From b54c0a6a7083d0d054994c9a48fcde1172fa4cb5 Mon Sep 17 00:00:00 2001 From: PeterWaIIace Date: Mon, 25 May 2026 14:31:00 +0200 Subject: [PATCH 01/14] adding rust engine and js --- .gitignore | 3 +- Makefile | 31 +- engine/Cargo.lock | 291 +++++++++ engine/Cargo.toml | 20 + engine/src/calibrator.rs | 177 ++++++ engine/src/eyegestures.rs | 145 +++++ engine/src/fixation.rs | 25 + engine/src/lib.rs | 32 + engine/src/python.rs | 56 ++ engine/src/wasm.rs | 41 ++ examples/simple_example_v4.py | 81 +++ eyeGestures/Legacy/Fixation.py | 23 + eyeGestures/Legacy/__init__.py | 471 +++++++++++++++ eyeGestures/{ => Legacy}/calibration_v1.py | 0 eyeGestures/{ => Legacy}/calibration_v2.py | 0 eyeGestures/Legacy/eye.py | 188 ++++++ eyeGestures/Legacy/face.py | 106 ++++ eyeGestures/{ => Legacy}/gazeEstimator.py | 0 eyeGestures/{ => Legacy}/gaze_contexter.py | 0 eyeGestures/Legacy/gevent.py | 57 ++ eyeGestures/Legacy/processing.py | 72 +++ eyeGestures/Legacy/requirements.txt | 7 + eyeGestures/{ => Legacy}/scalling_test.py | 0 .../{ => Legacy}/screenTracker/__init__.py | 0 .../{ => Legacy}/screenTracker/clusters.py | 0 .../{ => Legacy}/screenTracker/dataPoints.py | 0 .../{ => Legacy}/screenTracker/heatmap.py | 0 .../screenTracker/screenTracker.py | 0 .../{ => Legacy}/screenTracker_test.py | 0 eyeGestures/Legacy/utils.py | 240 ++++++++ eyeGestures/__init__.py | 407 ++----------- eyeGestures/eye.py | 22 +- eyeGestures/face.py | 65 +- pyproject.toml | 10 +- scripts/csv_reader.py | 48 -- scripts/heatmap_maker.py | 123 ---- tests/__init__.py | 0 tests/data/face_1.jpg | Bin 42883 -> 0 bytes tests/data/face_2.jpg | Bin 23980 -> 0 bytes tests/test_gestures.py | 65 -- tools/find_direction_vector.py | 125 ---- tools/pupilDetection.py | 43 -- uv.lock | 568 ++++++++++++++++++ web/License | 18 + web/Makefile | 17 + web/README.md | 140 +++++ web/demo/eyekey.html | 355 +++++++++++ web/example/EyeGesturesDark.png | Bin 0 -> 47767 bytes web/example/EyeGesturesLight.png | Bin 0 -> 39530 bytes web/example/eyegestures.css | 26 + web/example/simple.html | 90 +++ web/eyegestures.js | 2 + web/npm_test.js | 5 + web/package-lock.json | 141 +++++ web/package.json | 38 ++ web/src/eyegestures.js | 455 ++++++++++++++ web/test.js | 3 + 57 files changed, 4025 insertions(+), 807 deletions(-) create mode 100644 engine/Cargo.lock create mode 100644 engine/Cargo.toml create mode 100644 engine/src/calibrator.rs create mode 100644 engine/src/eyegestures.rs create mode 100644 engine/src/fixation.rs create mode 100644 engine/src/lib.rs create mode 100644 engine/src/python.rs create mode 100644 engine/src/wasm.rs create mode 100644 examples/simple_example_v4.py create mode 100644 eyeGestures/Legacy/Fixation.py create mode 100644 eyeGestures/Legacy/__init__.py rename eyeGestures/{ => Legacy}/calibration_v1.py (100%) rename eyeGestures/{ => Legacy}/calibration_v2.py (100%) create mode 100644 eyeGestures/Legacy/eye.py create mode 100644 eyeGestures/Legacy/face.py rename eyeGestures/{ => Legacy}/gazeEstimator.py (100%) rename eyeGestures/{ => Legacy}/gaze_contexter.py (100%) create mode 100644 eyeGestures/Legacy/gevent.py create mode 100644 eyeGestures/Legacy/processing.py create mode 100644 eyeGestures/Legacy/requirements.txt rename eyeGestures/{ => Legacy}/scalling_test.py (100%) rename eyeGestures/{ => Legacy}/screenTracker/__init__.py (100%) rename eyeGestures/{ => Legacy}/screenTracker/clusters.py (100%) rename eyeGestures/{ => Legacy}/screenTracker/dataPoints.py (100%) rename eyeGestures/{ => Legacy}/screenTracker/heatmap.py (100%) rename eyeGestures/{ => Legacy}/screenTracker/screenTracker.py (100%) rename eyeGestures/{ => Legacy}/screenTracker_test.py (100%) create mode 100644 eyeGestures/Legacy/utils.py delete mode 100644 scripts/csv_reader.py delete mode 100644 scripts/heatmap_maker.py delete mode 100644 tests/__init__.py delete mode 100644 tests/data/face_1.jpg delete mode 100644 tests/data/face_2.jpg delete mode 100644 tests/test_gestures.py delete mode 100644 tools/find_direction_vector.py delete mode 100644 tools/pupilDetection.py create mode 100644 uv.lock create mode 100644 web/License create mode 100644 web/Makefile create mode 100644 web/README.md create mode 100644 web/demo/eyekey.html create mode 100644 web/example/EyeGesturesDark.png create mode 100644 web/example/EyeGesturesLight.png create mode 100644 web/example/eyegestures.css create mode 100644 web/example/simple.html create mode 100644 web/eyegestures.js create mode 100644 web/npm_test.js create mode 100644 web/package-lock.json create mode 100644 web/package.json create mode 100644 web/src/eyegestures.js create mode 100644 web/test.js diff --git a/.gitignore b/.gitignore index bb41848..9736076 100644 --- a/.gitignore +++ b/.gitignore @@ -11,4 +11,5 @@ dist/* *.csv data/* .mypy_cache -.venvs \ No newline at end of file +.venvs +*.task \ No newline at end of file diff --git a/Makefile b/Makefile index bceca88..26ac2e5 100644 --- a/Makefile +++ b/Makefile @@ -1,11 +1,5 @@ -.PHONY: format format_check test +.PHONY: format format_check test min wasm python -# Detect OS and set PYTHON command accordingly -ifeq ($(OS),Windows_NT) - PYTHON := $(shell where python3) -else - PYTHON := $(shell which python3) -endif format: isort eyeGestures @@ -20,5 +14,28 @@ check: format_check flake8 eyeGestures mypy eyeGestures +# Web build + +MINIFY = npx terser +MINIFY_FLAGS = --compress --mangle +JS_SRC = web/src/eyegestures.js +MIN = $(JS_SRC:.js=.min.js) + +min: + $(MINIFY) $(JS_SRC) $(MINIFY_FLAGS) -o engine/pkg/eyegestures.min.js + +wasm: + cd ./engine && wasm-pack build --target web + $(MINIFY) $(JS_SRC) $(MINIFY_FLAGS) -o engine/pkg/eyegestures.min.js + +# Python buil +PYTHON = uv run + +python: + cd ./engine && maturin develop --release --no-default-features --features python + +clean: + rm -f src/eyegestures.min.js + test: ${PYTHON} -m unittest tests/test_* \ No newline at end of file diff --git a/engine/Cargo.lock b/engine/Cargo.lock new file mode 100644 index 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std::collections::VecDeque; + +struct LinearRegression { + x_data: Vec>, + y_data: Vec, + weights: Vec, + fitted: bool, +} + +impl LinearRegression { + fn new() -> Self { + Self { x_data: vec![], y_data: vec![], weights: vec![], fitted: false } + } + + fn add_sample(&mut self, x: Vec, y: f64) { + // Match JS MLR behavior: include a bias/intercept feature. + let mut x_with_bias = x; + x_with_bias.push(1.0); + self.x_data.push(x_with_bias); + self.y_data.push(y); + } + + fn fit(&mut self) { + let n = self.x_data.len(); + if n == 0 { + self.fitted = false; + return; + } + + let m = self.x_data[0].len(); + let mut xtx = vec![vec![0.0f64; m]; m]; + let mut xty = vec![0.0f64; m]; + + for i in 0..n { + for j in 0..m { + xty[j] += self.x_data[i][j] * self.y_data[i]; + for k in 0..m { + xtx[j][k] += self.x_data[i][j] * self.x_data[i][k]; + } + } + } + + for (i, row) in xtx.iter_mut().enumerate() { + row[i] += 1e-9; + } + + if let Some(w) = gaussian_elimination(xtx, xty) { + self.weights = w; + self.fitted = true; + } else { + self.fitted = false; + } + } + + fn predict(&self, x: &[f64]) -> f64 { + if !self.fitted { + return 0.0; + } + let weighted_sum: f64 = x.iter().zip(&self.weights).map(|(a, b)| a * b).sum(); + let bias = self.weights.get(x.len()).copied().unwrap_or(0.0); + weighted_sum + bias + } +} + +fn gaussian_elimination(mut a: Vec>, mut b: Vec) -> Option> { + let n = a.len(); + for i in 0..n { + let max_row = (i..n).max_by(|&r1, &r2| a[r1][i].abs().partial_cmp(&a[r2][i].abs()).unwrap())?; + a.swap(i, max_row); + b.swap(i, max_row); + + if a[i][i].abs() < 1e-12 { + return None; + } + + for k in i + 1..n { + let factor = a[k][i] / a[i][i]; + for j in i..n { + a[k][j] -= factor * a[i][j]; + } + b[k] -= factor * b[i]; + } + } + + let mut x = vec![0.0; n]; + for i in (0..n).rev() { + x[i] = b[i]; + for j in i + 1..n { + x[i] -= a[i][j] * x[j]; + } + x[i] /= a[i][i]; + } + Some(x) +} + +const CALIB_POINTS: [[f64; 2]; 25] = [ + [0.25, 0.25], [0.5, 0.75], [1.0, 0.5], [0.75, 0.5], [0.0, 0.75], + [0.5, 0.5], [1.0, 0.25], [0.75, 0.0], [0.25, 0.5], [0.5, 0.0], + [0.0, 0.5], [1.0, 1.0], [0.75, 1.0], [0.25, 0.0], [1.0, 0.0], + [0.0, 1.0], [0.25, 1.0], [0.75, 0.75],[0.5, 0.25], [0.0, 0.25], + [1.0, 0.5], [0.75, 0.25],[0.5, 1.0], [0.25, 0.75], [0.0, 0.0], +]; + +pub struct Calibrator { + tmp_x: VecDeque>, + stable_x: Vec>, + tmp_y: VecDeque<[f64; 2]>, + stable_y: Vec<[f64; 2]>, + reg_x: LinearRegression, + reg_y: LinearRegression, + point_index: usize, + pub fitted: bool, +} + +impl Calibrator { + pub fn new() -> Self { + Self { + tmp_x: VecDeque::new(), + stable_x: vec![], + tmp_y: VecDeque::new(), + stable_y: vec![], + reg_x: LinearRegression::new(), + reg_y: LinearRegression::new(), + point_index: 0, + fitted: false, + } + } + + pub fn add(&mut self, keypoints: Vec, target: [f64; 2]) { + self.tmp_x.push_back(keypoints); + self.tmp_y.push_back(target); + + if self.tmp_x.len() > 40 { + self.tmp_x.pop_front(); + self.tmp_y.pop_front(); + } + + let all_x: