Klaus 280 discard pose data when convex hull is larger than expected mouse size

nextflow/modules/pose_qc.nf

@@ -5,10 +5,12 @@
  * @param in_pose The input pose file
  *
  * @return Rendered video
+ *
+ * @publish ./qc Rendered pose video
  */
 process RENDER_POSE {
     label "tracking"
-    publishDir "compressed/pose/", mode:'copy'
+    publishDir "${params.pubdir}/qc", mode:'copy'
 
     input:
     tuple path(in_video), path(in_pose)
@@ -18,6 +20,32 @@ process RENDER_POSE {
 
     script:
     """
-    python3 /kumar_lab_models/mouse-tracking-runtime/render_pose.py --in-vid ${in_video} --in-pose ${in_pose} --out-vid ${in_video.baseName}_pose.mp4
+    mouse-tracking utils render-pose ${in_video} ${in_pose} ${in_video.baseName}_pose.mp4
+    """
+}
+
+/**
+ * Render fecal boli on a frame
+ *
+ * @param in_video The input video file
+ * @param in_pose The input pose file
+ *
+ * @return Rendered fecal boli video
+ *
+ * @publish ./qc Rendered fecal boli video
+ */
+process RENDER_BOLI {
+    label "tracking"
+    publishDir "${params.pubdir}/qc", mode:'copy'
+
+    input:
+    tuple path(in_video), path(in_pose)
+
+    output:
+    path "${in_video.baseName}_boli.avi"
+
+    script:
+    """
+    mouse-tracking utils render-fecal-boli ${in_video} ${in_pose} ${in_video.baseName}_boli.avi
     """
 }

nextflow/modules/single_mouse.nf

@@ -17,7 +17,7 @@ process PREDICT_SINGLE_MOUSE_SEGMENTATION {
     label "gpu"
     label "tracking"
     label "r_single_seg"
-    
+
     input:
     tuple path(video_file), path(in_pose_file)
 
@@ -90,6 +90,35 @@ process QC_SINGLE_MOUSE {
     """
 }
 
+/**
+ * Modifies a pose file to filter out large poses.
+ *
+ * @param tuple
+ *  - in_video The input video file
+ *  - in_pose_file The input pose file to modify
+ *
+ * @return tuple files
+ *  - Path to the video file.
+ *  - Path to the filtered pose file.
+ */
+process FILTER_LARGE_POSES {
+    label "tracking"
+
+    input:
+    tuple path(in_video), path(in_pose_file)
+
+    output:
+    tuple path("${in_video.baseName}_filtered.${in_video.extension}"), path("${in_video.baseName}_filtered.h5"), emit: files
+
+    script:
+    """
+    cp ${in_pose_file} ${in_video.baseName}_filtered.h5
+    ln -s ${in_video} ${in_video.baseName}_filtered.${in_video.extension}
+
+    mouse-tracking utils filter-large-area-pose ${in_video.baseName}_filtered.h5
+    """
+}
+
 /**
  * Clips a video and its corresponding pose file to a specified duration from the start.
  *

nextflow/workflows/single_mouse_pipeline.nf

@@ -2,7 +2,10 @@
  * This module contains the single mouse tracking pipeline.
  * It processes video input to track a single mouse.
  */
-include { PREDICT_SINGLE_MOUSE_SEGMENTATION; PREDICT_SINGLE_MOUSE_KEYPOINTS; CLIP_VIDEO_AND_POSE } from "${projectDir}/nextflow/modules/single_mouse"
+include { PREDICT_SINGLE_MOUSE_SEGMENTATION;
+          PREDICT_SINGLE_MOUSE_KEYPOINTS;
+          CLIP_VIDEO_AND_POSE;
+          FILTER_LARGE_POSES; } from "${projectDir}/nextflow/modules/single_mouse"
 include { PREDICT_ARENA_CORNERS } from "${projectDir}/nextflow/modules/static_objects"
 include { PREDICT_FECAL_BOLI } from "${projectDir}/nextflow/modules/fecal_boli"
 include { QC_SINGLE_MOUSE } from "${projectDir}/nextflow/modules/single_mouse"
@@ -40,14 +43,15 @@ workflow SINGLE_MOUSE_TRACKING {
     main:
     // Generate pose files
     pose_init = VIDEO_TO_POSE(input_video).files
-    // Pose v2 is output from this step
+    // Pose v2 is output from keypoint prediction step
     pose_v2_data = PREDICT_SINGLE_MOUSE_KEYPOINTS(pose_init).files
     if (params.align_videos) {
         pose_v2_data = CLIP_VIDEO_AND_POSE(pose_v2_data, params.clip_duration).files
     }
+    // Valid Pose v6 is produced when segmentation is added.
     pose_and_seg_data = PREDICT_SINGLE_MOUSE_SEGMENTATION(pose_v2_data).files
-    // Completed Pose v6 is output from this step
-    pose_with_corners = PREDICT_ARENA_CORNERS(pose_and_seg_data).files
+    filtered_pose_v6 = FILTER_LARGE_POSES(pose_and_seg_data).files
+    pose_with_corners = PREDICT_ARENA_CORNERS(filtered_pose_v6).files
     pose_v6_data = PREDICT_FECAL_BOLI(pose_with_corners).files
 
