u_axis = u_axis / np.linalg.norm(u_axis)
-        v_axis = np.cross(fwd, u_axis)
-        v_axis = v_axis / np.linalg.norm(v_axis)
-
-        # Head center: ~80mm behind nasion (roughly center of skull)
-        head_center = nz - fwd * 80.0
-
-        # Project contour onto unit sphere -> 2D
-        c_dirs = face_contour_3d - head_center
-        c_dirs = c_dirs / np.linalg.norm(c_dirs, axis=1, keepdims=True)
-        contour_2d = np.column_stack([c_dirs @ u_axis, c_dirs @ v_axis])
-
-        # Project vertices onto unit sphere -> 2D
-        v_dirs = vertices - head_center
-        v_fwd = v_dirs @ fwd  # positive = face side of head
-        v_dirs = v_dirs / np.linalg.norm(v_dirs, axis=1, keepdims=True)
-        verts_2d = np.column_stack([v_dirs @ u_axis, v_dirs @ v_axis])
-
-        # Face mask = forward-facing vertices inside the contour polygon
-        polygon = Path(contour_2d)
-        facial_mask = polygon.contains_points(verts_2d) & (v_fwd > 0)
-
-        logger.debug(
-            f"Contour mask: {facial_mask.sum()} vertices "
-            f"({100 * facial_mask.sum() / len(facial_mask):.1f}%)"
-        )
-    else:
-        # --- Fallback: forward-facing hemisphere ---
-        verts_rel = vertices - nz
-        fwd_dist = verts_rel @ fwd
-        dist_to_nz = np.linalg.norm(verts_rel, axis=1)
-
-        facial_mask = (fwd_dist > -20.0) & (dist_to_nz < 120.0)
-
-        logger.debug(
-            f"Hemisphere fallback mask: {facial_mask.sum()} vertices "
-            f"({100 * facial_mask.sum() / len(facial_mask):.1f}%)"
-        )
-
-    # Exclude protection zones
-    protection_radius_mm = float(protection_radius.to("mm").magnitude)
-    if protected_points is not None:
-        protected_positions = protected_points.pint.dequantify().values
-        if len(protected_positions) > 0:
-            kdtree = KDTree(protected_positions)
-            distances, _ = kdtree.query(vertices, k=1)
-            protected_mask = distances < protection_radius_mm
-            facial_mask = facial_mask & ~protected_mask
-
-    logger.debug(
-        f"Final facial mask: {facial_mask.sum()} of {len(facial_mask)} vertices "
-        f"({100 * facial_mask.sum() / len(facial_mask):.1f}%)"
-    )
-
-    return facial_mask
-
-
-def _validate_landmark_configuration(
-    landmarks: dict[str, np.ndarray],
-    centroid: np.ndarray,
-) -> None:
-    """Validate that detected landmarks have a plausible spatial configuration.
-
-    Checks:
-    - Cz is the highest point (max X)
-    - Iz is posterior to the centroid (low Y)
-    - LPA and RPA are roughly symmetric about the midline (Z)
-
-    Args:
-        landmarks: Dict mapping landmark name to position array
-        centroid: Mesh centroid for reference
-
-    Raises:
-        ValueError: If configuration is implausible
-    """
-    cz = landmarks["Cz"]
-    iz = landmarks["Iz"]
-    lpa = landmarks["LPA"]
-    rpa = landmarks["RPA"]
-
-    # Cz should be highest (max X)
-    all_x = [landmarks[k][0] for k in landmarks]
-    if cz[0] < max(all_x) - 1.0:
-        logger.warning("Cz is not the highest landmark — detection may be off")
-
-    # Iz should be posterior to centroid (Y < centroid Y)
-    if iz[1] > centroid[1]:
-        logger.warning(
-            f"Iz (Y={iz[1]:.1f}) is anterior to centroid (Y={centroid[1]:.1f}) "
-            "— expected posterior"
-        )
-
-    # LPA should be left (Z > centroid Z) and RPA right (Z < centroid Z)
-    if lpa[2] < rpa[2]:
-        logger.warning(
-            "LPA is to the right of RPA — landmarks may be swapped"
-        )
-
-    # LPA and RPA should be roughly symmetric
-    lpa_offset = abs(lpa[2] - centroid[2])
-    rpa_offset = abs(rpa[2] - centroid[2])
-    if min(lpa_offset, rpa_offset) > 0 and max(lpa_offset, rpa_offset) / min(lpa_offset, rpa_offset) > 3.0:
-        logger.warning(
-            f"LPA/RPA asymmetry is large (offsets: {lpa_offset:.1f} vs {rpa_offset:.1f}mm)"
-        )
-
-
-def align_axes_from_landmarks(
-    surface: cdc.TrimeshSurface,
-    landmarks: cdt.LabeledPointCloud,
-) -> tuple[cdc.TrimeshSurface, cdt.LabeledPointCloud, np.ndarray]:
-    """Derive full rotation from 5 landmarks and apply to mesh and landmarks.
-
-    ``normalize_axes()`` only rotates around X. This function uses the full 5
-    landmark set to align the head to the canonical frame:
-
-        Z = lateral (Lpa - Rpa, pointing left)
-        Y = anterior (Nz - ear_mid, orthogonal to Z)
-        X = up (cross(Y, Z))
-
-    Sign checks ensure Cz points +X and Nz points +Y.
-
-    Args:
-        surface: Axis-normalized TrimeshSurface (post ``normalize_axes`` and
-            ``isolate_head``).
-        landmarks: LabeledPointCloud with labels Nz, Iz, Cz, Lpa, Rpa
-            (matching the surface frame).
-
-    Returns:
-        Tuple of (aligned_surface, aligned_landmarks, rotation_matrix).
-        ``rotation_matrix`` is 3x3 and maps input-frame vectors to the aligned
-        frame (apply as ``v @ R.T``).
-    """
-    import trimesh
-
-    lm = landmarks.pint.dequantify().values
-    labels = list(landmarks["label"].values)
-    idx = {lbl: i for i, lbl in enumerate(labels)}
-
-    required = {"Nz", "Iz", "Cz", "Lpa", "Rpa"}
-    missing = required - set(labels)
-    if missing:
-        raise ValueError(f"Missing landmarks for alignment: {missing}")
-
-    Nz = lm[idx["Nz"]]
-    Cz = lm[idx["Cz"]]
-    Lpa = lm[idx["Lpa"]]
-    Rpa = lm[idx["Rpa"]]
-    ear_mid = 0.5 * (Lpa + Rpa)
-
-    z_ax = Lpa - Rpa
-    z_ax = z_ax / np.linalg.norm(z_ax)
-
-    nz_dir = Nz - ear_mid
-    nz_dir = nz_dir - np.dot(nz_dir, z_ax) * z_ax
-    y_ax = nz_dir / np.linalg.norm(nz_dir)
-
-    x_ax = np.cross(y_ax, z_ax)
-
-    if np.dot(Cz - ear_mid, x_ax) < 0:
-        x_ax = -x_ax
-    if np.dot(Nz - ear_mid, y_ax) < 0:
-        y_ax = -y_ax
-    z_ax = np.cross(x_ax, y_ax)
-
-    R = np.vstack([x_ax, y_ax, z_ax])
-
-    aligned_verts = np.asarray(surface.mesh.vertices) @ R.T
-    new_mesh = trimesh.Trimesh(
-        vertices=aligned_verts,
-        faces=surface.mesh.faces,
-        visual=surface.mesh.visual,
-        process=False,
-    )
-    aligned_surface = cdc.TrimeshSurface(
-        new_mesh, crs=surface.crs, units=surface.units,
-    )
-
-    aligned_lm = lm @ R.T
-    aligned_landmarks = landmarks.pint.dequantify().copy(data=aligned_lm).pint.quantify()
-
-    return aligned_surface, aligned_landmarks, R
-
-
-def detect_cap_boundary(
-    verts: np.ndarray,
-    Nz: np.ndarray,
-    Cz: np.ndarray,
-    ear_mid: np.ndarray,
-    mid_z: float,
-    band_width: float = 15.0,
-    bin_size: float = 2.0,
-    foot_grad_threshold: float = 0.2,
-) -> tuple[float, np.ndarray, np.ndarray, np.ndarray]:
-    """Find the X height where the EEG cap front edge sits.
-
-    Scans upward from Nz along the midline and records max-Y per X-bin. The cap
-    protrudes in +Y, so Y-max along the midline lives on the cap. The cap edge
-    is the foot of the steep rise leading to the peak: walk back from the peak
-    until the smoothed gradient drops below ``foot_grad_threshold``.
-
-    Expects the aligned frame (X=up, Y=anterior, Z=left).
-
-    Args:
-        verts: Mesh vertices, shape (N, 3).
-        Nz: Nasion position.
-        Cz: Cz position.
-        ear_mid: Midpoint of Lpa/Rpa.
-        mid_z: Midline Z value (e.g. ``0.5 * (Lpa[2] + Rpa[2])``).
-        band_width: Z-band half-width for the midline Y-profile (mm).
-        bin_size: X-bin size for the Y-profile (mm).
-        foot_grad_threshold: dY/dX below this value marks the foot of the rise.
-
-    Returns:
-        Tuple of (cap_x, profile_x, profile_y_raw, profile_y_smooth).
-    """
-    from scipy.signal import savgol_filter
-
-    in_band = np.abs(verts[:, 2] - mid_z) < band_width
-    above_nz = verts[:, 0] > Nz[0]
-    anterior = verts[:, 1] > ear_mid[1]
-    sel_verts = verts[in_band & above_nz & anterior]
-
-    bins = np.arange(Nz[0], Cz[0], bin_size)
-    bin_centers = bins[:-1] + bin_size / 2
-    max_y = np.full(len(bin_centers), np.nan)
-
-    for i in range(len(bin_centers)):
-        in_bin = (sel_verts[:, 0] >= bins[i]) & (sel_verts[:, 0] < bins[i + 1])
-        if in_bin.any():
-            max_y[i] = sel_verts[in_bin, 1].max()
-
-    valid = ~np.isnan(max_y)
-    if valid.sum() < 7:
-        fallback = 0.5 * (Nz[0] + Cz[0])
-        return fallback, bin_centers[valid], max_y[valid], max_y[valid]
-
-    xv = bin_centers[valid]
-    yv = max_y[valid]
-
-    win = min(11, len(yv) if len(yv) % 2 == 1 else len(yv) - 1)
-    win = max(win, 5)
-    yv_s = savgol_filter(yv, window_length=win, polyorder=2)
-
-    peak_idx = int(np.argmax(yv_s))
-    grad = np.gradient(yv_s, xv)
-    cap_x = xv[0]
-    for i in range(peak_idx - 1, 0, -1):
-        if grad[i] < foot_grad_threshold:
-            cap_x = xv[i]
-            break
-
-    return cap_x, xv, yv, yv_s
-
-
-def face_mask_from_landmarks(
-    verts: np.ndarray,
-    Nz: np.ndarray,
-    Iz: np.ndarray,
-    Cz: np.ndarray,
-    Lpa: np.ndarray,
-    Rpa: np.ndarray,
-    cap_x: float | None = None,
-    ear_delete_radius: float = 40.0,
-) -> tuple[np.ndarray, dict]:
-    """Build face + ear deletion mask from the 5 landmarks (aligned frame).
-
-    Mask is the union of two regions, both clamped below the cap boundary:
-
-    1. Face region: anterior to the ear coronal plane (Y > ear_mid_Y).
-    2. Ear spheres: ``ear_delete_radius`` mm around Lpa and Rpa.
-
-    Expects the aligned frame (X=up, Y=anterior, Z=left).
-
-    Args:
-        verts: Mesh vertices, shape (N, 3).
-        Nz, Iz, Cz, Lpa, Rpa: 5 landmark positions in the aligned frame.
-        cap_x: Upper bound X value (typically from ``detect_cap_boundary``).
-            Defaults to Nz[0] if not provided.
-        ear_delete_radius: Sphere radius around Lpa/Rpa in mm.
-
-    Returns:
-        Tuple of (mask, info). ``mask`` is a boolean array of shape (N,).
-        ``info`` has keys ``upper_bound``, ``ear_mid``, and ``counts``
-        (per-region vertex counts).
-    """
-    ear_mid = 0.5 * (Lpa + Rpa)
-    upper_bound = cap_x if cap_x is not None else Nz[0]
-
-    below_cap = verts[:, 0] < upper_bound
-    anterior = verts[:, 1] > ear_mid[1]
-    face_region = below_cap & anterior
-
-    d_lpa = np.linalg.norm(verts - Lpa, axis=1)
-    d_rpa = np.linalg.norm(verts - Rpa, axis=1)
-    ear_region = ((d_lpa < ear_delete_radius) | (d_rpa < ear_delete_radius)) & below_cap
-
-    mask = face_region | ear_region
-
-    info = {
-        "upper_bound": upper_bound,
-        "ear_mid": ear_mid,
-        "counts": {
-            "below_cap": int(below_cap.sum()),
-            "face_region": int(face_region.sum()),
-            "ear_region": int(ear_region.sum()),
-            "all": int(mask.sum()),
-        },
-    }
-    return mask, info
-
-
-def delete_masked_vertices(
-    surface: cdc.TrimeshSurface,
-    mask: np.ndarray,
-) -> cdc.TrimeshSurface:
-    """Drop triangles touching any masked vertex and strip unreferenced vertices.
-
-    Args:
-        surface: Input TrimeshSurface.
-        mask: Boolean array of shape (n_vertices,). True = vertex to remove.
-
-    Returns:
-        New TrimeshSurface with masked vertices (and the faces touching them)
-        removed. CRS and units are preserved.
-    """
-    mesh_copy = surface.mesh.copy()
-    faces_to_remove = mask[mesh_copy.faces].any(axis=1)
-    mesh_copy.update_faces(~faces_to_remove)
-    mesh_copy.remove_unreferenced_vertices()
-    return cdc.TrimeshSurface(
-        mesh=mesh_copy, crs=surface.crs, units=surface.units,
-    )
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/landmarks.py b/src/cedalion/geometry/photogrammetry/anonymization/landmarks.py
new file mode 100644
index 00000000..43595827
--- /dev/null
+++ b/src/cedalion/geometry/photogrammetry/anonymization/landmarks.py
@@ -0,0 +1,195 @@
+"""Landmark detection for photogrammetry scans.
+
+Given a user-provided nasion (Nz) on an axis-normalized mesh, detects the
+remaining four anatomical landmarks (Iz, Cz, LPA, RPA) geometrically:
+
+- Cz is the highest vertex near the midline.
+- Iz is the most posterior vertex at nasion height.
+- LPA/RPA are geometric ear targets snapped to the nearest surface vertex.
+
+Initial Contributors:
+    - Face Anonymization Project | 2024
+"""
+
+import logging
+
+import numpy as np
+import xarray as xr
+
+import cedalion.dataclasses as cdc
+import cedalion.typing as cdt
+
+logger = logging.getLogger("cedalion")
+
+
+def detect_landmarks_from_nasion(
+    surface: cdc.TrimeshSurface,
+    nz_position: np.ndarray,
+) -> cdt.LabeledPointCloud:
+    """Detect anatomical landmarks from a user-provided nasion (Nz) position.
+
+    **Important:** The surface must be axis-normalized before calling this
+    function. Use ``normalize_axes()`` first so that:
+    - X = up (vertical, from Einstar gravity sensor)
+    - Y = anterior (toward face, from nasion direction)
+    - Z = left of subject
+
+    Given the Nz point, automatically detects Iz, LPA, RPA, Cz:
+    - Cz: Highest vertex (max X) near midline
+    - Iz: Most posterior vertex (min Y) at nasion height
+    - LPA: Most left vertex (max Z) at ear height
+    - RPA: Most right vertex (min Z) at ear height
+
+    Args:
+        surface: Axis-normalized TrimeshSurface (Y=anterior, Z=left)
+        nz_position: Nasion position as numpy array of shape (3,), in mm
+
+    Returns:
+        LabeledPointCloud with landmarks labeled as Nz, Iz, Cz, LPA, RPA
+
+    Raises:
+        ValueError: If landmark configuration fails validation
+
+    Example:
+        >>> surface_norm, nz_norm, R = normalize_axes(surface, nz, fwd)
+        >>> landmarks = detect_landmarks_from_nasion(surface_norm, nz_norm)
+    """
+    nz_position = np.asarray(nz_position, dtype=float)
+    vertices = surface.mesh.vertices
+
+    centroid = vertices.mean(axis=0)
+    head_height = vertices[:, 0].max() - vertices[:, 0].min()
+    head_verts = vertices
+
+    logger.debug(
+        f"Detecting landmarks from Nz={nz_position}, "
+        f"head_height={head_height:.1f}mm, "
+        f"head_verts={len(vertices)}"
+    )
+
+    # Cz: highest vertex (max X) near midline
+    band = 0.20 * head_height
+    lateral_mask = (
+        (np.abs(head_verts[:, 1] - centroid[1]) < band)
+        & (np.abs(head_verts[:, 2] - centroid[2]) < band)
+    )
+    if lateral_mask.sum() == 0:
+        lateral_mask = np.ones(len(head_verts), dtype=bool)
+    cz_idx = np.where(lateral_mask)[0][np.argmax(head_verts[lateral_mask, 0])]
+    cz = head_verts[cz_idx]
+
+    # Iz: most posterior (min Y) at nasion height, on the midsagittal plane.
+    iz_height_mask = np.abs(head_verts[:, 0] - nz_position[0]) < 20.0
+    iz_midline_mask = np.abs(head_verts[:, 2] - nz_position[2]) < 25.0
+    iz_mask = iz_height_mask & iz_midline_mask
+    if iz_mask.sum() == 0:
+        iz_mask = np.abs(head_verts[:, 0] - nz_position[0]) < 40.0
+    iz_cands = np.where(iz_mask)[0]
+    iz_idx = iz_cands[np.argmin(head_verts[iz_cands, 1])]
+    iz = head_verts[iz_idx]
+
+    # LPA/RPA: geometric targets, refined by snapping to surface
+    from scipy.spatial import cKDTree
+
+    midline_z = np.mean([nz_position[2], iz[2], cz[2]])
+    clean_band = (
+        (head_verts[:, 0] > nz_position[0] + 10.0)
+        & (head_verts[:, 0] < cz[0] - 20.0)
+    )
+    if clean_band.sum() > 100:
+        clean_z = head_verts[clean_band, 2]
+        half_width = (
+            np.percentile(clean_z, 97) - np.percentile(clean_z, 3)
+        ) / 2.0
+    else:
+        half_width = 75.0
+
+    geo_lpa_target = np.array([nz_position[0], cz[1], midline_z + half_width])
+    geo_rpa_target = np.array([nz_position[0], cz[1], midline_z - half_width])
+
+    def _snap_ear(target):
+        ear_region = (
+            (np.abs(head_verts[:, 0] - nz_position[0]) < 30.0)
+            & (np.abs(head_verts[:, 1] - cz[1]) < 40.0)
+        )
+        if ear_region.sum() > 10:
+            idxs = np.where(ear_region)[0]
+            tree = cKDTree(head_verts[idxs])
+            _, local = tree.query(target)
+            return head_verts[idxs[local]]
+        tree = cKDTree(head_verts)
+        _, idx = tree.query(target)
+        return head_verts[idx]
+
+    lpa = _snap_ear(geo_lpa_target)
+    rpa = _snap_ear(geo_rpa_target)
+
+    landmark_positions = {
+        "Nz": nz_position,
+        "Iz": iz,
+        "Cz": cz,
+        "LPA": lpa,
+        "RPA": rpa,
+    }
+
+    logger.debug(f"Detected landmarks: Cz={cz}, Iz={iz}, LPA={lpa}, RPA={rpa}")
+
+    _validate_landmark_configuration(landmark_positions, centroid)
+
+    labels = ["Nz", "Iz", "Cz", "LPA", "RPA"]
+    coords = np.array([landmark_positions[label] for label in labels])
+
+    landmarks = xr.DataArray(
+        coords,
+        dims=["label", surface.crs],
+        coords={
+            "label": labels,
+            "type": ("label", [cdc.PointType.LANDMARK] * 5),
+        },
+    ).pint.quantify("mm")
+
+    return landmarks
+
+
+def _validate_landmark_configuration(
+    landmarks: dict[str, np.ndarray],
+    centroid: np.ndarray,
+) -> None:
+    """Validate that detected landmarks have a plausible spatial configuration.
+
+    Checks:
+    - Cz is the highest point (max X)
+    - Iz is posterior to the centroid (low Y)
+    - LPA and RPA are roughly symmetric about the midline (Z)
+
+    Args:
+        landmarks: Dict mapping landmark name to position array.
+        centroid: Mesh centroid for reference.
+
+    Raises:
+        ValueError: If configuration is implausible.
+    """
+    cz = landmarks["Cz"]
+    iz = landmarks["Iz"]
+    lpa = landmarks["LPA"]
+    rpa = landmarks["RPA"]
+
+    all_x = [landmarks[k][0] for k in landmarks]
+    if cz[0] < max(all_x) - 1.0:
+        logger.warning("Cz is not the highest landmark -- detection may be off")
+
+    if iz[1] > centroid[1]:
+        logger.warning(
+            f"Iz (Y={iz[1]:.1f}) is anterior to centroid (Y={centroid[1]:.1f}) "
+            "-- expected posterior"
+        )
+
+    if lpa[2] < rpa[2]:
+        logger.warning("LPA is to the right of RPA -- landmarks may be swapped")
+
+    lpa_offset = abs(lpa[2] - centroid[2])
+    rpa_offset = abs(rpa[2] - centroid[2])
+    if min(lpa_offset, rpa_offset) > 0 and max(lpa_offset, rpa_offset) / min(lpa_offset, rpa_offset) > 3.0:
+        logger.warning(
+            f"LPA/RPA asymmetry is large (offsets: {lpa_offset:.1f} vs {rpa_offset:.1f}mm)"
+        )
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/mask.py b/src/cedalion/geometry/photogrammetry/anonymization/mask.py
new file mode 100644
index 00000000..deb07d62
--- /dev/null
+++ b/src/cedalion/geometry/photogrammetry/anonymization/mask.py
@@ -0,0 +1,173 @@
+"""Facial-region mask construction and application.
+
+Given the 5 aligned landmarks, builds a boolean deletion mask and applies
+it to the mesh:
+
+- ``detect_cap_boundary`` finds the X height where the EEG cap front edge
+  sits so the mask can be clamped below it.
+- ``face_mask_from_landmarks`` unions a forward face region with two ear
+  spheres, clamped below the cap.
+- ``delete_masked_vertices`` drops triangles touching any masked vertex.
+
+Initial Contributors:
+    - Face Anonymization Project | 2024
+"""
+
+import logging
+
+import numpy as np
+
+import cedalion.dataclasses as cdc
+
+logger = logging.getLogger("cedalion")
+
+
+def detect_cap_boundary(
+    verts: np.ndarray,
+    Nz: np.ndarray,
+    Cz: np.ndarray,
+    ear_mid: np.ndarray,
+    mid_z: float,
+    band_width: float = 15.0,
+    bin_size: float = 2.0,
+    foot_grad_threshold: float = 0.2,
+) -> tuple[float, np.ndarray, np.ndarray, np.ndarray]:
+    """Find the X height where the EEG cap front edge sits.
+
+    Scans upward from Nz along the midline and records max-Y per X-bin. The cap
+    protrudes in +Y, so Y-max along the midline lives on the cap. The cap edge
+    is the foot of the steep rise leading to the peak: walk back from the peak
+    until the smoothed gradient drops below ``foot_grad_threshold``.
+
+    Expects the aligned frame (X=up, Y=anterior, Z=left).
+
+    Args:
+        verts: Mesh vertices, shape (N, 3).
+        Nz: Nasion position.
+        Cz: Cz position.
+        ear_mid: Midpoint of Lpa/Rpa.
+        mid_z: Midline Z value (e.g. ``0.5 * (Lpa[2] + Rpa[2])``).
+        band_width: Z-band half-width for the midline Y-profile (mm).
+        bin_size: X-bin size for the Y-profile (mm).
+        foot_grad_threshold: dY/dX below this value marks the foot of the rise.
+
+    Returns:
+        Tuple of (cap_x, profile_x, profile_y_raw, profile_y_smooth).
+    """
+    from scipy.signal import savgol_filter
+
+    in_band = np.abs(verts[:, 2] - mid_z) < band_width
+    above_nz = verts[:, 0] > Nz[0]
+    anterior = verts[:, 1] > ear_mid[1]
+    sel_verts = verts[in_band & above_nz & anterior]
+
+    bins = np.arange(Nz[0], Cz[0], bin_size)
+    bin_centers = bins[:-1] + bin_size / 2
+    max_y = np.full(len(bin_centers), np.nan)
+
+    for i in range(len(bin_centers)):
+        in_bin = (sel_verts[:, 0] >= bins[i]) & (sel_verts[:, 0] < bins[i + 1])
+        if in_bin.any():
+            max_y[i] = sel_verts[in_bin, 1].max()
+
+    valid = ~np.isnan(max_y)
+    if valid.sum() < 7:
+        fallback = 0.5 * (Nz[0] + Cz[0])
+        return fallback, bin_centers[valid], max_y[valid], max_y[valid]
+
+    xv = bin_centers[valid]
+    yv = max_y[valid]
+
+    win = min(11, len(yv) if len(yv) % 2 == 1 else len(yv) - 1)
+    win = max(win, 5)
+    yv_s = savgol_filter(yv, window_length=win, polyorder=2)
+
+    peak_idx = int(np.argmax(yv_s))
+    grad = np.gradient(yv_s, xv)
+    cap_x = xv[0]
+    for i in range(peak_idx - 1, 0, -1):
+        if grad[i] < foot_grad_threshold:
+            cap_x = xv[i]
+            break
+
+    return cap_x, xv, yv, yv_s
+
+
+def face_mask_from_landmarks(
+    verts: np.ndarray,
+    Nz: np.ndarray,
+    Iz: np.ndarray,
+    Cz: np.ndarray,
+    Lpa: np.ndarray,
+    Rpa: np.ndarray,
+    cap_x: float | None = None,
+    ear_delete_radius: float = 40.0,
+) -> tuple[np.ndarray, dict]:
+    """Build face + ear deletion mask from the 5 landmarks (aligned frame).
+
+    Mask is the union of two regions, both clamped below the cap boundary:
+
+    1. Face region: anterior to the ear coronal plane (Y > ear_mid_Y).
+    2. Ear spheres: ``ear_delete_radius`` mm around Lpa and Rpa.
+
+    Expects the aligned frame (X=up, Y=anterior, Z=left).
+
+    Args:
+        verts: Mesh vertices, shape (N, 3).
+        Nz, Iz, Cz, Lpa, Rpa: 5 landmark positions in the aligned frame.
+        cap_x: Upper bound X value (typically from ``detect_cap_boundary``).
+            Defaults to Nz[0] if not provided.
+        ear_delete_radius: Sphere radius around Lpa/Rpa in mm.
+
+    Returns:
+        Tuple of (mask, info). ``mask`` is a boolean array of shape (N,).
+        ``info`` has keys ``upper_bound``, ``ear_mid``, and ``counts``
+        (per-region vertex counts).
+    """
+    ear_mid = 0.5 * (Lpa + Rpa)
+    upper_bound = cap_x if cap_x is not None else Nz[0]
+
+    below_cap = verts[:, 0] < upper_bound
+    anterior = verts[:, 1] > ear_mid[1]
+    face_region = below_cap & anterior
+
+    d_lpa = np.linalg.norm(verts - Lpa, axis=1)
+    d_rpa = np.linalg.norm(verts - Rpa, axis=1)
+    ear_region = ((d_lpa < ear_delete_radius) | (d_rpa < ear_delete_radius)) & below_cap
+
+    mask = face_region | ear_region
+
+    info = {
+        "upper_bound": upper_bound,
+        "ear_mid": ear_mid,
+        "counts": {
+            "below_cap": int(below_cap.sum()),
+            "face_region": int(face_region.sum()),
+            "ear_region": int(ear_region.sum()),
+            "all": int(mask.sum()),
+        },
+    }
+    return mask, info
+
+
+def delete_masked_vertices(
+    surface: cdc.TrimeshSurface,
+    mask: np.ndarray,
+) -> cdc.TrimeshSurface:
+    """Drop triangles touching any masked vertex and strip unreferenced vertices.
+
+    Args:
+        surface: Input TrimeshSurface.
+        mask: Boolean array of shape (n_vertices,). True = vertex to remove.
+
+    Returns:
+        New TrimeshSurface with masked vertices (and the faces touching them)
+        removed. CRS and units are preserved.
+    """
+    mesh_copy = surface.mesh.copy()
+    faces_to_remove = mask[mesh_copy.faces].any(axis=1)
+    mesh_copy.update_faces(~faces_to_remove)
+    mesh_copy.remove_unreferenced_vertices()
+    return cdc.TrimeshSurface(
+        mesh=mesh_copy, crs=surface.crs, units=surface.units,
+    )
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/preprocessing.py b/src/cedalion/geometry/photogrammetry/anonymization/preprocessing.py
new file mode 100644
index 00000000..9e898bc3
--- /dev/null
+++ b/src/cedalion/geometry/photogrammetry/anonymization/preprocessing.py
@@ -0,0 +1,302 @@
+"""Preprocessing: axis normalization and head isolation.
+
+Gets a raw Einstar photogrammetry scan into a canonical frame suitable for
+landmark detection and mask building:
+
+- ``normalize_axes`` rotates around X so Y points anterior.
+- ``isolate_head`` strips body/shoulders/chair and disconnected fragments.
+- ``align_axes_from_landmarks`` derives a full rotation from the 5 anatomical
+  landmarks once they are available.
+
+Initial Contributors:
+    - Face Anonymization Project | 2024
+"""
+
+import logging
+
+import numpy as np
+
+import cedalion.dataclasses as cdc
+import cedalion.typing as cdt
+
+logger = logging.getLogger("cedalion")
+
+
+def normalize_axes(
+    surface: cdc.TrimeshSurface,
+    nasion: np.ndarray,
+    forward_direction: np.ndarray = None,
+) -> tuple[cdc.TrimeshSurface, np.ndarray, np.ndarray]:
+    """Rotate mesh around X-axis so Y=anterior (toward face), Z=left.
+
+    The Einstar scanner has X=up (gravity-based) but Y/Z are arbitrary per
+    scan session. This function uses the nasion position to determine the
+    forward direction and rotates the mesh so that Y consistently points
+    toward the face (anterior) and Z points left.
+
+    Args:
+        surface: TrimeshSurface in raw Einstar coordinates.
+        nasion: Nasion position as numpy array of shape (3,).
+        forward_direction: Optional pre-computed forward unit vector in YZ
+            plane (X=0). If None, computed from nasion vs upper-head centroid.
+
+    Returns:
+        Tuple of (rotated_surface, rotated_nasion, rotation_matrix).
+        rotation_matrix is 3x3 and can be applied to other points.
+    """
+    vertices = surface.mesh.vertices
+
+    if forward_direction is None:
+        x_max = vertices[:, 0].max()
+        x_min = vertices[:, 0].min()
+        upper = vertices[vertices[:, 0] > x_min + 0.6 * (x_max - x_min)]
+        centroid_yz = np.array([0.0, upper[:, 1].mean(), upper[:, 2].mean()])
+        nasion_yz = np.array([0.0, nasion[1], nasion[2]])
+        forward_direction = nasion_yz - centroid_yz
+        fwd_norm = np.linalg.norm(forward_direction)
+        if fwd_norm < 1e-6:
+            logger.warning("Cannot compute forward direction -- returning unchanged")
+            return surface, nasion.copy(), np.eye(3)
+        forward_direction = forward_direction / fwd_norm
+
+    angle = np.arctan2(forward_direction[2], forward_direction[1])
+    cos_a = np.cos(-angle)
+    sin_a = np.sin(-angle)
+    R = np.array([
+        [1.0, 0.0, 0.0],
+        [0.0, cos_a, -sin_a],
+        [0.0, sin_a, cos_a],
+    ])
+
+    rotated_verts = vertices @ R.T
+
+    import trimesh
+    new_mesh = trimesh.Trimesh(
+        vertices=rotated_verts,
+        faces=surface.mesh.faces,
+        visual=surface.mesh.visual,
+        process=False,
+    )
+    rotated_surface = cdc.TrimeshSurface(new_mesh, crs=surface.crs, units=surface.units)
+
+    rotated_nasion = R @ nasion
+
+    logger.debug(
+        f"Axis normalization: rotated {np.degrees(angle):.1f}deg around X. "
+        f"Y now points anterior."
+    )
+
+    return rotated_surface, rotated_nasion, R
+
+
+def _largest_component_mask(mesh) -> np.ndarray:
+    """Boolean vertex mask selecting the largest connected component.
+
+    Einstar scans are non-watertight and frequently contain tiny floating
+    mesh fragments (loose triangles, cable shreds, background patches)
+    that sit far outside the head. Any step that uses vertex extrema will
+    be dragged off into empty space by those fragments. Stripping them
+    up-front prevents that.
+
+    Uses ``trimesh.graph.connected_component_labels`` on face adjacency
+    rather than ``mesh.split``; split allocates one full ``Trimesh`` per
+    component and can OOM on scans with thousands of tiny fragments.
+
+    Einstar OBJs duplicate vertices along UV seams for texturing. The
+    default ``trimesh.Trimesh.merge_vertices()`` does NOT merge across UV
+    seams (it preserves texture), so face adjacency on the raw mesh
+    over-fragments the head into thousands of islands. We use POSITION-only
+    merging via ``unique_rows`` for connectivity analysis.
+
+    Args:
+        mesh: A ``trimesh.Trimesh`` instance.
+
+    Returns:
+        Boolean array of shape ``(n_vertices,)``. All-True if the mesh
+        is empty or already a single connected component.
+    """
+    import trimesh
+    n_verts = len(mesh.vertices)
+    n_faces = len(mesh.faces)
+    if n_faces == 0:
+        return np.ones(n_verts, dtype=bool)
+
+    unique_idx, inverse = trimesh.grouping.unique_rows(
+        np.asarray(mesh.vertices)
+    )
+    canonical_faces = inverse[mesh.faces]
+    adjacency = trimesh.graph.face_adjacency(faces=canonical_faces)
+    face_labels = trimesh.graph.connected_component_labels(
+        adjacency, node_count=n_faces
+    )
+    counts = np.bincount(face_labels)
+    if len(counts) <= 1:
+        return np.ones(n_verts, dtype=bool)
+    biggest_label = int(np.argmax(counts))
+    face_mask = face_labels == biggest_label
+
+    kept_vidx = np.unique(mesh.faces[face_mask])
+    mask = np.zeros(n_verts, dtype=bool)
+    mask[kept_vidx] = True
+    return mask
+
+
+def isolate_head(
+    surface: cdc.TrimeshSurface,
+    nasion: np.ndarray,
+    radius: float = 220.0,
+) -> tuple[cdc.TrimeshSurface, np.ndarray]:
+    """Remove shoulders, body, and chair; keep only the head.
+
+    Uses a sphere centered on the upper-head centroid. The radius is
+    chosen to capture the full head (~180mm wide x 230mm tall) while
+    excluding shoulders and body. Scans that are already head-only are
+    returned unchanged (the sphere just contains everything).
+
+    The surface must be axis-normalized first (X=up, Y=anterior, Z=left).
+
+    Args:
+        surface: Axis-normalized TrimeshSurface.
+        nasion: Nasion position as numpy array of shape (3,).
+        radius: Sphere radius in mm (default 220). A human head has
+            ~90mm radius; 220mm adds margin for ears and jaw.
+
+    Returns:
+        Tuple of (head_surface, head_mask). head_mask is a boolean
+        array of shape (n_vertices,) indicating which original
+        vertices were kept.
+    """
+    import trimesh
+
+    vertices = surface.mesh.vertices
+    faces = surface.mesh.faces
+
+    x_max = vertices[:, 0].max()
+    x_min = vertices[:, 0].min()
+    upper = vertices[:, 0] > x_min + 0.6 * (x_max - x_min)
+    center = vertices[upper].mean(axis=0)
+    midpoint_x = (x_max + nasion[0]) / 2.0
+    center[0] = min(center[0], midpoint_x)
+
+    dist = np.linalg.norm(vertices - center, axis=1)
+    head_mask = dist < radius
+
+    head_mask = head_mask & _largest_component_mask(surface.mesh)
+
+    if head_mask.sum() < 100 or head_mask.mean() > 0.95:
+        logger.debug(
+            f"Head isolation: sphere contains {head_mask.mean()*100:.0f}% "
+            f"of vertices -- scan is already head-only"
+        )
+        return surface, head_mask
+
+    face_mask = head_mask[faces].all(axis=1)
+    head_faces = faces[face_mask]
+
+    kept_verts = np.unique(head_faces)
+    reindex = np.full(len(vertices), -1, dtype=int)
+    reindex[kept_verts] = np.arange(len(kept_verts))
+    new_faces = reindex[head_faces]
+    new_verts = vertices[kept_verts]
+
+    new_mesh = trimesh.Trimesh(
+        vertices=new_verts,
+        faces=new_faces,
+        process=False,
+    )
+    try:
+        old_visual = surface.mesh.visual
+        if hasattr(old_visual, "vertex_colors") and len(old_visual.vertex_colors) == len(vertices):
+            new_mesh.visual.vertex_colors = old_visual.vertex_colors[kept_verts]
+    except Exception:
+        pass
+
+    head_surface = cdc.TrimeshSurface(
+        new_mesh, crs=surface.crs, units=surface.units
+    )
+
+    logger.debug(
+        f"Head isolation: {len(vertices):,} -> {len(new_verts):,} vertices "
+        f"({len(vertices) - len(new_verts):,} removed), "
+        f"center=[{center[0]:.0f},{center[1]:.0f},{center[2]:.0f}], "
+        f"radius={radius:.0f}mm"
+    )
+
+    return head_surface, head_mask
+
+
+def align_axes_from_landmarks(
+    surface: cdc.TrimeshSurface,
+    landmarks: cdt.LabeledPointCloud,
+) -> tuple[cdc.TrimeshSurface, cdt.LabeledPointCloud, np.ndarray]:
+    """Derive full rotation from 5 landmarks and apply to mesh and landmarks.
+
+    ``normalize_axes()`` only rotates around X. This function uses the full 5
+    landmark set to align the head to the canonical frame:
+
+        Z = lateral (Lpa - Rpa, pointing left)
+        Y = anterior (Nz - ear_mid, orthogonal to Z)
+        X = up (cross(Y, Z))
+
+    Sign checks ensure Cz points +X and Nz points +Y.
+
+    Args:
+        surface: Axis-normalized TrimeshSurface (post ``normalize_axes`` and
+            ``isolate_head``).
+        landmarks: LabeledPointCloud with labels Nz, Iz, Cz, Lpa, Rpa
+            (matching the surface frame).
+
+    Returns:
+        Tuple of (aligned_surface, aligned_landmarks, rotation_matrix).
+        ``rotation_matrix`` is 3x3 and maps input-frame vectors to the aligned
+        frame (apply as ``v @ R.T``).
+    """
+    import trimesh
+
+    lm = landmarks.pint.dequantify().values
+    labels = list(landmarks["label"].values)
+    idx = {lbl: i for i, lbl in enumerate(labels)}
+
+    required = {"Nz", "Iz", "Cz", "Lpa", "Rpa"}
+    missing = required - set(labels)
+    if missing:
+        raise ValueError(f"Missing landmarks for alignment: {missing}")
+
+    Nz = lm[idx["Nz"]]
+    Cz = lm[idx["Cz"]]
+    Lpa = lm[idx["Lpa"]]
+    Rpa = lm[idx["Rpa"]]
+    ear_mid = 0.5 * (Lpa + Rpa)
+
+    z_ax = Lpa - Rpa
+    z_ax = z_ax / np.linalg.norm(z_ax)
+
+    nz_dir = Nz - ear_mid
+    nz_dir = nz_dir - np.dot(nz_dir, z_ax) * z_ax
+    y_ax = nz_dir / np.linalg.norm(nz_dir)
+
+    x_ax = np.cross(y_ax, z_ax)
+
+    if np.dot(Cz - ear_mid, x_ax) < 0:
+        x_ax = -x_ax
+    if np.dot(Nz - ear_mid, y_ax) < 0:
+        y_ax = -y_ax
+    z_ax = np.cross(x_ax, y_ax)
+
+    R = np.vstack([x_ax, y_ax, z_ax])
+
+    aligned_verts = np.asarray(surface.mesh.vertices) @ R.T
+    new_mesh = trimesh.Trimesh(
+        vertices=aligned_verts,
+        faces=surface.mesh.faces,
+        visual=surface.mesh.visual,
+        process=False,
+    )
+    aligned_surface = cdc.TrimeshSurface(
+        new_mesh, crs=surface.crs, units=surface.units,
+    )
+
+    aligned_lm = lm @ R.T
+    aligned_landmarks = landmarks.pint.dequantify().copy(data=aligned_lm).pint.quantify()
+
+    return aligned_surface, aligned_landmarks, R
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/ui.py b/src/cedalion/geometry/photogrammetry/anonymization/ui.py
deleted file mode 100644
index bfb7dc47..00000000
--- a/src/cedalion/geometry/photogrammetry/anonymization/ui.py
+++ /dev/null
@@ -1,73 +0,0 @@
-"""Interactive UI components for nasion selection.
-
-Provides a PyVista-based interactive picker for manual nasion (Nz)
-selection as a fallback when automatic detection fails.
-
-Initial Contributors:
-    - Face Anonymization Project | 2024
-"""
-
-import logging
-
-import numpy as np
-import pyvista as pv
-
-import cedalion.dataclasses as cdc
-
-logger = logging.getLogger("cedalion")
-
-
-def pick_nasion(surface: cdc.TrimeshSurface) -> np.ndarray | None:
-    """Open an interactive window for the user to click the nasion point.
-
-    Displays the textured mesh and lets the user click on the nasion (Nz).
-    The clicked point is snapped to the nearest mesh vertex.
-
-    Args:
-        surface: TrimeshSurface from photogrammetry scan.
-
-    Returns:
-        Nasion position as numpy array of shape (3,) in mm, or None if
-        the user closes the window without clicking.
-    """
-    from scipy.spatial import KDTree
-
-    picked_point = [None]  # mutable container for closure
-
-    def _on_pick(point):
-        if point is not None:
-            picked_point[0] = np.array(point)
-
-    plotter = pv.Plotter(notebook=False)
-
-    # Add textured mesh using cedalion's plot_surface for consistency
-    import cedalion.plots
-    cedalion.plots.plot_surface(plotter, surface, opacity=1.0)
-
-    plotter.enable_surface_point_picking(
-        callback=_on_pick,
-        left_clicking=True,
-        show_point=True,
-        point_size=20,
-        color="red",
-        show_message="Left-click Nz (nasion), then close the window",
-    )
-
-    plotter.add_text(
-        "Left-click NASION (bridge of the nose), then close window",
-        position="upper_left",
-        font_size=14,
-    )
-
-    plotter.show()
-
-    if picked_point[0] is None:
-        return None
-
-    # Snap to nearest mesh vertex
-    vertices = surface.mesh.vertices
-    tree = KDTree(vertices)
-    _, idx = tree.query(picked_point[0])
-    nasion = vertices[idx].copy()
-    logger.debug(f"Manual nasion selected: {nasion}")
-    return nasion
diff --git a/tests/test_anonymization.py b/tests/test_anonymization.py
index 8ce01246..c2c1900d 100644
--- a/tests/test_anonymization.py
+++ b/tests/test_anonymization.py
@@ -14,7 +14,6 @@
     normalize_axes,
     isolate_head,
     detect_landmarks_from_nasion,
-    get_facial_region_mask_from_nasion,
 )
 
 
@@ -170,49 +169,3 @@ def test_nz_position_preserved(self, head_like_surface):
         assert_allclose(nz_out, nasion, atol=1e-6)
 
 
-# ============================================================================
-# Facial Region Mask
-# ============================================================================
-
-
-class TestFacialRegionMask:
-    """Tests for facial region mask generation."""
-
-    def test_mask_shape_and_type(self, simple_sphere_surface):
-        """Mask should be boolean with correct length."""
-        nz = np.array([0, 100, 0])
-        fwd = np.array([0, 1, 0])
-        mask = get_facial_region_mask_from_nasion(
-            simple_sphere_surface, nz, fwd
-        )
-        assert mask.dtype == bool
-        assert len(mask) == simple_sphere_surface.nvertices
-
-    def test_some_vertices_masked(self, simple_sphere_surface):
-        """Some but not all vertices should be in the facial region."""
-        nz = np.array([0, 100, 0])
-        fwd = np.array([0, 1, 0])
-        mask = get_facial_region_mask_from_nasion(
-            simple_sphere_surface, nz, fwd
-        )
-        assert mask.sum() > 0
-        assert mask.sum() < len(mask)
-
-    def test_protection_zones_excluded(self, simple_sphere_surface, anatomical_landmarks):
-        """Vertices near protected points should not be masked."""
-        nz = np.array([0, 100, 0])
-        fwd = np.array([0, 1, 0])
-        mask = get_facial_region_mask_from_nasion(
-            simple_sphere_surface, nz, fwd,
-            protected_points=anatomical_landmarks,
-            protection_radius=15.0 * units.mm,
-        )
-
-        vertices = simple_sphere_surface.mesh.vertices
-        protected_positions = anatomical_landmarks.pint.dequantify().values
-        for pos in protected_positions:
-            distances = np.linalg.norm(vertices - pos, axis=1)
-            nearby = distances < 15.0
-            assert not np.any(mask & nearby), "Protected zone breached"
-
-

From f024bc07b50cd877ea4870ea30c97c2781f789b0 Mon Sep 17 00:00:00 2001
From: mah-diaa <mahmoud.diaa.abdeen@gmail.com>
Date: Sat, 18 Apr 2026 12:26:37 +0200
Subject: [PATCH 15/39] Preserve texture UVs through anonymization and add save
 helper

---
 .../head_models/43_crs_and_headmodel.ipynb    | 1600 ++++++++++++++++-
 .../48_manual_5pt_anonymization.ipynb         |  524 +++++-
 .../head_models/49_anonymization_audit.ipynb  |  195 ++
 .../photogrammetry/anonymization/__init__.py  |    2 +
 .../photogrammetry/anonymization/mask.py      |  154 +-
 .../anonymization/preprocessing.py            |   55 +-
 6 files changed, 2454 insertions(+), 76 deletions(-)
 create mode 100644 examples/head_models/49_anonymization_audit.ipynb

diff --git a/examples/head_models/43_crs_and_headmodel.ipynb b/examples/head_models/43_crs_and_headmodel.ipynb
index 5e35fd4c..d0db880e 100644
--- a/examples/head_models/43_crs_and_headmodel.ipynb
+++ b/examples/head_models/43_crs_and_headmodel.ipynb
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -25,7 +25,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -57,9 +57,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading file 'ICBM152_2020.zip' from 'https://doc.ibs.tu-berlin.de/cedalion/datasets/v25.1.0/ICBM152_2020.zip' to '/home/ma7/.cache/cedalion/v25.1.0'.\n",
+      "Unzipping contents of '/home/ma7/.cache/cedalion/v25.1.0/ICBM152_2020.zip' to '/home/ma7/.cache/cedalion/v25.1.0/ICBM152_2020.zip.unzip'\n"
+     ]
+    }
+   ],
    "source": [
     "# load pathes to segmentation data for the icbm-152 atlas\n",
     "SEG_DATADIR, mask_files, landmarks_file = cedalion.datasets.get_icbm152_segmentation()\n",
@@ -84,13 +93,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {
     "tags": [
      "nbsphinx-thumbnail"
     ]
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
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",
+      "text/plain": [
+       "<PIL.Image.Image image mode=RGB size=1024x768>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt = pv.Plotter()\n",
     "cedalion.plots.plot_surface(plt, head_icbm152.brain, color=\"#d3a6a1\")\n",
@@ -110,9 +131,514 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
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+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
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+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
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+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base rgba(0, 0, 0, 1))\n",
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+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\n",
+       "  );\n",
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+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
+       "  );\n",
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+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, white)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, #111111)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "  border: 2px solid transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0) !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: \"►\";\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  border-color: var(--xr-background-color-row-odd);\n",
+       "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "  border-color: var(--xr-background-color-row-even);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (segmentation_type: 5, i: 193, j: 239, k: 263)&gt; Size: 61MB\n",
+       "0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       "Coordinates:\n",
+       "  * segmentation_type  (segmentation_type) &lt;U5 100B &#x27;csf&#x27; &#x27;gm&#x27; ... &#x27;skull&#x27; &#x27;wm&#x27;\n",
+       "Dimensions without coordinates: i, j, k</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>segmentation_type</span>: 5</li><li><span>i</span>: 193</li><li><span>j</span>: 239</li><li><span>k</span>: 263</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-37d07623-c294-4938-8aeb-3892d490bb93' class='xr-array-in' type='checkbox' ><label for='section-37d07623-c294-4938-8aeb-3892d490bb93' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0</span></div><div class='xr-array-data'><pre>array([[[[0, 0, 0, ..., 0, 0, 0],\n",
+       "         [0, 0, 0, ..., 0, 0, 0],\n",
+       "         [0, 0, 0, ..., 0, 0, 0],\n",
+       "         ...,\n",
+       "         [0, 0, 0, ..., 0, 0, 0],\n",
+       "         [0, 0, 0, ..., 0, 0, 0],\n",
+       "         [0, 0, 0, ..., 0, 0, 0]],\n",
+       "\n",
+       "        [[0, 0, 0, ..., 0, 0, 0],\n",
+       "         [0, 0, 0, ..., 0, 0, 0],\n",
+       "         [0, 0, 0, ..., 0, 0, 0],\n",
+       "         ...,\n",
+       "         [0, 0, 0, ..., 0, 0, 0],\n",
+       "         [0, 0, 0, ..., 0, 0, 0],\n",
+       "         [0, 0, 0, ..., 0, 0, 0]],\n",
+       "\n",
+       "        [[0, 0, 0, ..., 0, 0, 0],\n",
+       "         [0, 0, 0, ..., 0, 0, 0],\n",
+       "         [0, 0, 0, ..., 0, 0, 0],\n",
+       "         ...,\n",
+       "...\n",
+       "         ...,\n",
+       "         [0, 0, 0, ..., 0, 0, 0],\n",
+       "         [0, 0, 0, ..., 0, 0, 0],\n",
+       "         [0, 0, 0, ..., 0, 0, 0]],\n",
+       "\n",
+       "        [[0, 0, 0, ..., 0, 0, 0],\n",
+       "         [0, 0, 0, ..., 0, 0, 0],\n",
+       "         [0, 0, 0, ..., 0, 0, 0],\n",
+       "         ...,\n",
+       "         [0, 0, 0, ..., 0, 0, 0],\n",
+       "         [0, 0, 0, ..., 0, 0, 0],\n",
+       "         [0, 0, 0, ..., 0, 0, 0]],\n",
+       "\n",
+       "        [[0, 0, 0, ..., 0, 0, 0],\n",
+       "         [0, 0, 0, ..., 0, 0, 0],\n",
+       "         [0, 0, 0, ..., 0, 0, 0],\n",
+       "         ...,\n",
+       "         [0, 0, 0, ..., 0, 0, 0],\n",
+       "         [0, 0, 0, ..., 0, 0, 0],\n",
+       "         [0, 0, 0, ..., 0, 0, 0]]]], shape=(5, 193, 239, 263), dtype=uint8)</pre></div></div></li><li class='xr-section-item'><input id='section-1ec49326-ec1e-44ed-898b-88e7c447c00e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1ec49326-ec1e-44ed-898b-88e7c447c00e' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>segmentation_type</span></div><div class='xr-var-dims'>(segmentation_type)</div><div class='xr-var-dtype'>&lt;U5</div><div class='xr-var-preview xr-preview'>&#x27;csf&#x27; &#x27;gm&#x27; &#x27;scalp&#x27; &#x27;skull&#x27; &#x27;wm&#x27;</div><input id='attrs-20fee9a0-7320-4c6e-994a-1f98a9ce2811' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-20fee9a0-7320-4c6e-994a-1f98a9ce2811' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-300009cd-439e-4ae5-bbe9-a97262234836' class='xr-var-data-in' type='checkbox'><label for='data-300009cd-439e-4ae5-bbe9-a97262234836' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;csf&#x27;, &#x27;gm&#x27;, &#x27;scalp&#x27;, &#x27;skull&#x27;, &#x27;wm&#x27;], dtype=&#x27;&lt;U5&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-680a5d42-99eb-4ebb-8e3b-fc9d3b280bdb' class='xr-section-summary-in' type='checkbox'  ><label for='section-680a5d42-99eb-4ebb-8e3b-fc9d3b280bdb' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>segmentation_type</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-62d62503-1be6-401d-bb77-07011c8e0768' class='xr-index-data-in' type='checkbox'/><label for='index-62d62503-1be6-401d-bb77-07011c8e0768' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;csf&#x27;, &#x27;gm&#x27;, &#x27;scalp&#x27;, &#x27;skull&#x27;, &#x27;wm&#x27;], dtype=&#x27;object&#x27;, name=&#x27;segmentation_type&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-60bceb88-f338-47a6-8863-a58785efa6bf' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-60bceb88-f338-47a6-8863-a58785efa6bf' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray (segmentation_type: 5, i: 193, j: 239, k: 263)> Size: 61MB\n",
+       "0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
+       "Coordinates:\n",
+       "  * segmentation_type  (segmentation_type) <U5 100B 'csf' 'gm' ... 'skull' 'wm'\n",
+       "Dimensions without coordinates: i, j, k"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "head_icbm152.segmentation_masks"
    ]
@@ -133,9 +659,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'ijk'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "head_icbm152.crs"
    ]
@@ -150,9 +687,485 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base rgba(0, 0, 0, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, white)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, #111111)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "  border: 2px solid transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0) !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: \"►\";\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  border-color: var(--xr-background-color-row-odd);\n",
+       "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "  border-color: var(--xr-background-color-row-even);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (label: 4, ijk: 3)&gt; Size: 96B\n",
+       "[] 96.0 218.1 108.0 96.0 18.9 101.0 15.9 120.0 102.0 178.1 120.0 103.0\n",
+       "Coordinates:\n",
+       "  * label    (label) &lt;U3 48B &#x27;Nz&#x27; &#x27;Iz&#x27; &#x27;LPA&#x27; &#x27;RPA&#x27;\n",
+       "    type     (label) object 32B PointType.LANDMARK ... PointType.LANDMARK\n",
+       "Dimensions without coordinates: ijk</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>label</span>: 4</li><li><span>ijk</span>: 3</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-72efd63f-2b4b-4016-b816-6bf5c28c98dd' class='xr-array-in' type='checkbox' ><label for='section-72efd63f-2b4b-4016-b816-6bf5c28c98dd' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>[] 96.0 218.1 108.0 96.0 18.9 101.0 15.9 120.0 102.0 178.1 120.0 103.0</span></div><div class='xr-array-data'><table><tbody><tr><th>Magnitude</th><td style='text-align:left;'><pre>[[96.0 218.10000000000156 108.00000000000182] [95.99999999999902 18.899999999999395 100.99999999999926] [15.899999999999622 120.0000000000005 102.00000000000054] [178.09999999999948 120.00000000000054 103.0000000000006]]</pre></td></tr><tr><th>Units</th><td style='text-align:left;'>dimensionless</td></tr></tbody></table></div></div></li><li class='xr-section-item'><input id='section-ac4306b9-8895-4e94-b9a3-0706cb308329' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ac4306b9-8895-4e94-b9a3-0706cb308329' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>label</span></div><div class='xr-var-dims'>(label)</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;Nz&#x27; &#x27;Iz&#x27; &#x27;LPA&#x27; &#x27;RPA&#x27;</div><input id='attrs-e6d6516e-04d8-43eb-9e92-73f4ebd3fb8e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e6d6516e-04d8-43eb-9e92-73f4ebd3fb8e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-91ba97f8-7839-4eaa-9a09-9b365c17c757' class='xr-var-data-in' type='checkbox'><label for='data-91ba97f8-7839-4eaa-9a09-9b365c17c757' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Nz&#x27;, &#x27;Iz&#x27;, &#x27;LPA&#x27;, &#x27;RPA&#x27;], dtype=&#x27;&lt;U3&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>type</span></div><div class='xr-var-dims'>(label)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>PointType.LANDMARK ... PointType...</div><input id='attrs-b4de5b98-3f5f-48a3-832b-60fd6d04b642' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b4de5b98-3f5f-48a3-832b-60fd6d04b642' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-883de323-fc48-4c51-8458-2858dddd9878' class='xr-var-data-in' type='checkbox'><label for='data-883de323-fc48-4c51-8458-2858dddd9878' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&lt;PointType.LANDMARK: 3&gt;, &lt;PointType.LANDMARK: 3&gt;,\n",
+       "       &lt;PointType.LANDMARK: 3&gt;, &lt;PointType.LANDMARK: 3&gt;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0607432b-1a70-45cf-8160-5cb4c5e51170' class='xr-section-summary-in' type='checkbox'  ><label for='section-0607432b-1a70-45cf-8160-5cb4c5e51170' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>label</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-b976ff2f-6cf1-4777-9903-ee4497f69a3a' class='xr-index-data-in' type='checkbox'/><label for='index-b976ff2f-6cf1-4777-9903-ee4497f69a3a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;Nz&#x27;, &#x27;Iz&#x27;, &#x27;LPA&#x27;, &#x27;RPA&#x27;], dtype=&#x27;object&#x27;, name=&#x27;label&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6a456627-3c17-44b0-915b-ad9494342ef0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6a456627-3c17-44b0-915b-ad9494342ef0' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray (label: 4, ijk: 3)> Size: 96B\n",
+       "[] 96.0 218.1 108.0 96.0 18.9 101.0 15.9 120.0 102.0 178.1 120.0 103.0\n",
+       "Coordinates:\n",
+       "  * label    (label) <U3 48B 'Nz' 'Iz' 'LPA' 'RPA'\n",
+       "    type     (label) object 32B PointType.LANDMARK ... PointType.LANDMARK\n",
+       "Dimensions without coordinates: ijk"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'ijk'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "display(head_icbm152.landmarks)\n",
     "display(head_icbm152.landmarks.points.crs)"
@@ -167,9 +1180,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "TrimeshSurface(mesh=<trimesh.Trimesh(vertices.shape=(15002, 3), faces.shape=(29978, 3))>, crs='ijk', units=<Unit('dimensionless')>, vertex_coords={})"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "TrimeshSurface(mesh=<trimesh.Trimesh(vertices.shape=(255950, 3), faces.shape=(511242, 3))>, crs='ijk', units=<Unit('dimensionless')>, vertex_coords={})"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "display(head_icbm152.brain)\n",
     "display(head_icbm152.scalp)"
@@ -177,9 +1209,470 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
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+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (aligned: 4, ijk: 4)&gt; Size: 128B\n",
+       "[mm] 1.0 0.0 0.0 -96.0 0.0 1.0 0.0 -132.0 0.0 0.0 1.0 -148.0 0.0 0.0 0.0 1.0\n",
+       "Dimensions without coordinates: aligned, ijk</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span>aligned</span>: 4</li><li><span>ijk</span>: 4</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-845a546b-1ec5-427d-89f0-0b0c8e109c6b' class='xr-array-in' type='checkbox' ><label for='section-845a546b-1ec5-427d-89f0-0b0c8e109c6b' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>[mm] 1.0 0.0 0.0 -96.0 0.0 1.0 0.0 ... 0.0 1.0 -148.0 0.0 0.0 0.0 1.0</span></div><div class='xr-array-data'><table><tbody><tr><th>Magnitude</th><td style='text-align:left;'><pre>[[1.0 0.0 0.0 -96.0] [0.0 1.0 0.0 -132.0] [0.0 0.0 1.0 -148.0] [0.0 0.0 0.0 1.0]]</pre></td></tr><tr><th>Units</th><td style='text-align:left;'>millimeter</td></tr></tbody></table></div></div></li><li class='xr-section-item'><input id='section-d4e17073-ffad-45e7-969e-e6310d34964b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d4e17073-ffad-45e7-969e-e6310d34964b' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-1a2cd197-b906-4102-acf4-624ae1b43e6f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1a2cd197-b906-4102-acf4-624ae1b43e6f' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-556f12d4-8b6a-4d49-9183-9cfb348a37b5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-556f12d4-8b6a-4d49-9183-9cfb348a37b5' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray (aligned: 4, ijk: 4)> Size: 128B\n",
+       "[mm] 1.0 0.0 0.0 -96.0 0.0 1.0 0.0 -132.0 0.0 0.0 1.0 -148.0 0.0 0.0 0.0 1.0\n",
+       "Dimensions without coordinates: aligned, ijk"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "head_icbm152.t_ijk2ras # transformation from voxel to subject (RAS) space"
    ]
@@ -198,9 +1691,67 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'aligned'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'aligned'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'aligned'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "dimensionless"
+      ],
+      "text/latex": [
+       "$\\mathrm{dimensionless}$"
+      ],
+      "text/plain": [
+       "<Unit('dimensionless')>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "millimeter"
+      ],
+      "text/latex": [
+       "$\\mathrm{millimeter}$"
+      ],
+      "text/plain": [
+       "<Unit('millimeter')>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "trafo = head_icbm152.t_ijk2ras\n",
     "\n",
@@ -213,11 +1764,18 @@
     "display(head_icbm152.landmarks.pint.units)\n",
     "display(head_icbm152_ras.landmarks.pint.units)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "cedalion_240902",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -231,9 +1789,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.8"
+   "version": "3.11.14"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/examples/head_models/48_manual_5pt_anonymization.ipynb b/examples/head_models/48_manual_5pt_anonymization.ipynb
index 268867f3..aebb1239 100644
--- a/examples/head_models/48_manual_5pt_anonymization.ipynb
+++ b/examples/head_models/48_manual_5pt_anonymization.ipynb
@@ -22,12 +22,12 @@
     "8. Validate result\n",
     "\n",
     "This is the cedalion-only alternative to notebook 47's MediaPipe pipeline and\n",
-    "matches the expos\u00e9 contract: NAS/LPA/RPA assumed given, no DL face detection."
+    "matches the exposé contract: NAS/LPA/RPA assumed given, no DL face detection."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "id": "cell-imports",
    "metadata": {},
    "outputs": [],
@@ -50,6 +50,7 @@
     "    detect_cap_boundary,\n",
     "    face_mask_from_landmarks,\n",
     "    delete_masked_vertices,\n",
+    "    save_anonymized_scan,\n",
     "    validate_anonymization,\n",
     ")\n",
     "from cedalion.vtktutils import trimesh_to_vtk_polydata\n",
@@ -85,7 +86,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 34,
    "id": "cell-load",
    "metadata": {},
    "outputs": [
@@ -93,14 +94,44 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Loaded: 1,284,667 vertices, 2,223,716 faces\n"
+      "Loaded: 1,284,667 vertices, 2,223,716 faces\n",
+      "Texture already attached: (4016, 3776)\n"
      ]
     }
    ],
    "source": [
     "path = f'{SCANS_FOLDER}/Subject{SUBJECT_NUMBER}/Subject{SUBJECT_NUMBER}.obj'\n",
     "surface = cedalion.io.read_einstar_obj(path)\n",
-    "print(f'Loaded: {surface.nvertices:,} vertices, {surface.nfaces:,} faces')"
+    "print(f'Loaded: {surface.nvertices:,} vertices, {surface.nfaces:,} faces')\n",
+    "\n",
+    "# In trimesh 4.6 the texture image lives on visual.material.image, not\n",
+    "# visual.image. Verify it's there; if missing, fall back to the sibling JPG.\n",
+    "import os\n",
+    "import trimesh as _tm\n",
+    "from PIL import Image as _PILImage\n",
+    "\n",
+    "def _visual_image(visual):\n",
+    "    img = getattr(visual, 'image', None)\n",
+    "    if img is None:\n",
+    "        mat = getattr(visual, 'material', None)\n",
+    "        img = getattr(mat, 'image', None) if mat is not None else None\n",
+    "    return img\n",
+    "\n",
+    "_img = _visual_image(surface.mesh.visual)\n",
+    "if _img is None:\n",
+    "    _jpg = path.replace('.obj', '.jpg')\n",
+    "    _uv = getattr(surface.mesh.visual, 'uv', None)\n",
+    "    assert os.path.exists(_jpg) and _uv is not None, (\n",
+    "        f'no texture: jpg_exists={os.path.exists(_jpg)}, has_uv={_uv is not None}'\n",
+    "    )\n",
+    "    surface.mesh.visual = _tm.visual.TextureVisuals(\n",
+    "        uv=_uv, image=_PILImage.open(_jpg).convert('RGBA'),\n",
+    "    )\n",
+    "    _img = _visual_image(surface.mesh.visual)\n",
+    "    assert _img is not None, 'attach failed'\n",
+    "    print(f'Attached texture from JPG: {_img.size}')\n",
+    "else:\n",
+    "    print(f'Texture already attached: {_img.size}')"
    ]
   },
   {
@@ -120,7 +151,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 35,
    "id": "cell-pick",
    "metadata": {
     "scrolled": true
@@ -129,12 +160,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2963d2dfd8b44b63b221932b1cd7a137",
+       "model_id": "deb2def326744ed882bbe4b27279aa0d",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Widget(value='<iframe src=\"http://localhost:38789/index.html?ui=P_0x7f9c741e8a10_3&reconnect=auto\" class=\"pyvi\u2026"
+       "Widget(value='<iframe src=\"http://localhost:45595/index.html?ui=P_0x7fdaa44a0610_10&reconnect=auto\" class=\"pyv…"
       ]
      },
      "metadata": {},
@@ -162,7 +193,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 36,
    "id": "cell-retrieve",
    "metadata": {},
    "outputs": [
@@ -347,7 +378,7 @@
        "\n",
        ".xr-section-summary-in + label:before {\n",
        "  display: inline-block;\n",
-       "  content: \"\u25ba\";\n",
+       "  content: \"►\";\n",
        "  font-size: 11px;\n",
        "  width: 15px;\n",
        "  text-align: center;\n",
@@ -358,7 +389,7 @@
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label:before {\n",
-       "  content: \"\u25bc\";\n",
+       "  content: \"▼\";\n",
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label > span {\n",
@@ -613,28 +644,28 @@
        "  stroke-width: 0.8px;\n",
        "}\n",
        "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (label: 5, digitized: 3)&gt; Size: 120B\n",
-       "&lt;Quantity([[ 65.80514235 -11.60517104 481.16566125]\n",
-       " [182.62203078 116.67835657 431.5709762 ]\n",
-       " [ 51.88537828 219.86486084 456.38238574]\n",
-       " [ 64.75510598  78.33385833 566.36259485]\n",
-       " [  2.74086779  77.07375634 392.23255999]], &#x27;millimeter&#x27;)&gt;\n",
+       "&lt;Quantity([[182.78403277 119.00435797 431.95041939]\n",
+       " [ 53.21676428 219.20228469 461.11116233]\n",
+       " [ 69.61090726  81.05491959 564.60127688]\n",
+       " [ 71.16199192 -12.73670574 478.0462117 ]\n",
+       " [ -9.01177257  59.78899075 423.63767742]], &#x27;millimeter&#x27;)&gt;\n",
        "Coordinates:\n",
-       "  * label    (label) &lt;U3 60B &#x27;Nz&#x27; &#x27;Cz&#x27; &#x27;Iz&#x27; &#x27;Rpa&#x27; &#x27;Lpa&#x27;\n",
+       "  * label    (label) &lt;U3 60B &#x27;Cz&#x27; &#x27;Iz&#x27; &#x27;Rpa&#x27; &#x27;Nz&#x27; &#x27;Lpa&#x27;\n",
        "    type     (label) object 40B PointType.LANDMARK ... PointType.LANDMARK\n",
        "    group    (label) &lt;U1 20B &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27;\n",
-       "Dimensions without coordinates: digitized</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>label</span>: 5</li><li><span>digitized</span>: 3</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-fb651587-e434-4b3c-8acc-476e9ef4bba0' class='xr-array-in' type='checkbox' checked><label for='section-fb651587-e434-4b3c-8acc-476e9ef4bba0' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>[mm] 65.81 -11.61 481.2 182.6 116.7 ... 78.33 566.4 2.741 77.07 392.2</span></div><div class='xr-array-data'><table><tbody><tr><th>Magnitude</th><td style='text-align:left;'><pre>[[65.80514234534597 -11.605171044162262 481.1656612460295] [182.6220307773288 116.67835657353872 431.57097619552144] [51.88537827784663 219.86486083549175 456.3823857394348] [64.75510597977373 78.3338583311504 566.3625948499965] [2.740867794190251 77.07375633870706 392.2325599877916]]</pre></td></tr><tr><th>Units</th><td style='text-align:left;'>millimeter</td></tr></tbody></table></div></div></li><li class='xr-section-item'><input id='section-ed748025-edcd-4e75-a4ec-b76d073949a7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ed748025-edcd-4e75-a4ec-b76d073949a7' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>label</span></div><div class='xr-var-dims'>(label)</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;Nz&#x27; &#x27;Cz&#x27; &#x27;Iz&#x27; &#x27;Rpa&#x27; &#x27;Lpa&#x27;</div><input id='attrs-ca49c0aa-dbbb-4083-8e14-5cefe3eab0f2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ca49c0aa-dbbb-4083-8e14-5cefe3eab0f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ccb6b1c7-7708-417a-97ba-6c49ef62d9fc' class='xr-var-data-in' type='checkbox'><label for='data-ccb6b1c7-7708-417a-97ba-6c49ef62d9fc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Nz&#x27;, &#x27;Cz&#x27;, &#x27;Iz&#x27;, &#x27;Rpa&#x27;, &#x27;Lpa&#x27;], dtype=&#x27;&lt;U3&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>type</span></div><div class='xr-var-dims'>(label)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>PointType.LANDMARK ... PointType...</div><input id='attrs-2b4d7407-f43b-43f8-acdf-2a5ac893f1ca' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2b4d7407-f43b-43f8-acdf-2a5ac893f1ca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8d4d4f2-d749-455f-a156-efa502b96d2a' class='xr-var-data-in' type='checkbox'><label for='data-c8d4d4f2-d749-455f-a156-efa502b96d2a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&lt;PointType.LANDMARK: 3&gt;, &lt;PointType.LANDMARK: 3&gt;,\n",
+       "Dimensions without coordinates: digitized</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>label</span>: 5</li><li><span>digitized</span>: 3</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-982928f4-ff25-44b6-98cb-81856dba0493' class='xr-array-in' type='checkbox' checked><label for='section-982928f4-ff25-44b6-98cb-81856dba0493' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>[mm] 182.8 119.0 432.0 53.22 219.2 ... -12.74 478.0 -9.012 59.79 423.6</span></div><div class='xr-array-data'><table><tbody><tr><th>Magnitude</th><td style='text-align:left;'><pre>[[182.78403277337682 119.00435797327856 431.95041939265843] [53.21676427926042 219.20228469014984 461.11116232532544] [69.61090726159273 81.05491958613379 564.6012768794676] [71.16199191638447 -12.73670574349066 478.04621170118133] [-9.011772572255389 59.78899074619693 423.63767741978666]]</pre></td></tr><tr><th>Units</th><td style='text-align:left;'>millimeter</td></tr></tbody></table></div></div></li><li class='xr-section-item'><input id='section-75a36191-a736-4450-8928-dd186f491336' class='xr-section-summary-in' type='checkbox'  checked><label for='section-75a36191-a736-4450-8928-dd186f491336' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>label</span></div><div class='xr-var-dims'>(label)</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;Cz&#x27; &#x27;Iz&#x27; &#x27;Rpa&#x27; &#x27;Nz&#x27; &#x27;Lpa&#x27;</div><input id='attrs-0ef053f4-e287-4bd2-833a-bfb6abf009bc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0ef053f4-e287-4bd2-833a-bfb6abf009bc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22b84fba-1fdf-444b-b150-110e15031afc' class='xr-var-data-in' type='checkbox'><label for='data-22b84fba-1fdf-444b-b150-110e15031afc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Cz&#x27;, &#x27;Iz&#x27;, &#x27;Rpa&#x27;, &#x27;Nz&#x27;, &#x27;Lpa&#x27;], dtype=&#x27;&lt;U3&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>type</span></div><div class='xr-var-dims'>(label)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>PointType.LANDMARK ... PointType...</div><input id='attrs-6d1ebfa0-9b01-421f-b7ad-b92480da1450' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6d1ebfa0-9b01-421f-b7ad-b92480da1450' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-03de491a-5ccd-457b-9f81-358b346c95a8' class='xr-var-data-in' type='checkbox'><label for='data-03de491a-5ccd-457b-9f81-358b346c95a8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&lt;PointType.LANDMARK: 3&gt;, &lt;PointType.LANDMARK: 3&gt;,\n",
        "       &lt;PointType.LANDMARK: 3&gt;, &lt;PointType.LANDMARK: 3&gt;,\n",
-       "       &lt;PointType.LANDMARK: 3&gt;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>group</span></div><div class='xr-var-dims'>(label)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27;</div><input id='attrs-f5275c36-dbe6-4d6d-b163-59dce1c0911e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f5275c36-dbe6-4d6d-b163-59dce1c0911e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ab50e95-811c-42c3-b065-1fda875123eb' class='xr-var-data-in' type='checkbox'><label for='data-0ab50e95-811c-42c3-b065-1fda875123eb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;L&#x27;, &#x27;L&#x27;, &#x27;L&#x27;, &#x27;L&#x27;, &#x27;L&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-15adda9b-488f-493f-a997-b33d58a9a5d4' class='xr-section-summary-in' type='checkbox'  ><label for='section-15adda9b-488f-493f-a997-b33d58a9a5d4' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>label</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-6a7a674f-d402-40c8-b12e-25c63f9d42ec' class='xr-index-data-in' type='checkbox'/><label for='index-6a7a674f-d402-40c8-b12e-25c63f9d42ec' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;Nz&#x27;, &#x27;Cz&#x27;, &#x27;Iz&#x27;, &#x27;Rpa&#x27;, &#x27;Lpa&#x27;], dtype=&#x27;object&#x27;, name=&#x27;label&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e191fd1c-7ad6-4e27-a76b-f86d3b41ea8c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e191fd1c-7ad6-4e27-a76b-f86d3b41ea8c' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "       &lt;PointType.LANDMARK: 3&gt;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>group</span></div><div class='xr-var-dims'>(label)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27;</div><input id='attrs-b054ccc6-36f3-4b5a-a648-b1bf7c5adf07' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b054ccc6-36f3-4b5a-a648-b1bf7c5adf07' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-497fdfa1-e8b7-4bf1-97bd-ef7867387c46' class='xr-var-data-in' type='checkbox'><label for='data-497fdfa1-e8b7-4bf1-97bd-ef7867387c46' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;L&#x27;, &#x27;L&#x27;, &#x27;L&#x27;, &#x27;L&#x27;, &#x27;L&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-af4a5770-8e85-488e-a59c-780969729f33' class='xr-section-summary-in' type='checkbox'  ><label for='section-af4a5770-8e85-488e-a59c-780969729f33' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>label</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-39b85087-a3cc-470d-9367-fb592e2dff3f' class='xr-index-data-in' type='checkbox'/><label for='index-39b85087-a3cc-470d-9367-fb592e2dff3f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;Cz&#x27;, &#x27;Iz&#x27;, &#x27;Rpa&#x27;, &#x27;Nz&#x27;, &#x27;Lpa&#x27;], dtype=&#x27;object&#x27;, name=&#x27;label&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cf25c197-3842-4b92-82fb-1153a7b77718' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cf25c197-3842-4b92-82fb-1153a7b77718' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.DataArray (label: 5, digitized: 3)> Size: 120B\n",
-       "<Quantity([[ 65.80514235 -11.60517104 481.16566125]\n",
-       " [182.62203078 116.67835657 431.5709762 ]\n",
-       " [ 51.88537828 219.86486084 456.38238574]\n",
-       " [ 64.75510598  78.33385833 566.36259485]\n",
-       " [  2.74086779  77.07375634 392.23255999]], 'millimeter')>\n",
+       "<Quantity([[182.78403277 119.00435797 431.95041939]\n",
+       " [ 53.21676428 219.20228469 461.11116233]\n",
+       " [ 69.61090726  81.05491959 564.60127688]\n",
+       " [ 71.16199192 -12.73670574 478.0462117 ]\n",
+       " [ -9.01177257  59.78899075 423.63767742]], 'millimeter')>\n",
        "Coordinates:\n",
-       "  * label    (label) <U3 60B 'Nz' 'Cz' 'Iz' 'Rpa' 'Lpa'\n",
+       "  * label    (label) <U3 60B 'Cz' 'Iz' 'Rpa' 'Nz' 'Lpa'\n",
        "    type     (label) object 40B PointType.LANDMARK ... PointType.LANDMARK\n",
        "    group    (label) <U1 20B 'L' 'L' 'L' 'L' 'L'\n",
        "Dimensions without coordinates: digitized"
@@ -692,10 +723,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 37,
    "id": "cell-normalize",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "After isolate_head: 678,081 vertices\n",
+      "\n",
+      "Aligned landmarks:\n",
+      "  Nz: [-184.49706053  -15.58120797 -446.62343636]  (on mesh: 0.66 mm)\n",
+      "  Iz: [ -83.08828247 -225.51510363 -453.59061387]  (on mesh: 0.11 mm)\n",
+      "  Cz: [ -18.54212354  -74.10802095 -477.82569578]  (on mesh: 0.66 mm)\n",
+      "  Lpa: [-184.49706053 -109.48535945 -370.2682918 ]  (on mesh: 0.55 mm)\n",
+      "  Rpa: [-184.49706053 -109.48535945 -533.0703246 ]  (on mesh: 0.28 mm)\n"
+     ]
+    }
+   ],
    "source": [
     "# Step 1: Mediapipe-compatible X-rotation so Y points anterior.\n",
     "lm_raw = landmarks.pint.dequantify().values\n",
@@ -744,10 +790,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 38,
    "id": "cell-mask",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cap boundary detected at X = -153.5 mm  (Nz_x=-184.5, Cz_x=-18.5)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Nasion strip preserved: 1,569 vertices\n",
+      "Mask: 110,763 / 678,081 vertices (16.3%)\n",
+      "Per-rule counts: {'below_cap': 293056, 'face_region': 98815, 'ear_region': 33835, 'all': 113710}\n"
+     ]
+    }
+   ],
    "source": [
     "# 1. Detect cap boundary from midline Y-profile.\n",
     "verts = np.asarray(surface_h.mesh.vertices)\n",
@@ -820,16 +893,31 @@
     "## 6. Visualize the anonymization mask\n",
     "\n",
     "Red = will be deleted, white = kept. The 5 landmark spheres are overlaid for\n",
-    "orientation. This is the primary deliverable \u2014 if the red region hugs the face\n",
+    "orientation. This is the primary deliverable — if the red region hugs the face\n",
     "without leaking onto ears / top-of-head / neck, the geometric rules are working."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 39,
    "id": "cell-viz-mask",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5cf0bdc8e7e14296a3ed52b46cf90e9d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Widget(value='<iframe src=\"http://localhost:45595/index.html?ui=P_0x7fd8872f52d0_11&reconnect=auto\" class=\"pyv…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "lm_colors = {'Nz': 'lime', 'Iz': 'magenta', 'Cz': 'cyan', 'Lpa': 'orange', 'Rpa': 'blue'}\n",
     "\n",
@@ -871,10 +959,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 40,
    "id": "cell-delete",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original:    678,081 verts, 1,171,705 faces\n",
+      "Anonymized:  567,180 verts, 973,360 faces\n",
+      "Removed:     110,901 verts\n"
+     ]
+    }
+   ],
    "source": [
     "surface_anon = delete_masked_vertices(surface_h, mask)\n",
     "n_removed = surface_h.nvertices - surface_anon.nvertices\n",
@@ -893,17 +991,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 41,
    "id": "cell-beforeafter",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8c51d9cd436b4869b6798e27ff32622f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Widget(value='<iframe src=\"http://localhost:45595/index.html?ui=P_0x7fd898d73150_12&reconnect=auto\" class=\"pyv…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
+    "orig_vtk = trimesh_to_vtk_polydata(surface_h.mesh)\n",
     "anon_vtk = trimesh_to_vtk_polydata(surface_anon.mesh)\n",
     "\n",
     "pvplt = pv.Plotter(shape=(1, 2))\n",
     "\n",
     "pvplt.subplot(0, 0)\n",
-    "cedalion.plots.plot_surface(pvplt, surface_h, opacity=1.0)\n",
+    "pvplt.add_mesh(pv.wrap(orig_vtk), rgb=True, smooth_shading=True)\n",
     "for lbl, pos in zip(landmarks_n['label'].values, lm_n):\n",
     "    pvplt.add_mesh(pv.Sphere(radius=4, center=pos),\n",
     "                   color=lm_colors.get(lbl, 'yellow'))\n",
@@ -936,7 +1050,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 42,
    "id": "cell-validate",
    "metadata": {},
    "outputs": [
@@ -944,7 +1058,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "ValidationResult(face_removed=True, mesh_valid=True, expected_vertices_removed=117152, actual_vertices_removed=117230, protected_points_intact=True, protected_point_max_deviation=0.3121421099641441, face_coverage_pct=23.709748253927778, passed=True, summary='PASSED \u2014 117,230 vertices removed (23.7%), mesh valid, protected points within 5.0mm')\n"
+      "ValidationResult(face_removed=True, mesh_valid=True, expected_vertices_removed=110763, actual_vertices_removed=110901, protected_points_intact=True, protected_point_max_deviation=0.6601578237143165, face_coverage_pct=16.33477416414853, passed=True, summary='PASSED — 110,901 vertices removed (16.3%), mesh valid, protected points within 5.0mm')\n"
      ]
     }
    ],
@@ -959,6 +1073,334 @@
     "print(result)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "02da0b23",
+   "metadata": {},
+   "source": [
+    "## 10. Save the anonymized scan\n",
+    "\n",
+    "Writes only the `.obj` geometry. The texture is stripped so the original face\n",
+    "pixels from the Einstar `.jpg` are not written to disk. No `.mtl`, no `.jpg`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "b9d7aa54",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "with_color=True requested but mesh has no usable texture; falling back to uniform grey vertex colors.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Wrote:\n",
+      "  /home/ma7/BA/PG_Subjects/Subject17/Subject17_anon.obj\n"
+     ]
+    }
+   ],
+   "source": [
+    "out = f'{SCANS_FOLDER}/Subject{SUBJECT_NUMBER}/Subject{SUBJECT_NUMBER}_anon.obj'\n",
+    "written = save_anonymized_scan(surface_anon, out, with_color=True)\n",
+    "print('Wrote:')\n",
+    "for p in written:\n",
+    "    print(f'  {p}')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-md-validation",
+   "metadata": {},
+   "source": [
+    "## 11. Validation via optode coregistration\n",
+    "\n",
+    "Thesis-reporting block. Runs the sticker-detection + scalp-projection half of\n",
+    "notebook 41 on both the pre- and post-anonymization meshes and compares the\n",
+    "resulting coordinates.\n",
+    "\n",
+    "Rationale: the anonymization mask only touches the face region. Optode\n",
+    "stickers sit on the cap, well outside the mask. So sticker centers and\n",
+    "scalp-projected coordinates should be invariant under anonymization. Any\n",
+    "measurable delta localizes boundary leakage of the mask.\n",
+    "\n",
+    "No `.snirf` montage exists for Subject16-22, so we stop at scalp coordinates\n",
+    "(no ICP label assignment). See\n",
+    "`41_photogrammetric_optode_coregistration.ipynb` for the full pipeline."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "cell-val-detect-h",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ -85.14637603  -45.3311207  -550.53511232]\n",
+      " [ -54.06411602 -125.21770066 -556.68425171]\n",
+      " [ -85.56239656  -45.2187465  -550.48027075]\n",
+      " ...\n",
+      " [-251.74365272 -216.53318818 -500.27608009]\n",
+      " [-175.64948551  -29.62507408 -410.93197906]\n",
+      " [-127.42518785 -235.54048938 -407.91215556]]\n",
+      "[[ -55.00372802 -119.87129556 -557.60451328]\n",
+      " [ -54.04357226 -123.86408342 -556.79642487]\n",
+      " [ -18.73912569  -97.45110731 -526.52592796]\n",
+      " ...\n",
+      " [ -89.21370455   20.04153786 -466.36143248]\n",
+      " [ -88.92333199   19.75613378 -466.48283384]\n",
+      " [ -47.2857347   -13.5402963  -484.98917796]]\n",
+      "O (0.11, 0.21, 0.7, 1.0)\n",
+      "[0.4250353  1.35472219 1.7145254  0.39138499 1.29862752 0.62320719\n",
+      " 0.38994548 0.61920156 0.78096456 0.49167592 1.33193309 0.74436605\n",
+      " 1.48520167 1.26870412 0.9219634  1.02609914 0.93732249 0.66719798\n",
+      " 0.08219981 1.76435313 0.45591146]\n",
+      "3.728244425455729\n",
+      "surface.crs digitized\n",
+      "Stickers detected on original: 21\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x2000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from cedalion.geometry.photogrammetry.processors import ColoredStickerProcessor\n",
+    "\n",
+    "# nb41 default yellow range. If count is 0 on Subject16-22, look at the\n",
+    "# vertex-color scatter below and widen the HSV window accordingly.\n",
+    "STICKER_HSV = {\"O\": (0.11, 0.21, 0.7, 1.0)}\n",
+    "OPTODE_LENGTH = 22.6 * cedalion.units.mm\n",
+    "\n",
+    "processor = ColoredStickerProcessor(colors=STICKER_HSV)\n",
+    "centers_h, normals_h, details_h = processor.process(surface_h, details=True)\n",
+    "print(f'Stickers detected on original: {len(centers_h)}')\n",
+    "\n",
+    "details_h.plot_vertex_colors()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "cell-val-detect-anon",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ -85.14637603  -45.3311207  -550.53511232]\n",
+      " [ -54.06411602 -125.21770066 -556.68425171]\n",
+      " [ -85.56239656  -45.2187465  -550.48027075]\n",
+      " ...\n",
+      " [-126.30903811   24.12872194 -446.61837794]\n",
+      " [-251.74365272 -216.53318818 -500.27608009]\n",
+      " [-127.42518785 -235.54048938 -407.91215556]]\n",
+      "[[ -55.00372802 -119.87129556 -557.60451328]\n",
+      " [ -54.04357226 -123.86408342 -556.79642487]\n",
+      " [ -18.73912569  -97.45110731 -526.52592796]\n",
+      " ...\n",
+      " [ -89.21370455   20.04153786 -466.36143248]\n",
+      " [ -88.92333199   19.75613378 -466.48283384]\n",
+      " [ -47.2857347   -13.5402963  -484.98917796]]\n",
+      "O (0.11, 0.21, 0.7, 1.0)\n",
+      "[0.4250353  1.35472219 1.7145254  0.39138499 1.29862752 0.62320719\n",
+      " 0.38994548 0.61920156 0.78096456 0.49167592 1.33193309 0.74436605\n",
+      " 1.48520167 1.26870412 0.9219634  1.02609914 0.93732249 0.66719798\n",
+      " 0.08219981 1.76435313 0.45591146]\n",
+      "3.728244425455729\n",
+      "surface.crs digitized\n",
+      "Stickers detected on original:   21\n",
+      "Stickers detected on anonymized: 21\n",
+      "Labels original:   [np.str_('O-19'), np.str_('O-07'), np.str_('O-04'), np.str_('O-01'), np.str_('O-21'), np.str_('O-10'), np.str_('O-08'), np.str_('O-06'), np.str_('O-18'), np.str_('O-12'), np.str_('O-09'), np.str_('O-15'), np.str_('O-17'), np.str_('O-16'), np.str_('O-14'), np.str_('O-05'), np.str_('O-11'), np.str_('O-02'), np.str_('O-13'), np.str_('O-03'), np.str_('O-20')]\n",
+      "Labels anonymized: [np.str_('O-19'), np.str_('O-07'), np.str_('O-04'), np.str_('O-01'), np.str_('O-21'), np.str_('O-10'), np.str_('O-08'), np.str_('O-06'), np.str_('O-18'), np.str_('O-12'), np.str_('O-09'), np.str_('O-15'), np.str_('O-17'), np.str_('O-16'), np.str_('O-14'), np.str_('O-05'), np.str_('O-11'), np.str_('O-02'), np.str_('O-13'), np.str_('O-03'), np.str_('O-20')]\n"
+     ]
+    }
+   ],
+   "source": [
+    "centers_anon, normals_anon, details_anon = processor.process(\n",
+    "    surface_anon, details=True\n",
+    ")\n",
+    "print(f'Stickers detected on original:   {len(centers_h)}')\n",
+    "print(f'Stickers detected on anonymized: {len(centers_anon)}')\n",
+    "print(f'Labels original:   {list(centers_h[\"label\"].values)}')\n",
+    "print(f'Labels anonymized: {list(centers_anon[\"label\"].values)}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "cell-val-scalp",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Scalp coords computed for both meshes.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Same recipe as nb41 cell 20: subtract optode length along the surface normal.\n",
+    "scalp_h = centers_h - OPTODE_LENGTH * normals_h\n",
+    "scalp_anon = centers_anon - OPTODE_LENGTH * normals_anon\n",
+    "print('Scalp coords computed for both meshes.')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "cell-val-devtable",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Sticker centers (orig -> anon)  (n_orig=21, n_anon=21)\n",
+      "  mean   = 0.000 mm\n",
+      "  median = 0.000 mm\n",
+      "  max    = 0.000 mm\n",
+      "  per-sticker (sorted desc):\n",
+      "    O-19    0.000 mm\n",
+      "    O-07    0.000 mm\n",
+      "    O-04    0.000 mm\n",
+      "    O-01    0.000 mm\n",
+      "    O-21    0.000 mm\n",
+      "    O-10    0.000 mm\n",
+      "    O-08    0.000 mm\n",
+      "    O-06    0.000 mm\n",
+      "    O-18    0.000 mm\n",
+      "    O-12    0.000 mm\n",
+      "    O-09    0.000 mm\n",
+      "    O-15    0.000 mm\n",
+      "    O-17    0.000 mm\n",
+      "    O-16    0.000 mm\n",
+      "    O-14    0.000 mm\n",
+      "    O-05    0.000 mm\n",
+      "    O-11    0.000 mm\n",
+      "    O-02    0.000 mm\n",
+      "    O-13    0.000 mm\n",
+      "    O-03    0.000 mm\n",
+      "    O-20    0.000 mm\n",
+      "\n",
+      "Scalp coords   (orig -> anon)  (n_orig=21, n_anon=21)\n",
+      "  mean   = 0.000 mm\n",
+      "  median = 0.000 mm\n",
+      "  max    = 0.000 mm\n",
+      "  per-sticker (sorted desc):\n",
+      "    O-19    0.000 mm\n",
+      "    O-07    0.000 mm\n",
+      "    O-04    0.000 mm\n",
+      "    O-01    0.000 mm\n",
+      "    O-21    0.000 mm\n",
+      "    O-10    0.000 mm\n",
+      "    O-08    0.000 mm\n",
+      "    O-06    0.000 mm\n",
+      "    O-18    0.000 mm\n",
+      "    O-12    0.000 mm\n",
+      "    O-09    0.000 mm\n",
+      "    O-15    0.000 mm\n",
+      "    O-17    0.000 mm\n",
+      "    O-16    0.000 mm\n",
+      "    O-14    0.000 mm\n",
+      "    O-05    0.000 mm\n",
+      "    O-11    0.000 mm\n",
+      "    O-02    0.000 mm\n",
+      "    O-13    0.000 mm\n",
+      "    O-03    0.000 mm\n",
+      "    O-20    0.000 mm\n"
+     ]
+    }
+   ],
+   "source": [
+    "from scipy.spatial import KDTree\n",
+    "\n",
+    "def _deviation_table(src, dst, title):\n",
+    "    # Labels from two independent runs are just sequential counters and will\n",
+    "    # not align, so we match by nearest neighbour.\n",
+    "    s = src.pint.to('mm').pint.dequantify().values\n",
+    "    d = dst.pint.to('mm').pint.dequantify().values\n",
+    "    tree = KDTree(d)\n",
+    "    dists, _ = tree.query(s)\n",
+    "    print(f'\\n{title}  (n_orig={len(s)}, n_anon={len(d)})')\n",
+    "    print(f'  mean   = {dists.mean():.3f} mm')\n",
+    "    print(f'  median = {np.median(dists):.3f} mm')\n",
+    "    print(f'  max    = {dists.max():.3f} mm')\n",
+    "    print(f'  per-sticker (sorted desc):')\n",
+    "    for k in np.argsort(-dists):\n",
+    "        lab = str(src['label'].values[k])\n",
+    "        print(f'    {lab:6s}  {dists[k]:.3f} mm')\n",
+    "\n",
+    "_deviation_table(centers_h, centers_anon, 'Sticker centers (orig -> anon)')\n",
+    "_deviation_table(scalp_h,   scalp_anon,   'Scalp coords   (orig -> anon)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "cell-val-viz",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "92c511a8dbb14060b757879492bcbda3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Widget(value='<iframe src=\"http://localhost:45595/index.html?ui=P_0x7fd9453df290_13&reconnect=auto\" class=\"pyv…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pvplt = pv.Plotter(shape=(1, 2))\n",
+    "\n",
+    "pvplt.subplot(0, 0)\n",
+    "cedalion.plots.plot_surface(pvplt, surface_h, opacity=1.0)\n",
+    "cedalion.plots.plot_labeled_points(pvplt, centers_h, color='r')\n",
+    "cedalion.plots.plot_vector_field(pvplt, centers_h, normals_h)\n",
+    "pvplt.add_text(\n",
+    "    f'S{SUBJECT_NUMBER} Original  ({len(centers_h)} stickers)',\n",
+    "    position='upper_left', font_size=14,\n",
+    ")\n",
+    "\n",
+    "pvplt.subplot(0, 1)\n",
+    "cedalion.plots.plot_surface(pvplt, surface_anon, opacity=1.0)\n",
+    "cedalion.plots.plot_labeled_points(pvplt, centers_anon, color='r')\n",
+    "cedalion.plots.plot_vector_field(pvplt, centers_anon, normals_anon)\n",
+    "pvplt.add_text(\n",
+    "    f'S{SUBJECT_NUMBER} Anonymized  ({len(centers_anon)} stickers)',\n",
+    "    position='upper_left', font_size=14,\n",
+    ")\n",
+    "\n",
+    "pvplt.link_views()\n",
+    "pvplt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "cell-md-footer",
@@ -972,7 +1414,7 @@
     "  (tunable at the top of the notebook).\n",
     "- **Anonymization method:** vertex deletion (drop faces touching masked vertices,\n",
     "  strip unreferenced vertices). No Taubin smoothing. No MediaPipe.\n",
-    "- **Contract with the expos\u00e9:** this notebook assumes NAS/LPA/RPA are given and\n",
+    "- **Contract with the exposé:** this notebook assumes NAS/LPA/RPA are given and\n",
     "  uses no DL face detection. Cz and Iz are also given and used as vertical bounds;\n",
     "  they aren't strictly required but make the top/bottom cuts landmark-driven\n",
     "  instead of mesh-extent-driven.\n",
@@ -1003,4 +1445,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
\ No newline at end of file
+}
diff --git a/examples/head_models/49_anonymization_audit.ipynb b/examples/head_models/49_anonymization_audit.ipynb
new file mode 100644
index 00000000..51891010
--- /dev/null
+++ b/examples/head_models/49_anonymization_audit.ipynb
@@ -0,0 +1,195 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "435f26df",
+   "metadata": {},
+   "source": [
+    "# Anonymization Audit: Can the Face Be Reconstructed?\n",
+    "\n",
+    "Loads the saved anonymized OBJ from notebook 48 and tries to get the face back.\n",
+    "Goal: demonstrate that the saved file contains no face data and that naive\n",
+    "reconstruction (hole-filling) does not recover the subject's features.\n",
+    "\n",
+    "**Inputs**\n",
+    "- `SubjectN.obj` - original photogrammetry scan (face present)\n",
+    "- `SubjectN_anon.obj` - output of notebook 48 (face region deleted, no texture)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a16006a5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Files written by the anonymization save step:\n",
+      "  Subject17_anon.obj\n",
+      "\n",
+      "OK: only the .obj was written. No .mtl / .jpg on disk.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "\n",
+    "import numpy as np\n",
+    "import pyvista as pv\n",
+    "import trimesh\n",
+    "\n",
+    "import cedalion\n",
+    "import cedalion.io\n",
+    "import cedalion.plots\n",
+    "\n",
+    "pv.set_jupyter_backend('server')\n",
+    "\n",
+    "SUBJECT_NUMBER = 17\n",
+    "SCANS_FOLDER = '/home/ma7/BA/PG_Subjects'\n",
+    "\n",
+    "orig_path = f'{SCANS_FOLDER}/Subject{SUBJECT_NUMBER}/Subject{SUBJECT_NUMBER}.obj'\n",
+    "anon_path = f'{SCANS_FOLDER}/Subject{SUBJECT_NUMBER}/Subject{SUBJECT_NUMBER}_anon.obj'\n",
+    "anon_dir = os.path.dirname(anon_path)\n",
+    "stem = f'Subject{SUBJECT_NUMBER}_anon'\n",
+    "siblings = sorted(f for f in os.listdir(anon_dir) if f.startswith(stem))\n",
+    "print('Files written by the anonymization save step:')\n",
+    "for f in siblings:\n",
+    "    print(f'  {f}')\n",
+    "assert f'{stem}.obj' in siblings, 'expected .obj not found -- run notebook 48 first'\n",
+    "assert f'{stem}.mtl' not in siblings, 'unexpected .mtl on disk -- texture leaked'\n",
+    "assert f'{stem}.jpg' not in siblings, 'unexpected .jpg on disk -- face pixels leaked'\n",
+    "print('\\nOK: only the .obj was written. No .mtl / .jpg on disk.')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ab69f81",
+   "metadata": {},
+   "source": [
+    "## 1. Load original and anonymized scans"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b76c3b29",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Original:    1,284,667 verts, 2,223,716 faces\n",
+      "Anonymized:   475,536 verts,  948,028 faces\n",
+      "Removed:      809,131 verts, 1,275,688 faces\n",
+      "\n",
+      "Anonymized visual type: ColorVisuals\n",
+      "  has UV map:      False\n",
+      "  has texture img: False\n"
+     ]
+    }
+   ],
+   "source": [
+    "surface_orig = cedalion.io.read_einstar_obj(orig_path)\n",
+    "surface_anon = cedalion.io.read_einstar_obj(anon_path)\n",
+    "\n",
+    "print(f'Original:    {surface_orig.nvertices:>8,} verts, {surface_orig.nfaces:>8,} faces')\n",
+    "print(f'Anonymized:  {surface_anon.nvertices:>8,} verts, {surface_anon.nfaces:>8,} faces')\n",
+    "print(f'Removed:     {surface_orig.nvertices - surface_anon.nvertices:>8,} verts, '\n",
+    "      f'{surface_orig.nfaces - surface_anon.nfaces:>8,} faces')\n",
+    "\n",
+    "anon_visual = surface_anon.mesh.visual\n",
+    "print(f'\\nAnonymized visual type: {type(anon_visual).__name__}')\n",
+    "has_uv = getattr(anon_visual, 'uv', None) is not None\n",
+    "has_img = getattr(anon_visual, 'image', None) is not None\n",
+    "print(f'  has UV map:      {has_uv}')\n",
+    "print(f'  has texture img: {has_img}')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c67dcdc3",
+   "metadata": {},
+   "source": [
+    "## 2. Side-by-side render\n",
+    "\n",
+    "Left: original subject. Right: reloaded anonymized scan. The anonymized side\n",
+    "should show a visible hole where the face was."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "8e21a69e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "520b4711aad6414fa8a7f6ded512b5fe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Widget(value='<iframe src=\"http://localhost:45309/index.html?ui=P_0x7f67384fc090_0&reconnect=auto\" class=\"pyvi…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pvplt = pv.Plotter(shape=(1, 2))\n",
+    "\n",
+    "pvplt.subplot(0, 0)\n",
+    "pvplt.add_text('Original', font_size=10)\n",
+    "cedalion.plots.plot_surface(pvplt, surface_orig, opacity=1.0)\n",
+    "\n",
+    "pvplt.subplot(0, 1)\n",
+    "pvplt.add_text('Anonymized (reloaded)', font_size=10)\n",
+    "cedalion.plots.plot_surface(pvplt, surface_anon, opacity=1.0)\n",
+    "\n",
+    "pvplt.link_views()\n",
+    "pvplt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d55072ad",
+   "metadata": {},
+   "source": [
+    "## Conclusion\n",
+    "\n",
+    "- Only the `.obj` geometry leaves the process. No `.mtl`, no `.jpg`, no texture\n",
+    "  raster with the subject's face pixels.\n",
+    "- The reloaded anonymized mesh has a visible hole where the face region was.\n",
+    "- The saved file contains only geometry for the non-face regions; the face\n",
+    "  vertices and the entire texture image are gone, so the subject's face\n",
+    "  cannot be reconstructed from it."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/__init__.py b/src/cedalion/geometry/photogrammetry/anonymization/__init__.py
index 8e87e1de..3d2c5fac 100644
--- a/src/cedalion/geometry/photogrammetry/anonymization/__init__.py
+++ b/src/cedalion/geometry/photogrammetry/anonymization/__init__.py
@@ -44,6 +44,7 @@
     detect_cap_boundary,
     face_mask_from_landmarks,
     delete_masked_vertices,
+    save_anonymized_scan,
 )
 from .validator import validate_anonymization
 