Vec> = self.tmp_x.iter().chain(self.stable_x.iter()).cloned().collect(); + let all_y: Vec<[f64; 2]> = self.tmp_y.iter().chain(self.stable_y.iter()).cloned().collect(); + + self.reg_x = LinearRegression::new(); + self.reg_y = LinearRegression::new(); + for (x, y) in all_x.into_iter().zip(all_y.into_iter()) { + self.reg_x.add_sample(x.clone(), y[0]); + self.reg_y.add_sample(x, y[1]); + } + + self.reg_x.fit(); + self.reg_y.fit(); + + self.fitted = self.reg_x.fitted && self.reg_y.fitted; + } + + pub fn predict(&self, keypoints: &[f64]) -> [f64; 2] { + if !self.fitted { + return [0.0, 0.0]; + } + [self.reg_x.predict(keypoints), self.reg_y.predict(keypoints)] + } + + pub fn current_point(&self, screen_width: f64, screen_height: f64) -> [f64; 2] { + let p = CALIB_POINTS[self.point_index]; + [p[0] * screen_width, p[1] * screen_height] + } + + pub fn next_point(&mut self) { + let all_x: Vec> = self.tmp_x.drain(..).collect(); + let all_y: Vec<[f64; 2]> = self.tmp_y.drain(..).collect(); + self.stable_x.extend(all_x); + self.stable_y.extend(all_y); + self.point_index = (self.point_index + 1) % CALIB_POINTS.len(); + } + + pub fn reset(&mut self) { + *self = Calibrator::new(); + } +} diff --git a/engine/src/eyegestures.rs b/engine/src/eyegestures.rs new file mode 100644 index 0000000..0044156 --- /dev/null +++ b/engine/src/eyegestures.rs @@ -0,0 +1,145 @@ +use crate::{Calibrator, euclidean_distance}; + +pub struct GazeResult { + pub x: f64, + pub y: f64, + pub calibrating: bool, + pub calib_point: [f64; 2], +} + +/// Shared core gaze tracking logic (wasm + python) +pub struct EyeGesturesCore { + pub calibrator: Calibrator, + pub screen_width: f64, + pub screen_height: f64, + pub calib_counter: usize, + pub calib_max: usize, + pub frames_on_point: usize, + pub prev_calib: [f64; 2], + pub eye_calib_regions: Vec<[f64; 2]>, + pub buffer: Vec<[f64; 2]>, + pub start_width: f64, + pub start_height: f64, + pub head_start: [f64; 2], + pub last_keypoints: Vec, +} + +impl EyeGesturesCore { + pub fn new(screen_width: f64, screen_height: f64) -> Self { + Self { + calibrator: Calibrator::new(), + screen_width, + screen_height, + calib_counter: 0, + calib_max: 25, + frames_on_point: 0, + prev_calib: [0.0, 0.0], + eye_calib_regions: vec![], + buffer: vec![], + start_width: 0.0, + start_height: 0.0, + head_start: [0.0, 0.0], + last_keypoints: vec![], + } + } + + pub fn process_landmarks(&mut self, landmarks: &[[f64; 2]]) -> GazeResult { + let left_indices = [33, 133, 160, 159, 158, 157, 173, 155, 154, 153, 144, 145, 153, 246, 468]; + let right_indices = [362, 263, 387, 386, 385, 384, 398, 382, 381, 380, 374, 373, 374, 466, 473]; + + let offset_x = landmarks.iter().map(|l| l[0]).fold(f64::INFINITY, f64::min); + let offset_y = landmarks.iter().map(|l| l[1]).fold(f64::INFINITY, f64::min); + let max_x = landmarks.iter().map(|l| l[0]).fold(f64::NEG_INFINITY, f64::max); + let max_y = landmarks.iter().map(|l| l[1]).fold(f64::NEG_INFINITY, f64::max); + let width = max_x - offset_x; + let height = max_y - offset_y; + + if self.start_width * self.start_height == 0.0 { + self.start_width = width; + self.start_height = height; + } + + let scale_x = width / self.start_width; + let scale_y = height / self.start_height; + + if self.head_start == [0.0, 0.0] { + self.head_start = [offset_x * scale_x, offset_y * scale_y]; + } + + let to_coords = |indices: &[usize]| -> Vec<[f64; 2]> { + indices.iter().map(|&i| { + let l = landmarks[i]; + [((l[0] - offset_x) / width) * scale_x, + ((l[1] - offset_y) / height) * scale_y] + }).collect() + }; + + let left_eye = to_coords(&left_indices); + let right_eye = to_coords(&right_indices); + + let mut keypoints: Vec = left_eye.iter().chain(right_eye.iter()) + .flat_map(|p| [p[0], p[1]]) + .collect(); + keypoints.extend([scale_x, scale_y, width, height]); + keypoints.extend([ + offset_x * scale_x - self.head_start[0], + offset_y * scale_y - self.head_start[1], + ]); + + self.last_keypoints = keypoints.clone(); + + let raw = self.calibrator.predict(&keypoints); + self.buffer.push(raw); + if self.buffer.len() > 20 { + self.buffer.remove(0); + } + let point = self.buffer.iter().fold([0.0, 0.0], |acc, p| [acc[0] + p[0], acc[1] + p[1]]); + let point = [point[0] / self.buffer.len() as f64, point[1] / self.buffer.len() as f64]; + + let calibrating = self.calib_counter < self.calib_max; + let calib_point = self.calibrator.current_point(self.screen_width, self.screen_height); + + if calibrating { + self.eye_calib_regions.push(left_eye[0]); + self.calibrator.add(keypoints, calib_point); + + let dist = euclidean_distance(&point, &calib_point); + if dist < 0.1 * self.screen_width && self.frames_on_point > 20 { + self.calibrator.next_point(); + self.frames_on_point = 0; + self.buffer.clear(); + } else if dist < 0.1 * self.screen_width { + self.frames_on_point += 1; + } + + if self.prev_calib != calib_point { + self.prev_calib = calib_point; + self.calib_counter += 1; + } + } else { + let eye_calib_radius = 0.01; + let seen = self.eye_calib_regions.iter() + .any(|r| euclidean_distance(r, &left_eye[0]) < eye_calib_radius); + + if !seen { + self.calib_max += 5; + } + } + + let x = point[0].clamp(0.0, self.screen_width); + let y = point[1].clamp(0.0, self.screen_height); + + GazeResult { x, y, calibrating, calib_point } + } + + pub fn add_calibration_point(&mut self, x: f64, y: f64) { + self.calib_counter += 1; + self.calib_max += 1; + self.calibrator.add(self.last_keypoints.clone(), [x, y]); + } + + pub fn recalibrate(&mut self) { + self.calibrator.reset(); + self.calib_counter = 0; + } +} diff --git a/engine/src/fixation.rs b/engine/src/fixation.rs new file mode 100644 index 0000000..dafdd93 --- /dev/null +++ b/engine/src/fixation.rs @@ -0,0 +1,25 @@ +pub struct Fixation { + center: [f64; 2], + radius: f64, + pub level: f64, +} + +impl Fixation { + pub fn new(x: f64, y: f64, radius: f64) -> Self { + Self { center: [x, y], radius, level: 0.0 } + } + + pub fn process(&mut self, x: f64, y: f64) -> f64 { + let dx = x - self.center[0]; + let dy = y - self.center[1]; + + if dx * dx + dy * dy < self.radius * self.radius { + self.level = (self.level + 0.02).min(1.0); + } else { + self.center = [x, y]; + self.level = 0.0; + } + + self.level + } +} diff --git a/engine/src/lib.rs b/engine/src/lib.rs new file mode 100644 index 0000000..5c88a86 --- /dev/null +++ b/engine/src/lib.rs @@ -0,0 +1,32 @@ +mod calibrator; +mod fixation; +mod eyegestures; + +#[cfg(target_arch = "wasm32")] +pub mod wasm; + +#[cfg(feature = "python")] +pub mod python; + +#[cfg(feature = "python")] +use pyo3::prelude::*; + +pub use calibrator::Calibrator; +pub use fixation::Fixation; +pub use eyegestures::EyeGesturesCore; + +#[cfg(feature = "python")] +#[pymodule] +#[pyo3(name = "EyegesturesEngine")] +fn eyegestures_engine(_py: Python<'_>, module: &Bound<'_, PyModule>) -> PyResult<()> { + module.add_class::()?; + Ok(()) +} + +pub fn euclidean_distance(a: &[f64], b: &[f64]) -> f64 { + a.iter() + .zip(b.iter()) + .map(|(x, y)| (x - y).powi(2)) + .sum::() + .sqrt() +} diff --git a/engine/src/python.rs b/engine/src/python.rs new file mode 100644 index 0000000..6676904 --- /dev/null +++ b/engine/src/python.rs @@ -0,0 +1,56 @@ +use crate::eyegestures::EyeGesturesCore; +use pyo3::prelude::*; + +#[pyclass] +pub struct EyeGesturesEnginePython{ + core: EyeGesturesCore, +} + +#[pymethods] +impl EyeGesturesEnginePython { + #[new] + pub fn new(screen_width: f64, screen_height: f64) -> Self { + Self{ + core: EyeGesturesCore::new(screen_width, screen_height), + } + } + + /// Process landmarks and return [x, y, calibrating, calib_x, calib_y] + pub fn process(&mut self, landmarks: Vec) -> Vec { + let landmarks: Vec<[f64; 2]> = landmarks + .chunks_exact(2) + .map(|pair| [pair[0], pair[1]]) + .collect(); + + let result = self.core.process_landmarks(&landmarks); + vec![ + result.x, + result.y, + if result.calibrating { 1.0 } else { 0.0 }, + result.calib_point[0], + result.calib_point[1], + ] + } + + /// Manually add a calibration point at (x, y) + pub fn add_calibration_point(&mut self, x: f64, y: f64) { + self.core.add_calibration_point(x, y); + } + + /// Reset calibration state + pub fn recalibrate(&mut self) { + self.core.recalibrate(); + } + + /// Get current calibration counter + #[getter] + pub fn calib_counter(&self) -> usize { + self.core.calib_counter + } + + /// Get current calibration max + #[getter] + pub fn calib_max(&self) -> usize { + self.core.calib_max + } +} diff --git a/engine/src/wasm.rs b/engine/src/wasm.rs new file mode 100644 index 0000000..78d76bc --- /dev/null +++ b/engine/src/wasm.rs @@ -0,0 +1,41 @@ +use crate::eyegestures::EyeGesturesCore; +use wasm_bindgen::prelude::*; + +#[wasm_bindgen] +pub struct EyeGesturesEngineWasm{ + core: EyeGesturesCore, +} + +#[wasm_bindgen] +impl EyeGesturesEngineWasm { + #[wasm_bindgen(constructor)] + pub fn new(screen_width: f64, screen_height: f64) -> Self { + Self{ + core: EyeGesturesCore::new(screen_width, screen_height), + } + } + + pub fn process(&mut self, landmarks: Vec) -> Vec { + let