     // Publish the pose v2 results

pyproject.toml

@@ -19,6 +19,7 @@ dependencies = [
   "h5py>=3.11.0",
   "pydantic-settings>=2.10.1",
   "yacs>=0.1.8",
+  "plotnine>=0.12.0",
 ]
 
 [project.optional-dependencies]
@@ -28,6 +29,7 @@ gpu = [
   "torch==2.6.0",
   "torchvision==0.21.0",
   "torchaudio==2.6.0",
+  "nvidia-cusparselt-cu12==0.6.3",
 ]
 
 # CPU-only convenience for local tests (unchanged idea)

src/mouse_tracking/cli/qa.py

@@ -37,6 +37,25 @@ def single_pose(
     )
 
 
+@app.command()
+def single_feature(
+    pose: Path = typer.Argument(..., help="Path to the pose file to inspect"),
+    behavior: Path = typer.Argument(..., help="Path to the behavior table to inspect"),
+    output: Path | None = typer.Option(
+        None, help="Output filename. Will append row if already exists."
+    ),
+):
+    """Run single mouse feature inspection."""
+    # Dynamically set the output filename if not provided
+    if not output:
+        output = Path(
+            f"QA_features_{pose.stem}_{pd.Timestamp.now().strftime('%Y%m%d_%H%M%S')}.pdf"
+        )
+
+    # TODO implement desired plots of feature data for more in-depth inspection
+    raise NotImplementedError("Feature inspection is not yet implemented.")
+
+
 @app.command()
 def multi_pose():
     """Run multi pose quality assurance."""

src/mouse_tracking/cli/utils.py

@@ -6,13 +6,19 @@
 from rich import print
 
 from mouse_tracking import __version__
+from mouse_tracking.core.config.pose_utils import PoseUtilsConfig
 from mouse_tracking.matching.match_predictions import match_predictions
 from mouse_tracking.pose import render
-from mouse_tracking.pose.convert import downgrade_pose_file
 from mouse_tracking.utils import fecal_boli, static_objects
 from mouse_tracking.utils.clip_video import clip_video_auto, clip_video_manual
+from mouse_tracking.utils.writers import (
+    downgrade_pose_file,
+    filter_large_contours,
+    filter_large_keypoints,
+)
 
 app = typer.Typer()
+CONFIG = PoseUtilsConfig()
 
 
 def version_callback(value: bool) -> None:
@@ -54,6 +60,27 @@ def aggregate_fecal_boli(
     result.to_csv(output, index=False)
 
 
+@app.command()
+def render_fecal_boli_video(
+    in_video: Path = typer.Option(
+        ..., "--in-video", help="Path to the input video file"
+    ),
+    in_pose: Path = typer.Option(
+        ..., "--in-pose", help="Path to the input HDF5 pose file"
+    ),
+    out_video: Path = typer.Option(
+        ..., "--out-video", help="Path to the output video file"
+    ),
+):
+    """
+    Render fecal boli on video frames.
+
+    This command renders fecal boli from the pose file onto the input video.
+    Video playback is 1fps with original frame timestamp overlayed.
+    """
+    fecal_boli.render_fecal_boli_video(str(in_video), str(in_pose), str(out_video))
+
+
 clip_video_app = typer.Typer(help="Produce a video and pose clip aligned to criteria.")
 