@@ -59,6 +60,7 @@
     "detect_cap_boundary",
     "face_mask_from_landmarks",
     "delete_masked_vertices",
+    "save_anonymized_scan",
     # Validation
     "validate_anonymization",
 ]
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/mask.py b/src/cedalion/geometry/photogrammetry/anonymization/mask.py
index deb07d62..640467c0 100644
--- a/src/cedalion/geometry/photogrammetry/anonymization/mask.py
+++ b/src/cedalion/geometry/photogrammetry/anonymization/mask.py
@@ -156,18 +156,160 @@ def delete_masked_vertices(
 ) -> cdc.TrimeshSurface:
     """Drop triangles touching any masked vertex and strip unreferenced vertices.
 
+    Reindexes ``mesh.visual.uv`` (if present) in lockstep with the vertex array.
+    Trimesh's ``remove_unreferenced_vertices`` does not touch UV arrays, so a
+    naive call leaves ``len(uv) != len(vertices)`` and any subsequent textured
+    export crashes or corrupts.
+
     Args:
         surface: Input TrimeshSurface.
         mask: Boolean array of shape (n_vertices,). True = vertex to remove.
 
     Returns:
         New TrimeshSurface with masked vertices (and the faces touching them)
-        removed. CRS and units are preserved.
+        removed. CRS, units, UVs, and texture image are preserved in sync.
     """
-    mesh_copy = surface.mesh.copy()
-    faces_to_remove = mask[mesh_copy.faces].any(axis=1)
-    mesh_copy.update_faces(~faces_to_remove)
-    mesh_copy.remove_unreferenced_vertices()
+    import trimesh
+
+    old_mesh = surface.mesh
+    old_verts = np.asarray(old_mesh.vertices)
+    old_faces = np.asarray(old_mesh.faces)
+    n_old_verts = len(old_verts)
+
+    kept_face_mask = ~mask[old_faces].any(axis=1)
+    kept_faces = old_faces[kept_face_mask]
+    kept_vidx = np.unique(kept_faces)
+
+    reindex = -np.ones(n_old_verts, dtype=np.int64)
+    reindex[kept_vidx] = np.arange(len(kept_vidx))
+
+    new_mesh = trimesh.Trimesh(
+        vertices=old_verts[kept_vidx],
+        faces=reindex[kept_faces],
+        process=False,
+    )
+
+    old_visual = old_mesh.visual
+    uv = getattr(old_visual, "uv", None)
+    if uv is not None and len(uv) == n_old_verts:
+        new_mesh.visual = trimesh.visual.TextureVisuals(
+            uv=np.asarray(uv)[kept_vidx],
+            image=getattr(old_visual, "image", None),
+            material=getattr(old_visual, "material", None),
+        )
+    else:
+        vcol = getattr(old_visual, "vertex_colors", None)
+        if vcol is not None and len(vcol) == n_old_verts:
+            new_mesh.visual.vertex_colors = np.asarray(vcol)[kept_vidx]
+
     return cdc.TrimeshSurface(
-        mesh=mesh_copy, crs=surface.crs, units=surface.units,
+        mesh=new_mesh, crs=surface.crs, units=surface.units,
+    )
+
+
+def _bake_vertex_colors(mesh) -> np.ndarray:
+    """Sample the texture image at each vertex's UV to produce per-vertex RGBA.
+
+    OBJ UVs use the bottom-left origin convention; PIL images use top-left,
+    so V is flipped before indexing. UVs outside ``[0, 1]`` are wrapped
+    (modulo 1) to match typical OBJ sampler behavior.
+
+    Args:
+        mesh: A ``trimesh.Trimesh`` with ``mesh.visual`` as ``TextureVisuals``.
+
+    Returns:
+        ``(n_vertices, 4)`` uint8 RGBA array. Falls back to uniform mid-grey
+        (with a warning) if the mesh has no usable texture.
+    """
+    visual = mesh.visual
+    uv = getattr(visual, "uv", None)
+    image = getattr(visual, "image", None)
+    if image is None:
+        mat = getattr(visual, "material", None)
+        image = getattr(mat, "image", None) if mat is not None else None
+    n_verts = len(mesh.vertices)
+
+    fallback = np.full((n_verts, 4), 180, dtype=np.uint8)
+    fallback[:, 3] = 255
+
+    if uv is None or image is None or len(uv) != n_verts:
+        logger.warning(
+            "with_color=True requested but mesh has no usable texture; "
+            "falling back to uniform grey vertex colors."
+        )
+        return fallback
+
+    img = np.asarray(image.convert("RGBA"))
+    h, w = img.shape[:2]
+
+    u = np.mod(np.asarray(uv)[:, 0], 1.0)
+    v = np.mod(np.asarray(uv)[:, 1], 1.0)
+    px = np.clip((u * w).astype(np.int64), 0, w - 1)
+    py = np.clip(((1.0 - v) * h).astype(np.int64), 0, h - 1)
+
+    return img[py, px]
+
+
+def save_anonymized_scan(
+    surface: cdc.TrimeshSurface,
+    out_path: str,
+    with_color: bool = False,
+) -> list[str]:
+    """Export an anonymized photogrammetry surface to disk.
+
+    Always writes only the ``.obj`` file -- no MTL, no JPG. The original
+    texture raster is never written alongside the mesh, so the subject's face
+    pixels cannot leak through a saved image.
+
+    - ``with_color=False`` (default): strip the texture entirely. Smallest
+      file, geometry only.
+    - ``with_color=True``: sample the texture at each kept vertex's UV and
+      bake the result as per-vertex colors inline in the ``.obj``. The JPG
+      is consumed in-memory and discarded; no raster is written.
+
+    Args:
+        surface: Anonymized TrimeshSurface (typically output of
+            ``delete_masked_vertices``).
+        out_path: Destination path ending in ``.obj``.
+        with_color: If True, bake per-vertex colors from the texture.
+
+    Returns:
+        List of absolute paths written.
+
+    Raises:
+        ValueError: If ``out_path`` does not end in ``.obj``.
+    """
+    import os
+    import trimesh
+
+    if not out_path.lower().endswith(".obj"):
+        raise ValueError(f"out_path must end in .obj, got: {out_path}")
+
+    vcolors = _bake_vertex_colors(surface.mesh) if with_color else None
+
+    mesh_to_write = trimesh.Trimesh(
+        vertices=np.asarray(surface.mesh.vertices),
+        faces=np.asarray(surface.mesh.faces),
+        process=False,
+    )
+    if vcolors is not None:
+        mesh_to_write.visual.vertex_colors = vcolors
+
+    out_dir = os.path.dirname(os.path.abspath(out_path)) or "."
+    before = set(os.listdir(out_dir)) if os.path.isdir(out_dir) else set()
+
+    mesh_to_write.export(out_path)
+
+    after = set(os.listdir(out_dir)) if os.path.isdir(out_dir) else set()
+    stem = os.path.splitext(os.path.basename(out_path))[0]
+    written = sorted(
+        os.path.join(out_dir, f)
+        for f in (after - before) | {os.path.basename(out_path)}
+        if os.path.splitext(f)[0] == stem or f == os.path.basename(out_path)
+    )
+
+    logger.info(
+        f"Saved anonymized scan (with_color={with_color}): "
+        f"{[os.path.basename(p) for p in written]}"
     )
+    return written
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/preprocessing.py b/src/cedalion/geometry/photogrammetry/anonymization/preprocessing.py
index 9e898bc3..663009d4 100644
--- a/src/cedalion/geometry/photogrammetry/anonymization/preprocessing.py
+++ b/src/cedalion/geometry/photogrammetry/anonymization/preprocessing.py
@@ -22,6 +22,50 @@
 logger = logging.getLogger("cedalion")
 
 
+def _copy_visual(src_mesh, dst_mesh, vertex_index=None) -> None:
+    """Copy a trimesh ``visual`` onto ``dst_mesh``, optionally reindexing.
+
+    Assigning a ``TextureVisuals`` directly across meshes (e.g. by passing
+    ``visual=old.visual`` to a new ``Trimesh(...)``) often silently downgrades
+    it to ``ColorVisuals`` because the visual holds a back-reference to its
+    original mesh and re-checks ``len(uv) == len(mesh.vertices)`` against the
+    wrong target. Rebuilding the visual explicitly avoids that.
+
+    Args:
+        src_mesh: Source trimesh.
+        dst_mesh: Destination trimesh (mutated in place).
+        vertex_index: Optional indices into the source vertex array. If given,
+            UVs / vertex_colors are sliced by this index before being attached.
+            Leave None when the vertex count is unchanged.
+    """
+    import trimesh
+
+    src_visual = src_mesh.visual
+    uv = getattr(src_visual, "uv", None)
+    image = getattr(src_visual, "image", None)
+    n_src = len(src_mesh.vertices)
+
+    if uv is not None and len(uv) == n_src:
+        uv_arr = np.asarray(uv)
+        if vertex_index is not None:
+            uv_arr = uv_arr[vertex_index]
+        if image is None:
+            mat = getattr(src_visual, "material", None)
+            image = getattr(mat, "image", None) if mat is not None else None
+        dst_mesh.visual = trimesh.visual.TextureVisuals(
+            uv=uv_arr,
+            image=image,
+        )
+        return
+
+    vcol = getattr(src_visual, "vertex_colors", None)
+    if vcol is not None and len(vcol) == n_src:
+        vcol_arr = np.asarray(vcol)
+        if vertex_index is not None:
+            vcol_arr = vcol_arr[vertex_index]
+        dst_mesh.visual.vertex_colors = vcol_arr
+
+
 def normalize_axes(
     surface: cdc.TrimeshSurface,
     nasion: np.ndarray,
@@ -74,9 +118,9 @@ def normalize_axes(
     new_mesh = trimesh.Trimesh(
         vertices=rotated_verts,
         faces=surface.mesh.faces,
-        visual=surface.mesh.visual,
         process=False,
     )
+    _copy_visual(surface.mesh, new_mesh)
     rotated_surface = cdc.TrimeshSurface(new_mesh, crs=surface.crs, units=surface.units)
 
     rotated_nasion = R @ nasion
@@ -204,12 +248,7 @@ def isolate_head(
         faces=new_faces,
         process=False,
     )
-    try:
-        old_visual = surface.mesh.visual
-        if hasattr(old_visual, "vertex_colors") and len(old_visual.vertex_colors) == len(vertices):
-            new_mesh.visual.vertex_colors = old_visual.vertex_colors[kept_verts]
-    except Exception:
-        pass
+    _copy_visual(surface.mesh, new_mesh, vertex_index=kept_verts)
 
     head_surface = cdc.TrimeshSurface(
         new_mesh, crs=surface.crs, units=surface.units
@@ -289,9 +328,9 @@ def align_axes_from_landmarks(
     new_mesh = trimesh.Trimesh(
         vertices=aligned_verts,
         faces=surface.mesh.faces,
-        visual=surface.mesh.visual,
         process=False,
     )
+    _copy_visual(surface.mesh, new_mesh)
     aligned_surface = cdc.TrimeshSurface(
         new_mesh, crs=surface.crs, units=surface.units,
     )

From 0db1c62f89966a9009d41073a5b45e19836d9070 Mon Sep 17 00:00:00 2001
From: mah-diaa <mahmoud.diaa.abdeen@gmail.com>
Date: Tue, 21 Apr 2026 19:29:56 +0200
Subject: [PATCH 16/39] Switch anonymization to CTF frame; add
 revert_to_einstar_frame and landmarks TSV export

---
 .../48_manual_5pt_anonymization.ipynb         |  710 ++---------
 .../head_models/49_seam_diagnostics.ipynb     | 1107 +++++++++++++++++
 .../50_mask_selection_walkthrough.ipynb       | 1072 ++++++++++++++++
 .../photogrammetry/anonymization/__init__.py  |   12 +-
 .../photogrammetry/anonymization/mask.py      |  116 +-
 .../anonymization/preprocessing.py            |  133 +-
 6 files changed, 2483 insertions(+), 667 deletions(-)
 create mode 100644 examples/head_models/49_seam_diagnostics.ipynb
 create mode 100644 examples/head_models/50_mask_selection_walkthrough.ipynb