landmarks: Vec<[f64; 2]> = landmarks + .chunks_exact(2) + .map(|pair| [pair[0], pair[1]]) + .collect(); + + let result = self.core.process_landmarks(&landmarks); + vec![ + result.x, + result.y, + if result.calibrating { 1.0 } else { 0.0 }, + result.calib_point[0], + result.calib_point[1], + ] + } + + pub fn add_calibration_point(&mut self, x: f64, y: f64) { + self.core.add_calibration_point(x, y); + } + + pub fn recalibrate(&mut self) { + self.core.recalibrate(); + } +} diff --git a/examples/simple_example_v4.py b/examples/simple_example_v4.py new file mode 100644 index 0000000..d7db1b0 --- /dev/null +++ b/examples/simple_example_v4.py @@ -0,0 +1,81 @@ +import os +import sys +import cv2 +import pygame +import numpy as np + +pygame.init() +pygame.font.init() + +# Get the display dimensions +screen_info = pygame.display.Info() +screen_width = screen_info.current_w +screen_height = screen_info.current_h + +# Set up the screen +screen = pygame.display.set_mode((screen_width, screen_height)) +pygame.display.set_caption("EyeGestures v4 example") +font_size = 48 +bold_font = pygame.font.Font(None, font_size) +bold_font.set_bold(True) # Set the font to bold + +dir_path = os.path.dirname(os.path.realpath(__file__)) +sys.path.append(f'{dir_path}/..') + +from eyeGestures.utils import VideoCapture +from eyeGestures import EyeGestures_v4 + +gestures = EyeGestures_v4() +cap = VideoCapture(0) + +# Initialize Pygame +# Set up colors +RED = (255, 0, 100) +BLUE = (100, 0, 255) +GREEN = (0, 255, 0) +BLANK = (0,0,0) +WHITE = (255, 255, 255) + +clock = pygame.time.Clock() + +# Main game loop +running = True +iterator = 0 +prev_x = 0 +prev_y = 0 +while running: + # Event handling + for event in pygame.event.get(): + if event.type == pygame.QUIT: + running = False + elif event.type == pygame.KEYDOWN: + if event.key == pygame.K_q and pygame.key.get_mods() & pygame.KMOD_CTRL: + running = False + + + # Generate new random position for the cursor + ret, frame = cap.read() + frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + + point, calibrate, calib_point = gestures.step(frame, screen_width, screen_height - 100) + + screen.fill((0, 0, 0)) + # Display frame on Pygame + if calibrate: + if calib_point[0] != prev_x or calib_point[1] != prev_y: + iterator += 1 + prev_x = calib_point[0] + prev_y = calib_point[1] + # pygame.draw.circle(screen, GREEN, fit_point, calibration_radius) + pygame.draw.circle(screen, BLUE, calib_point, 50) + else: + pass + + pygame.draw.circle(screen, RED, point, 50) + pygame.display.flip() + + # Cap the frame rate + clock.tick(60) + +# Quit Pygame +pygame.quit() diff --git a/eyeGestures/Legacy/Fixation.py b/eyeGestures/Legacy/Fixation.py new file mode 100644 index 0000000..75fcb18 --- /dev/null +++ b/eyeGestures/Legacy/Fixation.py @@ -0,0 +1,23 @@ +"""Module providing a fixation detection.""" + + +class Fixation: + """Class performing Fixation""" + + def __init__(self, x: float, y: float, radius: int = 100) -> None: + self.radius = radius + self.fixation = 0.0 + self.x = x + self.y = y + + def process(self, x: float, y: float) -> float: + """Function processing x and y estimated points for fixation detection""" + + if (x - self.x) ** 2 + (y - self.y) ** 2 < self.radius**2: + self.fixation = min(self.fixation + 0.02, 1.0) + else: + self.x = x + self.y = y + self.fixation = 0.0 + + return self.fixation diff --git a/eyeGestures/Legacy/__init__.py b/eyeGestures/Legacy/__init__.py new file mode 100644 index 0000000..599731a --- /dev/null +++ b/eyeGestures/Legacy/__init__.py @@ -0,0 +1,471 @@ +import pickle +import time +from typing import Any, Dict, List, Optional, Tuple + +import cv2 +import numpy as np +import numpy.typing as npt + +import eyeGestures.screenTracker.dataPoints as dp +from eyeGestures.calibration_v1 import Calibrator as Calibrator_v1 +from eyeGestures.calibration_v2 import Calibrator as Calibrator_v2 +from eyeGestures.face import Face, FaceFinder +from eyeGestures.Fixation import Fixation +from eyeGestures.gazeEstimator import GazeTracker +from eyeGestures.gevent import Cevent, Gevent +from eyeGestures.utils import low_pass_filter_fourier, recoverable + +VERSION = "3.0.0" + + +class EyeGestures_v3: + """Main class for EyeGesture tracker. It configures and manages entire algorithm""" + + def __init__(self, calibration_radius: int = 1000) -> None: + self.calibration_radius = calibration_radius + + self.clb: Dict[str, Calibrator_v2] = dict() # Calibrator_v2() + self.cap = None + self.calibration: Dict[str, bool] = dict() + self.average_points: Dict[str, npt.NDArray[np.float64]] = dict() + self.filled_points: Dict[str, int] = dict() + self.enable_CN = False + self.calibrate_gestures = False + + self.finder = FaceFinder() + self.face = Face() + + # this has to be contexted + self.prev_timestamp: Dict[str, float] = dict() + self.prev_point: Dict[str, npt.NDArray[np.float64]] = dict() + self.fix: Optional[float] = None + self.velocity_max: Dict[str, int] = dict() + self.velocity_min: Dict[str, int] = dict() + self.fixationTracker: Dict[str, Fixation] = dict() + self.key_points_buffer: Dict[str, List[npt.NDArray[np.float64]]] = dict() + + self.starting_head_position = np.zeros((1, 2)) + self.starting_size = np.zeros((1, 2)) + + def saveModel(self, context: str = "main") -> Optional[bytes]: + if context in self.clb: + return pickle.dumps(self.clb[context]) + return None + + def loadModel(self, model: Any, context: str = "main") -> None: + self.clb[context] = pickle.loads(model) + + def uploadCalibrationMap(self, points: npt.NDArray[np.float64], context: str = "main") -> None: + self.addContext(context) + self.clb[context].updMatrix(np.array(points)) + + def getLandmarks(self, frame: cv2.typing.MatLike) -> Tuple[npt.NDArray[np.float64], bool, cv2.typing.MatLike]: + + frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + frame = cv2.flip(frame, 1) + + # try: + self.face.process(frame, self.finder.find(frame)) + + face_landmarks = self.face.get_landmarks() + assert face_landmarks is not None + l_eye = self.face.get_left_eye() + r_eye = self.face.get_right_eye() + l_eye_landmarks = l_eye.getLandmarks() + r_eye_landmarks = r_eye.getLandmarks() + blink = l_eye.getBlink() and r_eye.getBlink() + + # get x,y offset + x_offset = np.min(face_landmarks[:, 0]) + y_offset = np.min(face_landmarks[:, 1]) + x_width = np.max(face_landmarks[:, 0]) - x_offset + y_width = np.max(face_landmarks[:, 1]) - y_offset + + # get head position + head_offset = np.zeros((1, 2)) + scale_x = 1 + scale_y = 1 + if np.array_equal(self.starting_head_position, np.zeros((1, 2))): + self.starting_head_position = np.array([[x_offset, y_offset]]) + self.starting_size = np.array([[x_width, y_width]]) + else: + head_offset = np.array([[x_offset, y_offset]]) - self.starting_head_position + scale_x = self.starting_size[0, 0] / x_width + scale_y = self.starting_size[0, 1] / y_width + + # eye_events = np.array([event.blink,event.fixation]).reshape(1, 2) + assert l_eye_landmarks is not None + assert r_eye_landmarks is not None + key_points: npt.NDArray[np.float64] = np.concatenate( + ( + l_eye_landmarks, + r_eye_landmarks, + np.array([[scale_x, scale_y]]), + head_offset, + ) + ) + key_points[:, 0] = key_points[:, 0] - head_offset[:, 0] + key_points[:, 1] = key_points[:, 1] - head_offset[:, 1] + + key_points[:, 0] = key_points[:, 0] * scale_x + key_points[:, 1] = key_points[:, 1] * scale_y + + key_points[-1, 0] = head_offset[0, 0] + key_points[-1, 1] = head_offset[0, 1] + # print(self.starting_size,x_width,y_width) + subframe = frame[ + int(y_offset) : int(y_offset + y_width), + int(x_offset) : int(x_offset + x_width), + ] + return key_points, blink, subframe + + def whichAlgorithm(self, context: str = "main") -> str: + if context in self.clb: + return self.clb[context].whichAlgorithm() + return "None" + + def reset(self, context: str = "main") -> None: + self.filled_points[context] = 0 + if context in self.clb: + self.addContext(context) + + def setFixation(self, fix: float) -> None: + self.fix = fix + + def addContext(self, context: str) -> None: + if context not in self.clb: + self.clb[context] = Calibrator_v2(self.calibration_radius) + self.average_points[context] = np.zeros((20, 2)) + self.filled_points[context] = 0 + self.calibration[context] = False + self.prev_timestamp[context] = time.time() + self.prev_point[context] = np.array((0.0, 0.0)) + self.velocity_max[context] = 0 + self.velocity_min[context] = 100000000 + self.fixationTracker[context] = Fixation(0, 0, 100) + self.key_points_buffer[context] = [] + + @recoverable(ret_error_params=(None, None)) + def step( + self, frame: cv2.typing.MatLike, calibration: bool, width: int, height: int, context: str = "main" + ) -> Tuple[Optional[Gevent], Optional[Cevent]]: + self.addContext(context) + + self.calibration[context] = calibration + + key_points, blink, sub_frame = self.getLandmarks(frame) + + self.key_points_buffer[context].append(key_points) + if len(self.key_points_buffer[context]) > 10: + self.key_points_buffer[context].pop(0) + key_points = low_pass_filter_fourier(key_points, 200) + + y_point = self.clb[context].predict(key_points) + self.average_points[context][1:, :] = self.average_points[context][ + : (self.average_points[context].shape[0] - 1), : + ] + self.average_points[context][0, :] = y_point + + if ( + self.filled_points[context] < self.average_points[context].shape[0] + and (y_point != np.array([0.0, 0.0])).any() + ): + self.filled_points[context] += 1 + if self.filled_points[context] == 0: + self.filled_points[context] = 1 + + averaged_point = np.sum(self.average_points[context][:, :], axis=0) / (self.filled_points[context]) + fixation = self.fixationTracker[context].process(averaged_point[0], averaged_point[1]) + + duration = time.time() - self.prev_timestamp[context] + velocity = abs(averaged_point - self.prev_point[context]) / duration + velocity = np.sqrt(velocity[0] ** 2 + velocity[1] ** 2) + self.prev_point[context] = averaged_point + self.prev_timestamp[context] = time.time() + + self.velocity_max[context] = max(self.velocity_max[context], velocity) + self.velocity_min[context] = min(self.velocity_min[context], velocity) + + saccades = velocity > (self.velocity_max[context]) / 4 + + if self.calibration[context] and ( + self.clb[context].insideClbRadius(averaged_point, width, height) + or self.filled_points[context] < self.average_points[context].shape[0] * 10 + ): + self.clb[context].add(key_points, self.clb[context].getCurrentPoint(width, height)) + else: + self.clb[context].post_fit() + + if self.calibration[context] and self.clb[context].insideAcptcRadius(averaged_point, width, height): + if self.clb[context].isReadyToMove(): + self.clb[context].movePoint() + + gevent = Gevent( + point=averaged_point, + blink=blink, + fixation=fixation, + saccades=saccades, + sub_frame=sub_frame, + ) + cevent = Cevent( + self.clb[context].getCurrentPoint(width, height), + self.clb[context].acceptance_radius, + self.clb[context].calibration_radius, + ) + return (gevent, cevent) + + +class EyeGestures_v2: + """Main class for EyeGesture tracker. It configures and manages entire algorithm""" + + def __init__(self, calibration_radius: int = 1000) -> None: + self.monitor_width = 1 + self.monitor_height = 1 + self.calibration_radius = calibration_radius + + self.clb: Dict[str, Calibrator_v2] = dict() # Calibrator_v2() + self.cap = None + self.gestures = EyeGestures_v1(285, 115, 200, 100) + + self.calibration: Dict[str, bool] = dict() + + self.CN: int = 5 + + self.average_points: Dict[str, npt.NDArray[np.float64]] = dict() + self.filled_points: Dict[str, int] = dict() + self.enable_CN = False + self.calibrate_gestures = False + + self.fix = 0.8 + + def saveModel(self, context: str = "main") -> Optional[bytes]: + if context in self.clb: + return pickle.dumps(self.clb[context]) + return None + + def loadModel(self, model: Any, context: str = "main") -> None: + self.clb[context] = pickle.loads(model) + + def uploadCalibrationMap(self, points: npt.NDArray[np.float64], context="main") -> None: + self.addContext(context) + self.clb[context].updMatrix(np.array(points)) + + def getLandmarks( + self, frame: cv2.typing.MatLike, calibrate: bool = False, context: str = "main" + ) -> Tuple[npt.NDArray[np.float64], npt.NDArray[np.float64], bool, bool, Optional[Cevent]]: + + frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + frame = cv2.flip(frame, 1) + # frame = cv2.resize(frame, (360, 640)) + + event, cevent = self.gestures.step( + frame, + context, + calibrate, # set calibration - switch to False to stop calibration + self.monitor_width, + self.monitor_height, + 0, + 0, + self.fix, + 100, + ) + + if event is not None or cevent is not None: + cursor_x, cursor_y = event.point[0], event.point[1] + l_eye_landmarks = event.l_eye.getLandmarks() + r_eye_landmarks = event.r_eye.getLandmarks() + + cursors = np.array([cursor_x, cursor_y]).reshape(1, 2) + eye_events = np.array([event.blink, event.fixation]).reshape(1, 2) + key_points = np.concatenate((cursors, l_eye_landmarks, r_eye_landmarks, eye_events)) + return ( + np.array((cursor_x, cursor_y)), + key_points, + event.blink, + event.fixation, + cevent, + ) + return np.array((0.0, 0.0)), np.array([]), False, False, None + + def whichAlgorithm(self, context: str = "main") -> str: + if context in self.clb: + return self.clb[context].whichAlgorithm() + return "None" + + def setClassicImpact(self, impact: int) -> None: + self.CN = impact + + def reset(self, context: str = "main") -> None: + self.filled_points[context] = 0 + if context in self.clb: + self.addContext(context) + + def setFixation(self, fix: float) -> None: + self.fix = fix + + def setClassicalImpact(self, CN: int) -> None: + self.CN = CN + + def enableCNCalib(self) -> None: + self.enable_CN = True + + def disableCNCalib(self) -> None: + self.enable_CN = False + + def addContext(self, context: str) -> None: + if context not in self.clb: + self.clb[context] = Calibrator_v2(self.calibration_radius) + self.average_points[context] = np.zeros((20, 2)) + self.filled_points[context] = 0 + self.calibration[context] = False + + @recoverable(ret_error_params=(None, None)) + def step( + self, frame: cv2.typing.MatLike, calibration: bool, width: int, height: int, context: str = "main" + ) -> Tuple[Optional[Gevent], Optional[Cevent]]: + self.addContext(context) + + self.calibration[context] = calibration + self.monitor_width = width + self.monitor_height = height + + classic_point, key_points, blink, fixation, cevent = self.getLandmarks( + frame, self.calibrate_gestures and self.enable_CN, context=context + ) + + margin = 10 + if cevent is None: + raise ValueError("cevent must not be None") + if (classic_point[0] <= margin) and self.calibration[context]: + self.calibrate_gestures = cevent.calibration + elif (classic_point[0] >= width - margin) and self.calibration[context]: + self.calibrate_gestures = cevent.calibration + elif (cevent.point[1] <= margin) and self.calibration[context]: + self.calibrate_gestures = cevent.calibration + elif (classic_point[1] >= height - margin) and self.calibration[context]: + self.calibrate_gestures = cevent.calibration + else: + self.calibrate_gestures = False + + y_point = self.clb[context].predict(key_points) + self.average_points[context][1:, :] = self.average_points[context][ + : (self.average_points[context].shape[0] - 1), : + ] + if fixation <= self.fix: + self.average_points[context][0, :] = y_point + + if ( + self.filled_points[context] < self.average_points[context].shape[0] + and (y_point != np.array([0.0, 0.0])).any() + ): + self.filled_points[context] += 1 + averaged_point = (np.sum(self.average_points[context][:, :], axis=0) + (classic_point * self.CN)) / ( + self.filled_points[context] + self.CN + ) + + if self.calibration[context] and ( + self.clb[context].insideClbRadius(averaged_point, width, height) + or self.filled_points[context] < self.average_points[context].shape[0] * 10 + ): + self.clb[context].add(key_points, self.clb[context].getCurrentPoint(width, height)) + else: + self.clb[context].post_fit() + + if self.calibration[context] and self.clb[context].insideAcptcRadius(averaged_point, width, height): + if self.clb[context].isReadyToMove(): + self.clb[context].movePoint() + + gevent = Gevent(averaged_point, blink, fixation) + cevent = Cevent( + self.clb[context].getCurrentPoint(width, height), + self.clb[context].acceptance_radius, + self.clb[context].calibration_radius, + ) + return (gevent, cevent) + + +class EyeGestures_v1: + """Main class for EyeGesture tracker. It configures and manages entier algorithm""" + + def __init__(self, roi_x=225, roi_y=105, roi_width=80, roi_height=15): + + screen_width = 500 + screen_height = 500 + height = 250 + width = 250 + + roi_x = roi_x % screen_width + roi_y = roi_y % screen_height + roi_width = roi_width % screen_width + roi_height = roi_height % screen_height + + self.screen_width = screen_width + self.screen_height = screen_height + + self.screen = dp.Screen(screen_width, screen_height) + + self.gaze = GazeTracker( + screen_width, + screen_height, + width, + height, + roi_x, + roi_y, + roi_width, + roi_height, + ) + + self.calibrators = dict() + self.calibrate = False + + def getFeatures(self, image): + """[NOT RECOMMENDED] Function allowing for extraction of gaze features from image""" + + return self.gaze.getFeatures(image) + + # @timeit + # 0.011 - 0.015 s for execution + def step( + self, + image, + context, + calibration, + display_width, + display_height, + display_offset_x=0, + display_offset_y=0, + fixation_freeze=0.7, + freeze_radius=20, + offset_x=0, + offset_y=0, + ): + """Function performing estimation and returning event object""" + + display = dp.Display(display_width, display_height, display_offset_x, display_offset_y) + + # allow for random recalibration shot + event = self.gaze.estimate( + image, + display, + context, + calibration, + fixation_freeze, + freeze_radius, + offset_x, + offset_y, + ) + cevent = None + + if event is not None: + cursor_x, cursor_y = event.point[0], event.point[1] + if