 
@@ -227,3 +254,26 @@ def stitch_tracklets(
     This command stitches tracklets from the specified source.
     """
     match_predictions(in_pose)
+
+
+@app.command()
+def filter_large_area_pose(
+    in_pose: Path = typer.Argument(..., help="Input HDF5 pose file"),
+    max_area: int = typer.Option(
+        CONFIG.OFA_MAX_EXPECTED_AREA_PX,
+        help="Maximum area a pose can have, using a bounding box on keypoint pose.",
+    ),
+):
+    """
+    Filer pose by area.
+
+    This command unmarks identity of pose (both keypoint and segmentation) with large areas.
+    """
+    filter_large_keypoints(
+        in_pose,
+        max_area,
+    )
+    filter_large_contours(
+        in_pose,
+        max_area,
+    )

src/mouse_tracking/core/config/pose_utils.py

@@ -35,6 +35,9 @@ class PoseUtilsConfig(BaseSettings):
     MIN_JABS_CONFIDENCE: float = 0.3
     MIN_JABS_KEYPOINTS: int = 3
 
+    # Large animals are rarely larger than 100px in our OFA
+    OFA_MAX_EXPECTED_AREA_PX: int = 150 * 150
+
     # Colors
     MOUSE_COLORS: list[tuple[int, int, int]] = [
         (228, 26, 28),  # Red

src/mouse_tracking/pose/convert.py

@@ -1,14 +1,8 @@
 """Pose data conversion utilities."""
 
-import os
-import re
-
-import h5py
 import numpy as np
 
-from mouse_tracking.core.exceptions import InvalidPoseFileException
 from mouse_tracking.utils.run_length_encode import run_length_encode
-from mouse_tracking.utils.writers import write_pixel_per_cm_attr, write_pose_v2_data
 
 
 def v2_to_v3(pose_data, conf_data, threshold: float = 0.3):
@@ -95,53 +89,3 @@ def multi_to_v2(pose_data, conf_data, identity_data):
         return_list.append((cur_id, single_pose, single_conf))
 
     return return_list
-
-
-def downgrade_pose_file(pose_h5_path, disable_id: bool = False):
-    """Downgrades a multi-mouse pose file into multiple single mouse pose files.
-
-    Args:
-            pose_h5_path: input pose file
-            disable_id: bool to disable identity embedding tracks (if available) and use tracklet data instead
-    """
-    if not os.path.isfile(pose_h5_path):
-        raise FileNotFoundError(f"ERROR: missing file: {pose_h5_path}")
-    # Read in all the necessary data
-    with h5py.File(pose_h5_path, "r") as pose_h5:
-        if "version" in pose_h5["poseest"].attrs:
-            major_version = pose_h5["poseest"].attrs["version"][0]
-        else:
-            raise InvalidPoseFileException(
-                f"Pose file {pose_h5_path} did not have a valid version."
-            )
-        if major_version == 2:
-            print(f"Pose file {pose_h5_path} is already v2. Exiting.")
-            exit(0)
-
-        all_points = pose_h5["poseest/points"][:]
-        all_confidence = pose_h5["poseest/confidence"][:]
-        if major_version >= 4 and not disable_id:
-            all_track_id = pose_h5["poseest/instance_embed_id"][:]
-        elif major_version >= 3:
-            all_track_id = pose_h5["poseest/instance_track_id"][:]
-        try:
-            config_str = pose_h5["poseest/points"].attrs["config"]
-            model_str = pose_h5["poseest/points"].attrs["model"]
-        except (KeyError, AttributeError):
-            config_str = "unknown"
-            model_str = "unknown"
-        pose_attrs = pose_h5["poseest"].attrs
-        if "cm_per_pixel" in pose_attrs and "cm_per_pixel_source" in pose_attrs:
-            pixel_scaling = True
-            px_per_cm = pose_h5["poseest"].attrs["cm_per_pixel"]
-            source = pose_h5["poseest"].attrs["cm_per_pixel_source"]
-        else:
-            pixel_scaling = False
-
-    downgraded_pose_data = multi_to_v2(all_points, all_confidence, all_track_id)
-    new_file_base = re.sub("_pose_est_v[0-9]+\\.h5", "", pose_h5_path)
-    for animal_id, pose_data, conf_data in downgraded_pose_data:
-        out_fname = f"{new_file_base}_animal_{animal_id}_pose_est_v2.h5"
-        write_pose_v2_data(out_fname, pose_data, conf_data, config_str, model_str)
-        if pixel_scaling:
-            write_pixel_per_cm_attr(out_fname, px_per_cm, source)