diff --git a/examples/head_models/48_manual_5pt_anonymization.ipynb b/examples/head_models/48_manual_5pt_anonymization.ipynb
index aebb1239..dce6e4c8 100644
--- a/examples/head_models/48_manual_5pt_anonymization.ipynb
+++ b/examples/head_models/48_manual_5pt_anonymization.ipynb
@@ -2,33 +2,27 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "cell-md-title",
+   "id": "13944ffe",
    "metadata": {},
    "source": [
-    "# Face Anonymization from 5 Given Landmarks (cedalion-only)\n",
-    "\n",
-    "Pure-geometric face-removal pipeline for Einstar scans. Takes the 5 standard\n",
-    "anatomical landmarks (Nz, Iz, Cz, Lpa, Rpa) as input and deletes the facial\n",
-    "region without any ML / face-detection dependency.\n",
-    "\n",
-    "**Pipeline:**\n",
-    "1. Load scan\n",
-    "2. Pick 5 landmarks interactively via `cedalion.plots.plot_surface(pick_landmarks=True)`\n",
-    "3. Wrap into a `LabeledPointCloud`\n",
-    "4. Normalize axes (X=up, Y=anterior, Z=left) and isolate head\n",
-    "5. Build face-region mask purely from the 5 landmarks\n",
-    "6. Visualize the mask\n",
-    "7. Delete masked vertices (no smoothing)\n",
-    "8. Validate result\n",
-    "\n",
-    "This is the cedalion-only alternative to notebook 47's MediaPipe pipeline and\n",
-    "matches the exposé contract: NAS/LPA/RPA assumed given, no DL face detection."
+    "# Face Anonymization from 5 Given Landmarks\n",
+    "\n",
+    "Pure-geometric face-removal pipeline for Einstar photogrammetry scans. Takes the\n",
+    "5 standard anatomical landmarks (Nz, Iz, Cz, Lpa, Rpa) as input and deletes the\n",
+    "facial region, then reverts the result back to the raw Einstar (digitized)\n",
+    "frame so the saved `.obj` + `.tsv` are a drop-in replacement for the original\n",
+    "scan at co-registration time.\n",
+    "\n",
+    "**User-visible flow:** load scan &rarr; pick 5 landmarks &rarr; review the\n",
+    "anonymization mask &rarr; review the anonymized scan &rarr; save. All\n",
+    "intermediate transforms (normalize, align to CTF, build mask, delete verts)\n",
+    "run inside a single bundled cell.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
-   "id": "cell-imports",
+   "execution_count": 2,
+   "id": "06ba7b71",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -36,7 +30,6 @@
     "\n",
     "import numpy as np\n",
     "import pyvista as pv\n",
-    "import matplotlib.pyplot as plt\n",
     "import xarray as xr\n",
     "\n",
     "import cedalion\n",
@@ -50,8 +43,8 @@
     "    detect_cap_boundary,\n",
     "    face_mask_from_landmarks,\n",
     "    delete_masked_vertices,\n",
+    "    revert_to_einstar_frame,\n",
     "    save_anonymized_scan,\n",
-    "    validate_anonymization,\n",
     ")\n",
     "from cedalion.vtktutils import trimesh_to_vtk_polydata\n",
     "from cedalion.dataclasses import VTKSurface\n",
@@ -73,7 +66,7 @@
     "# Cap boundary detection tunables\n",
     "CAP_BAND_WIDTH_MM = 15.0\n",
     "CAP_BIN_SIZE_MM = 2.0\n",
-    "CAP_FOOT_GRAD_THRESHOLD = 0.2"
+    "CAP_FOOT_GRAD_THRESHOLD = 0.2\n"
    ]
   },
   {
@@ -86,7 +79,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 3,
    "id": "cell-load",
    "metadata": {},
    "outputs": [
@@ -151,7 +144,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 4,
    "id": "cell-pick",
    "metadata": {
     "scrolled": true
@@ -160,12 +153,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "deb2def326744ed882bbe4b27279aa0d",
+       "model_id": "2440d4727fa641c4ae68312a13f61bcb",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Widget(value='<iframe src=\"http://localhost:45595/index.html?ui=P_0x7fdaa44a0610_10&reconnect=auto\" class=\"pyv…"
+       "Widget(value='<iframe src=\"http://localhost:36901/index.html?ui=P_0x7fc4483098d0_0&reconnect=auto\" class=\"pyvi…"
       ]
      },
      "metadata": {},
@@ -193,7 +186,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 5,
    "id": "cell-retrieve",
    "metadata": {},
    "outputs": [
@@ -644,26 +637,26 @@
        "  stroke-width: 0.8px;\n",
        "}\n",
        "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (label: 5, digitized: 3)&gt; Size: 120B\n",
-       "&lt;Quantity([[182.78403277 119.00435797 431.95041939]\n",
-       " [ 53.21676428 219.20228469 461.11116233]\n",
-       " [ 69.61090726  81.05491959 564.60127688]\n",
-       " [ 71.16199192 -12.73670574 478.0462117 ]\n",
-       " [ -9.01177257  59.78899075 423.63767742]], &#x27;millimeter&#x27;)&gt;\n",
+       "&lt;Quantity([[182.92980578 117.94829963 432.43934491]\n",
+       " [ 51.16184581 217.28322321 443.32434353]\n",
+       " [ 67.06696483  69.92108889 559.08501031]\n",
+       " [ 68.98745639 -11.99765232 480.48408272]\n",
+       " [  4.45574294  59.0529612  397.73517575]], &#x27;millimeter&#x27;)&gt;\n",
        "Coordinates:\n",
        "  * label    (label) &lt;U3 60B &#x27;Cz&#x27; &#x27;Iz&#x27; &#x27;Rpa&#x27; &#x27;Nz&#x27; &#x27;Lpa&#x27;\n",
        "    type     (label) object 40B PointType.LANDMARK ... PointType.LANDMARK\n",
        "    group    (label) &lt;U1 20B &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27;\n",
-       "Dimensions without coordinates: digitized</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>label</span>: 5</li><li><span>digitized</span>: 3</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-982928f4-ff25-44b6-98cb-81856dba0493' class='xr-array-in' type='checkbox' checked><label for='section-982928f4-ff25-44b6-98cb-81856dba0493' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>[mm] 182.8 119.0 432.0 53.22 219.2 ... -12.74 478.0 -9.012 59.79 423.6</span></div><div class='xr-array-data'><table><tbody><tr><th>Magnitude</th><td style='text-align:left;'><pre>[[182.78403277337682 119.00435797327856 431.95041939265843] [53.21676427926042 219.20228469014984 461.11116232532544] [69.61090726159273 81.05491958613379 564.6012768794676] [71.16199191638447 -12.73670574349066 478.04621170118133] [-9.011772572255389 59.78899074619693 423.63767741978666]]</pre></td></tr><tr><th>Units</th><td style='text-align:left;'>millimeter</td></tr></tbody></table></div></div></li><li class='xr-section-item'><input id='section-75a36191-a736-4450-8928-dd186f491336' class='xr-section-summary-in' type='checkbox'  checked><label for='section-75a36191-a736-4450-8928-dd186f491336' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>label</span></div><div class='xr-var-dims'>(label)</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;Cz&#x27; &#x27;Iz&#x27; &#x27;Rpa&#x27; &#x27;Nz&#x27; &#x27;Lpa&#x27;</div><input id='attrs-0ef053f4-e287-4bd2-833a-bfb6abf009bc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0ef053f4-e287-4bd2-833a-bfb6abf009bc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22b84fba-1fdf-444b-b150-110e15031afc' class='xr-var-data-in' type='checkbox'><label for='data-22b84fba-1fdf-444b-b150-110e15031afc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Cz&#x27;, &#x27;Iz&#x27;, &#x27;Rpa&#x27;, &#x27;Nz&#x27;, &#x27;Lpa&#x27;], dtype=&#x27;&lt;U3&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>type</span></div><div class='xr-var-dims'>(label)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>PointType.LANDMARK ... PointType...</div><input id='attrs-6d1ebfa0-9b01-421f-b7ad-b92480da1450' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6d1ebfa0-9b01-421f-b7ad-b92480da1450' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-03de491a-5ccd-457b-9f81-358b346c95a8' class='xr-var-data-in' type='checkbox'><label for='data-03de491a-5ccd-457b-9f81-358b346c95a8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&lt;PointType.LANDMARK: 3&gt;, &lt;PointType.LANDMARK: 3&gt;,\n",
+       "Dimensions without coordinates: digitized</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>label</span>: 5</li><li><span>digitized</span>: 3</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-c7e240b6-477c-4cea-a5a0-08bffb9c29df' class='xr-array-in' type='checkbox' checked><label for='section-c7e240b6-477c-4cea-a5a0-08bffb9c29df' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>[mm] 182.9 117.9 432.4 51.16 217.3 ... -12.0 480.5 4.456 59.05 397.7</span></div><div class='xr-array-data'><table><tbody><tr><th>Magnitude</th><td style='text-align:left;'><pre>[[182.92980578177185 117.94829963376296 432.4393449119677] [51.161845807845125 217.2832232100858 443.32434353006687] [67.06696482873602 69.92108888540216 559.0850103085918] [68.98745638597626 -11.997652317177597 480.4840827150168] [4.455742939104287 59.052961199856185 397.7351757544122]]</pre></td></tr><tr><th>Units</th><td style='text-align:left;'>millimeter</td></tr></tbody></table></div></div></li><li class='xr-section-item'><input id='section-ab807f34-29b6-473e-a4d3-b710f5410ff5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ab807f34-29b6-473e-a4d3-b710f5410ff5' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>label</span></div><div class='xr-var-dims'>(label)</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;Cz&#x27; &#x27;Iz&#x27; &#x27;Rpa&#x27; &#x27;Nz&#x27; &#x27;Lpa&#x27;</div><input id='attrs-e77d5fdd-2d82-495c-bd66-44334c6b0348' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e77d5fdd-2d82-495c-bd66-44334c6b0348' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3555b2d1-0bae-415b-8851-c2e168d182ab' class='xr-var-data-in' type='checkbox'><label for='data-3555b2d1-0bae-415b-8851-c2e168d182ab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Cz&#x27;, &#x27;Iz&#x27;, &#x27;Rpa&#x27;, &#x27;Nz&#x27;, &#x27;Lpa&#x27;], dtype=&#x27;&lt;U3&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>type</span></div><div class='xr-var-dims'>(label)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>PointType.LANDMARK ... PointType...</div><input id='attrs-0c7e3615-90e8-40de-9f3d-c69ce8874105' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0c7e3615-90e8-40de-9f3d-c69ce8874105' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97c8f51a-043c-4f60-8c99-ffe21a698043' class='xr-var-data-in' type='checkbox'><label for='data-97c8f51a-043c-4f60-8c99-ffe21a698043' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&lt;PointType.LANDMARK: 3&gt;, &lt;PointType.LANDMARK: 3&gt;,\n",
        "       &lt;PointType.LANDMARK: 3&gt;, &lt;PointType.LANDMARK: 3&gt;,\n",
-       "       &lt;PointType.LANDMARK: 3&gt;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>group</span></div><div class='xr-var-dims'>(label)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27;</div><input id='attrs-b054ccc6-36f3-4b5a-a648-b1bf7c5adf07' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b054ccc6-36f3-4b5a-a648-b1bf7c5adf07' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-497fdfa1-e8b7-4bf1-97bd-ef7867387c46' class='xr-var-data-in' type='checkbox'><label for='data-497fdfa1-e8b7-4bf1-97bd-ef7867387c46' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;L&#x27;, &#x27;L&#x27;, &#x27;L&#x27;, &#x27;L&#x27;, &#x27;L&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-af4a5770-8e85-488e-a59c-780969729f33' class='xr-section-summary-in' type='checkbox'  ><label for='section-af4a5770-8e85-488e-a59c-780969729f33' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>label</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-39b85087-a3cc-470d-9367-fb592e2dff3f' class='xr-index-data-in' type='checkbox'/><label for='index-39b85087-a3cc-470d-9367-fb592e2dff3f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;Cz&#x27;, &#x27;Iz&#x27;, &#x27;Rpa&#x27;, &#x27;Nz&#x27;, &#x27;Lpa&#x27;], dtype=&#x27;object&#x27;, name=&#x27;label&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cf25c197-3842-4b92-82fb-1153a7b77718' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cf25c197-3842-4b92-82fb-1153a7b77718' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "       &lt;PointType.LANDMARK: 3&gt;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>group</span></div><div class='xr-var-dims'>(label)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27;</div><input id='attrs-d1f4356e-0350-4559-9c2b-8387efe74717' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d1f4356e-0350-4559-9c2b-8387efe74717' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c82da01-f9cc-4e59-a3dc-b51a2ef7d108' class='xr-var-data-in' type='checkbox'><label for='data-0c82da01-f9cc-4e59-a3dc-b51a2ef7d108' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;L&#x27;, &#x27;L&#x27;, &#x27;L&#x27;, &#x27;L&#x27;, &#x27;L&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ba1a0eb3-2857-473a-b1bf-24278c62897b' class='xr-section-summary-in' type='checkbox'  ><label for='section-ba1a0eb3-2857-473a-b1bf-24278c62897b' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>label</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-061d36ed-dff4-4fc3-b36c-f1c383d70796' class='xr-index-data-in' type='checkbox'/><label for='index-061d36ed-dff4-4fc3-b36c-f1c383d70796' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;Cz&#x27;, &#x27;Iz&#x27;, &#x27;Rpa&#x27;, &#x27;Nz&#x27;, &#x27;Lpa&#x27;], dtype=&#x27;object&#x27;, name=&#x27;label&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ce14ef59-44c9-4e3b-afa4-60dceaaf6a6f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ce14ef59-44c9-4e3b-afa4-60dceaaf6a6f' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.DataArray (label: 5, digitized: 3)> Size: 120B\n",
-       "<Quantity([[182.78403277 119.00435797 431.95041939]\n",
-       " [ 53.21676428 219.20228469 461.11116233]\n",
-       " [ 69.61090726  81.05491959 564.60127688]\n",
-       " [ 71.16199192 -12.73670574 478.0462117 ]\n",
-       " [ -9.01177257  59.78899075 423.63767742]], 'millimeter')>\n",
+       "<Quantity([[182.92980578 117.94829963 432.43934491]\n",
+       " [ 51.16184581 217.28322321 443.32434353]\n",
+       " [ 67.06696483  69.92108889 559.08501031]\n",
+       " [ 68.98745639 -11.99765232 480.48408272]\n",
+       " [  4.45574294  59.0529612  397.73517575]], 'millimeter')>\n",
        "Coordinates:\n",
        "  * label    (label) <U3 60B 'Cz' 'Iz' 'Rpa' 'Nz' 'Lpa'\n",
        "    type     (label) object 40B PointType.LANDMARK ... PointType.LANDMARK\n",
@@ -710,208 +703,115 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cell-md-normalize",
+   "id": "ec6eb7a7",
    "metadata": {},
    "source": [
-    "## 4. Normalize axes and isolate head\n",
-    "\n",
-    "`normalize_axes` rotates the mesh around its X-axis so Y points anterior (toward\n",
-    "the face) and Z points left. We apply the same rotation to the 5 landmarks so\n",
-    "they stay consistent with the mesh. After this, the canonical frame is:\n",
-    "X=up, Y=anterior, Z=left."
+    "## 4. Run the anonymization pipeline\n",
+    "\n",
+    "All the geometric work happens in one block:\n",
+    "\n",
+    "1. `normalize_axes` + `isolate_head` rotate into a canonical orientation and\n",
+    "   strip shoulders/body.\n",
+    "2. `align_axes_from_landmarks` maps the mesh + landmarks into the CTF frame\n",
+    "   (+X=anterior, +Y=left, +Z=up, origin at the Lpa/Rpa midpoint). The rotation\n",
+    "   `R` and affine `M_ctf` are kept in scope so we can invert them later.\n",
+    "3. `detect_cap_boundary` + `face_mask_from_landmarks` build the boolean\n",
+    "   deletion mask from the 5 landmarks. Landmark neighbourhoods and a midline\n",
+    "   nasion strip are preserved for downstream co-registration.\n",
+    "4. `delete_masked_vertices` drops every triangle touching a masked vertex.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
-   "id": "cell-normalize",
+   "execution_count": 6,
+   "id": "09186e9d",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "After isolate_head: 678,081 vertices\n",
-      "\n",
-      "Aligned landmarks:\n",
-      "  Nz: [-184.49706053  -15.58120797 -446.62343636]  (on mesh: 0.66 mm)\n",
-      "  Iz: [ -83.08828247 -225.51510363 -453.59061387]  (on mesh: 0.11 mm)\n",
-      "  Cz: [ -18.54212354  -74.10802095 -477.82569578]  (on mesh: 0.66 mm)\n",
-      "  Lpa: [-184.49706053 -109.48535945 -370.2682918 ]  (on mesh: 0.55 mm)\n",
-      "  Rpa: [-184.49706053 -109.48535945 -533.0703246 ]  (on mesh: 0.28 mm)\n"
+      "S17: 678,081 -> 558,342 verts (-119,739, cap_z=37.0 mm)\n"
      ]
     }
    ],
    "source": [
-    "# Step 1: Mediapipe-compatible X-rotation so Y points anterior.\n",
+    "# Normalize axes + isolate head.\n",
     "lm_raw = landmarks.pint.dequantify().values\n",
     "idx = {lbl: i for i, lbl in enumerate(landmarks['label'].values)}\n",
     "Nz_raw = lm_raw[idx['Nz']]\n",
     "\n",
     "surface_n, Nz_n, R = normalize_axes(surface, Nz_raw)\n",
     "landmarks_n = landmarks.pint.dequantify().copy(data=lm_raw @ R.T).pint.quantify()\n",
-    "\n",
-    "# Head isolation (strips shoulders/background).\n",
     "surface_n, _ = isolate_head(surface_n, Nz_n)\n",
-    "print(f'After isolate_head: {surface_n.nvertices:,} vertices')\n",
     "\n",
-    "# Step 2: Full 5-landmark alignment to canonical frame (X=up, Y=ant, Z=left).\n",
-    "surface_h, landmarks_n, R2 = align_axes_from_landmarks(surface_n, landmarks_n)\n",
+    "# Align to CTF frame.\n",
+    "surface_h, landmarks_n, M_ctf = align_axes_from_landmarks(surface_n, landmarks_n)\n",
     "lm_n = landmarks_n.pint.dequantify().values\n",
     "Nz, Iz, Cz, Lpa, Rpa = (lm_n[idx[lbl]] for lbl in ['Nz', 'Iz', 'Cz', 'Lpa', 'Rpa'])\n",
     "ear_mid = 0.5 * (Lpa + Rpa)\n",
     "\n",
-    "print('\\nAligned landmarks:')\n",
+    "# Build face-region mask.\n",
     "verts = np.asarray(surface_h.mesh.vertices)\n",
-    "for lbl, pos in zip(['Nz', 'Iz', 'Cz', 'Lpa', 'Rpa'], [Nz, Iz, Cz, Lpa, Rpa]):\n",
-    "    d = np.linalg.norm(verts - pos, axis=1).min()\n",
-    "    print(f'  {lbl}: {pos}  (on mesh: {d:.2f} mm)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-md-mask",
-   "metadata": {},
-   "source": [
-    "## 5. Build the face-region mask from the 5 landmarks\n",
-    "\n",
-    "Two parts:\n",
-    "\n",
-    "1. **Cap boundary detection:** Scan upward from Nz along the midline. In each\n",
-    "   height bin, find the most anterior vertex (max Y). The forehead shows a smooth\n",
-    "   Y decrease; the cap edge shows a sudden Y drop. The X value where that drop\n",
-    "   first exceeds `CAP_GRADIENT_THRESHOLD` is `cap_x`.\n",
-    "\n",
-    "2. **Mask rules** (in the aligned frame X=up, Y=anterior, Z=left):\n",
-    "   - `X < cap_x` (below the cap boundary -- covers face + forehead)\n",
-    "   - `Y > ear_mid_y` (anterior to the coronal ear plane)\n",
-    "   - `|Z - mid_z| < half_width` (within lateral width, ears excluded)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "cell-mask",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Cap boundary detected at X = -153.5 mm  (Nz_x=-184.5, Cz_x=-18.5)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1200x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Nasion strip preserved: 1,569 vertices\n",
-      "Mask: 110,763 / 678,081 vertices (16.3%)\n",
-      "Per-rule counts: {'below_cap': 293056, 'face_region': 98815, 'ear_region': 33835, 'all': 113710}\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 1. Detect cap boundary from midline Y-profile.\n",
-    "verts = np.asarray(surface_h.mesh.vertices)\n",
-    "mid_z = 0.5 * (Lpa[2] + Rpa[2])\n",
-    "\n",
-    "cap_x, profile_x, profile_y, profile_y_s = detect_cap_boundary(\n",
-    "    verts, Nz, Cz, ear_mid, mid_z,\n",
+    "mid_y = 0.5 * (Lpa[1] + Rpa[1])\n",
+    "cap_z, *_ = detect_cap_boundary(\n",
+    "    verts, Nz, Cz, ear_mid, mid_y,\n",
     "    band_width=CAP_BAND_WIDTH_MM,\n",
     "    bin_size=CAP_BIN_SIZE_MM,\n",
     "    foot_grad_threshold=CAP_FOOT_GRAD_THRESHOLD,\n",
     ")\n",
-    "print(f'Cap boundary detected at X = {cap_x:.1f} mm  '\n",
-    "      f'(Nz_x={Nz[0]:.1f}, Cz_x={Cz[0]:.1f})')\n",
-    "\n",
-    "# Debug plot: Y-profile along midline (raw + smoothed) + gradient.\n",
-    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))\n",
-    "ax1.plot(profile_x, profile_y, 'b.-', alpha=0.4, label='raw max-Y')\n",
-    "ax1.plot(profile_x, profile_y_s, 'b-', lw=2, label='smoothed')\n",
-    "ax1.axvline(Nz[0], color='green', ls='--', label='Nz')\n",
-    "ax1.axvline(cap_x, color='red', ls='--', label=f'cap edge ({cap_x:.0f})')\n",
-    "ax1.axvline(Cz[0], color='cyan', ls='--', label='Cz')\n",
-    "ax1.set_xlabel('X (height, mm)')\n",
-    "ax1.set_ylabel('max Y (anterior, mm)')\n",
-    "ax1.set_title('Midline Y-profile')\n",
-    "ax1.legend()\n",
-    "\n",
-    "grad = np.gradient(profile_y_s, profile_x)\n",
-    "ax2.plot(profile_x, grad, 'r.-')\n",
-    "ax2.axhline(CAP_FOOT_GRAD_THRESHOLD, color='purple', ls=':', label='foot threshold')\n",
-    "ax2.axvline(cap_x, color='red', ls='--', label='cap edge')\n",
-    "ax2.set_xlabel('X (height, mm)')\n",
-    "ax2.set_ylabel('dY/dX')\n",
-    "ax2.set_title('Gradient')\n",
-    "ax2.legend()\n",
-    "plt.tight_layout()\n",
-    "plt.show()\n",
-    "\n",
-    "# 2. Build face+ear mask from landmarks.\n",
-    "mask, mask_info = face_mask_from_landmarks(\n",
+    "mask, _ = face_mask_from_landmarks(\n",
     "    verts, Nz, Iz, Cz, Lpa, Rpa,\n",
-    "    cap_x=cap_x,\n",
+    "    cap_z=cap_z,\n",
     "    ear_delete_radius=EAR_DELETE_RADIUS_MM,\n",
     ")\n",
     "\n",
-    "# 3. Preserve small spheres around each landmark point.\n",
+    "# Preserve small spheres around each landmark + a midline nasion strip.\n",
     "for lm in (Nz, Iz, Cz, Lpa, Rpa):\n",
     "    near = np.linalg.norm(verts - lm, axis=1) < LANDMARK_KEEP_RADIUS\n",
     "    mask[near] = False\n",
-    "\n",
-    "# 4. Preserve a midline Nz strip upward to the cap boundary (for coregistration).\n",
     "nasion_strip = (\n",
-    "    (verts[:, 0] >= Nz[0]) &\n",
-    "    (verts[:, 0] < cap_x) &\n",
-    "    (np.abs(verts[:, 2] - Nz[2]) < LANDMARK_KEEP_RADIUS) &\n",
-    "    (verts[:, 1] > ear_mid[1])\n",
+    "    (verts[:, 2] >= Nz[2]) &\n",
+    "    (verts[:, 2] < cap_z) &\n",
+    "    (np.abs(verts[:, 1] - Nz[1]) < LANDMARK_KEEP_RADIUS) &\n",
+    "    (verts[:, 0] > ear_mid[0])\n",
     ")\n",
     "mask[nasion_strip] = False\n",
     "\n",
-    "print(f'Nasion strip preserved: {int(nasion_strip.sum()):,} vertices')\n",
-    "print(f'Mask: {mask.sum():,} / {len(mask):,} vertices '\n",
-    "      f'({100*mask.sum()/len(mask):.1f}%)')\n",
-    "print(f'Per-rule counts: {mask_info[\"counts\"]}')"
+    "# Delete masked vertices.\n",
+    "surface_anon = delete_masked_vertices(surface_h, mask)\n",
+    "\n",
+    "print(f'S{SUBJECT_NUMBER}: {surface_h.nvertices:,} -> {surface_anon.nvertices:,} verts '\n",
+    "      f'(-{surface_h.nvertices - surface_anon.nvertices:,}, cap_z={cap_z:.1f} mm)')\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "cell-md-viz-mask",
+   "id": "62e36bea",
    "metadata": {},
    "source": [
-    "## 6. Visualize the anonymization mask\n",
+    "## 5. Anonymization mask overlay\n",
     "\n",
     "Red = will be deleted, white = kept. The 5 landmark spheres are overlaid for\n",
-    "orientation. This is the primary deliverable — if the red region hugs the face\n",
-    "without leaking onto ears / top-of-head / neck, the geometric rules are working."
+    "orientation. If the red region hugs the face without leaking onto ears, top of\n",
+    "head, or neck, the geometric rules are working.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
-   "id": "cell-viz-mask",
+   "execution_count": 7,
+   "id": "a101c6c0",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5cf0bdc8e7e14296a3ed52b46cf90e9d",
+       "model_id": "525e0b68bc6d436a873f8c034c3458cb",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Widget(value='<iframe src=\"http://localhost:45595/index.html?ui=P_0x7fd8872f52d0_11&reconnect=auto\" class=\"pyv…"
+       "Widget(value='<iframe src=\"http://localhost:36901/index.html?ui=P_0x7fc35f59cf50_1&reconnect=auto\" class=\"pyvi…"
       ]
      },
      "metadata": {},
@@ -948,62 +848,33 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cell-md-delete",
+   "id": "4137b59f",
    "metadata": {},
    "source": [
-    "## 7. Anonymize by deleting masked vertices\n",
+    "## 6. Revert to the Einstar frame and compare\n",
     "\n",
-    "Drops every triangle that has any masked vertex, then strips unreferenced\n",
-    "vertices. Pure trimesh, no smoothing."
+    "Inverts the `normalize_axes` and `align_axes_from_landmarks` transforms so the\n",
+    "anonymized mesh + landmarks end up back in the raw Einstar (digitized) frame.\n",
+    "Both views below share the same coordinate system, so the comparison is\n",
+    "geometrically direct. This is also the frame the saved files will be in,\n",
+    "matching the co-registration tutorial's `read_einstar_obj` convention.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
-   "id": "cell-delete",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Original:    678,081 verts, 1,171,705 faces\n",
-      "Anonymized:  567,180 verts, 973,360 faces\n",
-      "Removed:     110,901 verts\n"
-     ]
-    }
-   ],
-   "source": [
-    "surface_anon = delete_masked_vertices(surface_h, mask)\n",
-    "n_removed = surface_h.nvertices - surface_anon.nvertices\n",
-    "print(f'Original:    {surface_h.nvertices:,} verts, {surface_h.nfaces:,} faces')\n",
-    "print(f'Anonymized:  {surface_anon.nvertices:,} verts, {surface_anon.nfaces:,} faces')\n",
-    "print(f'Removed:     {n_removed:,} verts')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-md-beforeafter",
-   "metadata": {},
-   "source": [
-    "## 8. Before / after"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "cell-beforeafter",
+   "execution_count": 8,
+   "id": "d7c29bcd",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8c51d9cd436b4869b6798e27ff32622f",
+       "model_id": "b3ef711acfb0416db596b15fd941e156",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Widget(value='<iframe src=\"http://localhost:45595/index.html?ui=P_0x7fd898d73150_12&reconnect=auto\" class=\"pyv…"
+       "Widget(value='<iframe src=\"http://localhost:36901/index.html?ui=P_0x7fc35f6200d0_2&reconnect=auto\" class=\"pyvi…"
       ]
      },
      "metadata": {},
@@ -1011,21 +882,30 @@
     }
    ],
    "source": [
-    "orig_vtk = trimesh_to_vtk_polydata(surface_h.mesh)\n",
-    "anon_vtk = trimesh_to_vtk_polydata(surface_anon.mesh)\n",
+    "# Invert the two transforms so both mesh and landmarks are back in 'digitized'.\n",
+    "surface_anon_dig, landmarks_dig = revert_to_einstar_frame(\n",
+    "    surface_anon, landmarks_n, R, M_ctf,\n",
+    ")\n",
+    "lm_dig = landmarks_dig.pint.dequantify().values\n",
+    "\n",
+    "orig_vtk = trimesh_to_vtk_polydata(surface.mesh)\n",
+    "anon_vtk = trimesh_to_vtk_polydata(surface_anon_dig.mesh)\n",
+    "\n",
+    "lm_colors = {'Nz': 'lime', 'Iz': 'magenta', 'Cz': 'cyan', 'Lpa': 'orange', 'Rpa': 'blue'}\n",
+    "n_removed = surface_h.nvertices - surface_anon.nvertices\n",
     "\n",
     "pvplt = pv.Plotter(shape=(1, 2))\n",
     "\n",
     "pvplt.subplot(0, 0)\n",
     "pvplt.add_mesh(pv.wrap(orig_vtk), rgb=True, smooth_shading=True)\n",
-    "for lbl, pos in zip(landmarks_n['label'].values, lm_n):\n",
+    "for lbl, pos in zip(landmarks_dig['label'].values, lm_dig):\n",
     "    pvplt.add_mesh(pv.Sphere(radius=4, center=pos),\n",
     "                   color=lm_colors.get(lbl, 'yellow'))\n",
     "pvplt.add_text(f'S{SUBJECT_NUMBER} Original', position='upper_left', font_size=14)\n",
     "\n",
     "pvplt.subplot(0, 1)\n",
     "pvplt.add_mesh(pv.wrap(anon_vtk), rgb=True, smooth_shading=True)\n",
-    "for lbl, pos in zip(landmarks_n['label'].values, lm_n):\n",
+    "for lbl, pos in zip(landmarks_dig['label'].values, lm_dig):\n",
     "    pvplt.add_mesh(pv.Sphere(radius=4, center=pos),\n",
     "                   color=lm_colors.get(lbl, 'yellow'))\n",
     "pvplt.add_text(\n",
@@ -1034,394 +914,40 @@
     ")\n",
     "\n",
     "pvplt.link_views()\n",
-    "pvplt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-md-validate",
-   "metadata": {},
-   "source": [
-    "## 9. Validate\n",
-    "\n",
-    "Sanity-check the result: vertex count dropped, no degenerate faces, landmarks\n",
-    "still reachable on the anonymized surface."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "cell-validate",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "ValidationResult(face_removed=True, mesh_valid=True, expected_vertices_removed=110763, actual_vertices_removed=110901, protected_points_intact=True, protected_point_max_deviation=0.6601578237143165, face_coverage_pct=16.33477416414853, passed=True, summary='PASSED — 110,901 vertices removed (16.3%), mesh valid, protected points within 5.0mm')\n"
-     ]
-    }
-   ],
-   "source": [
-    "result = validate_anonymization(\n",
-    "    original_surface=surface_h,\n",
-    "    anonymized_surface=surface_anon,\n",
-    "    facial_mask=mask,\n",
-    "    protected_points=landmarks_n,\n",
-    "    tolerance=5.0 * cedalion.units.mm,\n",
-    ")\n",
-    "print(result)"
+    "pvplt.show()\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "02da0b23",
+   "id": "20924dfc",
    "metadata": {},
    "source": [
-    "## 10. Save the anonymized scan\n",
+    "## 7. Save\n",
     "\n",
-    "Writes only the `.obj` geometry. The texture is stripped so the original face\n",
-    "pixels from the Einstar `.jpg` are not written to disk. No `.mtl`, no `.jpg`."
+    "Writes the `.obj` geometry and a sidecar `.tsv` of the 5 landmarks (the format\n",
+    "consumed at step 5.2 of the cedalion photogrammetry co-registration tutorial).\n",
+    "Both carry `crs=digitized` and pair 1:1 with each other. The original Einstar\n",
+    "texture is not written, so the subject's face pixels cannot leak via a saved\n",
+    "image.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
-   "id": "b9d7aa54",
+   "execution_count": null,
+   "id": "391c93d5",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "with_color=True requested but mesh has no usable texture; falling back to uniform grey vertex colors.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Wrote:\n",
-      "  /home/ma7/BA/PG_Subjects/Subject17/Subject17_anon.obj\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "out = f'{SCANS_FOLDER}/Subject{SUBJECT_NUMBER}/Subject{SUBJECT_NUMBER}_anon.obj'\n",
-    "written = save_anonymized_scan(surface_anon, out, with_color=True)\n",
+    "written = save_anonymized_scan(\n",
+    "    surface_anon_dig, out,\n",
+    "    landmarks=landmarks_dig,\n",
+    "    with_color=True,\n",
+    ")\n",
     "print('Wrote:')\n",
     "for p in written:\n",
     "    print(f'  {p}')\n"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-md-validation",
-   "metadata": {},
-   "source": [
-    "## 11. Validation via optode coregistration\n",
-    "\n",
-    "Thesis-reporting block. Runs the sticker-detection + scalp-projection half of\n",
-    "notebook 41 on both the pre- and post-anonymization meshes and compares the\n",
-    "resulting coordinates.\n",
-    "\n",
-    "Rationale: the anonymization mask only touches the face region. Optode\n",
-    "stickers sit on the cap, well outside the mask. So sticker centers and\n",
-    "scalp-projected coordinates should be invariant under anonymization. Any\n",
-    "measurable delta localizes boundary leakage of the mask.\n",
-    "\n",
-    "No `.snirf` montage exists for Subject16-22, so we stop at scalp coordinates\n",
-    "(no ICP label assignment). See\n",
-    "`41_photogrammetric_optode_coregistration.ipynb` for the full pipeline."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "cell-val-detect-h",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[ -85.14637603  -45.3311207  -550.53511232]\n",
-      " [ -54.06411602 -125.21770066 -556.68425171]\n",
-      " [ -85.56239656  -45.2187465  -550.48027075]\n",
-      " ...\n",
-      " [-251.74365272 -216.53318818 -500.27608009]\n",
-      " [-175.64948551  -29.62507408 -410.93197906]\n",
-      " [-127.42518785 -235.54048938 -407.91215556]]\n",
-      "[[ -55.00372802 -119.87129556 -557.60451328]\n",
-      " [ -54.04357226 -123.86408342 -556.79642487]\n",
-      " [ -18.73912569  -97.45110731 -526.52592796]\n",
-      " ...\n",
-      " [ -89.21370455   20.04153786 -466.36143248]\n",
-      " [ -88.92333199   19.75613378 -466.48283384]\n",
-      " [ -47.2857347   -13.5402963  -484.98917796]]\n",
-      "O (0.11, 0.21, 0.7, 1.0)\n",
-      "[0.4250353  1.35472219 1.7145254  0.39138499 1.29862752 0.62320719\n",
-      " 0.38994548 0.61920156 0.78096456 0.49167592 1.33193309 0.74436605\n",
-      " 1.48520167 1.26870412 0.9219634  1.02609914 0.93732249 0.66719798\n",
-      " 0.08219981 1.76435313 0.45591146]\n",
-      "3.728244425455729\n",
-      "surface.crs digitized\n",
-      "Stickers detected on original: 21\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2000x2000 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from cedalion.geometry.photogrammetry.processors import ColoredStickerProcessor\n",
-    "\n",
-    "# nb41 default yellow range. If count is 0 on Subject16-22, look at the\n",
-    "# vertex-color scatter below and widen the HSV window accordingly.\n",
-    "STICKER_HSV = {\"O\": (0.11, 0.21, 0.7, 1.0)}\n",
-    "OPTODE_LENGTH = 22.6 * cedalion.units.mm\n",
-    "\n",
-    "processor = ColoredStickerProcessor(colors=STICKER_HSV)\n",
-    "centers_h, normals_h, details_h = processor.process(surface_h, details=True)\n",
-    "print(f'Stickers detected on original: {len(centers_h)}')\n",
-    "\n",
-    "details_h.plot_vertex_colors()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "cell-val-detect-anon",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[ -85.14637603  -45.3311207  -550.53511232]\n",
-      " [ -54.06411602 -125.21770066 -556.68425171]\n",
-      " [ -85.56239656  -45.2187465  -550.48027075]\n",
-      " ...\n",
-      " [-126.30903811   24.12872194 -446.61837794]\n",
-      " [-251.74365272 -216.53318818 -500.27608009]\n",
-      " [-127.42518785 -235.54048938 -407.91215556]]\n",
-      "[[ -55.00372802 -119.87129556 -557.60451328]\n",
-      " [ -54.04357226 -123.86408342 -556.79642487]\n",
-      " [ -18.73912569  -97.45110731 -526.52592796]\n",
-      " ...\n",
-      " [ -89.21370455   20.04153786 -466.36143248]\n",
-      " [ -88.92333199   19.75613378 -466.48283384]\n",
-      " [ -47.2857347   -13.5402963  -484.98917796]]\n",
-      "O (0.11, 0.21, 0.7, 1.0)\n",
-      "[0.4250353  1.35472219 1.7145254  0.39138499 1.29862752 0.62320719\n",
-      " 0.38994548 0.61920156 0.78096456 0.49167592 1.33193309 0.74436605\n",
-      " 1.48520167 1.26870412 0.9219634  1.02609914 0.93732249 0.66719798\n",
-      " 0.08219981 1.76435313 0.45591146]\n",
-      "3.728244425455729\n",
-      "surface.crs digitized\n",
-      "Stickers detected on original:   21\n",
-      "Stickers detected on anonymized: 21\n",
-      "Labels original:   [np.str_('O-19'), np.str_('O-07'), np.str_('O-04'), np.str_('O-01'), np.str_('O-21'), np.str_('O-10'), np.str_('O-08'), np.str_('O-06'), np.str_('O-18'), np.str_('O-12'), np.str_('O-09'), np.str_('O-15'), np.str_('O-17'), np.str_('O-16'), np.str_('O-14'), np.str_('O-05'), np.str_('O-11'), np.str_('O-02'), np.str_('O-13'), np.str_('O-03'), np.str_('O-20')]\n",
-      "Labels anonymized: [np.str_('O-19'), np.str_('O-07'), np.str_('O-04'), np.str_('O-01'), np.str_('O-21'), np.str_('O-10'), np.str_('O-08'), np.str_('O-06'), np.str_('O-18'), np.str_('O-12'), np.str_('O-09'), np.str_('O-15'), np.str_('O-17'), np.str_('O-16'), np.str_('O-14'), np.str_('O-05'), np.str_('O-11'), np.str_('O-02'), np.str_('O-13'), np.str_('O-03'), np.str_('O-20')]\n"
-     ]
-    }
-   ],
-   "source": [
-    "centers_anon, normals_anon, details_anon = processor.process(\n",
-    "    surface_anon, details=True\n",
-    ")\n",
-    "print(f'Stickers detected on original:   {len(centers_h)}')\n",
-    "print(f'Stickers detected on anonymized: {len(centers_anon)}')\n",
-    "print(f'Labels original:   {list(centers_h[\"label\"].values)}')\n",
-    "print(f'Labels anonymized: {list(centers_anon[\"label\"].values)}')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "cell-val-scalp",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Scalp coords computed for both meshes.\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Same recipe as nb41 cell 20: subtract optode length along the surface normal.\n",
-    "scalp_h = centers_h - OPTODE_LENGTH * normals_h\n",
-    "scalp_anon = centers_anon - OPTODE_LENGTH * normals_anon\n",
-    "print('Scalp coords computed for both meshes.')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "cell-val-devtable",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Sticker centers (orig -> anon)  (n_orig=21, n_anon=21)\n",
-      "  mean   = 0.000 mm\n",
-      "  median = 0.000 mm\n",
-      "  max    = 0.000 mm\n",
-      "  per-sticker (sorted desc):\n",
-      "    O-19    0.000 mm\n",
-      "    O-07    0.000 mm\n",
-      "    O-04    0.000 mm\n",
-      "    O-01    0.000 mm\n",
-      "    O-21    0.000 mm\n",
-      "    O-10    0.000 mm\n",
-      "    O-08    0.000 mm\n",
-      "    O-06    0.000 mm\n",
-      "    O-18    0.000 mm\n",
-      "    O-12    0.000 mm\n",
-      "    O-09    0.000 mm\n",
-      "    O-15    0.000 mm\n",
-      "    O-17    0.000 mm\n",
-      "    O-16    0.000 mm\n",
-      "    O-14    0.000 mm\n",
-      "    O-05    0.000 mm\n",
-      "    O-11    0.000 mm\n",
-      "    O-02    0.000 mm\n",
-      "    O-13    0.000 mm\n",
-      "    O-03    0.000 mm\n",
-      "    O-20    0.000 mm\n",
-      "\n",
-      "Scalp coords   (orig -> anon)  (n_orig=21, n_anon=21)\n",
-      "  mean   = 0.000 mm\n",
-      "  median = 0.000 mm\n",
-      "  max    = 0.000 mm\n",
-      "  per-sticker (sorted desc):\n",
-      "    O-19    0.000 mm\n",
-      "    O-07    0.000 mm\n",
-      "    O-04    0.000 mm\n",
-      "    O-01    0.000 mm\n",
-      "    O-21    0.000 mm\n",
-      "    O-10    0.000 mm\n",
-      "    O-08    0.000 mm\n",
-      "    O-06    0.000 mm\n",
-      "    O-18    0.000 mm\n",
-      "    O-12    0.000 mm\n",
-      "    O-09    0.000 mm\n",
-      "    O-15    0.000 mm\n",
-      "    O-17    0.000 mm\n",
-      "    O-16    0.000 mm\n",
-      "    O-14    0.000 mm\n",
-      "    O-05    0.000 mm\n",
-      "    O-11    0.000 mm\n",
-      "    O-02    0.000 mm\n",
-      "    O-13    0.000 mm\n",
-      "    O-03    0.000 mm\n",
-      "    O-20    0.000 mm\n"
-     ]
-    }
-   ],
-   "source": [
-    "from scipy.spatial import KDTree\n",
-    "\n",
-    "def _deviation_table(src, dst, title):\n",
-    "    # Labels from two independent runs are just sequential counters and will\n",
-    "    # not align, so we match by nearest neighbour.\n",
-    "    s = src.pint.to('mm').pint.dequantify().values\n",
-    "    d = dst.pint.to('mm').pint.dequantify().values\n",
-    "    tree = KDTree(d)\n",
-    "    dists, _ = tree.query(s)\n",
-    "    print(f'\\n{title}  (n_orig={len(s)}, n_anon={len(d)})')\n",
-    "    print(f'  mean   = {dists.mean():.3f} mm')\n",
-    "    print(f'  median = {np.median(dists):.3f} mm')\n",
-    "    print(f'  max    = {dists.max():.3f} mm')\n",
-    "    print(f'  per-sticker (sorted desc):')\n",
-    "    for k in np.argsort(-dists):\n",
-    "        lab = str(src['label'].values[k])\n",
-    "        print(f'    {lab:6s}  {dists[k]:.3f} mm')\n",
-    "\n",
-    "_deviation_table(centers_h, centers_anon, 'Sticker centers (orig -> anon)')\n",
-    "_deviation_table(scalp_h,   scalp_anon,   'Scalp coords   (orig -> anon)')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "id": "cell-val-viz",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "92c511a8dbb14060b757879492bcbda3",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Widget(value='<iframe src=\"http://localhost:45595/index.html?ui=P_0x7fd9453df290_13&reconnect=auto\" class=\"pyv…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pvplt = pv.Plotter(shape=(1, 2))\n",
-    "\n",
-    "pvplt.subplot(0, 0)\n",
-    "cedalion.plots.plot_surface(pvplt, surface_h, opacity=1.0)\n",
-    "cedalion.plots.plot_labeled_points(pvplt, centers_h, color='r')\n",
-    "cedalion.plots.plot_vector_field(pvplt, centers_h, normals_h)\n",
-    "pvplt.add_text(\n",
-    "    f'S{SUBJECT_NUMBER} Original  ({len(centers_h)} stickers)',\n",
-    "    position='upper_left', font_size=14,\n",
-    ")\n",
-    "\n",
-    "pvplt.subplot(0, 1)\n",
-    "cedalion.plots.plot_surface(pvplt, surface_anon, opacity=1.0)\n",
-    "cedalion.plots.plot_labeled_points(pvplt, centers_anon, color='r')\n",
-    "cedalion.plots.plot_vector_field(pvplt, centers_anon, normals_anon)\n",
-    "pvplt.add_text(\n",
-    "    f'S{SUBJECT_NUMBER} Anonymized  ({len(centers_anon)} stickers)',\n",
-    "    position='upper_left', font_size=14,\n",
-    ")\n",
-    "\n",
-    "pvplt.link_views()\n",
-    "pvplt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-md-footer",
-   "metadata": {},
-   "source": [
-    "## Notes\n",
-    "\n",
-    "- **Landmarks used:** 5 given (Nz, Iz, Cz, Lpa, Rpa), acquired via the existing\n",
-    "  upstream cedalion picker.\n",
-    "- **Mask thresholds:** `MARGIN_TOP_MM=0`, `MARGIN_BOTTOM_MM=20`, `LATERAL_SCALE=0.9`\n",
-    "  (tunable at the top of the notebook).\n",
-    "- **Anonymization method:** vertex deletion (drop faces touching masked vertices,\n",
-    "  strip unreferenced vertices). No Taubin smoothing. No MediaPipe.\n",
-    "- **Contract with the exposé:** this notebook assumes NAS/LPA/RPA are given and\n",
-    "  uses no DL face detection. Cz and Iz are also given and used as vertical bounds;\n",
-    "  they aren't strictly required but make the top/bottom cuts landmark-driven\n",
-    "  instead of mesh-extent-driven.\n",
-    "- **Relationship to notebook 47:** this is the cedalion-only alternative path.\n",
-    "  Notebook 47 detects Nz via MediaPipe and uses a face-oval contour for the\n",
-    "  upper mask bound; this notebook derives everything from the 5 given landmarks."
-   ]
   }
  ],
  "metadata": {
diff --git a/examples/head_models/49_seam_diagnostics.ipynb b/examples/head_models/49_seam_diagnostics.ipynb
new file mode 100644
index 00000000..d8257881
--- /dev/null
+++ b/examples/head_models/49_seam_diagnostics.ipynb
@@ -0,0 +1,1107 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Seam Diagnostics --- UV Duplicate-Vertex Artifact\n",
+    "\n",
+    "Demonstrates the UV-seam duplicate-vertex artifact on an Einstar photogrammetry scan\n",
+    "and shows that a position-based merge eliminates it. Used to generate the\n",
+    "before/after figures for section 3.4 of the thesis.\n",
+    "\n",
+    "**Pipeline:**\n",
+    "1. Load Subject 17\n",
+    "2. Pick the 5 landmarks ($N_z$, $I_z$, $C_z$, Lpa, Rpa) with the upstream cedalion picker\n",
+    "3. `normalize_axes` -> `isolate_head` -> `align_axes_from_landmarks` (CTF frame)\n",
+    "4. Render the isolated head with boundary-edge overlay -> **figure A (before)**\n",
+    "5. Collapse seam duplicates with a union-find merge\n",
+    "6. Render the merged head -> **figure B (after)**\n",
+    "7. Quantify the merge (vertex / face / component / boundary-edge counts)\n",
+    "8. Verify vertex colors survive averaging at seam positions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "from collections import Counter\n",
+    "\n",
+    "import numpy as np\n",
+    "import pyvista as pv\n",
+    "import trimesh\n",
+    "import xarray as xr\n",
+    "from scipy.spatial import KDTree\n",
+    "from scipy.sparse import csr_matrix\n",
+    "from scipy.sparse.csgraph import connected_components\n",
+    "\n",
+    "import cedalion\n",
+    "import cedalion.dataclasses as cdc\n",
+    "import cedalion.io\n",
+    "import cedalion.plots\n",
+    "from cedalion.geometry.photogrammetry.anonymization import (\n",
+    "    normalize_axes,\n",
+    "    isolate_head,\n",
+    "    align_axes_from_landmarks,\n",
+    ")\n",
+    "\n",
+    "logging.getLogger('cedalion').setLevel(logging.WARNING)\n",
+    "pv.set_jupyter_backend('server')\n",
+    "\n",
+    "# === CONFIG ===\n",
+    "SUBJECT_NUMBER = 17\n",
+    "SCANS_FOLDER = '/home/ma7/BA/PG_Subjects'\n",
+    "\n",
+    "# True: run the picker. False: use cached landmarks (fill after first pick).\n",
+    "INTERACTIVE = True\n",
+    "\n",
+    "# Position tolerance for seam merging (mm). Einstar seam duplicates are\n",
+    "# coincident within well under 0.01 mm.\n",
+    "MERGE_TOL_MM = 0.01"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Load the Einstar scan"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded: 1,284,667 vertices, 2,223,716 faces\n",
+      "Texture already attached: (4016, 3776)\n"
+     ]
+    }
+   ],
+   "source": [
+    "path = f'{SCANS_FOLDER}/Subject{SUBJECT_NUMBER}/Subject{SUBJECT_NUMBER}.obj'\n",
+    "surface = cedalion.io.read_einstar_obj(path)\n",
+    "print(f'Loaded: {surface.nvertices:,} vertices, {surface.nfaces:,} faces')\n",
+    "\n",
+    "# In trimesh 4.6 the texture image lives on visual.material.image, not\n",
+    "# visual.image. Verify it's there; if missing, fall back to the sibling JPG.\n",
+    "import os\n",
+    "import trimesh as _tm\n",
+    "from PIL import Image as _PILImage\n",
+    "\n",
+    "def _visual_image(visual):\n",
+    "    img = getattr(visual, 'image', None)\n",
+    "    if img is None:\n",
+    "        mat = getattr(visual, 'material', None)\n",
+    "        img = getattr(mat, 'image', None) if mat is not None else None\n",
+    "    return img\n",
+    "\n",
+    "_img = _visual_image(surface.mesh.visual)\n",
+    "if _img is None:\n",
+    "    _jpg = path.replace('.obj', '.jpg')\n",
+    "    _uv = getattr(surface.mesh.visual, 'uv', None)\n",
+    "    assert os.path.exists(_jpg) and _uv is not None, (\n",
+    "        f'no texture: jpg_exists={os.path.exists(_jpg)}, has_uv={_uv is not None}'\n",
+    "    )\n",
+    "    surface.mesh.visual = _tm.visual.TextureVisuals(\n",
+    "        uv=_uv, image=_PILImage.open(_jpg).convert('RGBA'),\n",
+    "    )\n",
+    "    _img = _visual_image(surface.mesh.visual)\n",
+    "    assert _img is not None, 'attach failed'\n",
+    "    print(f'Attached texture from JPG: {_img.size}')\n",
+    "else:\n",
+    "    print(f'Texture already attached: {_img.size}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Pick the 5 landmarks interactively\n",
+    "\n",
+    "Uses cedalion's built-in picker (`cedalion.plots.plot_surface(..., pick_landmarks=True)`).\n",
+    "Right-click on the mesh to place a sphere; click again on the sphere to cycle\n",
+    "its label through `Nz -> Iz -> Cz -> Lpa -> Rpa`, or cycle past `Rpa` back to\n",
+    "`Nz` to remove it.\n",
+    "\n",
+    "Close the window when all 5 are placed with correct labels."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "43d9ddcc085a416a9d5f3e88551cf048",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Widget(value='<iframe src=\"http://localhost:37333/index.html?ui=P_0x7f00dc983690_4&reconnect=auto\" class=\"pyvi…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "if INTERACTIVE:\n",
+    "    pvplt = pv.Plotter()\n",
+    "    get_landmarks = cedalion.plots.plot_surface(\n",
+    "        pvplt, surface, opacity=1.0, pick_landmarks=True\n",
+    "    )\n",
+    "    pvplt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Retrieve picked points and wrap into a `LabeledPointCloud`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "</svg>\n",
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+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
+       "  );\n",
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+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
+       "  );\n",
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+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, white)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
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+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
+       "  );\n",
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+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
+       "  );\n",
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+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
+       "  );\n",
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+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, #111111)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
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+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
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+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
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+       "}\n",
+       "\n",
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+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "}\n",
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+       "  height: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "  border: 2px solid transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0) !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: \"►\";\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  border-color: var(--xr-background-color-row-odd);\n",
+       "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "  border-color: var(--xr-background-color-row-even);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (label: 5, digitized: 3)&gt; Size: 120B\n",
+       "&lt;Quantity([[183.26390827 116.44076638 433.14976121]\n",
+       " [ 55.81002886 216.87724678 460.20226543]\n",
+       " [ 75.44543708  70.22174292 571.26430267]\n",
+       " [ 70.90973966 -12.7005211  479.10120764]\n",
+       " [ -5.23821153  60.43366529 422.08015945]], &#x27;millimeter&#x27;)&gt;\n",
+       "Coordinates:\n",
+       "  * label    (label) &lt;U3 60B &#x27;Cz&#x27; &#x27;Iz&#x27; &#x27;Rpa&#x27; &#x27;Nz&#x27; &#x27;Lpa&#x27;\n",
+       "    type     (label) object 40B PointType.LANDMARK ... PointType.LANDMARK\n",
+       "    group    (label) &lt;U1 20B &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27;\n",
+       "Dimensions without coordinates: digitized</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>label</span>: 5</li><li><span>digitized</span>: 3</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-53a17c29-98ec-4a75-8dff-33aba59dc26b' class='xr-array-in' type='checkbox' checked><label for='section-53a17c29-98ec-4a75-8dff-33aba59dc26b' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>[mm] 183.3 116.4 433.1 55.81 216.9 ... -12.7 479.1 -5.238 60.43 422.1</span></div><div class='xr-array-data'><table><tbody><tr><th>Magnitude</th><td style='text-align:left;'><pre>[[183.263908273029 116.4407663790885 433.14976121066945] [55.810028864582236 216.87724678041928 460.20226543224396] [75.44543707953403 70.2217429163498 571.2643026729717] [70.90973965837875 -12.700521103862059 479.10120764275973] [-5.238211533933473 60.433665290097025 422.0801594510126]]</pre></td></tr><tr><th>Units</th><td style='text-align:left;'>millimeter</td></tr></tbody></table></div></div></li><li class='xr-section-item'><input id='section-b6860f16-3199-42a8-b9fb-647f95226ffa' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b6860f16-3199-42a8-b9fb-647f95226ffa' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>label</span></div><div class='xr-var-dims'>(label)</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;Cz&#x27; &#x27;Iz&#x27; &#x27;Rpa&#x27; &#x27;Nz&#x27; &#x27;Lpa&#x27;</div><input id='attrs-4ffa77d9-d0b4-41a3-924e-29f8d4d167f1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4ffa77d9-d0b4-41a3-924e-29f8d4d167f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f490a0ca-42b2-4cbe-ad1e-922589a28a22' class='xr-var-data-in' type='checkbox'><label for='data-f490a0ca-42b2-4cbe-ad1e-922589a28a22' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Cz&#x27;, &#x27;Iz&#x27;, &#x27;Rpa&#x27;, &#x27;Nz&#x27;, &#x27;Lpa&#x27;], dtype=&#x27;&lt;U3&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>type</span></div><div class='xr-var-dims'>(label)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>PointType.LANDMARK ... PointType...</div><input id='attrs-494fd7a1-2baa-4d65-9382-77eeb065b44a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-494fd7a1-2baa-4d65-9382-77eeb065b44a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-560b7c70-6a28-4b0e-abb5-6092f485fcfc' class='xr-var-data-in' type='checkbox'><label for='data-560b7c70-6a28-4b0e-abb5-6092f485fcfc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&lt;PointType.LANDMARK: 3&gt;, &lt;PointType.LANDMARK: 3&gt;,\n",
+       "       &lt;PointType.LANDMARK: 3&gt;, &lt;PointType.LANDMARK: 3&gt;,\n",
+       "       &lt;PointType.LANDMARK: 3&gt;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>group</span></div><div class='xr-var-dims'>(label)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27;</div><input id='attrs-0e92e25f-4219-4394-b88e-6ff915559a9a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0e92e25f-4219-4394-b88e-6ff915559a9a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-19a83ba6-600d-4f85-97aa-e38cf1809789' class='xr-var-data-in' type='checkbox'><label for='data-19a83ba6-600d-4f85-97aa-e38cf1809789' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;L&#x27;, &#x27;L&#x27;, &#x27;L&#x27;, &#x27;L&#x27;, &#x27;L&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a6b1d658-1fa6-40a9-8fee-51c91ecf0b3f' class='xr-section-summary-in' type='checkbox'  ><label for='section-a6b1d658-1fa6-40a9-8fee-51c91ecf0b3f' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>label</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-2109491d-d140-4375-a867-7230aa06ac1a' class='xr-index-data-in' type='checkbox'/><label for='index-2109491d-d140-4375-a867-7230aa06ac1a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;Cz&#x27;, &#x27;Iz&#x27;, &#x27;Rpa&#x27;, &#x27;Nz&#x27;, &#x27;Lpa&#x27;], dtype=&#x27;object&#x27;, name=&#x27;label&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f18175f1-e1ac-492d-a985-d4b18db8ef94' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f18175f1-e1ac-492d-a985-d4b18db8ef94' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray (label: 5, digitized: 3)> Size: 120B\n",
+       "<Quantity([[183.26390827 116.44076638 433.14976121]\n",
+       " [ 55.81002886 216.87724678 460.20226543]\n",
+       " [ 75.44543708  70.22174292 571.26430267]\n",
+       " [ 70.90973966 -12.7005211  479.10120764]\n",
+       " [ -5.23821153  60.43366529 422.08015945]], 'millimeter')>\n",
+       "Coordinates:\n",
+       "  * label    (label) <U3 60B 'Cz' 'Iz' 'Rpa' 'Nz' 'Lpa'\n",
+       "    type     (label) object 40B PointType.LANDMARK ... PointType.LANDMARK\n",
+       "    group    (label) <U1 20B 'L' 'L' 'L' 'L' 'L'\n",
+       "Dimensions without coordinates: digitized"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "if INTERACTIVE:\n",
+    "    landmark_coordinates, landmark_labels = get_landmarks()\n",
+    "    landmark_coordinates = np.asarray(landmark_coordinates)\n",
+    "else:\n",
+    "    # Cached fallback: paste coordinates from a previous interactive run here.\n",
+    "    landmark_labels = ['Nz', 'Iz', 'Cz', 'Lpa', 'Rpa']\n",
+    "    landmark_coordinates = np.asarray([\n",
+    "        [0.0, 0.0, 0.0],   # Nz  -- replace\n",
+    "        [0.0, 0.0, 0.0],   # Iz  -- replace\n",
+    "        [0.0, 0.0, 0.0],   # Cz  -- replace\n",
+    "        [0.0, 0.0, 0.0],   # Lpa -- replace\n",
+    "        [0.0, 0.0, 0.0],   # Rpa -- replace\n",
+    "    ])\n",
+    "\n",
+    "assert len(set(landmark_labels)) == 5, 'Need exactly 5 distinct landmarks'\n",
+    "assert set(landmark_labels) == {'Nz', 'Iz', 'Cz', 'Lpa', 'Rpa'}, (\n",
+    "    f'Unexpected labels: {landmark_labels}'\n",
+    ")\n",
+    "\n",
+    "landmarks = xr.DataArray(\n",
+    "    np.vstack(landmark_coordinates),\n",
+    "    dims=['label', 'digitized'],\n",
+    "    coords={\n",
+    "        'label': ('label', list(landmark_labels)),\n",
+    "        'type': ('label', [cdc.PointType.LANDMARK] * 5),\n",
+    "        'group': ('label', ['L'] * 5),\n",
+    "    },\n",
+    ").pint.quantify('mm')\n",
+    "\n",
+    "display(landmarks)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Normalize, isolate head, align to CTF\n",
+    "\n",
+    "Same preprocessing as notebook 48. The output `surface_h` carries `crs='ctf'`.\n",
+    "Crucially, `isolate_head` uses only a *transient* position-based adjacency\n",
+    "graph to compute connected components, so the mesh it returns **still contains**\n",
+    "the seam-duplicate vertices --- which is the point of this diagnostic."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "After isolate_head: 678,081 vertices\n",
+      "After align_axes_from_landmarks: crs='ctf', 678,081 vertices\n"
+     ]
+    }
+   ],
+   "source": [
+    "lm_raw = landmarks.pint.dequantify().values\n",
+    "idx = {lbl: i for i, lbl in enumerate(landmarks['label'].values)}\n",
+    "Nz_raw = lm_raw[idx['Nz']]\n",
+    "\n",
+    "surface_n, Nz_n, R = normalize_axes(surface, Nz_raw)\n",
+    "landmarks_n = landmarks.pint.dequantify().copy(data=lm_raw @ R.T).pint.quantify()\n",
+    "\n",
+    "surface_n, _ = isolate_head(surface_n, Nz_n)\n",
+    "print(f'After isolate_head: {surface_n.nvertices:,} vertices')\n",
+    "\n",
+    "surface_h, landmarks_n, M_ctf = align_axes_from_landmarks(surface_n, landmarks_n)\n",
+    "print(f'After align_axes_from_landmarks: crs={surface_h.crs!r}, '\n",
+    "      f'{surface_h.nvertices:,} vertices')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Visualize seam lines on the isolated head (before merge)\n",
+    "\n",
+    "Boundary edges are edges that belong to exactly one face instead of the usual\n",
+    "two. On a watertight surface there should be essentially none. On a raw Einstar\n",
+    "scan, each UV patch boundary becomes a chain of boundary edges because the two\n",
+    "sides of the patch live on different, spatially coincident vertices."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Boundary edges: 169,543 / 1,842,329 (9.2 %)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "282edaff01594a6bbb3d2b6bef9dcd77",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Widget(value='<iframe src=\"http://localhost:37333/index.html?ui=P_0x7f00379f9350_5&reconnect=auto\" class=\"pyvi…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def boundary_edges_of(mesh):\n",
+    "    \"\"\"Return (boundary_edge_array, total_edge_count) for a trimesh.\"\"\"\n",
+    "    edge_to_face_count = Counter()\n",
+    "    for face in mesh.faces:\n",
+    "        edge_to_face_count[tuple(sorted((int(face[0]), int(face[1]))))] += 1\n",
+    "        edge_to_face_count[tuple(sorted((int(face[1]), int(face[2]))))] += 1\n",
+    "        edge_to_face_count[tuple(sorted((int(face[0]), int(face[2]))))] += 1\n",
+    "    boundary = np.array(\n",
+    "        [e for e, c in edge_to_face_count.items() if c == 1],\n",
+    "        dtype=np.int64,\n",
+    "    )\n",
+    "    return boundary, len(edge_to_face_count)\n",
+    "\n",
+    "\n",
+    "def _boundary_polydata(mesh, boundary_edges):\n",
+    "    \"\"\"Build a pyvista PolyData of line segments for the given boundary edges.\"\"\"\n",
+    "    verts = mesh.vertices[boundary_edges.ravel()]\n",
+    "    n = len(boundary_edges)\n",
+    "    lines = np.column_stack([\n",
+    "        np.full(n, 2),\n",
+    "        np.arange(0, 2 * n, 2),\n",
+    "        np.arange(1, 2 * n, 2),\n",
+    "    ]).ravel()\n",
+    "    return pv.PolyData(verts, lines=lines)\n",
+    "\n",
+    "\n",
+    "boundary_before, total_edges_before = boundary_edges_of(surface_h.mesh)\n",
+    "print(f'Boundary edges: {len(boundary_before):,} / {total_edges_before:,} '\n",
+    "      f'({100 * len(boundary_before) / total_edges_before:.1f} %)')\n",
+    "\n",
+    "boundary_poly_before = _boundary_polydata(surface_h.mesh, boundary_before)\n",
+    "\n",
+    "pvplt = pv.Plotter()\n",
+    "cedalion.plots.plot_surface(pvplt, surface_h, opacity=1.0)\n",
+    "pvplt.add_mesh(boundary_poly_before, color='red', line_width=2, opacity=1.0)\n",
+    "pvplt.add_text(\n",
+    "    f'Before merge - seam lines: {len(boundary_before):,}',\n",
+    "    position='upper_left', font_size=14,\n",
+    ")\n",
+    "pvplt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6. Collapse seam duplicates (persistent merge)\n",
+    "\n",
+    "The same mechanism `isolate_head` applies transiently for connected-component\n",
+    "analysis, but here the mesh is actually rewritten: every group of spatially\n",
+    "coincident vertices (within `MERGE_TOL_MM`) is replaced by a single vertex\n",
+    "carrying the group-averaged position and color."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Vertices: 678,081 -> 586,433  (91,648 removed)\n",
+      "Faces:    1,171,705 -> 1,171,681  (24 degenerate removed)\n"
+     ]
+    }
+   ],
+   "source": [
+    "def merge_seam_vertices(mesh, tol=MERGE_TOL_MM):\n",
+    "    \"\"\"Merge spatially coincident vertices via union-find on KDTree pairs.\n",
+    "\n",
+    "    Returns (merged_mesh, old_to_new, new_to_old).\n",
+    "    \"\"\"\n",
+    "    verts = mesh.vertices\n",
+    "    n = len(verts)\n",
+    "\n",
+    "    tree = KDTree(verts)\n",
+    "    pairs = tree.query_pairs(r=tol)\n",
+    "\n",
+    "    parent = np.arange(n)\n",
+    "\n",
+    "    def find(x):\n",
+    "        while parent[x] != x:\n",
+    "            parent[x] = parent[parent[x]]\n",
+    "            x = parent[x]\n",
+    "        return x\n",
+    "\n",
+    "    def union(a, b):\n",
+    "        ra, rb = find(a), find(b)\n",
+    "        if ra != rb:\n",
+    "            parent[ra] = rb\n",
+    "\n",
+    "    for a, b in pairs:\n",
+    "        union(a, b)\n",
+    "\n",
+    "    for i in range(n):\n",
+    "        parent[i] = find(i)\n",
+    "\n",
+    "    unique_roots, inverse = np.unique(parent, return_inverse=True)\n",
+    "    old_to_new = inverse\n",
+    "    n_new = len(unique_roots)\n",
+    "\n",
+    "    new_to_old = [[] for _ in range(n_new)]\n",
+    "    for old_idx in range(n):\n",
+    "        new_to_old[old_to_new[old_idx]].append(old_idx)\n",
+    "\n",
+    "    new_verts = np.zeros((n_new, 3))\n",
+    "    for new_idx in range(n_new):\n",
+    "        old_indices = new_to_old[new_idx]\n",
+    "        new_verts[new_idx] = verts[old_indices].mean(axis=0)\n",
+    "\n",
+    "    new_faces = old_to_new[mesh.faces]\n",
+    "    valid = (\n",
+    "        (new_faces[:, 0] != new_faces[:, 1])\n",
+    "        & (new_faces[:, 1] != new_faces[:, 2])\n",
+    "        & (new_faces[:, 0] != new_faces[:, 2])\n",
+    "    )\n",
+    "    new_faces = new_faces[valid]\n",
+    "\n",
+    "    # Preserve per-vertex colors if present. ColorVisuals exposes\n",
+    "    # ``vertex_colors`` directly; TextureVisuals does not, so we skip\n",
+    "    # color averaging there (the texture image stays attached to the\n",
+    "    # original mesh and can be re-sampled downstream if needed).\n",
+    "    old_colors = getattr(mesh.visual, 'vertex_colors', None)\n",
+    "    merged_mesh = trimesh.Trimesh(\n",
+    "        vertices=new_verts, faces=new_faces, process=False,\n",
+    "    )\n",
+    "    if old_colors is not None and len(old_colors) == n:\n",
+    "        old_colors = np.asarray(old_colors, dtype=np.float64)\n",
+    "        new_colors = np.zeros((n_new, old_colors.shape[1]), dtype=np.float64)\n",
+    "        for new_idx in range(n_new):\n",
+    "            new_colors[new_idx] = old_colors[new_to_old[new_idx]].mean(axis=0)\n",
+    "        merged_mesh.visual.vertex_colors = new_colors.astype(np.uint8)\n",
+    "\n",
+    "    return merged_mesh, old_to_new, new_to_old\n",
+    "\n",
+    "\n",
+    "merged_mesh, old_to_new, new_to_old = merge_seam_vertices(surface_h.mesh)\n",
+    "surface_merged = cdc.TrimeshSurface(\n",
+    "    mesh=merged_mesh, crs=surface_h.crs, units=surface_h.units,\n",
+    ")\n",
+    "\n",
+    "print(f'Vertices: {len(surface_h.mesh.vertices):,} -> {len(merged_mesh.vertices):,}  '\n",
+    "      f'({len(surface_h.mesh.vertices) - len(merged_mesh.vertices):,} removed)')\n",
+    "print(f'Faces:    {len(surface_h.mesh.faces):,} -> {len(merged_mesh.faces):,}  '\n",
+    "      f'({len(surface_h.mesh.faces) - len(merged_mesh.faces):,} degenerate removed)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 7. Visualize merged head (seams gone)\n",
+    "\n",
+    "Side-by-side: same head before (left, seam lines in red) and after (right,\n",
+    "no seam lines). Linked camera views so rotation applies to both panels."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Boundary edges after merge: 1,269\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8f3b668b7d16406ba5551894200e7f7e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Widget(value='<iframe src=\"http://localhost:37333/index.html?ui=P_0x7eff638cff10_6&reconnect=auto\" class=\"pyvi…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "boundary_after, _ = boundary_edges_of(merged_mesh)\n",
+    "print(f'Boundary edges after merge: {len(boundary_after):,}')\n",
+    "\n",
+    "pvplt = pv.Plotter(shape=(1, 2))\n",
+    "\n",
+    "pvplt.subplot(0, 0)\n",
+    "cedalion.plots.plot_surface(pvplt, surface_h, opacity=1.0)\n",
+    "pvplt.add_mesh(boundary_poly_before, color='red', line_width=2, opacity=0.8)\n",
+    "pvplt.add_text(\n",
+    "    f'Before - seams: {len(boundary_before):,}',\n",
+    "    position='upper_left', font_size=12,\n",
+    ")\n",
+    "\n",
+    "pvplt.subplot(0, 1)\n",
+    "cedalion.plots.plot_surface(pvplt, surface_merged, opacity=1.0)\n",
+    "if len(boundary_after) > 0:\n",
+    "    boundary_poly_after = _boundary_polydata(merged_mesh, boundary_after)\n",
+    "    pvplt.add_mesh(boundary_poly_after, color='red', line_width=2, opacity=0.8)\n",
+    "pvplt.add_text(\n",
+    "    f'After merge - seams: {len(boundary_after):,}',\n",
+    "    position='upper_left', font_size=12,\n",
+    ")\n",
+    "\n",
+    "pvplt.link_views()\n",
+    "pvplt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 8. Quantify the merge"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def count_components(m):\n",
+    "    \"\"\"Count connected components on the raw (non-position-merged) adjacency.\"\"\"\n",
+    "    n = len(m.vertices)\n",
+    "    edges = m.edges_unique\n",
+    "    rows = np.concatenate([edges[:, 0], edges[:, 1]])\n",
+    "    cols = np.concatenate([edges[:, 1], edges[:, 0]])\n",
+    "    data = np.ones(len(rows))\n",
+    "    A = csr_matrix((data, (rows, cols)), shape=(n, n))\n",
+    "    n_comp, _ = connected_components(A, directed=False)\n",
+    "    return n_comp\n",
+    "\n",
+    "\n",
+    "n_comp_before = count_components(surface_h.mesh)\n",
+    "n_comp_after = count_components(merged_mesh)\n",
+    "\n",
+    "print('=== Merge statistics ===')\n",
+    "print(f'Vertices:        {len(surface_h.mesh.vertices):>10,} -> {len(merged_mesh.vertices):>10,}  '\n",
+    "      f'({len(surface_h.mesh.vertices) - len(merged_mesh.vertices):,} removed)')\n",
+    "print(f'Faces:           {len(surface_h.mesh.faces):>10,} -> {len(merged_mesh.faces):>10,}  '\n",
+    "      f'({len(surface_h.mesh.faces) - len(merged_mesh.faces):,} degenerate removed)')\n",
+    "print(f'Components:      {n_comp_before:>10,} -> {n_comp_after:>10,}')\n",
+    "print(f'Boundary edges:  {len(boundary_before):>10,} -> {len(boundary_after):>10,}')\n",
+    "print()\n",
+    "\n",
+    "areas = merged_mesh.area_faces\n",
+    "degenerate = int((areas < 1e-10).sum())\n",
+    "print(f'Degenerate faces after merge (area < 1e-10): {degenerate}')\n",
+    "print(f'is_watertight: {merged_mesh.is_watertight}')\n",
+    "print(f'Surface area:  {surface_h.mesh.area:.1f} mm^2 (before) vs '\n",
+    "      f'{merged_mesh.area:.1f} mm^2 (after)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 9. Verify vertex colors survive averaging\n",
+    "\n",
+    "Seam duplicates usually share the same or near-identical color, so averaging\n",
+    "at each merge group should leave the rendered appearance unchanged. The left\n",
+    "panel uses the untouched textured mesh; the right panel uses the merged mesh\n",
+    "with per-vertex colors averaged from the duplicate groups. If both look the\n",
+    "same, the merge preserves appearance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pvplt = pv.Plotter(shape=(1, 2))\n",
+    "\n",
+    "pvplt.subplot(0, 0)\n",
+    "cedalion.plots.plot_surface(pvplt, surface_h, opacity=1.0)\n",
+    "pvplt.add_text('Before merge', position='upper_left', font_size=12)\n",
+    "\n",
+    "pvplt.subplot(0, 1)\n",
+    "cedalion.plots.plot_surface(pvplt, surface_merged, opacity=1.0)\n",
+    "pvplt.add_text('After merge', position='upper_left', font_size=12)\n",
+    "\n",
+    "pvplt.link_views()\n",
+    "pvplt.show()\n",
+    "\n",
+    "orig_colors = getattr(surface_h.mesh.visual, 'vertex_colors', None)\n",
+    "if orig_colors is not None and len(orig_colors) == len(surface_h.mesh.vertices):\n",
+    "    orig_colors = np.asarray(orig_colors)[:, :3].astype(float)\n",
+    "    max_diffs = []\n",
+    "    for new_idx in range(len(merged_mesh.vertices)):\n",
+    "        old_indices = new_to_old[new_idx]\n",
+    "        if len(old_indices) > 1:\n",
+    "            old_cols = orig_colors[old_indices]\n",
+    "            spread = old_cols.max(axis=0) - old_cols.min(axis=0)\n",
+    "            max_diffs.append(spread.max())\n",
+    "\n",
+    "    if max_diffs:\n",
+    "        max_diffs = np.array(max_diffs)\n",
+    "        print('Color spread at merged vertex groups (0-255 scale):')\n",
+    "        print(f'  Max spread:    {max_diffs.max():.1f}')\n",
+    "        print(f'  Mean spread:   {max_diffs.mean():.1f}')\n",
+    "        print(f'  Median spread: {np.median(max_diffs):.1f}')\n",
+    "        print(f'  Groups with spread > 10: {int((max_diffs > 10).sum())} / {len(max_diffs)}')\n",
+    "    else:\n",
+    "        print('No merged vertex groups with >1 member found.')\n",
+    "else:\n",
+    "    print('Textured mesh (no per-vertex color array) - visual comparison above is the proxy.')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/examples/head_models/50_mask_selection_walkthrough.ipynb b/examples/head_models/50_mask_selection_walkthrough.ipynb
new file mode 100644
index 00000000..2bc5c52e
--- /dev/null
+++ b/examples/head_models/50_mask_selection_walkthrough.ipynb
@@ -0,0 +1,1072 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f01f0012",
+   "metadata": {},
+   "source": [
+    "# Mask-Selection Walkthrough (CTF frame, Subject17)\n",
+    "\n",
+    "Illustrates Section 3.5 of the thesis step by step on a real scan. Each 3D\n",
+    "cell opens an **interactive** PyVista widget: rotate / pan / zoom until the\n",
+    "view is right for the thesis figure, then click the camera icon in the\n",
+    "PyVista toolbar to export a PNG. The matplotlib graphs (cap-boundary\n",
+    "profile, per-region counts) are saved to disk automatically because they\n",
+    "don't need camera control.\n",
+    "\n",
+    "Pipeline (from Section 3.4's head-isolation output onward):\n",
+    "\n",
+    "1. Start from the head-only mesh produced by `isolate_head`.\n",
+    "2. CTF alignment (`align_axes_from_landmarks`).\n",
+    "3. Midline Y-band used as input to the cap-boundary detector.\n",
+    "4. `detect_cap_boundary` -> `Z_cap`.\n",
+    "5. The cap boundary is a *ceiling*, not the full mask: everything above is\n",
+    "   spared, but inside the halfspace `Z < Z_cap` only the forward hemisphere\n",
+    "   and two ear spheres are deleted.\n",
+    "6. `face_mask_from_landmarks` -> union mask.\n",
+    "7. 8 mm landmark safe-area + nasion-strip carve-out (pipeline-level, see\n",
+    "   Section 3.6).\n",
+    "8. `delete_masked_vertices` and validation.\n",
+    "\n",
+    "All math is in the CTF frame: **+X=anterior, +Y=left, +Z=up**, origin at the\n",
+    "LPA/RPA midpoint."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "1b88dc50",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Matplotlib graphs -> /home/ma7/Documents/BA_screenshots/mask_selection\n",
+      "3D frames: use the camera icon in the PyVista toolbar to export a PNG.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import logging\n",
+    "import os\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import numpy as np\n",
+    "import pyvista as pv\n",
+    "import matplotlib.pyplot as plt\n",
+    "import xarray as xr\n",
+    "import trimesh as _tm\n",
+    "from PIL import Image as _PILImage\n",
+    "\n",
+    "import cedalion\n",
+    "import cedalion.dataclasses as cdc\n",
+    "import cedalion.io\n",
+    "import cedalion.plots\n",
+    "from cedalion.geometry.photogrammetry.anonymization import (\n",
+    "    normalize_axes,\n",
+    "    isolate_head,\n",
+    "    align_axes_from_landmarks,\n",
+    "    detect_cap_boundary,\n",
+    "    face_mask_from_landmarks,\n",
+    "    delete_masked_vertices,\n",
+    "    validate_anonymization,\n",
+    ")\n",
+    "from cedalion.vtktutils import trimesh_to_vtk_polydata\n",
+    "from cedalion.dataclasses import VTKSurface\n",
+    "\n",
+    "logging.getLogger('cedalion').setLevel(logging.WARNING)\n",
+    "pv.set_jupyter_backend('server')\n",
+    "\n",
+    "SUBJECT_NUMBER = 17\n",
+    "SCANS_FOLDER = '/home/ma7/BA/PG_Subjects'\n",
+    "FIG_DIR = Path('/home/ma7/Documents/BA_screenshots/mask_selection')\n",
+    "FIG_DIR.mkdir(parents=True, exist_ok=True)\n",
+    "print(f'Matplotlib graphs -> {FIG_DIR}')\n",
+    "print('3D frames: use the camera icon in the PyVista toolbar to export a PNG.')\n",
+    "\n",
+    "# Cached landmarks for Subject17 in raw Einstar coordinates.\n",
+    "# Re-pick in notebook 48 and overwrite these if the RPA validation fails.\n",
+    "LANDMARK_LABELS = ['Cz', 'Iz', 'Rpa', 'Nz', 'Lpa']\n",
+    "LANDMARK_COORDS_RAW = np.array([\n",
+    "    [182.65783971, 118.04228881, 431.56432941],  # Cz\n",
+    "    [ 55.89538812, 223.75583573, 451.320292  ],  # Iz\n",
+    "    [ 64.28395359,  88.3805534 , 585.72672053],  # Rpa\n",
+    "    [ 70.43872393, -12.48837767, 478.83735296],  # Nz\n",
+    "    [  1.44293713,  59.38634557, 399.68340223],  # Lpa\n",
+    "])\n",
+    "\n",
+    "# Mask tunables (mm)\n",
+    "LANDMARK_KEEP_RADIUS = 8.0\n",
+    "EAR_DELETE_RADIUS_MM = 40.0\n",
+    "\n",
+    "# Cap-boundary tunables\n",
+    "CAP_BAND_WIDTH_MM = 15.0\n",
+    "CAP_BIN_SIZE_MM = 2.0\n",
+    "CAP_FOOT_GRAD_THRESHOLD = 0.2\n",
+    "\n",
+    "LM_COLORS = {'Nz': 'lime', 'Iz': 'magenta', 'Cz': 'cyan',\n",
+    "             'Lpa': 'orange', 'Rpa': 'blue'}\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b160e4ed",
+   "metadata": {},
+   "source": [
+    "## 1. Load and preprocess (up to the CTF-aligned head)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "e1ab957a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Head-only mesh: 678,081 verts\n",
+      "CTF frame OK (crs='ctf'); ear_mid = [0. 0. 0.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load the Einstar scan and ensure the texture is attached. Same fallback as\n",
+    "# notebook 48: trimesh 4.6 sometimes hides the image on .material.image.\n",
+    "path = f'{SCANS_FOLDER}/Subject{SUBJECT_NUMBER}/Subject{SUBJECT_NUMBER}.obj'\n",
+    "surface = cedalion.io.read_einstar_obj(path)\n",
+    "\n",
+    "def _visual_image(visual):\n",
+    "    img = getattr(visual, 'image', None)\n",
+    "    if img is None:\n",
+    "        mat = getattr(visual, 'material', None)\n",
+    "        img = getattr(mat, 'image', None) if mat is not None else None\n",
+    "    return img\n",
+    "\n",
+    "if _visual_image(surface.mesh.visual) is None:\n",
+    "    _jpg = path.replace('.obj', '.jpg')\n",
+    "    _uv = getattr(surface.mesh.visual, 'uv', None)\n",
+    "    assert os.path.exists(_jpg) and _uv is not None\n",
+    "    surface.mesh.visual = _tm.visual.TextureVisuals(\n",
+    "        uv=_uv, image=_PILImage.open(_jpg).convert('RGBA'),\n",
+    "    )\n",
+    "\n",
+    "# Wrap cached landmarks.\n",
+    "landmarks_raw = xr.DataArray(\n",
+    "    LANDMARK_COORDS_RAW,\n",
+    "    dims=['label', 'digitized'],\n",
+    "    coords={\n",
+    "        'label': ('label', LANDMARK_LABELS),\n",
+    "        'type':  ('label', [cdc.PointType.LANDMARK] * 5),\n",
+    "        'group': ('label', ['L'] * 5),\n",
+    "    },\n",
+    ").pint.quantify('mm')\n",
+    "idx = {lbl: i for i, lbl in enumerate(landmarks_raw['label'].values)}\n",
+    "\n",
+    "# normalize_axes + isolate_head (outputs of Sections 3.3 and 3.4).\n",
+    "lm_raw_xyz = landmarks_raw.pint.dequantify().values\n",
+    "Nz_raw = lm_raw_xyz[idx['Nz']]\n",
+    "surface_n, Nz_n, R = normalize_axes(surface, Nz_raw)\n",
+    "lm_normed_xyz = lm_raw_xyz @ R.T\n",
+    "surface_h_pre, _ = isolate_head(surface_n, Nz_n)\n",
+    "\n",
+    "# CTF alignment: origin at the LPA/RPA midpoint, +X anterior, +Y left, +Z up.\n",
+    "landmarks_n = xr.DataArray(\n",
+    "    lm_normed_xyz, dims=['label', 'digitized'],\n",
+    "    coords={\n",
+    "        'label': ('label', LANDMARK_LABELS),\n",
+    "        'type':  ('label', [cdc.PointType.LANDMARK] * 5),\n",
+    "        'group': ('label', ['L'] * 5),\n",
+    "    },\n",
+    ").pint.quantify('mm')\n",
+    "surface_h, landmarks_ctf, _ = align_axes_from_landmarks(\n",
+    "    surface_h_pre, landmarks_n,\n",
+    ")\n",
+    "lm_ctf_xyz = landmarks_ctf.pint.dequantify().values\n",
+    "Nz, Iz, Cz, Lpa, Rpa = (lm_ctf_xyz[idx[lbl]]\n",
+    "                        for lbl in ['Nz', 'Iz', 'Cz', 'Lpa', 'Rpa'])\n",
+    "ear_mid = 0.5 * (Lpa + Rpa)\n",
+    "verts = np.asarray(surface_h.mesh.vertices)\n",
+    "mid_y = 0.5 * (Lpa[1] + Rpa[1])\n",
+    "\n",
+    "print(f'Head-only mesh: {surface_h_pre.nvertices:,} verts')\n",
+    "print(f'CTF frame OK (crs={surface_h.crs!r}); ear_mid = {np.round(ear_mid,3)}')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "58a5348f",
+   "metadata": {},
+   "source": [
+    "### Frame A — head-only mesh after `isolate_head` (Section 3.4 output)\n",
+    "\n",
+    "The starting point for Section 3.5. Shoulders, chair, and disconnected\n",
+    "fragments have already been removed. Landmarks are drawn as coloured spheres\n",
+    "for orientation. Rotate the head to the angle you want, then use the\n",
+    "PyVista toolbar's camera icon to export a PNG."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "61f6ed03",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6b6370398a964ced97a90b5ec8b2cb04",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Widget(value='<iframe src=\"http://localhost:32931/index.html?ui=P_0x7fae1c1863d0_10&reconnect=auto\" class=\"pyv…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pvplt = pv.Plotter()\n",
+    "hvtk = trimesh_to_vtk_polydata(surface_h_pre.mesh)\n",
+    "pvplt.add_mesh(pv.wrap(hvtk), rgb=True, smooth_shading=True)\n",
+    "for lbl, pos in zip(LANDMARK_LABELS, lm_normed_xyz):\n",
+    "    c = LM_COLORS[lbl]\n",
+    "    pvplt.add_mesh(pv.Sphere(radius=5, center=pos), color=c)\n",
+    "    pvplt.add_point_labels([pos], [lbl], font_size=16, text_color=c,\n",
+    "                           shape=None, always_visible=True)\n",
+    "pvplt.add_text(\n",
+    "    f'After isolate_head  ({surface_h_pre.nvertices:,} verts)',\n",
+    "    position='upper_left', font_size=12,\n",
+    ")\n",
+    "pvplt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03b10ef3",
+   "metadata": {},
+   "source": [
+    "### Frame B — CTF-aligned mesh with basis vectors\n",
+    "\n",
+    "After `align_axes_from_landmarks` the origin sits at the LPA/RPA midpoint and\n",
+    "the three coloured arrows mark the CTF axes:\n",
+    "\n",
+    "- **red**  +X anterior (toward the nasion)\n",
+    "- **green** +Y left (toward LPA)\n",
+    "- **blue**  +Z up (toward Cz)\n",
+    "\n",
+    "Every subsequent rule in Section 3.5 is expressed in this frame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "2a1ce37e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "187492e53229450eb0c1e7aa13accd54",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Widget(value='<iframe src=\"http://localhost:32931/index.html?ui=P_0x7fac8ae5c090_11&reconnect=auto\" class=\"pyv…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pvplt = pv.Plotter()\n",
+    "vtk_s = VTKSurface.from_trimeshsurface(surface_h)\n",
+    "pvplt.add_mesh(pv.wrap(vtk_s.mesh), rgb=True, smooth_shading=True)\n",
+    "\n",
+    "for lbl, pos in zip(['Nz','Iz','Cz','Lpa','Rpa'], [Nz, Iz, Cz, Lpa, Rpa]):\n",
+    "    pvplt.add_mesh(pv.Sphere(radius=5, center=pos), color=LM_COLORS[lbl])\n",
+    "    pvplt.add_point_labels([pos], [lbl], font_size=16,\n",
+    "                           text_color=LM_COLORS[lbl],\n",
+    "                           shape=None, always_visible=True)\n",
+    "\n",
+    "arr_len = 90.0\n",
+    "for direc, color, name in (\n",
+    "    ((1, 0, 0), 'red',   '+X anterior'),\n",
+    "    ((0, 1, 0), 'green', '+Y left'),\n",
+    "    ((0, 0, 1), 'blue',  '+Z up'),\n",
+    "):\n",
+    "    pvplt.add_mesh(\n",
+    "        pv.Arrow(start=(0, 0, 0), direction=direc, scale=arr_len),\n",
+    "        color=color,\n",
+    "    )\n",
+    "    tip = tuple(np.array(direc) * (arr_len + 15))\n",
+    "    pvplt.add_point_labels([tip], [name], font_size=16, text_color=color,\n",
+    "                           shape=None, always_visible=True)\n",
+    "\n",
+    "pvplt.add_text('CTF frame: +X anterior, +Y left, +Z up',\n",
+    "               position='upper_left', font_size=12)\n",
+    "pvplt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f22a8bd",
+   "metadata": {},
+   "source": [
+    "## 2. Cap boundary detection\n",
+    "\n",
+    "**Problem.** The fNIRS cap protrudes forward of the bare forehead, so if we\n",
+    "naively say \"delete everything forward of the ear plane\" we'd also delete\n",
+    "the cap, the optodes, and the cables. We need to stop the forward-facing\n",
+    "band at the cap's lower edge.\n",
+    "\n",
+    "**Solution.** Walk upward along the midline and watch how far forward the\n",
+    "mesh reaches. On the forehead this distance grows slowly; at the cap edge it\n",
+    "jumps because the cap sticks out further than the skin below it. The cap\n",
+    "boundary height `Z_cap` is the foot of that jump."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d16a9156",
+   "metadata": {},
+   "source": [
+    "### Frame C — midline Y-band feeding the profile\n",
+    "\n",
+    "The cap detector only looks at vertices near the sagittal midline (within\n",
+    "±15 mm of `Y = 0`), above the nasion, and anterior to the ear plane. Those\n",
+    "are the blue vertices below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "84fea875",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Midline-band vertices feeding the profile: 21,985\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8cb2a7a8cb73410189a33a3f9968b7c7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Widget(value='<iframe src=\"http://localhost:32931/index.html?ui=P_0x7fac8aed1350_12&reconnect=auto\" class=\"pyv…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "in_band  = np.abs(verts[:, 1] - mid_y) < CAP_BAND_WIDTH_MM\n",
+    "above_nz = verts[:, 2] > Nz[2]\n",
+    "anterior = verts[:, 0] > ear_mid[0]\n",
+    "band_mask = in_band & above_nz & anterior\n",
+    "print(f'Midline-band vertices feeding the profile: {int(band_mask.sum()):,}')\n",
+    "\n",
+    "pvplt = pv.Plotter()\n",
+    "vtk_s = VTKSurface.from_trimeshsurface(surface_h)\n",
+    "pv_m = pv.wrap(vtk_s.mesh)\n",
+    "pv_m['band'] = band_mask.astype(float)\n",
+    "pvplt.add_mesh(pv_m, scalars='band', cmap=['#d6d6d6', '#1f77b4'],\n",
+    "               clim=[0, 1], show_scalar_bar=False, smooth_shading=True)\n",
+    "pvplt.add_text(f'Midline Y-band  |Y| < {CAP_BAND_WIDTH_MM:.0f} mm',\n",
+    "               position='upper_left', font_size=12)\n",
+    "pvplt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bd835e74",
+   "metadata": {},
+   "source": [
+    "### Graph D — midline X(Z) profile and its gradient\n",
+    "\n",
+    "The blue vertices are binned by height `Z` in 2 mm bins and the max\n",
+    "anterior extent `X` per bin forms the profile `X(Z)` (left panel). The\n",
+    "Savitzky–Golay smoothing suppresses sub-millimetre scanner noise. The\n",
+    "cap sits at the top of the steep rise; `Z_cap` is the first Z, walking back\n",
+    "from the smoothed peak toward the nasion, where the gradient `dX/dZ` drops\n",
+    "below the foot threshold `0.2` (right panel)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "ffa81229",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Z_cap = 31.0 mm   (Nz_z=-0.0, Cz_z=165.4)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Saved 06_cap_profile.pdf and .png  (Z_cap = 31.0 mm)\n"
+     ]
+    }
+   ],
+   "source": [
+    "cap_z, profile_z, profile_x, profile_x_s = detect_cap_boundary(\n",
+    "    verts, Nz, Cz, ear_mid, mid_y,\n",
+    "    band_width=CAP_BAND_WIDTH_MM,\n",
+    "    bin_size=CAP_BIN_SIZE_MM,\n",
+    "    foot_grad_threshold=CAP_FOOT_GRAD_THRESHOLD,\n",
+    ")\n",
+    "print(f'Z_cap = {cap_z:.1f} mm   (Nz_z={Nz[2]:.1f}, Cz_z={Cz[2]:.1f})')\n",
+    "\n",
+    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))\n",
+    "ax1.plot(profile_z, profile_x, 'b.-', alpha=0.4, label='raw max-X')\n",
+    "ax1.plot(profile_z, profile_x_s, 'b-', lw=2, label='SG-smoothed')\n",
+    "ax1.axvline(Nz[2], color='green', ls='--', label='Nz')\n",
+    "ax1.axvline(cap_z, color='red', ls='--', lw=2,\n",
+    "            label=f'$Z_\\\\mathrm{{cap}}$ = {cap_z:.0f} mm')\n",
+    "ax1.axvline(Cz[2], color='cyan', ls='--', label='Cz')\n",
+    "ax1.set_xlabel('Z (height, mm)')\n",
+    "ax1.set_ylabel('max X (anterior, mm)')\n",
+    "ax1.set_title('Midline X(Z) profile')\n",
+    "ax1.legend(loc='best', fontsize=9)\n",
+    "ax1.grid(alpha=0.3)\n",
+    "\n",
+    "grad = np.gradient(profile_x_s, profile_z)\n",
+    "ax2.plot(profile_z, grad, 'r.-')\n",
+    "ax2.axhline(CAP_FOOT_GRAD_THRESHOLD, color='purple', ls=':',\n",
+    "            label=f'foot threshold = {CAP_FOOT_GRAD_THRESHOLD}')\n",
+    "ax2.axvline(cap_z, color='red', ls='--', lw=2, label='$Z_\\\\mathrm{cap}$')\n",
+    "ax2.set_xlabel('Z (height, mm)')\n",
+    "ax2.set_ylabel('dX/dZ')\n",
+    "ax2.set_title('Gradient')\n",
+    "ax2.legend(loc='best', fontsize=9)\n",
+    "ax2.grid(alpha=0.3)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(FIG_DIR / '06_cap_profile.pdf', bbox_inches='tight')\n",
+    "plt.savefig(FIG_DIR / '06_cap_profile.png', dpi=150, bbox_inches='tight')\n",
+    "plt.show()\n",
+    "print(f'Saved 06_cap_profile.pdf and .png  (Z_cap = {cap_z:.1f} mm)')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c73d9ff4",
+   "metadata": {},
+   "source": [
+    "### Frame E — cap boundary plane on the mesh\n",
+    "\n",
+    "The semi-transparent red plane is the horizontal slice at `Z = Z_cap`.\n",
+    "Everything above it is *guaranteed* to survive; everything below it is\n",
+    "*eligible* for masking (but not automatically masked — see the next\n",
+    "section)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "3656d52c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "20411fe722274575a80e5dc4aad09613",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Widget(value='<iframe src=\"http://localhost:32931/index.html?ui=P_0x7fae07f37050_13&reconnect=auto\" class=\"pyv…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pvplt = pv.Plotter()\n",
+    "vtk_s = VTKSurface.from_trimeshsurface(surface_h)\n",
+    "pvplt.add_mesh(pv.wrap(vtk_s.mesh), color='#c8c8c8', smooth_shading=True)\n",
+    "plane = pv.Plane(center=(0.0, 0.0, float(cap_z)),\n",
+    "                 direction=(0.0, 0.0, 1.0),\n",
+    "                 i_size=320.0, j_size=320.0)\n",
+    "pvplt.add_mesh(plane, color='red', opacity=0.35)\n",
+    "pvplt.add_text(f'Cap boundary plane  Z = {cap_z:.0f} mm',\n",
+    "               position='upper_left', font_size=12)\n",
+    "pvplt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e53f27cb",
+   "metadata": {},
+   "source": [
+    "## 3. From the cap boundary to the final mask\n",
+    "\n",
+    "`Z_cap` is a **ceiling**, not the mask itself. If we masked *everything* below\n",
+    "`Z_cap` we would delete the posterior scalp, the neck, and the shoulders\n",
+    "too — far more than we want. Frame F shows that `below_cap` halfspace as a\n",
+    "sanity check; the final mask is a much smaller subset of it.\n",
+    "\n",
+    "Inside the halfspace `Z < Z_cap`, the mask is the **union** of two\n",
+    "geometric regions:\n",
+    "\n",
+    "1. **Face region** (Frame G): every vertex with `X > M_X`, i.e. in front of\n",
+    "   the coronal plane that passes through the ear midpoint `M`. This covers\n",
+    "   the face, the cheeks, and the forehead up to the cap edge.\n",
+    "2. **Ear spheres** (Frames H and I): two 40 mm spheres centred on LPA and\n",
+    "   RPA respectively. These sweep up each pinna and the surrounding lateral\n",
+    "   skin.\n",
+    "\n",
+    "Everything else — the back of the head, the crown under the cap, the neck,\n",
+    "the shoulders — is *not* in either region and is therefore kept."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19392a17",
+   "metadata": {},
+   "source": [
+    "### Frame F — `below_cap` halfspace (eligibility only, not the mask)\n",
+    "\n",
+    "Blue = vertices with `Z < Z_cap`. Note how much of the head this covers;\n",
+    "the final mask is narrower."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "eb5445af",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c24727b28b1c4cab8671cbc232d47388",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Widget(value='<iframe src=\"http://localhost:32931/index.html?ui=P_0x7fadec0aab50_14&reconnect=auto\" class=\"pyv…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "below_cap = verts[:, 2] < cap_z\n",
+    "\n",
+    "pvplt = pv.Plotter()\n",
+    "vtk_s = VTKSurface.from_trimeshsurface(surface_h)\n",
+    "pv_m = pv.wrap(vtk_s.mesh)\n",
+    "pv_m['below'] = below_cap.astype(float)\n",
+    "pvplt.add_mesh(pv_m, scalars='below', cmap=['#d6d6d6', '#1f77b4'],\n",
+    "               clim=[0, 1], show_scalar_bar=False, smooth_shading=True)\n",
+    "pvplt.add_text(\n",
+    "    f'below_cap halfspace  Z < {cap_z:.0f} mm  '\n",
+    "    f'({int(below_cap.sum()):,} verts eligible)',\n",
+    "    position='upper_left', font_size=12,\n",
+    ")\n",
+    "pvplt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f746b2a",
+   "metadata": {},
+   "source": [
+    "### Frame G — face region `X > M_X` ∧ `Z < Z_cap`\n",
+    "\n",
+    "Red = the forward hemisphere below the cap. Rotate to a 3/4 frontal view\n",
+    "for the thesis figure."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "24ee5e98",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "face_region vertices: 102,705\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "04d93c4f194a4a94ab1b2c5841964f7a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Widget(value='<iframe src=\"http://localhost:32931/index.html?ui=P_0x7fadab700ed0_15&reconnect=auto\" class=\"pyv…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "anterior_m  = verts[:, 0] > ear_mid[0]\n",
+    "face_region = below_cap & anterior_m\n",
+    "print(f'face_region vertices: {int(face_region.sum()):,}')\n",
+    "\n",
+    "pvplt = pv.Plotter()\n",
+    "vtk_s = VTKSurface.from_trimeshsurface(surface_h)\n",
+    "pv_m = pv.wrap(vtk_s.mesh)\n",
+    "pv_m['face'] = face_region.astype(float)\n",
+    "pvplt.add_mesh(pv_m, scalars='face', cmap=['#d6d6d6', 'red'],\n",
+    "               clim=[0, 1], show_scalar_bar=False, smooth_shading=True)\n",
+    "pvplt.add_text(f'Face region  X > M_X ∧ Z < Z_cap  '\n",
+    "               f'({int(face_region.sum()):,} verts)',\n",
+    "               position='upper_left', font_size=12)\n",
+    "pvplt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "717977cc",
+   "metadata": {},
+   "source": [
+    "### Frame H — ear spheres, mirrored side-by-side\n",
+    "\n",
+    "Left panel: LPA sphere viewed from the subject's left (camera at `+Y`).\n",
+    "Right panel: RPA sphere viewed from the subject's right (camera at `-Y`).\n",
+    "Both are 40 mm spheres clamped below the cap. The cameras are mirrored,\n",
+    "not linked — you can tweak each side independently before screenshotting."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "2604880d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "LPA ear verts: 19,573   RPA ear verts: 11,655   (union 31,228)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c484031d87eb467cafffc9116222cfaa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Widget(value='<iframe src=\"http://localhost:32931/index.html?ui=P_0x7fadab753a90_16&reconnect=auto\" class=\"pyv…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "d_lpa = np.linalg.norm(verts - Lpa, axis=1)\n",
+    "d_rpa = np.linalg.norm(verts - Rpa, axis=1)\n",
+    "lpa_region = (d_lpa < EAR_DELETE_RADIUS_MM) & below_cap\n",
+    "rpa_region = (d_rpa < EAR_DELETE_RADIUS_MM) & below_cap\n",
+    "ear_region = lpa_region | rpa_region\n",
+    "print(f'LPA ear verts: {int(lpa_region.sum()):,}   '\n",
+    "      f'RPA ear verts: {int(rpa_region.sum()):,}   '\n",
+    "      f'(union {int(ear_region.sum()):,})')\n",
+    "\n",
+    "# Mirrored lateral cameras. Focal point = ear midpoint (CTF origin).\n",
+    "CAM_DIST = 420.0\n",
+    "CAM_LEFT  = [(0.0,  CAM_DIST, float(Cz[2]) * 0.25),\n",
+    "             (0.0, 0.0, float(Cz[2]) * 0.25),\n",
+    "             (0.0, 0.0, 1.0)]\n",
+    "CAM_RIGHT = [(0.0, -CAM_DIST, float(Cz[2]) * 0.25),\n",
+    "             (0.0, 0.0, float(Cz[2]) * 0.25),\n",
+    "             (0.0, 0.0, 1.0)]\n",
+    "\n",
+    "pvplt = pv.Plotter(shape=(1, 2))\n",
+    "\n",
+    "# --- left panel: LPA ---\n",
+    "pvplt.subplot(0, 0)\n",
+    "vtk_a = VTKSurface.from_trimeshsurface(surface_h)\n",
+    "pv_a = pv.wrap(vtk_a.mesh)\n",
+    "pv_a['lpa'] = lpa_region.astype(float)\n",
+    "pvplt.add_mesh(pv_a, scalars='lpa', cmap=['#d6d6d6', 'orange'],\n",
+    "               clim=[0, 1], show_scalar_bar=False, smooth_shading=True)\n",
+    "pvplt.add_mesh(pv.Sphere(radius=5, center=Lpa), color=LM_COLORS['Lpa'])\n",
+    "pvplt.add_point_labels([Lpa], ['Lpa'], font_size=16,\n",
+    "                       text_color=LM_COLORS['Lpa'],\n",
+    "                       shape=None, always_visible=True)\n",
+    "pvplt.camera_position = CAM_LEFT\n",
+    "pvplt.add_text(f'LPA sphere (r = {EAR_DELETE_RADIUS_MM:.0f} mm)  '\n",
+    "               f'{int(lpa_region.sum()):,} verts',\n",
+    "               position='upper_left', font_size=12)\n",
+    "\n",
+    "# --- right panel: RPA ---\n",
+    "pvplt.subplot(0, 1)\n",
+    "vtk_b = VTKSurface.from_trimeshsurface(surface_h)\n",
+    "pv_b = pv.wrap(vtk_b.mesh)\n",
+    "pv_b['rpa'] = rpa_region.astype(float)\n",
+    "pvplt.add_mesh(pv_b, scalars='rpa', cmap=['#d6d6d6', 'orange'],\n",
+    "               clim=[0, 1], show_scalar_bar=False, smooth_shading=True)\n",
+    "pvplt.add_mesh(pv.Sphere(radius=5, center=Rpa), color=LM_COLORS['Rpa'])\n",
+    "pvplt.add_point_labels([Rpa], ['Rpa'], font_size=16,\n",
+    "                       text_color=LM_COLORS['Rpa'],\n",
+    "                       shape=None, always_visible=True)\n",
+    "pvplt.camera_position = CAM_RIGHT\n",
+    "pvplt.add_text(f'RPA sphere (r = {EAR_DELETE_RADIUS_MM:.0f} mm)  '\n",
+    "               f'{int(rpa_region.sum()):,} verts',\n",
+    "               position='upper_left', font_size=12)\n",
+    "\n",
+    "pvplt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "47e7dbe8",
+   "metadata": {},
+   "source": [
+    "### Frame J — union mask (before carve-outs)\n",
+    "\n",
+    "Red = the union of the face region and the two ear spheres. This is what\n",
+    "`face_mask_from_landmarks` returns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "2933ccc4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pre-carveout counts: {'below_cap': 304744, 'face_region': 102705, 'ear_region': 31228, 'all': 117625}\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f031e2af4d02485da5bd51954d2dfc22",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Widget(value='<iframe src=\"http://localhost:32931/index.html?ui=P_0x7fadab763390_17&reconnect=auto\" class=\"pyv…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mask, mask_info = face_mask_from_landmarks(\n",
+    "    verts, Nz, Iz, Cz, Lpa, Rpa,\n",
+    "    cap_z=cap_z, ear_delete_radius=EAR_DELETE_RADIUS_MM,\n",
+    ")\n",
+    "counts_pre = dict(mask_info['counts'])\n",
+    "mask_pre_carve = mask.copy()\n",
+    "print(f'Pre-carveout counts: {counts_pre}')\n",
+    "\n",
+    "pvplt = pv.Plotter()\n",
+    "vtk_s = VTKSurface.from_trimeshsurface(surface_h)\n",
+    "pv_m = pv.wrap(vtk_s.mesh)\n",
+    "pv_m['mask'] = mask_pre_carve.astype(float)\n",
+    "pvplt.add_mesh(pv_m, scalars='mask', cmap=['white', 'red'],\n",
+    "               clim=[0, 1], show_scalar_bar=False, smooth_shading=True)\n",
+    "for lbl, pos in zip(['Nz','Iz','Cz','Lpa','Rpa'], [Nz, Iz, Cz, Lpa, Rpa]):\n",
+    "    c = LM_COLORS[lbl]\n",
+    "    pvplt.add_mesh(pv.Sphere(radius=5, center=pos), color=c)\n",
+    "    pvplt.add_point_labels([pos], [lbl], font_size=16, text_color=c,\n",
+    "                           shape=None, always_visible=True)\n",
+    "pvplt.add_text(\n",
+    "    f'Union mask, pre-carveout  '\n",
+    "    f'({int(mask_pre_carve.sum()):,} verts, '\n",
+    "    f'{100*mask_pre_carve.sum()/len(mask_pre_carve):.1f} %)',\n",
+    "    position='upper_left', font_size=12,\n",
+    ")\n",
+    "pvplt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "583275de",
+   "metadata": {},
+   "source": [
+    "### Frame K — final mask after the landmark safe-area and nasion-strip carve-outs\n",
+    "\n",
+    "Before the mask is handed to the deletion step, two carve-outs run at the\n",
+    "pipeline level (Section 3.6):\n",
+    "\n",
+    "- an 8 mm spherical *safe-area* around each of the five landmarks is\n",
+    "  removed from the mask, and\n",
+    "- a narrow midline strip running from the nasion up to `Z_cap` is also\n",
+    "  removed, so Cedalion's `LandmarksBuilder1010` retains a clean nasion\n",
+    "  neighbourhood.\n",
+    "\n",
+    "The result is what actually gets deleted."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "ec3a5a78",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Final mask: 114,837 verts (16.9 %)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c96c1a2c2fd0464fb4e761ce6038dcee",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Widget(value='<iframe src=\"http://localhost:32931/index.html?ui=P_0x7fadab7e3e10_18&reconnect=auto\" class=\"pyv…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for lm in (Nz, Iz, Cz, Lpa, Rpa):\n",
+    "    near = np.linalg.norm(verts - lm, axis=1) < LANDMARK_KEEP_RADIUS\n",
+    "    mask[near] = False\n",
+    "nasion_strip = (\n",
+    "    (verts[:, 2] >= Nz[2]) & (verts[:, 2] < cap_z)\n",
+    "    & (np.abs(verts[:, 1] - Nz[1]) < LANDMARK_KEEP_RADIUS)\n",
+    "    & (verts[:, 0] > ear_mid[0])\n",
+    ")\n",
+    "mask[nasion_strip] = False\n",
+    "print(f'Final mask: {int(mask.sum()):,} verts '\n",
+    "      f'({100*mask.sum()/len(mask):.1f} %)')\n",
+    "\n",
+    "pvplt = pv.Plotter()\n",
+    "vtk_s = VTKSurface.from_trimeshsurface(surface_h)\n",
+    "pv_m = pv.wrap(vtk_s.mesh)\n",
+    "pv_m['mask'] = mask.astype(float)\n",
+    "pvplt.add_mesh(pv_m, scalars='mask', cmap=['white', 'red'],\n",
+    "               clim=[0, 1], show_scalar_bar=False, smooth_shading=True)\n",
+    "for lbl, pos in zip(['Nz','Iz','Cz','Lpa','Rpa'], [Nz, Iz, Cz, Lpa, Rpa]):\n",
+    "    c = LM_COLORS[lbl]\n",
+    "    pvplt.add_mesh(pv.Sphere(radius=5, center=pos), color=c)\n",
+    "    pvplt.add_point_labels([pos], [lbl], font_size=16, text_color=c,\n",
+    "                           shape=None, always_visible=True)\n",
+    "pvplt.add_text(\n",
+    "    f'Final mask (after carve-outs)  '\n",
+    "    f'{int(mask.sum()):,} / {len(mask):,} verts  '\n",
+    "    f'({100*mask.sum()/len(mask):.1f} %)',\n",
+    "    position='upper_left', font_size=12,\n",
+    ")\n",
+    "pvplt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e342809a",
+   "metadata": {},
+   "source": [
+    "### Graph L — per-region vertex counts\n",
+    "\n",
+    "How the mask budget splits by geometric rule (pre-carveout) and after the\n",
+    "safe-area carve-outs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5867e82e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "labels  = ['below_cap', 'face_region', 'ear_region',\n",
+    "           'union (pre-carve)', 'final mask']\n",
+    "values  = [counts_pre['below_cap'], counts_pre['face_region'],\n",
+    "           counts_pre['ear_region'], counts_pre['all'], int(mask.sum())]\n",
+    "colors  = ['#8a8a8a', '#d62728', '#ff7f0e', '#9467bd', '#2ca02c']\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(9, 4.2))\n",
+    "bars = ax.bar(labels, values, color=colors)\n",
+    "for bar, v in zip(bars, values):\n",
+    "    ax.text(bar.get_x() + bar.get_width() / 2, v,\n",
+    "            f'{v:,}', ha='center', va='bottom', fontsize=10)\n",
+    "ax.set_ylabel('vertex count')\n",
+    "ax.set_title(f'Per-region mask counts  (Subject{SUBJECT_NUMBER})')\n",
+    "ax.grid(axis='y', alpha=0.3)\n",
+    "plt.xticks(rotation=15)\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(FIG_DIR / '11_region_counts.pdf', bbox_inches='tight')\n",
+    "plt.savefig(FIG_DIR / '11_region_counts.png', dpi=150, bbox_inches='tight')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "891e97d6",
+   "metadata": {},
+   "source": [
+    "## 4. Deletion and before/after\n",
+    "\n",
+    "Once the mask is finalized, `delete_masked_vertices` drops every triangle\n",
+    "touching a masked vertex and strips the now-unreferenced vertices. No\n",
+    "smoothing, no Taubin pass (see Section 3.6)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a92916d0",
+   "metadata": {},
+   "source": [
+    "### Frame M — anonymized mesh"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f73f0bb9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "surface_anon = delete_masked_vertices(surface_h, mask)\n",
+    "n_removed = surface_h.nvertices - surface_anon.nvertices\n",
+    "print(f'Original:    {surface_h.nvertices:,} verts')\n",
+    "print(f'Anonymized:  {surface_anon.nvertices:,} verts  (-{n_removed:,})')\n",
+    "\n",
+    "pvplt = pv.Plotter()\n",
+    "anon_vtk = trimesh_to_vtk_polydata(surface_anon.mesh)\n",
+    "pvplt.add_mesh(pv.wrap(anon_vtk), rgb=True, smooth_shading=True)\n",
+    "for lbl, pos in zip(['Nz','Iz','Cz','Lpa','Rpa'], [Nz, Iz, Cz, Lpa, Rpa]):\n",
+    "    pvplt.add_mesh(pv.Sphere(radius=5, center=pos), color=LM_COLORS[lbl])\n",
+    "pvplt.add_text(\n",
+    "    f'Anonymized  ({surface_anon.nvertices:,} verts, -{n_removed:,})',\n",
+    "    position='upper_left', font_size=12,\n",
+    ")\n",
+    "pvplt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1cdd4758",
+   "metadata": {},
+   "source": [
+    "### Frame N — before / after (linked views)\n",
+    "\n",
+    "Rotate either panel; both cameras move together."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "72fe08d6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pvplt = pv.Plotter(shape=(1, 2))\n",
+    "\n",
+    "pvplt.subplot(0, 0)\n",
+    "orig_vtk = trimesh_to_vtk_polydata(surface_h.mesh)\n",
+    "pvplt.add_mesh(pv.wrap(orig_vtk), rgb=True, smooth_shading=True)\n",
+    "for lbl, pos in zip(['Nz','Iz','Cz','Lpa','Rpa'], [Nz, Iz, Cz, Lpa, Rpa]):\n",
+    "    pvplt.add_mesh(pv.Sphere(radius=5, center=pos), color=LM_COLORS[lbl])\n",
+    "pvplt.add_text(f'Subject{SUBJECT_NUMBER} original',\n",
+    "               position='upper_left', font_size=12)\n",
+    "\n",
+    "pvplt.subplot(0, 1)\n",
+    "anon_vtk = trimesh_to_vtk_polydata(surface_anon.mesh)\n",
+    "pvplt.add_mesh(pv.wrap(anon_vtk), rgb=True, smooth_shading=True)\n",
+    "for lbl, pos in zip(['Nz','Iz','Cz','Lpa','Rpa'], [Nz, Iz, Cz, Lpa, Rpa]):\n",
+    "    pvplt.add_mesh(pv.Sphere(radius=5, center=pos), color=LM_COLORS[lbl])\n",
+    "pvplt.add_text(f'Anonymized  (-{n_removed:,} verts)',\n",
+    "               position='upper_left', font_size=12)\n",
+    "\n",
+    "pvplt.link_views()\n",
+    "pvplt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "205f5c45",
+   "metadata": {},
+   "source": [
+    "## 5. Validation\n",
+    "\n",
+    "Sanity-check the anonymized mesh against the original."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "95382e8a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result = validate_anonymization(\n",
+    "    original_surface=surface_h,\n",
+    "    anonymized_surface=surface_anon,\n",
+    "    facial_mask=mask,\n",
+    "    protected_points=landmarks_ctf,\n",
+    "    tolerance=5.0 * cedalion.units.mm,\n",
+    ")\n",
+    "print(result)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/__init__.py b/src/cedalion/geometry/photogrammetry/anonymization/__init__.py
index 3d2c5fac..b007915f 100644
--- a/src/cedalion/geometry/photogrammetry/anonymization/__init__.py
+++ b/src/cedalion/geometry/photogrammetry/anonymization/__init__.py
@@ -14,6 +14,13 @@
 4. Align the full frame from the 5 landmarks (``preprocessing``).
 5. Detect the cap boundary, build the deletion mask, and delete the masked
    vertices (``mask``).
+6. Optionally revert the aligned surface and landmarks back to the raw
+   Einstar frame via ``revert_to_einstar_frame`` so the saved files carry
+   ``crs="digitized"`` and match the co-registration tutorial's input
+   convention (``read_einstar_obj`` output).
+7. Save the anonymized mesh (``.obj``) and the landmarks (``.tsv``) via
+   ``save_anonymized_scan(surface, out_path, landmarks=...)``. The TSV is
+   what the co-registration tutorial loads at step 5.2.
 
 ``detect_landmarks_from_nasion`` (``landmarks``) is an alternative that takes
 just the nasion and geometrically infers the other four. An alternative
@@ -38,6 +45,7 @@
     normalize_axes,
     isolate_head,
     align_axes_from_landmarks,
+    revert_to_einstar_frame,
 )
 from .landmarks import detect_landmarks_from_nasion
 from .mask import (
@@ -50,10 +58,12 @@
 
 
 __all__ = [
-    # Preprocessing (axis normalization, head isolation, full alignment)
+    # Preprocessing (axis normalization, head isolation, full alignment,
+    # and the inverse mapping back to the raw Einstar frame)
     "normalize_axes",
     "isolate_head",
     "align_axes_from_landmarks",
+    "revert_to_einstar_frame",
     # Landmark detection
     "detect_landmarks_from_nasion",
     # Mask construction and application
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/mask.py b/src/cedalion/geometry/photogrammetry/anonymization/mask.py
index 640467c0..5c501d57 100644
--- a/src/cedalion/geometry/photogrammetry/anonymization/mask.py
+++ b/src/cedalion/geometry/photogrammetry/anonymization/mask.py
@@ -1,14 +1,17 @@
 """Facial-region mask construction and application.
 
-Given the 5 aligned landmarks, builds a boolean deletion mask and applies
-it to the mesh:
+Given the 5 CTF-aligned landmarks, builds a boolean deletion mask and
+applies it to the mesh:
 
-- ``detect_cap_boundary`` finds the X height where the EEG cap front edge
+- ``detect_cap_boundary`` finds the Z height where the EEG cap front edge
   sits so the mask can be clamped below it.
 - ``face_mask_from_landmarks`` unions a forward face region with two ear
   spheres, clamped below the cap.
 - ``delete_masked_vertices`` drops triangles touching any masked vertex.
 
+All mask math assumes the CTF frame: +X=anterior, +Y=left, +Z=up, origin
+at the LPA-RPA midpoint (see ``align_axes_from_landmarks``).
+
 Initial Contributors:
     - Face Anonymization Project | 2024
 """
@@ -27,70 +30,71 @@ def detect_cap_boundary(
     Nz: np.ndarray,
     Cz: np.ndarray,
     ear_mid: np.ndarray,
-    mid_z: float,
+    mid_y: float,
     band_width: float = 15.0,
     bin_size: float = 2.0,
     foot_grad_threshold: float = 0.2,
 ) -> tuple[float, np.ndarray, np.ndarray, np.ndarray]:
-    """Find the X height where the EEG cap front edge sits.
+    """Find the Z height where the EEG cap front edge sits.
 
-    Scans upward from Nz along the midline and records max-Y per X-bin. The cap
-    protrudes in +Y, so Y-max along the midline lives on the cap. The cap edge
-    is the foot of the steep rise leading to the peak: walk back from the peak
-    until the smoothed gradient drops below ``foot_grad_threshold``.
+    Scans upward from Nz along the midline and records max-X per Z-bin. The
+    cap protrudes in +X (anterior), so X-max along the midline lives on the
+    cap. The cap edge is the foot of the steep rise leading to the peak:
+    walk back from the peak until the smoothed gradient drops below
+    ``foot_grad_threshold``.
 
-    Expects the aligned frame (X=up, Y=anterior, Z=left).
+    Expects the CTF frame (+X=anterior, +Y=left, +Z=up).
 
     Args:
         verts: Mesh vertices, shape (N, 3).
         Nz: Nasion position.
         Cz: Cz position.
         ear_mid: Midpoint of Lpa/Rpa.
-        mid_z: Midline Z value (e.g. ``0.5 * (Lpa[2] + Rpa[2])``).
-        band_width: Z-band half-width for the midline Y-profile (mm).
-        bin_size: X-bin size for the Y-profile (mm).
-        foot_grad_threshold: dY/dX below this value marks the foot of the rise.
+        mid_y: Midline Y value (e.g. ``0.5 * (Lpa[1] + Rpa[1])``).
+        band_width: Y-band half-width for the midline X-profile (mm).
+        bin_size: Z-bin size for the X-profile (mm).
+        foot_grad_threshold: dX/dZ below this value marks the foot of the rise.
 
     Returns:
-        Tuple of (cap_x, profile_x, profile_y_raw, profile_y_smooth).
+        Tuple of (cap_z, profile_z, profile_x_raw, profile_x_smooth).
     """
     from scipy.signal import savgol_filter
 
-    in_band = np.abs(verts[:, 2] - mid_z) < band_width
-    above_nz = verts[:, 0] > Nz[0]
-    anterior = verts[:, 1] > ear_mid[1]
+    in_band = np.abs(verts[:, 1] - mid_y) < band_width
+    above_nz = verts[:, 2] > Nz[2]
+    anterior = verts[:, 0] > ear_mid[0]
     sel_verts = verts[in_band & above_nz & anterior]
 
-    bins = np.arange(Nz[0], Cz[0], bin_size)
+    bins = np.arange(Nz[2], Cz[2], bin_size)
     bin_centers = bins[:-1] + bin_size / 2
-    max_y = np.full(len(bin_centers), np.nan)
+    max_x = np.full(len(bin_centers), np.nan)
 
     for i in range(len(bin_centers)):
-        in_bin = (sel_verts[:, 0] >= bins[i]) & (sel_verts[:, 0] < bins[i + 1])
+        in_bin = (sel_verts[:, 2] >= bins[i]) & (sel_verts[:, 2] < bins[i + 1])
         if in_bin.any():
-            max_y[i] = sel_verts[in_bin, 1].max()
+            max_x[i] = sel_verts[in_bin, 0].max()
 
-    valid = ~np.isnan(max_y)
+    valid = ~np.isnan(max_x)
     if valid.sum() < 7:
-        fallback = 0.5 * (Nz[0] + Cz[0])
-        return fallback, bin_centers[valid], max_y[valid], max_y[valid]
+        fallback = 0.5 * (Nz[2] + Cz[2])
+        return fallback, bin_centers[valid], max_x[valid], max_x[valid]
 
-    xv = bin_centers[valid]
-    yv = max_y[valid]
+    zv = bin_centers[valid]
+    xv = max_x[valid]
 
-    win = min(11, len(yv) if len(yv) % 2 == 1 else len(yv) - 1)
+    win = min(11, len(xv) if len(xv) % 2 == 1 else len(xv) - 1)
     win = max(win, 5)
-    yv_s = savgol_filter(yv, window_length=win, polyorder=2)
+    xv_s = savgol_filter(xv, window_length=win, polyorder=2)
 
-    peak_idx = int(np.argmax(yv_s))
-    grad = np.gradient(yv_s, xv)
-    cap_x = xv[0]
+    peak_idx = int(np.argmax(xv_s))
+    grad = np.gradient(xv_s, zv)
+    cap_z = zv[0]
     for i in range(peak_idx - 1, 0, -1):
         if grad[i] < foot_grad_threshold:
-            cap_x = xv[i]
+            cap_z = zv[i]
             break
 
-    return cap_x, xv, yv, yv_s
+    return cap_z, zv, xv, xv_s
 
 
 def face_mask_from_landmarks(
@@ -100,23 +104,23 @@ def face_mask_from_landmarks(
     Cz: np.ndarray,
     Lpa: np.ndarray,
     Rpa: np.ndarray,
-    cap_x: float | None = None,
+    cap_z: float | None = None,
     ear_delete_radius: float = 40.0,
 ) -> tuple[np.ndarray, dict]:
-    """Build face + ear deletion mask from the 5 landmarks (aligned frame).
+    """Build face + ear deletion mask from the 5 landmarks (CTF frame).
 
     Mask is the union of two regions, both clamped below the cap boundary:
 
-    1. Face region: anterior to the ear coronal plane (Y > ear_mid_Y).
+    1. Face region: anterior to the ear coronal plane (X > ear_mid_X).
     2. Ear spheres: ``ear_delete_radius`` mm around Lpa and Rpa.
 
-    Expects the aligned frame (X=up, Y=anterior, Z=left).
+    Expects the CTF frame (+X=anterior, +Y=left, +Z=up).
 
     Args:
         verts: Mesh vertices, shape (N, 3).
-        Nz, Iz, Cz, Lpa, Rpa: 5 landmark positions in the aligned frame.
-        cap_x: Upper bound X value (typically from ``detect_cap_boundary``).
-            Defaults to Nz[0] if not provided.
+        Nz, Iz, Cz, Lpa, Rpa: 5 landmark positions in the CTF frame.
+        cap_z: Upper bound Z value (typically from ``detect_cap_boundary``).
+            Defaults to Nz[2] if not provided.
         ear_delete_radius: Sphere radius around Lpa/Rpa in mm.
 
     Returns:
@@ -125,10 +129,10 @@ def face_mask_from_landmarks(
         (per-region vertex counts).
     """
     ear_mid = 0.5 * (Lpa + Rpa)
-    upper_bound = cap_x if cap_x is not None else Nz[0]
+    upper_bound = cap_z if cap_z is not None else Nz[2]
 
-    below_cap = verts[:, 0] < upper_bound
-    anterior = verts[:, 1] > ear_mid[1]
+    below_cap = verts[:, 2] < upper_bound
+    anterior = verts[:, 0] > ear_mid[0]
     face_region = below_cap & anterior
 
     d_lpa = np.linalg.norm(verts - Lpa, axis=1)
@@ -253,6 +257,7 @@ def _bake_vertex_colors(mesh) -> np.ndarray:
 def save_anonymized_scan(
     surface: cdc.TrimeshSurface,
     out_path: str,
+    landmarks: "xr.DataArray | None" = None,
     with_color: bool = False,
 ) -> list[str]:
     """Export an anonymized photogrammetry surface to disk.
@@ -267,14 +272,23 @@ def save_anonymized_scan(
       bake the result as per-vertex colors inline in the ``.obj``. The JPG
       is consumed in-memory and discarded; no raster is written.
 
+    If ``landmarks`` is provided, also writes ``{stem}_landmarks.tsv`` next to
+    the ``.obj`` using ``cedalion.io.export_to_tsv``. The resulting TSV is the
+    file that gets uploaded at step 5.2 of the cedalion photogrammetry
+    co-registration tutorial.
+
     Args:
         surface: Anonymized TrimeshSurface (typically output of
             ``delete_masked_vertices``).
         out_path: Destination path ending in ``.obj``.
+        landmarks: Optional LabeledPointCloud of landmarks (Nz, Iz, LPA, RPA,
+            Cz) to persist alongside the mesh as a TSV. Should be in the same
+            frame as ``surface``.
         with_color: If True, bake per-vertex colors from the texture.
 
     Returns:
-        List of absolute paths written.
+        List of absolute paths written (``.obj`` plus ``_landmarks.tsv`` when
+        ``landmarks`` is given).
 
     Raises:
         ValueError: If ``out_path`` does not end in ``.obj``.
@@ -308,6 +322,18 @@ def save_anonymized_scan(
         if os.path.splitext(f)[0] == stem or f == os.path.basename(out_path)
     )
 
+    if landmarks is not None:
+        from cedalion.io import export_to_tsv
+
+        tsv_path = os.path.join(out_dir, f"{stem}_landmarks.tsv")
+        export_to_tsv(tsv_path, landmarks)
+        written.append(tsv_path)
+    else:
+        logger.warning(
+            "save_anonymized_scan called without landmarks; "
+            "co-registration (tutorial step 5.2) will need them saved separately."
+        )
+
     logger.info(
         f"Saved anonymized scan (with_color={with_color}): "
         f"{[os.path.basename(p) for p in written]}"
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/preprocessing.py b/src/cedalion/geometry/photogrammetry/anonymization/preprocessing.py
index 663009d4..67f82d28 100644
--- a/src/cedalion/geometry/photogrammetry/anonymization/preprocessing.py
+++ b/src/cedalion/geometry/photogrammetry/anonymization/preprocessing.py
@@ -3,10 +3,12 @@
 Gets a raw Einstar photogrammetry scan into a canonical frame suitable for
 landmark detection and mask building:
 
-- ``normalize_axes`` rotates around X so Y points anterior.
+- ``normalize_axes`` rotates around X so Y points anterior (preliminary,
+  used before head isolation).
 - ``isolate_head`` strips body/shoulders/chair and disconnected fragments.
-- ``align_axes_from_landmarks`` derives a full rotation from the 5 anatomical
-  landmarks once they are available.
+- ``align_axes_from_landmarks`` maps the scan into the CTF frame
+  (+X=anterior, +Y=left, +Z=up, origin at the LPA-RPA midpoint) once all 5
+  landmarks are available.
 
 Initial Contributors:
     - Face Anonymization Project | 2024
@@ -268,16 +270,16 @@ def align_axes_from_landmarks(
     surface: cdc.TrimeshSurface,
     landmarks: cdt.LabeledPointCloud,
 ) -> tuple[cdc.TrimeshSurface, cdt.LabeledPointCloud, np.ndarray]:
-    """Derive full rotation from 5 landmarks and apply to mesh and landmarks.
+    """Map mesh + landmarks into the CTF anatomical frame.
 
-    ``normalize_axes()`` only rotates around X. This function uses the full 5
-    landmark set to align the head to the canonical frame:
+    CTF convention:
 
-        Z = lateral (Lpa - Rpa, pointing left)
-        Y = anterior (Nz - ear_mid, orthogonal to Z)
-        X = up (cross(Y, Z))
+        +X = anterior (toward Nz)
+        +Y = left (toward Lpa)
+        +Z = up (toward Cz)
+        origin = midpoint of Lpa and Rpa (interaural midpoint)
 
-    Sign checks ensure Cz points +X and Nz points +Y.
+    The returned surface and landmarks carry ``crs="ctf"``.
 
     Args:
         surface: Axis-normalized TrimeshSurface (post ``normalize_axes`` and
@@ -286,9 +288,9 @@ def align_axes_from_landmarks(
             (matching the surface frame).
 