context in self.calibrators: + self.calibrate = self.calibrators[context].calibrate(cursor_x, cursor_y, event.fixation) + else: + self.calibrators[context] = Calibrator_v1(display_width, display_height, cursor_x, cursor_y) + self.calibrate = self.calibrators[context].calibrate(cursor_x, cursor_y, event.fixation) + + cpoint = self.calibrators[context].get_current_point() + calibration = self.calibrate and cpoint != (0, 0) + cevent = Cevent(cpoint, 100, 100, calibration) + + return (event, cevent) diff --git a/eyeGestures/calibration_v1.py b/eyeGestures/Legacy/calibration_v1.py similarity index 100% rename from eyeGestures/calibration_v1.py rename to eyeGestures/Legacy/calibration_v1.py diff --git a/eyeGestures/calibration_v2.py b/eyeGestures/Legacy/calibration_v2.py similarity index 100% rename from eyeGestures/calibration_v2.py rename to eyeGestures/Legacy/calibration_v2.py diff --git a/eyeGestures/Legacy/eye.py b/eyeGestures/Legacy/eye.py new file mode 100644 index 0000000..184c4ee --- /dev/null +++ b/eyeGestures/Legacy/eye.py @@ -0,0 +1,188 @@ +"""Module providing a extraction of eye from face object.""" + +from typing import Optional, Tuple + +import cv2 +import mediapipe as mp +import numpy as np +import numpy.typing as npt + +from eyeGestures.utils import Buffor + + +class Eye: + """Class storing data related and representing a eye""" + + LEFT_EYE_KEYPOINTS = np.array(list(mp.solutions.face_mesh.FACEMESH_LEFT_EYE))[:, 0] + RIGHT_EYE_KEYPOINTS = np.array(list(mp.solutions.face_mesh.FACEMESH_RIGHT_EYE))[:, 0] + LEFT_EYE_PUPIL_KEYPOINT = [473] + RIGHT_EYE_PUPIL_KEYPOINT = [468] + + # LEFT_EYE_KEYPOINTS = [36, 37, 38, 39, 40, 41] # keypoint indices for left eye + # RIGHT_EYE_KEYPOINTS = [42, 43, 44, 45, 46, 47] # keypoint indices for right eye + + scale = (150, 100) + + def __init__(self, side: int) -> None: + + # check if eye is left or right + if side == 1: + self.side = "right" + self.pupil_index = self.RIGHT_EYE_PUPIL_KEYPOINT + elif side == 0: + self.side = "left" + self.pupil_index = self.LEFT_EYE_PUPIL_KEYPOINT + + self.x = 0 + self.y = 0 + self.width = 0 + self.height = 0 + self.center_x = 0 + self.center_y = 0 + self.image: Optional[cv2.typing.MatLike] = None + self.pupil: Optional[npt.NDArray[np.float64]] = None + self.offset: Optional[npt.NDArray[np.float64]] = None + self.region: Optional[npt.NDArray[np.float64]] = None + self.cut_image: Optional[cv2.typing.MatLike] = None + self.landmarks: Optional[npt.NDArray[np.float64]] = None + + # self._process(self.image,self.region) + + def update( + self, image: cv2.typing.MatLike, landmarks: npt.NDArray[np.float64], offset: npt.NDArray[np.float64] + ) -> None: + """function updating data stored inside eye object""" + + self.image = image + self.offset = offset + self.landmarks = landmarks + # check if eye is left or right + if self.side == "right": + self.region = np.array(landmarks[self.RIGHT_EYE_KEYPOINTS]) + # landmarks[self.RIGHT_EYE_KEYPOINTS], dtype=np.int32) + + elif self.side == "left": + self.region = np.array(landmarks[self.LEFT_EYE_KEYPOINTS]) + # landmarks[self.LEFT_EYE_KEYPOINTS], dtype=np.int32) + + self.pupil = landmarks[self.pupil_index][0] + assert self.region is not None + self._process(self.image, self.region) + + def getCenter(self) -> Tuple[float, float]: + """function returning center of eye""" + + return (float(self.center_x), float(self.center_y)) + + def getPos(self) -> Tuple[int, int]: + """function returning position of eye in the image""" + + return (int(self.x), int(self.y)) + + def getPupil(self) -> Optional[npt.NDArray[np.float64]]: + """function returning pupil object""" + + # return self.pupil.getCoords() + return self.pupil + + def getBlink(self) -> bool: + """function returning blink event""" + # return self.pupil.getCoords() + return (self.height) <= 3 # 2x margin + + def getImage(self) -> Optional[cv2.typing.MatLike]: + """function returning image of the eye cut from the entire face image""" + + # TODO: draw additional parameters + return self.cut_image + + def getGaze(self, gaze_buffor: Buffor, y_correction: int = 0, x_correction: int = 0) -> npt.NDArray[np.float64]: + """function returning gaze position""" + + # pupilCoords = self.pupil.getCoords() + assert self.offset is not None + assert self.region is not None + assert self.pupil is not None + center = np.array((self.center_x, self.center_y)) - self.offset + + region_corrected = self.region - self.offset + pupil_corrected = self.pupil - self.offset + + vectors = region_corrected - center + pupil = pupil_corrected - center + + vectors = vectors - pupil + gaze_vector = np.zeros((2)) + + gaze_vector[1] = np.sum(vectors, axis=0)[1] * 10 - y_correction + gaze_vector[0] = -np.sum(vectors, axis=0)[0] * 10 - x_correction + + # print("gaze_vector: ",gaze_vector) + gaze_buffor.add(gaze_vector) + return gaze_buffor.getAvg() + + def getOpenness(self) -> float: + """function returning eye openness""" + + return self.height / 2 + + def getLandmarks(self) -> Optional[npt.NDArray[np.float64]]: + """function returning eye landmarks""" + + return self.region + + def getBoundingBox(self) -> Tuple[int, int, int, int]: + return (int(self.x), int(self.y), int(self.width), int(self.height)) + + def _process(self, image: cv2.typing.MatLike, region: npt.NDArray[np.float64]) -> None: + h, w, _ = image.shape + + mask = np.full((h, w), 0, dtype=np.uint8) + background = np.zeros((h, w), dtype=np.uint8) + + region_int = np.array(region, dtype=np.int32) + cv2.fillPoly(mask, [region_int], (0,)) + + masked_image = cv2.bitwise_not(background, cv2.cvtColor(image.copy(), cv2.COLOR_BGR2GRAY), mask=mask) + + margin = 2 + min_x = np.min(region_int[:, 0]) - margin + max_x = np.max(region_int[:, 0]) + margin + min_y = np.min(region_int[:, 1]) - margin + max_y = np.max(region_int[:, 1]) + margin + + self.x = min_x + self.y = min_y + + self.width = np.max(region_int[:, 0]) - np.min(region_int[:, 0]) + self.height = np.max(region_int[:, 1]) - np.min(region_int[:, 1]) + + self.center_x = (min_x + max_x) / 2 + self.center_y = (min_y + max_y) / 2 + + # HACKETY_HACK: + assert self.pupil is not None + self.pupil[1] = np.min(region[:, 1]) + + self.cut_image = masked_image[min_y:max_y, min_x:max_x] + # print(f"here: {self.cut_image.shape,min_y,max_y,min_x,max_x}") + # self.cut_image = cv2.cvtColor(self.cut_image, cv2.COLOR_GRAY2BGR) + + # for point in self.region: + # point = point - (min_x, min_y) + # cv2.circle(self.cut_image, point.astype( + # int), 1, (255, 0, 0, 150), 1) + + # pupil = self.pupil - (min_x, min_y) + + # cv2.circle(self.cut_image, pupil.astype(int), 1, (0, 255, 0, 150), 1) + + # self.cut_image = cv2.resize(self.cut_image, self.scale) + + # save cut_image to buffor and get avg from previous buffors + # self.eyeBuffer.add(self.cut_image) + # self.cut_image = np.array(self.eyeBuffer.getAvg(), dtype=np.uint8) + + # LEGACY + # org_scale = (max_x - min_x,max_y - min_y) + # self.pupil = pupil.Pupil(self.cut_image, min_x, min_y, self.scale, org_scale) diff --git a/eyeGestures/Legacy/face.py b/eyeGestures/Legacy/face.py new file mode 100644 index 0000000..42575eb --- /dev/null +++ b/eyeGestures/Legacy/face.py @@ -0,0 +1,106 @@ +"""Module providing finding and extraction of face from image.""" + +from typing import Any, NamedTuple, Optional, Tuple + +import cv2 +import mediapipe as mp +import numpy as np +import numpy.typing as npt + +from eyeGestures.eye import Eye + + +class FaceFinder: + """Class helping finding face""" + + def __init__(self) -> None: + self.mp_face_mesh = mp.solutions.face_mesh.FaceMesh( + refine_landmarks=True, + static_image_mode=False, + min_detection_confidence=0.5, + min_tracking_confidence=0.5, + ) + + def find(self, image: cv2.typing.MatLike) -> Optional[Any]: + """Find face mesh""" + + assert len(image.shape) > 2 + + try: + face_mesh = self.mp_face_mesh.