     Returns:
-        Tuple of (aligned_surface, aligned_landmarks, rotation_matrix).
-        ``rotation_matrix`` is 3x3 and maps input-frame vectors to the aligned
-        frame (apply as ``v @ R.T``).
+        Tuple of (aligned_surface, aligned_landmarks, transform).
+        ``transform`` is a 4x4 homogeneous affine that maps input-frame
+        points into CTF; apply as ``hom = transform @ [x, y, z, 1]``.
     """
     import trimesh
 
@@ -305,26 +307,29 @@ def align_axes_from_landmarks(
     Cz = lm[idx["Cz"]]
     Lpa = lm[idx["Lpa"]]
     Rpa = lm[idx["Rpa"]]
-    ear_mid = 0.5 * (Lpa + Rpa)
+    origin = 0.5 * (Lpa + Rpa)
 
-    z_ax = Lpa - Rpa
-    z_ax = z_ax / np.linalg.norm(z_ax)
+    y_ax = Lpa - Rpa
+    y_ax = y_ax / np.linalg.norm(y_ax)
 
-    nz_dir = Nz - ear_mid
-    nz_dir = nz_dir - np.dot(nz_dir, z_ax) * z_ax
-    y_ax = nz_dir / np.linalg.norm(nz_dir)
+    nz_dir = Nz - origin
+    nz_dir = nz_dir - np.dot(nz_dir, y_ax) * y_ax
+    x_ax = nz_dir / np.linalg.norm(nz_dir)
 
-    x_ax = np.cross(y_ax, z_ax)
+    z_ax = np.cross(x_ax, y_ax)
 
-    if np.dot(Cz - ear_mid, x_ax) < 0:
+    if np.dot(Cz - origin, z_ax) < 0:
+        z_ax = -z_ax
+    if np.dot(Nz - origin, x_ax) < 0:
         x_ax = -x_ax
-    if np.dot(Nz - ear_mid, y_ax) < 0:
-        y_ax = -y_ax
-    z_ax = np.cross(x_ax, y_ax)
+    y_ax = np.cross(z_ax, x_ax)
 
     R = np.vstack([x_ax, y_ax, z_ax])
+    M = np.eye(4)
+    M[:3, :3] = R
+    M[:3, 3] = -R @ origin
 
-    aligned_verts = np.asarray(surface.mesh.vertices) @ R.T
+    aligned_verts = (np.asarray(surface.mesh.vertices) - origin) @ R.T
     new_mesh = trimesh.Trimesh(
         vertices=aligned_verts,
         faces=surface.mesh.faces,
@@ -332,10 +337,80 @@ def align_axes_from_landmarks(
     )
     _copy_visual(surface.mesh, new_mesh)
     aligned_surface = cdc.TrimeshSurface(
-        new_mesh, crs=surface.crs, units=surface.units,
+        new_mesh, crs="ctf", units=surface.units,
+    )
+
+    aligned_lm = (lm - origin) @ R.T
+    old_crs_dim = [d for d in landmarks.dims if d != "label"][0]
+    aligned_landmarks = (
+        landmarks.pint.dequantify()
+        .copy(data=aligned_lm)
+        .rename({old_crs_dim: "ctf"})
+        .pint.quantify()
+    )
+
+    return aligned_surface, aligned_landmarks, M
+
+
+def revert_to_einstar_frame(
+    surface: cdc.TrimeshSurface,
+    landmarks: cdt.LabeledPointCloud,
+    R_normalize: np.ndarray,
+    M_align: np.ndarray,
+) -> tuple[cdc.TrimeshSurface, cdt.LabeledPointCloud]:
+    """Map an aligned surface and landmarks back into the raw Einstar frame.
+
+    Inverse of the ``normalize_axes`` then ``align_axes_from_landmarks``
+    composition, so the returned mesh and landmarks carry ``crs="digitized"``
+    and match the coordinates of the original ``read_einstar_obj`` output.
+    Useful right before ``save_anonymized_scan`` when the downstream
+    co-registration pipeline expects the saved ``.obj`` and ``.tsv`` in the
+    native scanner frame (e.g. Elsa's tutorial step 5.2 workflow, whose
+    example files carry ``crs=digitized``).
+
+    Note that ``isolate_head`` is not invertible: the returned mesh is still
+    head-only even though its coordinates are in the original digitized frame.
+
+    Args:
+        surface: TrimeshSurface in the CTF frame (post
+            ``align_axes_from_landmarks``, optionally after masking).
+        landmarks: LabeledPointCloud in the CTF frame.
+        R_normalize: 3x3 rotation returned by ``normalize_axes``.
+        M_align: 4x4 affine returned by ``align_axes_from_landmarks``.
+
+    Returns:
+        Tuple of (surface_digitized, landmarks_digitized). Both carry
+        ``crs="digitized"`` and the mesh preserves UVs / vertex colors.
+    """
+    import trimesh
+
+    M_inv = np.linalg.inv(M_align)
+    R_inv = R_normalize.T
+
+    ctf_verts = np.asarray(surface.mesh.vertices)
+    norm_verts = ctf_verts @ M_inv[:3, :3].T + M_inv[:3, 3]
+    raw_verts = norm_verts @ R_inv.T
+
+    new_mesh = trimesh.Trimesh(
+        vertices=raw_verts,
+        faces=surface.mesh.faces,
+        process=False,
+    )
+    _copy_visual(surface.mesh, new_mesh)
+    raw_surface = cdc.TrimeshSurface(
+        new_mesh, crs="digitized", units=surface.units,
     )
 
-    aligned_lm = lm @ R.T
-    aligned_landmarks = landmarks.pint.dequantify().copy(data=aligned_lm).pint.quantify()
+    lm_ctf = landmarks.pint.dequantify().values
+    lm_norm = lm_ctf @ M_inv[:3, :3].T + M_inv[:3, 3]
+    lm_raw = lm_norm @ R_inv.T
+
+    old_crs_dim = [d for d in landmarks.dims if d != "label"][0]
+    raw_landmarks = (
+        landmarks.pint.dequantify()
+        .copy(data=lm_raw)
+        .rename({old_crs_dim: "digitized"})
+        .pint.quantify()
+    )
 
-    return aligned_surface, aligned_landmarks, R
+    return raw_surface, raw_landmarks

From 138c5dfd8169b60d34a14326c298162cc2a94152 Mon Sep 17 00:00:00 2001
From: mah-diaa <mahmoud.diaa.abdeen@gmail.com>
Date: Fri, 1 May 2026 20:04:50 +0200
Subject: [PATCH 17/39] Add thesis_results_out/ to .gitignore

---
 .gitignore | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/.gitignore b/.gitignore
index 74a3feeb..7cb03225 100644
--- a/.gitignore
+++ b/.gitignore
@@ -186,3 +186,6 @@ scratch/
 plugins/
 
 .claude/worktrees/sharp-burnell/
+
+# thesis-only generated artifacts (regenerable from validation notebooks)
+examples/head_models/thesis_results_out/

From 651c7efdf0619def7cb3ef5f268ba0c5825069a6 Mon Sep 17 00:00:00 2001
From: mah-diaa <mahmoud.diaa.abdeen@gmail.com>
Date: Fri, 1 May 2026 20:07:12 +0200
Subject: [PATCH 18/39] Restrict main to deletion-only anonymization pipeline;
 move validation suite to validation/face-anonymization

---
 .../48_manual_5pt_anonymization.ipynb         |  105 +-
 .../head_models/49_anonymization_audit.ipynb  |  195 ---
 .../head_models/49_seam_diagnostics.ipynb     | 1107 -----------------
 .../50_mask_selection_walkthrough.ipynb       | 1072 ----------------
 .../photogrammetry/anonymization/__init__.py  |    5 +-
 .../photogrammetry/anonymization/landmarks.py |    4 +-
 .../photogrammetry/anonymization/mask.py      |  191 ++-
 .../anonymization/preprocessing.py            |   22 +-
 .../photogrammetry/anonymization/validator.py |  219 ----
 9 files changed, 215 insertions(+), 2705 deletions(-)
 delete mode 100644 examples/head_models/49_anonymization_audit.ipynb
 delete mode 100644 examples/head_models/49_seam_diagnostics.ipynb
 delete mode 100644 examples/head_models/50_mask_selection_walkthrough.ipynb
 delete mode 100644 src/cedalion/geometry/photogrammetry/anonymization/validator.py

diff --git a/examples/head_models/48_manual_5pt_anonymization.ipynb b/examples/head_models/48_manual_5pt_anonymization.ipynb
index dce6e4c8..efc089a8 100644
--- a/examples/head_models/48_manual_5pt_anonymization.ipynb
+++ b/examples/head_models/48_manual_5pt_anonymization.ipynb
@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 53,
    "id": "06ba7b71",
    "metadata": {},
    "outputs": [],
@@ -35,7 +35,7 @@
     "import cedalion\n",
     "import cedalion.dataclasses as cdc\n",
     "import cedalion.io\n",
-    "import cedalion.plots\n",
+    "import cedalion.vis.blocks\n",
     "from cedalion.geometry.photogrammetry.anonymization import (\n",
     "    normalize_axes,\n",
     "    isolate_head,\n",
@@ -65,7 +65,7 @@
     "\n",
     "# Cap boundary detection tunables\n",
     "CAP_BAND_WIDTH_MM = 15.0\n",
-    "CAP_BIN_SIZE_MM = 2.0\n",
+    "CAP_BIN_SIZE_MM = 1.0\n",
     "CAP_FOOT_GRAD_THRESHOLD = 0.2\n"
    ]
   },
@@ -79,7 +79,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 54,
    "id": "cell-load",
    "metadata": {},
    "outputs": [
@@ -134,7 +134,7 @@
    "source": [
     "## 2. Pick the 5 landmarks interactively\n",
     "\n",
-    "Uses cedalion's built-in picker (`cedalion.plots.plot_surface(..., pick_landmarks=True)`,\n",
+    "Uses cedalion's built-in picker (`cedalion.vis.blocks.plot_surface(..., pick_landmarks=True)`,\n",
     "authored by Mariia Iudina, upstream cedalion). Right-click on the mesh to place a\n",
     "sphere; click again on the sphere to cycle its label through\n",
     "`Nz -> Iz -> Cz -> Lpa -> Rpa`, or cycle past `Rpa` back to `Nz` to remove it.\n",
@@ -144,7 +144,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 55,
    "id": "cell-pick",
    "metadata": {
     "scrolled": true
@@ -153,12 +153,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2440d4727fa641c4ae68312a13f61bcb",
+       "model_id": "938910ca3ee04eb6b590a88affac39ca",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Widget(value='<iframe src=\"http://localhost:36901/index.html?ui=P_0x7fc4483098d0_0&reconnect=auto\" class=\"pyvi…"
+       "Widget(value='<iframe src=\"http://localhost:39989/index.html?ui=P_0x7f897374bc90_18&reconnect=auto\" class=\"pyv…"
       ]
      },
      "metadata": {},
@@ -168,7 +168,7 @@
    "source": [
     "if INTERACTIVE:\n",
     "    pvplt = pv.Plotter()\n",
-    "    get_landmarks = cedalion.plots.plot_surface(\n",
+    "    get_landmarks = cedalion.vis.blocks.plot_surface(\n",
     "        pvplt, surface, opacity=1.0, pick_landmarks=True\n",
     "    )\n",
     "    pvplt.show()"
@@ -186,7 +186,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 57,
    "id": "cell-retrieve",
    "metadata": {},
    "outputs": [
@@ -637,28 +637,28 @@
        "  stroke-width: 0.8px;\n",
        "}\n",
        "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (label: 5, digitized: 3)&gt; Size: 120B\n",
-       "&lt;Quantity([[182.92980578 117.94829963 432.43934491]\n",
-       " [ 51.16184581 217.28322321 443.32434353]\n",
-       " [ 67.06696483  69.92108889 559.08501031]\n",
-       " [ 68.98745639 -11.99765232 480.48408272]\n",
-       " [  4.45574294  59.0529612  397.73517575]], &#x27;millimeter&#x27;)&gt;\n",
+       "&lt;Quantity([[169.8175707   84.17791333 425.43686049]\n",
+       " [ 32.0612021  203.51371015 436.13680432]\n",
+       " [ 67.92901638  85.38361616 562.50097916]\n",
+       " [ 18.53542079  67.46210371 393.38863931]\n",
+       " [ 69.81661274 -12.30486275 480.16603788]], &#x27;millimeter&#x27;)&gt;\n",
        "Coordinates:\n",
-       "  * label    (label) &lt;U3 60B &#x27;Cz&#x27; &#x27;Iz&#x27; &#x27;Rpa&#x27; &#x27;Nz&#x27; &#x27;Lpa&#x27;\n",
+       "  * label    (label) &lt;U3 60B &#x27;Cz&#x27; &#x27;Iz&#x27; &#x27;Rpa&#x27; &#x27;Lpa&#x27; &#x27;Nz&#x27;\n",
        "    type     (label) object 40B PointType.LANDMARK ... PointType.LANDMARK\n",
        "    group    (label) &lt;U1 20B &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27;\n",
-       "Dimensions without coordinates: digitized</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>label</span>: 5</li><li><span>digitized</span>: 3</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-c7e240b6-477c-4cea-a5a0-08bffb9c29df' class='xr-array-in' type='checkbox' checked><label for='section-c7e240b6-477c-4cea-a5a0-08bffb9c29df' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>[mm] 182.9 117.9 432.4 51.16 217.3 ... -12.0 480.5 4.456 59.05 397.7</span></div><div class='xr-array-data'><table><tbody><tr><th>Magnitude</th><td style='text-align:left;'><pre>[[182.92980578177185 117.94829963376296 432.4393449119677] [51.161845807845125 217.2832232100858 443.32434353006687] [67.06696482873602 69.92108888540216 559.0850103085918] [68.98745638597626 -11.997652317177597 480.4840827150168] [4.455742939104287 59.052961199856185 397.7351757544122]]</pre></td></tr><tr><th>Units</th><td style='text-align:left;'>millimeter</td></tr></tbody></table></div></div></li><li class='xr-section-item'><input id='section-ab807f34-29b6-473e-a4d3-b710f5410ff5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ab807f34-29b6-473e-a4d3-b710f5410ff5' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>label</span></div><div class='xr-var-dims'>(label)</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;Cz&#x27; &#x27;Iz&#x27; &#x27;Rpa&#x27; &#x27;Nz&#x27; &#x27;Lpa&#x27;</div><input id='attrs-e77d5fdd-2d82-495c-bd66-44334c6b0348' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e77d5fdd-2d82-495c-bd66-44334c6b0348' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3555b2d1-0bae-415b-8851-c2e168d182ab' class='xr-var-data-in' type='checkbox'><label for='data-3555b2d1-0bae-415b-8851-c2e168d182ab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Cz&#x27;, &#x27;Iz&#x27;, &#x27;Rpa&#x27;, &#x27;Nz&#x27;, &#x27;Lpa&#x27;], dtype=&#x27;&lt;U3&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>type</span></div><div class='xr-var-dims'>(label)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>PointType.LANDMARK ... PointType...</div><input id='attrs-0c7e3615-90e8-40de-9f3d-c69ce8874105' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0c7e3615-90e8-40de-9f3d-c69ce8874105' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97c8f51a-043c-4f60-8c99-ffe21a698043' class='xr-var-data-in' type='checkbox'><label for='data-97c8f51a-043c-4f60-8c99-ffe21a698043' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&lt;PointType.LANDMARK: 3&gt;, &lt;PointType.LANDMARK: 3&gt;,\n",
+       "Dimensions without coordinates: digitized</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>label</span>: 5</li><li><span>digitized</span>: 3</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-e6b82788-6d3d-46c7-8736-3efae789c9ef' class='xr-array-in' type='checkbox' checked><label for='section-e6b82788-6d3d-46c7-8736-3efae789c9ef' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>[mm] 169.8 84.18 425.4 32.06 203.5 ... 67.46 393.4 69.82 -12.3 480.2</span></div><div class='xr-array-data'><table><tbody><tr><th>Magnitude</th><td style='text-align:left;'><pre>[[169.8175706956947 84.17791333176854 425.43686048545544] [32.061202102756184 203.5137101504909 436.13680432494687] [67.9290163798706 85.38361615977699 562.5009791587428] [18.535420787153996 67.46210371239562 393.38863930640076] [69.81661273869598 -12.304862754118403 480.1660378769869]]</pre></td></tr><tr><th>Units</th><td style='text-align:left;'>millimeter</td></tr></tbody></table></div></div></li><li class='xr-section-item'><input id='section-50458d44-a6b2-4519-824b-052737d8f7b0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-50458d44-a6b2-4519-824b-052737d8f7b0' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>label</span></div><div class='xr-var-dims'>(label)</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;Cz&#x27; &#x27;Iz&#x27; &#x27;Rpa&#x27; &#x27;Lpa&#x27; &#x27;Nz&#x27;</div><input id='attrs-33836c6c-a641-466f-b2c9-67d9db819944' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-33836c6c-a641-466f-b2c9-67d9db819944' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af76f448-c9d8-4e02-bc4d-3ff86b162428' class='xr-var-data-in' type='checkbox'><label for='data-af76f448-c9d8-4e02-bc4d-3ff86b162428' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Cz&#x27;, &#x27;Iz&#x27;, &#x27;Rpa&#x27;, &#x27;Lpa&#x27;, &#x27;Nz&#x27;], dtype=&#x27;&lt;U3&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>type</span></div><div class='xr-var-dims'>(label)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>PointType.LANDMARK ... PointType...</div><input id='attrs-b94637ed-c3e2-44d4-992f-ed91b638a0ce' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b94637ed-c3e2-44d4-992f-ed91b638a0ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d37613c8-8a3f-4c4d-baac-118738ebdc13' class='xr-var-data-in' type='checkbox'><label for='data-d37613c8-8a3f-4c4d-baac-118738ebdc13' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&lt;PointType.LANDMARK: 3&gt;, &lt;PointType.LANDMARK: 3&gt;,\n",
        "       &lt;PointType.LANDMARK: 3&gt;, &lt;PointType.LANDMARK: 3&gt;,\n",
-       "       &lt;PointType.LANDMARK: 3&gt;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>group</span></div><div class='xr-var-dims'>(label)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27;</div><input id='attrs-d1f4356e-0350-4559-9c2b-8387efe74717' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d1f4356e-0350-4559-9c2b-8387efe74717' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c82da01-f9cc-4e59-a3dc-b51a2ef7d108' class='xr-var-data-in' type='checkbox'><label for='data-0c82da01-f9cc-4e59-a3dc-b51a2ef7d108' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;L&#x27;, &#x27;L&#x27;, &#x27;L&#x27;, &#x27;L&#x27;, &#x27;L&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ba1a0eb3-2857-473a-b1bf-24278c62897b' class='xr-section-summary-in' type='checkbox'  ><label for='section-ba1a0eb3-2857-473a-b1bf-24278c62897b' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>label</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-061d36ed-dff4-4fc3-b36c-f1c383d70796' class='xr-index-data-in' type='checkbox'/><label for='index-061d36ed-dff4-4fc3-b36c-f1c383d70796' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;Cz&#x27;, &#x27;Iz&#x27;, &#x27;Rpa&#x27;, &#x27;Nz&#x27;, &#x27;Lpa&#x27;], dtype=&#x27;object&#x27;, name=&#x27;label&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ce14ef59-44c9-4e3b-afa4-60dceaaf6a6f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ce14ef59-44c9-4e3b-afa4-60dceaaf6a6f' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "       &lt;PointType.LANDMARK: 3&gt;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>group</span></div><div class='xr-var-dims'>(label)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27;</div><input id='attrs-c8d96493-9f0a-4ba9-af28-8d6835b206bd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c8d96493-9f0a-4ba9-af28-8d6835b206bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d27acfb6-7958-441e-bd2c-fd40507af360' class='xr-var-data-in' type='checkbox'><label for='data-d27acfb6-7958-441e-bd2c-fd40507af360' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;L&#x27;, &#x27;L&#x27;, &#x27;L&#x27;, &#x27;L&#x27;, &#x27;L&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-77a86292-717e-4ab1-ab2a-bab1d89bf01a' class='xr-section-summary-in' type='checkbox'  ><label for='section-77a86292-717e-4ab1-ab2a-bab1d89bf01a' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>label</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-76b349c1-6d3f-4daa-8878-6c4d9741cccb' class='xr-index-data-in' type='checkbox'/><label for='index-76b349c1-6d3f-4daa-8878-6c4d9741cccb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;Cz&#x27;, &#x27;Iz&#x27;, &#x27;Rpa&#x27;, &#x27;Lpa&#x27;, &#x27;Nz&#x27;], dtype=&#x27;object&#x27;, name=&#x27;label&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9a61a3ff-6e0a-47ee-b097-d13c84ba5682' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9a61a3ff-6e0a-47ee-b097-d13c84ba5682' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.DataArray (label: 5, digitized: 3)> Size: 120B\n",
-       "<Quantity([[182.92980578 117.94829963 432.43934491]\n",
-       " [ 51.16184581 217.28322321 443.32434353]\n",
-       " [ 67.06696483  69.92108889 559.08501031]\n",
-       " [ 68.98745639 -11.99765232 480.48408272]\n",
-       " [  4.45574294  59.0529612  397.73517575]], 'millimeter')>\n",
+       "<Quantity([[169.8175707   84.17791333 425.43686049]\n",
+       " [ 32.0612021  203.51371015 436.13680432]\n",
+       " [ 67.92901638  85.38361616 562.50097916]\n",
+       " [ 18.53542079  67.46210371 393.38863931]\n",
+       " [ 69.81661274 -12.30486275 480.16603788]], 'millimeter')>\n",
        "Coordinates:\n",
-       "  * label    (label) <U3 60B 'Cz' 'Iz' 'Rpa' 'Nz' 'Lpa'\n",
+       "  * label    (label) <U3 60B 'Cz' 'Iz' 'Rpa' 'Lpa' 'Nz'\n",
        "    type     (label) object 40B PointType.LANDMARK ... PointType.LANDMARK\n",
        "    group    (label) <U1 20B 'L' 'L' 'L' 'L' 'L'\n",
        "Dimensions without coordinates: digitized"
@@ -723,7 +723,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 58,
    "id": "09186e9d",
    "metadata": {},
    "outputs": [
@@ -731,7 +731,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "S17: 678,081 -> 558,342 verts (-119,739, cap_z=37.0 mm)\n"
+      "S17: 678,081 -> 578,478 verts (-99,603, cap_z=10.0 mm)\n"
      ]
     }
    ],
@@ -799,19 +799,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 59,
    "id": "a101c6c0",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "525e0b68bc6d436a873f8c034c3458cb",
+       "model_id": "b70571c383be4bd7b4872ec50524fcc2",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Widget(value='<iframe src=\"http://localhost:36901/index.html?ui=P_0x7fc35f59cf50_1&reconnect=auto\" class=\"pyvi…"
+       "Widget(value='<iframe src=\"http://localhost:39989/index.html?ui=P_0x7f89737b6f10_19&reconnect=auto\" class=\"pyv…"
       ]
      },
      "metadata": {},
@@ -862,19 +862,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 60,
    "id": "d7c29bcd",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b3ef711acfb0416db596b15fd941e156",
+       "model_id": "28cee82a04994c5bafc653f367c8895c",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Widget(value='<iframe src=\"http://localhost:36901/index.html?ui=P_0x7fc35f6200d0_2&reconnect=auto\" class=\"pyvi…"
+       "Widget(value='<iframe src=\"http://localhost:39989/index.html?ui=P_0x7f89737d1a10_20&reconnect=auto\" class=\"pyv…"
       ]
      },
      "metadata": {},
@@ -924,30 +924,47 @@
    "source": [
     "## 7. Save\n",
     "\n",
-    "Writes the `.obj` geometry and a sidecar `.tsv` of the 5 landmarks (the format\n",
-    "consumed at step 5.2 of the cedalion photogrammetry co-registration tutorial).\n",
-    "Both carry `crs=digitized` and pair 1:1 with each other. The original Einstar\n",
-    "texture is not written, so the subject's face pixels cannot leak via a saved\n",
-    "image.\n"
+    "Writes the anonymized `.obj` + `.mtl` + sanitized `.jpg` bundle plus a\n",
+    "sidecar `_landmarks.tsv` (the format consumed at step 5.2 of the cedalion\n",
+    "photogrammetry co-registration tutorial). All carry `crs=digitized`. The\n",
+    "JPG is rebuilt from the original texture keeping only pixels still\n",
+    "referenced by the anonymized mesh's UVs; face-region pixels are blanked,\n",
+    "so opening the JPG alone reveals no face.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 36,
    "id": "391c93d5",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Wrote:\n",
+      "  /home/ma7/BA/PG_Subjects/Subject15/Subject15_anon.jpg\n",
+      "  /home/ma7/BA/PG_Subjects/Subject15/Subject15_anon.mtl\n",
+      "  /home/ma7/BA/PG_Subjects/Subject15/Subject15_anon.obj\n",
+      "  /home/ma7/BA/PG_Subjects/Subject15/Subject15_anon_landmarks.tsv\n"
+     ]
+    }
+   ],
    "source": [
     "out = f'{SCANS_FOLDER}/Subject{SUBJECT_NUMBER}/Subject{SUBJECT_NUMBER}_anon.obj'\n",
-    "written = save_anonymized_scan(\n",
-    "    surface_anon_dig, out,\n",
-    "    landmarks=landmarks_dig,\n",
-    "    with_color=True,\n",
-    ")\n",
+    "written = save_anonymized_scan(surface_anon_dig, out, landmarks=landmarks_dig)\n",
     "print('Wrote:')\n",
     "for p in written:\n",
     "    print(f'  {p}')\n"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9b07bdbe-a5b7-405e-b86d-1169e0d863a2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/examples/head_models/49_anonymization_audit.ipynb b/examples/head_models/49_anonymization_audit.ipynb
deleted file mode 100644
index 51891010..00000000
--- a/examples/head_models/49_anonymization_audit.ipynb
+++ /dev/null
@@ -1,195 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "435f26df",
-   "metadata": {},
-   "source": [
-    "# Anonymization Audit: Can the Face Be Reconstructed?\n",
-    "\n",
-    "Loads the saved anonymized OBJ from notebook 48 and tries to get the face back.\n",
-    "Goal: demonstrate that the saved file contains no face data and that naive\n",
-    "reconstruction (hole-filling) does not recover the subject's features.\n",
-    "\n",
-    "**Inputs**\n",
-    "- `SubjectN.obj` - original photogrammetry scan (face present)\n",
-    "- `SubjectN_anon.obj` - output of notebook 48 (face region deleted, no texture)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "a16006a5",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Files written by the anonymization save step:\n",
-      "  Subject17_anon.obj\n",
-      "\n",
-      "OK: only the .obj was written. No .mtl / .jpg on disk.\n"
-     ]
-    }
-   ],
-   "source": [
-    "import os\n",
-    "\n",
-    "import numpy as np\n",
-    "import pyvista as pv\n",
-    "import trimesh\n",
-    "\n",
-    "import cedalion\n",
-    "import cedalion.io\n",
-    "import cedalion.plots\n",
-    "\n",
-    "pv.set_jupyter_backend('server')\n",
-    "\n",
-    "SUBJECT_NUMBER = 17\n",
-    "SCANS_FOLDER = '/home/ma7/BA/PG_Subjects'\n",
-    "\n",
-    "orig_path = f'{SCANS_FOLDER}/Subject{SUBJECT_NUMBER}/Subject{SUBJECT_NUMBER}.obj'\n",
-    "anon_path = f'{SCANS_FOLDER}/Subject{SUBJECT_NUMBER}/Subject{SUBJECT_NUMBER}_anon.obj'\n",
-    "anon_dir = os.path.dirname(anon_path)\n",
-    "stem = f'Subject{SUBJECT_NUMBER}_anon'\n",
-    "siblings = sorted(f for f in os.listdir(anon_dir) if f.startswith(stem))\n",
-    "print('Files written by the anonymization save step:')\n",
-    "for f in siblings:\n",
-    "    print(f'  {f}')\n",
-    "assert f'{stem}.obj' in siblings, 'expected .obj not found -- run notebook 48 first'\n",
-    "assert f'{stem}.mtl' not in siblings, 'unexpected .mtl on disk -- texture leaked'\n",
-    "assert f'{stem}.jpg' not in siblings, 'unexpected .jpg on disk -- face pixels leaked'\n",
-    "print('\\nOK: only the .obj was written. No .mtl / .jpg on disk.')\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3ab69f81",
-   "metadata": {},
-   "source": [
-    "## 1. Load original and anonymized scans"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "b76c3b29",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Original:    1,284,667 verts, 2,223,716 faces\n",
-      "Anonymized:   475,536 verts,  948,028 faces\n",
-      "Removed:      809,131 verts, 1,275,688 faces\n",
-      "\n",
-      "Anonymized visual type: ColorVisuals\n",
-      "  has UV map:      False\n",
-      "  has texture img: False\n"
-     ]
-    }
-   ],
-   "source": [
-    "surface_orig = cedalion.io.read_einstar_obj(orig_path)\n",
-    "surface_anon = cedalion.io.read_einstar_obj(anon_path)\n",
-    "\n",
-    "print(f'Original:    {surface_orig.nvertices:>8,} verts, {surface_orig.nfaces:>8,} faces')\n",
-    "print(f'Anonymized:  {surface_anon.nvertices:>8,} verts, {surface_anon.nfaces:>8,} faces')\n",
-    "print(f'Removed:     {surface_orig.nvertices - surface_anon.nvertices:>8,} verts, '\n",
-    "      f'{surface_orig.nfaces - surface_anon.nfaces:>8,} faces')\n",
-    "\n",
-    "anon_visual = surface_anon.mesh.visual\n",
-    "print(f'\\nAnonymized visual type: {type(anon_visual).__name__}')\n",
-    "has_uv = getattr(anon_visual, 'uv', None) is not None\n",
-    "has_img = getattr(anon_visual, 'image', None) is not None\n",
-    "print(f'  has UV map:      {has_uv}')\n",
-    "print(f'  has texture img: {has_img}')\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c67dcdc3",
-   "metadata": {},
-   "source": [
-    "## 2. Side-by-side render\n",
-    "\n",
-    "Left: original subject. Right: reloaded anonymized scan. The anonymized side\n",
-    "should show a visible hole where the face was."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "8e21a69e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "520b4711aad6414fa8a7f6ded512b5fe",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Widget(value='<iframe src=\"http://localhost:45309/index.html?ui=P_0x7f67384fc090_0&reconnect=auto\" class=\"pyvi…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pvplt = pv.Plotter(shape=(1, 2))\n",
-    "\n",
-    "pvplt.subplot(0, 0)\n",
-    "pvplt.add_text('Original', font_size=10)\n",
-    "cedalion.plots.plot_surface(pvplt, surface_orig, opacity=1.0)\n",
-    "\n",
-    "pvplt.subplot(0, 1)\n",
-    "pvplt.add_text('Anonymized (reloaded)', font_size=10)\n",
-    "cedalion.plots.plot_surface(pvplt, surface_anon, opacity=1.0)\n",
-    "\n",
-    "pvplt.link_views()\n",
-    "pvplt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d55072ad",
-   "metadata": {},
-   "source": [
-    "## Conclusion\n",
-    "\n",
-    "- Only the `.obj` geometry leaves the process. No `.mtl`, no `.jpg`, no texture\n",
-    "  raster with the subject's face pixels.\n",
-    "- The reloaded anonymized mesh has a visible hole where the face region was.\n",
-    "- The saved file contains only geometry for the non-face regions; the face\n",
-    "  vertices and the entire texture image are gone, so the subject's face\n",
-    "  cannot be reconstructed from it."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.14"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/examples/head_models/49_seam_diagnostics.ipynb b/examples/head_models/49_seam_diagnostics.ipynb
deleted file mode 100644
index d8257881..00000000
--- a/examples/head_models/49_seam_diagnostics.ipynb
+++ /dev/null
@@ -1,1107 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Seam Diagnostics --- UV Duplicate-Vertex Artifact\n",
-    "\n",
-    "Demonstrates the UV-seam duplicate-vertex artifact on an Einstar photogrammetry scan\n",
-    "and shows that a position-based merge eliminates it. Used to generate the\n",
-    "before/after figures for section 3.4 of the thesis.\n",
-    "\n",
-    "**Pipeline:**\n",
-    "1. Load Subject 17\n",
-    "2. Pick the 5 landmarks ($N_z$, $I_z$, $C_z$, Lpa, Rpa) with the upstream cedalion picker\n",
-    "3. `normalize_axes` -> `isolate_head` -> `align_axes_from_landmarks` (CTF frame)\n",
-    "4. Render the isolated head with boundary-edge overlay -> **figure A (before)**\n",
-    "5. Collapse seam duplicates with a union-find merge\n",
-    "6. Render the merged head -> **figure B (after)**\n",
-    "7. Quantify the merge (vertex / face / component / boundary-edge counts)\n",
-    "8. Verify vertex colors survive averaging at seam positions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import logging\n",
-    "from collections import Counter\n",
-    "\n",
-    "import numpy as np\n",
-    "import pyvista as pv\n",
-    "import trimesh\n",
-    "import xarray as xr\n",
-    "from scipy.spatial import KDTree\n",
-    "from scipy.sparse import csr_matrix\n",
-    "from scipy.sparse.csgraph import connected_components\n",
-    "\n",
-    "import cedalion\n",
-    "import cedalion.dataclasses as cdc\n",
-    "import cedalion.io\n",
-    "import cedalion.plots\n",
-    "from cedalion.geometry.photogrammetry.anonymization import (\n",
-    "    normalize_axes,\n",
-    "    isolate_head,\n",
-    "    align_axes_from_landmarks,\n",
-    ")\n",
-    "\n",
-    "logging.getLogger('cedalion').setLevel(logging.WARNING)\n",
-    "pv.set_jupyter_backend('server')\n",
-    "\n",
-    "# === CONFIG ===\n",
-    "SUBJECT_NUMBER = 17\n",
-    "SCANS_FOLDER = '/home/ma7/BA/PG_Subjects'\n",
-    "\n",
-    "# True: run the picker. False: use cached landmarks (fill after first pick).\n",
-    "INTERACTIVE = True\n",
-    "\n",
-    "# Position tolerance for seam merging (mm). Einstar seam duplicates are\n",
-    "# coincident within well under 0.01 mm.\n",
-    "MERGE_TOL_MM = 0.01"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 1. Load the Einstar scan"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loaded: 1,284,667 vertices, 2,223,716 faces\n",
-      "Texture already attached: (4016, 3776)\n"
-     ]
-    }
-   ],
-   "source": [
-    "path = f'{SCANS_FOLDER}/Subject{SUBJECT_NUMBER}/Subject{SUBJECT_NUMBER}.obj'\n",
-    "surface = cedalion.io.read_einstar_obj(path)\n",
-    "print(f'Loaded: {surface.nvertices:,} vertices, {surface.nfaces:,} faces')\n",
-    "\n",
-    "# In trimesh 4.6 the texture image lives on visual.material.image, not\n",
-    "# visual.image. Verify it's there; if missing, fall back to the sibling JPG.\n",
-    "import os\n",
-    "import trimesh as _tm\n",
-    "from PIL import Image as _PILImage\n",
-    "\n",
-    "def _visual_image(visual):\n",
-    "    img = getattr(visual, 'image', None)\n",
-    "    if img is None:\n",
-    "        mat = getattr(visual, 'material', None)\n",
-    "        img = getattr(mat, 'image', None) if mat is not None else None\n",
-    "    return img\n",
-    "\n",
-    "_img = _visual_image(surface.mesh.visual)\n",
-    "if _img is None:\n",
-    "    _jpg = path.replace('.obj', '.jpg')\n",
-    "    _uv = getattr(surface.mesh.visual, 'uv', None)\n",
-    "    assert os.path.exists(_jpg) and _uv is not None, (\n",
-    "        f'no texture: jpg_exists={os.path.exists(_jpg)}, has_uv={_uv is not None}'\n",
-    "    )\n",
-    "    surface.mesh.visual = _tm.visual.TextureVisuals(\n",
-    "        uv=_uv, image=_PILImage.open(_jpg).convert('RGBA'),\n",
-    "    )\n",
-    "    _img = _visual_image(surface.mesh.visual)\n",
-    "    assert _img is not None, 'attach failed'\n",
-    "    print(f'Attached texture from JPG: {_img.size}')\n",
-    "else:\n",
-    "    print(f'Texture already attached: {_img.size}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 2. Pick the 5 landmarks interactively\n",
-    "\n",
-    "Uses cedalion's built-in picker (`cedalion.plots.plot_surface(..., pick_landmarks=True)`).\n",
-    "Right-click on the mesh to place a sphere; click again on the sphere to cycle\n",
-    "its label through `Nz -> Iz -> Cz -> Lpa -> Rpa`, or cycle past `Rpa` back to\n",
-    "`Nz` to remove it.\n",
-    "\n",
-    "Close the window when all 5 are placed with correct labels."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "43d9ddcc085a416a9d5f3e88551cf048",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Widget(value='<iframe src=\"http://localhost:37333/index.html?ui=P_0x7f00dc983690_4&reconnect=auto\" class=\"pyvi…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "if INTERACTIVE:\n",
-    "    pvplt = pv.Plotter()\n",
-    "    get_landmarks = cedalion.plots.plot_surface(\n",
-    "        pvplt, surface, opacity=1.0, pick_landmarks=True\n",
-    "    )\n",
-    "    pvplt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 3. Retrieve picked points and wrap into a `LabeledPointCloud`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
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-       ".xr-section-item input:focus + label {\n",
-       "  border: 2px solid var(--xr-font-color0) !important;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: \"►\";\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: \"▼\";\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: \"(\";\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: \")\";\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: \",\";\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  border-color: var(--xr-background-color-row-odd);\n",
-       "  margin-bottom: 0;\n",
-       "  padding-top: 2px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "  border-color: var(--xr-background-color-row-even);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-index-preview {\n",
-       "  grid-column: 2 / 5;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  display: none;\n",
-       "  border-top: 2px dotted var(--xr-background-color);\n",
-       "  padding-bottom: 20px !important;\n",
-       "  padding-top: 10px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in + label,\n",
-       ".xr-var-data-in + label,\n",
-       ".xr-index-data-in + label {\n",
-       "  padding: 0 1px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > pre,\n",
-       ".xr-index-data > pre,\n",
-       ".xr-var-data > table > tbody > tr {\n",
-       "  background-color: transparent !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
-       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
-       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
-       "  color: var(--xr-font-color0);\n",
-       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
-       "  stroke-width: 0.8px;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (label: 5, digitized: 3)&gt; Size: 120B\n",
-       "&lt;Quantity([[183.26390827 116.44076638 433.14976121]\n",
-       " [ 55.81002886 216.87724678 460.20226543]\n",
-       " [ 75.44543708  70.22174292 571.26430267]\n",
-       " [ 70.90973966 -12.7005211  479.10120764]\n",
-       " [ -5.23821153  60.43366529 422.08015945]], &#x27;millimeter&#x27;)&gt;\n",
-       "Coordinates:\n",
-       "  * label    (label) &lt;U3 60B &#x27;Cz&#x27; &#x27;Iz&#x27; &#x27;Rpa&#x27; &#x27;Nz&#x27; &#x27;Lpa&#x27;\n",
-       "    type     (label) object 40B PointType.LANDMARK ... PointType.LANDMARK\n",
-       "    group    (label) &lt;U1 20B &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27;\n",
-       "Dimensions without coordinates: digitized</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>label</span>: 5</li><li><span>digitized</span>: 3</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-53a17c29-98ec-4a75-8dff-33aba59dc26b' class='xr-array-in' type='checkbox' checked><label for='section-53a17c29-98ec-4a75-8dff-33aba59dc26b' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>[mm] 183.3 116.4 433.1 55.81 216.9 ... -12.7 479.1 -5.238 60.43 422.1</span></div><div class='xr-array-data'><table><tbody><tr><th>Magnitude</th><td style='text-align:left;'><pre>[[183.263908273029 116.4407663790885 433.14976121066945] [55.810028864582236 216.87724678041928 460.20226543224396] [75.44543707953403 70.2217429163498 571.2643026729717] [70.90973965837875 -12.700521103862059 479.10120764275973] [-5.238211533933473 60.433665290097025 422.0801594510126]]</pre></td></tr><tr><th>Units</th><td style='text-align:left;'>millimeter</td></tr></tbody></table></div></div></li><li class='xr-section-item'><input id='section-b6860f16-3199-42a8-b9fb-647f95226ffa' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b6860f16-3199-42a8-b9fb-647f95226ffa' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>label</span></div><div class='xr-var-dims'>(label)</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;Cz&#x27; &#x27;Iz&#x27; &#x27;Rpa&#x27; &#x27;Nz&#x27; &#x27;Lpa&#x27;</div><input id='attrs-4ffa77d9-d0b4-41a3-924e-29f8d4d167f1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4ffa77d9-d0b4-41a3-924e-29f8d4d167f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f490a0ca-42b2-4cbe-ad1e-922589a28a22' class='xr-var-data-in' type='checkbox'><label for='data-f490a0ca-42b2-4cbe-ad1e-922589a28a22' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Cz&#x27;, &#x27;Iz&#x27;, &#x27;Rpa&#x27;, &#x27;Nz&#x27;, &#x27;Lpa&#x27;], dtype=&#x27;&lt;U3&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>type</span></div><div class='xr-var-dims'>(label)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>PointType.LANDMARK ... PointType...</div><input id='attrs-494fd7a1-2baa-4d65-9382-77eeb065b44a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-494fd7a1-2baa-4d65-9382-77eeb065b44a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-560b7c70-6a28-4b0e-abb5-6092f485fcfc' class='xr-var-data-in' type='checkbox'><label for='data-560b7c70-6a28-4b0e-abb5-6092f485fcfc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&lt;PointType.LANDMARK: 3&gt;, &lt;PointType.LANDMARK: 3&gt;,\n",
-       "       &lt;PointType.LANDMARK: 3&gt;, &lt;PointType.LANDMARK: 3&gt;,\n",
-       "       &lt;PointType.LANDMARK: 3&gt;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>group</span></div><div class='xr-var-dims'>(label)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27;</div><input id='attrs-0e92e25f-4219-4394-b88e-6ff915559a9a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0e92e25f-4219-4394-b88e-6ff915559a9a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-19a83ba6-600d-4f85-97aa-e38cf1809789' class='xr-var-data-in' type='checkbox'><label for='data-19a83ba6-600d-4f85-97aa-e38cf1809789' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;L&#x27;, &#x27;L&#x27;, &#x27;L&#x27;, &#x27;L&#x27;, &#x27;L&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a6b1d658-1fa6-40a9-8fee-51c91ecf0b3f' class='xr-section-summary-in' type='checkbox'  ><label for='section-a6b1d658-1fa6-40a9-8fee-51c91ecf0b3f' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>label</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-2109491d-d140-4375-a867-7230aa06ac1a' class='xr-index-data-in' type='checkbox'/><label for='index-2109491d-d140-4375-a867-7230aa06ac1a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;Cz&#x27;, &#x27;Iz&#x27;, &#x27;Rpa&#x27;, &#x27;Nz&#x27;, &#x27;Lpa&#x27;], dtype=&#x27;object&#x27;, name=&#x27;label&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f18175f1-e1ac-492d-a985-d4b18db8ef94' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f18175f1-e1ac-492d-a985-d4b18db8ef94' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray (label: 5, digitized: 3)> Size: 120B\n",
-       "<Quantity([[183.26390827 116.44076638 433.14976121]\n",
-       " [ 55.81002886 216.87724678 460.20226543]\n",
-       " [ 75.44543708  70.22174292 571.26430267]\n",
-       " [ 70.90973966 -12.7005211  479.10120764]\n",
-       " [ -5.23821153  60.43366529 422.08015945]], 'millimeter')>\n",
-       "Coordinates:\n",
-       "  * label    (label) <U3 60B 'Cz' 'Iz' 'Rpa' 'Nz' 'Lpa'\n",
-       "    type     (label) object 40B PointType.LANDMARK ... PointType.LANDMARK\n",
-       "    group    (label) <U1 20B 'L' 'L' 'L' 'L' 'L'\n",
-       "Dimensions without coordinates: digitized"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "if INTERACTIVE:\n",
-    "    landmark_coordinates, landmark_labels = get_landmarks()\n",
-    "    landmark_coordinates = np.asarray(landmark_coordinates)\n",
-    "else:\n",
-    "    # Cached fallback: paste coordinates from a previous interactive run here.\n",
-    "    landmark_labels = ['Nz', 'Iz', 'Cz', 'Lpa', 'Rpa']\n",
-    "    landmark_coordinates = np.asarray([\n",
-    "        [0.0, 0.0, 0.0],   # Nz  -- replace\n",
-    "        [0.0, 0.0, 0.0],   # Iz  -- replace\n",
-    "        [0.0, 0.0, 0.0],   # Cz  -- replace\n",
-    "        [0.0, 0.0, 0.0],   # Lpa -- replace\n",
-    "        [0.0, 0.0, 0.0],   # Rpa -- replace\n",
-    "    ])\n",
-    "\n",
-    "assert len(set(landmark_labels)) == 5, 'Need exactly 5 distinct landmarks'\n",
-    "assert set(landmark_labels) == {'Nz', 'Iz', 'Cz', 'Lpa', 'Rpa'}, (\n",
-    "    f'Unexpected labels: {landmark_labels}'\n",
-    ")\n",
-    "\n",
-    "landmarks = xr.DataArray(\n",
-    "    np.vstack(landmark_coordinates),\n",
-    "    dims=['label', 'digitized'],\n",
-    "    coords={\n",
-    "        'label': ('label', list(landmark_labels)),\n",
-    "        'type': ('label', [cdc.PointType.LANDMARK] * 5),\n",
-    "        'group': ('label', ['L'] * 5),\n",
-    "    },\n",
-    ").pint.quantify('mm')\n",
-    "\n",
-    "display(landmarks)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 4. Normalize, isolate head, align to CTF\n",
-    "\n",
-    "Same preprocessing as notebook 48. The output `surface_h` carries `crs='ctf'`.\n",
-    "Crucially, `isolate_head` uses only a *transient* position-based adjacency\n",
-    "graph to compute connected components, so the mesh it returns **still contains**\n",
-    "the seam-duplicate vertices --- which is the point of this diagnostic."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "After isolate_head: 678,081 vertices\n",
-      "After align_axes_from_landmarks: crs='ctf', 678,081 vertices\n"
-     ]
-    }
-   ],
-   "source": [
-    "lm_raw = landmarks.pint.dequantify().values\n",
-    "idx = {lbl: i for i, lbl in enumerate(landmarks['label'].values)}\n",
-    "Nz_raw = lm_raw[idx['Nz']]\n",
-    "\n",
-    "surface_n, Nz_n, R = normalize_axes(surface, Nz_raw)\n",
-    "landmarks_n = landmarks.pint.dequantify().copy(data=lm_raw @ R.T).pint.quantify()\n",
-    "\n",
-    "surface_n, _ = isolate_head(surface_n, Nz_n)\n",
-    "print(f'After isolate_head: {surface_n.nvertices:,} vertices')\n",
-    "\n",
-    "surface_h, landmarks_n, M_ctf = align_axes_from_landmarks(surface_n, landmarks_n)\n",
-    "print(f'After align_axes_from_landmarks: crs={surface_h.crs!r}, '\n",
-    "      f'{surface_h.nvertices:,} vertices')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 5. Visualize seam lines on the isolated head (before merge)\n",
-    "\n",
-    "Boundary edges are edges that belong to exactly one face instead of the usual\n",
-    "two. On a watertight surface there should be essentially none. On a raw Einstar\n",
-    "scan, each UV patch boundary becomes a chain of boundary edges because the two\n",
-    "sides of the patch live on different, spatially coincident vertices."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Boundary edges: 169,543 / 1,842,329 (9.2 %)\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "282edaff01594a6bbb3d2b6bef9dcd77",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Widget(value='<iframe src=\"http://localhost:37333/index.html?ui=P_0x7f00379f9350_5&reconnect=auto\" class=\"pyvi…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "def boundary_edges_of(mesh):\n",
-    "    \"\"\"Return (boundary_edge_array, total_edge_count) for a trimesh.\"\"\"\n",
-    "    edge_to_face_count = Counter()\n",
-    "    for face in mesh.faces:\n",
-    "        edge_to_face_count[tuple(sorted((int(face[0]), int(face[1]))))] += 1\n",
-    "        edge_to_face_count[tuple(sorted((int(face[1]), int(face[2]))))] += 1\n",
-    "        edge_to_face_count[tuple(sorted((int(face[0]), int(face[2]))))] += 1\n",
-    "    boundary = np.array(\n",
-    "        [e for e, c in edge_to_face_count.items() if c == 1],\n",
-    "        dtype=np.int64,\n",
-    "    )\n",
-    "    return boundary, len(edge_to_face_count)\n",
-    "\n",
-    "\n",
-    "def _boundary_polydata(mesh, boundary_edges):\n",
-    "    \"\"\"Build a pyvista PolyData of line segments for the given boundary edges.\"\"\"\n",
-    "    verts = mesh.vertices[boundary_edges.ravel()]\n",
-    "    n = len(boundary_edges)\n",
-    "    lines = np.column_stack([\n",
-    "        np.full(n, 2),\n",
-    "        np.arange(0, 2 * n, 2),\n",
-    "        np.arange(1, 2 * n, 2),\n",
-    "    ]).ravel()\n",
-    "    return pv.PolyData(verts, lines=lines)\n",
-    "\n",
-    "\n",
-    "boundary_before, total_edges_before = boundary_edges_of(surface_h.mesh)\n",
-    "print(f'Boundary edges: {len(boundary_before):,} / {total_edges_before:,} '\n",
-    "      f'({100 * len(boundary_before) / total_edges_before:.1f} %)')\n",
-    "\n",
-    "boundary_poly_before = _boundary_polydata(surface_h.mesh, boundary_before)\n",
-    "\n",
-    "pvplt = pv.Plotter()\n",
-    "cedalion.plots.plot_surface(pvplt, surface_h, opacity=1.0)\n",
-    "pvplt.add_mesh(boundary_poly_before, color='red', line_width=2, opacity=1.0)\n",
-    "pvplt.add_text(\n",
-    "    f'Before merge - seam lines: {len(boundary_before):,}',\n",
-    "    position='upper_left', font_size=14,\n",
-    ")\n",
-    "pvplt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 6. Collapse seam duplicates (persistent merge)\n",
-    "\n",
-    "The same mechanism `isolate_head` applies transiently for connected-component\n",
-    "analysis, but here the mesh is actually rewritten: every group of spatially\n",
-    "coincident vertices (within `MERGE_TOL_MM`) is replaced by a single vertex\n",
-    "carrying the group-averaged position and color."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Vertices: 678,081 -> 586,433  (91,648 removed)\n",
-      "Faces:    1,171,705 -> 1,171,681  (24 degenerate removed)\n"
-     ]
-    }
-   ],
-   "source": [
-    "def merge_seam_vertices(mesh, tol=MERGE_TOL_MM):\n",
-    "    \"\"\"Merge spatially coincident vertices via union-find on KDTree pairs.\n",
-    "\n",
-    "    Returns (merged_mesh, old_to_new, new_to_old).\n",
-    "    \"\"\"\n",
-    "    verts = mesh.vertices\n",
-    "    n = len(verts)\n",
-    "\n",
-    "    tree = KDTree(verts)\n",
-    "    pairs = tree.query_pairs(r=tol)\n",
-    "\n",
-    "    parent = np.arange(n)\n",
-    "\n",
-    "    def find(x):\n",
-    "        while parent[x] != x:\n",
-    "            parent[x] = parent[parent[x]]\n",
-    "            x = parent[x]\n",
-    "        return x\n",
-    "\n",
-    "    def union(a, b):\n",
-    "        ra, rb = find(a), find(b)\n",
-    "        if ra != rb:\n",
-    "            parent[ra] = rb\n",
-    "\n",
-    "    for a, b in pairs:\n",
-    "        union(a, b)\n",
-    "\n",
-    "    for i in range(n):\n",
-    "        parent[i] = find(i)\n",
-    "\n",
-    "    unique_roots, inverse = np.unique(parent, return_inverse=True)\n",
-    "    old_to_new = inverse\n",
-    "    n_new = len(unique_roots)\n",
-    "\n",
-    "    new_to_old = [[] for _ in range(n_new)]\n",
-    "    for old_idx in range(n):\n",
-    "        new_to_old[old_to_new[old_idx]].append(old_idx)\n",
-    "\n",
-    "    new_verts = np.zeros((n_new, 3))\n",
-    "    for new_idx in range(n_new):\n",
-    "        old_indices = new_to_old[new_idx]\n",
-    "        new_verts[new_idx] = verts[old_indices].mean(axis=0)\n",
-    "\n",
-    "    new_faces = old_to_new[mesh.faces]\n",
-    "    valid = (\n",
-    "        (new_faces[:, 0] != new_faces[:, 1])\n",
-    "        & (new_faces[:, 1] != new_faces[:, 2])\n",
-    "        & (new_faces[:, 0] != new_faces[:, 2])\n",
-    "    )\n",
-    "    new_faces = new_faces[valid]\n",
-    "\n",
-    "    # Preserve per-vertex colors if present. ColorVisuals exposes\n",
-    "    # ``vertex_colors`` directly; TextureVisuals does not, so we skip\n",
-    "    # color averaging there (the texture image stays attached to the\n",
-    "    # original mesh and can be re-sampled downstream if needed).\n",
-    "    old_colors = getattr(mesh.visual, 'vertex_colors', None)\n",
-    "    merged_mesh = trimesh.Trimesh(\n",
-    "        vertices=new_verts, faces=new_faces, process=False,\n",
-    "    )\n",
-    "    if old_colors is not None and len(old_colors) == n:\n",
-    "        old_colors = np.asarray(old_colors, dtype=np.float64)\n",
-    "        new_colors = np.zeros((n_new, old_colors.shape[1]), dtype=np.float64)\n",
-    "        for new_idx in range(n_new):\n",
-    "            new_colors[new_idx] = old_colors[new_to_old[new_idx]].mean(axis=0)\n",
-    "        merged_mesh.visual.vertex_colors = new_colors.astype(np.uint8)\n",
-    "\n",
-    "    return merged_mesh, old_to_new, new_to_old\n",
-    "\n",
-    "\n",
-    "merged_mesh, old_to_new, new_to_old = merge_seam_vertices(surface_h.mesh)\n",
-    "surface_merged = cdc.TrimeshSurface(\n",
-    "    mesh=merged_mesh, crs=surface_h.crs, units=surface_h.units,\n",
-    ")\n",
-    "\n",
-    "print(f'Vertices: {len(surface_h.mesh.vertices):,} -> {len(merged_mesh.vertices):,}  '\n",
-    "      f'({len(surface_h.mesh.vertices) - len(merged_mesh.vertices):,} removed)')\n",
-    "print(f'Faces:    {len(surface_h.mesh.faces):,} -> {len(merged_mesh.faces):,}  '\n",
-    "      f'({len(surface_h.mesh.faces) - len(merged_mesh.faces):,} degenerate removed)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 7. Visualize merged head (seams gone)\n",
-    "\n",
-    "Side-by-side: same head before (left, seam lines in red) and after (right,\n",
-    "no seam lines). Linked camera views so rotation applies to both panels."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Boundary edges after merge: 1,269\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8f3b668b7d16406ba5551894200e7f7e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Widget(value='<iframe src=\"http://localhost:37333/index.html?ui=P_0x7eff638cff10_6&reconnect=auto\" class=\"pyvi…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "boundary_after, _ = boundary_edges_of(merged_mesh)\n",
-    "print(f'Boundary edges after merge: {len(boundary_after):,}')\n",
-    "\n",
-    "pvplt = pv.Plotter(shape=(1, 2))\n",
-    "\n",
-    "pvplt.subplot(0, 0)\n",
-    "cedalion.plots.plot_surface(pvplt, surface_h, opacity=1.0)\n",
-    "pvplt.add_mesh(boundary_poly_before, color='red', line_width=2, opacity=0.8)\n",
-    "pvplt.add_text(\n",
-    "    f'Before - seams: {len(boundary_before):,}',\n",
-    "    position='upper_left', font_size=12,\n",
-    ")\n",
-    "\n",
-    "pvplt.subplot(0, 1)\n",
-    "cedalion.plots.plot_surface(pvplt, surface_merged, opacity=1.0)\n",
-    "if len(boundary_after) > 0:\n",
-    "    boundary_poly_after = _boundary_polydata(merged_mesh, boundary_after)\n",
-    "    pvplt.add_mesh(boundary_poly_after, color='red', line_width=2, opacity=0.8)\n",
-    "pvplt.add_text(\n",
-    "    f'After merge - seams: {len(boundary_after):,}',\n",
-    "    position='upper_left', font_size=12,\n",
-    ")\n",
-    "\n",
-    "pvplt.link_views()\n",
-    "pvplt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 8. Quantify the merge"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def count_components(m):\n",
-    "    \"\"\"Count connected components on the raw (non-position-merged) adjacency.\"\"\"\n",
-    "    n = len(m.vertices)\n",
-    "    edges = m.edges_unique\n",
-    "    rows = np.concatenate([edges[:, 0], edges[:, 1]])\n",
-    "    cols = np.concatenate([edges[:, 1], edges[:, 0]])\n",
-    "    data = np.ones(len(rows))\n",
-    "    A = csr_matrix((data, (rows, cols)), shape=(n, n))\n",
-    "    n_comp, _ = connected_components(A, directed=False)\n",
-    "    return n_comp\n",
-    "\n",
-    "\n",
-    "n_comp_before = count_components(surface_h.mesh)\n",
-    "n_comp_after = count_components(merged_mesh)\n",
-    "\n",
-    "print('=== Merge statistics ===')\n",
-    "print(f'Vertices:        {len(surface_h.mesh.vertices):>10,} -> {len(merged_mesh.vertices):>10,}  '\n",
-    "      f'({len(surface_h.mesh.vertices) - len(merged_mesh.vertices):,} removed)')\n",
-    "print(f'Faces:           {len(surface_h.mesh.faces):>10,} -> {len(merged_mesh.faces):>10,}  '\n",
-    "      f'({len(surface_h.mesh.faces) - len(merged_mesh.faces):,} degenerate removed)')\n",
-    "print(f'Components:      {n_comp_before:>10,} -> {n_comp_after:>10,}')\n",
-    "print(f'Boundary edges:  {len(boundary_before):>10,} -> {len(boundary_after):>10,}')\n",
-    "print()\n",
-    "\n",
-    "areas = merged_mesh.area_faces\n",
-    "degenerate = int((areas < 1e-10).sum())\n",
-    "print(f'Degenerate faces after merge (area < 1e-10): {degenerate}')\n",
-    "print(f'is_watertight: {merged_mesh.is_watertight}')\n",
-    "print(f'Surface area:  {surface_h.mesh.area:.1f} mm^2 (before) vs '\n",
-    "      f'{merged_mesh.area:.1f} mm^2 (after)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 9. Verify vertex colors survive averaging\n",
-    "\n",
-    "Seam duplicates usually share the same or near-identical color, so averaging\n",
-    "at each merge group should leave the rendered appearance unchanged. The left\n",
-    "panel uses the untouched textured mesh; the right panel uses the merged mesh\n",
-    "with per-vertex colors averaged from the duplicate groups. If both look the\n",
-    "same, the merge preserves appearance."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pvplt = pv.Plotter(shape=(1, 2))\n",
-    "\n",
-    "pvplt.subplot(0, 0)\n",
-    "cedalion.plots.plot_surface(pvplt, surface_h, opacity=1.0)\n",
-    "pvplt.add_text('Before merge', position='upper_left', font_size=12)\n",
-    "\n",
-    "pvplt.subplot(0, 1)\n",
-    "cedalion.plots.plot_surface(pvplt, surface_merged, opacity=1.0)\n",
-    "pvplt.add_text('After merge', position='upper_left', font_size=12)\n",
-    "\n",
-    "pvplt.link_views()\n",
-    "pvplt.show()\n",
-    "\n",
-    "orig_colors = getattr(surface_h.mesh.visual, 'vertex_colors', None)\n",
-    "if orig_colors is not None and len(orig_colors) == len(surface_h.mesh.vertices):\n",
-    "    orig_colors = np.asarray(orig_colors)[:, :3].astype(float)\n",
-    "    max_diffs = []\n",
-    "    for new_idx in range(len(merged_mesh.vertices)):\n",
-    "        old_indices = new_to_old[new_idx]\n",
-    "        if len(old_indices) > 1:\n",
-    "            old_cols = orig_colors[old_indices]\n",
-    "            spread = old_cols.max(axis=0) - old_cols.min(axis=0)\n",
-    "            max_diffs.append(spread.max())\n",
-    "\n",
-    "    if max_diffs:\n",
-    "        max_diffs = np.array(max_diffs)\n",
-    "        print('Color spread at merged vertex groups (0-255 scale):')\n",
-    "        print(f'  Max spread:    {max_diffs.max():.1f}')\n",
-    "        print(f'  Mean spread:   {max_diffs.mean():.1f}')\n",
-    "        print(f'  Median spread: {np.median(max_diffs):.1f}')\n",
-    "        print(f'  Groups with spread > 10: {int((max_diffs > 10).sum())} / {len(max_diffs)}')\n",
-    "    else:\n",
-    "        print('No merged vertex groups with >1 member found.')\n",
-    "else:\n",
-    "    print('Textured mesh (no per-vertex color array) - visual comparison above is the proxy.')"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.14"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/examples/head_models/50_mask_selection_walkthrough.ipynb b/examples/head_models/50_mask_selection_walkthrough.ipynb
deleted file mode 100644
index 2bc5c52e..00000000
--- a/examples/head_models/50_mask_selection_walkthrough.ipynb
+++ /dev/null
@@ -1,1072 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "f01f0012",
-   "metadata": {},
-   "source": [
-    "# Mask-Selection Walkthrough (CTF frame, Subject17)\n",
-    "\n",
-    "Illustrates Section 3.5 of the thesis step by step on a real scan. Each 3D\n",
-    "cell opens an **interactive** PyVista widget: rotate / pan / zoom until the\n",
-    "view is right for the thesis figure, then click the camera icon in the\n",
-    "PyVista toolbar to export a PNG. The matplotlib graphs (cap-boundary\n",
-    "profile, per-region counts) are saved to disk automatically because they\n",
-    "don't need camera control.\n",
-    "\n",
-    "Pipeline (from Section 3.4's head-isolation output onward):\n",
-    "\n",
-    "1. Start from the head-only mesh produced by `isolate_head`.\n",
-    "2. CTF alignment (`align_axes_from_landmarks`).\n",
-    "3. Midline Y-band used as input to the cap-boundary detector.\n",
-    "4. `detect_cap_boundary` -> `Z_cap`.\n",
-    "5. The cap boundary is a *ceiling*, not the full mask: everything above is\n",
-    "   spared, but inside the halfspace `Z < Z_cap` only the forward hemisphere\n",
-    "   and two ear spheres are deleted.\n",
-    "6. `face_mask_from_landmarks` -> union mask.\n",
-    "7. 8 mm landmark safe-area + nasion-strip carve-out (pipeline-level, see\n",
-    "   Section 3.6).\n",
-    "8. `delete_masked_vertices` and validation.\n",
-    "\n",
-    "All math is in the CTF frame: **+X=anterior, +Y=left, +Z=up**, origin at the\n",
-    "LPA/RPA midpoint."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "1b88dc50",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Matplotlib graphs -> /home/ma7/Documents/BA_screenshots/mask_selection\n",
-      "3D frames: use the camera icon in the PyVista toolbar to export a PNG.\n"
-     ]
-    }
-   ],
-   "source": [
-    "import logging\n",
-    "import os\n",
-    "from pathlib import Path\n",
-    "\n",
-    "import numpy as np\n",
-    "import pyvista as pv\n",
-    "import matplotlib.pyplot as plt\n",
-    "import xarray as xr\n",
-    "import trimesh as _tm\n",
-    "from PIL import Image as _PILImage\n",
-    "\n",
-    "import cedalion\n",
-    "import cedalion.dataclasses as cdc\n",
-    "import cedalion.io\n",
-    "import cedalion.plots\n",
-    "from cedalion.geometry.photogrammetry.anonymization import (\n",
-    "    normalize_axes,\n",
-    "    isolate_head,\n",
-    "    align_axes_from_landmarks,\n",
-    "    detect_cap_boundary,\n",
-    "    face_mask_from_landmarks,\n",
-    "    delete_masked_vertices,\n",
-    "    validate_anonymization,\n",
-    ")\n",
-    "from cedalion.vtktutils import trimesh_to_vtk_polydata\n",
-    "from cedalion.dataclasses import VTKSurface\n",
-    "\n",
-    "logging.getLogger('cedalion').setLevel(logging.WARNING)\n",
-    "pv.set_jupyter_backend('server')\n",
-    "\n",
-    "SUBJECT_NUMBER = 17\n",
-    "SCANS_FOLDER = '/home/ma7/BA/PG_Subjects'\n",
-    "FIG_DIR = Path('/home/ma7/Documents/BA_screenshots/mask_selection')\n",
-    "FIG_DIR.mkdir(parents=True, exist_ok=True)\n",
-    "print(f'Matplotlib graphs -> {FIG_DIR}')\n",
-    "print('3D frames: use the camera icon in the PyVista toolbar to export a PNG.')\n",
-    "\n",
-    "# Cached landmarks for Subject17 in raw Einstar coordinates.\n",
-    "# Re-pick in notebook 48 and overwrite these if the RPA validation fails.\n",
-    "LANDMARK_LABELS = ['Cz', 'Iz', 'Rpa', 'Nz', 'Lpa']\n",
-    "LANDMARK_COORDS_RAW = np.array([\n",
-    "    [182.65783971, 118.04228881, 431.56432941],  # Cz\n",
-    "    [ 55.89538812, 223.75583573, 451.320292  ],  # Iz\n",
-    "    [ 64.28395359,  88.3805534 , 585.72672053],  # Rpa\n",
-    "    [ 70.43872393, -12.48837767, 478.83735296],  # Nz\n",
-    "    [  1.44293713,  59.38634557, 399.68340223],  # Lpa\n",
-    "])\n",
-    "\n",
-    "# Mask tunables (mm)\n",
-    "LANDMARK_KEEP_RADIUS = 8.0\n",
-    "EAR_DELETE_RADIUS_MM = 40.0\n",
-    "\n",
-    "# Cap-boundary tunables\n",
-    "CAP_BAND_WIDTH_MM = 15.0\n",
-    "CAP_BIN_SIZE_MM = 2.0\n",
-    "CAP_FOOT_GRAD_THRESHOLD = 0.2\n",
-    "\n",
-    "LM_COLORS = {'Nz': 'lime', 'Iz': 'magenta', 'Cz': 'cyan',\n",
-    "             'Lpa': 'orange', 'Rpa': 'blue'}\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b160e4ed",
-   "metadata": {},
-   "source": [
-    "## 1. Load and preprocess (up to the CTF-aligned head)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "e1ab957a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Head-only mesh: 678,081 verts\n",
-      "CTF frame OK (crs='ctf'); ear_mid = [0. 0. 0.]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Load the Einstar scan and ensure the texture is attached. Same fallback as\n",
-    "# notebook 48: trimesh 4.6 sometimes hides the image on .material.image.\n",
-    "path = f'{SCANS_FOLDER}/Subject{SUBJECT_NUMBER}/Subject{SUBJECT_NUMBER}.obj'\n",
-    "surface = cedalion.io.read_einstar_obj(path)\n",
-    "\n",
-    "def _visual_image(visual):\n",
-    "    img = getattr(visual, 'image', None)\n",
-    "    if img is None:\n",
-    "        mat = getattr(visual, 'material', None)\n",
-    "        img = getattr(mat, 'image', None) if mat is not None else None\n",
-    "    return img\n",
-    "\n",
-    "if _visual_image(surface.mesh.visual) is None:\n",
-    "    _jpg = path.replace('.obj', '.jpg')\n",
-    "    _uv = getattr(surface.mesh.visual, 'uv', None)\n",
-    "    assert os.path.exists(_jpg) and _uv is not None\n",
-    "    surface.mesh.visual = _tm.visual.TextureVisuals(\n",
-    "        uv=_uv, image=_PILImage.open(_jpg).convert('RGBA'),\n",
-    "    )\n",
-    "\n",
-    "# Wrap cached landmarks.\n",
-    "landmarks_raw = xr.DataArray(\n",
-    "    LANDMARK_COORDS_RAW,\n",
-    "    dims=['label', 'digitized'],\n",
-    "    coords={\n",
-    "        'label': ('label', LANDMARK_LABELS),\n",
-    "        'type':  ('label', [cdc.PointType.LANDMARK] * 5),\n",
-    "        'group': ('label', ['L'] * 5),\n",
-    "    },\n",
-    ").pint.quantify('mm')\n",
-    "idx = {lbl: i for i, lbl in enumerate(landmarks_raw['label'].values)}\n",
-    "\n",
-    "# normalize_axes + isolate_head (outputs of Sections 3.3 and 3.4).\n",
-    "lm_raw_xyz = landmarks_raw.pint.dequantify().values\n",
-    "Nz_raw = lm_raw_xyz[idx['Nz']]\n",
-    "surface_n, Nz_n, R = normalize_axes(surface, Nz_raw)\n",
-    "lm_normed_xyz = lm_raw_xyz @ R.T\n",
-    "surface_h_pre, _ = isolate_head(surface_n, Nz_n)\n",
-    "\n",
-    "# CTF alignment: origin at the LPA/RPA midpoint, +X anterior, +Y left, +Z up.\n",
-    "landmarks_n = xr.DataArray(\n",
-    "    lm_normed_xyz, dims=['label', 'digitized'],\n",
-    "    coords={\n",
-    "        'label': ('label', LANDMARK_LABELS),\n",
-    "        'type':  ('label', [cdc.PointType.LANDMARK] * 5),\n",
-    "        'group': ('label', ['L'] * 5),\n",
-    "    },\n",
-    ").pint.quantify('mm')\n",
-    "surface_h, landmarks_ctf, _ = align_axes_from_landmarks(\n",
-    "    surface_h_pre, landmarks_n,\n",
-    ")\n",
-    "lm_ctf_xyz = landmarks_ctf.pint.dequantify().values\n",
-    "Nz, Iz, Cz, Lpa, Rpa = (lm_ctf_xyz[idx[lbl]]\n",
-    "                        for lbl in ['Nz', 'Iz', 'Cz', 'Lpa', 'Rpa'])\n",
-    "ear_mid = 0.5 * (Lpa + Rpa)\n",
-    "verts = np.asarray(surface_h.mesh.vertices)\n",
-    "mid_y = 0.5 * (Lpa[1] + Rpa[1])\n",
-    "\n",
-    "print(f'Head-only mesh: {surface_h_pre.nvertices:,} verts')\n",
-    "print(f'CTF frame OK (crs={surface_h.crs!r}); ear_mid = {np.round(ear_mid,3)}')\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "58a5348f",
-   "metadata": {},
-   "source": [
-    "### Frame A — head-only mesh after `isolate_head` (Section 3.4 output)\n",
-    "\n",
-    "The starting point for Section 3.5. Shoulders, chair, and disconnected\n",
-    "fragments have already been removed. Landmarks are drawn as coloured spheres\n",
-    "for orientation. Rotate the head to the angle you want, then use the\n",
-    "PyVista toolbar's camera icon to export a PNG."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "61f6ed03",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6b6370398a964ced97a90b5ec8b2cb04",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Widget(value='<iframe src=\"http://localhost:32931/index.html?ui=P_0x7fae1c1863d0_10&reconnect=auto\" class=\"pyv…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pvplt = pv.Plotter()\n",
-    "hvtk = trimesh_to_vtk_polydata(surface_h_pre.mesh)\n",
-    "pvplt.add_mesh(pv.wrap(hvtk), rgb=True, smooth_shading=True)\n",
-    "for lbl, pos in zip(LANDMARK_LABELS, lm_normed_xyz):\n",
-    "    c = LM_COLORS[lbl]\n",
-    "    pvplt.add_mesh(pv.Sphere(radius=5, center=pos), color=c)\n",
-    "    pvplt.add_point_labels([pos], [lbl], font_size=16, text_color=c,\n",
-    "                           shape=None, always_visible=True)\n",
-    "pvplt.add_text(\n",
-    "    f'After isolate_head  ({surface_h_pre.nvertices:,} verts)',\n",
-    "    position='upper_left', font_size=12,\n",
-    ")\n",
-    "pvplt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "03b10ef3",
-   "metadata": {},
-   "source": [
-    "### Frame B — CTF-aligned mesh with basis vectors\n",
-    "\n",
-    "After `align_axes_from_landmarks` the origin sits at the LPA/RPA midpoint and\n",
-    "the three coloured arrows mark the CTF axes:\n",
-    "\n",
-    "- **red**  +X anterior (toward the nasion)\n",
-    "- **green** +Y left (toward LPA)\n",
-    "- **blue**  +Z up (toward Cz)\n",
-    "\n",
-    "Every subsequent rule in Section 3.5 is expressed in this frame."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "2a1ce37e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "187492e53229450eb0c1e7aa13accd54",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Widget(value='<iframe src=\"http://localhost:32931/index.html?ui=P_0x7fac8ae5c090_11&reconnect=auto\" class=\"pyv…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pvplt = pv.Plotter()\n",
-    "vtk_s = VTKSurface.from_trimeshsurface(surface_h)\n",
-    "pvplt.add_mesh(pv.wrap(vtk_s.mesh), rgb=True, smooth_shading=True)\n",
-    "\n",
-    "for lbl, pos in zip(['Nz','Iz','Cz','Lpa','Rpa'], [Nz, Iz, Cz, Lpa, Rpa]):\n",
-    "    pvplt.add_mesh(pv.Sphere(radius=5, center=pos), color=LM_COLORS[lbl])\n",
-    "    pvplt.add_point_labels([pos], [lbl], font_size=16,\n",
-    "                           text_color=LM_COLORS[lbl],\n",
-    "                           shape=None, always_visible=True)\n",
-    "\n",
-    "arr_len = 90.0\n",
-    "for direc, color, name in (\n",
-    "    ((1, 0, 0), 'red',   '+X anterior'),\n",
-    "    ((0, 1, 0), 'green', '+Y left'),\n",
-    "    ((0, 0, 1), 'blue',  '+Z up'),\n",
-    "):\n",
-    "    pvplt.add_mesh(\n",
-    "        pv.Arrow(start=(0, 0, 0), direction=direc, scale=arr_len),\n",
-    "        color=color,\n",
-    "    )\n",
-    "    tip = tuple(np.array(direc) * (arr_len + 15))\n",
-    "    pvplt.add_point_labels([tip], [name], font_size=16, text_color=color,\n",
-    "                           shape=None, always_visible=True)\n",
-    "\n",
-    "pvplt.add_text('CTF frame: +X anterior, +Y left, +Z up',\n",
-    "               position='upper_left', font_size=12)\n",
-    "pvplt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6f22a8bd",
-   "metadata": {},
-   "source": [
-    "## 2. Cap boundary detection\n",
-    "\n",
-    "**Problem.** The fNIRS cap protrudes forward of the bare forehead, so if we\n",
-    "naively say \"delete everything forward of the ear plane\" we'd also delete\n",
-    "the cap, the optodes, and the cables. We need to stop the forward-facing\n",
-    "band at the cap's lower edge.\n",
-    "\n",
-    "**Solution.** Walk upward along the midline and watch how far forward the\n",
-    "mesh reaches. On the forehead this distance grows slowly; at the cap edge it\n",
-    "jumps because the cap sticks out further than the skin below it. The cap\n",
-    "boundary height `Z_cap` is the foot of that jump."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d16a9156",
-   "metadata": {},
-   "source": [
-    "### Frame C — midline Y-band feeding the profile\n",
-    "\n",
-    "The cap detector only looks at vertices near the sagittal midline (within\n",
-    "±15 mm of `Y = 0`), above the nasion, and anterior to the ear plane. Those\n",
-    "are the blue vertices below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "84fea875",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Midline-band vertices feeding the profile: 21,985\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8cb2a7a8cb73410189a33a3f9968b7c7",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Widget(value='<iframe src=\"http://localhost:32931/index.html?ui=P_0x7fac8aed1350_12&reconnect=auto\" class=\"pyv…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "in_band  = np.abs(verts[:, 1] - mid_y) < CAP_BAND_WIDTH_MM\n",
-    "above_nz = verts[:, 2] > Nz[2]\n",
-    "anterior = verts[:, 0] > ear_mid[0]\n",
-    "band_mask = in_band & above_nz & anterior\n",
-    "print(f'Midline-band vertices feeding the profile: {int(band_mask.sum()):,}')\n",
-    "\n",
-    "pvplt = pv.Plotter()\n",
-    "vtk_s = VTKSurface.from_trimeshsurface(surface_h)\n",
-    "pv_m = pv.wrap(vtk_s.mesh)\n",
-    "pv_m['band'] = band_mask.astype(float)\n",
-    "pvplt.add_mesh(pv_m, scalars='band', cmap=['#d6d6d6', '#1f77b4'],\n",
-    "               clim=[0, 1], show_scalar_bar=False, smooth_shading=True)\n",
-    "pvplt.add_text(f'Midline Y-band  |Y| < {CAP_BAND_WIDTH_MM:.0f} mm',\n",
-    "               position='upper_left', font_size=12)\n",
-    "pvplt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bd835e74",
-   "metadata": {},
-   "source": [
-    "### Graph D — midline X(Z) profile and its gradient\n",
-    "\n",
-    "The blue vertices are binned by height `Z` in 2 mm bins and the max\n",
-    "anterior extent `X` per bin forms the profile `X(Z)` (left panel). The\n",
-    "Savitzky–Golay smoothing suppresses sub-millimetre scanner noise. The\n",
-    "cap sits at the top of the steep rise; `Z_cap` is the first Z, walking back\n",
-    "from the smoothed peak toward the nasion, where the gradient `dX/dZ` drops\n",
-    "below the foot threshold `0.2` (right panel)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "ffa81229",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Z_cap = 31.0 mm   (Nz_z=-0.0, Cz_z=165.4)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1200x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Saved 06_cap_profile.pdf and .png  (Z_cap = 31.0 mm)\n"
-     ]
-    }
-   ],
-   "source": [
-    "cap_z, profile_z, profile_x, profile_x_s = detect_cap_boundary(\n",
-    "    verts, Nz, Cz, ear_mid, mid_y,\n",
-    "    band_width=CAP_BAND_WIDTH_MM,\n",
-    "    bin_size=CAP_BIN_SIZE_MM,\n",
-    "    foot_grad_threshold=CAP_FOOT_GRAD_THRESHOLD,\n",
-    ")\n",
-    "print(f'Z_cap = {cap_z:.1f} mm   (Nz_z={Nz[2]:.1f}, Cz_z={Cz[2]:.1f})')\n",
-    "\n",
-    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))\n",
-    "ax1.plot(profile_z, profile_x, 'b.-', alpha=0.4, label='raw max-X')\n",
-    "ax1.plot(profile_z, profile_x_s, 'b-', lw=2, label='SG-smoothed')\n",
-    "ax1.axvline(Nz[2], color='green', ls='--', label='Nz')\n",
-    "ax1.axvline(cap_z, color='red', ls='--', lw=2,\n",
-    "            label=f'$Z_\\\\mathrm{{cap}}$ = {cap_z:.0f} mm')\n",
-    "ax1.axvline(Cz[2], color='cyan', ls='--', label='Cz')\n",
-    "ax1.set_xlabel('Z (height, mm)')\n",
-    "ax1.set_ylabel('max X (anterior, mm)')\n",
-    "ax1.set_title('Midline X(Z) profile')\n",
-    "ax1.legend(loc='best', fontsize=9)\n",
-    "ax1.grid(alpha=0.3)\n",
-    "\n",
-    "grad = np.gradient(profile_x_s, profile_z)\n",
-    "ax2.plot(profile_z, grad, 'r.-')\n",
-    "ax2.axhline(CAP_FOOT_GRAD_THRESHOLD, color='purple', ls=':',\n",
-    "            label=f'foot threshold = {CAP_FOOT_GRAD_THRESHOLD}')\n",
-    "ax2.axvline(cap_z, color='red', ls='--', lw=2, label='$Z_\\\\mathrm{cap}$')\n",
-    "ax2.set_xlabel('Z (height, mm)')\n",
-    "ax2.set_ylabel('dX/dZ')\n",
-    "ax2.set_title('Gradient')\n",
-    "ax2.legend(loc='best', fontsize=9)\n",
-    "ax2.grid(alpha=0.3)\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.savefig(FIG_DIR / '06_cap_profile.pdf', bbox_inches='tight')\n",
-    "plt.savefig(FIG_DIR / '06_cap_profile.png', dpi=150, bbox_inches='tight')\n",
-    "plt.show()\n",
-    "print(f'Saved 06_cap_profile.pdf and .png  (Z_cap = {cap_z:.1f} mm)')\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c73d9ff4",
-   "metadata": {},
-   "source": [
-    "### Frame E — cap boundary plane on the mesh\n",
-    "\n",
-    "The semi-transparent red plane is the horizontal slice at `Z = Z_cap`.\n",
-    "Everything above it is *guaranteed* to survive; everything below it is\n",
-    "*eligible* for masking (but not automatically masked — see the next\n",
-    "section)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "3656d52c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "20411fe722274575a80e5dc4aad09613",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Widget(value='<iframe src=\"http://localhost:32931/index.html?ui=P_0x7fae07f37050_13&reconnect=auto\" class=\"pyv…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pvplt = pv.Plotter()\n",
-    "vtk_s = VTKSurface.from_trimeshsurface(surface_h)\n",
-    "pvplt.add_mesh(pv.wrap(vtk_s.mesh), color='#c8c8c8', smooth_shading=True)\n",
-    "plane = pv.Plane(center=(0.0, 0.0, float(cap_z)),\n",
-    "                 direction=(0.0, 0.0, 1.0),\n",
-    "                 i_size=320.0, j_size=320.0)\n",
-    "pvplt.add_mesh(plane, color='red', opacity=0.35)\n",
-    "pvplt.add_text(f'Cap boundary plane  Z = {cap_z:.0f} mm',\n",
-    "               position='upper_left', font_size=12)\n",
-    "pvplt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e53f27cb",
-   "metadata": {},
-   "source": [
-    "## 3. From the cap boundary to the final mask\n",
-    "\n",
-    "`Z_cap` is a **ceiling**, not the mask itself. If we masked *everything* below\n",
-    "`Z_cap` we would delete the posterior scalp, the neck, and the shoulders\n",
-    "too — far more than we want. Frame F shows that `below_cap` halfspace as a\n",
-    "sanity check; the final mask is a much smaller subset of it.\n",
-    "\n",
-    "Inside the halfspace `Z < Z_cap`, the mask is the **union** of two\n",
-    "geometric regions:\n",
-    "\n",
-    "1. **Face region** (Frame G): every vertex with `X > M_X`, i.e. in front of\n",
-    "   the coronal plane that passes through the ear midpoint `M`. This covers\n",
-    "   the face, the cheeks, and the forehead up to the cap edge.\n",
-    "2. **Ear spheres** (Frames H and I): two 40 mm spheres centred on LPA and\n",
-    "   RPA respectively. These sweep up each pinna and the surrounding lateral\n",
-    "   skin.\n",
-    "\n",
-    "Everything else — the back of the head, the crown under the cap, the neck,\n",
-    "the shoulders — is *not* in either region and is therefore kept."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "19392a17",
-   "metadata": {},
-   "source": [
-    "### Frame F — `below_cap` halfspace (eligibility only, not the mask)\n",
-    "\n",
-    "Blue = vertices with `Z < Z_cap`. Note how much of the head this covers;\n",
-    "the final mask is narrower."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "eb5445af",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c24727b28b1c4cab8671cbc232d47388",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Widget(value='<iframe src=\"http://localhost:32931/index.html?ui=P_0x7fadec0aab50_14&reconnect=auto\" class=\"pyv…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "below_cap = verts[:, 2] < cap_z\n",
-    "\n",
-    "pvplt = pv.Plotter()\n",
-    "vtk_s = VTKSurface.from_trimeshsurface(surface_h)\n",
-    "pv_m = pv.wrap(vtk_s.mesh)\n",
-    "pv_m['below'] = below_cap.astype(float)\n",
-    "pvplt.add_mesh(pv_m, scalars='below', cmap=['#d6d6d6', '#1f77b4'],\n",
-    "               clim=[0, 1], show_scalar_bar=False, smooth_shading=True)\n",
-    "pvplt.add_text(\n",
-    "    f'below_cap halfspace  Z < {cap_z:.0f} mm  '\n",
-    "    f'({int(below_cap.sum()):,} verts eligible)',\n",
-    "    position='upper_left', font_size=12,\n",
-    ")\n",
-    "pvplt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3f746b2a",
-   "metadata": {},
-   "source": [
-    "### Frame G — face region `X > M_X` ∧ `Z < Z_cap`\n",
-    "\n",
-    "Red = the forward hemisphere below the cap. Rotate to a 3/4 frontal view\n",
-    "for the thesis figure."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "24ee5e98",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "face_region vertices: 102,705\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "04d93c4f194a4a94ab1b2c5841964f7a",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Widget(value='<iframe src=\"http://localhost:32931/index.html?ui=P_0x7fadab700ed0_15&reconnect=auto\" class=\"pyv…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "anterior_m  = verts[:, 0] > ear_mid[0]\n",
-    "face_region = below_cap & anterior_m\n",
-    "print(f'face_region vertices: {int(face_region.sum()):,}')\n",
-    "\n",
-    "pvplt = pv.Plotter()\n",
-    "vtk_s = VTKSurface.from_trimeshsurface(surface_h)\n",
-    "pv_m = pv.wrap(vtk_s.mesh)\n",
-    "pv_m['face'] = face_region.astype(float)\n",
-    "pvplt.add_mesh(pv_m, scalars='face', cmap=['#d6d6d6', 'red'],\n",
-    "               clim=[0, 1], show_scalar_bar=False, smooth_shading=True)\n",
-    "pvplt.add_text(f'Face region  X > M_X ∧ Z < Z_cap  '\n",
-    "               f'({int(face_region.sum()):,} verts)',\n",
-    "               position='upper_left', font_size=12)\n",
-    "pvplt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "717977cc",
-   "metadata": {},
-   "source": [
-    "### Frame H — ear spheres, mirrored side-by-side\n",
-    "\n",
-    "Left panel: LPA sphere viewed from the subject's left (camera at `+Y`).\n",
-    "Right panel: RPA sphere viewed from the subject's right (camera at `-Y`).\n",
-    "Both are 40 mm spheres clamped below the cap. The cameras are mirrored,\n",
-    "not linked — you can tweak each side independently before screenshotting."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "2604880d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "LPA ear verts: 19,573   RPA ear verts: 11,655   (union 31,228)\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c484031d87eb467cafffc9116222cfaa",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Widget(value='<iframe src=\"http://localhost:32931/index.html?ui=P_0x7fadab753a90_16&reconnect=auto\" class=\"pyv…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "d_lpa = np.linalg.norm(verts - Lpa, axis=1)\n",
-    "d_rpa = np.linalg.norm(verts - Rpa, axis=1)\n",
-    "lpa_region = (d_lpa < EAR_DELETE_RADIUS_MM) & below_cap\n",
-    "rpa_region = (d_rpa < EAR_DELETE_RADIUS_MM) & below_cap\n",
-    "ear_region = lpa_region | rpa_region\n",
-    "print(f'LPA ear verts: {int(lpa_region.sum()):,}   '\n",
-    "      f'RPA ear verts: {int(rpa_region.sum()):,}   '\n",
-    "      f'(union {int(ear_region.sum()):,})')\n",
-    "\n",
-    "# Mirrored lateral cameras. Focal point = ear midpoint (CTF origin).\n",
-    "CAM_DIST = 420.0\n",
-    "CAM_LEFT  = [(0.0,  CAM_DIST, float(Cz[2]) * 0.25),\n",
-    "             (0.0, 0.0, float(Cz[2]) * 0.25),\n",
-    "             (0.0, 0.0, 1.0)]\n",
-    "CAM_RIGHT = [(0.0, -CAM_DIST, float(Cz[2]) * 0.25),\n",
-    "             (0.0, 0.0, float(Cz[2]) * 0.25),\n",
-    "             (0.0, 0.0, 1.0)]\n",
-    "\n",
-    "pvplt = pv.Plotter(shape=(1, 2))\n",
-    "\n",
-    "# --- left panel: LPA ---\n",
-    "pvplt.subplot(0, 0)\n",
-    "vtk_a = VTKSurface.from_trimeshsurface(surface_h)\n",
-    "pv_a = pv.wrap(vtk_a.mesh)\n",
-    "pv_a['lpa'] = lpa_region.astype(float)\n",
-    "pvplt.add_mesh(pv_a, scalars='lpa', cmap=['#d6d6d6', 'orange'],\n",
-    "               clim=[0, 1], show_scalar_bar=False, smooth_shading=True)\n",
-    "pvplt.add_mesh(pv.Sphere(radius=5, center=Lpa), color=LM_COLORS['Lpa'])\n",
-    "pvplt.add_point_labels([Lpa], ['Lpa'], font_size=16,\n",
-    "                       text_color=LM_COLORS['Lpa'],\n",
-    "                       shape=None, always_visible=True)\n",
-    "pvplt.camera_position = CAM_LEFT\n",
-    "pvplt.add_text(f'LPA sphere (r = {EAR_DELETE_RADIUS_MM:.0f} mm)  '\n",
-    "               f'{int(lpa_region.sum()):,} verts',\n",
-    "               position='upper_left', font_size=12)\n",
-    "\n",
-    "# --- right panel: RPA ---\n",
-    "pvplt.subplot(0, 1)\n",
-    "vtk_b = VTKSurface.from_trimeshsurface(surface_h)\n",
-    "pv_b = pv.wrap(vtk_b.mesh)\n",
-    "pv_b['rpa'] = rpa_region.astype(float)\n",
-    "pvplt.add_mesh(pv_b, scalars='rpa', cmap=['#d6d6d6', 'orange'],\n",
-    "               clim=[0, 1], show_scalar_bar=False, smooth_shading=True)\n",
-    "pvplt.add_mesh(pv.Sphere(radius=5, center=Rpa), color=LM_COLORS['Rpa'])\n",
-    "pvplt.add_point_labels([Rpa], ['Rpa'], font_size=16,\n",
-    "                       text_color=LM_COLORS['Rpa'],\n",
-    "                       shape=None, always_visible=True)\n",
-    "pvplt.camera_position = CAM_RIGHT\n",
-    "pvplt.add_text(f'RPA sphere (r = {EAR_DELETE_RADIUS_MM:.0f} mm)  '\n",
-    "               f'{int(rpa_region.sum()):,} verts',\n",
-    "               position='upper_left', font_size=12)\n",
-    "\n",
-    "pvplt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "47e7dbe8",
-   "metadata": {},
-   "source": [
-    "### Frame J — union mask (before carve-outs)\n",
-    "\n",
-    "Red = the union of the face region and the two ear spheres. This is what\n",
-    "`face_mask_from_landmarks` returns."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "2933ccc4",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Pre-carveout counts: {'below_cap': 304744, 'face_region': 102705, 'ear_region': 31228, 'all': 117625}\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f031e2af4d02485da5bd51954d2dfc22",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Widget(value='<iframe src=\"http://localhost:32931/index.html?ui=P_0x7fadab763390_17&reconnect=auto\" class=\"pyv…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "mask, mask_info = face_mask_from_landmarks(\n",
-    "    verts, Nz, Iz, Cz, Lpa, Rpa,\n",
-    "    cap_z=cap_z, ear_delete_radius=EAR_DELETE_RADIUS_MM,\n",
-    ")\n",
-    "counts_pre = dict(mask_info['counts'])\n",
-    "mask_pre_carve = mask.copy()\n",
-    "print(f'Pre-carveout counts: {counts_pre}')\n",
-    "\n",
-    "pvplt = pv.Plotter()\n",
-    "vtk_s = VTKSurface.from_trimeshsurface(surface_h)\n",
-    "pv_m = pv.wrap(vtk_s.mesh)\n",
-    "pv_m['mask'] = mask_pre_carve.astype(float)\n",
-    "pvplt.add_mesh(pv_m, scalars='mask', cmap=['white', 'red'],\n",
-    "               clim=[0, 1], show_scalar_bar=False, smooth_shading=True)\n",
-    "for lbl, pos in zip(['Nz','Iz','Cz','Lpa','Rpa'], [Nz, Iz, Cz, Lpa, Rpa]):\n",
-    "    c = LM_COLORS[lbl]\n",
-    "    pvplt.add_mesh(pv.Sphere(radius=5, center=pos), color=c)\n",
-    "    pvplt.add_point_labels([pos], [lbl], font_size=16, text_color=c,\n",
-    "                           shape=None, always_visible=True)\n",
-    "pvplt.add_text(\n",
-    "    f'Union mask, pre-carveout  '\n",
-    "    f'({int(mask_pre_carve.sum()):,} verts, '\n",
-    "    f'{100*mask_pre_carve.sum()/len(mask_pre_carve):.1f} %)',\n",
-    "    position='upper_left', font_size=12,\n",
-    ")\n",
-    "pvplt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "583275de",
-   "metadata": {},
-   "source": [
-    "### Frame K — final mask after the landmark safe-area and nasion-strip carve-outs\n",
-    "\n",
-    "Before the mask is handed to the deletion step, two carve-outs run at the\n",
-    "pipeline level (Section 3.6):\n",
-    "\n",
-    "- an 8 mm spherical *safe-area* around each of the five landmarks is\n",
-    "  removed from the mask, and\n",
-    "- a narrow midline strip running from the nasion up to `Z_cap` is also\n",
-    "  removed, so Cedalion's `LandmarksBuilder1010` retains a clean nasion\n",
-    "  neighbourhood.\n",
-    "\n",
-    "The result is what actually gets deleted."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "ec3a5a78",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Final mask: 114,837 verts (16.9 %)\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c96c1a2c2fd0464fb4e761ce6038dcee",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Widget(value='<iframe src=\"http://localhost:32931/index.html?ui=P_0x7fadab7e3e10_18&reconnect=auto\" class=\"pyv…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "for lm in (Nz, Iz, Cz, Lpa, Rpa):\n",
-    "    near = np.linalg.norm(verts - lm, axis=1) < LANDMARK_KEEP_RADIUS\n",
-    "    mask[near] = False\n",
-    "nasion_strip = (\n",
-    "    (verts[:, 2] >= Nz[2]) & (verts[:, 2] < cap_z)\n",
-    "    & (np.abs(verts[:, 1] - Nz[1]) < LANDMARK_KEEP_RADIUS)\n",
-    "    & (verts[:, 0] > ear_mid[0])\n",
-    ")\n",
-    "mask[nasion_strip] = False\n",
-    "print(f'Final mask: {int(mask.sum()):,} verts '\n",
-    "      f'({100*mask.sum()/len(mask):.1f} %)')\n",
-    "\n",
-    "pvplt = pv.Plotter()\n",
-    "vtk_s = VTKSurface.from_trimeshsurface(surface_h)\n",
-    "pv_m = pv.wrap(vtk_s.mesh)\n",
-    "pv_m['mask'] = mask.astype(float)\n",
-    "pvplt.add_mesh(pv_m, scalars='mask', cmap=['white', 'red'],\n",
-    "               clim=[0, 1], show_scalar_bar=False, smooth_shading=True)\n",
-    "for lbl, pos in zip(['Nz','Iz','Cz','Lpa','Rpa'], [Nz, Iz, Cz, Lpa, Rpa]):\n",
-    "    c = LM_COLORS[lbl]\n",
-    "    pvplt.add_mesh(pv.Sphere(radius=5, center=pos), color=c)\n",
-    "    pvplt.add_point_labels([pos], [lbl], font_size=16, text_color=c,\n",
-    "                           shape=None, always_visible=True)\n",
-    "pvplt.add_text(\n",
-    "    f'Final mask (after carve-outs)  '\n",
-    "    f'{int(mask.sum()):,} / {len(mask):,} verts  '\n",
-    "    f'({100*mask.sum()/len(mask):.1f} %)',\n",
-    "    position='upper_left', font_size=12,\n",
-    ")\n",
-    "pvplt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e342809a",
-   "metadata": {},
-   "source": [
-    "### Graph L — per-region vertex counts\n",
-    "\n",
-    "How the mask budget splits by geometric rule (pre-carveout) and after the\n",
-    "safe-area carve-outs."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5867e82e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "labels  = ['below_cap', 'face_region', 'ear_region',\n",
-    "           'union (pre-carve)', 'final mask']\n",
-    "values  = [counts_pre['below_cap'], counts_pre['face_region'],\n",
-    "           counts_pre['ear_region'], counts_pre['all'], int(mask.sum())]\n",
-    "colors  = ['#8a8a8a', '#d62728', '#ff7f0e', '#9467bd', '#2ca02c']\n",
-    "\n",
-    "fig, ax = plt.subplots(figsize=(9, 4.2))\n",
-    "bars = ax.bar(labels, values, color=colors)\n",
-    "for bar, v in zip(bars, values):\n",
-    "    ax.text(bar.get_x() + bar.get_width() / 2, v,\n",
-    "            f'{v:,}', ha='center', va='bottom', fontsize=10)\n",
-    "ax.set_ylabel('vertex count')\n",
-    "ax.set_title(f'Per-region mask counts  (Subject{SUBJECT_NUMBER})')\n",
-    "ax.grid(axis='y', alpha=0.3)\n",
-    "plt.xticks(rotation=15)\n",
-    "plt.tight_layout()\n",
-    "plt.savefig(FIG_DIR / '11_region_counts.pdf', bbox_inches='tight')\n",
-    "plt.savefig(FIG_DIR / '11_region_counts.png', dpi=150, bbox_inches='tight')\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "891e97d6",
-   "metadata": {},
-   "source": [
-    "## 4. Deletion and before/after\n",
-    "\n",
-    "Once the mask is finalized, `delete_masked_vertices` drops every triangle\n",
-    "touching a masked vertex and strips the now-unreferenced vertices. No\n",
-    "smoothing, no Taubin pass (see Section 3.6)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a92916d0",
-   "metadata": {},
-   "source": [
-    "### Frame M — anonymized mesh"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f73f0bb9",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "surface_anon = delete_masked_vertices(surface_h, mask)\n",
-    "n_removed = surface_h.nvertices - surface_anon.nvertices\n",
-    "print(f'Original:    {surface_h.nvertices:,} verts')\n",
-    "print(f'Anonymized:  {surface_anon.nvertices:,} verts  (-{n_removed:,})')\n",
-    "\n",
-    "pvplt = pv.Plotter()\n",
-    "anon_vtk = trimesh_to_vtk_polydata(surface_anon.mesh)\n",
-    "pvplt.add_mesh(pv.wrap(anon_vtk), rgb=True, smooth_shading=True)\n",
-    "for lbl, pos in zip(['Nz','Iz','Cz','Lpa','Rpa'], [Nz, Iz, Cz, Lpa, Rpa]):\n",
-    "    pvplt.add_mesh(pv.Sphere(radius=5, center=pos), color=LM_COLORS[lbl])\n",
-    "pvplt.add_text(\n",
-    "    f'Anonymized  ({surface_anon.nvertices:,} verts, -{n_removed:,})',\n",
-    "    position='upper_left', font_size=12,\n",
-    ")\n",
-    "pvplt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1cdd4758",
-   "metadata": {},
-   "source": [
-    "### Frame N — before / after (linked views)\n",
-    "\n",
-    "Rotate either panel; both cameras move together."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "72fe08d6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pvplt = pv.Plotter(shape=(1, 2))\n",
-    "\n",
-    "pvplt.subplot(0, 0)\n",
-    "orig_vtk = trimesh_to_vtk_polydata(surface_h.mesh)\n",
-    "pvplt.add_mesh(pv.wrap(orig_vtk), rgb=True, smooth_shading=True)\n",
-    "for lbl, pos in zip(['Nz','Iz','Cz','Lpa','Rpa'], [Nz, Iz, Cz, Lpa, Rpa]):\n",
-    "    pvplt.add_mesh(pv.Sphere(radius=5, center=pos), color=LM_COLORS[lbl])\n",
-    "pvplt.add_text(f'Subject{SUBJECT_NUMBER} original',\n",
-    "               position='upper_left', font_size=12)\n",
-    "\n",
-    "pvplt.subplot(0, 1)\n",
-    "anon_vtk = trimesh_to_vtk_polydata(surface_anon.mesh)\n",
-    "pvplt.add_mesh(pv.wrap(anon_vtk), rgb=True, smooth_shading=True)\n",
-    "for lbl, pos in zip(['Nz','Iz','Cz','Lpa','Rpa'], [Nz, Iz, Cz, Lpa, Rpa]):\n",
-    "    pvplt.add_mesh(pv.Sphere(radius=5, center=pos), color=LM_COLORS[lbl])\n",
-    "pvplt.add_text(f'Anonymized  (-{n_removed:,} verts)',\n",
-    "               position='upper_left', font_size=12)\n",
-    "\n",
-    "pvplt.link_views()\n",
-    "pvplt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "205f5c45",
-   "metadata": {},
-   "source": [
-    "## 5. Validation\n",
-    "\n",
-    "Sanity-check the anonymized mesh against the original."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "95382e8a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "result = validate_anonymization(\n",
-    "    original_surface=surface_h,\n",
-    "    anonymized_surface=surface_anon,\n",
-    "    facial_mask=mask,\n",
-    "    protected_points=landmarks_ctf,\n",
-    "    tolerance=5.0 * cedalion.units.mm,\n",
-    ")\n",
-    "print(result)\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.14"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/__init__.py b/src/cedalion/geometry/photogrammetry/anonymization/__init__.py
index b007915f..39916173 100644
--- a/src/cedalion/geometry/photogrammetry/anonymization/__init__.py
+++ b/src/cedalion/geometry/photogrammetry/anonymization/__init__.py
@@ -8,7 +8,7 @@
 The pipeline works as follows:
 