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) + if face_mesh.multi_face_landmarks is None: + return None + + return face_mesh + except Exception as e: + print(f"Exception in FaceFinder: {e}") + return None + + +class Face: + """Class keeping and processing face landmarks""" + + def __init__(self) -> None: + self.eye_left = Eye(0) + self.eye_right = Eye(1) + self.landmarks: Optional[npt.NDArray[np.float64]] = None + self.image_h: Optional[int] = None + self.image_w: Optional[int] = None + self.face: Optional[NamedTuple] = None + + def get_bounding_box(self) -> Tuple[int, int, int, int]: + """Get bounding box of face""" + if self.landmarks is not None: + margin = 0 + min_x = np.min(self.landmarks[:, 0]) - margin + max_x = np.max(self.landmarks[:, 0]) + margin + min_y = np.min(self.landmarks[:, 1]) - margin + max_y = np.max(self.landmarks[:, 1]) + margin + + width = int((max_x - min_x)) + height = int((max_y - min_y)) + x = int(min_x) + y = int(min_y) + return (x, y, width, height) + return (0, 0, 0, 0) + + def get_left_eye(self) -> Eye: + """Get left eye""" + return self.eye_left + + def get_right_eye(self) -> Eye: + """Get right eye""" + return self.eye_right + + def get_landmarks(self) -> Optional[npt.NDArray[np.float64]]: + """Get landmarks""" + return self.landmarks + + def _landmarks(self, face: Any) -> npt.NDArray[np.float64]: + + __complex_landmark_points = face.multi_face_landmarks + __complex_landmarks = __complex_landmark_points[0].landmark + + __face_landmarks = [] + for landmark in __complex_landmarks: + __face_landmarks.append((landmark.x * self.image_w, landmark.y * self.image_h)) + + return np.array(__face_landmarks) + + def process(self, image: cv2.typing.MatLike, face: Optional[Any]) -> None: + """Process face landmarks on image""" + # try: + self.face = face + self.image_h, self.image_w, _ = image.shape + self.landmarks = self._landmarks(self.face) + # self.nose = nose.Nose(image,self.landmarks,self.getBoundingBox()) + + x, y, _, _ = self.get_bounding_box() + offset = np.array((x, y)) + # offset = offset - self.nose.getHeadTiltOffset() + + self.eye_left.update(image, self.landmarks, offset) + self.eye_right.update(image, self.landmarks, offset) + # except Exception as e: + # print(f"Caught exception: {e}") + # return None diff --git a/eyeGestures/gazeEstimator.py b/eyeGestures/Legacy/gazeEstimator.py similarity index 100% rename from eyeGestures/gazeEstimator.py rename to eyeGestures/Legacy/gazeEstimator.py diff --git a/eyeGestures/gaze_contexter.py b/eyeGestures/Legacy/gaze_contexter.py similarity index 100% rename from eyeGestures/gaze_contexter.py rename to eyeGestures/Legacy/gaze_contexter.py diff --git a/eyeGestures/Legacy/gevent.py b/eyeGestures/Legacy/gevent.py new file mode 100644 index 0000000..7bc0181 --- /dev/null +++ b/eyeGestures/Legacy/gevent.py @@ -0,0 +1,57 @@ +"""Module providing a Gaze Events.""" + +from typing import Any, Optional + +import cv2 +import numpy as np +import numpy.typing as npt + +from eyeGestures.eye import Eye + + +class Gevent: + """Class representing gaze event, with tracked points scaled to screen, blink and fixation.""" + + def __init__( + self, + point: npt.NDArray[np.float64], + blink: bool, + fixation: float, + l_eye: Optional[Eye] = None, + r_eye: Optional[Eye] = None, + screen_man: Optional[Any] = None, + roi: Optional[Any] = None, + edges: Optional[Any] = None, + cluster: Optional[Any] = None, + context: Optional[Any] = None, + saccades: bool = False, + sub_frame: Optional[cv2.typing.MatLike] = None, + ): + + self.point = point + self.blink = blink + self.fixation = fixation + self.saccades = saccades + + # ALL DEBUG DATA + self.roi = roi + self.edges = edges + self.l_eye = l_eye + self.r_eye = r_eye + self.cluster = cluster + self.context = context + self.screen_man = screen_man + self.sub_frame = sub_frame + + +class Cevent: + """Class representing gaze event, with tracked points scaled to screen, blink and fixation.""" + + def __init__( + self, point: npt.NDArray[np.float64], acceptance_radius: int, calibration_radius: int, calibration: bool = False + ) -> None: + + self.point = point + self.acceptance_radius = acceptance_radius + self.calibration_radius = calibration_radius + self.calibration = calibration diff --git a/eyeGestures/Legacy/processing.py b/eyeGestures/Legacy/processing.py new file mode 100644 index 0000000..9a5f8e5 --- /dev/null +++ b/eyeGestures/Legacy/processing.py @@ -0,0 +1,72 @@ +"""Module providing a processing pupil position to gaze direction.""" + +from typing import Tuple + +import numpy as np +import numpy.typing as npt + + +class EyeProcessor: + """Class providing processing for eye to obtain gaze direction.""" + + def __init__(self, scale_w=250, scale_h=250): + self.scale_w = scale_w + self.scale_h = scale_h + self.min_x = 0 + self.max_x = 0 + self.min_y = 0 + self.max_y = 0 + self.pupil = None + self.landmarks = None + + def append(self, pupil: Tuple[int, int], landmarks: npt.NDArray[np.float64], pupilBuffor): + """Function appending new pupil point to tracker.""" + + self.pupil = pupil + self.landmarks = landmarks + + # get center: + margin = 5 + self.min_x = np.min(self.landmarks[:, 0]) - margin + self.max_x = np.max(self.landmarks[:, 0]) + margin + self.min_y = np.min(self.landmarks[:, 1]) - margin + self.max_y = np.max(self.landmarks[:, 1]) + margin + + assert self.pupil[0] > self.min_x + assert self.pupil[1] > self.min_y + + width = self.max_x - self.min_x + height = (self.max_y - self.min_y) / 2 + + pupilBuffor.add( + self.__convertPoint( + self.pupil, + width=self.scale_w, + height=self.scale_h, + scale_w=width, + scale_h=height, + offset=(self.min_x, self.min_y), + ) + ) + + def __convertPoint(self, point, width=1.0, height=1.0, scale_w=1.0, scale_h=1.0, offset=(0.0, 0.0)): + (min_x, min_y) = offset + x = int(((point[0] - min_x) / scale_w) * width) + y = int(((point[1] - min_y) / scale_h) * height) + return (x, y) + + def getAvgPupil(self, width, height, pupilBuffor): + """Function returning average point based on tracked.""" + + if width is not None and height is not None: + _retPupil = self.__convertPoint( + pupilBuffor.getAvg(), + width=width, + height=height, + scale_w=self.scale_w, + scale_h=self.scale_h, + ) + else: + _retPupil = pupilBuffor.getAvg() + + return _retPupil diff --git a/eyeGestures/Legacy/requirements.txt b/eyeGestures/Legacy/requirements.txt new file mode 100644 index 0000000..4436618 --- /dev/null +++ b/eyeGestures/Legacy/requirements.txt @@ -0,0 +1,7 @@ +mediapipe==0.10.8 +numpy==1.23.4 +opencv_contrib_python==4.8.1.78 +opencv_python==4.8.1.78 +opencv_python_headless==4.8.1.78 +scikit_learn==1.5.0 +scipy==1.11.4 diff --git a/eyeGestures/scalling_test.py b/eyeGestures/Legacy/scalling_test.py similarity index 100% rename from eyeGestures/scalling_test.py rename to eyeGestures/Legacy/scalling_test.py diff --git a/eyeGestures/screenTracker/__init__.py b/eyeGestures/Legacy/screenTracker/__init__.py similarity index 100% rename from eyeGestures/screenTracker/__init__.py rename to eyeGestures/Legacy/screenTracker/__init__.py diff --git a/eyeGestures/screenTracker/clusters.py b/eyeGestures/Legacy/screenTracker/clusters.py similarity index 100% rename from eyeGestures/screenTracker/clusters.py rename to eyeGestures/Legacy/screenTracker/clusters.py diff --git a/eyeGestures/screenTracker/dataPoints.py b/eyeGestures/Legacy/screenTracker/dataPoints.py similarity index 100% rename from eyeGestures/screenTracker/dataPoints.py rename to eyeGestures/Legacy/screenTracker/dataPoints.py diff --git a/eyeGestures/screenTracker/heatmap.py b/eyeGestures/Legacy/screenTracker/heatmap.py similarity index 100% rename from eyeGestures/screenTracker/heatmap.py rename to eyeGestures/Legacy/screenTracker/heatmap.py diff --git a/eyeGestures/screenTracker/screenTracker.py b/eyeGestures/Legacy/screenTracker/screenTracker.py similarity index 100% rename from eyeGestures/screenTracker/screenTracker.py rename to eyeGestures/Legacy/screenTracker/screenTracker.py diff --git a/eyeGestures/screenTracker_test.py b/eyeGestures/Legacy/screenTracker_test.py similarity index 100% rename from eyeGestures/screenTracker_test.py rename to eyeGestures/Legacy/screenTracker_test.py diff --git a/eyeGestures/Legacy/utils.py b/eyeGestures/Legacy/utils.py new file mode 100644 index 0000000..aba78ab --- /dev/null +++ b/eyeGestures/Legacy/utils.py @@ -0,0 +1,240 @@ +import pickle +import platform +import queue +import threading +import time + +import cv2 +import numpy as np +import numpy.typing as npt + +# Make predictions for new data points + + +def recoverable(ret_error_params=()): + def decorator(func): + """ + timeit + """ + + def inner(*args, **kwargs): + """ + inner + """ + try: + return func(*args, **kwargs) + except Exception as e: + print(f"Caugh error: {e}") + return ret_error_params + + return inner + + return decorator + + +def timeit(func): + """ + timeit + """ + + def inner(*args, **kwargs): + """ + inner + """ + start = time.time() + ret = func(*args, **kwargs) + print(f"Elapsed time: {time.time() - start}") + return ret + + return inner + + +def low_pass_filter_fourier(data: npt.NDArray[np.float64], cutoff_frequency: int) -> npt.NDArray[np.float64]: + # Apply Fourier Transform-based filter column-wise + filtered_data = np.zeros_like(data, dtype=float) + for col in range(data.shape[1]): # Iterate over each column + fft_data = np.fft.fft(data[:, col]) + frequencies = np.fft.fftfreq(len(data[:, col])) + # Apply the low-pass filter + fft_data[np.abs(frequencies) > cutoff_frequency] = 0 + # Perform Inverse Fourier Transform + filtered_data[:, col] = np.fft.ifft(fft_data).real + return filtered_data + + +def shape_to_np(shape, dtype="int"): + """ + shape_to_np + """ + coords = np.zeros((68, 2), dtype=dtype) + for i in range(0, 68): + coords[i] = (shape.part(i).x, shape.part(i).y) + return coords + + +def make_image_grid(images, rows, cols): + """ + Make a grid of images. + + Parameters: + - images: list of images to form the grid (all images must be of the same size and type) + - rows, cols: number of rows and columns in the grid + + Returns: + - grid_image: image grid as a single image + """ + # Check if the list of images is not empty and that we have enough to fill the grid + assert images, "List of images is empty" + # assert len(images) >= rows * cols, "Not enough images to fill the grid" + + # Get image dimensions + img_h, img_w = images[0].shape[:2] + + if len(images[0].shape) > 2: + # Create a black canvas to draw the grid on + grid_image = np.zeros((img_h * rows, img_w * cols, images[0].shape[2]), dtype=np.uint8) + else: + grid_image = np.zeros((img_h * rows, img_w * cols), dtype=np.uint8) + + # Copy images to the grid + for i, img in enumerate(images): + if i >= rows * cols: + break # Stop if we have filled the grid + row = i // cols + col = i % cols + grid_image[row * img_h : (row + 1) * img_h, col * img_w : (col + 1) * img_w] = img + + return grid_image + + +class var: + + def __init__(self, _var): + self.__var = _var + + def set(self, _var): + self.__var = _var + + def get(self): + return self.__var + + +class Buffor: + + def __init__(self, length): + self.length = length + self.__buffor = [] + + def add(self, _var): + if len(self.__buffor) >= self.length: + self.__buffor.pop(0) + + self.__buffor.append(_var) + + def getAvg(self, lenght=0): + return np.sum(self.__buffor[-lenght:], axis=0) / len(self.__buffor[-lenght:]) + + def getBuffor(self): + return self.__buffor + + def loadBuffor(self, buffor): + self.__buffor = buffor + + def getLast(self): + return self.__buffor[0] + + def getFirst(self): + return self.__buffor[len(self.__buffor) - 1] + + def getLen(self): + return len(self.__buffor) + + def isFull(self): + return len(self.__buffor) >= self.length + + def flush(self): + tmp = self.__buffor[-1] + self.__buffor = [] + self.__buffor.append(tmp) + + def clear(self): + self.__buffor = [] + + +# Bufforless + + +class VideoCapture: + """Wrapper on openCV2 stream making it bufforless and adding camera search""" + + def __init__(self, name, bufforless=True): + self.bufforless = bufforless + self.run = True + + if isinstance(name, str): + if ".pkl" in name: + self.stream = False + else: + self.stream = True + else: + self.stream = True + + if self.stream: + self.prev_frame = None + + self.__openCam(name) + + self.q = queue.Queue() + self.t = threading.Thread(target=self.__reader) + self.t.start() + else: + self.frames = [] + with open(name, "rb") as file: + self.frames = pickle.load(file) + + def __openCam(self, name): + if isinstance(name, int): + if "Windows" in platform.system(): + self.cap = cv2.VideoCapture(name, cv2.CAP_DSHOW) + else: + self.cap = cv2.VideoCapture(name) + + if self.cap is None or not self.cap.isOpened(): + print(f"Was unable to open camera: {name}.") + print(f"Trying to open camera: {name}.") + if name + 1 < 10: + self.__openCam(name + 1) + else: + self.cap = cv2.VideoCapture(name) + + def __reader(self): + while self.run: + ret, frame = self.cap.read() + if not ret: + break + if not self.q.empty() and self.bufforless: + try: + self.q.get_nowait() + except queue.Empty: + pass + self.q.put((ret, frame)) + + self.flush() + + def flush(self): + while not self.q.empty(): + self.q.get() + + def read(self): + """Function returning latest frame""" + if self.stream: + return self.q.get() + frame = self.frames.pop(0) + self.frames.pop(0) + return ((len(self.frames) >= 1), frame) + + def close(self): + """Function closing stream""" + self.run = False + self.t.join() + self.cap.release() diff --git a/eyeGestures/__init__.py b/eyeGestures/__init__.py index 599731a..2dcd1bf 100644 --- a/eyeGestures/__init__.py +++ b/eyeGestures/__init__.py @@ -6,58 +6,40 @@ import numpy as np import numpy.typing as npt -import eyeGestures.screenTracker.dataPoints as dp -from eyeGestures.calibration_v1 import Calibrator as Calibrator_v1 -from eyeGestures.calibration_v2 import Calibrator as Calibrator_v2 from eyeGestures.face import Face, FaceFinder from eyeGestures.Fixation import Fixation -from eyeGestures.gazeEstimator import GazeTracker -from eyeGestures.gevent import Cevent, Gevent -from eyeGestures.utils import low_pass_filter_fourier, recoverable +from eyeGestures.utils import recoverable -VERSION = "3.0.0" +try: + from EyegesturesEngine import EyeGesturesEnginePython as RustEyeGesturesEngine +except ImportError: + RustEyeGesturesEngine = None +VERSION = "4.0.0" -class EyeGestures_v3: +class EyeGestures_v4: """Main class for EyeGesture tracker. It configures and manages entire algorithm""" def __init__(self, calibration_radius: int = 1000) -> None: self.calibration_radius = calibration_radius - self.clb: Dict[str, Calibrator_v2] = dict() # Calibrator_v2() - self.cap = None - self.calibration: Dict[str, bool] = dict() - self.average_points: Dict[str, npt.NDArray[np.float64]] = dict() - self.filled_points: Dict[str, int] = dict() - self.enable_CN = False - self.calibrate_gestures = False + self.average_points: npt.NDArray[np.float64] = np.zeros((1, 2)) + self.calibration: bool = False + self.filled_points: int = 0 self.finder = FaceFinder() self.face = Face() + self.engine = None - # this has to be contexted - self.prev_timestamp: Dict[str, float] = dict() - self.prev_point: Dict[str, npt.NDArray[np.float64]] = dict() self.fix: Optional[float] = None - self.velocity_max: Dict[str, int] = dict() - self.velocity_min: Dict[str, int] = dict() self.fixationTracker: Dict[str, Fixation] = dict() self.key_points_buffer: Dict[str, List[npt.NDArray[np.float64]]] = dict() self.starting_head_position = np.zeros((1, 2)) self.starting_size = np.zeros((1, 2)) - - def saveModel(self, context: str = "main") -> Optional[bytes]: - if context in self.clb: - return pickle.dumps(self.clb[context]) - return None - - def loadModel(self, model: Any, context: str = "main") -> None: - self.clb[context] = pickle.loads(model) - + def uploadCalibrationMap(self, points: npt.NDArray[np.float64], context: str = "main") -> None: - self.addContext(context) - self.clb[context].updMatrix(np.array(points)) + self.clb.updMatrix(np.array(points)) def getLandmarks(self, frame: cv2.typing.MatLike) -> Tuple[npt.NDArray[np.float64], bool, cv2.typing.MatLike]: @@ -66,7 +48,6 @@ def getLandmarks(self, frame: cv2.typing.MatLike) -> Tuple[npt.NDArray[np.float6 # try: self.face.process(frame, self.finder.find(frame)) - face_landmarks = self.face.get_landmarks() assert face_landmarks is not None l_eye = self.face.get_left_eye() @@ -120,352 +101,32 @@ def getLandmarks(self, frame: cv2.typing.MatLike) -> Tuple[npt.NDArray[np.float6 return key_points, blink, subframe def whichAlgorithm(self, context: str = "main") -> str: - if context in self.clb: - return self.clb[context].whichAlgorithm() - return "None" + return "rust engine" def reset(self, context: str = "main") -> None: - self.filled_points[context] = 0 - if context in self.clb: - self.addContext(context) + self.filled_points = 0 def setFixation(self, fix: float) -> None: self.fix = fix - def addContext(self, context: str) -> None: - if context not in self.clb: - self.clb[context] = Calibrator_v2(self.calibration_radius) - self.average_points[context] = np.zeros((20, 2)) - self.filled_points[context] = 0 - self.calibration[context] = False - self.prev_timestamp[context] = time.time() - self.prev_point[context] = np.array((0.0, 0.0)) - self.velocity_max[context] = 0 - self.velocity_min[context] = 100000000 - self.fixationTracker[context] = Fixation(0, 0, 100) - self.key_points_buffer[context] = [] - @recoverable(ret_error_params=(None, None)) + @recoverable(ret_error_params=(None, None, None)) def step( - self, frame: cv2.typing.MatLike, calibration: bool, width: int, height: int, context: str = "main" - ) -> Tuple[Optional[Gevent], Optional[Cevent]]: - self.addContext(context) - - self.calibration[context] = calibration - - key_points, blink, sub_frame = self.getLandmarks(frame) - - self.key_points_buffer[context].append(key_points) - if len(self.key_points_buffer[context]) > 10: - self.key_points_buffer[context].pop(0) - key_points = low_pass_filter_fourier(key_points, 200) - - y_point = self.clb[context].predict(key_points) - self.average_points[context][1:, :] = self.average_points[context][ - : (self.average_points[context].shape[0] - 1), : - ] - self.average_points[context][0, :] = y_point - - if ( - self.filled_points[context] < self.average_points[context].shape[0] - and (y_point != np.array([0.0, 0.0])).any() - ): - self.filled_points[context] += 1 - if self.filled_points[context] == 0: - self.filled_points[context] = 1 - - averaged_point = np.sum(self.average_points[context][:, :], axis=0) / (self.filled_points[context]) - fixation = self.fixationTracker[context].process(averaged_point[0], averaged_point[1]) - - duration = time.time() - self.prev_timestamp[context] - velocity = abs(averaged_point - self.prev_point[context]) / duration - velocity = np.sqrt(velocity[0] ** 2 + velocity[1] ** 2) - self.prev_point[context] = averaged_point - self.prev_timestamp[context] = time.time() - - self.velocity_max[context] = max(self.velocity_max[context], velocity) - self.velocity_min[context] = min(self.velocity_min[context], velocity) - - saccades = velocity > (self.velocity_max[context]) / 