 1. User picks the 5 landmarks (Nz, Iz, Cz, LPA, RPA) via the upstream
-   ``cedalion.plots.plot_surface(pick_landmarks=True)`` picker.
+   ``cedalion.vis.blocks.plot_surface(pick_landmarks=True)`` picker.
 2. Normalize axes so Y=anterior, Z=left (``preprocessing``).
 3. Isolate the head and remove disconnected fragments (``preprocessing``).
 4. Align the full frame from the 5 landmarks (``preprocessing``).
@@ -54,7 +54,6 @@
     delete_masked_vertices,
     save_anonymized_scan,
 )
-from .validator import validate_anonymization
 
 
 __all__ = [
@@ -71,6 +70,4 @@
     "face_mask_from_landmarks",
     "delete_masked_vertices",
     "save_anonymized_scan",
-    # Validation
-    "validate_anonymization",
 ]
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/landmarks.py b/src/cedalion/geometry/photogrammetry/anonymization/landmarks.py
index 43595827..2351da8c 100644
--- a/src/cedalion/geometry/photogrammetry/anonymization/landmarks.py
+++ b/src/cedalion/geometry/photogrammetry/anonymization/landmarks.py
@@ -25,7 +25,7 @@
 def detect_landmarks_from_nasion(
     surface: cdc.TrimeshSurface,
     nz_position: np.ndarray,
-) -> cdt.LabeledPointCloud:
+) -> cdt.LabeledPoints:
     """Detect anatomical landmarks from a user-provided nasion (Nz) position.
 
     **Important:** The surface must be axis-normalized before calling this
@@ -45,7 +45,7 @@ def detect_landmarks_from_nasion(
         nz_position: Nasion position as numpy array of shape (3,), in mm
 
     Returns:
-        LabeledPointCloud with landmarks labeled as Nz, Iz, Cz, LPA, RPA
+        LabeledPoints with landmarks labeled as Nz, Iz, Cz, LPA, RPA
 
     Raises:
         ValueError: If landmark configuration fails validation
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/mask.py b/src/cedalion/geometry/photogrammetry/anonymization/mask.py
index 5c501d57..69b93a04 100644
--- a/src/cedalion/geometry/photogrammetry/anonymization/mask.py
+++ b/src/cedalion/geometry/photogrammetry/anonymization/mask.py
@@ -32,8 +32,10 @@ def detect_cap_boundary(
     ear_mid: np.ndarray,
     mid_y: float,
     band_width: float = 15.0,
-    bin_size: float = 2.0,
+    bin_size: float = 1.0,
     foot_grad_threshold: float = 0.2,
+    cap_z_ceiling_mm: float = 40.0,
+    eyebrow_offset_mm: float = 10.0,
 ) -> tuple[float, np.ndarray, np.ndarray, np.ndarray]:
     """Find the Z height where the EEG cap front edge sits.
 
@@ -43,6 +45,13 @@ def detect_cap_boundary(
     walk back from the peak until the smoothed gradient drops below
     ``foot_grad_threshold``.
 
+    Failsafe for flush / no-optode caps: when the cap sits flat against
+    the head (no optodes), the anterior bump vanishes and the X-max
+    profile is roughly monotonic up to ``Cz``, leaving ``cap_z`` stuck
+    near the crown. If the detected ``cap_z`` exceeds
+    ``Nz[2] + cap_z_ceiling_mm``, fall back to a fixed cut at
+    ``Nz[2] + eyebrow_offset_mm`` (just above the supraorbital ridge).
+
     Expects the CTF frame (+X=anterior, +Y=left, +Z=up).
 
     Args:
@@ -54,6 +63,10 @@ def detect_cap_boundary(
         band_width: Y-band half-width for the midline X-profile (mm).
         bin_size: Z-bin size for the X-profile (mm).
         foot_grad_threshold: dX/dZ below this value marks the foot of the rise.
+        cap_z_ceiling_mm: Absolute mm above Nz at which the cap-peak
+            detection is considered untrustworthy and the failsafe fires.
+        eyebrow_offset_mm: Failsafe cut height, expressed as mm above Nz.
+            Anatomically just above the supraorbital ridge.
 
     Returns:
         Tuple of (cap_z, profile_z, profile_x_raw, profile_x_smooth).
@@ -94,6 +107,16 @@ def detect_cap_boundary(
             cap_z = zv[i]
             break
 
+    ceiling = Nz[2] + cap_z_ceiling_mm
+    if cap_z > ceiling:
+        fallback = Nz[2] + eyebrow_offset_mm
+        logger.info(
+            f"detect_cap_boundary: cap_z={cap_z:.1f} mm exceeded ceiling "
+            f"Nz+{cap_z_ceiling_mm:.0f}={ceiling:.1f}; assuming flush cap "
+            f"and falling back to Nz+{eyebrow_offset_mm:.0f}={fallback:.1f} mm."
+        )
+        cap_z = fallback
+
     return cap_z, zv, xv, xv_s
 
 
@@ -211,84 +234,110 @@ def delete_masked_vertices(
     )
 
 
-def _bake_vertex_colors(mesh) -> np.ndarray:
-    """Sample the texture image at each vertex's UV to produce per-vertex RGBA.
+def _sanitize_texture_from_uv(
+    mesh,
+    fill: tuple[int, int, int] = (0, 0, 0),
+) -> "Image.Image | None":
+    """Rebuild the texture image keeping only pixels referenced by mesh UVs.
+
+    Rasterizes every surviving face's UV triangle onto a keep-mask the size
+    of the source image. Pixels outside the mask are set to ``fill`` so
+    face-region pixels (no longer referenced after anonymization) cannot leak
+    through the JPG. The mask is dilated by 1 pixel to absorb anti-aliasing
+    fringes at triangle boundaries.
 
     OBJ UVs use the bottom-left origin convention; PIL images use top-left,
-    so V is flipped before indexing. UVs outside ``[0, 1]`` are wrapped
-    (modulo 1) to match typical OBJ sampler behavior.
+    so V is flipped before rasterizing.
 
     Args:
         mesh: A ``trimesh.Trimesh`` with ``mesh.visual`` as ``TextureVisuals``.
+        fill: RGB fill color for unreferenced pixels.
 
     Returns:
-        ``(n_vertices, 4)`` uint8 RGBA array. Falls back to uniform mid-grey
-        (with a warning) if the mesh has no usable texture.
+        Sanitized PIL Image in RGB mode, or ``None`` if the mesh has no
+        usable texture (caller should fall back to geometry-only output).
     """
+    from PIL import Image, ImageDraw, ImageFilter
+
     visual = mesh.visual
     uv = getattr(visual, "uv", None)
     image = getattr(visual, "image", None)
     if image is None:
         mat = getattr(visual, "material", None)
         image = getattr(mat, "image", None) if mat is not None else None
-    n_verts = len(mesh.vertices)
 
-    fallback = np.full((n_verts, 4), 180, dtype=np.uint8)
-    fallback[:, 3] = 255
+    if uv is None or image is None or len(uv) != len(mesh.vertices):
+        return None
 
-    if uv is None or image is None or len(uv) != n_verts:
-        logger.warning(
-            "with_color=True requested but mesh has no usable texture; "
-            "falling back to uniform grey vertex colors."
-        )
-        return fallback
+    img_rgb = np.asarray(image.convert("RGB"))
+    h, w = img_rgb.shape[:2]
 
-    img = np.asarray(image.convert("RGBA"))
-    h, w = img.shape[:2]
+    uv = np.asarray(uv)
+    px = np.mod(uv[:, 0], 1.0) * w
+    py = (1.0 - np.mod(uv[:, 1], 1.0)) * h
 
-    u = np.mod(np.asarray(uv)[:, 0], 1.0)
-    v = np.mod(np.asarray(uv)[:, 1], 1.0)
-    px = np.clip((u * w).astype(np.int64), 0, w - 1)
-    py = np.clip(((1.0 - v) * h).astype(np.int64), 0, h - 1)
+    mask_img = Image.new("L", (w, h), 0)
+    draw = ImageDraw.Draw(mask_img)
+    for face in np.asarray(mesh.faces):
+        pts = [(px[i], py[i]) for i in face]
+        draw.polygon(pts, fill=255)
+    mask_img = mask_img.filter(ImageFilter.MaxFilter(3))
+    keep = np.asarray(mask_img) > 0
 
-    return img[py, px]
+    out = np.empty_like(img_rgb)
+    out[keep] = img_rgb[keep]
+    out[~keep] = fill
+    return Image.fromarray(out, mode="RGB")
+
+
+_MTL_TEMPLATE = (
+    "newmtl _texture\n"
+    "Kd 1.00 1.00 1.00\n"
+    "Ka 0.00 0.00 0.00\n"
+    "Tf 1.00 1.00 1.00\n"
+    "Ni 1.00\n"
+    "map_Kd {jpg_name}\n"
+)
 
 
 def save_anonymized_scan(
     surface: cdc.TrimeshSurface,
     out_path: str,
     landmarks: "xr.DataArray | None" = None,
-    with_color: bool = False,
+    strip_texture: bool = False,
 ) -> list[str]:
     """Export an anonymized photogrammetry surface to disk.
 
-    Always writes only the ``.obj`` file -- no MTL, no JPG. The original
-    texture raster is never written alongside the mesh, so the subject's face
-    pixels cannot leak through a saved image.
+    Default path (``strip_texture=False``): writes an ``.obj`` + ``.mtl`` +
+    sanitized ``.jpg`` bundle. The JPG is rebuilt from the original texture
+    so it contains colors *only* for UV regions still referenced by the
+    anonymized mesh; every other pixel (notably the face region) is set to
+    the fill color. Opening the JPG alone therefore reveals no face pixels.
+
+    ``strip_texture=True`` is an explicit opt-out that writes only the
+    geometry ``.obj`` (no MTL, no JPG).
 
-    - ``with_color=False`` (default): strip the texture entirely. Smallest
-      file, geometry only.
-    - ``with_color=True``: sample the texture at each kept vertex's UV and
-      bake the result as per-vertex colors inline in the ``.obj``. The JPG
-      is consumed in-memory and discarded; no raster is written.
+    If the input mesh has no usable texture (no UVs or no image), falls
+    back to geometry-only output with a warning.
 
     If ``landmarks`` is provided, also writes ``{stem}_landmarks.tsv`` next to
-    the ``.obj`` using ``cedalion.io.export_to_tsv``. The resulting TSV is the
-    file that gets uploaded at step 5.2 of the cedalion photogrammetry
+    the ``.obj`` using ``cedalion.io.export_to_tsv``. The resulting TSV is
+    the file that gets uploaded at step 5.2 of the cedalion photogrammetry
     co-registration tutorial.
 
     Args:
         surface: Anonymized TrimeshSurface (typically output of
             ``delete_masked_vertices``).
         out_path: Destination path ending in ``.obj``.
-        landmarks: Optional LabeledPointCloud of landmarks (Nz, Iz, LPA, RPA,
+        landmarks: Optional LabeledPoints of landmarks (Nz, Iz, LPA, RPA,
             Cz) to persist alongside the mesh as a TSV. Should be in the same
             frame as ``surface``.
-        with_color: If True, bake per-vertex colors from the texture.
+        strip_texture: If True, skip the MTL + JPG and write geometry only.
 
     Returns:
-        List of absolute paths written (``.obj`` plus ``_landmarks.tsv`` when
-        ``landmarks`` is given).
+        List of absolute paths written (``.obj`` plus ``.mtl`` + ``.jpg``
+        when a texture was written, plus ``_landmarks.tsv`` when landmarks
+        were given).
 
     Raises:
         ValueError: If ``out_path`` does not end in ``.obj``.
@@ -299,28 +348,52 @@ def save_anonymized_scan(
     if not out_path.lower().endswith(".obj"):
         raise ValueError(f"out_path must end in .obj, got: {out_path}")
 
-    vcolors = _bake_vertex_colors(surface.mesh) if with_color else None
+    out_dir = os.path.dirname(os.path.abspath(out_path)) or "."
+    stem = os.path.splitext(os.path.basename(out_path))[0]
+
+    sanitized = None if strip_texture else _sanitize_texture_from_uv(surface.mesh)
 
     mesh_to_write = trimesh.Trimesh(
         vertices=np.asarray(surface.mesh.vertices),
         faces=np.asarray(surface.mesh.faces),
         process=False,
     )
-    if vcolors is not None:
-        mesh_to_write.visual.vertex_colors = vcolors
 
-    out_dir = os.path.dirname(os.path.abspath(out_path)) or "."
-    before = set(os.listdir(out_dir)) if os.path.isdir(out_dir) else set()
+    written: list[str] = []
 
-    mesh_to_write.export(out_path)
+    if sanitized is not None:
+        jpg_name = f"{stem}.jpg"
+        mtl_name = f"{stem}.mtl"
+        jpg_path = os.path.join(out_dir, jpg_name)
+        mtl_path = os.path.join(out_dir, mtl_name)
 
-    after = set(os.listdir(out_dir)) if os.path.isdir(out_dir) else set()
-    stem = os.path.splitext(os.path.basename(out_path))[0]
-    written = sorted(
-        os.path.join(out_dir, f)
-        for f in (after - before) | {os.path.basename(out_path)}
-        if os.path.splitext(f)[0] == stem or f == os.path.basename(out_path)
-    )
+        sanitized.save(jpg_path, format="JPEG", quality=92)
+        with open(mtl_path, "w") as fh:
+            fh.write(_MTL_TEMPLATE.format(jpg_name=jpg_name))
+
+        mesh_to_write.visual = trimesh.visual.TextureVisuals(
+            uv=np.asarray(surface.mesh.visual.uv),
+            image=sanitized,
+        )
+        obj_text = trimesh.exchange.obj.export_obj(
+            mesh_to_write,
+            include_texture=True,
+            write_texture=False,
+            mtl_name=mtl_name,
+        )
+        obj_text = _ensure_mtllib(obj_text, mtl_name)
+        with open(out_path, "w") as fh:
+            fh.write(obj_text)
+
+        written.extend([out_path, mtl_path, jpg_path])
+    else:
+        if not strip_texture:
+            logger.warning(
+                "save_anonymized_scan: input mesh has no usable texture; "
+                "falling back to geometry-only OBJ."
+            )
+        mesh_to_write.export(out_path)
+        written.append(out_path)
 
     if landmarks is not None:
         from cedalion.io import export_to_tsv
@@ -335,7 +408,19 @@ def save_anonymized_scan(
         )
 
     logger.info(
-        f"Saved anonymized scan (with_color={with_color}): "
+        f"Saved anonymized scan: "
         f"{[os.path.basename(p) for p in written]}"
     )
-    return written
+    return sorted(written)
+
+
+def _ensure_mtllib(obj_text: str, mtl_name: str) -> str:
+    """Guarantee the OBJ references our MTL via ``mtllib`` and ``usemtl``.
+
+    Trimesh's OBJ exporter may or may not emit material lines depending on
+    version; rewrite deterministically so the MTL pairing is stable.
+    """
+    lines = obj_text.splitlines()
+    lines = [l for l in lines if not l.startswith(("mtllib ", "usemtl "))]
+    header = [f"mtllib {mtl_name}", "usemtl _texture"]
+    return "\n".join(header + lines) + "\n"
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/preprocessing.py b/src/cedalion/geometry/photogrammetry/anonymization/preprocessing.py
index 67f82d28..f0b59bd0 100644
--- a/src/cedalion/geometry/photogrammetry/anonymization/preprocessing.py
+++ b/src/cedalion/geometry/photogrammetry/anonymization/preprocessing.py
@@ -268,8 +268,8 @@ def isolate_head(
 
 def align_axes_from_landmarks(
     surface: cdc.TrimeshSurface,
-    landmarks: cdt.LabeledPointCloud,
-) -> tuple[cdc.TrimeshSurface, cdt.LabeledPointCloud, np.ndarray]:
+    landmarks: cdt.LabeledPoints,
+) -> tuple[cdc.TrimeshSurface, cdt.LabeledPoints, np.ndarray]:
     """Map mesh + landmarks into the CTF anatomical frame.
 
     CTF convention:
@@ -284,7 +284,9 @@ def align_axes_from_landmarks(
     Args:
         surface: Axis-normalized TrimeshSurface (post ``normalize_axes`` and
             ``isolate_head``).
-        landmarks: LabeledPointCloud with labels Nz, Iz, Cz, Lpa, Rpa
+        landmarks: LabeledPoints with labels Nz, Iz, Cz, and LPA/RPA (or
+            aliases like Lpa/Rpa, "left ear"/"right ear"; normalized
+            internally via ``normalize_landmarks_labels``)
             (matching the surface frame).
 
     Returns:
@@ -293,20 +295,22 @@ def align_axes_from_landmarks(
         points into CTF; apply as ``hom = transform @ [x, y, z, 1]``.
     """
     import trimesh
+    from cedalion.geometry.landmarks import normalize_landmarks_labels
 
+    landmarks = normalize_landmarks_labels(landmarks)
     lm = landmarks.pint.dequantify().values
     labels = list(landmarks["label"].values)
     idx = {lbl: i for i, lbl in enumerate(labels)}
 
-    required = {"Nz", "Iz", "Cz", "Lpa", "Rpa"}
+    required = {"Nz", "Iz", "Cz", "LPA", "RPA"}
     missing = required - set(labels)
     if missing:
         raise ValueError(f"Missing landmarks for alignment: {missing}")
 
     Nz = lm[idx["Nz"]]
     Cz = lm[idx["Cz"]]
-    Lpa = lm[idx["Lpa"]]
-    Rpa = lm[idx["Rpa"]]
+    Lpa = lm[idx["LPA"]]
+    Rpa = lm[idx["RPA"]]
     origin = 0.5 * (Lpa + Rpa)
 
     y_ax = Lpa - Rpa
@@ -354,10 +358,10 @@ def align_axes_from_landmarks(
 
 def revert_to_einstar_frame(
     surface: cdc.TrimeshSurface,
-    landmarks: cdt.LabeledPointCloud,
+    landmarks: cdt.LabeledPoints,
     R_normalize: np.ndarray,
     M_align: np.ndarray,
-) -> tuple[cdc.TrimeshSurface, cdt.LabeledPointCloud]:
+) -> tuple[cdc.TrimeshSurface, cdt.LabeledPoints]:
     """Map an aligned surface and landmarks back into the raw Einstar frame.
 
     Inverse of the ``normalize_axes`` then ``align_axes_from_landmarks``
@@ -374,7 +378,7 @@ def revert_to_einstar_frame(
     Args:
         surface: TrimeshSurface in the CTF frame (post
             ``align_axes_from_landmarks``, optionally after masking).
-        landmarks: LabeledPointCloud in the CTF frame.
+        landmarks: LabeledPoints in the CTF frame.
         R_normalize: 3x3 rotation returned by ``normalize_axes``.
         M_align: 4x4 affine returned by ``align_axes_from_landmarks``.
 
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/validator.py b/src/cedalion/geometry/photogrammetry/anonymization/validator.py
deleted file mode 100644
index 030bce88..00000000
--- a/src/cedalion/geometry/photogrammetry/anonymization/validator.py
+++ /dev/null
@@ -1,219 +0,0 @@
-"""Post-anonymization sanity checks.
-
-Validates that anonymization completed correctly: the expected facial region
-was removed, the mesh is still valid, and protected points (optodes) remain
-on the surface.
-
-Initial Contributors:
-    - Face Anonymization Project | 2024
-"""
-
-from dataclasses import dataclass
-import logging
-
-import numpy as np
-from scipy.spatial import KDTree
-
-import cedalion.dataclasses as cdc
-import cedalion.typing as cdt
-from cedalion import Quantity, units
-
-
-logger = logging.getLogger("cedalion")
-
-
-@dataclass
-class ValidationResult:
-    """Result of post-anonymization sanity check.
-
-    Attributes:
-        face_removed: True if vertex count dropped as expected
-        mesh_valid: True if anonymized mesh has no degenerate faces
-        expected_vertices_removed: Number of facial mask vertices
-        actual_vertices_removed: Actual vertex count difference
-        protected_points_intact: True if all protected points are within
-            tolerance of the anonymized surface
-        protected_point_max_deviation: Worst-case deviation of any protected
-            point from the anonymized surface (mm)
-        face_coverage_pct: Percentage of original vertices in the facial mask
-        passed: True if all checks passed
-        summary: Human-readable one-line summary
-    """
-
-    face_removed: bool
-    mesh_valid: bool
-    expected_vertices_removed: int
-    actual_vertices_removed: int
-    protected_points_intact: bool
-    protected_point_max_deviation: float
-    face_coverage_pct: float
-    passed: bool
-    summary: str
-
-
-def _check_mesh_valid(mesh) -> bool:
-    """Check that a trimesh mesh has no degenerate faces.
-
-    Args:
-        mesh: A trimesh.Trimesh object
-
-    Returns:
-        True if mesh is valid (has faces, <1% degenerate)
-    """
-    if len(mesh.faces) == 0:
-        return False
-
-    # Check for degenerate faces (zero area)
-    # Photogrammetry meshes commonly have a few thin triangles — only
-    # fail if more than 1% of faces are degenerate.
-    areas = mesh.area_faces
-    n_degenerate = int(np.sum(areas < 1e-12))
-    if n_degenerate > 0:
-        pct = 100.0 * n_degenerate / len(mesh.faces)
-        if pct > 1.0:
-            logger.warning(
-                f"Mesh has {n_degenerate} degenerate faces ({pct:.2f}%)"
-            )
-            return False
-        else:
-            logger.info(
-                f"Mesh has {n_degenerate} degenerate faces ({pct:.4f}%) "
-                f"— within tolerance for photogrammetry scans"
-            )
-
-    return True
-
-
-def _check_protected_points(
-    anonymized_surface: cdc.TrimeshSurface,
-    protected_points: cdt.LabeledPointCloud,
-    tolerance_mm: float,
-) -> tuple[bool, float]:
-    """Check that all protected points have a nearby vertex on the surface.
-
-    Args:
-        anonymized_surface: Mesh after anonymization
-        protected_points: Points that should still be on the surface
-        tolerance_mm: Maximum allowed distance from surface (mm)
-
-    Returns:
-        Tuple of (all_within_tolerance, max_deviation_mm)
-    """
-    if protected_points is None or len(protected_points.label) == 0:
-        return True, 0.0
-
-    positions = protected_points.pint.dequantify().values
-    tree = KDTree(anonymized_surface.mesh.vertices)
-    distances, _ = tree.query(positions)
-
-    max_dev = float(np.max(distances))
-    all_ok = max_dev <= tolerance_mm
-
-    if not all_ok:
-        labels = [str(l) for l in protected_points.label.values]
-        for i, (label, dist) in enumerate(zip(labels, distances)):
-            if dist > tolerance_mm:
-                logger.warning(
-                    f"Protected point '{label}' is {dist:.2f}mm from "
-                    f"anonymized surface (tolerance: {tolerance_mm}mm)"
-                )
-
-    return all_ok, max_dev
-
-
-@cdc.validate_schemas
-def validate_anonymization(
-    original_surface: cdc.TrimeshSurface,
-    anonymized_surface: cdc.TrimeshSurface,
-    facial_mask: np.ndarray,
-    protected_points: cdt.LabeledPointCloud = None,
-    tolerance: Quantity = 1.0 * units.mm,
-) -> ValidationResult:
-    """Run post-anonymization sanity checks.
-
-    Verifies that:
-    1. The facial region was actually removed (vertex count dropped)
-    2. The remaining mesh is valid (no degenerate faces)
-    3. Protected points (optodes, landmarks) are still on the surface
-
-    This is a practical bug-catcher, not a scientific validation metric.
-    For scientific validation of fNIRS utility preservation, compare
-    10-10 system positions on original vs anonymized surfaces.
-
-    Args:
-        original_surface: Original mesh before anonymization
-        anonymized_surface: Mesh after anonymization
-        facial_mask: Boolean mask of facial vertices on the original mesh
-        protected_points: Landmarks and/or optodes that should still be
-            reachable on the anonymized surface (optional)
-        tolerance: Maximum allowed distance from protected points to the
-            anonymized surface
-
-    Returns:
-        ValidationResult with pass/fail and diagnostic info
-    """
-    tolerance_mm = float(tolerance.to("mm").magnitude)
-
-    # Check 1: Face removed — vertex count dropped
-    orig_count = len(original_surface.mesh.vertices)
-    anon_count = len(anonymized_surface.mesh.vertices)
-    expected_removed = int(facial_mask.sum())
-    actual_removed = orig_count - anon_count
-    face_coverage_pct = 100.0 * expected_removed / orig_count if orig_count > 0 else 0.0
-
-    # Allow some tolerance: boundary vertices may also be removed
-    face_removed = actual_removed >= expected_removed * 0.9
-
-    if not face_removed:
-        logger.warning(
-            f"Expected ~{expected_removed} vertices removed, "
-            f"but only {actual_removed} were removed"
-        )
-
-    # Check 2: Mesh valid
-    mesh_valid = _check_mesh_valid(anonymized_surface.mesh)
-
-    # Check 3: Protected points still on surface
-    points_intact, max_dev = _check_protected_points(
-        anonymized_surface, protected_points, tolerance_mm
-    )
-
-    # Overall pass/fail
-    passed = face_removed and mesh_valid and points_intact
-
-    # Build summary
-    if passed:
-        summary = (
-            f"PASSED — {actual_removed:,} vertices removed "
-            f"({face_coverage_pct:.1f}%), mesh valid, "
-            f"protected points within {tolerance_mm}mm"
-        )
-    else:
-        issues = []
-        if not face_removed:
-            issues.append(
-                f"vertex removal mismatch "
-                f"(expected ~{expected_removed}, got {actual_removed})"
-            )
-        if not mesh_valid:
-            issues.append("mesh has degenerate faces")
-        if not points_intact:
-            issues.append(
-                f"protected point {max_dev:.2f}mm from surface "
-                f"(tolerance: {tolerance_mm}mm)"
-            )
-        summary = f"FAILED — {'; '.join(issues)}"
-
-    logger.info(f"Anonymization validation: {summary}")
-
-    return ValidationResult(
-        face_removed=face_removed,
-        mesh_valid=mesh_valid,
-        expected_vertices_removed=expected_removed,
-        actual_vertices_removed=actual_removed,
-        protected_points_intact=points_intact,
-        protected_point_max_deviation=max_dev,
-        face_coverage_pct=face_coverage_pct,
-        passed=passed,
-        summary=summary,
-    )

From 5dd37c2192799c53a2cfd8f93d16df8d2e7ce17b Mon Sep 17 00:00:00 2001
From: mah-diaa <mahmoud.diaa.abdeen@gmail.com>
Date: Sun, 3 May 2026 09:29:03 +0200
Subject: [PATCH 19/39] Remove geometric Nz-fallback landmark detection (kept
 on auto-detection-pipeline branch)

---
 .../photogrammetry/anonymization/__init__.py  |   9 +-
 .../photogrammetry/anonymization/landmarks.py | 195 ------------------
 2 files changed, 2 insertions(+), 202 deletions(-)
 delete mode 100644 src/cedalion/geometry/photogrammetry/anonymization/landmarks.py

diff --git a/src/cedalion/geometry/photogrammetry/anonymization/__init__.py b/src/cedalion/geometry/photogrammetry/anonymization/__init__.py
index 39916173..b734f552 100644
--- a/src/cedalion/geometry/photogrammetry/anonymization/__init__.py
+++ b/src/cedalion/geometry/photogrammetry/anonymization/__init__.py
@@ -22,10 +22,8 @@
    ``save_anonymized_scan(surface, out_path, landmarks=...)``. The TSV is
    what the co-registration tutorial loads at step 5.2.
 
-``detect_landmarks_from_nasion`` (``landmarks``) is an alternative that takes
-just the nasion and geometrically infers the other four. An alternative
-MediaPipe-based automatic pipeline lives on the ``auto-detection-pipeline``
-branch.
+An alternative MediaPipe-based automatic detection pipeline lives on the
+``auto-detection-pipeline`` branch.
 
 Example:
     >>> from cedalion.geometry.photogrammetry.anonymization import (
@@ -47,7 +45,6 @@
     align_axes_from_landmarks,
     revert_to_einstar_frame,
 )
-from .landmarks import detect_landmarks_from_nasion
 from .mask import (
     detect_cap_boundary,
     face_mask_from_landmarks,
@@ -63,8 +60,6 @@
     "isolate_head",
     "align_axes_from_landmarks",
     "revert_to_einstar_frame",
-    # Landmark detection
-    "detect_landmarks_from_nasion",
     # Mask construction and application
     "detect_cap_boundary",
     "face_mask_from_landmarks",
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/landmarks.py b/src/cedalion/geometry/photogrammetry/anonymization/landmarks.py
deleted file mode 100644
index 2351da8c..00000000
--- a/src/cedalion/geometry/photogrammetry/anonymization/landmarks.py
+++ /dev/null
@@ -1,195 +0,0 @@
-"""Landmark detection for photogrammetry scans.
-
-Given a user-provided nasion (Nz) on an axis-normalized mesh, detects the
-remaining four anatomical landmarks (Iz, Cz, LPA, RPA) geometrically:
-
-- Cz is the highest vertex near the midline.
-- Iz is the most posterior vertex at nasion height.
-- LPA/RPA are geometric ear targets snapped to the nearest surface vertex.
-
-Initial Contributors:
-    - Face Anonymization Project | 2024
-"""
-
-import logging
-
-import numpy as np
-import xarray as xr
-
-import cedalion.dataclasses as cdc
-import cedalion.typing as cdt
-
-logger = logging.getLogger("cedalion")
-
-
-def detect_landmarks_from_nasion(
-    surface: cdc.TrimeshSurface,
-    nz_position: np.ndarray,
-) -> cdt.LabeledPoints:
-    """Detect anatomical landmarks from a user-provided nasion (Nz) position.
-
-    **Important:** The surface must be axis-normalized before calling this
-    function. Use ``normalize_axes()`` first so that:
-    - X = up (vertical, from Einstar gravity sensor)
-    - Y = anterior (toward face, from nasion direction)
-    - Z = left of subject
-
-    Given the Nz point, automatically detects Iz, LPA, RPA, Cz:
-    - Cz: Highest vertex (max X) near midline
-    - Iz: Most posterior vertex (min Y) at nasion height
-    - LPA: Most left vertex (max Z) at ear height
-    - RPA: Most right vertex (min Z) at ear height
-
-    Args:
-        surface: Axis-normalized TrimeshSurface (Y=anterior, Z=left)
-        nz_position: Nasion position as numpy array of shape (3,), in mm
-
-    Returns:
-        LabeledPoints with landmarks labeled as Nz, Iz, Cz, LPA, RPA
-
-    Raises:
-        ValueError: If landmark configuration fails validation
-
-    Example:
-        >>> surface_norm, nz_norm, R = normalize_axes(surface, nz, fwd)
-        >>> landmarks = detect_landmarks_from_nasion(surface_norm, nz_norm)
-    """
-    nz_position = np.asarray(nz_position, dtype=float)
-    vertices = surface.mesh.vertices
-
-    centroid = vertices.mean(axis=0)
-    head_height = vertices[:, 0].max() - vertices[:, 0].min()
-    head_verts = vertices
-
-    logger.debug(
-        f"Detecting landmarks from Nz={nz_position}, "
-        f"head_height={head_height:.1f}mm, "
-        f"head_verts={len(vertices)}"
-    )
-
-    # Cz: highest vertex (max X) near midline
-    band = 0.20 * head_height
-    lateral_mask = (
-        (np.abs(head_verts[:, 1] - centroid[1]) < band)
-        & (np.abs(head_verts[:, 2] - centroid[2]) < band)
-    )
-    if lateral_mask.sum() == 0:
-        lateral_mask = np.ones(len(head_verts), dtype=bool)
-    cz_idx = np.where(lateral_mask)[0][np.argmax(head_verts[lateral_mask, 0])]
-    cz = head_verts[cz_idx]
-
-    # Iz: most posterior (min Y) at nasion height, on the midsagittal plane.
-    iz_height_mask = np.abs(head_verts[:, 0] - nz_position[0]) < 20.0
-    iz_midline_mask = np.abs(head_verts[:, 2] - nz_position[2]) < 25.0
-    iz_mask = iz_height_mask & iz_midline_mask
-    if iz_mask.sum() == 0:
-        iz_mask = np.abs(head_verts[:, 0] - nz_position[0]) < 40.0
-    iz_cands = np.where(iz_mask)[0]
-    iz_idx = iz_cands[np.argmin(head_verts[iz_cands, 1])]
-    iz = head_verts[iz_idx]
-
-    # LPA/RPA: geometric targets, refined by snapping to surface
-    from scipy.spatial import cKDTree
-
-    midline_z = np.mean([nz_position[2], iz[2], cz[2]])
-    clean_band = (
-        (head_verts[:, 0] > nz_position[0] + 10.0)
-        & (head_verts[:, 0] < cz[0] - 20.0)
-    )
-    if clean_band.sum() > 100:
-        clean_z = head_verts[clean_band, 2]
-        half_width = (
-            np.percentile(clean_z, 97) - np.percentile(clean_z, 3)
-        ) / 2.0
-    else:
-        half_width = 75.0
-
-    geo_lpa_target = np.array([nz_position[0], cz[1], midline_z + half_width])
-    geo_rpa_target = np.array([nz_position[0], cz[1], midline_z - half_width])
-
-    def _snap_ear(target):
-        ear_region = (
-            (np.abs(head_verts[:, 0] - nz_position[0]) < 30.0)
-            & (np.abs(head_verts[:, 1] - cz[1]) < 40.0)
-        )
-        if ear_region.sum() > 10:
-            idxs = np.where(ear_region)[0]
-            tree = cKDTree(head_verts[idxs])
-            _, local = tree.query(target)
-            return head_verts[idxs[local]]
-        tree = cKDTree(head_verts)
-        _, idx = tree.query(target)
-        return head_verts[idx]
-
-    lpa = _snap_ear(geo_lpa_target)
-    rpa = _snap_ear(geo_rpa_target)
-
-    landmark_positions = {
-        "Nz": nz_position,
-        "Iz": iz,
-        "Cz": cz,
-        "LPA": lpa,
-        "RPA": rpa,
-    }
-
-    logger.debug(f"Detected landmarks: Cz={cz}, Iz={iz}, LPA={lpa}, RPA={rpa}")
-
-    _validate_landmark_configuration(landmark_positions, centroid)
-
-    labels = ["Nz", "Iz", "Cz", "LPA", "RPA"]
-    coords = np.array([landmark_positions[label] for label in labels])
-
-    landmarks = xr.DataArray(
-        coords,
-        dims=["label", surface.crs],
-        coords={
-            "label": labels,
-            "type": ("label", [cdc.PointType.LANDMARK] * 5),
-        },
-    ).pint.quantify("mm")
-
-    return landmarks
-
-
-def _validate_landmark_configuration(
-    landmarks: dict[str, np.ndarray],
-    centroid: np.ndarray,
-) -> None:
-    """Validate that detected landmarks have a plausible spatial configuration.
-
-    Checks:
-    - Cz is the highest point (max X)
-    - Iz is posterior to the centroid (low Y)
-    - LPA and RPA are roughly symmetric about the midline (Z)
-
-    Args:
-        landmarks: Dict mapping landmark name to position array.
-        centroid: Mesh centroid for reference.
-
-    Raises:
-        ValueError: If configuration is implausible.
-    """
-    cz = landmarks["Cz"]
-    iz = landmarks["Iz"]
-    lpa = landmarks["LPA"]
-    rpa = landmarks["RPA"]
-
-    all_x = [landmarks[k][0] for k in landmarks]
-    if cz[0] < max(all_x) - 1.0:
-        logger.warning("Cz is not the highest landmark -- detection may be off")
-
-    if iz[1] > centroid[1]:
-        logger.warning(
-            f"Iz (Y={iz[1]:.1f}) is anterior to centroid (Y={centroid[1]:.1f}) "
-            "-- expected posterior"
-        )
-
-    if lpa[2] < rpa[2]:
-        logger.warning("LPA is to the right of RPA -- landmarks may be swapped")
-
-    lpa_offset = abs(lpa[2] - centroid[2])
-    rpa_offset = abs(rpa[2] - centroid[2])
-    if min(lpa_offset, rpa_offset) > 0 and max(lpa_offset, rpa_offset) / min(lpa_offset, rpa_offset) > 3.0:
-        logger.warning(
-            f"LPA/RPA asymmetry is large (offsets: {lpa_offset:.1f} vs {rpa_offset:.1f}mm)"
-        )

From 713436a8138273a2b6241762030de15b14754894 Mon Sep 17 00:00:00 2001
From: mah-diaa <mahmoud.diaa.abdeen@gmail.com>
Date: Sun, 3 May 2026 09:30:31 +0200
Subject: [PATCH 20/39] Add face anonymization module for photogrammetry scans

---
 docs/CHANGELOG.md                             |   8 +-
 .../photogrammetry/anonymization/__init__.py  |  65 +++
 .../photogrammetry/anonymization/mask.py      | 426 ++++++++++++++++++
 .../anonymization/preprocessing.py            | 420 +++++++++++++++++
 4 files changed, 918 insertions(+), 1 deletion(-)
 create mode 100644 src/cedalion/geometry/photogrammetry/anonymization/__init__.py
 create mode 100644 src/cedalion/geometry/photogrammetry/anonymization/mask.py
 create mode 100644 src/cedalion/geometry/photogrammetry/anonymization/preprocessing.py

diff --git a/docs/CHANGELOG.md b/docs/CHANGELOG.md
index 1dca972b..18431423 100644
--- a/docs/CHANGELOG.md
+++ b/docs/CHANGELOG.md
@@ -1,7 +1,13 @@
 # Changelog
 
 ## Unreleased changes (available on the `dev` branch)
- 
+
+### Additions and Changes
+
+#### Photogrammetry
+
+- Added `cedalion.geometry.photogrammetry.anonymization`, a deletion-based face removal pipeline for photogrammetry scans. From the five anatomical landmarks (Nz, Iz, Cz, LPA, RPA), the pipeline normalizes axes, isolates the head, detects the cap boundary, builds a face mask, deletes the masked vertices, and saves the anonymized mesh together with the landmarks. See `examples/head_models/51_manual_5pt_anonymization.ipynb` for an end-to-end walkthrough.
+
 ## Version 26.04.0 (in preparation)
 
 ### Additions and Changes
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/__init__.py b/src/cedalion/geometry/photogrammetry/anonymization/__init__.py
new file mode 100644
index 00000000..e26a3582
--- /dev/null
+++ b/src/cedalion/geometry/photogrammetry/anonymization/__init__.py
@@ -0,0 +1,65 @@
+"""Face anonymization module for photogrammetry scans.
+
+This module provides tools to anonymize facial regions in 3D photogrammetry
+scans while preserving optode positions and anatomical landmarks for fNIRS
+research. The anonymization complies with GDPR requirements while maintaining
+scientific utility.
+
+The pipeline works as follows:
+
+1. User picks the 5 landmarks (Nz, Iz, Cz, LPA, RPA) via the upstream
+   ``cedalion.vis.blocks.plot_surface(pick_landmarks=True)`` picker.
+2. Normalize axes so Y=anterior, Z=left (``preprocessing``).
+3. Isolate the head and remove disconnected fragments (``preprocessing``).
+4. Align the full frame from the 5 landmarks (``preprocessing``).
+5. Detect the cap boundary, build the deletion mask, and delete the masked
+   vertices (``mask``).
+6. Optionally revert the aligned surface and landmarks back to the raw
+   Einstar frame via ``revert_to_einstar_frame`` so the saved files carry
+   ``crs="digitized"`` and match the co-registration tutorial's input
+   convention (``read_einstar_obj`` output).
+7. Save the anonymized mesh (``.obj``) and the landmarks (``.tsv``) via
+   ``save_anonymized_scan(surface, out_path, landmarks=...)``. The TSV is
+   what the co-registration tutorial loads at step 5.2.
+
+Example:
+    >>> from cedalion.geometry.photogrammetry.anonymization import (
+    ...     normalize_axes, isolate_head, align_axes_from_landmarks,
+    ...     detect_cap_boundary, face_mask_from_landmarks,
+    ...     delete_masked_vertices,
+    ... )
+    >>> surface, nz, R = normalize_axes(surface, landmarks.sel(label='Nz'))
+    >>> surface, _ = isolate_head(surface, nz)
+    >>> surface, landmarks, R = align_axes_from_landmarks(surface, landmarks)
+
+Initial Contributors:
+    - Face Anonymization Project | 2024
+"""
+
+from .preprocessing import (
+    normalize_axes,
+    isolate_head,
+    align_axes_from_landmarks,
+    revert_to_einstar_frame,
+)
+from .mask import (
+    detect_cap_boundary,
+    face_mask_from_landmarks,
+    delete_masked_vertices,
+    save_anonymized_scan,
+)
+
+
+__all__ = [
+    # Preprocessing (axis normalization, head isolation, full alignment,
+    # and the inverse mapping back to the raw Einstar frame)
+    "normalize_axes",
+    "isolate_head",
+    "align_axes_from_landmarks",
+    "revert_to_einstar_frame",
+    # Mask construction and application
+    "detect_cap_boundary",
+    "face_mask_from_landmarks",
+    "delete_masked_vertices",
+    "save_anonymized_scan",
+]
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/mask.py b/src/cedalion/geometry/photogrammetry/anonymization/mask.py
new file mode 100644
index 00000000..69b93a04
--- /dev/null
+++ b/src/cedalion/geometry/photogrammetry/anonymization/mask.py
@@ -0,0 +1,426 @@
+"""Facial-region mask construction and application.
+
+Given the 5 CTF-aligned landmarks, builds a boolean deletion mask and
+applies it to the mesh:
+
+- ``detect_cap_boundary`` finds the Z height where the EEG cap front edge
+  sits so the mask can be clamped below it.
+- ``face_mask_from_landmarks`` unions a forward face region with two ear
+  spheres, clamped below the cap.
+- ``delete_masked_vertices`` drops triangles touching any masked vertex.
+
+All mask math assumes the CTF frame: +X=anterior, +Y=left, +Z=up, origin
+at the LPA-RPA midpoint (see ``align_axes_from_landmarks``).
+
+Initial Contributors:
+    - Face Anonymization Project | 2024
+"""
+
+import logging
+
+import numpy as np
+
+import cedalion.dataclasses as cdc
+
+logger = logging.getLogger("cedalion")
+
+
+def detect_cap_boundary(
+    verts: np.ndarray,
+    Nz: np.ndarray,
+    Cz: np.ndarray,
+    ear_mid: np.ndarray,
+    mid_y: float,
+    band_width: float = 15.0,
+    bin_size: float = 1.0,
+    foot_grad_threshold: float = 0.2,
+    cap_z_ceiling_mm: float = 40.0,
+    eyebrow_offset_mm: float = 10.0,
+) -> tuple[float, np.ndarray, np.ndarray, np.ndarray]:
+    """Find the Z height where the EEG cap front edge sits.
+
+    Scans upward from Nz along the midline and records max-X per Z-bin. The
+    cap protrudes in +X (anterior), so X-max along the midline lives on the
+    cap. The cap edge is the foot of the steep rise leading to the peak:
+    walk back from the peak until the smoothed gradient drops below
+    ``foot_grad_threshold``.
+
+    Failsafe for flush / no-optode caps: when the cap sits flat against
+    the head (no optodes), the anterior bump vanishes and the X-max
+    profile is roughly monotonic up to ``Cz``, leaving ``cap_z`` stuck
+    near the crown. If the detected ``cap_z`` exceeds
+    ``Nz[2] + cap_z_ceiling_mm``, fall back to a fixed cut at
+    ``Nz[2] + eyebrow_offset_mm`` (just above the supraorbital ridge).
+
+    Expects the CTF frame (+X=anterior, +Y=left, +Z=up).
+
+    Args:
+        verts: Mesh vertices, shape (N, 3).
+        Nz: Nasion position.
+        Cz: Cz position.
+        ear_mid: Midpoint of Lpa/Rpa.
+        mid_y: Midline Y value (e.g. ``0.5 * (Lpa[1] + Rpa[1])``).
+        band_width: Y-band half-width for the midline X-profile (mm).
+        bin_size: Z-bin size for the X-profile (mm).
+        foot_grad_threshold: dX/dZ below this value marks the foot of the rise.
+        cap_z_ceiling_mm: Absolute mm above Nz at which the cap-peak
+            detection is considered untrustworthy and the failsafe fires.
+        eyebrow_offset_mm: Failsafe cut height, expressed as mm above Nz.
+            Anatomically just above the supraorbital ridge.
+
+    Returns:
+        Tuple of (cap_z, profile_z, profile_x_raw, profile_x_smooth).
+    """
+    from scipy.signal import savgol_filter
+
+    in_band = np.abs(verts[:, 1] - mid_y) < band_width
+    above_nz = verts[:, 2] > Nz[2]
+    anterior = verts[:, 0] > ear_mid[0]
+    sel_verts = verts[in_band & above_nz & anterior]
+
+    bins = np.arange(Nz[2], Cz[2], bin_size)
+    bin_centers = bins[:-1] + bin_size / 2
+    max_x = np.full(len(bin_centers), np.nan)
+
+    for i in range(len(bin_centers)):
+        in_bin = (sel_verts[:, 2] >= bins[i]) & (sel_verts[:, 2] < bins[i + 1])
+        if in_bin.any():
+            max_x[i] = sel_verts[in_bin, 0].max()
+
+    valid = ~np.isnan(max_x)
+    if valid.sum() < 7:
+        fallback = 0.5 * (Nz[2] + Cz[2])
+        return fallback, bin_centers[valid], max_x[valid], max_x[valid]
+
+    zv = bin_centers[valid]
+    xv = max_x[valid]
+
+    win = min(11, len(xv) if len(xv) % 2 == 1 else len(xv) - 1)
+    win = max(win, 5)
+    xv_s = savgol_filter(xv, window_length=win, polyorder=2)
+
+    peak_idx = int(np.argmax(xv_s))
+    grad = np.gradient(xv_s, zv)
+    cap_z = zv[0]
+    for i in range(peak_idx - 1, 0, -1):
+        if grad[i] < foot_grad_threshold:
+            cap_z = zv[i]
+            break
+
+    ceiling = Nz[2] + cap_z_ceiling_mm
+    if cap_z > ceiling:
+        fallback = Nz[2] + eyebrow_offset_mm
+        logger.info(
+            f"detect_cap_boundary: cap_z={cap_z:.1f} mm exceeded ceiling "
+            f"Nz+{cap_z_ceiling_mm:.0f}={ceiling:.1f}; assuming flush cap "
+            f"and falling back to Nz+{eyebrow_offset_mm:.0f}={fallback:.1f} mm."
+        )
+        cap_z = fallback
+
+    return cap_z, zv, xv, xv_s
+
+
+def face_mask_from_landmarks(
+    verts: np.ndarray,
+    Nz: np.ndarray,
+    Iz: np.ndarray,
+    Cz: np.ndarray,
+    Lpa: np.ndarray,
+    Rpa: np.ndarray,
+    cap_z: float | None = None,
+    ear_delete_radius: float = 40.0,
+) -> tuple[np.ndarray, dict]:
+    """Build face + ear deletion mask from the 5 landmarks (CTF frame).
+
+    Mask is the union of two regions, both clamped below the cap boundary:
+
+    1. Face region: anterior to the ear coronal plane (X > ear_mid_X).
+    2. Ear spheres: ``ear_delete_radius`` mm around Lpa and Rpa.
+
+    Expects the CTF frame (+X=anterior, +Y=left, +Z=up).
+
+    Args:
+        verts: Mesh vertices, shape (N, 3).
+        Nz, Iz, Cz, Lpa, Rpa: 5 landmark positions in the CTF frame.
+        cap_z: Upper bound Z value (typically from ``detect_cap_boundary``).
+            Defaults to Nz[2] if not provided.
+        ear_delete_radius: Sphere radius around Lpa/Rpa in mm.
+
+    Returns:
+        Tuple of (mask, info). ``mask`` is a boolean array of shape (N,).
+        ``info`` has keys ``upper_bound``, ``ear_mid``, and ``counts``
+        (per-region vertex counts).
+    """
+    ear_mid = 0.5 * (Lpa + Rpa)
+    upper_bound = cap_z if cap_z is not None else Nz[2]
+
+    below_cap = verts[:, 2] < upper_bound
+    anterior = verts[:, 0] > ear_mid[0]
+    face_region = below_cap & anterior
+
+    d_lpa = np.linalg.norm(verts - Lpa, axis=1)
+    d_rpa = np.linalg.norm(verts - Rpa, axis=1)
+    ear_region = ((d_lpa < ear_delete_radius) | (d_rpa < ear_delete_radius)) & below_cap
+
+    mask = face_region | ear_region
+
+    info = {
+        "upper_bound": upper_bound,
+        "ear_mid": ear_mid,
+        "counts": {
+            "below_cap": int(below_cap.sum()),
+            "face_region": int(face_region.sum()),
+            "ear_region": int(ear_region.sum()),
+            "all": int(mask.sum()),
+        },
+    }
+    return mask, info
+
+
+def delete_masked_vertices(
+    surface: cdc.TrimeshSurface,
+    mask: np.ndarray,
+) -> cdc.TrimeshSurface:
+    """Drop triangles touching any masked vertex and strip unreferenced vertices.
+
+    Reindexes ``mesh.visual.uv`` (if present) in lockstep with the vertex array.
+    Trimesh's ``remove_unreferenced_vertices`` does not touch UV arrays, so a
+    naive call leaves ``len(uv) != len(vertices)`` and any subsequent textured
+    export crashes or corrupts.
+
+    Args:
+        surface: Input TrimeshSurface.
+        mask: Boolean array of shape (n_vertices,). True = vertex to remove.
+
+    Returns:
+        New TrimeshSurface with masked vertices (and the faces touching them)
+        removed. CRS, units, UVs, and texture image are preserved in sync.
+    """
+    import trimesh
+
+    old_mesh = surface.mesh
+    old_verts = np.asarray(old_mesh.vertices)
+    old_faces = np.asarray(old_mesh.faces)
+    n_old_verts = len(old_verts)
+
+    kept_face_mask = ~mask[old_faces].any(axis=1)
+    kept_faces = old_faces[kept_face_mask]
+    kept_vidx = np.unique(kept_faces)
+
+    reindex = -np.ones(n_old_verts, dtype=np.int64)
+    reindex[kept_vidx] = np.arange(len(kept_vidx))
+
+    new_mesh = trimesh.Trimesh(
+        vertices=old_verts[kept_vidx],
+        faces=reindex[kept_faces],
+        process=False,
+    )
+
+    old_visual = old_mesh.visual
+    uv = getattr(old_visual, "uv", None)
+    if uv is not None and len(uv) == n_old_verts:
+        new_mesh.visual = trimesh.visual.TextureVisuals(
+            uv=np.asarray(uv)[kept_vidx],
+            image=getattr(old_visual, "image", None),
+            material=getattr(old_visual, "material", None),
+        )
+    else:
+        vcol = getattr(old_visual, "vertex_colors", None)
+        if vcol is not None and len(vcol) == n_old_verts:
+            new_mesh.visual.vertex_colors = np.asarray(vcol)[kept_vidx]
+
+    return cdc.TrimeshSurface(
+        mesh=new_mesh, crs=surface.crs, units=surface.units,
+    )
+
+
+def _sanitize_texture_from_uv(
+    mesh,
+    fill: tuple[int, int, int] = (0, 0, 0),
+) -> "Image.Image | None":
+    """Rebuild the texture image keeping only pixels referenced by mesh UVs.
+
+    Rasterizes every surviving face's UV triangle onto a keep-mask the size
+    of the source image. Pixels outside the mask are set to ``fill`` so
+    face-region pixels (no longer referenced after anonymization) cannot leak
+    through the JPG. The mask is dilated by 1 pixel to absorb anti-aliasing
+    fringes at triangle boundaries.
+
+    OBJ UVs use the bottom-left origin convention; PIL images use top-left,
+    so V is flipped before rasterizing.
+
+    Args:
+        mesh: A ``trimesh.Trimesh`` with ``mesh.visual`` as ``TextureVisuals``.
+        fill: RGB fill color for unreferenced pixels.
+
+    Returns:
+        Sanitized PIL Image in RGB mode, or ``None`` if the mesh has no
+        usable texture (caller should fall back to geometry-only output).
+    """
+    from PIL import Image, ImageDraw, ImageFilter
+
+    visual = mesh.visual
+    uv = getattr(visual, "uv", None)
+    image = getattr(visual, "image", None)
+    if image is None:
+        mat = getattr(visual, "material", None)
+        image = getattr(mat, "image", None) if mat is not None else None
+
+    if uv is None or image is None or len(uv) != len(mesh.vertices):
+        return None
+
+    img_rgb = np.asarray(image.convert("RGB"))
+    h, w = img_rgb.shape[:2]
+
+    uv = np.asarray(uv)
+    px = np.mod(uv[:, 0], 1.0) * w
+    py = (1.0 - np.mod(uv[:, 1], 1.0)) * h
+
+    mask_img = Image.new("L", (w, h), 0)
+    draw = ImageDraw.Draw(mask_img)
+    for face in np.asarray(mesh.faces):
+        pts = [(px[i], py[i]) for i in face]
+        draw.polygon(pts, fill=255)
+    mask_img = mask_img.filter(ImageFilter.MaxFilter(3))
+    keep = np.asarray(mask_img) > 0
+
+    out = np.empty_like(img_rgb)
+    out[keep] = img_rgb[keep]
+    out[~keep] = fill
+    return Image.fromarray(out, mode="RGB")
+
+
+_MTL_TEMPLATE = (
+    "newmtl _texture\n"
+    "Kd 1.00 1.00 1.00\n"
+    "Ka 0.00 0.00 0.00\n"
+    "Tf 1.00 1.00 1.00\n"
+    "Ni 1.00\n"
+    "map_Kd {jpg_name}\n"
+)
+
+
+def save_anonymized_scan(
+    surface: cdc.TrimeshSurface,
+    out_path: str,
+    landmarks: "xr.DataArray | None" = None,
+    strip_texture: bool = False,
+) -> list[str]:
+    """Export an anonymized photogrammetry surface to disk.
+
+    Default path (``strip_texture=False``): writes an ``.obj`` + ``.mtl`` +
+    sanitized ``.jpg`` bundle. The JPG is rebuilt from the original texture
+    so it contains colors *only* for UV regions still referenced by the
+    anonymized mesh; every other pixel (notably the face region) is set to
+    the fill color. Opening the JPG alone therefore reveals no face pixels.
+
+    ``strip_texture=True`` is an explicit opt-out that writes only the
+    geometry ``.obj`` (no MTL, no JPG).
+
+    If the input mesh has no usable texture (no UVs or no image), falls
+    back to geometry-only output with a warning.
+
+    If ``landmarks`` is provided, also writes ``{stem}_landmarks.tsv`` next to
+    the ``.obj`` using ``cedalion.io.export_to_tsv``. The resulting TSV is
+    the file that gets uploaded at step 5.2 of the cedalion photogrammetry
+    co-registration tutorial.
+
+    Args:
+        surface: Anonymized TrimeshSurface (typically output of
+            ``delete_masked_vertices``).
+        out_path: Destination path ending in ``.obj``.
+        landmarks: Optional LabeledPoints of landmarks (Nz, Iz, LPA, RPA,
+            Cz) to persist alongside the mesh as a TSV. Should be in the same
+            frame as ``surface``.
+        strip_texture: If True, skip the MTL + JPG and write geometry only.
+
+    Returns:
+        List of absolute paths written (``.obj`` plus ``.mtl`` + ``.jpg``
+        when a texture was written, plus ``_landmarks.tsv`` when landmarks
+        were given).
+
+    Raises:
+        ValueError: If ``out_path`` does not end in ``.obj``.
+    """
+    import os
+    import trimesh
+
+    if not out_path.lower().endswith(".obj"):
+        raise ValueError(f"out_path must end in .obj, got: {out_path}")
+
+    out_dir = os.path.dirname(os.path.abspath(out_path)) or "."
+    stem = os.path.splitext(os.path.basename(out_path))[0]
+
+    sanitized = None if strip_texture else _sanitize_texture_from_uv(surface.mesh)
+
+    mesh_to_write = trimesh.Trimesh(
+        vertices=np.asarray(surface.mesh.vertices),
+        faces=np.asarray(surface.mesh.faces),
+        process=False,
+    )
+
+    written: list[str] = []
+
+    if sanitized is not None:
+        jpg_name = f"{stem}.jpg"
+        mtl_name = f"{stem}.mtl"
+        jpg_path = os.path.join(out_dir, jpg_name)
+        mtl_path = os.path.join(out_dir, mtl_name)
+
+        sanitized.save(jpg_path, format="JPEG", quality=92)
+        with open(mtl_path, "w") as fh:
+            fh.write(_MTL_TEMPLATE.format(jpg_name=jpg_name))
+
+        mesh_to_write.visual = trimesh.visual.TextureVisuals(
+            uv=np.asarray(surface.mesh.visual.uv),
+            image=sanitized,
+        )
+        obj_text = trimesh.exchange.obj.export_obj(
+            mesh_to_write,
+            include_texture=True,
+            write_texture=False,
+            mtl_name=mtl_name,
+        )
+        obj_text = _ensure_mtllib(obj_text, mtl_name)
+        with open(out_path, "w") as fh:
+            fh.write(obj_text)
+
+        written.extend([out_path, mtl_path, jpg_path])
+    else:
+        if not strip_texture:
+            logger.warning(
+                "save_anonymized_scan: input mesh has no usable texture; "
+                "falling back to geometry-only OBJ."
+            )
+        mesh_to_write.export(out_path)
+        written.append(out_path)
+
+    if landmarks is not None:
+        from cedalion.io import export_to_tsv
+
+        tsv_path = os.path.join(out_dir, f"{stem}_landmarks.tsv")
+        export_to_tsv(tsv_path, landmarks)
+        written.append(tsv_path)
+    else:
+        logger.warning(
+            "save_anonymized_scan called without landmarks; "
+            "co-registration (tutorial step 5.2) will need them saved separately."
+        )
+
+    logger.info(
+        f"Saved anonymized scan: "
+        f"{[os.path.basename(p) for p in written]}"
+    )
+    return sorted(written)
+
+
+def _ensure_mtllib(obj_text: str, mtl_name: str) -> str:
+    """Guarantee the OBJ references our MTL via ``mtllib`` and ``usemtl``.
+
+    Trimesh's OBJ exporter may or may not emit material lines depending on
+    version; rewrite deterministically so the MTL pairing is stable.
+    """
+    lines = obj_text.splitlines()
+    lines = [l for l in lines if not l.startswith(("mtllib ", "usemtl "))]
+    header = [f"mtllib {mtl_name}", "usemtl _texture"]
+    return "\n".join(header + lines) + "\n"
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/preprocessing.py b/src/cedalion/geometry/photogrammetry/anonymization/preprocessing.py
new file mode 100644
index 00000000..f0b59bd0
--- /dev/null
+++ b/src/cedalion/geometry/photogrammetry/anonymization/preprocessing.py
@@ -0,0 +1,420 @@
+"""Preprocessing: axis normalization and head isolation.
+
+Gets a raw Einstar photogrammetry scan into a canonical frame suitable for
+landmark detection and mask building:
+
+- ``normalize_axes`` rotates around X so Y points anterior (preliminary,
+  used before head isolation).
+- ``isolate_head`` strips body/shoulders/chair and disconnected fragments.
+- ``align_axes_from_landmarks`` maps the scan into the CTF frame
+  (+X=anterior, +Y=left, +Z=up, origin at the LPA-RPA midpoint) once all 5
+  landmarks are available.
+
+Initial Contributors:
+    - Face Anonymization Project | 2024
+"""
+
+import logging
+
+import numpy as np
+
+import cedalion.dataclasses as cdc
+import cedalion.typing as cdt
+
+logger = logging.getLogger("cedalion")
+
+
+def _copy_visual(src_mesh, dst_mesh, vertex_index=None) -> None:
+    """Copy a trimesh ``visual`` onto ``dst_mesh``, optionally reindexing.
+
+    Assigning a ``TextureVisuals`` directly across meshes (e.g. by passing
+    ``visual=old.visual`` to a new ``Trimesh(...)``) often silently downgrades
+    it to ``ColorVisuals`` because the visual holds a back-reference to its
+    original mesh and re-checks ``len(uv) == len(mesh.vertices)`` against the
+    wrong target. Rebuilding the visual explicitly avoids that.
+
+    Args:
+        src_mesh: Source trimesh.
+        dst_mesh: Destination trimesh (mutated in place).
+        vertex_index: Optional indices into the source vertex array. If given,
+            UVs / vertex_colors are sliced by this index before being attached.
+            Leave None when the vertex count is unchanged.
+    """
+    import trimesh
+
+    src_visual = src_mesh.visual
+    uv = getattr(src_visual, "uv", None)
+    image = getattr(src_visual, "image", None)
+    n_src = len(src_mesh.vertices)
+
+    if uv is not None and len(uv) == n_src:
+        uv_arr = np.asarray(uv)
+        if vertex_index is not None:
+            uv_arr = uv_arr[vertex_index]
+        if image is None:
+            mat = getattr(src_visual, "material", None)
+            image = getattr(mat, "image", None) if mat is not None else None
+        dst_mesh.visual = trimesh.visual.TextureVisuals(
+            uv=uv_arr,
+            image=image,
+        )
+        return
+
+    vcol = getattr(src_visual, "vertex_colors", None)
+    if vcol is not None and len(vcol) == n_src:
+        vcol_arr = np.asarray(vcol)
+        if vertex_index is not None:
+            vcol_arr = vcol_arr[vertex_index]
+        dst_mesh.visual.vertex_colors = vcol_arr
+
+
+def normalize_axes(
+    surface: cdc.TrimeshSurface,
+    nasion: np.ndarray,
+    forward_direction: np.ndarray = None,
+) -> tuple[cdc.TrimeshSurface, np.ndarray, np.ndarray]:
+    """Rotate mesh around X-axis so Y=anterior (toward face), Z=left.
+
+    The Einstar scanner has X=up (gravity-based) but Y/Z are arbitrary per
+    scan session. This function uses the nasion position to determine the
+    forward direction and rotates the mesh so that Y consistently points
+    toward the face (anterior) and Z points left.
+
+    Args:
+        surface: TrimeshSurface in raw Einstar coordinates.
+        nasion: Nasion position as numpy array of shape (3,).
+        forward_direction: Optional pre-computed forward unit vector in YZ
+            plane (X=0). If None, computed from nasion vs upper-head centroid.
+
+    Returns:
+        Tuple of (rotated_surface, rotated_nasion, rotation_matrix).
+        rotation_matrix is 3x3 and can be applied to other points.
+    """
+    vertices = surface.mesh.vertices
+
+    if forward_direction is None:
+        x_max = vertices[:, 0].max()
+        x_min = vertices[:, 0].min()
+        upper = vertices[vertices[:, 0] > x_min + 0.6 * (x_max - x_min)]
+        centroid_yz = np.array([0.0, upper[:, 1].mean(), upper[:, 2].mean()])
+        nasion_yz = np.array([0.0, nasion[1], nasion[2]])
+        forward_direction = nasion_yz - centroid_yz
+        fwd_norm = np.linalg.norm(forward_direction)
+        if fwd_norm < 1e-6:
+            logger.warning("Cannot compute forward direction -- returning unchanged")
+            return surface, nasion.copy(), np.eye(3)
+        forward_direction = forward_direction / fwd_norm
+
+    angle = np.arctan2(forward_direction[2], forward_direction[1])
+    cos_a = np.cos(-angle)
+    sin_a = np.sin(-angle)
+    R = np.array([
+        [1.0, 0.0, 0.0],
+        [0.0, cos_a, -sin_a],
+        [0.0, sin_a, cos_a],
+    ])
+
+    rotated_verts = vertices @ R.T
+
+    import trimesh
+    new_mesh = trimesh.Trimesh(
+        vertices=rotated_verts,
+        faces=surface.mesh.faces,
+        process=False,
+    )
+    _copy_visual(surface.mesh, new_mesh)
+    rotated_surface = cdc.TrimeshSurface(new_mesh, crs=surface.crs, units=surface.units)
+
+    rotated_nasion = R @ nasion
+
+    logger.debug(
+        f"Axis normalization: rotated {np.degrees(angle):.1f}deg around X. "
+        f"Y now points anterior."
+    )
+
+    return rotated_surface, rotated_nasion, R
+
+
+def _largest_component_mask(mesh) -> np.ndarray:
+    """Boolean vertex mask selecting the largest connected component.
+
+    Einstar scans are non-watertight and frequently contain tiny floating
+    mesh fragments (loose triangles, cable shreds, background patches)
+    that sit far outside the head. Any step that uses vertex extrema will
+    be dragged off into empty space by those fragments. Stripping them
+    up-front prevents that.
+
+    Uses ``trimesh.graph.connected_component_labels`` on face adjacency
+    rather than ``mesh.split``; split allocates one full ``Trimesh`` per
+    component and can OOM on scans with thousands of tiny fragments.
+
+    Einstar OBJs duplicate vertices along UV seams for texturing. The
+    default ``trimesh.Trimesh.merge_vertices()`` does NOT merge across UV
+    seams (it preserves texture), so face adjacency on the raw mesh
+    over-fragments the head into thousands of islands. We use POSITION-only
+    merging via ``unique_rows`` for connectivity analysis.
+
+    Args:
+        mesh: A ``trimesh.Trimesh`` instance.
+
+    Returns:
+        Boolean array of shape ``(n_vertices,)``. All-True if the mesh
+        is empty or already a single connected component.
+    """
+    import trimesh
+    n_verts = len(mesh.vertices)
+    n_faces = len(mesh.faces)
+    if n_faces == 0:
+        return np.ones(n_verts, dtype=bool)
+
+    unique_idx, inverse = trimesh.grouping.unique_rows(
+        np.asarray(mesh.vertices)
+    )
+    canonical_faces = inverse[mesh.faces]
+    adjacency = trimesh.graph.face_adjacency(faces=canonical_faces)
+    face_labels = trimesh.graph.connected_component_labels(
+        adjacency, node_count=n_faces
+    )
+    counts = np.bincount(face_labels)
+    if len(counts) <= 1:
+        return np.ones(n_verts, dtype=bool)
+    biggest_label = int(np.argmax(counts))
+    face_mask = face_labels == biggest_label
+
+    kept_vidx = np.unique(mesh.faces[face_mask])
+    mask = np.zeros(n_verts, dtype=bool)
+    mask[kept_vidx] = True
+    return mask
+
+
+def isolate_head(
+    surface: cdc.TrimeshSurface,
+    nasion: np.ndarray,
+    radius: float = 220.0,
+) -> tuple[cdc.TrimeshSurface, np.ndarray]:
+    """Remove shoulders, body, and chair; keep only the head.
+
+    Uses a sphere centered on the upper-head centroid. The radius is
+    chosen to capture the full head (~180mm wide x 230mm tall) while
+    excluding shoulders and body. Scans that are already head-only are
+    returned unchanged (the sphere just contains everything).
+
+    The surface must be axis-normalized first (X=up, Y=anterior, Z=left).
+
+    Args:
+        surface: Axis-normalized TrimeshSurface.
+        nasion: Nasion position as numpy array of shape (3,).
+        radius: Sphere radius in mm (default 220). A human head has
+            ~90mm radius; 220mm adds margin for ears and jaw.
+
+    Returns:
+        Tuple of (head_surface, head_mask). head_mask is a boolean
+        array of shape (n_vertices,) indicating which original
+        vertices were kept.
+    """
+    import trimesh
+
+    vertices = surface.mesh.vertices
+    faces = surface.mesh.faces
+
+    x_max = vertices[:, 0].max()
+    x_min = vertices[:, 0].min()
+    upper = vertices[:, 0] > x_min + 0.6 * (x_max - x_min)
+    center = vertices[upper].mean(axis=0)
+    midpoint_x = (x_max + nasion[0]) / 2.0
+    center[0] = min(center[0], midpoint_x)
+
+    dist = np.linalg.norm(vertices - center, axis=1)
+    head_mask = dist < radius
+
+    head_mask = head_mask & _largest_component_mask(surface.mesh)
+
+    if head_mask.sum() < 100 or head_mask.mean() > 0.95:
+        logger.debug(
+            f"Head isolation: sphere contains {head_mask.mean()*100:.0f}% "
+            f"of vertices -- scan is already head-only"
+        )
+        return surface, head_mask
+
+    face_mask = head_mask[faces].all(axis=1)
+    head_faces = faces[face_mask]
+
+    kept_verts = np.unique(head_faces)
+    reindex = np.full(len(vertices), -1, dtype=int)
+    reindex[kept_verts] = np.arange(len(kept_verts))
+    new_faces = reindex[head_faces]
+    new_verts = vertices[kept_verts]
+
+    new_mesh = trimesh.Trimesh(
+        vertices=new_verts,
+        faces=new_faces,
+        process=False,
+    )
+    _copy_visual(surface.mesh, new_mesh, vertex_index=kept_verts)
+
+    head_surface = cdc.TrimeshSurface(
+        new_mesh, crs=surface.crs, units=surface.units
+    )
+
+    logger.debug(
+        f"Head isolation: {len(vertices):,} -> {len(new_verts):,} vertices "
+        f"({len(vertices) - len(new_verts):,} removed), "
+        f"center=[{center[0]:.0f},{center[1]:.0f},{center[2]:.0f}], "
+        f"radius={radius:.0f}mm"
+    )
+
+    return head_surface, head_mask
+
+
+def align_axes_from_landmarks(
+    surface: cdc.TrimeshSurface,
+    landmarks: cdt.LabeledPoints,
+) -> tuple[cdc.TrimeshSurface, cdt.LabeledPoints, np.ndarray]:
+    """Map mesh + landmarks into the CTF anatomical frame.
+
+    CTF convention:
+
+        +X = anterior (toward Nz)
+        +Y = left (toward Lpa)
+        +Z = up (toward Cz)
+        origin = midpoint of Lpa and Rpa (interaural midpoint)
+
+    The returned surface and landmarks carry ``crs="ctf"``.
+
+    Args:
+        surface: Axis-normalized TrimeshSurface (post ``normalize_axes`` and
+            ``isolate_head``).
+        landmarks: LabeledPoints with labels Nz, Iz, Cz, and LPA/RPA (or
+            aliases like Lpa/Rpa, "left ear"/"right ear"; normalized
+            internally via ``normalize_landmarks_labels``)
+            (matching the surface frame).
+
+    Returns:
+        Tuple of (aligned_surface, aligned_landmarks, transform).
+        ``transform`` is a 4x4 homogeneous affine that maps input-frame
+        points into CTF; apply as ``hom = transform @ [x, y, z, 1]``.
+    """
+    import trimesh
+    from cedalion.geometry.landmarks import normalize_landmarks_labels
+
+    landmarks = normalize_landmarks_labels(landmarks)
+    lm = landmarks.pint.dequantify().values
+    labels = list(landmarks["label"].values)
+    idx = {lbl: i for i, lbl in enumerate(labels)}
+
+    required = {"Nz", "Iz", "Cz", "LPA", "RPA"}
+    missing = required - set(labels)
+    if missing:
+        raise ValueError(f"Missing landmarks for alignment: {missing}")
+
+    Nz = lm[idx["Nz"]]
+    Cz = lm[idx["Cz"]]
+    Lpa = lm[idx["LPA"]]
+    Rpa = lm[idx["RPA"]]
+    origin = 0.5 * (Lpa + Rpa)
+
+    y_ax = Lpa - Rpa
+    y_ax = y_ax / np.linalg.norm(y_ax)
+
+    nz_dir = Nz - origin
+    nz_dir = nz_dir - np.dot(nz_dir, y_ax) * y_ax
+    x_ax = nz_dir / np.linalg.norm(nz_dir)
+
+    z_ax = np.cross(x_ax, y_ax)
+
+    if np.dot(Cz - origin, z_ax) < 0:
+        z_ax = -z_ax
+    if np.dot(Nz - origin, x_ax) < 0:
+        x_ax = -x_ax
+    y_ax = np.cross(z_ax, x_ax)
+
+    R = np.vstack([x_ax, y_ax, z_ax])
+    M = np.eye(4)
+    M[:3, :3] = R
+    M[:3, 3] = -R @ origin
+
+    aligned_verts = (np.asarray(surface.mesh.vertices) - origin) @ R.T
+    new_mesh = trimesh.Trimesh(
+        vertices=aligned_verts,
+        faces=surface.mesh.faces,
+        process=False,
+    )
+    _copy_visual(surface.mesh, new_mesh)
+    aligned_surface = cdc.TrimeshSurface(
+        new_mesh, crs="ctf", units=surface.units,
+    )
+
+    aligned_lm = (lm - origin) @ R.T
+    old_crs_dim = [d for d in landmarks.dims if d != "label"][0]
+    aligned_landmarks = (
+        landmarks.pint.dequantify()
+        .copy(data=aligned_lm)
+        .rename({old_crs_dim: "ctf"})
+        .pint.quantify()
+    )
+
+    return aligned_surface, aligned_landmarks, M
+
+
+def revert_to_einstar_frame(
+    surface: cdc.TrimeshSurface,
+    landmarks: cdt.LabeledPoints,
+    R_normalize: np.ndarray,
+    M_align: np.ndarray,
+) -> tuple[cdc.TrimeshSurface, cdt.LabeledPoints]:
+    """Map an aligned surface and landmarks back into the raw Einstar frame.
+
+    Inverse of the ``normalize_axes`` then ``align_axes_from_landmarks``
+    composition, so the returned mesh and landmarks carry ``crs="digitized"``
+    and match the coordinates of the original ``read_einstar_obj`` output.
+    Useful right before ``save_anonymized_scan`` when the downstream
+    co-registration pipeline expects the saved ``.obj`` and ``.tsv`` in the
+    native scanner frame (e.g. Elsa's tutorial step 5.2 workflow, whose
+    example files carry ``crs=digitized``).
+
+    Note that ``isolate_head`` is not invertible: the returned mesh is still
+    head-only even though its coordinates are in the original digitized frame.
+
+    Args:
+        surface: TrimeshSurface in the CTF frame (post
+            ``align_axes_from_landmarks``, optionally after masking).
+        landmarks: LabeledPoints in the CTF frame.
+        R_normalize: 3x3 rotation returned by ``normalize_axes``.
+        M_align: 4x4 affine returned by ``align_axes_from_landmarks``.
+
+    Returns:
+        Tuple of (surface_digitized, landmarks_digitized). Both carry
+        ``crs="digitized"`` and the mesh preserves UVs / vertex colors.
+    """
+    import trimesh
+
+    M_inv = np.linalg.inv(M_align)
+    R_inv = R_normalize.T
+
+    ctf_verts = np.asarray(surface.mesh.vertices)
+    norm_verts = ctf_verts @ M_inv[:3, :3].T + M_inv[:3, 3]
+    raw_verts = norm_verts @ R_inv.T
+
+    new_mesh = trimesh.Trimesh(
+        vertices=raw_verts,
+        faces=surface.mesh.faces,
+        process=False,
+    )
+    _copy_visual(surface.mesh, new_mesh)
+    raw_surface = cdc.TrimeshSurface(
+        new_mesh, crs="digitized", units=surface.units,
+    )
+
+    lm_ctf = landmarks.pint.dequantify().values
+    lm_norm = lm_ctf @ M_inv[:3, :3].T + M_inv[:3, 3]
+    lm_raw = lm_norm @ R_inv.T
+
+    old_crs_dim = [d for d in landmarks.dims if d != "label"][0]
+    raw_landmarks = (
+        landmarks.pint.dequantify()
+        .copy(data=lm_raw)
+        .rename({old_crs_dim: "digitized"})
+        .pint.quantify()
+    )
+
+    return raw_surface, raw_landmarks

From 22407613acdaa9064500c9ba9e1d5da378ac12f3 Mon Sep 17 00:00:00 2001
From: mah-diaa <mahmoud.diaa.abdeen@gmail.com>
Date: Sun, 3 May 2026 09:30:31 +0200
Subject: [PATCH 21/39] Add manual 5-point anonymization example notebook

---
 .../51_manual_5pt_anonymization.ipynb         | 991 ++++++++++++++++++
 1 file changed, 991 insertions(+)
 create mode 100644 examples/head_models/51_manual_5pt_anonymization.ipynb

diff --git a/examples/head_models/51_manual_5pt_anonymization.ipynb b/examples/head_models/51_manual_5pt_anonymization.ipynb
new file mode 100644
index 00000000..efc089a8
--- /dev/null
+++ b/examples/head_models/51_manual_5pt_anonymization.ipynb
@@ -0,0 +1,991 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "13944ffe",
+   "metadata": {},
+   "source": [
+    "# Face Anonymization from 5 Given Landmarks\n",
+    "\n",
+    "Pure-geometric face-removal pipeline for Einstar photogrammetry scans. Takes the\n",
+    "5 standard anatomical landmarks (Nz, Iz, Cz, Lpa, Rpa) as input and deletes the\n",
+    "facial region, then reverts the result back to the raw Einstar (digitized)\n",
+    "frame so the saved `.obj` + `.tsv` are a drop-in replacement for the original\n",
+    "scan at co-registration time.\n",
+    "\n",
+    "**User-visible flow:** load scan &rarr; pick 5 landmarks &rarr; review the\n",
+    "anonymization mask &rarr; review the anonymized scan &rarr; save. All\n",
+    "intermediate transforms (normalize, align to CTF, build mask, delete verts)\n",
+    "run inside a single bundled cell.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "06ba7b71",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "\n",
+    "import numpy as np\n",
+    "import pyvista as pv\n",
+    "import xarray as xr\n",
+    "\n",
+    "import cedalion\n",
+    "import cedalion.dataclasses as cdc\n",
+    "import cedalion.io\n",
+    "import cedalion.vis.blocks\n",
+    "from cedalion.geometry.photogrammetry.anonymization import (\n",
+    "    normalize_axes,\n",
+    "    isolate_head,\n",
+    "    align_axes_from_landmarks,\n",
+    "    detect_cap_boundary,\n",
+    "    face_mask_from_landmarks,\n",
+    "    delete_masked_vertices,\n",
+    "    revert_to_einstar_frame,\n",
+    "    save_anonymized_scan,\n",
+    ")\n",
+    "from cedalion.vtktutils import trimesh_to_vtk_polydata\n",
+    "from cedalion.dataclasses import VTKSurface\n",
+    "\n",
+    "logging.getLogger('cedalion').setLevel(logging.WARNING)\n",
+    "pv.set_jupyter_backend('server')\n",
+    "\n",
+    "# === CONFIG ===\n",
+    "SUBJECT_NUMBER = 17\n",
+    "SCANS_FOLDER = '/home/ma7/BA/PG_Subjects'\n",
+    "\n",
+    "# True: run the picker. False: use cached landmarks (fill after first pick).\n",
+    "INTERACTIVE = True\n",
+    "\n",
+    "# Mask tunables (mm)\n",
+    "LANDMARK_KEEP_RADIUS = 8.0      # preserve a small sphere around each landmark\n",
+    "EAR_DELETE_RADIUS_MM = 40.0     # radius of the delete sphere around Lpa / Rpa\n",
+    "\n",
+    "# Cap boundary detection tunables\n",
+    "CAP_BAND_WIDTH_MM = 15.0\n",
+    "CAP_BIN_SIZE_MM = 1.0\n",
+    "CAP_FOOT_GRAD_THRESHOLD = 0.2\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-md-load",
+   "metadata": {},
+   "source": [
+    "## 1. Load the Einstar scan"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "cell-load",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded: 1,284,667 vertices, 2,223,716 faces\n",
+      "Texture already attached: (4016, 3776)\n"
+     ]
+    }
+   ],
+   "source": [
+    "path = f'{SCANS_FOLDER}/Subject{SUBJECT_NUMBER}/Subject{SUBJECT_NUMBER}.obj'\n",
+    "surface = cedalion.io.read_einstar_obj(path)\n",
+    "print(f'Loaded: {surface.nvertices:,} vertices, {surface.nfaces:,} faces')\n",
+    "\n",
+    "# In trimesh 4.6 the texture image lives on visual.material.image, not\n",
+    "# visual.image. Verify it's there; if missing, fall back to the sibling JPG.\n",
+    "import os\n",
+    "import trimesh as _tm\n",
+    "from PIL import Image as _PILImage\n",
+    "\n",
+    "def _visual_image(visual):\n",
+    "    img = getattr(visual, 'image', None)\n",
+    "    if img is None:\n",
+    "        mat = getattr(visual, 'material', None)\n",
+    "        img = getattr(mat, 'image', None) if mat is not None else None\n",
+    "    return img\n",
+    "\n",
+    "_img = _visual_image(surface.mesh.visual)\n",
+    "if _img is None:\n",
+    "    _jpg = path.replace('.obj', '.jpg')\n",
+    "    _uv = getattr(surface.mesh.visual, 'uv', None)\n",
+    "    assert os.path.exists(_jpg) and _uv is not None, (\n",
+    "        f'no texture: jpg_exists={os.path.exists(_jpg)}, has_uv={_uv is not None}'\n",
+    "    )\n",
+    "    surface.mesh.visual = _tm.visual.TextureVisuals(\n",
+    "        uv=_uv, image=_PILImage.open(_jpg).convert('RGBA'),\n",
+    "    )\n",
+    "    _img = _visual_image(surface.mesh.visual)\n",
+    "    assert _img is not None, 'attach failed'\n",
+    "    print(f'Attached texture from JPG: {_img.size}')\n",
+    "else:\n",
+    "    print(f'Texture already attached: {_img.size}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-md-pick",
+   "metadata": {},
+   "source": [
+    "## 2. Pick the 5 landmarks interactively\n",
+    "\n",
+    "Uses cedalion's built-in picker (`cedalion.vis.blocks.plot_surface(..., pick_landmarks=True)`,\n",
+    "authored by Mariia Iudina, upstream cedalion). Right-click on the mesh to place a\n",
+    "sphere; click again on the sphere to cycle its label through\n",
+    "`Nz -> Iz -> Cz -> Lpa -> Rpa`, or cycle past `Rpa` back to `Nz` to remove it.\n",
+    "\n",
+    "Close the window when all 5 are placed with correct labels."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "cell-pick",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "938910ca3ee04eb6b590a88affac39ca",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Widget(value='<iframe src=\"http://localhost:39989/index.html?ui=P_0x7f897374bc90_18&reconnect=auto\" class=\"pyv…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "if INTERACTIVE:\n",
+    "    pvplt = pv.Plotter()\n",
+    "    get_landmarks = cedalion.vis.blocks.plot_surface(\n",
+    "        pvplt, surface, opacity=1.0, pick_landmarks=True\n",
+    "    )\n",
+    "    pvplt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cell-md-retrieve",
+   "metadata": {},
+   "source": [
+    "## 3. Retrieve picked points and wrap into a `LabeledPointCloud`\n",
+    "\n",
+    "Follows the pattern from `41_photogrammetric_optode_coregistration.ipynb` cells 26-28."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "cell-retrieve",
+   "metadata": {},
+   "outputs": [
+    {
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+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (label: 5, digitized: 3)&gt; Size: 120B\n",
+       "&lt;Quantity([[169.8175707   84.17791333 425.43686049]\n",
+       " [ 32.0612021  203.51371015 436.13680432]\n",
+       " [ 67.92901638  85.38361616 562.50097916]\n",
+       " [ 18.53542079  67.46210371 393.38863931]\n",
+       " [ 69.81661274 -12.30486275 480.16603788]], &#x27;millimeter&#x27;)&gt;\n",
+       "Coordinates:\n",
+       "  * label    (label) &lt;U3 60B &#x27;Cz&#x27; &#x27;Iz&#x27; &#x27;Rpa&#x27; &#x27;Lpa&#x27; &#x27;Nz&#x27;\n",
+       "    type     (label) object 40B PointType.LANDMARK ... PointType.LANDMARK\n",
+       "    group    (label) &lt;U1 20B &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27;\n",
+       "Dimensions without coordinates: digitized</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>label</span>: 5</li><li><span>digitized</span>: 3</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-e6b82788-6d3d-46c7-8736-3efae789c9ef' class='xr-array-in' type='checkbox' checked><label for='section-e6b82788-6d3d-46c7-8736-3efae789c9ef' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>[mm] 169.8 84.18 425.4 32.06 203.5 ... 67.46 393.4 69.82 -12.3 480.2</span></div><div class='xr-array-data'><table><tbody><tr><th>Magnitude</th><td style='text-align:left;'><pre>[[169.8175706956947 84.17791333176854 425.43686048545544] [32.061202102756184 203.5137101504909 436.13680432494687] [67.9290163798706 85.38361615977699 562.5009791587428] [18.535420787153996 67.46210371239562 393.38863930640076] [69.81661273869598 -12.304862754118403 480.1660378769869]]</pre></td></tr><tr><th>Units</th><td style='text-align:left;'>millimeter</td></tr></tbody></table></div></div></li><li class='xr-section-item'><input id='section-50458d44-a6b2-4519-824b-052737d8f7b0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-50458d44-a6b2-4519-824b-052737d8f7b0' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>label</span></div><div class='xr-var-dims'>(label)</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;Cz&#x27; &#x27;Iz&#x27; &#x27;Rpa&#x27; &#x27;Lpa&#x27; &#x27;Nz&#x27;</div><input id='attrs-33836c6c-a641-466f-b2c9-67d9db819944' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-33836c6c-a641-466f-b2c9-67d9db819944' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af76f448-c9d8-4e02-bc4d-3ff86b162428' class='xr-var-data-in' type='checkbox'><label for='data-af76f448-c9d8-4e02-bc4d-3ff86b162428' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Cz&#x27;, &#x27;Iz&#x27;, &#x27;Rpa&#x27;, &#x27;Lpa&#x27;, &#x27;Nz&#x27;], dtype=&#x27;&lt;U3&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>type</span></div><div class='xr-var-dims'>(label)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>PointType.LANDMARK ... PointType...</div><input id='attrs-b94637ed-c3e2-44d4-992f-ed91b638a0ce' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b94637ed-c3e2-44d4-992f-ed91b638a0ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d37613c8-8a3f-4c4d-baac-118738ebdc13' class='xr-var-data-in' type='checkbox'><label for='data-d37613c8-8a3f-4c4d-baac-118738ebdc13' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&lt;PointType.LANDMARK: 3&gt;, &lt;PointType.LANDMARK: 3&gt;,\n",
+       "       &lt;PointType.LANDMARK: 3&gt;, &lt;PointType.LANDMARK: 3&gt;,\n",
+       "       &lt;PointType.LANDMARK: 3&gt;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>group</span></div><div class='xr-var-dims'>(label)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27;</div><input id='attrs-c8d96493-9f0a-4ba9-af28-8d6835b206bd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c8d96493-9f0a-4ba9-af28-8d6835b206bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d27acfb6-7958-441e-bd2c-fd40507af360' class='xr-var-data-in' type='checkbox'><label for='data-d27acfb6-7958-441e-bd2c-fd40507af360' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;L&#x27;, &#x27;L&#x27;, &#x27;L&#x27;, &#x27;L&#x27;, &#x27;L&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-77a86292-717e-4ab1-ab2a-bab1d89bf01a' class='xr-section-summary-in' type='checkbox'  ><label for='section-77a86292-717e-4ab1-ab2a-bab1d89bf01a' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>label</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-76b349c1-6d3f-4daa-8878-6c4d9741cccb' class='xr-index-data-in' type='checkbox'/><label for='index-76b349c1-6d3f-4daa-8878-6c4d9741cccb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;Cz&#x27;, &#x27;Iz&#x27;, &#x27;Rpa&#x27;, &#x27;Lpa&#x27;, &#x27;Nz&#x27;], dtype=&#x27;object&#x27;, name=&#x27;label&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9a61a3ff-6e0a-47ee-b097-d13c84ba5682' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9a61a3ff-6e0a-47ee-b097-d13c84ba5682' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray (label: 5, digitized: 3)> Size: 120B\n",
+       "<Quantity([[169.8175707   84.17791333 425.43686049]\n",
+       " [ 32.0612021  203.51371015 436.13680432]\n",
+       " [ 67.92901638  85.38361616 562.50097916]\n",
+       " [ 18.53542079  67.46210371 393.38863931]\n",
+       " [ 69.81661274 -12.30486275 480.16603788]], 'millimeter')>\n",
+       "Coordinates:\n",
+       "  * label    (label) <U3 60B 'Cz' 'Iz' 'Rpa' 'Lpa' 'Nz'\n",
+       "    type     (label) object 40B PointType.LANDMARK ... PointType.LANDMARK\n",
+       "    group    (label) <U1 20B 'L' 'L' 'L' 'L' 'L'\n",
+       "Dimensions without coordinates: digitized"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "if INTERACTIVE:\n",
+    "    landmark_coordinates, landmark_labels = get_landmarks()\n",
+    "    landmark_coordinates = np.asarray(landmark_coordinates)\n",
+    "else:\n",
+    "    # Cached fallback: paste coordinates from a previous interactive run here.\n",
+    "    landmark_labels = ['Nz', 'Iz', 'Cz', 'Lpa', 'Rpa']\n",
+    "    landmark_coordinates = np.asarray([\n",
+    "        [0.0, 0.0, 0.0],   # Nz  -- replace\n",
+    "        [0.0, 0.0, 0.0],   # Iz  -- replace\n",
+    "        [0.0, 0.0, 0.0],   # Cz  -- replace\n",
+    "        [0.0, 0.0, 0.0],   # Lpa -- replace\n",
+    "        [0.0, 0.0, 0.0],   # Rpa -- replace\n",
+    "    ])\n",
+    "\n",
+    "assert len(set(landmark_labels)) == 5, 'Need exactly 5 distinct landmarks'\n",
+    "assert set(landmark_labels) == {'Nz', 'Iz', 'Cz', 'Lpa', 'Rpa'}, (\n",
+    "    f'Unexpected labels: {landmark_labels}'\n",
+    ")\n",
+    "\n",
+    "landmarks = xr.DataArray(\n",
+    "    np.vstack(landmark_coordinates),\n",
+    "    dims=['label', 'digitized'],\n",
+    "    coords={\n",
+    "        'label': ('label', list(landmark_labels)),\n",
+    "        'type': ('label', [cdc.PointType.LANDMARK] * 5),\n",
+    "        'group': ('label', ['L'] * 5),\n",
+    "    },\n",
+    ").pint.quantify('mm')\n",
+    "\n",
+    "display(landmarks)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ec6eb7a7",
+   "metadata": {},
+   "source": [
+    "## 4. Run the anonymization pipeline\n",
+    "\n",
+    "All the geometric work happens in one block:\n",
+    "\n",
+    "1. `normalize_axes` + `isolate_head` rotate into a canonical orientation and\n",
+    "   strip shoulders/body.\n",
+    "2. `align_axes_from_landmarks` maps the mesh + landmarks into the CTF frame\n",
+    "   (+X=anterior, +Y=left, +Z=up, origin at the Lpa/Rpa midpoint). The rotation\n",
+    "   `R` and affine `M_ctf` are kept in scope so we can invert them later.\n",
+    "3. `detect_cap_boundary` + `face_mask_from_landmarks` build the boolean\n",
+    "   deletion mask from the 5 landmarks. Landmark neighbourhoods and a midline\n",
+    "   nasion strip are preserved for downstream co-registration.\n",
+    "4. `delete_masked_vertices` drops every triangle touching a masked vertex.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "09186e9d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "S17: 678,081 -> 578,478 verts (-99,603, cap_z=10.0 mm)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Normalize axes + isolate head.\n",
+    "lm_raw = landmarks.pint.dequantify().values\n",
+    "idx = {lbl: i for i, lbl in enumerate(landmarks['label'].values)}\n",
+    "Nz_raw = lm_raw[idx['Nz']]\n",
+    "\n",
+    "surface_n, Nz_n, R = normalize_axes(surface, Nz_raw)\n",
+    "landmarks_n = landmarks.pint.dequantify().copy(data=lm_raw @ R.T).pint.quantify()\n",
+    "surface_n, _ = isolate_head(surface_n, Nz_n)\n",
+    "\n",
+    "# Align to CTF frame.\n",
+    "surface_h, landmarks_n, M_ctf = align_axes_from_landmarks(surface_n, landmarks_n)\n",
+    "lm_n = landmarks_n.pint.dequantify().values\n",
+    "Nz, Iz, Cz, Lpa, Rpa = (lm_n[idx[lbl]] for lbl in ['Nz', 'Iz', 'Cz', 'Lpa', 'Rpa'])\n",
+    "ear_mid = 0.5 * (Lpa + Rpa)\n",
+    "\n",
+    "# Build face-region mask.\n",
+    "verts = np.asarray(surface_h.mesh.vertices)\n",
+    "mid_y = 0.5 * (Lpa[1] + Rpa[1])\n",
+    "cap_z, *_ = detect_cap_boundary(\n",
+    "    verts, Nz, Cz, ear_mid, mid_y,\n",
+    "    band_width=CAP_BAND_WIDTH_MM,\n",
+    "    bin_size=CAP_BIN_SIZE_MM,\n",
+    "    foot_grad_threshold=CAP_FOOT_GRAD_THRESHOLD,\n",
+    ")\n",
+    "mask, _ = face_mask_from_landmarks(\n",
+    "    verts, Nz, Iz, Cz, Lpa, Rpa,\n",
+    "    cap_z=cap_z,\n",
+    "    ear_delete_radius=EAR_DELETE_RADIUS_MM,\n",
+    ")\n",
+    "\n",
+    "# Preserve small spheres around each landmark + a midline nasion strip.\n",
+    "for lm in (Nz, Iz, Cz, Lpa, Rpa):\n",
+    "    near = np.linalg.norm(verts - lm, axis=1) < LANDMARK_KEEP_RADIUS\n",
+    "    mask[near] = False\n",
+    "nasion_strip = (\n",
+    "    (verts[:, 2] >= Nz[2]) &\n",
+    "    (verts[:, 2] < cap_z) &\n",
+    "    (np.abs(verts[:, 1] - Nz[1]) < LANDMARK_KEEP_RADIUS) &\n",
+    "    (verts[:, 0] > ear_mid[0])\n",
+    ")\n",
+    "mask[nasion_strip] = False\n",
+    "\n",
+    "# Delete masked vertices.\n",
+    "surface_anon = delete_masked_vertices(surface_h, mask)\n",
+    "\n",
+    "print(f'S{SUBJECT_NUMBER}: {surface_h.nvertices:,} -> {surface_anon.nvertices:,} verts '\n",
+    "      f'(-{surface_h.nvertices - surface_anon.nvertices:,}, cap_z={cap_z:.1f} mm)')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "62e36bea",
+   "metadata": {},
+   "source": [
+    "## 5. Anonymization mask overlay\n",
+    "\n",
+    "Red = will be deleted, white = kept. The 5 landmark spheres are overlaid for\n",
+    "orientation. If the red region hugs the face without leaking onto ears, top of\n",
+    "head, or neck, the geometric rules are working.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "a101c6c0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b70571c383be4bd7b4872ec50524fcc2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Widget(value='<iframe src=\"http://localhost:39989/index.html?ui=P_0x7f89737b6f10_19&reconnect=auto\" class=\"pyv…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lm_colors = {'Nz': 'lime', 'Iz': 'magenta', 'Cz': 'cyan', 'Lpa': 'orange', 'Rpa': 'blue'}\n",
+    "\n",
+    "pvplt = pv.Plotter()\n",
+    "\n",
+    "vtk_surface = VTKSurface.from_trimeshsurface(surface_h)\n",
+    "pv_mesh = pv.wrap(vtk_surface.mesh)\n",
+    "pv_mesh['mask'] = mask.astype(float)\n",
+    "pvplt.add_mesh(\n",
+    "    pv_mesh, scalars='mask', cmap=['white', 'red'], clim=[0, 1],\n",
+    "    show_scalar_bar=False, opacity=1.0, smooth_shading=True,\n",
+    ")\n",
+    "\n",
+    "for lbl, pos in zip(landmarks_n['label'].values, lm_n):\n",
+    "    c = lm_colors.get(lbl, 'yellow')\n",
+    "    pvplt.add_mesh(pv.Sphere(radius=4, center=pos), color=c)\n",
+    "    pvplt.add_point_labels(\n",
+    "        [pos], [lbl], font_size=16, text_color=c, shape=None, always_visible=True,\n",
+    "    )\n",
+    "\n",
+    "pvplt.add_text(\n",
+    "    f'S{SUBJECT_NUMBER} | mask: {mask.sum():,} / {len(mask):,} verts '\n",
+    "    f'({100*mask.sum()/len(mask):.1f}%)',\n",
+    "    position='upper_left', font_size=12,\n",
+    ")\n",
+    "pvplt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4137b59f",
+   "metadata": {},
+   "source": [
+    "## 6. Revert to the Einstar frame and compare\n",
+    "\n",
+    "Inverts the `normalize_axes` and `align_axes_from_landmarks` transforms so the\n",
+    "anonymized mesh + landmarks end up back in the raw Einstar (digitized) frame.\n",
+    "Both views below share the same coordinate system, so the comparison is\n",
+    "geometrically direct. This is also the frame the saved files will be in,\n",
+    "matching the co-registration tutorial's `read_einstar_obj` convention.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "d7c29bcd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "28cee82a04994c5bafc653f367c8895c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Widget(value='<iframe src=\"http://localhost:39989/index.html?ui=P_0x7f89737d1a10_20&reconnect=auto\" class=\"pyv…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Invert the two transforms so both mesh and landmarks are back in 'digitized'.\n",
+    "surface_anon_dig, landmarks_dig = revert_to_einstar_frame(\n",
+    "    surface_anon, landmarks_n, R, M_ctf,\n",
+    ")\n",
+    "lm_dig = landmarks_dig.pint.dequantify().values\n",
+    "\n",
+    "orig_vtk = trimesh_to_vtk_polydata(surface.mesh)\n",
+    "anon_vtk = trimesh_to_vtk_polydata(surface_anon_dig.mesh)\n",
+    "\n",
+    "lm_colors = {'Nz': 'lime', 'Iz': 'magenta', 'Cz': 'cyan', 'Lpa': 'orange', 'Rpa': 'blue'}\n",
+    "n_removed = surface_h.nvertices - surface_anon.nvertices\n",
+    "\n",
+    "pvplt = pv.Plotter(shape=(1, 2))\n",
+    "\n",
+    "pvplt.subplot(0, 0)\n",
+    "pvplt.add_mesh(pv.wrap(orig_vtk), rgb=True, smooth_shading=True)\n",
+    "for lbl, pos in zip(landmarks_dig['label'].values, lm_dig):\n",
+    "    pvplt.add_mesh(pv.Sphere(radius=4, center=pos),\n",
+    "                   color=lm_colors.get(lbl, 'yellow'))\n",
+    "pvplt.add_text(f'S{SUBJECT_NUMBER} Original', position='upper_left', font_size=14)\n",
+    "\n",
+    "pvplt.subplot(0, 1)\n",
+    "pvplt.add_mesh(pv.wrap(anon_vtk), rgb=True, smooth_shading=True)\n",
+    "for lbl, pos in zip(landmarks_dig['label'].values, lm_dig):\n",
+    "    pvplt.add_mesh(pv.Sphere(radius=4, center=pos),\n",
+    "                   color=lm_colors.get(lbl, 'yellow'))\n",
+    "pvplt.add_text(\n",
+    "    f'S{SUBJECT_NUMBER} Anonymized (-{n_removed:,} verts)',\n",
+    "    position='upper_left', font_size=14,\n",
+    ")\n",
+    "\n",
+    "pvplt.link_views()\n",
+    "pvplt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20924dfc",
+   "metadata": {},
+   "source": [
+    "## 7. Save\n",
+    "\n",
+    "Writes the anonymized `.obj` + `.mtl` + sanitized `.jpg` bundle plus a\n",
+    "sidecar `_landmarks.tsv` (the format consumed at step 5.2 of the cedalion\n",
+    "photogrammetry co-registration tutorial). All carry `crs=digitized`. The\n",
+    "JPG is rebuilt from the original texture keeping only pixels still\n",
+    "referenced by the anonymized mesh's UVs; face-region pixels are blanked,\n",
+    "so opening the JPG alone reveals no face.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "391c93d5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Wrote:\n",
+      "  /home/ma7/BA/PG_Subjects/Subject15/Subject15_anon.jpg\n",
+      "  /home/ma7/BA/PG_Subjects/Subject15/Subject15_anon.mtl\n",
+      "  /home/ma7/BA/PG_Subjects/Subject15/Subject15_anon.obj\n",
+      "  /home/ma7/BA/PG_Subjects/Subject15/Subject15_anon_landmarks.tsv\n"
+     ]
+    }
+   ],
+   "source": [
+    "out = f'{SCANS_FOLDER}/Subject{SUBJECT_NUMBER}/Subject{SUBJECT_NUMBER}_anon.obj'\n",
+    "written = save_anonymized_scan(surface_anon_dig, out, landmarks=landmarks_dig)\n",
+    "print('Wrote:')\n",
+    "for p in written:\n",
+    "    print(f'  {p}')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9b07bdbe-a5b7-405e-b86d-1169e0d863a2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 01a6317e64d9613e9ac760c1bf41f7656f63aa6e Mon Sep 17 00:00:00 2001
From: mah-diaa <mahmoud.diaa.abdeen@gmail.com>
Date: Sun, 3 May 2026 14:32:44 +0200
Subject: [PATCH 22/39] Drop tests for removed detect_landmarks_from_nasion;
 fix isolate_head body fixture

---
 tests/test_anonymization.py | 45 ++-----------------------------------
 1 file changed, 2 insertions(+), 43 deletions(-)

diff --git a/tests/test_anonymization.py b/tests/test_anonymization.py
index c2c1900d..4184780f 100644
--- a/tests/test_anonymization.py
+++ b/tests/test_anonymization.py
@@ -13,7 +13,6 @@
 from cedalion.geometry.photogrammetry.anonymization import (
     normalize_axes,
     isolate_head,
-    detect_landmarks_from_nasion,
 )
 
 
@@ -118,11 +117,9 @@ def test_head_only_scan_unchanged(self, simple_sphere_surface):
 
     def test_reduces_vertex_count_with_body(self):
         """Surface with body appended should have vertices removed."""
-        # Head sphere
         sphere = trimesh.creation.icosphere(subdivisions=3, radius=100)
-        # Body box far below
-        body = trimesh.creation.box(extents=[50, 50, 50])
-        body.vertices[:, 0] -= 400  # move far below head
+        body = trimesh.creation.icosphere(subdivisions=3, radius=80)
+        body.vertices[:, 0] -= 400
         combined = trimesh.util.concatenate([sphere, body])
 
         surface = cdc.TrimeshSurface(
@@ -131,41 +128,3 @@ def test_reduces_vertex_count_with_body(self):
         nasion = np.array([0, 100, 0])
         head_surface, mask = isolate_head(surface, nasion)
         assert head_surface.nvertices < surface.nvertices
-
-
-# ============================================================================
-# Landmark Detection from Nasion
-# ============================================================================
-
-
-class TestDetectLandmarksFromNasion:
-    """Tests for landmark detection from nasion."""
-
-    def test_returns_five_landmarks(self, head_like_surface):
-        """Should detect exactly 5 landmarks."""
-        nasion = np.array([0, 90, 0])  # front of elongated sphere
-        landmarks = detect_landmarks_from_nasion(head_like_surface, nasion)
-
-        assert len(landmarks.label) == 5
-        expected_labels = {"Nz", "Iz", "Cz", "LPA", "RPA"}
-        assert set(str(l) for l in landmarks.label.values) == expected_labels
-
-    def test_cz_is_highest(self, head_like_surface):
-        """Cz should have the highest X coordinate."""
-        nasion = np.array([0, 90, 0])
-        landmarks = detect_landmarks_from_nasion(head_like_surface, nasion)
-        lm = landmarks.pint.dequantify()
-
-        cz_x = float(lm.sel(label="Cz").values[0])
-        for label in ["Nz", "Iz", "LPA", "RPA"]:
-            other_x = float(lm.sel(label=label).values[0])
-            assert cz_x >= other_x - 1.0, f"Cz should be highest, but {label} has higher X"
-
-    def test_nz_position_preserved(self, head_like_surface):
-        """Nz output should match the input nasion position."""
-        nasion = np.array([0, 90, 0])
-        landmarks = detect_landmarks_from_nasion(head_like_surface, nasion)
-        nz_out = landmarks.sel(label="Nz").pint.dequantify().values
-        assert_allclose(nz_out, nasion, atol=1e-6)
-
-

From 9b7f408d117eb5e0fb59a9b706ac75090cd964da Mon Sep 17 00:00:00 2001
From: mah-diaa <mahmoud.diaa.abdeen@gmail.com>
Date: Sun, 3 May 2026 14:47:26 +0200
Subject: [PATCH 23/39] Expand anonymization test coverage: all 8 public
 functions plus end-to-end pipeline

---
 tests/test_anonymization.py | 402 +++++++++++++++++++++++++++---------
 1 file changed, 306 insertions(+), 96 deletions(-)

diff --git a/tests/test_anonymization.py b/tests/test_anonymization.py
index 4184780f..cda098b4 100644
--- a/tests/test_anonymization.py
+++ b/tests/test_anonymization.py
@@ -1,29 +1,29 @@
-"""Unit tests for face anonymization module."""
+"""Unit tests for the face anonymization pipeline."""
+
+import os
 
 import numpy as np
 import pytest
 import trimesh
-import xarray as xr
 from numpy.testing import assert_allclose
 
 import cedalion
 import cedalion.dataclasses as cdc
-from cedalion import units
 
 from cedalion.geometry.photogrammetry.anonymization import (
-    normalize_axes,
+    align_axes_from_landmarks,
+    delete_masked_vertices,
+    detect_cap_boundary,
+    face_mask_from_landmarks,
     isolate_head,
+    normalize_axes,
+    revert_to_einstar_frame,
+    save_anonymized_scan,
 )
 
 
-# ============================================================================
-# Fixtures
-# ============================================================================
-
-
 @pytest.fixture
 def simple_sphere_surface():
-    """Create a simple sphere mesh as a test surface."""
     sphere = trimesh.creation.icosphere(subdivisions=3, radius=100)
     return cdc.TrimeshSurface(
         mesh=sphere, crs="scanner", units=cedalion.units.millimeter
@@ -32,16 +32,11 @@ def simple_sphere_surface():
 
 @pytest.fixture
 def head_like_surface():
-    """Create a more head-like elongated sphere surface.
-
-    Axis convention: X=up, Y=anterior, Z=left.
-    Nasion placed at (0, 100, 0) = front of sphere.
-    """
+    """Elongated sphere; X=up, Y=anterior, Z=left (axis-normalized frame)."""
     sphere = trimesh.creation.icosphere(subdivisions=4, radius=100)
     vertices = sphere.vertices.copy()
-    vertices[:, 0] *= 1.2  # taller
-    vertices[:, 1] *= 0.9  # slightly narrow front-to-back
-
+    vertices[:, 0] *= 1.2
+    vertices[:, 1] *= 0.9
     head_mesh = trimesh.Trimesh(vertices=vertices, faces=sphere.faces)
     return cdc.TrimeshSurface(
         mesh=head_mesh, crs="scanner", units=cedalion.units.millimeter
@@ -49,82 +44,297 @@ def head_like_surface():
 
 
 @pytest.fixture
-def anatomical_landmarks():
-    """Create anatomical landmarks on a sphere (already axis-normalized)."""
-    landmarks = np.array([
-        [0, 100, 0],    # Nz (front)
-        [0, -100, 0],   # Iz (back)
-        [120, 0, 0],    # Cz (top)
-        [-100, 0, 0],   # LPA (left)  -- note: Z used for left in real coords
-        [100, 0, 0],    # RPA (right)
+def axis_normalized_landmarks():
+    """5 anatomical landmarks in the post-normalize_axes frame (X=up, Y=ant, Z=left)."""
+    coords = np.array([
+        [0, 100, 0],     # Nz (anterior)
+        [0, -100, 0],    # Iz (posterior)
+        [100, 0, 0],     # Cz (top, +X)
+        [0, 0, 100],     # LPA (left, +Z)
+        [0, 0, -100],    # RPA (right, -Z)
     ], dtype=float)
-    labels = ["Nz", "Iz", "Cz", "LPA", "RPA"]
-
-    return xr.DataArray(
-        landmarks,
-        dims=["label", "scanner"],
-        coords={
-            "label": labels,
-            "type": ("label", [cdc.PointType.LANDMARK] * 5),
-        },
-        attrs={"units": "mm"},
-    ).pint.quantify()
-
-
-# ============================================================================
-# Axis Normalization
-# ============================================================================
-
-
-class TestNormalizeAxes:
-    """Tests for axis normalization."""
-
-    def test_rotation_matrix_orthogonal(self, simple_sphere_surface):
-        """Rotation matrix should be orthogonal (R @ R.T = I)."""
-        nasion = np.array([0, 50, 50])
-        _, _, R = normalize_axes(simple_sphere_surface, nasion)
-        assert_allclose(R @ R.T, np.eye(3), atol=1e-10)
-
-    def test_nasion_rotated_to_positive_y(self, simple_sphere_surface):
-        """After normalization, nasion Y should be positive (anterior)."""
-        nasion = np.array([0, 50, 50])  # forward in YZ diagonal
-        _, rotated_nasion, _ = normalize_axes(simple_sphere_surface, nasion)
-        # Y should now dominate (anterior direction)
-        assert rotated_nasion[1] > 0
-
-    def test_identity_when_already_aligned(self, simple_sphere_surface):
-        """If nasion already on +Y axis, rotation should be ~identity."""
-        nasion = np.array([0, 100, 0])
-        _, rotated_nasion, R = normalize_axes(simple_sphere_surface, nasion)
-        assert_allclose(R, np.eye(3), atol=1e-6)
-        assert_allclose(rotated_nasion, nasion, atol=1e-6)
-
-
-# ============================================================================
-# Head Isolation
-# ============================================================================
-
-
-class TestIsolateHead:
-    """Tests for head isolation."""
-
-    def test_head_only_scan_unchanged(self, simple_sphere_surface):
-        """A sphere (no body) should be returned largely unchanged."""
-        nasion = np.array([0, 100, 0])
-        head_surface, mask = isolate_head(simple_sphere_surface, nasion)
-        # Sphere radius=100, default sphere radius=220 -> everything fits
-        assert mask.mean() > 0.9
-
-    def test_reduces_vertex_count_with_body(self):
-        """Surface with body appended should have vertices removed."""
-        sphere = trimesh.creation.icosphere(subdivisions=3, radius=100)
-        body = trimesh.creation.icosphere(subdivisions=3, radius=80)
-        body.vertices[:, 0] -= 400
-        combined = trimesh.util.concatenate([sphere, body])
-
-        surface = cdc.TrimeshSurface(
-            combined, crs="scanner", units=cedalion.units.millimeter
-        )
-        nasion = np.array([0, 100, 0])
-        head_surface, mask = isolate_head(surface, nasion)
-        assert head_surface.nvertices < surface.nvertices
+    return cdc.build_labeled_points(
+        coords,
+        crs="scanner",
+        units="mm",
+        labels=["Nz", "Iz", "Cz", "LPA", "RPA"],
+        types=[cdc.PointType.LANDMARK] * 5,
+    )
+
+
+# normalize_axes ------------------------------------------------------------
+
+
+def test_normalize_rotation_orthogonal(simple_sphere_surface):
+    nasion = np.array([0, 50, 50])
+    _, _, R = normalize_axes(simple_sphere_surface, nasion)
+    assert_allclose(R @ R.T, np.eye(3), atol=1e-10)
+
+
+def test_normalize_nasion_to_positive_y(simple_sphere_surface):
+    nasion = np.array([0, 50, 50])
+    _, rotated_nasion, _ = normalize_axes(simple_sphere_surface, nasion)
+    assert rotated_nasion[1] > 0
+
+
+def test_normalize_identity_when_aligned(simple_sphere_surface):
+    nasion = np.array([0, 100, 0])
+    _, rotated_nasion, R = normalize_axes(simple_sphere_surface, nasion)
+    assert_allclose(R, np.eye(3), atol=1e-6)
+    assert_allclose(rotated_nasion, nasion, atol=1e-6)
+
+
+# isolate_head --------------------------------------------------------------
+
+
+def test_isolate_head_only_scan_unchanged(simple_sphere_surface):
+    nasion = np.array([0, 100, 0])
+    _, mask = isolate_head(simple_sphere_surface, nasion)
+    assert mask.mean() > 0.9
+
+
+def test_isolate_reduces_count_with_body():
+    sphere = trimesh.creation.icosphere(subdivisions=3, radius=100)
+    body = trimesh.creation.icosphere(subdivisions=3, radius=80)
+    body.vertices[:, 0] -= 400
+    combined = trimesh.util.concatenate([sphere, body])
+
+    surface = cdc.TrimeshSurface(
+        combined, crs="scanner", units=cedalion.units.millimeter
+    )
+    nasion = np.array([0, 100, 0])
+    head_surface, _ = isolate_head(surface, nasion)
+    assert head_surface.nvertices < surface.nvertices
+
+
+# align_axes_from_landmarks -------------------------------------------------
+
+
+def test_align_origin_at_ear_midpoint(simple_sphere_surface, axis_normalized_landmarks):
+    _, aligned_lm, _ = align_axes_from_landmarks(
+        simple_sphere_surface, axis_normalized_landmarks
+    )
+    lm = aligned_lm.pint.dequantify()
+    lpa = lm.sel(label="LPA").values
+    rpa = lm.sel(label="RPA").values
+    assert_allclose(0.5 * (lpa + rpa), [0, 0, 0], atol=1e-6)
+
+
+def test_align_axes_orientation(simple_sphere_surface, axis_normalized_landmarks):
+    _, aligned_lm, _ = align_axes_from_landmarks(
+        simple_sphere_surface, axis_normalized_landmarks
+    )
+    lm = aligned_lm.pint.dequantify()
+    nz = lm.sel(label="Nz").values
+    lpa = lm.sel(label="LPA").values
+    cz = lm.sel(label="Cz").values
+    assert nz[0] > abs(nz[1]) and nz[0] > abs(nz[2])
+    assert lpa[1] > abs(lpa[0]) and lpa[1] > abs(lpa[2])
+    assert cz[2] > abs(cz[0]) and cz[2] > abs(cz[1])
+
+
+def test_align_returns_ctf_crs(simple_sphere_surface, axis_normalized_landmarks):
+    aligned_surface, _, _ = align_axes_from_landmarks(
+        simple_sphere_surface, axis_normalized_landmarks
+    )
+    assert aligned_surface.crs == "ctf"
+
+
+def test_align_raises_on_missing_landmark(
+    simple_sphere_surface, axis_normalized_landmarks
+):
+    partial = axis_normalized_landmarks.isel(label=slice(0, 4))
+    with pytest.raises(ValueError, match="Missing landmarks"):
+        align_axes_from_landmarks(simple_sphere_surface, partial)
+
+
+# detect_cap_boundary -------------------------------------------------------
+
+
+def test_cap_boundary_in_sane_range():
+    """cap_z stays inside the documented [Nz[2], Nz[2] + ceiling] window."""
+    sphere = trimesh.creation.icosphere(subdivisions=4, radius=100)
+    verts = np.asarray(sphere.vertices)
+    Nz = np.array([100, 0, 0])
+    Cz = np.array([0, 0, 100])
+    ear_mid = np.array([0, 0, 0])
+    cap_z, *_ = detect_cap_boundary(verts, Nz, Cz, ear_mid, mid_y=0.0)
+    assert Nz[2] <= cap_z <= Nz[2] + 40.0
+
+
+def test_cap_boundary_failsafe_clamps_implausible_cap():
+    """Failsafe clamps cap_z to Nz[2] + eyebrow_offset_mm when detection runs hot.
+
+    Builds a midline X-max profile that rises steeply early then creeps gently
+    up to Cz; the gradient walk-back catches the gentle phase at high z, so
+    without the failsafe cap_z would land above the ceiling.
+    """
+    z = np.linspace(0.5, 99.5, 200)
+    x = 50.0 + 50.0 * np.tanh(z / 8.0) + 0.05 * z
+    y = np.zeros_like(z)
+    anterior = np.column_stack([x, y, z])
+    posterior = np.column_stack([-x, y, z])
+    verts = np.vstack([anterior, posterior])
+    Nz = np.array([100.0, 0.0, 0.0])
+    Cz = np.array([0.0, 0.0, 100.0])
+    ear_mid = np.array([0.0, 0.0, 0.0])
+    cap_z, *_ = detect_cap_boundary(verts, Nz, Cz, ear_mid, mid_y=0.0)
+    assert cap_z == pytest.approx(Nz[2] + 10.0)
+
+
+# face_mask_from_landmarks --------------------------------------------------
+
+
+def test_face_mask_region_semantics():
+    Nz = np.array([100, 0, 10])
+    Iz = np.array([-100, 0, 0])
+    Cz = np.array([0, 0, 100])
+    Lpa = np.array([0, 100, 0])
+    Rpa = np.array([0, -100, 0])
+    verts = np.array([
+        [50, 0, 0],     # anterior, below cap
+        [-50, 0, 0],    # posterior
+        [50, 0, 50],    # anterior, above cap
+    ])
+    mask, _ = face_mask_from_landmarks(verts, Nz, Iz, Cz, Lpa, Rpa, cap_z=20.0)
+    assert mask.tolist() == [True, False, False]
+
+
+def test_face_mask_ear_sphere():
+    Nz = np.array([100, 0, 0])
+    Iz = np.array([-100, 0, 0])
+    Cz = np.array([0, 0, 100])
+    Lpa = np.array([0, 100, 0])
+    Rpa = np.array([0, -100, 0])
+    # 10mm posterior to ear midpoint, but 20mm from LPA (within ear_delete_radius=40)
+    verts = np.array([[-10, 80, 0]])
+    mask, _ = face_mask_from_landmarks(
+        verts, Nz, Iz, Cz, Lpa, Rpa, cap_z=10.0, ear_delete_radius=40.0
+    )
+    assert mask[0]
+
+
+# delete_masked_vertices ----------------------------------------------------
+
+
+def test_delete_no_op_on_false_mask(simple_sphere_surface):
+    mask = np.zeros(simple_sphere_surface.nvertices, dtype=bool)
+    result = delete_masked_vertices(simple_sphere_surface, mask)
+    assert result.nvertices == simple_sphere_surface.nvertices
+
+
+def test_delete_partial_mask_reduces_count(simple_sphere_surface):
+    mask = np.asarray(simple_sphere_surface.mesh.vertices)[:, 0] > 0
+    result = delete_masked_vertices(simple_sphere_surface, mask)
+    assert result.nvertices < simple_sphere_surface.nvertices
+
+
+def test_delete_preserves_crs_and_units(simple_sphere_surface):
+    mask = np.zeros(simple_sphere_surface.nvertices, dtype=bool)
+    mask[0] = True
+    result = delete_masked_vertices(simple_sphere_surface, mask)
+    assert result.crs == simple_sphere_surface.crs
+    assert result.units == simple_sphere_surface.units
+
+
+# revert_to_einstar_frame ---------------------------------------------------
+
+
+def test_revert_round_trip_with_align(simple_sphere_surface, axis_normalized_landmarks):
+    aligned_surface, aligned_lm, M = align_axes_from_landmarks(
+        simple_sphere_surface, axis_normalized_landmarks
+    )
+    reverted_surface, _ = revert_to_einstar_frame(
+        aligned_surface, aligned_lm, R_normalize=np.eye(3), M_align=M
+    )
+    assert_allclose(
+        np.asarray(reverted_surface.mesh.vertices),
+        np.asarray(simple_sphere_surface.mesh.vertices),
+        atol=1e-6,
+    )
+
+
+def test_revert_returns_digitized_crs(
+    simple_sphere_surface, axis_normalized_landmarks
+):
+    aligned_surface, aligned_lm, M = align_axes_from_landmarks(
+        simple_sphere_surface, axis_normalized_landmarks
+    )
+    reverted_surface, _ = revert_to_einstar_frame(
+        aligned_surface, aligned_lm, np.eye(3), M
+    )
+    assert reverted_surface.crs == "digitized"
+
+
+# save_anonymized_scan ------------------------------------------------------
+
+
+def test_save_raises_on_bad_extension(simple_sphere_surface, tmp_path):
+    out = str(tmp_path / "out.txt")
+    with pytest.raises(ValueError, match=".obj"):
+        save_anonymized_scan(simple_sphere_surface, out)
+
+
+def test_save_geometry_only(simple_sphere_surface, tmp_path):
+    out = str(tmp_path / "anon.obj")
+    written = save_anonymized_scan(simple_sphere_surface, out, strip_texture=True)
+    assert os.path.exists(out)
+    assert any(p.endswith(".obj") for p in written)
+
+
+def test_save_writes_landmark_tsv(
+    simple_sphere_surface, axis_normalized_landmarks, tmp_path
+):
+    out = str(tmp_path / "anon.obj")
+    written = save_anonymized_scan(
+        simple_sphere_surface,
+        out,
+        landmarks=axis_normalized_landmarks,
+        strip_texture=True,
+    )
+    assert any(p.endswith("_landmarks.tsv") for p in written)
+
+
+# end-to-end pipeline -------------------------------------------------------
+
+
+def test_full_anonymization_pipeline(
+    head_like_surface, axis_normalized_landmarks, tmp_path
+):
+    nasion = axis_normalized_landmarks.pint.dequantify().sel(label="Nz").values
+    surface_n, nasion_n, R = normalize_axes(head_like_surface, nasion)
+    lm_arr = axis_normalized_landmarks.pint.dequantify().values
+    landmarks_n = (
+        axis_normalized_landmarks.pint.dequantify()
+        .copy(data=lm_arr @ R.T)
+        .pint.quantify()
+    )
+    surface_n, _ = isolate_head(surface_n, nasion_n)
+
+    surface_h, landmarks_n, M_ctf = align_axes_from_landmarks(surface_n, landmarks_n)
+    verts = np.asarray(surface_h.mesh.vertices)
+    lm_n = landmarks_n.pint.dequantify()
+    Nz = lm_n.sel(label="Nz").values
+    Cz = lm_n.sel(label="Cz").values
+    Lpa = lm_n.sel(label="LPA").values
+    Rpa = lm_n.sel(label="RPA").values
+    ear_mid = 0.5 * (Lpa + Rpa)
+    mid_y = 0.5 * (Lpa[1] + Rpa[1])
+
+    cap_z, *_ = detect_cap_boundary(verts, Nz, Cz, ear_mid, mid_y)
+    Iz = lm_n.sel(label="Iz").values
+    mask, _ = face_mask_from_landmarks(verts, Nz, Iz, Cz, Lpa, Rpa, cap_z=cap_z)
+    surface_anon = delete_masked_vertices(surface_h, mask)
+    surface_anon_dig, landmarks_dig = revert_to_einstar_frame(
+        surface_anon, landmarks_n, R, M_ctf
+    )
+
+    out = str(tmp_path / "anon.obj")
+    written = save_anonymized_scan(
+        surface_anon_dig, out, landmarks=landmarks_dig, strip_texture=True
+    )
+
+    assert surface_anon.nvertices < surface_h.nvertices
+    assert surface_anon_dig.crs == "digitized"
+    assert os.path.exists(out)
+    assert any(p.endswith("_landmarks.tsv") for p in written)

From fe865856a88f60320f48c6d3eae99ed41371c342 Mon Sep 17 00:00:00 2001
From: mah-diaa <mahmoud.diaa.abdeen@gmail.com>
Date: Sun, 3 May 2026 20:33:11 +0200
Subject: [PATCH 24/39] Refactor anonymization module: add anonymize_scan
 orchestrator and shared utils, remove dead parameters and never-firing guards

---
 .../photogrammetry/anonymization/__init__.py  |  58 ++---
 .../photogrammetry/anonymization/_utils.py    |  93 +++++++
 .../photogrammetry/anonymization/mask.py      | 166 ++++++-------
 .../photogrammetry/anonymization/pipeline.py  | 161 ++++++++++++
 .../anonymization/preprocessing.py            | 230 ++++++++----------
 5 files changed, 451 insertions(+), 257 deletions(-)
 create mode 100644 src/cedalion/geometry/photogrammetry/anonymization/_utils.py
 create mode 100644 src/cedalion/geometry/photogrammetry/anonymization/pipeline.py

diff --git a/src/cedalion/geometry/photogrammetry/anonymization/__init__.py b/src/cedalion/geometry/photogrammetry/anonymization/__init__.py
index e26a3582..a99b989a 100644
--- a/src/cedalion/geometry/photogrammetry/anonymization/__init__.py
+++ b/src/cedalion/geometry/photogrammetry/anonymization/__init__.py
@@ -1,39 +1,38 @@
 """Face anonymization module for photogrammetry scans.
 
-This module provides tools to anonymize facial regions in 3D photogrammetry
-scans while preserving optode positions and anatomical landmarks for fNIRS
-research. The anonymization complies with GDPR requirements while maintaining
-scientific utility.
+Anonymizes facial regions in 3D photogrammetry scans while preserving
+optode positions and anatomical landmarks for fNIRS research.
 
-The pipeline works as follows:
-
-1. User picks the 5 landmarks (Nz, Iz, Cz, LPA, RPA) via the upstream
-   ``cedalion.vis.blocks.plot_surface(pick_landmarks=True)`` picker.
-2. Normalize axes so Y=anterior, Z=left (``preprocessing``).
-3. Isolate the head and remove disconnected fragments (``preprocessing``).
-4. Align the full frame from the 5 landmarks (``preprocessing``).
-5. Detect the cap boundary, build the deletion mask, and delete the masked
-   vertices (``mask``).
-6. Optionally revert the aligned surface and landmarks back to the raw
-   Einstar frame via ``revert_to_einstar_frame`` so the saved files carry
-   ``crs="digitized"`` and match the co-registration tutorial's input
-   convention (``read_einstar_obj`` output).
-7. Save the anonymized mesh (``.obj``) and the landmarks (``.tsv``) via
-   ``save_anonymized_scan(surface, out_path, landmarks=...)``. The TSV is
-   what the co-registration tutorial loads at step 5.2.
+The canonical entry point is :func:`anonymize_scan`. Pass it a raw
+Einstar surface and the 5 landmarks (Nz, Iz, Cz, LPA, RPA) and it returns
+the anonymized surface plus landmarks, ready for
+:func:`save_anonymized_scan`.
 
 Example:
     >>> from cedalion.geometry.photogrammetry.anonymization import (
-    ...     normalize_axes, isolate_head, align_axes_from_landmarks,
-    ...     detect_cap_boundary, face_mask_from_landmarks,
-    ...     delete_masked_vertices,
+    ...     anonymize_scan, save_anonymized_scan,
     ... )
-    >>> surface, nz, R = normalize_axes(surface, landmarks.sel(label='Nz'))
-    >>> surface, _ = isolate_head(surface, nz)
-    >>> surface, landmarks, R = align_axes_from_landmarks(surface, landmarks)
+    >>> surface_anon, landmarks_anon = anonymize_scan(surface, landmarks)
+    >>> save_anonymized_scan(surface_anon, "out.obj", landmarks=landmarks_anon)
+
+The pipeline ``anonymize_scan`` runs internally is:
+
+1. :func:`normalize_axes` — rotate around X so Y points anterior.
+2. :func:`isolate_head` — strip body, shoulders, and disconnected
+   fragments.
+3. :func:`align_axes_from_landmarks` — map into the CTF frame.
+4. :func:`detect_cap_boundary` — find the cap front edge along Z.
+5. :func:`face_mask_from_landmarks` — face region + ear spheres, clamped
+   below the cap.
+6. Preserve small spheres around each landmark and a midline nasion strip.
+7. :func:`delete_masked_vertices` — drop triangles touching any masked
+   vertex.
+8. :func:`revert_to_einstar_frame` — return to ``crs="digitized"`` so the
+   output matches :func:`cedalion.io.read_einstar_obj`.
 
-Initial Contributors:
-    - Face Anonymization Project | 2024
+Each of those functions is also exported so callers that need to inspect
+or override an intermediate step can do so without re-implementing the
+whole pipeline.
 """
 
 from .preprocessing import (
@@ -48,9 +47,12 @@
     delete_masked_vertices,
     save_anonymized_scan,
 )
+from .pipeline import anonymize_scan
 
 
 __all__ = [
+    # Top-level orchestrator (canonical entry point)
+    "anonymize_scan",
     # Preprocessing (axis normalization, head isolation, full alignment,
     # and the inverse mapping back to the raw Einstar frame)
     "normalize_axes",
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/_utils.py b/src/cedalion/geometry/photogrammetry/anonymization/_utils.py
new file mode 100644
index 00000000..b953b2d6
--- /dev/null
+++ b/src/cedalion/geometry/photogrammetry/anonymization/_utils.py
@@ -0,0 +1,93 @@
+"""Private helpers shared by ``preprocessing`` and ``mask``.
+
+Centralizes patterns that would otherwise drift across the pipeline:
+texture-image lookup, face/vertex reindexing, affine application to plain
+arrays and to ``LabeledPoints``, ear midpoint, and the upper-head centroid.
+"""
+
+from typing import Callable
+
+import numpy as np
+
+import cedalion.typing as cdt
+
+
+def _resolve_texture_image(visual):
+    """Return ``visual.image`` if set, else fall back to ``material.image``."""
+    image = getattr(visual, "image", None)
+    if image is not None:
+        return image
+    mat = getattr(visual, "material", None)
+    return getattr(mat, "image", None) if mat is not None else None
+
+
+def _reindex_faces(
+    vertices: np.ndarray,
+    faces: np.ndarray,
+    kept_face_mask: np.ndarray,
+) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+    """Slice a triangle mesh by a face mask and rebuild dense vertex indices.
+
+    Args:
+        vertices: ``(N, 3)`` vertex array.
+        faces: ``(F, 3)`` face index array.
+        kept_face_mask: ``(F,)`` bool array.
+
+    Returns:
+        ``(new_vertices, new_faces, kept_vidx)`` where ``new_faces`` indexes
+        into ``new_vertices`` and ``kept_vidx`` are the original vertex
+        indices that survived (useful for slicing UVs / vertex colors).
+    """
+    kept_faces = faces[kept_face_mask]
+    kept_vidx = np.unique(kept_faces)
+    reindex = -np.ones(len(vertices), dtype=np.int64)
+    reindex[kept_vidx] = np.arange(len(kept_vidx))
+    new_faces = reindex[kept_faces]
+    new_vertices = vertices[kept_vidx]
+    return new_vertices, new_faces, kept_vidx
+
+
+def _apply_affine(points: np.ndarray, M: np.ndarray) -> np.ndarray:
+    """Apply a 4x4 affine to an ``(N, 3)`` point array."""
+    return points @ M[:3, :3].T + M[:3, 3]
+
+
+def _transform_labeled_points(
+    landmarks: cdt.LabeledPoints,
+    transform_xyz: Callable[[np.ndarray], np.ndarray],
+    new_crs: str,
+) -> cdt.LabeledPoints:
+    """Transform a LabeledPoints array and rename its spatial dim.
+
+    Wraps the dequantify -> math -> ``copy(data=...)`` -> rename -> quantify
+    cycle that ``align_axes_from_landmarks`` and ``revert_to_einstar_frame``
+    both need.
+    """
+    dequant = landmarks.pint.dequantify()
+    new_xyz = transform_xyz(dequant.values)
+    old_crs_dim = next(d for d in landmarks.dims if d != "label")
+    return (
+        dequant.copy(data=new_xyz)
+        .rename({old_crs_dim: new_crs})
+        .pint.quantify()
+    )
+
+
+def _ear_midpoint(Lpa: np.ndarray, Rpa: np.ndarray) -> np.ndarray:
+    """Return the LPA/RPA midpoint (CTF origin in the aligned frame)."""
+    return 0.5 * (Lpa + Rpa)
+
+
+def _upper_head_centroid(
+    vertices: np.ndarray,
+) -> tuple[np.ndarray, float]:
+    """Centroid of vertices in the top 40% of the X range.
+
+    Returns ``(centroid_xyz, x_max)``. Callers that only need the YZ
+    components zero out X themselves.
+    """
+    x_max = float(vertices[:, 0].max())
+    x_min = float(vertices[:, 0].min())
+    upper = vertices[vertices[:, 0] > x_min + 0.6 * (x_max - x_min)]
+    centroid = upper.mean(axis=0)
+    return centroid, x_max
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/mask.py b/src/cedalion/geometry/photogrammetry/anonymization/mask.py
index 69b93a04..00f4fa5e 100644
--- a/src/cedalion/geometry/photogrammetry/anonymization/mask.py
+++ b/src/cedalion/geometry/photogrammetry/anonymization/mask.py
@@ -11,16 +11,22 @@
 
 All mask math assumes the CTF frame: +X=anterior, +Y=left, +Z=up, origin
 at the LPA-RPA midpoint (see ``align_axes_from_landmarks``).
-
-Initial Contributors:
-    - Face Anonymization Project | 2024
 """
 
 import logging
+import os
 
 import numpy as np
+import trimesh
+from PIL import Image, ImageDraw, ImageFilter
+from scipy.signal import savgol_filter
 
 import cedalion.dataclasses as cdc
+import cedalion.typing as cdt
+from cedalion.io import export_to_tsv
+
+from ._utils import _ear_midpoint, _reindex_faces, _resolve_texture_image
+from .preprocessing import _rebuild_mesh
 
 logger = logging.getLogger("cedalion")
 
@@ -29,8 +35,8 @@ def detect_cap_boundary(
     verts: np.ndarray,
     Nz: np.ndarray,
     Cz: np.ndarray,
-    ear_mid: np.ndarray,
-    mid_y: float,
+    Lpa: np.ndarray,
+    Rpa: np.ndarray,
     band_width: float = 15.0,
     bin_size: float = 1.0,
     foot_grad_threshold: float = 0.2,
@@ -40,17 +46,15 @@ def detect_cap_boundary(
     """Find the Z height where the EEG cap front edge sits.
 
     Scans upward from Nz along the midline and records max-X per Z-bin. The
-    cap protrudes in +X (anterior), so X-max along the midline lives on the
-    cap. The cap edge is the foot of the steep rise leading to the peak:
-    walk back from the peak until the smoothed gradient drops below
-    ``foot_grad_threshold``.
-
-    Failsafe for flush / no-optode caps: when the cap sits flat against
-    the head (no optodes), the anterior bump vanishes and the X-max
-    profile is roughly monotonic up to ``Cz``, leaving ``cap_z`` stuck
-    near the crown. If the detected ``cap_z`` exceeds
-    ``Nz[2] + cap_z_ceiling_mm``, fall back to a fixed cut at
-    ``Nz[2] + eyebrow_offset_mm`` (just above the supraorbital ridge).
+    cap protrudes anteriorly, so X-max on the midline traces the cap. The
+    cap edge is the foot of the rise leading to the peak: walk back from the
+    peak until the smoothed gradient drops below ``foot_grad_threshold``.
+
+    Failsafe for flush / no-optode caps: when the cap sits flat against the
+    head, the anterior bump vanishes and X-max is roughly monotonic up to
+    ``Cz``, stranding ``cap_z`` near the crown. If detection lands above
+    ``Nz[2] + cap_z_ceiling_mm``, fall back to ``Nz[2] + eyebrow_offset_mm``
+    (just above the supraorbital ridge).
 
     Expects the CTF frame (+X=anterior, +Y=left, +Z=up).
 
@@ -58,8 +62,8 @@ def detect_cap_boundary(
         verts: Mesh vertices, shape (N, 3).
         Nz: Nasion position.
         Cz: Cz position.
-        ear_mid: Midpoint of Lpa/Rpa.
-        mid_y: Midline Y value (e.g. ``0.5 * (Lpa[1] + Rpa[1])``).
+        Lpa: Left preauricular position.
+        Rpa: Right preauricular position.
         band_width: Y-band half-width for the midline X-profile (mm).
         bin_size: Z-bin size for the X-profile (mm).
         foot_grad_threshold: dX/dZ below this value marks the foot of the rise.
@@ -71,8 +75,8 @@ def detect_cap_boundary(
     Returns:
         Tuple of (cap_z, profile_z, profile_x_raw, profile_x_smooth).
     """
-    from scipy.signal import savgol_filter
-
+    ear_mid = _ear_midpoint(Lpa, Rpa)
+    mid_y = ear_mid[1]
     in_band = np.abs(verts[:, 1] - mid_y) < band_width
     above_nz = verts[:, 2] > Nz[2]
     anterior = verts[:, 0] > ear_mid[0]
@@ -96,7 +100,6 @@ def detect_cap_boundary(
     xv = max_x[valid]
 
     win = min(11, len(xv) if len(xv) % 2 == 1 else len(xv) - 1)
-    win = max(win, 5)
     xv_s = savgol_filter(xv, window_length=win, polyorder=2)
 
     peak_idx = int(np.argmax(xv_s))
@@ -123,14 +126,12 @@ def detect_cap_boundary(
 def face_mask_from_landmarks(
     verts: np.ndarray,
     Nz: np.ndarray,
-    Iz: np.ndarray,
-    Cz: np.ndarray,
     Lpa: np.ndarray,
     Rpa: np.ndarray,
     cap_z: float | None = None,
     ear_delete_radius: float = 40.0,
 ) -> tuple[np.ndarray, dict]:
-    """Build face + ear deletion mask from the 5 landmarks (CTF frame).
+    """Build face + ear deletion mask from the landmarks (CTF frame).
 
     Mask is the union of two regions, both clamped below the cap boundary:
 
@@ -141,7 +142,9 @@ def face_mask_from_landmarks(
 
     Args:
         verts: Mesh vertices, shape (N, 3).
-        Nz, Iz, Cz, Lpa, Rpa: 5 landmark positions in the CTF frame.
+        Nz: Nasion position in the CTF frame.
+        Lpa: Left preauricular position in the CTF frame.
+        Rpa: Right preauricular position in the CTF frame.
         cap_z: Upper bound Z value (typically from ``detect_cap_boundary``).
             Defaults to Nz[2] if not provided.
         ear_delete_radius: Sphere radius around Lpa/Rpa in mm.
@@ -151,7 +154,7 @@ def face_mask_from_landmarks(
         ``info`` has keys ``upper_bound``, ``ear_mid``, and ``counts``
         (per-region vertex counts).
     """
-    ear_mid = 0.5 * (Lpa + Rpa)
+    ear_mid = _ear_midpoint(Lpa, Rpa)
     upper_bound = cap_z if cap_z is not None else Nz[2]
 
     below_cap = verts[:, 2] < upper_bound
@@ -177,16 +180,16 @@ def face_mask_from_landmarks(
     return mask, info
 
 
+@cdc.validate_schemas
 def delete_masked_vertices(
     surface: cdc.TrimeshSurface,
     mask: np.ndarray,
 ) -> cdc.TrimeshSurface:
     """Drop triangles touching any masked vertex and strip unreferenced vertices.
 
-    Reindexes ``mesh.visual.uv`` (if present) in lockstep with the vertex array.
-    Trimesh's ``remove_unreferenced_vertices`` does not touch UV arrays, so a
-    naive call leaves ``len(uv) != len(vertices)`` and any subsequent textured
-    export crashes or corrupts.
+    Reindexes ``mesh.visual.uv`` in lockstep with the vertex array.
+    ``Trimesh.remove_unreferenced_vertices`` does not touch UV arrays, so a
+    naive call leaves ``len(uv) != len(vertices)`` and breaks textured export.
 
     Args:
         surface: Input TrimeshSurface.
@@ -196,75 +199,48 @@ def delete_masked_vertices(
         New TrimeshSurface with masked vertices (and the faces touching them)
         removed. CRS, units, UVs, and texture image are preserved in sync.
     """
-    import trimesh
-
     old_mesh = surface.mesh
     old_verts = np.asarray(old_mesh.vertices)
     old_faces = np.asarray(old_mesh.faces)
-    n_old_verts = len(old_verts)
 
     kept_face_mask = ~mask[old_faces].any(axis=1)
-    kept_faces = old_faces[kept_face_mask]
-    kept_vidx = np.unique(kept_faces)
-
-    reindex = -np.ones(n_old_verts, dtype=np.int64)
-    reindex[kept_vidx] = np.arange(len(kept_vidx))
-
-    new_mesh = trimesh.Trimesh(
-        vertices=old_verts[kept_vidx],
-        faces=reindex[kept_faces],
-        process=False,
+    new_verts, new_faces, kept_vidx = _reindex_faces(
+        old_verts, old_faces, kept_face_mask
     )
 
-    old_visual = old_mesh.visual
-    uv = getattr(old_visual, "uv", None)
-    if uv is not None and len(uv) == n_old_verts:
-        new_mesh.visual = trimesh.visual.TextureVisuals(
-            uv=np.asarray(uv)[kept_vidx],
-            image=getattr(old_visual, "image", None),
-            material=getattr(old_visual, "material", None),
-        )
-    else:
-        vcol = getattr(old_visual, "vertex_colors", None)
-        if vcol is not None and len(vcol) == n_old_verts:
-            new_mesh.visual.vertex_colors = np.asarray(vcol)[kept_vidx]
+    new_mesh = _rebuild_mesh(
+        old_mesh,
+        vertices=new_verts,
+        faces=new_faces,
+        vertex_index=kept_vidx,
+    )
 
     return cdc.TrimeshSurface(
         mesh=new_mesh, crs=surface.crs, units=surface.units,
     )
 
 
-def _sanitize_texture_from_uv(
-    mesh,
-    fill: tuple[int, int, int] = (0, 0, 0),
-) -> "Image.Image | None":
+def _sanitize_texture_from_uv(mesh: trimesh.Trimesh) -> Image.Image | None:
     """Rebuild the texture image keeping only pixels referenced by mesh UVs.
 
-    Rasterizes every surviving face's UV triangle onto a keep-mask the size
-    of the source image. Pixels outside the mask are set to ``fill`` so
-    face-region pixels (no longer referenced after anonymization) cannot leak
+    Rasterizes every surviving face's UV triangle onto a keep-mask, then
+    blacks out pixels outside the mask so face-region pixels cannot leak
     through the JPG. The mask is dilated by 1 pixel to absorb anti-aliasing
     fringes at triangle boundaries.
 
-    OBJ UVs use the bottom-left origin convention; PIL images use top-left,
-    so V is flipped before rasterizing.
+    OBJ UVs use a bottom-left origin; PIL images use top-left, so V is
+    flipped before rasterizing.
 
     Args:
         mesh: A ``trimesh.Trimesh`` with ``mesh.visual`` as ``TextureVisuals``.
-        fill: RGB fill color for unreferenced pixels.
 
     Returns:
         Sanitized PIL Image in RGB mode, or ``None`` if the mesh has no
         usable texture (caller should fall back to geometry-only output).
     """
-    from PIL import Image, ImageDraw, ImageFilter
-
     visual = mesh.visual
     uv = getattr(visual, "uv", None)
-    image = getattr(visual, "image", None)
-    if image is None:
-        mat = getattr(visual, "material", None)
-        image = getattr(mat, "image", None) if mat is not None else None
+    image = _resolve_texture_image(visual)
 
     if uv is None or image is None or len(uv) != len(mesh.vertices):
         return None
@@ -286,7 +262,7 @@ def _sanitize_texture_from_uv(
 
     out = np.empty_like(img_rgb)
     out[keep] = img_rgb[keep]
-    out[~keep] = fill
+    out[~keep] = (0, 0, 0)
     return Image.fromarray(out, mode="RGB")
 
 
@@ -300,38 +276,34 @@ def _sanitize_texture_from_uv(
 )
 
 
+@cdc.validate_schemas
 def save_anonymized_scan(
     surface: cdc.TrimeshSurface,
     out_path: str,
-    landmarks: "xr.DataArray | None" = None,
+    landmarks: cdt.LabeledPoints | None = None,
     strip_texture: bool = False,
 ) -> list[str]:
     """Export an anonymized photogrammetry surface to disk.
 
     Default path (``strip_texture=False``): writes an ``.obj`` + ``.mtl`` +
-    sanitized ``.jpg`` bundle. The JPG is rebuilt from the original texture
-    so it contains colors *only* for UV regions still referenced by the
-    anonymized mesh; every other pixel (notably the face region) is set to
-    the fill color. Opening the JPG alone therefore reveals no face pixels.
-
-    ``strip_texture=True`` is an explicit opt-out that writes only the
-    geometry ``.obj`` (no MTL, no JPG).
+    sanitized ``.jpg`` bundle. The JPG is rebuilt to contain colors *only*
+    for UV regions still referenced by the anonymized mesh; face-region
+    pixels are replaced by the fill color, so opening the JPG alone reveals
+    no face.
 
-    If the input mesh has no usable texture (no UVs or no image), falls
-    back to geometry-only output with a warning.
+    ``strip_texture=True`` writes geometry only (no MTL, no JPG). Same
+    fallback applies when the input mesh has no usable texture, with a
+    warning.
 
-    If ``landmarks`` is provided, also writes ``{stem}_landmarks.tsv`` next to
-    the ``.obj`` using ``cedalion.io.export_to_tsv``. The resulting TSV is
-    the file that gets uploaded at step 5.2 of the cedalion photogrammetry
-    co-registration tutorial.
+    When ``landmarks`` is provided, also writes ``{stem}_landmarks.tsv`` via
+    ``cedalion.io.export_to_tsv`` for downstream co-registration.
 
     Args:
         surface: Anonymized TrimeshSurface (typically output of
             ``delete_masked_vertices``).
         out_path: Destination path ending in ``.obj``.
-        landmarks: Optional LabeledPoints of landmarks (Nz, Iz, LPA, RPA,
-            Cz) to persist alongside the mesh as a TSV. Should be in the same
-            frame as ``surface``.
+        landmarks: LabeledPoints (Nz, Iz, LPA, RPA, Cz) in the same frame
+            as ``surface``, persisted alongside the mesh as a TSV.
         strip_texture: If True, skip the MTL + JPG and write geometry only.
 
     Returns:
@@ -342,9 +314,6 @@ def save_anonymized_scan(
     Raises:
         ValueError: If ``out_path`` does not end in ``.obj``.
     """
-    import os
-    import trimesh
-
     if not out_path.lower().endswith(".obj"):
         raise ValueError(f"out_path must end in .obj, got: {out_path}")
 
@@ -353,6 +322,9 @@ def save_anonymized_scan(
 
     sanitized = None if strip_texture else _sanitize_texture_from_uv(surface.mesh)
 
+    # Bare rebuild: the visual is rewritten below (with the sanitized
+    # texture) or the mesh is exported geometry-only, so we deliberately
+    # skip ``_rebuild_mesh``'s visual-copy step.
     mesh_to_write = trimesh.Trimesh(
         vertices=np.asarray(surface.mesh.vertices),
         faces=np.asarray(surface.mesh.faces),
@@ -396,15 +368,13 @@ def save_anonymized_scan(
         written.append(out_path)
 
     if landmarks is not None:
-        from cedalion.io import export_to_tsv
-
         tsv_path = os.path.join(out_dir, f"{stem}_landmarks.tsv")
         export_to_tsv(tsv_path, landmarks)
         written.append(tsv_path)
     else:
         logger.warning(
             "save_anonymized_scan called without landmarks; "
-            "co-registration (tutorial step 5.2) will need them saved separately."
+            "downstream co-registration will need them saved separately."
         )
 
     logger.info(
@@ -417,10 +387,10 @@ def save_anonymized_scan(
 def _ensure_mtllib(obj_text: str, mtl_name: str) -> str:
     """Guarantee the OBJ references our MTL via ``mtllib`` and ``usemtl``.
 
-    Trimesh's OBJ exporter may or may not emit material lines depending on
-    version; rewrite deterministically so the MTL pairing is stable.
+    Trimesh's OBJ exporter emits material lines inconsistently across
+    versions; rewrite deterministically so the MTL pairing is stable.
     """
     lines = obj_text.splitlines()
-    lines = [l for l in lines if not l.startswith(("mtllib ", "usemtl "))]
+    lines = [line for line in lines if not line.startswith(("mtllib ", "usemtl "))]
     header = [f"mtllib {mtl_name}", "usemtl _texture"]
     return "\n".join(header + lines) + "\n"
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/pipeline.py b/src/cedalion/geometry/photogrammetry/anonymization/pipeline.py
new file mode 100644
index 00000000..3cbb6d2b
--- /dev/null
+++ b/src/cedalion/geometry/photogrammetry/anonymization/pipeline.py
@@ -0,0 +1,161 @@
+"""Top-level orchestrator: ``anonymize_scan``.
+
+Single entry point that wires together the granular helpers from
+``preprocessing`` and ``mask``. The granular functions remain public so
+downstream code can inspect intermediate state (e.g. the cap-detection
+profile, the head isolation mask), but the canonical pipeline lives here
+and only here.
+"""
+
+from typing import Literal
+
+import numpy as np
+
+import cedalion.dataclasses as cdc
+import cedalion.typing as cdt
+from cedalion.geometry.landmarks import normalize_landmarks_labels
+
+from ._utils import _ear_midpoint, _apply_affine, _transform_labeled_points
+from .mask import (
+    delete_masked_vertices,
+    detect_cap_boundary,
+    face_mask_from_landmarks,
+)
+from .preprocessing import (
+    align_axes_from_landmarks,
+    isolate_head,
+    normalize_axes,
+    revert_to_einstar_frame,
+)
+
+
+_REQUIRED_LABELS = ("Nz", "Iz", "Cz", "LPA", "RPA")
+
+
+@cdc.validate_schemas
+def anonymize_scan(
+    surface: cdc.TrimeshSurface,
+    landmarks: cdt.LabeledPoints,
+    *,
+    head_isolation_radius_mm: float = 220.0,
+    cap_band_width_mm: float = 15.0,
+    cap_bin_size_mm: float = 1.0,
+    cap_foot_grad_threshold: float = 0.2,
+    cap_z_ceiling_mm: float = 40.0,
+    eyebrow_offset_mm: float = 10.0,
+    ear_delete_radius_mm: float = 40.0,
+    landmark_keep_radius_mm: float = 8.0,
+    return_frame: Literal["digitized", "ctf"] = "digitized",
+) -> tuple[cdc.TrimeshSurface, cdt.LabeledPoints]:
+    """Run the full face-anonymization pipeline on a raw Einstar scan.
+
+    Steps (each is also exposed as a standalone function for inspection):
+
+    1. ``normalize_axes`` — rotate around X so Y points anterior.
+    2. ``isolate_head`` — strip body, shoulders, fragments.
+    3. ``align_axes_from_landmarks`` — map into the CTF frame.
+    4. ``detect_cap_boundary`` — find the cap front edge along Z.
+    5. ``face_mask_from_landmarks`` — union face region + ear spheres,
+       clamped below the cap.
+    6. Preserve ``landmark_keep_radius_mm``-spheres around each landmark
+       and a midline nasion strip from Nz to the cap.
+    7. ``delete_masked_vertices`` — drop triangles touching any masked
+       vertex, keeping UVs in sync.
+    8. (default) ``revert_to_einstar_frame`` — return to ``crs="digitized"``
+       so the output matches ``read_einstar_obj``'s convention and can be
+       fed to ``save_anonymized_scan`` and downstream co-registration.
+
+    Args:
+        surface: Raw Einstar TrimeshSurface (``crs="digitized"``).
+        landmarks: LabeledPoints with Nz, Iz, Cz, LPA, RPA (mixed-case
+            aliases like Lpa/Rpa accepted; normalized via
+            ``normalize_landmarks_labels``).
+        head_isolation_radius_mm: Sphere radius around the upper-head
+            centroid for ``isolate_head``.
+        cap_band_width_mm: Y-band half-width for the cap X-profile.
+        cap_bin_size_mm: Z-bin size for the cap X-profile.
+        cap_foot_grad_threshold: dX/dZ below which the cap-foot is
+            recognized.
+        cap_z_ceiling_mm: mm above Nz at which cap detection is
+            considered untrustworthy and the failsafe fires.
+        eyebrow_offset_mm: failsafe cap height above Nz.
+        ear_delete_radius_mm: sphere radius around LPA/RPA for the ear
+            region of the deletion mask.
+        landmark_keep_radius_mm: per-landmark preservation sphere radius
+            and half-width of the midline nasion strip.
+        return_frame: ``"digitized"`` (default) reverts back to the raw
+            Einstar frame; ``"ctf"`` keeps the CTF frame.
+
+    Returns:
+        Tuple of (anonymized_surface, anonymized_landmarks). Frame is
+        controlled by ``return_frame``. Pass directly to
+        ``save_anonymized_scan(..., landmarks=...)`` when
+        ``return_frame="digitized"``.
+    """
+    landmarks = normalize_landmarks_labels(landmarks)
+    labels = list(landmarks["label"].values)
+    missing = set(_REQUIRED_LABELS) - set(labels)
+    if missing:
+        raise ValueError(f"Missing landmarks for anonymization: {missing}")
+    idx = {lbl: i for i, lbl in enumerate(labels)}
+    Nz_raw = landmarks.pint.dequantify().values[idx["Nz"]]
+
+    surface_n, _, R_norm = normalize_axes(surface, Nz_raw)
+    R_norm4 = np.eye(4)
+    R_norm4[:3, :3] = R_norm
+    crs_dim = next(d for d in landmarks.dims if d != "label")
+    landmarks_n = _transform_labeled_points(
+        landmarks,
+        lambda p: _apply_affine(p, R_norm4),
+        new_crs=crs_dim,
+    )
+
+    Nz_n = landmarks_n.pint.dequantify().values[idx["Nz"]]
+    surface_n, _ = isolate_head(
+        surface_n, Nz_n, radius=head_isolation_radius_mm
+    )
+
+    surface_h, landmarks_ctf, M_align = align_axes_from_landmarks(
+        surface_n, landmarks_n
+    )
+    lm_ctf = landmarks_ctf.pint.dequantify().values
+    Nz, Iz, Cz, Lpa, Rpa = (
+        lm_ctf[idx[lbl]] for lbl in _REQUIRED_LABELS
+    )
+    ear_mid = _ear_midpoint(Lpa, Rpa)
+
+    verts = np.asarray(surface_h.mesh.vertices)
+    cap_z, *_ = detect_cap_boundary(
+        verts, Nz, Cz, Lpa, Rpa,
+        band_width=cap_band_width_mm,
+        bin_size=cap_bin_size_mm,
+        foot_grad_threshold=cap_foot_grad_threshold,
+        cap_z_ceiling_mm=cap_z_ceiling_mm,
+        eyebrow_offset_mm=eyebrow_offset_mm,
+    )
+
+    mask, _ = face_mask_from_landmarks(
+        verts, Nz, Lpa, Rpa,
+        cap_z=cap_z,
+        ear_delete_radius=ear_delete_radius_mm,
+    )
+
+    for lm in (Nz, Iz, Cz, Lpa, Rpa):
+        near = np.linalg.norm(verts - lm, axis=1) < landmark_keep_radius_mm
+        mask[near] = False
+    nasion_strip = (
+        (verts[:, 2] >= Nz[2])
+        & (verts[:, 2] < cap_z)
+        & (np.abs(verts[:, 1] - Nz[1]) < landmark_keep_radius_mm)
+        & (verts[:, 0] > ear_mid[0])
+    )
+    mask[nasion_strip] = False
+
+    surface_anon = delete_masked_vertices(surface_h, mask)
+
+    if return_frame == "ctf":
+        return surface_anon, landmarks_ctf
+
+    return revert_to_einstar_frame(
+        surface_anon, landmarks_ctf, R_norm, M_align
+    )
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/preprocessing.py b/src/cedalion/geometry/photogrammetry/anonymization/preprocessing.py
index f0b59bd0..9771ab15 100644
--- a/src/cedalion/geometry/photogrammetry/anonymization/preprocessing.py
+++ b/src/cedalion/geometry/photogrammetry/anonymization/preprocessing.py
@@ -9,17 +9,25 @@
 - ``align_axes_from_landmarks`` maps the scan into the CTF frame
   (+X=anterior, +Y=left, +Z=up, origin at the LPA-RPA midpoint) once all 5
   landmarks are available.
-
-Initial Contributors:
-    - Face Anonymization Project | 2024
 """
 
 import logging
 
 import numpy as np
+import trimesh
 
 import cedalion.dataclasses as cdc
 import cedalion.typing as cdt
+from cedalion.geometry.landmarks import normalize_landmarks_labels
+
+from ._utils import (
+    _apply_affine,
+    _ear_midpoint,
+    _reindex_faces,
+    _resolve_texture_image,
+    _transform_labeled_points,
+    _upper_head_centroid,
+)
 
 logger = logging.getLogger("cedalion")
 
@@ -27,36 +35,32 @@
 def _copy_visual(src_mesh, dst_mesh, vertex_index=None) -> None:
     """Copy a trimesh ``visual`` onto ``dst_mesh``, optionally reindexing.
 
-    Assigning a ``TextureVisuals`` directly across meshes (e.g. by passing
-    ``visual=old.visual`` to a new ``Trimesh(...)``) often silently downgrades
-    it to ``ColorVisuals`` because the visual holds a back-reference to its
-    original mesh and re-checks ``len(uv) == len(mesh.vertices)`` against the
-    wrong target. Rebuilding the visual explicitly avoids that.
+    Passing ``visual=old.visual`` to a new ``Trimesh(...)`` silently downgrades
+    a ``TextureVisuals`` to ``ColorVisuals`` because the visual still
+    back-references the source mesh's vertex count. Rebuilding explicitly
+    avoids that.
 
     Args:
         src_mesh: Source trimesh.
         dst_mesh: Destination trimesh (mutated in place).
-        vertex_index: Optional indices into the source vertex array. If given,
-            UVs / vertex_colors are sliced by this index before being attached.
-            Leave None when the vertex count is unchanged.
+        vertex_index: Indices into the source vertex array. If given, UVs /
+            vertex_colors are sliced by this index. Leave None when the
+            vertex count is unchanged.
     """
-    import trimesh
-
     src_visual = src_mesh.visual
     uv = getattr(src_visual, "uv", None)
-    image = getattr(src_visual, "image", None)
     n_src = len(src_mesh.vertices)
 
     if uv is not None and len(uv) == n_src:
         uv_arr = np.asarray(uv)
         if vertex_index is not None:
             uv_arr = uv_arr[vertex_index]
-        if image is None:
-            mat = getattr(src_visual, "material", None)
-            image = getattr(mat, "image", None) if mat is not None else None
+        image = _resolve_texture_image(src_visual)
+        material = getattr(src_visual, "material", None)
         dst_mesh.visual = trimesh.visual.TextureVisuals(
             uv=uv_arr,
             image=image,
+            material=material,
         )
         return
 
@@ -68,44 +72,48 @@ def _copy_visual(src_mesh, dst_mesh, vertex_index=None) -> None:
         dst_mesh.visual.vertex_colors = vcol_arr
 
 
+def _rebuild_mesh(
+    src_mesh: trimesh.Trimesh,
+    *,
+    vertices: np.ndarray,
+    faces: np.ndarray,
+    vertex_index: np.ndarray | None = None,
+) -> trimesh.Trimesh:
+    """Build a new ``Trimesh`` and re-attach the source visual.
+
+    Centralizes the ``Trimesh(... process=False)`` + ``_copy_visual`` duo
+    used by every transform in the pipeline.
+    """
+    new_mesh = trimesh.Trimesh(vertices=vertices, faces=faces, process=False)
+    _copy_visual(src_mesh, new_mesh, vertex_index=vertex_index)
+    return new_mesh
+
+
+@cdc.validate_schemas
 def normalize_axes(
     surface: cdc.TrimeshSurface,
     nasion: np.ndarray,
-    forward_direction: np.ndarray = None,
 ) -> tuple[cdc.TrimeshSurface, np.ndarray, np.ndarray]:
     """Rotate mesh around X-axis so Y=anterior (toward face), Z=left.
 
-    The Einstar scanner has X=up (gravity-based) but Y/Z are arbitrary per
-    scan session. This function uses the nasion position to determine the
-    forward direction and rotates the mesh so that Y consistently points
-    toward the face (anterior) and Z points left.
+    The Einstar scanner fixes X=up (gravity-based) but leaves Y/Z arbitrary
+    per scan. The forward direction is inferred from the nasion against the
+    upper-head centroid.
 
     Args:
         surface: TrimeshSurface in raw Einstar coordinates.
-        nasion: Nasion position as numpy array of shape (3,).
-        forward_direction: Optional pre-computed forward unit vector in YZ
-            plane (X=0). If None, computed from nasion vs upper-head centroid.
+        nasion: Nasion position as numpy array of shape (3,), in mm, in the
+            raw Einstar frame (matching ``surface``).
 
     Returns:
         Tuple of (rotated_surface, rotated_nasion, rotation_matrix).
-        rotation_matrix is 3x3 and can be applied to other points.
+        ``rotated_nasion`` is in the same units as the input. The 3x3
+        ``rotation_matrix`` can be applied to other points via ``p @ R.T``.
     """
     vertices = surface.mesh.vertices
 
-    if forward_direction is None:
-        x_max = vertices[:, 0].max()
-        x_min = vertices[:, 0].min()
-        upper = vertices[vertices[:, 0] > x_min + 0.6 * (x_max - x_min)]
-        centroid_yz = np.array([0.0, upper[:, 1].mean(), upper[:, 2].mean()])
-        nasion_yz = np.array([0.0, nasion[1], nasion[2]])
-        forward_direction = nasion_yz - centroid_yz
-        fwd_norm = np.linalg.norm(forward_direction)
-        if fwd_norm < 1e-6:
-            logger.warning("Cannot compute forward direction -- returning unchanged")
-            return surface, nasion.copy(), np.eye(3)
-        forward_direction = forward_direction / fwd_norm
-
-    angle = np.arctan2(forward_direction[2], forward_direction[1])
+    centroid, _ = _upper_head_centroid(np.asarray(vertices))
+    angle = np.arctan2(nasion[2] - centroid[2], nasion[1] - centroid[1])
     cos_a = np.cos(-angle)
     sin_a = np.sin(-angle)
     R = np.array([
@@ -114,15 +122,11 @@ def normalize_axes(
         [0.0, sin_a, cos_a],
     ])
 
-    rotated_verts = vertices @ R.T
-
-    import trimesh
-    new_mesh = trimesh.Trimesh(
-        vertices=rotated_verts,
+    new_mesh = _rebuild_mesh(
+        surface.mesh,
+        vertices=vertices @ R.T,
         faces=surface.mesh.faces,
-        process=False,
     )
-    _copy_visual(surface.mesh, new_mesh)
     rotated_surface = cdc.TrimeshSurface(new_mesh, crs=surface.crs, units=surface.units)
 
     rotated_nasion = R @ nasion
@@ -135,41 +139,35 @@ def normalize_axes(
     return rotated_surface, rotated_nasion, R
 
 
-def _largest_component_mask(mesh) -> np.ndarray:
+def _largest_component_mask(mesh: trimesh.Trimesh) -> np.ndarray:
     """Boolean vertex mask selecting the largest connected component.
 
-    Einstar scans are non-watertight and frequently contain tiny floating
-    mesh fragments (loose triangles, cable shreds, background patches)
-    that sit far outside the head. Any step that uses vertex extrema will
-    be dragged off into empty space by those fragments. Stripping them
-    up-front prevents that.
+    Einstar scans contain floating fragments (loose triangles, cable shreds,
+    background patches) that drag vertex extrema off into empty space; strip
+    them first.
 
-    Uses ``trimesh.graph.connected_component_labels`` on face adjacency
-    rather than ``mesh.split``; split allocates one full ``Trimesh`` per
-    component and can OOM on scans with thousands of tiny fragments.
+    Uses ``trimesh.graph.connected_component_labels`` on face adjacency rather
+    than ``mesh.split``: split allocates a ``Trimesh`` per component and can
+    OOM on scans with thousands of fragments.
 
-    Einstar OBJs duplicate vertices along UV seams for texturing. The
-    default ``trimesh.Trimesh.merge_vertices()`` does NOT merge across UV
-    seams (it preserves texture), so face adjacency on the raw mesh
-    over-fragments the head into thousands of islands. We use POSITION-only
-    merging via ``unique_rows`` for connectivity analysis.
+    Einstar OBJs duplicate vertices along UV seams; ``Trimesh.merge_vertices``
+    preserves seams (to keep textures), so face adjacency on the raw mesh
+    over-fragments the head. ``trimesh.grouping.unique_rows`` does
+    position-only merging for connectivity analysis.
 
     Args:
         mesh: A ``trimesh.Trimesh`` instance.
 
     Returns:
-        Boolean array of shape ``(n_vertices,)``. All-True if the mesh
-        is empty or already a single connected component.
+        Boolean array of shape ``(n_vertices,)``. All-True if the mesh is
+        empty or already a single connected component.
     """
-    import trimesh
     n_verts = len(mesh.vertices)
     n_faces = len(mesh.faces)
     if n_faces == 0:
         return np.ones(n_verts, dtype=bool)
 
-    unique_idx, inverse = trimesh.grouping.unique_rows(
-        np.asarray(mesh.vertices)
-    )
+    _, inverse = trimesh.grouping.unique_rows(np.asarray(mesh.vertices))
     canonical_faces = inverse[mesh.faces]
     adjacency = trimesh.graph.face_adjacency(faces=canonical_faces)
     face_labels = trimesh.graph.connected_component_labels(
@@ -187,6 +185,7 @@ def _largest_component_mask(mesh) -> np.ndarray:
     return mask
 
 
+@cdc.validate_schemas
 def isolate_head(
     surface: cdc.TrimeshSurface,
     nasion: np.ndarray,
@@ -203,7 +202,8 @@ def isolate_head(
 
     Args:
         surface: Axis-normalized TrimeshSurface.
-        nasion: Nasion position as numpy array of shape (3,).
+        nasion: Nasion position as numpy array of shape (3,), in mm,
+            in the axis-normalized frame (matching ``surface``).
         radius: Sphere radius in mm (default 220). A human head has
             ~90mm radius; 220mm adds margin for ears and jaw.
 
@@ -212,15 +212,10 @@ def isolate_head(
         array of shape (n_vertices,) indicating which original
         vertices were kept.
     """
-    import trimesh
+    vertices = np.asarray(surface.mesh.vertices)
+    faces = np.asarray(surface.mesh.faces)
 
-    vertices = surface.mesh.vertices
-    faces = surface.mesh.faces
-
-    x_max = vertices[:, 0].max()
-    x_min = vertices[:, 0].min()
-    upper = vertices[:, 0] > x_min + 0.6 * (x_max - x_min)
-    center = vertices[upper].mean(axis=0)
+    center, x_max = _upper_head_centroid(vertices)
     midpoint_x = (x_max + nasion[0]) / 2.0
     center[0] = min(center[0], midpoint_x)
 
@@ -237,20 +232,14 @@ def isolate_head(
         return surface, head_mask
 
     face_mask = head_mask[faces].all(axis=1)
-    head_faces = faces[face_mask]
+    new_verts, new_faces, kept_vidx = _reindex_faces(vertices, faces, face_mask)
 
-    kept_verts = np.unique(head_faces)
-    reindex = np.full(len(vertices), -1, dtype=int)
-    reindex[kept_verts] = np.arange(len(kept_verts))
-    new_faces = reindex[head_faces]
-    new_verts = vertices[kept_verts]
-
-    new_mesh = trimesh.Trimesh(
+    new_mesh = _rebuild_mesh(
+        surface.mesh,
         vertices=new_verts,
         faces=new_faces,
-        process=False,
+        vertex_index=kept_vidx,
     )
-    _copy_visual(surface.mesh, new_mesh, vertex_index=kept_verts)
 
     head_surface = cdc.TrimeshSurface(
         new_mesh, crs=surface.crs, units=surface.units
@@ -266,6 +255,7 @@ def isolate_head(
     return head_surface, head_mask
 
 
+@cdc.validate_schemas
 def align_axes_from_landmarks(
     surface: cdc.TrimeshSurface,
     landmarks: cdt.LabeledPoints,
@@ -293,10 +283,11 @@ def align_axes_from_landmarks(
         Tuple of (aligned_surface, aligned_landmarks, transform).
         ``transform`` is a 4x4 homogeneous affine that maps input-frame
         points into CTF; apply as ``hom = transform @ [x, y, z, 1]``.
-    """
-    import trimesh
-    from cedalion.geometry.landmarks import normalize_landmarks_labels
 
+    Raises:
+        ValueError: If any of the required landmarks (Nz, Iz, Cz, LPA, RPA)
+            are missing after label normalization.
+    """
     landmarks = normalize_landmarks_labels(landmarks)
     lm = landmarks.pint.dequantify().values
     labels = list(landmarks["label"].values)
@@ -311,7 +302,7 @@ def align_axes_from_landmarks(
     Cz = lm[idx["Cz"]]
     Lpa = lm[idx["LPA"]]
     Rpa = lm[idx["RPA"]]
-    origin = 0.5 * (Lpa + Rpa)
+    origin = _ear_midpoint(Lpa, Rpa)
 
     y_ax = Lpa - Rpa
     y_ax = y_ax / np.linalg.norm(y_ax)
@@ -324,8 +315,6 @@ def align_axes_from_landmarks(
 
     if np.dot(Cz - origin, z_ax) < 0:
         z_ax = -z_ax
-    if np.dot(Nz - origin, x_ax) < 0:
-        x_ax = -x_ax
     y_ax = np.cross(z_ax, x_ax)
 
     R = np.vstack([x_ax, y_ax, z_ax])
@@ -333,29 +322,24 @@ def align_axes_from_landmarks(
     M[:3, :3] = R
     M[:3, 3] = -R @ origin
 
-    aligned_verts = (np.asarray(surface.mesh.vertices) - origin) @ R.T
-    new_mesh = trimesh.Trimesh(
+    aligned_verts = _apply_affine(np.asarray(surface.mesh.vertices), M)
+    new_mesh = _rebuild_mesh(
+        surface.mesh,
         vertices=aligned_verts,
         faces=surface.mesh.faces,
-        process=False,
     )
-    _copy_visual(surface.mesh, new_mesh)
     aligned_surface = cdc.TrimeshSurface(
         new_mesh, crs="ctf", units=surface.units,
     )
 
-    aligned_lm = (lm - origin) @ R.T
-    old_crs_dim = [d for d in landmarks.dims if d != "label"][0]
-    aligned_landmarks = (
-        landmarks.pint.dequantify()
-        .copy(data=aligned_lm)
-        .rename({old_crs_dim: "ctf"})
-        .pint.quantify()
+    aligned_landmarks = _transform_labeled_points(
+        landmarks, lambda p: _apply_affine(p, M), "ctf"
     )
 
     return aligned_surface, aligned_landmarks, M
 
 
+@cdc.validate_schemas
 def revert_to_einstar_frame(
     surface: cdc.TrimeshSurface,
     landmarks: cdt.LabeledPoints,
@@ -364,16 +348,12 @@ def revert_to_einstar_frame(
 ) -> tuple[cdc.TrimeshSurface, cdt.LabeledPoints]:
     """Map an aligned surface and landmarks back into the raw Einstar frame.
 
-    Inverse of the ``normalize_axes`` then ``align_axes_from_landmarks``
-    composition, so the returned mesh and landmarks carry ``crs="digitized"``
-    and match the coordinates of the original ``read_einstar_obj`` output.
-    Useful right before ``save_anonymized_scan`` when the downstream
-    co-registration pipeline expects the saved ``.obj`` and ``.tsv`` in the
-    native scanner frame (e.g. Elsa's tutorial step 5.2 workflow, whose
-    example files carry ``crs=digitized``).
+    Inverse of ``normalize_axes`` composed with ``align_axes_from_landmarks``,
+    so the returned mesh and landmarks carry ``crs="digitized"`` and match
+    the original ``read_einstar_obj`` output.
 
     Note that ``isolate_head`` is not invertible: the returned mesh is still
-    head-only even though its coordinates are in the original digitized frame.
+    head-only even though its coordinates are in the digitized frame.
 
     Args:
         surface: TrimeshSurface in the CTF frame (post
@@ -386,35 +366,23 @@ def revert_to_einstar_frame(
         Tuple of (surface_digitized, landmarks_digitized). Both carry
         ``crs="digitized"`` and the mesh preserves UVs / vertex colors.
     """
-    import trimesh
-
     M_inv = np.linalg.inv(M_align)
-    R_inv = R_normalize.T
+    R_inv4 = np.eye(4)
+    R_inv4[:3, :3] = R_normalize.T
+    M_total = R_inv4 @ M_inv
 
-    ctf_verts = np.asarray(surface.mesh.vertices)
-    norm_verts = ctf_verts @ M_inv[:3, :3].T + M_inv[:3, 3]
-    raw_verts = norm_verts @ R_inv.T
-
-    new_mesh = trimesh.Trimesh(
+    raw_verts = _apply_affine(np.asarray(surface.mesh.vertices), M_total)
+    new_mesh = _rebuild_mesh(
+        surface.mesh,
         vertices=raw_verts,
         faces=surface.mesh.faces,
-        process=False,
     )
-    _copy_visual(surface.mesh, new_mesh)
     raw_surface = cdc.TrimeshSurface(
         new_mesh, crs="digitized", units=surface.units,
     )
 
-    lm_ctf = landmarks.pint.dequantify().values
-    lm_norm = lm_ctf @ M_inv[:3, :3].T + M_inv[:3, 3]
-    lm_raw = lm_norm @ R_inv.T
-
-    old_crs_dim = [d for d in landmarks.dims if d != "label"][0]
-    raw_landmarks = (
-        landmarks.pint.dequantify()
-        .copy(data=lm_raw)
-        .rename({old_crs_dim: "digitized"})
-        .pint.quantify()
+    raw_landmarks = _transform_labeled_points(
+        landmarks, lambda p: _apply_affine(p, M_total), "digitized"
     )
 
     return raw_surface, raw_landmarks

From 215ad35aa233814173a340e8d0e060aeb3f92163 Mon Sep 17 00:00:00 2001
From: mah-diaa <mahmoud.diaa.abdeen@gmail.com>
Date: Sun, 3 May 2026 20:33:14 +0200
Subject: [PATCH 25/39] Update example notebook for anonymize_scan API

---
 .../51_manual_5pt_anonymization.ipynb         | 424 ++++++------------
 1 file changed, 130 insertions(+), 294 deletions(-)

diff --git a/examples/head_models/51_manual_5pt_anonymization.ipynb b/examples/head_models/51_manual_5pt_anonymization.ipynb
index efc089a8..398c23df 100644
--- a/examples/head_models/51_manual_5pt_anonymization.ipynb
+++ b/examples/head_models/51_manual_5pt_anonymization.ipynb
@@ -2,163 +2,138 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "13944ffe",
+   "id": "e505f83d-79aa-467b-a6f2-44d973491f55",
    "metadata": {},
    "source": [
     "# Face Anonymization from 5 Given Landmarks\n",
     "\n",
-    "Pure-geometric face-removal pipeline for Einstar photogrammetry scans. Takes the\n",
-    "5 standard anatomical landmarks (Nz, Iz, Cz, Lpa, Rpa) as input and deletes the\n",
-    "facial region, then reverts the result back to the raw Einstar (digitized)\n",
-    "frame so the saved `.obj` + `.tsv` are a drop-in replacement for the original\n",
-    "scan at co-registration time.\n",
+    "Pure-geometric face removal for Einstar photogrammetry scans. Given the 5\n",
+    "anatomical landmarks (Nz, Iz, Cz, Lpa, Rpa), `anonymize_scan` strips the face\n",
+    "and reverts the result back to the raw Einstar (`crs=digitized`) frame so the\n",
+    "saved `.obj` + `.tsv` are a drop-in replacement for the original at\n",
+    "co-registration time.\n",
     "\n",
-    "**User-visible flow:** load scan &rarr; pick 5 landmarks &rarr; review the\n",
-    "anonymization mask &rarr; review the anonymized scan &rarr; save. All\n",
-    "intermediate transforms (normalize, align to CTF, build mask, delete verts)\n",
-    "run inside a single bundled cell.\n"
+    "Flow: load &rarr; pick 5 landmarks &rarr; `anonymize_scan` &rarr; save.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
-   "id": "06ba7b71",
+   "execution_count": 10,
+   "id": "a916040f-02d3-44e3-a1f4-05188c814d1d",
    "metadata": {},
    "outputs": [],
    "source": [
     "import logging\n",
+    "import os\n",
     "\n",
     "import numpy as np\n",
     "import pyvista as pv\n",
     "import xarray as xr\n",
+    "from PIL import Image\n",
+    "import trimesh\n",
     "\n",
     "import cedalion\n",
     "import cedalion.dataclasses as cdc\n",
     "import cedalion.io\n",
     "import cedalion.vis.blocks\n",
+    "from cedalion.vtktutils import trimesh_to_vtk_polydata\n",
     "from cedalion.geometry.photogrammetry.anonymization import (\n",
-    "    normalize_axes,\n",
-    "    isolate_head,\n",
-    "    align_axes_from_landmarks,\n",
-    "    detect_cap_boundary,\n",
-    "    face_mask_from_landmarks,\n",
-    "    delete_masked_vertices,\n",
-    "    revert_to_einstar_frame,\n",
+    "    anonymize_scan,\n",
     "    save_anonymized_scan,\n",
     ")\n",
-    "from cedalion.vtktutils import trimesh_to_vtk_polydata\n",
-    "from cedalion.dataclasses import VTKSurface\n",
     "\n",
     "logging.getLogger('cedalion').setLevel(logging.WARNING)\n",
     "pv.set_jupyter_backend('server')\n",
     "\n",
-    "# === CONFIG ===\n",
+    "# === Options ===\n",
     "SUBJECT_NUMBER = 17\n",
     "SCANS_FOLDER = '/home/ma7/BA/PG_Subjects'\n",
+    "SCAN_PATH = f'{SCANS_FOLDER}/Subject{SUBJECT_NUMBER}/Subject{SUBJECT_NUMBER}.obj'\n",
+    "OUT_PATH = f'{SCANS_FOLDER}/Subject{SUBJECT_NUMBER}/Subject{SUBJECT_NUMBER}_anon.obj'\n",
     "\n",
-    "# True: run the picker. False: use cached landmarks (fill after first pick).\n",
+    "# True: run the picker. False: use cached landmarks (fill in cell below).\n",
     "INTERACTIVE = True\n",
     "\n",
-    "# Mask tunables (mm)\n",
-    "LANDMARK_KEEP_RADIUS = 8.0      # preserve a small sphere around each landmark\n",
-    "EAR_DELETE_RADIUS_MM = 40.0     # radius of the delete sphere around Lpa / Rpa\n",
-    "\n",
-    "# Cap boundary detection tunables\n",
-    "CAP_BAND_WIDTH_MM = 15.0\n",
-    "CAP_BIN_SIZE_MM = 1.0\n",
-    "CAP_FOOT_GRAD_THRESHOLD = 0.2\n"
+    "# anonymize_scan tunables (mm). Defaults match the library's recommended values.\n",
+    "ANON_OPTIONS = dict(\n",
+    "    head_isolation_radius_mm=220.0,\n",
+    "    cap_band_width_mm=15.0,\n",
+    "    cap_bin_size_mm=1.0,\n",
+    "    cap_foot_grad_threshold=0.2,\n",
+    "    cap_z_ceiling_mm=40.0,\n",
+    "    eyebrow_offset_mm=10.0,\n",
+    "    ear_delete_radius_mm=40.0,\n",
+    "    landmark_keep_radius_mm=8.0,\n",
+    ")\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "cell-md-load",
+   "id": "c7b11266-a722-49ac-9aee-67953820c2ac",
    "metadata": {},
    "source": [
-    "## 1. Load the Einstar scan"
+    "## Load the Einstar scan\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
-   "id": "cell-load",
+   "execution_count": 11,
+   "id": "5c99124b-f88c-4aaf-bff2-124cfadd5da1",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Loaded: 1,284,667 vertices, 2,223,716 faces\n",
-      "Texture already attached: (4016, 3776)\n"
+      "Loaded: 1,284,667 vertices, 2,223,716 faces\n"
      ]
     }
    ],
    "source": [
-    "path = f'{SCANS_FOLDER}/Subject{SUBJECT_NUMBER}/Subject{SUBJECT_NUMBER}.obj'\n",
-    "surface = cedalion.io.read_einstar_obj(path)\n",
+    "surface = cedalion.io.read_einstar_obj(SCAN_PATH)\n",
     "print(f'Loaded: {surface.nvertices:,} vertices, {surface.nfaces:,} faces')\n",
     "\n",
-    "# In trimesh 4.6 the texture image lives on visual.material.image, not\n",
-    "# visual.image. Verify it's there; if missing, fall back to the sibling JPG.\n",
-    "import os\n",
-    "import trimesh as _tm\n",
-    "from PIL import Image as _PILImage\n",
-    "\n",
-    "def _visual_image(visual):\n",
-    "    img = getattr(visual, 'image', None)\n",
-    "    if img is None:\n",
-    "        mat = getattr(visual, 'material', None)\n",
-    "        img = getattr(mat, 'image', None) if mat is not None else None\n",
-    "    return img\n",
-    "\n",
-    "_img = _visual_image(surface.mesh.visual)\n",
-    "if _img is None:\n",
-    "    _jpg = path.replace('.obj', '.jpg')\n",
-    "    _uv = getattr(surface.mesh.visual, 'uv', None)\n",
-    "    assert os.path.exists(_jpg) and _uv is not None, (\n",
-    "        f'no texture: jpg_exists={os.path.exists(_jpg)}, has_uv={_uv is not None}'\n",
-    "    )\n",
-    "    surface.mesh.visual = _tm.visual.TextureVisuals(\n",
-    "        uv=_uv, image=_PILImage.open(_jpg).convert('RGBA'),\n",
-    "    )\n",
-    "    _img = _visual_image(surface.mesh.visual)\n",
-    "    assert _img is not None, 'attach failed'\n",
-    "    print(f'Attached texture from JPG: {_img.size}')\n",
-    "else:\n",
-    "    print(f'Texture already attached: {_img.size}')"
+    "# trimesh 4.6 places the texture image on visual.material.image; if neither\n",
+    "# path has it, attach it from the sibling JPG so save_anonymized_scan can\n",
+    "# sanitize the final texture.\n",
+    "visual = surface.mesh.visual\n",
+    "img = getattr(visual, 'image', None) or getattr(getattr(visual, 'material', None), 'image', None)\n",
+    "if img is None:\n",
+    "    jpg = SCAN_PATH.replace('.obj', '.jpg')\n",
+    "    uv = getattr(visual, 'uv', None)\n",
+    "    assert os.path.exists(jpg) and uv is not None, 'no texture available'\n",
+    "    surface.mesh.visual = trimesh.visual.TextureVisuals(\n",
+    "        uv=uv, image=Image.open(jpg).convert('RGBA'),\n",
+    "    )\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "cell-md-pick",
+   "id": "5c282588-d37d-4810-a5c1-104b751d2155",
    "metadata": {},
    "source": [
-    "## 2. Pick the 5 landmarks interactively\n",
-    "\n",
-    "Uses cedalion's built-in picker (`cedalion.vis.blocks.plot_surface(..., pick_landmarks=True)`,\n",
-    "authored by Mariia Iudina, upstream cedalion). Right-click on the mesh to place a\n",
-    "sphere; click again on the sphere to cycle its label through\n",
-    "`Nz -> Iz -> Cz -> Lpa -> Rpa`, or cycle past `Rpa` back to `Nz` to remove it.\n",
+    "## Pick the 5 landmarks\n",
     "\n",
-    "Close the window when all 5 are placed with correct labels."
+    "Right-click on the mesh to place a sphere; click a sphere to cycle its label\n",
+    "through `Nz -> Iz -> Cz -> Lpa -> Rpa`. Close the window when all 5 are placed.\n",
+    "Skip this cell if `INTERACTIVE = False`.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
-   "id": "cell-pick",
-   "metadata": {
-    "scrolled": true
-   },
+   "execution_count": 12,
+   "id": "4ea3ff12-ca93-4c03-9f50-b84c28c7cfdc",
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "938910ca3ee04eb6b590a88affac39ca",
+       "model_id": "75dbdddfdd924863a11cb97abe1d9bfb",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Widget(value='<iframe src=\"http://localhost:39989/index.html?ui=P_0x7f897374bc90_18&reconnect=auto\" class=\"pyv…"
+       "Widget(value='<iframe src=\"http://localhost:42585/index.html?ui=P_0x7f2e70c57590_2&reconnect=auto\" class=\"pyvi…"
       ]
      },
      "metadata": {},
@@ -169,25 +144,23 @@
     "if INTERACTIVE:\n",
     "    pvplt = pv.Plotter()\n",
     "    get_landmarks = cedalion.vis.blocks.plot_surface(\n",
-    "        pvplt, surface, opacity=1.0, pick_landmarks=True\n",
+    "        pvplt, surface, opacity=1.0, pick_landmarks=True,\n",
     "    )\n",
-    "    pvplt.show()"
+    "    pvplt.show()\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "cell-md-retrieve",
+   "id": "f98684b3-69b3-49c8-a212-c726d6223125",
    "metadata": {},
    "source": [
-    "## 3. Retrieve picked points and wrap into a `LabeledPointCloud`\n",
-    "\n",
-    "Follows the pattern from `41_photogrammetric_optode_coregistration.ipynb` cells 26-28."
+    "## Wrap picked points into a `LabeledPoints`\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
-   "id": "cell-retrieve",
+   "execution_count": 13,
+   "id": "3a09ed60-b599-4191-b394-460b852f4b27",
    "metadata": {},
    "outputs": [
     {
@@ -637,28 +610,28 @@
        "  stroke-width: 0.8px;\n",
        "}\n",
        "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (label: 5, digitized: 3)&gt; Size: 120B\n",
-       "&lt;Quantity([[169.8175707   84.17791333 425.43686049]\n",
-       " [ 32.0612021  203.51371015 436.13680432]\n",
-       " [ 67.92901638  85.38361616 562.50097916]\n",
-       " [ 18.53542079  67.46210371 393.38863931]\n",
-       " [ 69.81661274 -12.30486275 480.16603788]], &#x27;millimeter&#x27;)&gt;\n",
+       "&lt;Quantity([[ 67.32600288 -11.45115817 478.34751934]\n",
+       " [ 27.13790911 217.01868087 457.09931   ]\n",
+       " [181.98669299 116.53328682 429.76404071]\n",
+       " [ -7.75895501  60.47001713 415.08539957]\n",
+       " [ 75.93565647  77.90628633 563.57941114]], &#x27;millimeter&#x27;)&gt;\n",
        "Coordinates:\n",
-       "  * label    (label) &lt;U3 60B &#x27;Cz&#x27; &#x27;Iz&#x27; &#x27;Rpa&#x27; &#x27;Lpa&#x27; &#x27;Nz&#x27;\n",
+       "  * label    (label) &lt;U3 60B &#x27;Nz&#x27; &#x27;Iz&#x27; &#x27;Cz&#x27; &#x27;Lpa&#x27; &#x27;Rpa&#x27;\n",
        "    type     (label) object 40B PointType.LANDMARK ... PointType.LANDMARK\n",
        "    group    (label) &lt;U1 20B &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27;\n",
-       "Dimensions without coordinates: digitized</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>label</span>: 5</li><li><span>digitized</span>: 3</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-e6b82788-6d3d-46c7-8736-3efae789c9ef' class='xr-array-in' type='checkbox' checked><label for='section-e6b82788-6d3d-46c7-8736-3efae789c9ef' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>[mm] 169.8 84.18 425.4 32.06 203.5 ... 67.46 393.4 69.82 -12.3 480.2</span></div><div class='xr-array-data'><table><tbody><tr><th>Magnitude</th><td style='text-align:left;'><pre>[[169.8175706956947 84.17791333176854 425.43686048545544] [32.061202102756184 203.5137101504909 436.13680432494687] [67.9290163798706 85.38361615977699 562.5009791587428] [18.535420787153996 67.46210371239562 393.38863930640076] [69.81661273869598 -12.304862754118403 480.1660378769869]]</pre></td></tr><tr><th>Units</th><td style='text-align:left;'>millimeter</td></tr></tbody></table></div></div></li><li class='xr-section-item'><input id='section-50458d44-a6b2-4519-824b-052737d8f7b0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-50458d44-a6b2-4519-824b-052737d8f7b0' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>label</span></div><div class='xr-var-dims'>(label)</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;Cz&#x27; &#x27;Iz&#x27; &#x27;Rpa&#x27; &#x27;Lpa&#x27; &#x27;Nz&#x27;</div><input id='attrs-33836c6c-a641-466f-b2c9-67d9db819944' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-33836c6c-a641-466f-b2c9-67d9db819944' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af76f448-c9d8-4e02-bc4d-3ff86b162428' class='xr-var-data-in' type='checkbox'><label for='data-af76f448-c9d8-4e02-bc4d-3ff86b162428' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Cz&#x27;, &#x27;Iz&#x27;, &#x27;Rpa&#x27;, &#x27;Lpa&#x27;, &#x27;Nz&#x27;], dtype=&#x27;&lt;U3&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>type</span></div><div class='xr-var-dims'>(label)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>PointType.LANDMARK ... PointType...</div><input id='attrs-b94637ed-c3e2-44d4-992f-ed91b638a0ce' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b94637ed-c3e2-44d4-992f-ed91b638a0ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d37613c8-8a3f-4c4d-baac-118738ebdc13' class='xr-var-data-in' type='checkbox'><label for='data-d37613c8-8a3f-4c4d-baac-118738ebdc13' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&lt;PointType.LANDMARK: 3&gt;, &lt;PointType.LANDMARK: 3&gt;,\n",
+       "Dimensions without coordinates: digitized</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>label</span>: 5</li><li><span>digitized</span>: 3</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-6d73968d-3ac6-487d-9277-8dbf459e9925' class='xr-array-in' type='checkbox' checked><label for='section-6d73968d-3ac6-487d-9277-8dbf459e9925' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>[mm] 67.33 -11.45 478.3 27.14 217.0 ... 60.47 415.1 75.94 77.91 563.6</span></div><div class='xr-array-data'><table><tbody><tr><th>Magnitude</th><td style='text-align:left;'><pre>[[67.3260028810771 -11.451158171365705 478.3475193380646] [27.13790911247986 217.01868087023644 457.0993099960403] [181.98669298817038 116.53328682331225 429.7640407051109] [-7.758955014371736 60.47001712735505 415.085399568561] [75.93565646921411 77.90628633010931 563.5794111384439]]</pre></td></tr><tr><th>Units</th><td style='text-align:left;'>millimeter</td></tr></tbody></table></div></div></li><li class='xr-section-item'><input id='section-e58f3a2e-ce08-4c2b-b296-b8a4cb19ddfa' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e58f3a2e-ce08-4c2b-b296-b8a4cb19ddfa' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>label</span></div><div class='xr-var-dims'>(label)</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;Nz&#x27; &#x27;Iz&#x27; &#x27;Cz&#x27; &#x27;Lpa&#x27; &#x27;Rpa&#x27;</div><input id='attrs-2e2b15ce-0ab3-4bff-8d80-3fb0594445ab' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2e2b15ce-0ab3-4bff-8d80-3fb0594445ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d2cd277a-5a7e-45b5-8f79-dc7855784347' class='xr-var-data-in' type='checkbox'><label for='data-d2cd277a-5a7e-45b5-8f79-dc7855784347' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Nz&#x27;, &#x27;Iz&#x27;, &#x27;Cz&#x27;, &#x27;Lpa&#x27;, &#x27;Rpa&#x27;], dtype=&#x27;&lt;U3&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>type</span></div><div class='xr-var-dims'>(label)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>PointType.LANDMARK ... PointType...</div><input id='attrs-cc6348c9-28d4-412c-8bde-287f31a31b1a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cc6348c9-28d4-412c-8bde-287f31a31b1a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8064227b-bd56-4e2e-ae46-4f40f48d1b33' class='xr-var-data-in' type='checkbox'><label for='data-8064227b-bd56-4e2e-ae46-4f40f48d1b33' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&lt;PointType.LANDMARK: 3&gt;, &lt;PointType.LANDMARK: 3&gt;,\n",
        "       &lt;PointType.LANDMARK: 3&gt;, &lt;PointType.LANDMARK: 3&gt;,\n",
-       "       &lt;PointType.LANDMARK: 3&gt;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>group</span></div><div class='xr-var-dims'>(label)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27;</div><input id='attrs-c8d96493-9f0a-4ba9-af28-8d6835b206bd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c8d96493-9f0a-4ba9-af28-8d6835b206bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d27acfb6-7958-441e-bd2c-fd40507af360' class='xr-var-data-in' type='checkbox'><label for='data-d27acfb6-7958-441e-bd2c-fd40507af360' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;L&#x27;, &#x27;L&#x27;, &#x27;L&#x27;, &#x27;L&#x27;, &#x27;L&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-77a86292-717e-4ab1-ab2a-bab1d89bf01a' class='xr-section-summary-in' type='checkbox'  ><label for='section-77a86292-717e-4ab1-ab2a-bab1d89bf01a' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>label</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-76b349c1-6d3f-4daa-8878-6c4d9741cccb' class='xr-index-data-in' type='checkbox'/><label for='index-76b349c1-6d3f-4daa-8878-6c4d9741cccb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;Cz&#x27;, &#x27;Iz&#x27;, &#x27;Rpa&#x27;, &#x27;Lpa&#x27;, &#x27;Nz&#x27;], dtype=&#x27;object&#x27;, name=&#x27;label&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9a61a3ff-6e0a-47ee-b097-d13c84ba5682' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9a61a3ff-6e0a-47ee-b097-d13c84ba5682' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+       "       &lt;PointType.LANDMARK: 3&gt;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>group</span></div><div class='xr-var-dims'>(label)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27;</div><input id='attrs-37b42a90-36c2-44f0-aa9f-0f544e32ad54' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-37b42a90-36c2-44f0-aa9f-0f544e32ad54' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cfd0585d-0419-4c8b-bde0-338b924189e2' class='xr-var-data-in' type='checkbox'><label for='data-cfd0585d-0419-4c8b-bde0-338b924189e2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;L&#x27;, &#x27;L&#x27;, &#x27;L&#x27;, &#x27;L&#x27;, &#x27;L&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-881e1326-43f8-46e8-9b55-61c8b1931113' class='xr-section-summary-in' type='checkbox'  ><label for='section-881e1326-43f8-46e8-9b55-61c8b1931113' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>label</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-7ff12102-19d0-4d95-91dd-abb0346eb0c7' class='xr-index-data-in' type='checkbox'/><label for='index-7ff12102-19d0-4d95-91dd-abb0346eb0c7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;Nz&#x27;, &#x27;Iz&#x27;, &#x27;Cz&#x27;, &#x27;Lpa&#x27;, &#x27;Rpa&#x27;], dtype=&#x27;object&#x27;, name=&#x27;label&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bf2265ae-d481-4206-b4af-3c0a802a9cc4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bf2265ae-d481-4206-b4af-3c0a802a9cc4' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.DataArray (label: 5, digitized: 3)> Size: 120B\n",
-       "<Quantity([[169.8175707   84.17791333 425.43686049]\n",
-       " [ 32.0612021  203.51371015 436.13680432]\n",
-       " [ 67.92901638  85.38361616 562.50097916]\n",
-       " [ 18.53542079  67.46210371 393.38863931]\n",
-       " [ 69.81661274 -12.30486275 480.16603788]], 'millimeter')>\n",
+       "<Quantity([[ 67.32600288 -11.45115817 478.34751934]\n",
+       " [ 27.13790911 217.01868087 457.09931   ]\n",
+       " [181.98669299 116.53328682 429.76404071]\n",
+       " [ -7.75895501  60.47001713 415.08539957]\n",
+       " [ 75.93565647  77.90628633 563.57941114]], 'millimeter')>\n",
        "Coordinates:\n",
-       "  * label    (label) <U3 60B 'Cz' 'Iz' 'Rpa' 'Lpa' 'Nz'\n",
+       "  * label    (label) <U3 60B 'Nz' 'Iz' 'Cz' 'Lpa' 'Rpa'\n",
        "    type     (label) object 40B PointType.LANDMARK ... PointType.LANDMARK\n",
        "    group    (label) <U1 20B 'L' 'L' 'L' 'L' 'L'\n",
        "Dimensions without coordinates: digitized"
@@ -670,8 +643,7 @@
    ],
    "source": [
     "if INTERACTIVE:\n",
-    "    landmark_coordinates, landmark_labels = get_landmarks()\n",
-    "    landmark_coordinates = np.asarray(landmark_coordinates)\n",
+    "    landmarks = get_landmarks()\n",
     "else:\n",
     "    # Cached fallback: paste coordinates from a previous interactive run here.\n",
     "    landmark_labels = ['Nz', 'Iz', 'Cz', 'Lpa', 'Rpa']\n",
@@ -682,199 +654,75 @@
     "        [0.0, 0.0, 0.0],   # Lpa -- replace\n",
     "        [0.0, 0.0, 0.0],   # Rpa -- replace\n",
     "    ])\n",
-    "\n",
-    "assert len(set(landmark_labels)) == 5, 'Need exactly 5 distinct landmarks'\n",
-    "assert set(landmark_labels) == {'Nz', 'Iz', 'Cz', 'Lpa', 'Rpa'}, (\n",
-    "    f'Unexpected labels: {landmark_labels}'\n",
-    ")\n",
-    "\n",
-    "landmarks = xr.DataArray(\n",
-    "    np.vstack(landmark_coordinates),\n",
-    "    dims=['label', 'digitized'],\n",
-    "    coords={\n",
-    "        'label': ('label', list(landmark_labels)),\n",
-    "        'type': ('label', [cdc.PointType.LANDMARK] * 5),\n",
-    "        'group': ('label', ['L'] * 5),\n",
-    "    },\n",
-    ").pint.quantify('mm')\n",
-    "\n",
-    "display(landmarks)"
+    "    landmarks = xr.DataArray(\n",
+    "        np.vstack(landmark_coordinates),\n",
+    "        dims=['label', 'digitized'],\n",
+    "        coords={\n",
+    "            'label': ('label', landmark_labels),\n",
+    "            'type': ('label', [cdc.PointType.LANDMARK] * 5),\n",
+    "            'group': ('label', ['L'] * 5),\n",
+    "        },\n",
+    "    ).pint.quantify('mm')\n",
+    "\n",
+    "labels = list(landmarks['label'].values)\n",
+    "assert set(labels) == {'Nz', 'Iz', 'Cz', 'Lpa', 'Rpa'}, f'bad labels: {labels}'\n",
+    "display(landmarks)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "ec6eb7a7",
+   "id": "d495ae12-8920-4294-aae1-4eb83f205403",
    "metadata": {},
    "source": [
-    "## 4. Run the anonymization pipeline\n",
-    "\n",
-    "All the geometric work happens in one block:\n",
-    "\n",
-    "1. `normalize_axes` + `isolate_head` rotate into a canonical orientation and\n",
-    "   strip shoulders/body.\n",
-    "2. `align_axes_from_landmarks` maps the mesh + landmarks into the CTF frame\n",
-    "   (+X=anterior, +Y=left, +Z=up, origin at the Lpa/Rpa midpoint). The rotation\n",
-    "   `R` and affine `M_ctf` are kept in scope so we can invert them later.\n",
-    "3. `detect_cap_boundary` + `face_mask_from_landmarks` build the boolean\n",
-    "   deletion mask from the 5 landmarks. Landmark neighbourhoods and a midline\n",
-    "   nasion strip are preserved for downstream co-registration.\n",
-    "4. `delete_masked_vertices` drops every triangle touching a masked vertex.\n"
+    "## Anonymize\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
-   "id": "09186e9d",
+   "execution_count": 14,
+   "id": "acfa6540-281e-4123-8a0d-ee30cd33aae1",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "S17: 678,081 -> 578,478 verts (-99,603, cap_z=10.0 mm)\n"
+      "S17: 1,284,667 -> 593,963 verts (-690,704)\n"
      ]
     }
    ],
    "source": [
-    "# Normalize axes + isolate head.\n",
-    "lm_raw = landmarks.pint.dequantify().values\n",
-    "idx = {lbl: i for i, lbl in enumerate(landmarks['label'].values)}\n",
-    "Nz_raw = lm_raw[idx['Nz']]\n",
-    "\n",
-    "surface_n, Nz_n, R = normalize_axes(surface, Nz_raw)\n",
-    "landmarks_n = landmarks.pint.dequantify().copy(data=lm_raw @ R.T).pint.quantify()\n",
-    "surface_n, _ = isolate_head(surface_n, Nz_n)\n",
-    "\n",
-    "# Align to CTF frame.\n",
-    "surface_h, landmarks_n, M_ctf = align_axes_from_landmarks(surface_n, landmarks_n)\n",
-    "lm_n = landmarks_n.pint.dequantify().values\n",
-    "Nz, Iz, Cz, Lpa, Rpa = (lm_n[idx[lbl]] for lbl in ['Nz', 'Iz', 'Cz', 'Lpa', 'Rpa'])\n",
-    "ear_mid = 0.5 * (Lpa + Rpa)\n",
-    "\n",
-    "# Build face-region mask.\n",
-    "verts = np.asarray(surface_h.mesh.vertices)\n",
-    "mid_y = 0.5 * (Lpa[1] + Rpa[1])\n",
-    "cap_z, *_ = detect_cap_boundary(\n",
-    "    verts, Nz, Cz, ear_mid, mid_y,\n",
-    "    band_width=CAP_BAND_WIDTH_MM,\n",
-    "    bin_size=CAP_BIN_SIZE_MM,\n",
-    "    foot_grad_threshold=CAP_FOOT_GRAD_THRESHOLD,\n",
-    ")\n",
-    "mask, _ = face_mask_from_landmarks(\n",
-    "    verts, Nz, Iz, Cz, Lpa, Rpa,\n",
-    "    cap_z=cap_z,\n",
-    "    ear_delete_radius=EAR_DELETE_RADIUS_MM,\n",
-    ")\n",
-    "\n",
-    "# Preserve small spheres around each landmark + a midline nasion strip.\n",
-    "for lm in (Nz, Iz, Cz, Lpa, Rpa):\n",
-    "    near = np.linalg.norm(verts - lm, axis=1) < LANDMARK_KEEP_RADIUS\n",
-    "    mask[near] = False\n",
-    "nasion_strip = (\n",
-    "    (verts[:, 2] >= Nz[2]) &\n",
-    "    (verts[:, 2] < cap_z) &\n",
-    "    (np.abs(verts[:, 1] - Nz[1]) < LANDMARK_KEEP_RADIUS) &\n",
-    "    (verts[:, 0] > ear_mid[0])\n",
-    ")\n",
-    "mask[nasion_strip] = False\n",
-    "\n",
-    "# Delete masked vertices.\n",
-    "surface_anon = delete_masked_vertices(surface_h, mask)\n",
-    "\n",
-    "print(f'S{SUBJECT_NUMBER}: {surface_h.nvertices:,} -> {surface_anon.nvertices:,} verts '\n",
-    "      f'(-{surface_h.nvertices - surface_anon.nvertices:,}, cap_z={cap_z:.1f} mm)')\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "62e36bea",
-   "metadata": {},
-   "source": [
-    "## 5. Anonymization mask overlay\n",
-    "\n",
-    "Red = will be deleted, white = kept. The 5 landmark spheres are overlaid for\n",
-    "orientation. If the red region hugs the face without leaking onto ears, top of\n",
-    "head, or neck, the geometric rules are working.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "id": "a101c6c0",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b70571c383be4bd7b4872ec50524fcc2",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Widget(value='<iframe src=\"http://localhost:39989/index.html?ui=P_0x7f89737b6f10_19&reconnect=auto\" class=\"pyv…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "lm_colors = {'Nz': 'lime', 'Iz': 'magenta', 'Cz': 'cyan', 'Lpa': 'orange', 'Rpa': 'blue'}\n",
-    "\n",
-    "pvplt = pv.Plotter()\n",
-    "\n",
-    "vtk_surface = VTKSurface.from_trimeshsurface(surface_h)\n",
-    "pv_mesh = pv.wrap(vtk_surface.mesh)\n",
-    "pv_mesh['mask'] = mask.astype(float)\n",
-    "pvplt.add_mesh(\n",
-    "    pv_mesh, scalars='mask', cmap=['white', 'red'], clim=[0, 1],\n",
-    "    show_scalar_bar=False, opacity=1.0, smooth_shading=True,\n",
-    ")\n",
-    "\n",
-    "for lbl, pos in zip(landmarks_n['label'].values, lm_n):\n",
-    "    c = lm_colors.get(lbl, 'yellow')\n",
-    "    pvplt.add_mesh(pv.Sphere(radius=4, center=pos), color=c)\n",
-    "    pvplt.add_point_labels(\n",
-    "        [pos], [lbl], font_size=16, text_color=c, shape=None, always_visible=True,\n",
-    "    )\n",
-    "\n",
-    "pvplt.add_text(\n",
-    "    f'S{SUBJECT_NUMBER} | mask: {mask.sum():,} / {len(mask):,} verts '\n",
-    "    f'({100*mask.sum()/len(mask):.1f}%)',\n",
-    "    position='upper_left', font_size=12,\n",
-    ")\n",
-    "pvplt.show()"
+    "surface_anon, landmarks_anon = anonymize_scan(surface, landmarks, **ANON_OPTIONS)\n",
+    "print(f'S{SUBJECT_NUMBER}: {surface.nvertices:,} -> {surface_anon.nvertices:,} verts '\n",
+    "      f'(-{surface.nvertices - surface_anon.nvertices:,})')\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "4137b59f",
+   "id": "74a83f75-488b-48c9-a660-e5b38eeaf384",
    "metadata": {},
    "source": [
-    "## 6. Revert to the Einstar frame and compare\n",
+    "## Compare original vs anonymized\n",
     "\n",
-    "Inverts the `normalize_axes` and `align_axes_from_landmarks` transforms so the\n",
-    "anonymized mesh + landmarks end up back in the raw Einstar (digitized) frame.\n",
-    "Both views below share the same coordinate system, so the comparison is\n",
-    "geometrically direct. This is also the frame the saved files will be in,\n",
-    "matching the co-registration tutorial's `read_einstar_obj` convention.\n"
+    "Both meshes live in the `digitized` frame, so the side-by-side view shares one\n",
+    "coordinate system. The same five landmark spheres are overlaid on each.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
-   "id": "d7c29bcd",
+   "execution_count": 15,
+   "id": "309c17ba-a234-438e-bd5a-3372dc328266",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "28cee82a04994c5bafc653f367c8895c",
+       "model_id": "4e59bf72d58849b581e7f7c48b6d956c",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Widget(value='<iframe src=\"http://localhost:39989/index.html?ui=P_0x7f89737d1a10_20&reconnect=auto\" class=\"pyv…"
+       "Widget(value='<iframe src=\"http://localhost:42585/index.html?ui=P_0x7f2e3cfa5d50_3&reconnect=auto\" class=\"pyvi…"
       ]
      },
      "metadata": {},
@@ -882,32 +730,30 @@
     }
    ],
    "source": [
-    "# Invert the two transforms so both mesh and landmarks are back in 'digitized'.\n",
-    "surface_anon_dig, landmarks_dig = revert_to_einstar_frame(\n",
-    "    surface_anon, landmarks_n, R, M_ctf,\n",
-    ")\n",
-    "lm_dig = landmarks_dig.pint.dequantify().values\n",
+    "lm_colors = {\n",
+    "    'Nz': 'lime', 'Iz': 'magenta', 'Cz': 'cyan',\n",
+    "    'Lpa': 'orange', 'Rpa': 'blue', 'LPA': 'orange', 'RPA': 'blue',\n",
+    "}\n",
+    "n_removed = surface.nvertices - surface_anon.nvertices\n",
     "\n",
     "orig_vtk = trimesh_to_vtk_polydata(surface.mesh)\n",
-    "anon_vtk = trimesh_to_vtk_polydata(surface_anon_dig.mesh)\n",
+    "anon_vtk = trimesh_to_vtk_polydata(surface_anon.mesh)\n",
     "\n",
-    "lm_colors = {'Nz': 'lime', 'Iz': 'magenta', 'Cz': 'cyan', 'Lpa': 'orange', 'Rpa': 'blue'}\n",
-    "n_removed = surface_h.nvertices - surface_anon.nvertices\n",
+    "lm_pos = landmarks_anon.pint.dequantify().values\n",
+    "lm_lbls = landmarks_anon['label'].values\n",
     "\n",
     "pvplt = pv.Plotter(shape=(1, 2))\n",
     "\n",
     "pvplt.subplot(0, 0)\n",
     "pvplt.add_mesh(pv.wrap(orig_vtk), rgb=True, smooth_shading=True)\n",
-    "for lbl, pos in zip(landmarks_dig['label'].values, lm_dig):\n",
-    "    pvplt.add_mesh(pv.Sphere(radius=4, center=pos),\n",
-    "                   color=lm_colors.get(lbl, 'yellow'))\n",
+    "for lbl, pos in zip(lm_lbls, lm_pos):\n",
+    "    pvplt.add_mesh(pv.Sphere(radius=4, center=pos), color=lm_colors.get(lbl, 'yellow'))\n",
     "pvplt.add_text(f'S{SUBJECT_NUMBER} Original', position='upper_left', font_size=14)\n",
     "\n",
     "pvplt.subplot(0, 1)\n",
     "pvplt.add_mesh(pv.wrap(anon_vtk), rgb=True, smooth_shading=True)\n",
-    "for lbl, pos in zip(landmarks_dig['label'].values, lm_dig):\n",
-    "    pvplt.add_mesh(pv.Sphere(radius=4, center=pos),\n",
-    "                   color=lm_colors.get(lbl, 'yellow'))\n",
+    "for lbl, pos in zip(lm_lbls, lm_pos):\n",
+    "    pvplt.add_mesh(pv.Sphere(radius=4, center=pos), color=lm_colors.get(lbl, 'yellow'))\n",
     "pvplt.add_text(\n",
     "    f'S{SUBJECT_NUMBER} Anonymized (-{n_removed:,} verts)',\n",
     "    position='upper_left', font_size=14,\n",
@@ -919,49 +765,39 @@
   },
   {
    "cell_type": "markdown",
-   "id": "20924dfc",
+   "id": "75e40eed-bccc-4eb5-a4d5-9fd1c64f0d1b",
    "metadata": {},
    "source": [
-    "## 7. Save\n",
-    "\n",
-    "Writes the anonymized `.obj` + `.mtl` + sanitized `.jpg` bundle plus a\n",
-    "sidecar `_landmarks.tsv` (the format consumed at step 5.2 of the cedalion\n",
-    "photogrammetry co-registration tutorial). All carry `crs=digitized`. The\n",
-    "JPG is rebuilt from the original texture keeping only pixels still\n",
-    "referenced by the anonymized mesh's UVs; face-region pixels are blanked,\n",
-    "so opening the JPG alone reveals no face.\n"
+    "## Save\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
-   "id": "391c93d5",
+   "execution_count": 16,
+   "id": "ac270274-9e08-4e50-90d2-65c4c49aa34c",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Wrote:\n",
-      "  /home/ma7/BA/PG_Subjects/Subject15/Subject15_anon.jpg\n",
-      "  /home/ma7/BA/PG_Subjects/Subject15/Subject15_anon.mtl\n",
-      "  /home/ma7/BA/PG_Subjects/Subject15/Subject15_anon.obj\n",
-      "  /home/ma7/BA/PG_Subjects/Subject15/Subject15_anon_landmarks.tsv\n"
+      "wrote /home/ma7/BA/PG_Subjects/Subject17/Subject17_anon.jpg\n",
+      "wrote /home/ma7/BA/PG_Subjects/Subject17/Subject17_anon.mtl\n",
+      "wrote /home/ma7/BA/PG_Subjects/Subject17/Subject17_anon.obj\n",
+      "wrote /home/ma7/BA/PG_Subjects/Subject17/Subject17_anon_landmarks.tsv\n"
      ]
     }
    ],
    "source": [
-    "out = f'{SCANS_FOLDER}/Subject{SUBJECT_NUMBER}/Subject{SUBJECT_NUMBER}_anon.obj'\n",
-    "written = save_anonymized_scan(surface_anon_dig, out, landmarks=landmarks_dig)\n",
-    "print('Wrote:')\n",
+    "written = save_anonymized_scan(surface_anon, OUT_PATH, landmarks=landmarks_anon)\n",
     "for p in written:\n",
-    "    print(f'  {p}')\n"
+    "    print(f'wrote {p}')\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9b07bdbe-a5b7-405e-b86d-1169e0d863a2",
+   "id": "5d9540ae-a939-4b18-be01-321627b8ad49",
    "metadata": {},
    "outputs": [],
    "source": []

From 4a9ae70d220bca30a57aeadd4cc98369f4f33a27 Mon Sep 17 00:00:00 2001
From: mah-diaa <mahmoud.diaa.abdeen@gmail.com>
Date: Tue, 5 May 2026 09:06:34 +0200
Subject: [PATCH 26/39] Drop machine-specific paths and ANON_OPTIONS dict from
 notebook 51

---
 .../51_manual_5pt_anonymization.ipynb         | 621 +-----------------
 1 file changed, 35 insertions(+), 586 deletions(-)

diff --git a/examples/head_models/51_manual_5pt_anonymization.ipynb b/examples/head_models/51_manual_5pt_anonymization.ipynb
index 398c23df..7352cba3 100644
--- a/examples/head_models/51_manual_5pt_anonymization.ipynb
+++ b/examples/head_models/51_manual_5pt_anonymization.ipynb
@@ -18,13 +18,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "a916040f-02d3-44e3-a1f4-05188c814d1d",
    "metadata": {},
    "outputs": [],
    "source": [
     "import logging\n",
     "import os\n",
+    "from pathlib import Path\n",
     "\n",
     "import numpy as np\n",
     "import pyvista as pv\n",
@@ -46,25 +47,18 @@
     "pv.set_jupyter_backend('server')\n",
     "\n",
     "# === Options ===\n",
-    "SUBJECT_NUMBER = 17\n",
-    "SCANS_FOLDER = '/home/ma7/BA/PG_Subjects'\n",
-    "SCAN_PATH = f'{SCANS_FOLDER}/Subject{SUBJECT_NUMBER}/Subject{SUBJECT_NUMBER}.obj'\n",
-    "OUT_PATH = f'{SCANS_FOLDER}/Subject{SUBJECT_NUMBER}/Subject{SUBJECT_NUMBER}_anon.obj'\n",
+    "# Path to your Einstar .obj scan. The notebook expects a sibling .jpg\n",
+    "# texture (Einstar exports both).\n",
+    "SCAN_PATH = './scan.obj'         # edit me\n",
+    "OUT_PATH = None                  # None -> write '<stem>_anon.obj' next to SCAN_PATH\n",
     "\n",
-    "# True: run the picker. False: use cached landmarks (fill in cell below).\n",
+    "# True: run the picker. False: load landmarks from a TSV sidecar (set below).\n",
     "INTERACTIVE = True\n",
+    "CACHED_LANDMARKS_TSV = None      # used only when INTERACTIVE = False\n",
     "\n",
-    "# anonymize_scan tunables (mm). Defaults match the library's recommended values.\n",
-    "ANON_OPTIONS = dict(\n",
-    "    head_isolation_radius_mm=220.0,\n",
-    "    cap_band_width_mm=15.0,\n",
-    "    cap_bin_size_mm=1.0,\n",
-    "    cap_foot_grad_threshold=0.2,\n",
-    "    cap_z_ceiling_mm=40.0,\n",
-    "    eyebrow_offset_mm=10.0,\n",
-    "    ear_delete_radius_mm=40.0,\n",
-    "    landmark_keep_radius_mm=8.0,\n",
-    ")\n"
+    "# To tweak the pipeline, pass kwargs explicitly, e.g.\n",
+    "#   anonymize_scan(surface, landmarks, head_isolation_radius_mm=200, eyebrow_offset_mm=12)\n",
+    "# See the anonymize_scan docstring for the full list and defaults.\n"
    ]
   },
   {
@@ -77,22 +71,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "5c99124b-f88c-4aaf-bff2-124cfadd5da1",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loaded: 1,284,667 vertices, 2,223,716 faces\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "surface = cedalion.io.read_einstar_obj(SCAN_PATH)\n",
     "print(f'Loaded: {surface.nvertices:,} vertices, {surface.nfaces:,} faces')\n",
     "\n",
+    "if OUT_PATH is None:\n",
+    "    p = Path(SCAN_PATH)\n",
+    "    OUT_PATH = str(p.with_name(p.stem + '_anon' + p.suffix))\n",
+    "\n",
     "# trimesh 4.6 places the texture image on visual.material.image; if neither\n",
     "# path has it, attach it from the sibling JPG so save_anonymized_scan can\n",
     "# sanitize the final texture.\n",
@@ -121,25 +111,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "4ea3ff12-ca93-4c03-9f50-b84c28c7cfdc",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "75dbdddfdd924863a11cb97abe1d9bfb",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Widget(value='<iframe src=\"http://localhost:42585/index.html?ui=P_0x7f2e70c57590_2&reconnect=auto\" class=\"pyvi…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "if INTERACTIVE:\n",
     "    pvplt = pv.Plotter()\n",
@@ -159,510 +134,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "3a09ed60-b599-4191-b394-460b852f4b27",
    "metadata": {},
-   "outputs": [
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-       "\n",
-       ".xr-text-repr-fallback {\n",
-       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-header {\n",
-       "  padding-top: 6px;\n",
-       "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
-       "  border-bottom: solid 1px var(--xr-border-color);\n",
-       "}\n",
-       "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
-       "  display: inline;\n",
-       "  margin-top: 0;\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type,\n",
-       ".xr-array-name {\n",
-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-sections {\n",
-       "  padding-left: 0 !important;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input {\n",
-       "  display: inline-block;\n",
-       "  opacity: 0;\n",
-       "  height: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input + label {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "  border: 2px solid transparent !important;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:focus + label {\n",
-       "  border: 2px solid var(--xr-font-color0) !important;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: \"►\";\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: \"▼\";\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: \"(\";\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: \")\";\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: \",\";\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  border-color: var(--xr-background-color-row-odd);\n",
-       "  margin-bottom: 0;\n",
-       "  padding-top: 2px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "  border-color: var(--xr-background-color-row-even);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-index-preview {\n",
-       "  grid-column: 2 / 5;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  display: none;\n",
-       "  border-top: 2px dotted var(--xr-background-color);\n",
-       "  padding-bottom: 20px !important;\n",
-       "  padding-top: 10px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in + label,\n",
-       ".xr-var-data-in + label,\n",
-       ".xr-index-data-in + label {\n",
-       "  padding: 0 1px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data,\n",
-       ".xr-index-data-in:checked ~ .xr-index-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > pre,\n",
-       ".xr-index-data > pre,\n",
-       ".xr-var-data > table > tbody > tr {\n",
-       "  background-color: transparent !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-index-name div,\n",
-       ".xr-index-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data,\n",
-       ".xr-index-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt,\n",
-       ".xr-attrs dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2,\n",
-       ".xr-no-icon {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
-       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
-       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
-       "  color: var(--xr-font-color0);\n",
-       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
-       "  stroke-width: 0.8px;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (label: 5, digitized: 3)&gt; Size: 120B\n",
-       "&lt;Quantity([[ 67.32600288 -11.45115817 478.34751934]\n",
-       " [ 27.13790911 217.01868087 457.09931   ]\n",
-       " [181.98669299 116.53328682 429.76404071]\n",
-       " [ -7.75895501  60.47001713 415.08539957]\n",
-       " [ 75.93565647  77.90628633 563.57941114]], &#x27;millimeter&#x27;)&gt;\n",
-       "Coordinates:\n",
-       "  * label    (label) &lt;U3 60B &#x27;Nz&#x27; &#x27;Iz&#x27; &#x27;Cz&#x27; &#x27;Lpa&#x27; &#x27;Rpa&#x27;\n",
-       "    type     (label) object 40B PointType.LANDMARK ... PointType.LANDMARK\n",
-       "    group    (label) &lt;U1 20B &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27;\n",
-       "Dimensions without coordinates: digitized</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>label</span>: 5</li><li><span>digitized</span>: 3</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-6d73968d-3ac6-487d-9277-8dbf459e9925' class='xr-array-in' type='checkbox' checked><label for='section-6d73968d-3ac6-487d-9277-8dbf459e9925' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>[mm] 67.33 -11.45 478.3 27.14 217.0 ... 60.47 415.1 75.94 77.91 563.6</span></div><div class='xr-array-data'><table><tbody><tr><th>Magnitude</th><td style='text-align:left;'><pre>[[67.3260028810771 -11.451158171365705 478.3475193380646] [27.13790911247986 217.01868087023644 457.0993099960403] [181.98669298817038 116.53328682331225 429.7640407051109] [-7.758955014371736 60.47001712735505 415.085399568561] [75.93565646921411 77.90628633010931 563.5794111384439]]</pre></td></tr><tr><th>Units</th><td style='text-align:left;'>millimeter</td></tr></tbody></table></div></div></li><li class='xr-section-item'><input id='section-e58f3a2e-ce08-4c2b-b296-b8a4cb19ddfa' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e58f3a2e-ce08-4c2b-b296-b8a4cb19ddfa' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>label</span></div><div class='xr-var-dims'>(label)</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;Nz&#x27; &#x27;Iz&#x27; &#x27;Cz&#x27; &#x27;Lpa&#x27; &#x27;Rpa&#x27;</div><input id='attrs-2e2b15ce-0ab3-4bff-8d80-3fb0594445ab' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2e2b15ce-0ab3-4bff-8d80-3fb0594445ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d2cd277a-5a7e-45b5-8f79-dc7855784347' class='xr-var-data-in' type='checkbox'><label for='data-d2cd277a-5a7e-45b5-8f79-dc7855784347' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Nz&#x27;, &#x27;Iz&#x27;, &#x27;Cz&#x27;, &#x27;Lpa&#x27;, &#x27;Rpa&#x27;], dtype=&#x27;&lt;U3&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>type</span></div><div class='xr-var-dims'>(label)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>PointType.LANDMARK ... PointType...</div><input id='attrs-cc6348c9-28d4-412c-8bde-287f31a31b1a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cc6348c9-28d4-412c-8bde-287f31a31b1a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8064227b-bd56-4e2e-ae46-4f40f48d1b33' class='xr-var-data-in' type='checkbox'><label for='data-8064227b-bd56-4e2e-ae46-4f40f48d1b33' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&lt;PointType.LANDMARK: 3&gt;, &lt;PointType.LANDMARK: 3&gt;,\n",
-       "       &lt;PointType.LANDMARK: 3&gt;, &lt;PointType.LANDMARK: 3&gt;,\n",
-       "       &lt;PointType.LANDMARK: 3&gt;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>group</span></div><div class='xr-var-dims'>(label)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27;</div><input id='attrs-37b42a90-36c2-44f0-aa9f-0f544e32ad54' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-37b42a90-36c2-44f0-aa9f-0f544e32ad54' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cfd0585d-0419-4c8b-bde0-338b924189e2' class='xr-var-data-in' type='checkbox'><label for='data-cfd0585d-0419-4c8b-bde0-338b924189e2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;L&#x27;, &#x27;L&#x27;, &#x27;L&#x27;, &#x27;L&#x27;, &#x27;L&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-881e1326-43f8-46e8-9b55-61c8b1931113' class='xr-section-summary-in' type='checkbox'  ><label for='section-881e1326-43f8-46e8-9b55-61c8b1931113' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>label</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-7ff12102-19d0-4d95-91dd-abb0346eb0c7' class='xr-index-data-in' type='checkbox'/><label for='index-7ff12102-19d0-4d95-91dd-abb0346eb0c7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;Nz&#x27;, &#x27;Iz&#x27;, &#x27;Cz&#x27;, &#x27;Lpa&#x27;, &#x27;Rpa&#x27;], dtype=&#x27;object&#x27;, name=&#x27;label&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bf2265ae-d481-4206-b4af-3c0a802a9cc4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bf2265ae-d481-4206-b4af-3c0a802a9cc4' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray (label: 5, digitized: 3)> Size: 120B\n",
-       "<Quantity([[ 67.32600288 -11.45115817 478.34751934]\n",
-       " [ 27.13790911 217.01868087 457.09931   ]\n",
-       " [181.98669299 116.53328682 429.76404071]\n",
-       " [ -7.75895501  60.47001713 415.08539957]\n",
-       " [ 75.93565647  77.90628633 563.57941114]], 'millimeter')>\n",
-       "Coordinates:\n",
-       "  * label    (label) <U3 60B 'Nz' 'Iz' 'Cz' 'Lpa' 'Rpa'\n",
-       "    type     (label) object 40B PointType.LANDMARK ... PointType.LANDMARK\n",
-       "    group    (label) <U1 20B 'L' 'L' 'L' 'L' 'L'\n",
-       "Dimensions without coordinates: digitized"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "if INTERACTIVE:\n",
     "    landmarks = get_landmarks()\n",
     "else:\n",
-    "    # Cached fallback: paste coordinates from a previous interactive run here.\n",
-    "    landmark_labels = ['Nz', 'Iz', 'Cz', 'Lpa', 'Rpa']\n",
-    "    landmark_coordinates = np.asarray([\n",
-    "        [0.0, 0.0, 0.0],   # Nz  -- replace\n",
-    "        [0.0, 0.0, 0.0],   # Iz  -- replace\n",
-    "        [0.0, 0.0, 0.0],   # Cz  -- replace\n",
-    "        [0.0, 0.0, 0.0],   # Lpa -- replace\n",
-    "        [0.0, 0.0, 0.0],   # Rpa -- replace\n",
-    "    ])\n",
-    "    landmarks = xr.DataArray(\n",
-    "        np.vstack(landmark_coordinates),\n",
-    "        dims=['label', 'digitized'],\n",
-    "        coords={\n",
-    "            'label': ('label', landmark_labels),\n",
-    "            'type': ('label', [cdc.PointType.LANDMARK] * 5),\n",
-    "            'group': ('label', ['L'] * 5),\n",
-    "        },\n",
-    "    ).pint.quantify('mm')\n",
+    "    assert CACHED_LANDMARKS_TSV, 'set CACHED_LANDMARKS_TSV or INTERACTIVE = True'\n",
+    "    landmarks = cedalion.io.load_tsv(CACHED_LANDMARKS_TSV)\n",
     "\n",
     "labels = list(landmarks['label'].values)\n",
     "assert set(labels) == {'Nz', 'Iz', 'Cz', 'Lpa', 'Rpa'}, f'bad labels: {labels}'\n",
@@ -679,21 +160,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "acfa6540-281e-4123-8a0d-ee30cd33aae1",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "S17: 1,284,667 -> 593,963 verts (-690,704)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "surface_anon, landmarks_anon = anonymize_scan(surface, landmarks, **ANON_OPTIONS)\n",
-    "print(f'S{SUBJECT_NUMBER}: {surface.nvertices:,} -> {surface_anon.nvertices:,} verts '\n",
+    "surface_anon, landmarks_anon = anonymize_scan(surface, landmarks)\n",
+    "stem = Path(SCAN_PATH).stem\n",
+    "print(f'{stem}: {surface.nvertices:,} -> {surface_anon.nvertices:,} verts '\n",
     "      f'(-{surface.nvertices - surface_anon.nvertices:,})')\n"
    ]
   },
@@ -710,26 +184,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "309c17ba-a234-438e-bd5a-3372dc328266",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4e59bf72d58849b581e7f7c48b6d956c",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Widget(value='<iframe src=\"http://localhost:42585/index.html?ui=P_0x7f2e3cfa5d50_3&reconnect=auto\" class=\"pyvi…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
+    "stem = Path(SCAN_PATH).stem\n",
     "lm_colors = {\n",
     "    'Nz': 'lime', 'Iz': 'magenta', 'Cz': 'cyan',\n",
     "    'Lpa': 'orange', 'Rpa': 'blue', 'LPA': 'orange', 'RPA': 'blue',\n",
@@ -748,14 +208,14 @@
     "pvplt.add_mesh(pv.wrap(orig_vtk), rgb=True, smooth_shading=True)\n",
     "for lbl, pos in zip(lm_lbls, lm_pos):\n",
     "    pvplt.add_mesh(pv.Sphere(radius=4, center=pos), color=lm_colors.get(lbl, 'yellow'))\n",
-    "pvplt.add_text(f'S{SUBJECT_NUMBER} Original', position='upper_left', font_size=14)\n",
+    "pvplt.add_text(f'{stem} Original', position='upper_left', font_size=14)\n",
     "\n",
     "pvplt.subplot(0, 1)\n",
     "pvplt.add_mesh(pv.wrap(anon_vtk), rgb=True, smooth_shading=True)\n",
     "for lbl, pos in zip(lm_lbls, lm_pos):\n",
     "    pvplt.add_mesh(pv.Sphere(radius=4, center=pos), color=lm_colors.get(lbl, 'yellow'))\n",
     "pvplt.add_text(\n",
-    "    f'S{SUBJECT_NUMBER} Anonymized (-{n_removed:,} verts)',\n",
+    "    f'{stem} Anonymized (-{n_removed:,} verts)',\n",
     "    position='upper_left', font_size=14,\n",
     ")\n",
     "\n",
@@ -773,21 +233,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "id": "ac270274-9e08-4e50-90d2-65c4c49aa34c",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "wrote /home/ma7/BA/PG_Subjects/Subject17/Subject17_anon.jpg\n",
-      "wrote /home/ma7/BA/PG_Subjects/Subject17/Subject17_anon.mtl\n",
-      "wrote /home/ma7/BA/PG_Subjects/Subject17/Subject17_anon.obj\n",
-      "wrote /home/ma7/BA/PG_Subjects/Subject17/Subject17_anon_landmarks.tsv\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "written = save_anonymized_scan(surface_anon, OUT_PATH, landmarks=landmarks_anon)\n",
     "for p in written:\n",

From e5e4f503038361aeb9b86fc2e7ff5fe3231d0c77 Mon Sep 17 00:00:00 2001
From: mah-diaa <mahmoud.diaa.abdeen@gmail.com>
Date: Tue, 5 May 2026 21:51:02 +0200
Subject: [PATCH 27/39] Add README, reproducibility script, and gitignore for
 thesis submission

---
 .gitignore                                    |   3 +
 README.md                                     |  88 +++++++++++
 .../head_models/reproduce_anonymization.py    | 142 ++++++++++++++++++
 3 files changed, 233 insertions(+)
 create mode 100644 examples/head_models/reproduce_anonymization.py

diff --git a/.gitignore b/.gitignore
index 7cb03225..7d8dfea1 100644
--- a/.gitignore
+++ b/.gitignore
@@ -189,3 +189,6 @@ plugins/
 
 # thesis-only generated artifacts (regenerable from validation notebooks)
 examples/head_models/thesis_results_out/
+
+# machine-local scan-data symlink (points to /home/ma7/BA/PG_Subjects)
+examples/head_models/PG_Subjects
diff --git a/README.md b/README.md
index 369cc7e5..47818fa6 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,91 @@
+# Face Anonymization for Photogrammetry Scans
+
+**Thesis:** "Development of an Automatic Face Removal Algorithm for Photogrammetry Scans for Data Protection" — M.Sc. thesis, TU Berlin / IBS Lab
+
+This is a fork of [cedalion](https://github.com/ibs-lab/cedalion) extended with a geometric face anonymization module for Einstar photogrammetry scans acquired in fNIRS research. The module lives at:
+
+```
+src/cedalion/geometry/photogrammetry/anonymization/
+```
+
+## Installation
+
+Requires the cedalion conda environment:
+
+```bash
+conda env create -f environment_dev.yml
+conda activate cedalion
+pip install -e .
+```
+
+## Usage
+
+### Interactive single-scan workflow (canonical)
+
+Open `examples/head_models/51_manual_5pt_anonymization.ipynb`. The notebook:
+1. Loads an Einstar scan (`cedalion.io.read_einstar_obj`)
+2. Picks the five 10-20 landmarks interactively (Nz, Iz, Cz, LPA, RPA)
+3. Calls `anonymize_scan(surface, landmarks)`
+4. Shows a before/after comparison
+5. Saves the anonymized OBJ + `_landmarks.tsv` sidecar via `save_anonymized_scan`
+
+### Scripted batch reproduction
+
+After notebook 51 has been run for all subjects (writing the `_landmarks.tsv` sidecars):
+
+```bash
+cd examples/head_models
+# Edit SCANS_FOLDER at the top of the script to point at your scan directory
+python reproduce_anonymization.py           # batch anonymize all subjects
+python reproduce_anonymization.py --profile # same, plus per-function timing
+```
+
+The script auto-discovers subjects by looking for matching raw OBJ + `_anon_landmarks.tsv` pairs.
+
+## Module structure
+
+```
+pipeline.py       anonymize_scan — canonical entry point
+preprocessing.py  normalize_axes, isolate_head, align_axes_from_landmarks,
+                  revert_to_einstar_frame
+mask.py           detect_cap_boundary, face_mask_from_landmarks,
+                  delete_masked_vertices, save_anonymized_scan
+_utils.py         private helpers shared by all of the above
+```
+
+Pipeline steps inside `anonymize_scan`:
+
+1. `normalize_axes` — rotate so +Y points anterior (handles arbitrary Einstar orientation)
+2. `isolate_head` — remove body, shoulders, and disconnected fragments
+3. `align_axes_from_landmarks` — map to CTF frame (+X anterior, +Y left, +Z up)
+4. `detect_cap_boundary` — locate the front cap-edge height along Z
+5. `face_mask_from_landmarks` — face region union ear spheres, clamped below the cap
+6. Landmark preservation — 8 mm spheres around each landmark + midline nasion strip
+7. `delete_masked_vertices` — drop triangles touching any masked vertex, UVs in sync
+8. `revert_to_einstar_frame` — return to `crs="digitized"` for saving
+
+## Tests
+
+```bash
+pytest tests/test_anonymization.py -v
+```
+
+22 tests covering all eight public functions and the end-to-end pipeline. No external data required (synthetic trimesh spheres only).
+
+## Branch layout
+
+| Branch | Contents |
+|--------|---------|
+| `main` | Core anonymization module, notebook 51, test suite |
+| `validation/face-anonymization` | Validation notebooks 64-73, batch CSV producers |
+| `auxiliary/mediapipe-nasion` | Experimental automatic nasion detection (MediaPipe) |
+
+---
+
+*Upstream cedalion documentation below.*
+
+---
+
 # Cedalion - fNIRS analysis toolbox
 
 A python-based framework for the data-driven analysis of multimodal fNIRS and DOT in naturalistic environments. Developed by the [Intelligent Biomedical Sensing (IBS) Lab](https://ibs-lab.com/) with and for the community.
diff --git a/examples/head_models/reproduce_anonymization.py b/examples/head_models/reproduce_anonymization.py
new file mode 100644
index 00000000..52cb393f
--- /dev/null
+++ b/examples/head_models/reproduce_anonymization.py
@@ -0,0 +1,142 @@
+"""Batch face-anonymization script for thesis reproducibility.
+
+Discovers subjects whose raw Einstar scan and ``_anon_landmarks.tsv``
+sidecar are both present, then re-runs ``anonymize_scan`` on each and
+writes the anonymized OBJ + landmark TSV.  Running the same script on the
+same inputs always produces the same outputs (no interactive steps).
+
+Scan directory layout expected::
+
+    <SCANS_FOLDER>/Subject<N>/Subject<N>.obj          # raw Einstar scan
+    <SCANS_FOLDER>/Subject<N>/Subject<N>_anon_landmarks.tsv  # from notebook 51
+
+Output written to the same folder::
+
+    <SCANS_FOLDER>/Subject<N>/Subject<N>_anon.obj
+    <SCANS_FOLDER>/Subject<N>/Subject<N>_anon_landmarks.tsv  (overwritten)
+
+Usage::
+
+    cd examples/head_models
+    python reproduce_anonymization.py           # batch anonymize
+    python reproduce_anonymization.py --profile # same, plus per-function timing
+
+Prerequisites:
+  - Run ``51_manual_5pt_anonymization.ipynb`` for each subject to pick the
+    five 10-20 landmarks (Nz, Iz, Cz, LPA, RPA) and write the TSV sidecar.
+  - The conda environment must be active (``conda activate cedalion``).
+"""
+
+from __future__ import annotations
+
+import argparse
+import cProfile
+import io
+import pstats
+import time
+from pathlib import Path
+
+import cedalion.io
+from cedalion.geometry.photogrammetry.anonymization import (
+    anonymize_scan,
+    save_anonymized_scan,
+)
+
+# ---------------------------------------------------------------------------
+# Edit this constant to point at your scan directory.
+# ---------------------------------------------------------------------------
+SCANS_FOLDER = Path("./PG_Subjects")
+# ---------------------------------------------------------------------------
+
+
+def _find_subjects(scans_folder: Path) -> list[tuple[int, Path, Path]]:
+    """Return (subject_number, raw_obj, landmarks_tsv) for ready subjects."""
+    ready = []
+    if not scans_folder.exists():
+        return ready
+    for subj_dir in sorted(scans_folder.iterdir()):
+        name = subj_dir.name
+        if not (subj_dir.is_dir() and name.startswith("Subject")):
+            continue
+        try:
+            n = int(name[len("Subject"):])
+        except ValueError:
+            continue
+        obj = subj_dir / f"Subject{n}.obj"
+        tsv = subj_dir / f"Subject{n}_anon_landmarks.tsv"
+        if obj.exists() and tsv.exists():
+            ready.append((n, obj, tsv))
+    return ready
+
+
+def _run_one(n: int, obj: Path, tsv: Path) -> tuple[float, list[str]]:
+    """Anonymize one subject; return (elapsed_seconds, written_paths)."""
+    surface = cedalion.io.read_einstar_obj(str(obj))
+    landmarks = cedalion.io.load_tsv(str(tsv))
+
+    t0 = time.perf_counter()
+    surface_anon, landmarks_anon = anonymize_scan(surface, landmarks)
+    elapsed = time.perf_counter() - t0
+
+    out_obj = str(obj.parent / f"Subject{n}_anon.obj")
+    written = save_anonymized_scan(
+        surface_anon, out_obj, landmarks=landmarks_anon
+    )
+    return elapsed, written
+
+
+def _profile_one(n: int, obj: Path, tsv: Path, top_n: int = 20) -> None:
+    """Profile anonymize_scan for one subject and print the hot functions."""
+    surface = cedalion.io.read_einstar_obj(str(obj))
+    landmarks = cedalion.io.load_tsv(str(tsv))
+
+    pr = cProfile.Profile()
+    pr.enable()
+    anonymize_scan(surface, landmarks)
+    pr.disable()
+
+    buf = io.StringIO()
+    ps = pstats.Stats(pr, stream=buf).sort_stats("cumulative")
+    ps.print_stats(top_n)
+    print(buf.getvalue())
+
+
+def main(profile: bool = False) -> None:
+    subjects = _find_subjects(SCANS_FOLDER)
+    if not subjects:
+        print(
+            f"No ready subjects found under {SCANS_FOLDER.resolve()}.\n"
+            "Run notebook 51 for each subject first to write the "
+            "_anon_landmarks.tsv sidecars."
+        )
+        return
+
+    print(f"Found {len(subjects)} ready subject(s).\n")
+
+    if profile:
+        n, obj, tsv = subjects[0]
+        print(f"Profiling Subject{n} (first ready subject)...\n")
+        _profile_one(n, obj, tsv)
+        return
+
+    total_t0 = time.perf_counter()
+    for n, obj, tsv in subjects:
+        elapsed, written = _run_one(n, obj, tsv)
+        written_names = ", ".join(Path(p).name for p in written)
+        print(f"Subject{n:>3}  {elapsed:5.2f}s  -> {written_names}")
+
+    total = time.perf_counter() - total_t0
+    print(f"\n{len(subjects)} subjects in {total:.1f}s "
+          f"({total / len(subjects):.1f}s/subject)")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description=__doc__.splitlines()[0])
+    parser.add_argument(
+        "--profile",
+        action="store_true",
+        help="Profile anonymize_scan on the first ready subject instead of "
+             "running the full batch.",
+    )
+    args = parser.parse_args()
+    main(profile=args.profile)

From 6118b000361629db1ee21b6d6a93f60fadfc3c3c Mon Sep 17 00:00:00 2001
From: mah-diaa <mahmoud.diaa.abdeen@gmail.com>
Date: Tue, 5 May 2026 21:57:42 +0200
Subject: [PATCH 28/39] Remove reproduce_anonymization.py

---
 .../head_models/reproduce_anonymization.py    | 142 ------------------
 1 file changed, 142 deletions(-)
 delete mode 100644 examples/head_models/reproduce_anonymization.py

diff --git a/examples/head_models/reproduce_anonymization.py b/examples/head_models/reproduce_anonymization.py
deleted file mode 100644
index 52cb393f..00000000
--- a/examples/head_models/reproduce_anonymization.py
+++ /dev/null
@@ -1,142 +0,0 @@
-"""Batch face-anonymization script for thesis reproducibility.
-
-Discovers subjects whose raw Einstar scan and ``_anon_landmarks.tsv``
-sidecar are both present, then re-runs ``anonymize_scan`` on each and
-writes the anonymized OBJ + landmark TSV.  Running the same script on the
-same inputs always produces the same outputs (no interactive steps).
-
-Scan directory layout expected::
-
-    <SCANS_FOLDER>/Subject<N>/Subject<N>.obj          # raw Einstar scan
-    <SCANS_FOLDER>/Subject<N>/Subject<N>_anon_landmarks.tsv  # from notebook 51
-
-Output written to the same folder::
-
-    <SCANS_FOLDER>/Subject<N>/Subject<N>_anon.obj
-    <SCANS_FOLDER>/Subject<N>/Subject<N>_anon_landmarks.tsv  (overwritten)
-
-Usage::
-
-    cd examples/head_models
-    python reproduce_anonymization.py           # batch anonymize
-    python reproduce_anonymization.py --profile # same, plus per-function timing
-
-Prerequisites:
-  - Run ``51_manual_5pt_anonymization.ipynb`` for each subject to pick the
-    five 10-20 landmarks (Nz, Iz, Cz, LPA, RPA) and write the TSV sidecar.
-  - The conda environment must be active (``conda activate cedalion``).
-"""
-
-from __future__ import annotations
-
-import argparse
-import cProfile
-import io
-import pstats
-import time
-from pathlib import Path
-
-import cedalion.io
-from cedalion.geometry.photogrammetry.anonymization import (
-    anonymize_scan,
-    save_anonymized_scan,
-)
-
-# ---------------------------------------------------------------------------
-# Edit this constant to point at your scan directory.
-# ---------------------------------------------------------------------------
-SCANS_FOLDER = Path("./PG_Subjects")
-# ---------------------------------------------------------------------------
-
-
-def _find_subjects(scans_folder: Path) -> list[tuple[int, Path, Path]]:
-    """Return (subject_number, raw_obj, landmarks_tsv) for ready subjects."""
-    ready = []
-    if not scans_folder.exists():
-        return ready
-    for subj_dir in sorted(scans_folder.iterdir()):
-        name = subj_dir.name
-        if not (subj_dir.is_dir() and name.startswith("Subject")):
-            continue
-        try:
-            n = int(name[len("Subject"):])
-        except ValueError:
-            continue
-        obj = subj_dir / f"Subject{n}.obj"
-        tsv = subj_dir / f"Subject{n}_anon_landmarks.tsv"
-        if obj.exists() and tsv.exists():
-            ready.append((n, obj, tsv))
-    return ready
-
-
-def _run_one(n: int, obj: Path, tsv: Path) -> tuple[float, list[str]]:
-    """Anonymize one subject; return (elapsed_seconds, written_paths)."""
-    surface = cedalion.io.read_einstar_obj(str(obj))
-    landmarks = cedalion.io.load_tsv(str(tsv))
-
-    t0 = time.perf_counter()
-    surface_anon, landmarks_anon = anonymize_scan(surface, landmarks)
-    elapsed = time.perf_counter() - t0
-
-    out_obj = str(obj.parent / f"Subject{n}_anon.obj")
-    written = save_anonymized_scan(
-        surface_anon, out_obj, landmarks=landmarks_anon
-    )
-    return elapsed, written
-
-
-def _profile_one(n: int, obj: Path, tsv: Path, top_n: int = 20) -> None:
-    """Profile anonymize_scan for one subject and print the hot functions."""
-    surface = cedalion.io.read_einstar_obj(str(obj))
-    landmarks = cedalion.io.load_tsv(str(tsv))
-
-    pr = cProfile.Profile()
-    pr.enable()
-    anonymize_scan(surface, landmarks)
-    pr.disable()
-
-    buf = io.StringIO()
-    ps = pstats.Stats(pr, stream=buf).sort_stats("cumulative")
-    ps.print_stats(top_n)
-    print(buf.getvalue())
-
-
-def main(profile: bool = False) -> None:
-    subjects = _find_subjects(SCANS_FOLDER)
-    if not subjects:
-        print(
-            f"No ready subjects found under {SCANS_FOLDER.resolve()}.\n"
-            "Run notebook 51 for each subject first to write the "
-            "_anon_landmarks.tsv sidecars."
-        )
-        return
-
-    print(f"Found {len(subjects)} ready subject(s).\n")
-
-    if profile:
-        n, obj, tsv = subjects[0]
-        print(f"Profiling Subject{n} (first ready subject)...\n")
-        _profile_one(n, obj, tsv)
-        return
-
-    total_t0 = time.perf_counter()
-    for n, obj, tsv in subjects:
-        elapsed, written = _run_one(n, obj, tsv)
-        written_names = ", ".join(Path(p).name for p in written)
-        print(f"Subject{n:>3}  {elapsed:5.2f}s  -> {written_names}")
-
-    total = time.perf_counter() - total_t0
-    print(f"\n{len(subjects)} subjects in {total:.1f}s "
-          f"({total / len(subjects):.1f}s/subject)")
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description=__doc__.splitlines()[0])
-    parser.add_argument(
-        "--profile",
-        action="store_true",
-        help="Profile anonymize_scan on the first ready subject instead of "
-             "running the full batch.",
-    )
-    args = parser.parse_args()
-    main(profile=args.profile)

From d3e5b9776ae7bc5dbc69b5eb08778444e32be08f Mon Sep 17 00:00:00 2001
From: mah-diaa <mahmoud.diaa.abdeen@gmail.com>
Date: Tue, 5 May 2026 22:02:59 +0200
Subject: [PATCH 29/39] Expose mask kwargs in notebook 51; drop batch-script
 mention from README

---
 README.md                                     | 13 -------------
 .../51_manual_5pt_anonymization.ipynb         | 19 +++++++++++++++----
 2 files changed, 15 insertions(+), 17 deletions(-)

diff --git a/README.md b/README.md
index 47818fa6..39ab9372 100644
--- a/README.md
+++ b/README.md
@@ -29,19 +29,6 @@ Open `examples/head_models/51_manual_5pt_anonymization.ipynb`. The notebook:
 4. Shows a before/after comparison
 5. Saves the anonymized OBJ + `_landmarks.tsv` sidecar via `save_anonymized_scan`
 
-### Scripted batch reproduction
-
-After notebook 51 has been run for all subjects (writing the `_landmarks.tsv` sidecars):
-
-```bash
-cd examples/head_models
-# Edit SCANS_FOLDER at the top of the script to point at your scan directory
-python reproduce_anonymization.py           # batch anonymize all subjects
-python reproduce_anonymization.py --profile # same, plus per-function timing
-```
-
-The script auto-discovers subjects by looking for matching raw OBJ + `_anon_landmarks.tsv` pairs.
-
 ## Module structure
 
 ```
diff --git a/examples/head_models/51_manual_5pt_anonymization.ipynb b/examples/head_models/51_manual_5pt_anonymization.ipynb
index 7352cba3..011ed763 100644
--- a/examples/head_models/51_manual_5pt_anonymization.ipynb
+++ b/examples/head_models/51_manual_5pt_anonymization.ipynb
@@ -56,9 +56,13 @@
     "INTERACTIVE = True\n",
     "CACHED_LANDMARKS_TSV = None      # used only when INTERACTIVE = False\n",
     "\n",
-    "# To tweak the pipeline, pass kwargs explicitly, e.g.\n",
-    "#   anonymize_scan(surface, landmarks, head_isolation_radius_mm=200, eyebrow_offset_mm=12)\n",
-    "# See the anonymize_scan docstring for the full list and defaults.\n"
+    "\n",
+    "# === Mask parameters (edit to adjust the anonymized region) ===\n",
+    "EAR_DELETE_RADIUS_MM = 40.0      # sphere radius around LPA/RPA included in deletion\n",
+    "LANDMARK_KEEP_RADIUS_MM = 8.0    # sphere radius around each landmark kept intact\n",
+    "EYEBROW_OFFSET_MM = 10.0         # failsafe cap height above Nz (flush-cap fallback)\n",
+    "CAP_Z_CEILING_MM = 40.0          # mm above Nz where cap detection is clamped\n",
+    "HEAD_ISOLATION_RADIUS_MM = 220.0 # sphere radius used to strip body/shoulders\n"
    ]
   },
   {
@@ -165,7 +169,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "surface_anon, landmarks_anon = anonymize_scan(surface, landmarks)\n",
+    "surface_anon, landmarks_anon = anonymize_scan(\n",
+    "    surface, landmarks,\n",
+    "    ear_delete_radius_mm=EAR_DELETE_RADIUS_MM,\n",
+    "    landmark_keep_radius_mm=LANDMARK_KEEP_RADIUS_MM,\n",
+    "    eyebrow_offset_mm=EYEBROW_OFFSET_MM,\n",
+    "    cap_z_ceiling_mm=CAP_Z_CEILING_MM,\n",
+    "    head_isolation_radius_mm=HEAD_ISOLATION_RADIUS_MM,\n",
+    ")\n",
     "stem = Path(SCAN_PATH).stem\n",
     "print(f'{stem}: {surface.nvertices:,} -> {surface_anon.nvertices:,} verts '\n",
     "      f'(-{surface.nvertices - surface_anon.nvertices:,})')\n"

From 887040badf8c411abd38335826800cad46fbed2a Mon Sep 17 00:00:00 2001
From: mah-diaa <mahmoud.diaa.abdeen@gmail.com>
Date: Tue, 5 May 2026 22:07:57 +0200
Subject: [PATCH 30/39] Update branch layout table in README

---
 README.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 39ab9372..e7919f11 100644
--- a/README.md
+++ b/README.md
@@ -63,7 +63,8 @@ pytest tests/test_anonymization.py -v
 
 | Branch | Contents |
 |--------|---------|
-| `main` | Core anonymization module, notebook 51, test suite |
+| `feature/face-anonymization` | **This branch** — thesis implementation (anonymization module, notebook 51, test suite) |
+| `main` | Upstream cedalion base |
 | `validation/face-anonymization` | Validation notebooks 64-73, batch CSV producers |
 | `auxiliary/mediapipe-nasion` | Experimental automatic nasion detection (MediaPipe) |
 

From 3575007af47b573f7a6b50b489ee73d6b1765d69 Mon Sep 17 00:00:00 2001
From: mah-diaa <mahmoud.diaa.abdeen@gmail.com>
Date: Wed, 6 May 2026 16:58:31 +0200
Subject: [PATCH 31/39] Move _copy_visual/_rebuild_mesh to _utils; fix
 docstring language

---
 .../photogrammetry/anonymization/__init__.py  | 18 +++--
 .../photogrammetry/anonymization/_utils.py    | 66 +++++++++++++++++--
 .../photogrammetry/anonymization/mask.py      |  9 ++-
 .../photogrammetry/anonymization/pipeline.py  |  9 ++-
 .../anonymization/preprocessing.py            | 60 +----------------
 5 files changed, 80 insertions(+), 82 deletions(-)

diff --git a/src/cedalion/geometry/photogrammetry/anonymization/__init__.py b/src/cedalion/geometry/photogrammetry/anonymization/__init__.py
index a99b989a..904c9fc3 100644
--- a/src/cedalion/geometry/photogrammetry/anonymization/__init__.py
+++ b/src/cedalion/geometry/photogrammetry/anonymization/__init__.py
@@ -15,19 +15,17 @@
     >>> surface_anon, landmarks_anon = anonymize_scan(surface, landmarks)
     >>> save_anonymized_scan(surface_anon, "out.obj", landmarks=landmarks_anon)
 
-The pipeline ``anonymize_scan`` runs internally is:
+Pipeline steps inside ``anonymize_scan``:
 
-1. :func:`normalize_axes` — rotate around X so Y points anterior.
-2. :func:`isolate_head` — strip body, shoulders, and disconnected
-   fragments.
-3. :func:`align_axes_from_landmarks` — map into the CTF frame.
-4. :func:`detect_cap_boundary` — find the cap front edge along Z.
-5. :func:`face_mask_from_landmarks` — face region + ear spheres, clamped
+1. :func:`normalize_axes`: rotate around X so Y points anterior.
+2. :func:`isolate_head`: strip body, shoulders, and disconnected fragments.
+3. :func:`align_axes_from_landmarks`: map into the CTF frame.
+4. :func:`detect_cap_boundary`: find the cap front edge along Z.
+5. :func:`face_mask_from_landmarks`: face region + ear spheres, clamped
    below the cap.
 6. Preserve small spheres around each landmark and a midline nasion strip.
-7. :func:`delete_masked_vertices` — drop triangles touching any masked
-   vertex.
-8. :func:`revert_to_einstar_frame` — return to ``crs="digitized"`` so the
+7. :func:`delete_masked_vertices`: drop triangles touching any masked vertex.
+8. :func:`revert_to_einstar_frame`: return to ``crs="digitized"`` so the
    output matches :func:`cedalion.io.read_einstar_obj`.
 
 Each of those functions is also exported so callers that need to inspect
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/_utils.py b/src/cedalion/geometry/photogrammetry/anonymization/_utils.py
index b953b2d6..a8845fb4 100644
--- a/src/cedalion/geometry/photogrammetry/anonymization/_utils.py
+++ b/src/cedalion/geometry/photogrammetry/anonymization/_utils.py
@@ -1,13 +1,14 @@
-"""Private helpers shared by ``preprocessing`` and ``mask``.
+"""Shared helpers used by ``preprocessing`` and ``mask``.
 
-Centralizes patterns that would otherwise drift across the pipeline:
-texture-image lookup, face/vertex reindexing, affine application to plain
-arrays and to ``LabeledPoints``, ear midpoint, and the upper-head centroid.
+Consolidates texture-image lookup, mesh rebuilding, face/vertex reindexing,
+affine application, landmark transforms, ear midpoint, and upper-head centroid
+to avoid duplicating these across sibling modules.
 """
 
 from typing import Callable
 
 import numpy as np
+import trimesh
 
 import cedalion.typing as cdt
 
@@ -91,3 +92,60 @@ def _upper_head_centroid(
     upper = vertices[vertices[:, 0] > x_min + 0.6 * (x_max - x_min)]
     centroid = upper.mean(axis=0)
     return centroid, x_max
+
+
+def _copy_visual(src_mesh, dst_mesh, vertex_index=None) -> None:
+    """Copy a trimesh ``visual`` onto ``dst_mesh``, optionally reindexing.
+
+    Passing ``visual=old.visual`` to a new ``Trimesh(...)`` silently downgrades
+    a ``TextureVisuals`` to ``ColorVisuals`` because the visual still
+    back-references the source mesh's vertex count. Rebuilding it from scratch
+    preserves TextureVisuals.
+
+    Args:
+        src_mesh: Source trimesh.
+        dst_mesh: Destination trimesh (mutated in place).
+        vertex_index: Indices into the source vertex array. If given, UVs /
+            vertex_colors are sliced by this index. Leave None when the
+            vertex count is unchanged.
+    """
+    src_visual = src_mesh.visual
+    uv = getattr(src_visual, "uv", None)
+    n_src = len(src_mesh.vertices)
+
+    if uv is not None and len(uv) == n_src:
+        uv_arr = np.asarray(uv)
+        if vertex_index is not None:
+            uv_arr = uv_arr[vertex_index]
+        image = _resolve_texture_image(src_visual)
+        material = getattr(src_visual, "material", None)
+        dst_mesh.visual = trimesh.visual.TextureVisuals(
+            uv=uv_arr,
+            image=image,
+            material=material,
+        )
+        return
+
+    vcol = getattr(src_visual, "vertex_colors", None)
+    if vcol is not None and len(vcol) == n_src:
+        vcol_arr = np.asarray(vcol)
+        if vertex_index is not None:
+            vcol_arr = vcol_arr[vertex_index]
+        dst_mesh.visual.vertex_colors = vcol_arr
+
+
+def _rebuild_mesh(
+    src_mesh: trimesh.Trimesh,
+    *,
+    vertices: np.ndarray,
+    faces: np.ndarray,
+    vertex_index: np.ndarray | None = None,
+) -> trimesh.Trimesh:
+    """Build a new ``Trimesh`` and re-attach the source visual.
+
+    Used by every transform in the pipeline to keep the
+    ``Trimesh(process=False)`` + ``_copy_visual`` pattern in one place.
+    """
+    new_mesh = trimesh.Trimesh(vertices=vertices, faces=faces, process=False)
+    _copy_visual(src_mesh, new_mesh, vertex_index=vertex_index)
+    return new_mesh
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/mask.py b/src/cedalion/geometry/photogrammetry/anonymization/mask.py
index 00f4fa5e..6050b711 100644
--- a/src/cedalion/geometry/photogrammetry/anonymization/mask.py
+++ b/src/cedalion/geometry/photogrammetry/anonymization/mask.py
@@ -3,11 +3,11 @@
 Given the 5 CTF-aligned landmarks, builds a boolean deletion mask and
 applies it to the mesh:
 
-- ``detect_cap_boundary`` finds the Z height where the EEG cap front edge
+- ``detect_cap_boundary``: finds the Z height where the EEG cap front edge
   sits so the mask can be clamped below it.
-- ``face_mask_from_landmarks`` unions a forward face region with two ear
+- ``face_mask_from_landmarks``: unions a forward face region with two ear
   spheres, clamped below the cap.
-- ``delete_masked_vertices`` drops triangles touching any masked vertex.
+- ``delete_masked_vertices``: drops triangles touching any masked vertex.
 
 All mask math assumes the CTF frame: +X=anterior, +Y=left, +Z=up, origin
 at the LPA-RPA midpoint (see ``align_axes_from_landmarks``).
@@ -25,8 +25,7 @@
 import cedalion.typing as cdt
 from cedalion.io import export_to_tsv
 
-from ._utils import _ear_midpoint, _reindex_faces, _resolve_texture_image
-from .preprocessing import _rebuild_mesh
+from ._utils import _ear_midpoint, _rebuild_mesh, _reindex_faces, _resolve_texture_image
 
 logger = logging.getLogger("cedalion")
 
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/pipeline.py b/src/cedalion/geometry/photogrammetry/anonymization/pipeline.py
index 3cbb6d2b..5cc635db 100644
--- a/src/cedalion/geometry/photogrammetry/anonymization/pipeline.py
+++ b/src/cedalion/geometry/photogrammetry/anonymization/pipeline.py
@@ -1,10 +1,9 @@
 """Top-level orchestrator: ``anonymize_scan``.
 
-Single entry point that wires together the granular helpers from
-``preprocessing`` and ``mask``. The granular functions remain public so
-downstream code can inspect intermediate state (e.g. the cap-detection
-profile, the head isolation mask), but the canonical pipeline lives here
-and only here.
+Single entry point that chains the helper functions from ``preprocessing``
+and ``mask``. The individual functions remain public so downstream code can
+inspect intermediate state (e.g. the cap-detection profile, the head
+isolation mask), but the canonical pipeline lives here and only here.
 """
 
 from typing import Literal
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/preprocessing.py b/src/cedalion/geometry/photogrammetry/anonymization/preprocessing.py
index 9771ab15..c365cd9d 100644
--- a/src/cedalion/geometry/photogrammetry/anonymization/preprocessing.py
+++ b/src/cedalion/geometry/photogrammetry/anonymization/preprocessing.py
@@ -1,6 +1,6 @@
 """Preprocessing: axis normalization and head isolation.
 
-Gets a raw Einstar photogrammetry scan into a canonical frame suitable for
+Gets a raw Einstar photogrammetry scan into a standard reference frame suitable for
 landmark detection and mask building:
 
 - ``normalize_axes`` rotates around X so Y points anterior (preliminary,
@@ -23,6 +23,7 @@
 from ._utils import (
     _apply_affine,
     _ear_midpoint,
+    _rebuild_mesh,
     _reindex_faces,
     _resolve_texture_image,
     _transform_labeled_points,
@@ -32,63 +33,6 @@
 logger = logging.getLogger("cedalion")
 
 
-def _copy_visual(src_mesh, dst_mesh, vertex_index=None) -> None:
-    """Copy a trimesh ``visual`` onto ``dst_mesh``, optionally reindexing.
-
-    Passing ``visual=old.visual`` to a new ``Trimesh(...)`` silently downgrades
-    a ``TextureVisuals`` to ``ColorVisuals`` because the visual still
-    back-references the source mesh's vertex count. Rebuilding explicitly
-    avoids that.
-
-    Args:
-        src_mesh: Source trimesh.
-        dst_mesh: Destination trimesh (mutated in place).
-        vertex_index: Indices into the source vertex array. If given, UVs /
-            vertex_colors are sliced by this index. Leave None when the
-            vertex count is unchanged.
-    """
-    src_visual = src_mesh.visual
-    uv = getattr(src_visual, "uv", None)
-    n_src = len(src_mesh.vertices)
-
-    if uv is not None and len(uv) == n_src:
-        uv_arr = np.asarray(uv)
-        if vertex_index is not None:
-            uv_arr = uv_arr[vertex_index]
-        image = _resolve_texture_image(src_visual)
-        material = getattr(src_visual, "material", None)
-        dst_mesh.visual = trimesh.visual.TextureVisuals(
-            uv=uv_arr,
-            image=image,
-            material=material,
-        )
-        return
-
-    vcol = getattr(src_visual, "vertex_colors", None)
-    if vcol is not None and len(vcol) == n_src:
-        vcol_arr = np.asarray(vcol)
-        if vertex_index is not None:
-            vcol_arr = vcol_arr[vertex_index]
-        dst_mesh.visual.vertex_colors = vcol_arr
-
-
-def _rebuild_mesh(
-    src_mesh: trimesh.Trimesh,
-    *,
-    vertices: np.ndarray,
-    faces: np.ndarray,
-    vertex_index: np.ndarray | None = None,
-) -> trimesh.Trimesh:
-    """Build a new ``Trimesh`` and re-attach the source visual.
-
-    Centralizes the ``Trimesh(... process=False)`` + ``_copy_visual`` duo
-    used by every transform in the pipeline.
-    """
-    new_mesh = trimesh.Trimesh(vertices=vertices, faces=faces, process=False)
-    _copy_visual(src_mesh, new_mesh, vertex_index=vertex_index)
-    return new_mesh
-
-
 @cdc.validate_schemas
 def normalize_axes(
     surface: cdc.TrimeshSurface,

From 9ec2df421d68873edc9db6cbd67f688e6950913b Mon Sep 17 00:00:00 2001
From: mah-diaa <mahmoud.diaa.abdeen@gmail.com>
Date: Wed, 6 May 2026 17:05:58 +0200
Subject: [PATCH 32/39] Remove unused _resolve_texture_image import; fix em
 dashes in pipeline docstring

---
 .../photogrammetry/anonymization/pipeline.py       | 14 +++++++-------
 .../photogrammetry/anonymization/preprocessing.py  |  1 -
 2 files changed, 7 insertions(+), 8 deletions(-)

diff --git a/src/cedalion/geometry/photogrammetry/anonymization/pipeline.py b/src/cedalion/geometry/photogrammetry/anonymization/pipeline.py
index 5cc635db..523482e4 100644
--- a/src/cedalion/geometry/photogrammetry/anonymization/pipeline.py
+++ b/src/cedalion/geometry/photogrammetry/anonymization/pipeline.py
@@ -50,17 +50,17 @@ def anonymize_scan(
 
     Steps (each is also exposed as a standalone function for inspection):
 
-    1. ``normalize_axes`` — rotate around X so Y points anterior.
-    2. ``isolate_head`` — strip body, shoulders, fragments.
-    3. ``align_axes_from_landmarks`` — map into the CTF frame.
-    4. ``detect_cap_boundary`` — find the cap front edge along Z.
-    5. ``face_mask_from_landmarks`` — union face region + ear spheres,
+    1. ``normalize_axes``: rotate around X so Y points anterior.
+    2. ``isolate_head``: strip body, shoulders, fragments.
+    3. ``align_axes_from_landmarks``: map into the CTF frame.
+    4. ``detect_cap_boundary``: find the cap front edge along Z.
+    5. ``face_mask_from_landmarks``: union face region + ear spheres,
        clamped below the cap.
     6. Preserve ``landmark_keep_radius_mm``-spheres around each landmark
        and a midline nasion strip from Nz to the cap.
-    7. ``delete_masked_vertices`` — drop triangles touching any masked
+    7. ``delete_masked_vertices``: drop triangles touching any masked
        vertex, keeping UVs in sync.
-    8. (default) ``revert_to_einstar_frame`` — return to ``crs="digitized"``
+    8. (default) ``revert_to_einstar_frame``: return to ``crs="digitized"``
        so the output matches ``read_einstar_obj``'s convention and can be
        fed to ``save_anonymized_scan`` and downstream co-registration.
 
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/preprocessing.py b/src/cedalion/geometry/photogrammetry/anonymization/preprocessing.py
index c365cd9d..db2f9d6e 100644
--- a/src/cedalion/geometry/photogrammetry/anonymization/preprocessing.py
+++ b/src/cedalion/geometry/photogrammetry/anonymization/preprocessing.py
@@ -25,7 +25,6 @@
     _ear_midpoint,
     _rebuild_mesh,
     _reindex_faces,
-    _resolve_texture_image,
     _transform_labeled_points,
     _upper_head_centroid,
 )

From f0781913fd9dbca920df73234221c9d969a9a831 Mon Sep 17 00:00:00 2001
From: mah-diaa <mahmoud.diaa.abdeen@gmail.com>
Date: Wed, 6 May 2026 17:09:04 +0200
Subject: [PATCH 33/39] Add anonymize_scan tests: smoke, ctf frame, missing
 landmark

---
 tests/test_anonymization.py | 34 ++++++++++++++++++++++++++++++++++
 1 file changed, 34 insertions(+)

diff --git a/tests/test_anonymization.py b/tests/test_anonymization.py
index c0912da9..afffd686 100644
--- a/tests/test_anonymization.py
+++ b/tests/test_anonymization.py
@@ -12,6 +12,7 @@
 
 from cedalion.geometry.photogrammetry.anonymization import (
     align_axes_from_landmarks,
+    anonymize_scan,
     delete_masked_vertices,
     detect_cap_boundary,
     face_mask_from_landmarks,
@@ -293,6 +294,39 @@ def test_save_writes_landmark_tsv(
     assert any(p.endswith("_landmarks.tsv") for p in written)
 
 
+# anonymize_scan (orchestrator) ---------------------------------------------
+
+
+def test_anonymize_scan_reduces_vertices(head_like_surface, axis_normalized_landmarks):
+    surface_anon, _ = anonymize_scan(head_like_surface, axis_normalized_landmarks)
+    assert surface_anon.nvertices < head_like_surface.nvertices
+
+
+def test_anonymize_scan_returns_digitized_frame(
+    head_like_surface, axis_normalized_landmarks
+):
+    surface_anon, landmarks_anon = anonymize_scan(
+        head_like_surface, axis_normalized_landmarks
+    )
+    assert surface_anon.crs == "digitized"
+    assert "digitized" in landmarks_anon.dims
+
+
+def test_anonymize_scan_return_frame_ctf(head_like_surface, axis_normalized_landmarks):
+    surface_anon, landmarks_anon = anonymize_scan(
+        head_like_surface, axis_normalized_landmarks, return_frame="ctf"
+    )
+    assert surface_anon.crs == "ctf"
+
+
+def test_anonymize_scan_raises_on_missing_landmark(
+    head_like_surface, axis_normalized_landmarks
+):
+    partial = axis_normalized_landmarks.isel(label=slice(0, 3))
+    with pytest.raises(ValueError, match="Missing landmarks"):
+        anonymize_scan(head_like_surface, partial)
+
+
 # end-to-end pipeline -------------------------------------------------------
 
 

From 34e26885e761d4432e89a6e1df2ef1860a8944ec Mon Sep 17 00:00:00 2001
From: mah-diaa <mahmoud.diaa.abdeen@gmail.com>
Date: Wed, 6 May 2026 17:32:39 +0200
Subject: [PATCH 34/39] test + readme: add docstrings, file tree, and test
 approach docs

---
 README.md                   | 30 +++++++++++++++-----
 tests/test_anonymization.py | 55 +++++++++++++++++--------------------
 2 files changed, 48 insertions(+), 37 deletions(-)

diff --git a/README.md b/README.md
index e7919f11..2d71bf53 100644
--- a/README.md
+++ b/README.md
@@ -32,12 +32,24 @@ Open `examples/head_models/51_manual_5pt_anonymization.ipynb`. The notebook:
 ## Module structure
 
 ```
-pipeline.py       anonymize_scan — canonical entry point
-preprocessing.py  normalize_axes, isolate_head, align_axes_from_landmarks,
-                  revert_to_einstar_frame
-mask.py           detect_cap_boundary, face_mask_from_landmarks,
-                  delete_masked_vertices, save_anonymized_scan
-_utils.py         private helpers shared by all of the above
+src/cedalion/geometry/photogrammetry/anonymization/
+├── __init__.py        public API — re-exports all functions listed below
+├── pipeline.py        anonymize_scan (canonical entry point)
+├── preprocessing.py   normalize_axes, isolate_head, align_axes_from_landmarks,
+│                      revert_to_einstar_frame
+├── mask.py            detect_cap_boundary, face_mask_from_landmarks,
+│                      delete_masked_vertices, save_anonymized_scan
+└── _utils.py          private helpers shared by preprocessing and mask
+                       (_rebuild_mesh, _copy_visual, _reindex_faces,
+                        _apply_affine, _transform_labeled_points,
+                        _ear_midpoint, _upper_head_centroid,
+                        _resolve_texture_image)
+
+examples/head_models/
+└── 51_manual_5pt_anonymization.ipynb   interactive workflow notebook
+
+tests/
+└── test_anonymization.py               26 unit tests
 ```
 
 Pipeline steps inside `anonymize_scan`:
@@ -57,7 +69,11 @@ Pipeline steps inside `anonymize_scan`:
 pytest tests/test_anonymization.py -v
 ```
 
-22 tests covering all eight public functions and the end-to-end pipeline. No external data required (synthetic trimesh spheres only).
+26 tests covering all eight public functions and the end-to-end pipeline. No real scan data is required: all tests build synthetic geometry using `trimesh.creation.icosphere`, which is the same approach used throughout the cedalion test suite (see `test_geodesics.py`, `test_dataclasses_geometry.py`). Three pytest fixtures are shared across tests:
+
+- `simple_sphere_surface` — unit icosphere as a minimal `TrimeshSurface` for geometry-only checks
+- `head_like_surface` — slightly elongated icosphere (X scaled ×1.2) that mimics head proportions and produces a non-trivial face region after masking
+- `axis_normalized_landmarks` — five `LabeledPoints` (Nz, Iz, Cz, LPA, RPA) placed on the sphere axes, matching the post-`normalize_axes` coordinate frame
 
 ## Branch layout
 
diff --git a/tests/test_anonymization.py b/tests/test_anonymization.py
index afffd686..e4719467 100644
--- a/tests/test_anonymization.py
+++ b/tests/test_anonymization.py
@@ -25,6 +25,7 @@
 
 @pytest.fixture
 def simple_sphere_surface():
+    """Unit sphere as a minimal TrimeshSurface for geometry-only tests."""
     sphere = trimesh.creation.icosphere(subdivisions=3, radius=100)
     return cdc.TrimeshSurface(
         mesh=sphere, crs="scanner", units=cedalion.units.millimeter
@@ -63,38 +64,37 @@ def axis_normalized_landmarks():
     )
 
 
-# normalize_axes ------------------------------------------------------------
-
-
 def test_normalize_rotation_orthogonal(simple_sphere_surface):
+    """Rotation matrix returned by normalize_axes is orthogonal."""
     nasion = np.array([0, 50, 50])
     _, _, R = normalize_axes(simple_sphere_surface, nasion)
     assert_allclose(R @ R.T, np.eye(3), atol=1e-10)
 
 
 def test_normalize_nasion_to_positive_y(simple_sphere_surface):
+    """After normalization the rotated nasion has a positive Y component."""
     nasion = np.array([0, 50, 50])
     _, rotated_nasion, _ = normalize_axes(simple_sphere_surface, nasion)
     assert rotated_nasion[1] > 0
 
 
 def test_normalize_identity_when_aligned(simple_sphere_surface):
+    """No rotation is applied when the nasion already points along +Y."""
     nasion = np.array([0, 100, 0])
     _, rotated_nasion, R = normalize_axes(simple_sphere_surface, nasion)
     assert_allclose(R, np.eye(3), atol=1e-6)
     assert_allclose(rotated_nasion, nasion, atol=1e-6)
 
 
-# isolate_head --------------------------------------------------------------
-
-
 def test_isolate_head_only_scan_unchanged(simple_sphere_surface):
+    """A head-only scan (no body) is returned with almost all vertices kept."""
     nasion = np.array([0, 100, 0])
     _, mask = isolate_head(simple_sphere_surface, nasion)
     assert mask.mean() > 0.9
 
 
 def test_isolate_reduces_count_with_body():
+    """Body sphere 400 mm away is removed; only the head sphere survives."""
     sphere = trimesh.creation.icosphere(subdivisions=3, radius=100)
     body = trimesh.creation.icosphere(subdivisions=3, radius=80)
     body.vertices[:, 0] -= 400
@@ -108,10 +108,8 @@ def test_isolate_reduces_count_with_body():
     assert head_surface.nvertices < surface.nvertices
 
 
-# align_axes_from_landmarks -------------------------------------------------
-
-
 def test_align_origin_at_ear_midpoint(simple_sphere_surface, axis_normalized_landmarks):
+    """CTF origin is placed at the midpoint of LPA and RPA."""
     _, aligned_lm, _ = align_axes_from_landmarks(
         simple_sphere_surface, axis_normalized_landmarks
     )
@@ -122,6 +120,7 @@ def test_align_origin_at_ear_midpoint(simple_sphere_surface, axis_normalized_lan
 
 
 def test_align_axes_orientation(simple_sphere_surface, axis_normalized_landmarks):
+    """Nz points along +X, LPA along +Y, and Cz along +Z in the CTF frame."""
     _, aligned_lm, _ = align_axes_from_landmarks(
         simple_sphere_surface, axis_normalized_landmarks
     )
@@ -135,6 +134,7 @@ def test_align_axes_orientation(simple_sphere_surface, axis_normalized_landmarks
 
 
 def test_align_returns_ctf_crs(simple_sphere_surface, axis_normalized_landmarks):
+    """Aligned surface carries crs='ctf'."""
     aligned_surface, _, _ = align_axes_from_landmarks(
         simple_sphere_surface, axis_normalized_landmarks
     )
@@ -144,14 +144,12 @@ def test_align_returns_ctf_crs(simple_sphere_surface, axis_normalized_landmarks)
 def test_align_raises_on_missing_landmark(
     simple_sphere_surface, axis_normalized_landmarks
 ):
+    """ValueError is raised when a required landmark is absent."""
     partial = axis_normalized_landmarks.isel(label=slice(0, 4))
     with pytest.raises(ValueError, match="Missing landmarks"):
         align_axes_from_landmarks(simple_sphere_surface, partial)
 
 
-# detect_cap_boundary -------------------------------------------------------
-
-
 def test_cap_boundary_in_sane_range():
     """cap_z stays inside the documented [Nz[2], Nz[2] + ceiling] window."""
     sphere = trimesh.creation.icosphere(subdivisions=4, radius=100)
@@ -185,10 +183,8 @@ def test_cap_boundary_failsafe_clamps_implausible_cap():
     assert cap_z == pytest.approx(Nz[2] + 10.0)
 
 
-# face_mask_from_landmarks --------------------------------------------------
-
-
 def test_face_mask_region_semantics():
+    """Anterior-below-cap vertex is masked; posterior and above-cap are not."""
     Nz = np.array([100, 0, 10])
     Lpa = np.array([0, 100, 0])
     Rpa = np.array([0, -100, 0])
@@ -202,6 +198,7 @@ def test_face_mask_region_semantics():
 
 
 def test_face_mask_ear_sphere():
+    """Vertex near LPA is masked even when posterior to the ear coronal plane."""
     Nz = np.array([100, 0, 0])
     Lpa = np.array([0, 100, 0])
     Rpa = np.array([0, -100, 0])
@@ -213,22 +210,22 @@ def test_face_mask_ear_sphere():
     assert mask[0]
 
 
-# delete_masked_vertices ----------------------------------------------------
-
-
 def test_delete_no_op_on_false_mask(simple_sphere_surface):
+    """All-False mask leaves the vertex count unchanged."""
     mask = np.zeros(simple_sphere_surface.nvertices, dtype=bool)
     result = delete_masked_vertices(simple_sphere_surface, mask)
     assert result.nvertices == simple_sphere_surface.nvertices
 
 
 def test_delete_partial_mask_reduces_count(simple_sphere_surface):
+    """Masking one hemisphere reduces the vertex count."""
     mask = np.asarray(simple_sphere_surface.mesh.vertices)[:, 0] > 0
     result = delete_masked_vertices(simple_sphere_surface, mask)
     assert result.nvertices < simple_sphere_surface.nvertices
 
 
 def test_delete_preserves_crs_and_units(simple_sphere_surface):
+    """CRS and units are propagated unchanged after vertex deletion."""
     mask = np.zeros(simple_sphere_surface.nvertices, dtype=bool)
     mask[0] = True
     result = delete_masked_vertices(simple_sphere_surface, mask)
@@ -236,10 +233,8 @@ def test_delete_preserves_crs_and_units(simple_sphere_surface):
     assert result.units == simple_sphere_surface.units
 
 
-# revert_to_einstar_frame ---------------------------------------------------
-
-
 def test_revert_round_trip_with_align(simple_sphere_surface, axis_normalized_landmarks):
+    """align then revert recovers the original vertex positions."""
     aligned_surface, aligned_lm, M = align_axes_from_landmarks(
         simple_sphere_surface, axis_normalized_landmarks
     )
@@ -256,6 +251,7 @@ def test_revert_round_trip_with_align(simple_sphere_surface, axis_normalized_lan
 def test_revert_returns_digitized_crs(
     simple_sphere_surface, axis_normalized_landmarks
 ):
+    """Reverted surface carries crs='digitized'."""
     aligned_surface, aligned_lm, M = align_axes_from_landmarks(
         simple_sphere_surface, axis_normalized_landmarks
     )
@@ -265,16 +261,15 @@ def test_revert_returns_digitized_crs(
     assert reverted_surface.crs == "digitized"
 
 
-# save_anonymized_scan ------------------------------------------------------
-
-
 def test_save_raises_on_bad_extension(simple_sphere_surface, tmp_path):
+    """ValueError is raised when out_path does not end in .obj."""
     out = str(tmp_path / "out.txt")
     with pytest.raises(ValueError, match=".obj"):
         save_anonymized_scan(simple_sphere_surface, out)
 
 
 def test_save_geometry_only(simple_sphere_surface, tmp_path):
+    """strip_texture=True writes an OBJ file without MTL or JPG."""
     out = str(tmp_path / "anon.obj")
     written = save_anonymized_scan(simple_sphere_surface, out, strip_texture=True)
     assert os.path.exists(out)
@@ -284,6 +279,7 @@ def test_save_geometry_only(simple_sphere_surface, tmp_path):
 def test_save_writes_landmark_tsv(
     simple_sphere_surface, axis_normalized_landmarks, tmp_path
 ):
+    """Passing landmarks produces a _landmarks.tsv alongside the OBJ."""
     out = str(tmp_path / "anon.obj")
     written = save_anonymized_scan(
         simple_sphere_surface,
@@ -294,10 +290,8 @@ def test_save_writes_landmark_tsv(
     assert any(p.endswith("_landmarks.tsv") for p in written)
 
 
-# anonymize_scan (orchestrator) ---------------------------------------------
-
-
 def test_anonymize_scan_reduces_vertices(head_like_surface, axis_normalized_landmarks):
+    """Anonymized surface has fewer vertices than the input (face region deleted)."""
     surface_anon, _ = anonymize_scan(head_like_surface, axis_normalized_landmarks)
     assert surface_anon.nvertices < head_like_surface.nvertices
 
@@ -305,6 +299,7 @@ def test_anonymize_scan_reduces_vertices(head_like_surface, axis_normalized_land
 def test_anonymize_scan_returns_digitized_frame(
     head_like_surface, axis_normalized_landmarks
 ):
+    """Default return frame is 'digitized' for both surface and landmarks."""
     surface_anon, landmarks_anon = anonymize_scan(
         head_like_surface, axis_normalized_landmarks
     )
@@ -313,6 +308,7 @@ def test_anonymize_scan_returns_digitized_frame(
 
 
 def test_anonymize_scan_return_frame_ctf(head_like_surface, axis_normalized_landmarks):
+    """return_frame='ctf' keeps the surface in the CTF coordinate frame."""
     surface_anon, landmarks_anon = anonymize_scan(
         head_like_surface, axis_normalized_landmarks, return_frame="ctf"
     )
@@ -322,17 +318,16 @@ def test_anonymize_scan_return_frame_ctf(head_like_surface, axis_normalized_land
 def test_anonymize_scan_raises_on_missing_landmark(
     head_like_surface, axis_normalized_landmarks
 ):
+    """ValueError is raised when fewer than 5 required landmarks are provided."""
     partial = axis_normalized_landmarks.isel(label=slice(0, 3))
     with pytest.raises(ValueError, match="Missing landmarks"):
         anonymize_scan(head_like_surface, partial)
 
 
-# end-to-end pipeline -------------------------------------------------------
-
-
 def test_full_anonymization_pipeline(
     head_like_surface, axis_normalized_landmarks, tmp_path
 ):
+    """End-to-end pipeline: normalize, isolate, align, mask, revert, save."""
     nasion = axis_normalized_landmarks.pint.dequantify().sel(label="Nz").values
     surface_n, nasion_n, R = normalize_axes(head_like_surface, nasion)
     lm_arr = axis_normalized_landmarks.pint.dequantify().values

From 1640ab8ebfe3772c52c72aa66d3eb380116f9f87 Mon Sep 17 00:00:00 2001
From: mah-diaa <mahmoud.diaa.abdeen@gmail.com>
Date: Wed, 6 May 2026 17:37:17 +0200
Subject: [PATCH 35/39] readme: remove em dashes and AI phrasing

---
 README.md | 36 ++++++++++++++++++------------------
 1 file changed, 18 insertions(+), 18 deletions(-)

diff --git a/README.md b/README.md
index 2d71bf53..3bee9bca 100644
--- a/README.md
+++ b/README.md
@@ -1,8 +1,8 @@
 # Face Anonymization for Photogrammetry Scans
 
-**Thesis:** "Development of an Automatic Face Removal Algorithm for Photogrammetry Scans for Data Protection" — M.Sc. thesis, TU Berlin / IBS Lab
+**Thesis (M.Sc., TU Berlin / IBS Lab):** "Development of a Landmark-Aware Face-Removal Algorithm for Photogrammetric Head Scans for Data Protection"
 
-This is a fork of [cedalion](https://github.com/ibs-lab/cedalion) extended with a geometric face anonymization module for Einstar photogrammetry scans acquired in fNIRS research. The module lives at:
+This is a fork of [cedalion](https://github.com/ibs-lab/cedalion) with a geometric face anonymization module for Einstar photogrammetry scans used in fNIRS research. The module lives at:
 
 ```
 src/cedalion/geometry/photogrammetry/anonymization/
@@ -20,7 +20,7 @@ pip install -e .
 
 ## Usage
 
-### Interactive single-scan workflow (canonical)
+### Interactive single-scan workflow
 
 Open `examples/head_models/51_manual_5pt_anonymization.ipynb`. The notebook:
 1. Loads an Einstar scan (`cedalion.io.read_einstar_obj`)
@@ -33,8 +33,8 @@ Open `examples/head_models/51_manual_5pt_anonymization.ipynb`. The notebook:
 
 ```
 src/cedalion/geometry/photogrammetry/anonymization/
-├── __init__.py        public API — re-exports all functions listed below
-├── pipeline.py        anonymize_scan (canonical entry point)
+├── __init__.py        public API, re-exports all functions listed below
+├── pipeline.py        anonymize_scan (entry point)
 ├── preprocessing.py   normalize_axes, isolate_head, align_axes_from_landmarks,
 │                      revert_to_einstar_frame
 ├── mask.py            detect_cap_boundary, face_mask_from_landmarks,
@@ -54,14 +54,14 @@ tests/
 
 Pipeline steps inside `anonymize_scan`:
 
-1. `normalize_axes` — rotate so +Y points anterior (handles arbitrary Einstar orientation)
-2. `isolate_head` — remove body, shoulders, and disconnected fragments
-3. `align_axes_from_landmarks` — map to CTF frame (+X anterior, +Y left, +Z up)
-4. `detect_cap_boundary` — locate the front cap-edge height along Z
-5. `face_mask_from_landmarks` — face region union ear spheres, clamped below the cap
-6. Landmark preservation — 8 mm spheres around each landmark + midline nasion strip
-7. `delete_masked_vertices` — drop triangles touching any masked vertex, UVs in sync
-8. `revert_to_einstar_frame` — return to `crs="digitized"` for saving
+1. `normalize_axes`: rotate so +Y points anterior (handles arbitrary Einstar orientation)
+2. `isolate_head`: remove body, shoulders, and disconnected fragments
+3. `align_axes_from_landmarks`: map to CTF frame (+X anterior, +Y left, +Z up)
+4. `detect_cap_boundary`: locate the front cap-edge height along Z
+5. `face_mask_from_landmarks`: face region union ear spheres, clamped below the cap
+6. Landmark preservation: 8 mm spheres around each landmark + midline nasion strip
+7. `delete_masked_vertices`: drop triangles touching any masked vertex, UVs in sync
+8. `revert_to_einstar_frame`: return to `crs="digitized"` for saving
 
 ## Tests
 
@@ -69,17 +69,17 @@ Pipeline steps inside `anonymize_scan`:
 pytest tests/test_anonymization.py -v
 ```
 
-26 tests covering all eight public functions and the end-to-end pipeline. No real scan data is required: all tests build synthetic geometry using `trimesh.creation.icosphere`, which is the same approach used throughout the cedalion test suite (see `test_geodesics.py`, `test_dataclasses_geometry.py`). Three pytest fixtures are shared across tests:
+26 tests covering all eight public functions and the end-to-end pipeline. No real scan data needed: all tests build synthetic geometry with `trimesh.creation.icosphere`, the same approach used elsewhere in cedalion (see `test_geodesics.py`, `test_dataclasses_geometry.py`). Three fixtures are shared:
 
-- `simple_sphere_surface` — unit icosphere as a minimal `TrimeshSurface` for geometry-only checks
-- `head_like_surface` — slightly elongated icosphere (X scaled ×1.2) that mimics head proportions and produces a non-trivial face region after masking
-- `axis_normalized_landmarks` — five `LabeledPoints` (Nz, Iz, Cz, LPA, RPA) placed on the sphere axes, matching the post-`normalize_axes` coordinate frame
+- `simple_sphere_surface`: unit icosphere for geometry-only checks
+- `head_like_surface`: elongated icosphere (X scaled x1.2), giving the masking step a real face region to remove
+- `axis_normalized_landmarks`: five `LabeledPoints` (Nz, Iz, Cz, LPA, RPA) on the sphere axes in the post-`normalize_axes` frame
 
 ## Branch layout
 
 | Branch | Contents |
 |--------|---------|
-| `feature/face-anonymization` | **This branch** — thesis implementation (anonymization module, notebook 51, test suite) |
+| `feature/face-anonymization` | **This branch**: thesis implementation (anonymization module, notebook 51, test suite) |
 | `main` | Upstream cedalion base |
 | `validation/face-anonymization` | Validation notebooks 64-73, batch CSV producers |
 | `auxiliary/mediapipe-nasion` | Experimental automatic nasion detection (MediaPipe) |

From 8cec88bb9c1bb97bf00c0f6c6e24908e50191701 Mon Sep 17 00:00:00 2001
From: mah-diaa <mahmoud.diaa.abdeen@gmail.com>
Date: Thu, 7 May 2026 08:50:52 +0200
Subject: [PATCH 36/39] anonymization: drop landmarks kwarg and tsv sidecar
 from save_anonymized_scan

---
 .../51_manual_5pt_anonymization.ipynb         | 608 ++++++++++++++++--
 .../photogrammetry/anonymization/__init__.py  |   2 +-
 .../photogrammetry/anonymization/mask.py      |  21 +-
 .../photogrammetry/anonymization/pipeline.py  |   5 +-
 tests/test_anonymization.py                   |  21 +-
 5 files changed, 556 insertions(+), 101 deletions(-)

diff --git a/examples/head_models/51_manual_5pt_anonymization.ipynb b/examples/head_models/51_manual_5pt_anonymization.ipynb
index 011ed763..687469b2 100644
--- a/examples/head_models/51_manual_5pt_anonymization.ipynb
+++ b/examples/head_models/51_manual_5pt_anonymization.ipynb
@@ -10,8 +10,7 @@
     "Pure-geometric face removal for Einstar photogrammetry scans. Given the 5\n",
     "anatomical landmarks (Nz, Iz, Cz, Lpa, Rpa), `anonymize_scan` strips the face\n",
     "and reverts the result back to the raw Einstar (`crs=digitized`) frame so the\n",
-    "saved `.obj` + `.tsv` are a drop-in replacement for the original at\n",
-    "co-registration time.\n",
+    "saved `.obj` is a drop-in replacement for the original at co-registration time.\n",
     "\n",
     "Flow: load &rarr; pick 5 landmarks &rarr; `anonymize_scan` &rarr; save.\n"
    ]
@@ -22,48 +21,7 @@
    "id": "a916040f-02d3-44e3-a1f4-05188c814d1d",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "import logging\n",
-    "import os\n",
-    "from pathlib import Path\n",
-    "\n",
-    "import numpy as np\n",
-    "import pyvista as pv\n",
-    "import xarray as xr\n",
-    "from PIL import Image\n",
-    "import trimesh\n",
-    "\n",
-    "import cedalion\n",
-    "import cedalion.dataclasses as cdc\n",
-    "import cedalion.io\n",
-    "import cedalion.vis.blocks\n",
-    "from cedalion.vtktutils import trimesh_to_vtk_polydata\n",
-    "from cedalion.geometry.photogrammetry.anonymization import (\n",
-    "    anonymize_scan,\n",
-    "    save_anonymized_scan,\n",
-    ")\n",
-    "\n",
-    "logging.getLogger('cedalion').setLevel(logging.WARNING)\n",
-    "pv.set_jupyter_backend('server')\n",
-    "\n",
-    "# === Options ===\n",
-    "# Path to your Einstar .obj scan. The notebook expects a sibling .jpg\n",
-    "# texture (Einstar exports both).\n",
-    "SCAN_PATH = './scan.obj'         # edit me\n",
-    "OUT_PATH = None                  # None -> write '<stem>_anon.obj' next to SCAN_PATH\n",
-    "\n",
-    "# True: run the picker. False: load landmarks from a TSV sidecar (set below).\n",
-    "INTERACTIVE = True\n",
-    "CACHED_LANDMARKS_TSV = None      # used only when INTERACTIVE = False\n",
-    "\n",
-    "\n",
-    "# === Mask parameters (edit to adjust the anonymized region) ===\n",
-    "EAR_DELETE_RADIUS_MM = 40.0      # sphere radius around LPA/RPA included in deletion\n",
-    "LANDMARK_KEEP_RADIUS_MM = 8.0    # sphere radius around each landmark kept intact\n",
-    "EYEBROW_OFFSET_MM = 10.0         # failsafe cap height above Nz (flush-cap fallback)\n",
-    "CAP_Z_CEILING_MM = 40.0          # mm above Nz where cap detection is clamped\n",
-    "HEAD_ISOLATION_RADIUS_MM = 220.0 # sphere radius used to strip body/shoulders\n"
-   ]
+   "source": "import logging\nimport os\nfrom pathlib import Path\n\nimport numpy as np\nimport pyvista as pv\nimport xarray as xr\nfrom PIL import Image\nimport trimesh\n\nimport cedalion\nimport cedalion.dataclasses as cdc\nimport cedalion.io\nimport cedalion.vis.blocks\nfrom cedalion.vtktutils import trimesh_to_vtk_polydata\nfrom cedalion.geometry.photogrammetry.anonymization import (\n    anonymize_scan,\n    save_anonymized_scan,\n)\n\nlogging.getLogger('cedalion').setLevel(logging.WARNING)\npv.set_jupyter_backend('server')\n\n# === Options ===\n# Path to your Einstar .obj scan. The notebook expects a sibling .jpg\n# texture (Einstar exports both).\nSCAN_PATH = './scan.obj'         # edit me\nOUT_PATH = None                  # None -> write '<stem>_anon.obj' next to SCAN_PATH\n\n# True: run the picker. False: load landmarks from a TSV sidecar (set below).\nINTERACTIVE = True\nCACHED_LANDMARKS_TSV = None      # used only when INTERACTIVE = False\n\n\n# === Mask parameters (edit to adjust the anonymized region) ===\nEAR_DELETE_RADIUS_MM = 40.0      # sphere radius around LPA/RPA included in deletion\nLANDMARK_KEEP_RADIUS_MM = 8.0    # sphere radius around each landmark kept intact\nEYEBROW_OFFSET_MM = 10.0         # failsafe cap height above Nz (flush-cap fallback)\nCAP_Z_CEILING_MM = 40.0          # mm above Nz where cap detection is clamped\nHEAD_ISOLATION_RADIUS_MM = 220.0 # sphere radius used to strip body/shoulders\n"
   },
   {
    "cell_type": "markdown",
@@ -75,10 +33,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "5c99124b-f88c-4aaf-bff2-124cfadd5da1",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded: 943,539 vertices, 1,736,738 faces\n"
+     ]
+    }
+   ],
    "source": [
     "surface = cedalion.io.read_einstar_obj(SCAN_PATH)\n",
     "print(f'Loaded: {surface.nvertices:,} vertices, {surface.nfaces:,} faces')\n",
@@ -115,10 +81,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "4ea3ff12-ca93-4c03-9f50-b84c28c7cfdc",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8931ae160417497089d51fca6c1cd106",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Widget(value='<iframe src=\"http://localhost:44253/index.html?ui=P_0x7fe28a702550_2&reconnect=auto\" class=\"pyvi…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "if INTERACTIVE:\n",
     "    pvplt = pv.Plotter()\n",
@@ -138,10 +119,488 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "3a09ed60-b599-4191-b394-460b852f4b27",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
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+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, white)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, #111111)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
+       "  );\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "  border: 2px solid transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0) !important;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: \"►\";\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  border-color: var(--xr-background-color-row-odd);\n",
+       "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "  border-color: var(--xr-background-color-row-even);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (label: 5, digitized: 3)&gt; Size: 120B\n",
+       "&lt;Quantity([[135.82489764 134.11959626 388.34999789]\n",
+       " [ 83.19674841 -38.36289545 470.02017819]\n",
+       " [226.49436905 -13.33052629 371.30306422]\n",
+       " [144.87231487  60.99825005 490.28171817]\n",
+       " [ 74.20606761  16.62082718 368.93477872]], &#x27;millimeter&#x27;)&gt;\n",
+       "Coordinates:\n",
+       "  * label    (label) &lt;U3 60B &#x27;Nz&#x27; &#x27;Iz&#x27; &#x27;Cz&#x27; &#x27;Lpa&#x27; &#x27;Rpa&#x27;\n",
+       "    type     (label) object 40B PointType.LANDMARK ... PointType.LANDMARK\n",
+       "    group    (label) &lt;U1 20B &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27;\n",
+       "Dimensions without coordinates: digitized</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>label</span>: 5</li><li><span>digitized</span>: 3</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-813f730a-31f8-46bf-90d1-411fbb4c4615' class='xr-array-in' type='checkbox' checked><label for='section-813f730a-31f8-46bf-90d1-411fbb4c4615' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>[mm] 135.8 134.1 388.3 83.2 -38.36 ... 61.0 490.3 74.21 16.62 368.9</span></div><div class='xr-array-data'><table><tbody><tr><th>Magnitude</th><td style='text-align:left;'><pre>[[135.82489764260836 134.1195962551057 388.34999788776486] [83.19674841020856 -38.36289544683734 470.02017819060416] [226.49436904915945 -13.33052629089562 371.3030642194328] [144.8723148742921 60.99825004860746 490.2817181736568] [74.20606761144799 16.620827183111732 368.93477871973005]]</pre></td></tr><tr><th>Units</th><td style='text-align:left;'>millimeter</td></tr></tbody></table></div></div></li><li class='xr-section-item'><input id='section-863b5ce2-1e30-44df-a965-c675e3ec206a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-863b5ce2-1e30-44df-a965-c675e3ec206a' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>label</span></div><div class='xr-var-dims'>(label)</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;Nz&#x27; &#x27;Iz&#x27; &#x27;Cz&#x27; &#x27;Lpa&#x27; &#x27;Rpa&#x27;</div><input id='attrs-1ae75da9-4f24-4008-a9c8-26fb768aedac' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1ae75da9-4f24-4008-a9c8-26fb768aedac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42f5893b-9bcf-42d6-83af-eb15277be687' class='xr-var-data-in' type='checkbox'><label for='data-42f5893b-9bcf-42d6-83af-eb15277be687' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Nz&#x27;, &#x27;Iz&#x27;, &#x27;Cz&#x27;, &#x27;Lpa&#x27;, &#x27;Rpa&#x27;], dtype=&#x27;&lt;U3&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>type</span></div><div class='xr-var-dims'>(label)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>PointType.LANDMARK ... PointType...</div><input id='attrs-f6113b8c-f472-4a7c-8e45-0014e5560c6a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f6113b8c-f472-4a7c-8e45-0014e5560c6a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6830bc7-aae4-4b53-a423-f215588a0e4a' class='xr-var-data-in' type='checkbox'><label for='data-e6830bc7-aae4-4b53-a423-f215588a0e4a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&lt;PointType.LANDMARK: 3&gt;, &lt;PointType.LANDMARK: 3&gt;,\n",
+       "       &lt;PointType.LANDMARK: 3&gt;, &lt;PointType.LANDMARK: 3&gt;,\n",
+       "       &lt;PointType.LANDMARK: 3&gt;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>group</span></div><div class='xr-var-dims'>(label)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27; &#x27;L&#x27;</div><input id='attrs-6c9359df-b959-47dc-a89d-4a4aad3e038f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6c9359df-b959-47dc-a89d-4a4aad3e038f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f0e9a6dc-56be-4e46-b876-bec43da6a675' class='xr-var-data-in' type='checkbox'><label for='data-f0e9a6dc-56be-4e46-b876-bec43da6a675' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;L&#x27;, &#x27;L&#x27;, &#x27;L&#x27;, &#x27;L&#x27;, &#x27;L&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6aee2e82-d78d-4eaf-a768-be84733a8bf3' class='xr-section-summary-in' type='checkbox'  ><label for='section-6aee2e82-d78d-4eaf-a768-be84733a8bf3' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>label</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-0351f477-b136-47da-b290-ed9b4ed249d6' class='xr-index-data-in' type='checkbox'/><label for='index-0351f477-b136-47da-b290-ed9b4ed249d6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;Nz&#x27;, &#x27;Iz&#x27;, &#x27;Cz&#x27;, &#x27;Lpa&#x27;, &#x27;Rpa&#x27;], dtype=&#x27;object&#x27;, name=&#x27;label&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e7577a12-af76-4399-ba73-750b2be851de' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e7577a12-af76-4399-ba73-750b2be851de' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray (label: 5, digitized: 3)> Size: 120B\n",
+       "<Quantity([[135.82489764 134.11959626 388.34999789]\n",
+       " [ 83.19674841 -38.36289545 470.02017819]\n",
+       " [226.49436905 -13.33052629 371.30306422]\n",
+       " [144.87231487  60.99825005 490.28171817]\n",
+       " [ 74.20606761  16.62082718 368.93477872]], 'millimeter')>\n",
+       "Coordinates:\n",
+       "  * label    (label) <U3 60B 'Nz' 'Iz' 'Cz' 'Lpa' 'Rpa'\n",
+       "    type     (label) object 40B PointType.LANDMARK ... PointType.LANDMARK\n",
+       "    group    (label) <U1 20B 'L' 'L' 'L' 'L' 'L'\n",
+       "Dimensions without coordinates: digitized"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "if INTERACTIVE:\n",
     "    landmarks = get_landmarks()\n",
@@ -164,10 +623,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "acfa6540-281e-4123-8a0d-ee30cd33aae1",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Subject13: 943,539 -> 590,551 verts (-352,988)\n"
+     ]
+    }
+   ],
    "source": [
     "surface_anon, landmarks_anon = anonymize_scan(\n",
     "    surface, landmarks,\n",
@@ -195,10 +662,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "309c17ba-a234-438e-bd5a-3372dc328266",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "687724dd208b4edca3d05ca2c1e3db15",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Widget(value='<iframe src=\"http://localhost:44253/index.html?ui=P_0x7fe109fd7890_3&reconnect=auto\" class=\"pyvi…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "stem = Path(SCAN_PATH).stem\n",
     "lm_colors = {\n",
@@ -244,12 +726,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "id": "ac270274-9e08-4e50-90d2-65c4c49aa34c",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "wrote /tmp/Subject13_notebook_test_anon.jpg\n",
+      "wrote /tmp/Subject13_notebook_test_anon.mtl\n",
+      "wrote /tmp/Subject13_notebook_test_anon.obj\n"
+     ]
+    }
+   ],
    "source": [
-    "written = save_anonymized_scan(surface_anon, OUT_PATH, landmarks=landmarks_anon)\n",
+    "written = save_anonymized_scan(surface_anon, OUT_PATH)\n",
     "for p in written:\n",
     "    print(f'wrote {p}')\n"
    ]
@@ -284,4 +776,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}
\ No newline at end of file
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/__init__.py b/src/cedalion/geometry/photogrammetry/anonymization/__init__.py
index 904c9fc3..2abe8a63 100644
--- a/src/cedalion/geometry/photogrammetry/anonymization/__init__.py
+++ b/src/cedalion/geometry/photogrammetry/anonymization/__init__.py
@@ -13,7 +13,7 @@
     ...     anonymize_scan, save_anonymized_scan,
     ... )
     >>> surface_anon, landmarks_anon = anonymize_scan(surface, landmarks)
-    >>> save_anonymized_scan(surface_anon, "out.obj", landmarks=landmarks_anon)
+    >>> save_anonymized_scan(surface_anon, "out.obj")
 
 Pipeline steps inside ``anonymize_scan``:
 
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/mask.py b/src/cedalion/geometry/photogrammetry/anonymization/mask.py
index 6050b711..f55975ae 100644
--- a/src/cedalion/geometry/photogrammetry/anonymization/mask.py
+++ b/src/cedalion/geometry/photogrammetry/anonymization/mask.py
@@ -22,8 +22,6 @@
 from scipy.signal import savgol_filter
 
 import cedalion.dataclasses as cdc
-import cedalion.typing as cdt
-from cedalion.io import export_to_tsv
 
 from ._utils import _ear_midpoint, _rebuild_mesh, _reindex_faces, _resolve_texture_image
 
@@ -279,7 +277,6 @@ def _sanitize_texture_from_uv(mesh: trimesh.Trimesh) -> Image.Image | None:
 def save_anonymized_scan(
     surface: cdc.TrimeshSurface,
     out_path: str,
-    landmarks: cdt.LabeledPoints | None = None,
     strip_texture: bool = False,
 ) -> list[str]:
     """Export an anonymized photogrammetry surface to disk.
@@ -294,21 +291,15 @@ def save_anonymized_scan(
     fallback applies when the input mesh has no usable texture, with a
     warning.
 
-    When ``landmarks`` is provided, also writes ``{stem}_landmarks.tsv`` via
-    ``cedalion.io.export_to_tsv`` for downstream co-registration.
-
     Args:
         surface: Anonymized TrimeshSurface (typically output of
             ``delete_masked_vertices``).
         out_path: Destination path ending in ``.obj``.
-        landmarks: LabeledPoints (Nz, Iz, LPA, RPA, Cz) in the same frame
-            as ``surface``, persisted alongside the mesh as a TSV.
         strip_texture: If True, skip the MTL + JPG and write geometry only.
 
     Returns:
         List of absolute paths written (``.obj`` plus ``.mtl`` + ``.jpg``
-        when a texture was written, plus ``_landmarks.tsv`` when landmarks
-        were given).
+        when a texture was written).
 
     Raises:
         ValueError: If ``out_path`` does not end in ``.obj``.
@@ -366,16 +357,6 @@ def save_anonymized_scan(
         mesh_to_write.export(out_path)
         written.append(out_path)
 
-    if landmarks is not None:
-        tsv_path = os.path.join(out_dir, f"{stem}_landmarks.tsv")
-        export_to_tsv(tsv_path, landmarks)
-        written.append(tsv_path)
-    else:
-        logger.warning(
-            "save_anonymized_scan called without landmarks; "
-            "downstream co-registration will need them saved separately."
-        )
-
     logger.info(
         f"Saved anonymized scan: "
         f"{[os.path.basename(p) for p in written]}"
diff --git a/src/cedalion/geometry/photogrammetry/anonymization/pipeline.py b/src/cedalion/geometry/photogrammetry/anonymization/pipeline.py
index 523482e4..66a1ee8b 100644
--- a/src/cedalion/geometry/photogrammetry/anonymization/pipeline.py
+++ b/src/cedalion/geometry/photogrammetry/anonymization/pipeline.py
@@ -87,9 +87,8 @@ def anonymize_scan(
 
     Returns:
         Tuple of (anonymized_surface, anonymized_landmarks). Frame is
-        controlled by ``return_frame``. Pass directly to
-        ``save_anonymized_scan(..., landmarks=...)`` when
-        ``return_frame="digitized"``.
+        controlled by ``return_frame``. The surface can be written with
+        ``save_anonymized_scan`` when ``return_frame="digitized"``.
     """
     landmarks = normalize_landmarks_labels(landmarks)
     labels = list(landmarks["label"].values)
diff --git a/tests/test_anonymization.py b/tests/test_anonymization.py
index e4719467..7fbf18e6 100644
--- a/tests/test_anonymization.py
+++ b/tests/test_anonymization.py
@@ -276,20 +276,6 @@ def test_save_geometry_only(simple_sphere_surface, tmp_path):
     assert any(p.endswith(".obj") for p in written)
 
 
-def test_save_writes_landmark_tsv(
-    simple_sphere_surface, axis_normalized_landmarks, tmp_path
-):
-    """Passing landmarks produces a _landmarks.tsv alongside the OBJ."""
-    out = str(tmp_path / "anon.obj")
-    written = save_anonymized_scan(
-        simple_sphere_surface,
-        out,
-        landmarks=axis_normalized_landmarks,
-        strip_texture=True,
-    )
-    assert any(p.endswith("_landmarks.tsv") for p in written)
-
-
 def test_anonymize_scan_reduces_vertices(head_like_surface, axis_normalized_landmarks):
     """Anonymized surface has fewer vertices than the input (face region deleted)."""
     surface_anon, _ = anonymize_scan(head_like_surface, axis_normalized_landmarks)
@@ -349,16 +335,13 @@ def test_full_anonymization_pipeline(
     cap_z, *_ = detect_cap_boundary(verts, Nz, Cz, Lpa, Rpa)
     mask, _ = face_mask_from_landmarks(verts, Nz, Lpa, Rpa, cap_z=cap_z)
     surface_anon = delete_masked_vertices(surface_h, mask)
-    surface_anon_dig, landmarks_dig = revert_to_einstar_frame(
+    surface_anon_dig, _ = revert_to_einstar_frame(
         surface_anon, landmarks_n, R, M_ctf
     )
 
     out = str(tmp_path / "anon.obj")
-    written = save_anonymized_scan(
-        surface_anon_dig, out, landmarks=landmarks_dig, strip_texture=True
-    )
+    save_anonymized_scan(surface_anon_dig, out, strip_texture=True)
 
     assert surface_anon.nvertices < surface_h.nvertices
     assert surface_anon_dig.crs == "digitized"
     assert os.path.exists(out)
-    assert any(p.endswith("_landmarks.tsv") for p in written)

From 3b196c9e6d43c385a7da887bf995a9713771c4a5 Mon Sep 17 00:00:00 2001
From: mah-diaa <mahmoud.diaa.abdeen@gmail.com>
Date: Thu, 7 May 2026 08:59:44 +0200
Subject: [PATCH 37/39] readme: drop tsv-sidecar mention; describe
 validation-branch notebooks and headline results

---
 README.md | 19 +++++++++++++++++--
 1 file changed, 17 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 3bee9bca..9bedc19f 100644
--- a/README.md
+++ b/README.md
@@ -27,7 +27,7 @@ Open `examples/head_models/51_manual_5pt_anonymization.ipynb`. The notebook:
 2. Picks the five 10-20 landmarks interactively (Nz, Iz, Cz, LPA, RPA)
 3. Calls `anonymize_scan(surface, landmarks)`
 4. Shows a before/after comparison
-5. Saves the anonymized OBJ + `_landmarks.tsv` sidecar via `save_anonymized_scan`
+5. Saves the anonymized OBJ + sanitized JPG texture bundle via `save_anonymized_scan`
 
 ## Module structure
 
@@ -81,9 +81,24 @@ pytest tests/test_anonymization.py -v
 |--------|---------|
 | `feature/face-anonymization` | **This branch**: thesis implementation (anonymization module, notebook 51, test suite) |
 | `main` | Upstream cedalion base |
-| `validation/face-anonymization` | Validation notebooks 64-73, batch CSV producers |
+| `validation/face-anonymization` | Measurement notebooks behind the thesis results tables (see below) |
 | `auxiliary/mediapipe-nasion` | Experimental automatic nasion detection (MediaPipe) |
 
+### Validation branch contents
+
+The notebooks on `validation/face-anonymization` are the measurement harness behind the thesis results tables. They consume the shipped pipeline's output and quantify it; they are not part of the pipeline itself.
+
+| Notebook | What it checks | Headline result on the 11-subject cohort |
+|---|---|---|
+| `64_batch_validation` | Driver that runs the four validators below on every subject and emits per-subject CSVs | per-subject CSVs feeding the thesis tables |
+| `66_preservation_check` | 10-20 landmark deviation + bit-exact vertex preservation across the surviving surface | 0.000 mm across all 55 landmark measurements (5 landmarks x 11 subjects) |
+| `68_coreg_invariance` | Cedalion `ColoredStickerProcessor` re-run on original vs anonymized mesh (optode-cap subcohort) | 131 matched stickers, 0 mm deviation in sticker centres and scalp-projected optode positions |
+| `70_auxiliary_nasion` | MediaPipe Face Landmarker auto-nasion vs the manually picked nasion | 13.68 mm cohort mean offset, 30.78 mm worst case |
+| `72_face_detectability_comparison` | MediaPipe Face Detector hit counts under a 21-view sweep: original vs vertex-deletion vs noise-perturbation | 93/231 -> 34/231 (63% reduction); optode-cap subcohort 32/147 -> 4/147 (88%); noise reaches 28/231 (Wilcoxon p=0.375, not distinguishable from deletion) |
+| `73_s8_mediapipe_boxes` | BlazeFace bounding box and six-keypoint inspection on a single subject | qualitative figure for the discussion of why post-deletion residual hits are silhouette artefacts |
+
+Helper modules `_thesis_data.py`, `_thesis_pipeline.py`, `_validator_noise.py`, and `_validator_render.py` factor the cohort-walk, pipeline call, noise-perturbation operator, and contact-sheet rendering used by these notebooks.
+
 ---
 
 *Upstream cedalion documentation below.*

From d530df01202b390e9abe6b62cf7395a4ddd42fdb Mon Sep 17 00:00:00 2001
From: mah-diaa <mahmoud.diaa.abdeen@gmail.com>
Date: Thu, 7 May 2026 13:33:01 +0200
Subject: [PATCH 38/39] readme: list notebook 69 in validation-branch table

---
 README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/README.md b/README.md
index 9bedc19f..6654b069 100644
--- a/README.md
+++ b/README.md
@@ -93,6 +93,7 @@ The notebooks on `validation/face-anonymization` are the measurement harness beh
 | `64_batch_validation` | Driver that runs the four validators below on every subject and emits per-subject CSVs | per-subject CSVs feeding the thesis tables |
 | `66_preservation_check` | 10-20 landmark deviation + bit-exact vertex preservation across the surviving surface | 0.000 mm across all 55 landmark measurements (5 landmarks x 11 subjects) |
 | `68_coreg_invariance` | Cedalion `ColoredStickerProcessor` re-run on original vs anonymized mesh (optode-cap subcohort) | 131 matched stickers, 0 mm deviation in sticker centres and scalp-projected optode positions |
+| `69_coreg_visual_check` | Side-by-side render of detected stickers on original vs anonymized mesh; flags any original detection without an anon counterpart in red so the operator can verify by eye | qualitative; supports the operator-verification step backing notebook 68 |
 | `70_auxiliary_nasion` | MediaPipe Face Landmarker auto-nasion vs the manually picked nasion | 13.68 mm cohort mean offset, 30.78 mm worst case |
 | `72_face_detectability_comparison` | MediaPipe Face Detector hit counts under a 21-view sweep: original vs vertex-deletion vs noise-perturbation | 93/231 -> 34/231 (63% reduction); optode-cap subcohort 32/147 -> 4/147 (88%); noise reaches 28/231 (Wilcoxon p=0.375, not distinguishable from deletion) |
 | `73_s8_mediapipe_boxes` | BlazeFace bounding box and six-keypoint inspection on a single subject | qualitative figure for the discussion of why post-deletion residual hits are silhouette artefacts |

From af893781e3f6960f8a004ade2c5e818553e10e61 Mon Sep 17 00:00:00 2001
From: mah-diaa <mahmoud.diaa.abdeen@gmail.com>
Date: Thu, 7 May 2026 13:43:38 +0200
Subject: [PATCH 39/39] readme + nb 51 intro: tighten prose, drop AI-style
 padding

---
 README.md                                              |  8 ++++----
 examples/head_models/51_manual_5pt_anonymization.ipynb | 10 +++++-----
 2 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/README.md b/README.md
index 6654b069..f8788f85 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
 # Face Anonymization for Photogrammetry Scans
 
-**Thesis (M.Sc., TU Berlin / IBS Lab):** "Development of a Landmark-Aware Face-Removal Algorithm for Photogrammetric Head Scans for Data Protection"
+M.Sc. thesis at TU Berlin / IBS Lab: "Development of a Landmark-Aware Face-Removal Algorithm for Photogrammetric Head Scans for Data Protection".
 
 This is a fork of [cedalion](https://github.com/ibs-lab/cedalion) with a geometric face anonymization module for Einstar photogrammetry scans used in fNIRS research. The module lives at:
 
@@ -86,19 +86,19 @@ pytest tests/test_anonymization.py -v
 
 ### Validation branch contents
 
-The notebooks on `validation/face-anonymization` are the measurement harness behind the thesis results tables. They consume the shipped pipeline's output and quantify it; they are not part of the pipeline itself.
+The notebooks on `validation/face-anonymization` produce the per-subject numbers in the thesis results tables. They run on top of the shipped pipeline's output and do not modify it.
 
 | Notebook | What it checks | Headline result on the 11-subject cohort |
 |---|---|---|
 | `64_batch_validation` | Driver that runs the four validators below on every subject and emits per-subject CSVs | per-subject CSVs feeding the thesis tables |
 | `66_preservation_check` | 10-20 landmark deviation + bit-exact vertex preservation across the surviving surface | 0.000 mm across all 55 landmark measurements (5 landmarks x 11 subjects) |
 | `68_coreg_invariance` | Cedalion `ColoredStickerProcessor` re-run on original vs anonymized mesh (optode-cap subcohort) | 131 matched stickers, 0 mm deviation in sticker centres and scalp-projected optode positions |
-| `69_coreg_visual_check` | Side-by-side render of detected stickers on original vs anonymized mesh; flags any original detection without an anon counterpart in red so the operator can verify by eye | qualitative; supports the operator-verification step backing notebook 68 |
+| `69_coreg_visual_check` | Side-by-side render of detected stickers on the original and anonymized mesh; any original-side detection without a match on the anonymized side is drawn in red so the operator can spot it | qualitative; the manual visibility check that backs notebook 68 |
 | `70_auxiliary_nasion` | MediaPipe Face Landmarker auto-nasion vs the manually picked nasion | 13.68 mm cohort mean offset, 30.78 mm worst case |
 | `72_face_detectability_comparison` | MediaPipe Face Detector hit counts under a 21-view sweep: original vs vertex-deletion vs noise-perturbation | 93/231 -> 34/231 (63% reduction); optode-cap subcohort 32/147 -> 4/147 (88%); noise reaches 28/231 (Wilcoxon p=0.375, not distinguishable from deletion) |
 | `73_s8_mediapipe_boxes` | BlazeFace bounding box and six-keypoint inspection on a single subject | qualitative figure for the discussion of why post-deletion residual hits are silhouette artefacts |
 
-Helper modules `_thesis_data.py`, `_thesis_pipeline.py`, `_validator_noise.py`, and `_validator_render.py` factor the cohort-walk, pipeline call, noise-perturbation operator, and contact-sheet rendering used by these notebooks.
+Helper modules `_thesis_data.py`, `_thesis_pipeline.py`, `_validator_noise.py`, and `_validator_render.py` hold the cohort walk, pipeline call, noise-perturbation operator, and contact-sheet rendering shared across these notebooks.
 
 ---
 
diff --git a/examples/head_models/51_manual_5pt_anonymization.ipynb b/examples/head_models/51_manual_5pt_anonymization.ipynb
index 687469b2..79cef4cc 100644
--- a/examples/head_models/51_manual_5pt_anonymization.ipynb
+++ b/examples/head_models/51_manual_5pt_anonymization.ipynb
@@ -7,10 +7,10 @@
    "source": [
     "# Face Anonymization from 5 Given Landmarks\n",
     "\n",
-    "Pure-geometric face removal for Einstar photogrammetry scans. Given the 5\n",
-    "anatomical landmarks (Nz, Iz, Cz, Lpa, Rpa), `anonymize_scan` strips the face\n",
-    "and reverts the result back to the raw Einstar (`crs=digitized`) frame so the\n",
-    "saved `.obj` is a drop-in replacement for the original at co-registration time.\n",
+    "Geometric face removal for Einstar photogrammetry scans. Given the 5\n",
+    "landmarks (Nz, Iz, Cz, Lpa, Rpa), `anonymize_scan` deletes the face and\n",
+    "returns the result in the raw Einstar (`crs=digitized`) frame, so the\n",
+    "saved `.obj` lines up with the original at co-registration.\n",
     "\n",
     "Flow: load &rarr; pick 5 landmarks &rarr; `anonymize_scan` &rarr; save.\n"
    ]
@@ -776,4 +776,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
\ No newline at end of file
+}