4 - - if self.calibration[context] and ( - self.clb[context].insideClbRadius(averaged_point, width, height) - or self.filled_points[context] < self.average_points[context].shape[0] * 10 - ): - self.clb[context].add(key_points, self.clb[context].getCurrentPoint(width, height)) - else: - self.clb[context].post_fit() - - if self.calibration[context] and self.clb[context].insideAcptcRadius(averaged_point, width, height): - if self.clb[context].isReadyToMove(): - self.clb[context].movePoint() - - gevent = Gevent( - point=averaged_point, - blink=blink, - fixation=fixation, - saccades=saccades, - sub_frame=sub_frame, - ) - cevent = Cevent( - self.clb[context].getCurrentPoint(width, height), - self.clb[context].acceptance_radius, - self.clb[context].calibration_radius, - ) - return (gevent, cevent) - - -class EyeGestures_v2: - """Main class for EyeGesture tracker. It configures and manages entire algorithm""" - - def __init__(self, calibration_radius: int = 1000) -> None: - self.monitor_width = 1 - self.monitor_height = 1 - self.calibration_radius = calibration_radius - - self.clb: Dict[str, Calibrator_v2] = dict() # Calibrator_v2() - self.cap = None - self.gestures = EyeGestures_v1(285, 115, 200, 100) - - self.calibration: Dict[str, bool] = dict() - - self.CN: int = 5 - - self.average_points: Dict[str, npt.NDArray[np.float64]] = dict() - self.filled_points: Dict[str, int] = dict() - self.enable_CN = False - self.calibrate_gestures = False - - self.fix = 0.8 - - def saveModel(self, context: str = "main") -> Optional[bytes]: - if context in self.clb: - return pickle.dumps(self.clb[context]) - return None - - def loadModel(self, model: Any, context: str = "main") -> None: - self.clb[context] = pickle.loads(model) - - def uploadCalibrationMap(self, points: npt.NDArray[np.float64], context="main") -> None: - self.addContext(context) - self.clb[context].updMatrix(np.array(points)) - - def getLandmarks( - self, frame: cv2.typing.MatLike, calibrate: bool = False, context: str = "main" - ) -> Tuple[npt.NDArray[np.float64], npt.NDArray[np.float64], bool, bool, Optional[Cevent]]: - - frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) - frame = cv2.flip(frame, 1) - # frame = cv2.resize(frame, (360, 640)) - - event, cevent = self.gestures.step( - frame, - context, - calibrate, # set calibration - switch to False to stop calibration - self.monitor_width, - self.monitor_height, - 0, - 0, - self.fix, - 100, - ) - - if event is not None or cevent is not None: - cursor_x, cursor_y = event.point[0], event.point[1] - l_eye_landmarks = event.l_eye.getLandmarks() - r_eye_landmarks = event.r_eye.getLandmarks() - - cursors = np.array([cursor_x, cursor_y]).reshape(1, 2) - eye_events = np.array([event.blink, event.fixation]).reshape(1, 2) - key_points = np.concatenate((cursors, l_eye_landmarks, r_eye_landmarks, eye_events)) - return ( - np.array((cursor_x, cursor_y)), - key_points, - event.blink, - event.fixation, - cevent, - ) - return np.array((0.0, 0.0)), np.array([]), False, False, None - - def whichAlgorithm(self, context: str = "main") -> str: - if context in self.clb: - return self.clb[context].whichAlgorithm() - return "None" - - def setClassicImpact(self, impact: int) -> None: - self.CN = impact - - def reset(self, context: str = "main") -> None: - self.filled_points[context] = 0 - if context in self.clb: - self.addContext(context) - - def setFixation(self, fix: float) -> None: - self.fix = fix - - def setClassicalImpact(self, CN: int) -> None: - self.CN = CN - - def enableCNCalib(self) -> None: - self.enable_CN = True - - def disableCNCalib(self) -> None: - self.enable_CN = False - - def addContext(self, context: str) -> None: - if context not in self.clb: - self.clb[context] = Calibrator_v2(self.calibration_radius) - self.average_points[context] = np.zeros((20, 2)) - self.filled_points[context] = 0 - self.calibration[context] = False - - @recoverable(ret_error_params=(None, None)) - def step( - self, frame: cv2.typing.MatLike, calibration: bool, width: int, height: int, context: str = "main" - ) -> Tuple[Optional[Gevent], Optional[Cevent]]: - self.addContext(context) - - self.calibration[context] = calibration - self.monitor_width = width - self.monitor_height = height - - classic_point, key_points, blink, fixation, cevent = self.getLandmarks( - frame, self.calibrate_gestures and self.enable_CN, context=context - ) - - margin = 10 - if cevent is None: - raise ValueError("cevent must not be None") - if (classic_point[0] <= margin) and self.calibration[context]: - self.calibrate_gestures = cevent.calibration - elif (classic_point[0] >= width - margin) and self.calibration[context]: - self.calibrate_gestures = cevent.calibration - elif (cevent.point[1] <= margin) and self.calibration[context]: - self.calibrate_gestures = cevent.calibration - elif (classic_point[1] >= height - margin) and self.calibration[context]: - self.calibrate_gestures = cevent.calibration - else: - self.calibrate_gestures = False - - y_point = self.clb[context].predict(key_points) - self.average_points[context][1:, :] = self.average_points[context][ - : (self.average_points[context].shape[0] - 1), : - ] - if fixation <= self.fix: - self.average_points[context][0, :] = y_point - - if ( - self.filled_points[context] < self.average_points[context].shape[0] - and (y_point != np.array([0.0, 0.0])).any() - ): - self.filled_points[context] += 1 - averaged_point = (np.sum(self.average_points[context][:, :], axis=0) + (classic_point * self.CN)) / ( - self.filled_points[context] + self.CN - ) - - if self.calibration[context] and ( - self.clb[context].insideClbRadius(averaged_point, width, height) - or self.filled_points[context] < self.average_points[context].shape[0] * 10 - ): - self.clb[context].add(key_points, self.clb[context].getCurrentPoint(width, height)) - else: - self.clb[context].post_fit() - - if self.calibration[context] and self.clb[context].insideAcptcRadius(averaged_point, width, height): - if self.clb[context].isReadyToMove(): - self.clb[context].movePoint() - - gevent = Gevent(averaged_point, blink, fixation) - cevent = Cevent( - self.clb[context].getCurrentPoint(width, height), - self.clb[context].acceptance_radius, - self.clb[context].calibration_radius, - ) - return (gevent, cevent) - - -class EyeGestures_v1: - """Main class for EyeGesture tracker. It configures and manages entier algorithm""" - - def __init__(self, roi_x=225, roi_y=105, roi_width=80, roi_height=15): - - screen_width = 500 - screen_height = 500 - height = 250 - width = 250 - - roi_x = roi_x % screen_width - roi_y = roi_y % screen_height - roi_width = roi_width % screen_width - roi_height = roi_height % screen_height - - self.screen_width = screen_width - self.screen_height = screen_height - - self.screen = dp.Screen(screen_width, screen_height) - - self.gaze = GazeTracker( - screen_width, - screen_height, - width, - height, - roi_x, - roi_y, - roi_width, - roi_height, - ) - - self.calibrators = dict() - self.calibrate = False - - def getFeatures(self, image): - """[NOT RECOMMENDED] Function allowing for extraction of gaze features from image""" - - return self.gaze.getFeatures(image) - - # @timeit - # 0.011 - 0.015 s for execution - def step( - self, - image, - context, - calibration, - display_width, - display_height, - display_offset_x=0, - display_offset_y=0, - fixation_freeze=0.7, - freeze_radius=20, - offset_x=0, - offset_y=0, - ): - """Function performing estimation and returning event object""" - - display = dp.Display(display_width, display_height, display_offset_x, display_offset_y) - - # allow for random recalibration shot - event = self.gaze.estimate( - image, - display, - context, - calibration, - fixation_freeze, - freeze_radius, - offset_x, - offset_y, - ) - cevent = None - - if event is not None: - cursor_x, cursor_y = event.point[0], event.point[1] - if context in self.calibrators: - self.calibrate = self.calibrators[context].calibrate(cursor_x, cursor_y, event.fixation) - else: - self.calibrators[context] = Calibrator_v1(display_width, display_height, cursor_x, cursor_y) - self.calibrate = self.calibrators[context].calibrate(cursor_x, cursor_y, event.fixation) - - cpoint = self.calibrators[context].get_current_point() - calibration = self.calibrate and cpoint != (0, 0) - cevent = Cevent(cpoint, 100, 100, calibration) - - return (event, cevent) + self, frame: cv2.typing.MatLike, width: int, height: int,): + + if self.engine is None: + self.engine = RustEyeGesturesEngine(width, height) + + landmarks = self.finder.find(frame).face_landmarks[0] + scaled_landmarks = [] + for landmark in landmarks: + scaled_landmarks.append((landmark.x, landmark.y)) + scaled_landmarks = np.array(scaled_landmarks) + result = self.engine.process(scaled_landmarks.flatten().tolist()) + + x, y = result[0], result[1] + is_calibrating = result[2] == 1.0 + calib_x, calib_y = result[3], result[4] + calib_x = min(max(calib_x, 0), width) + calib_y = min(max(calib_y, 0), height) + return [x, y], is_calibrating, [calib_x, calib_y] diff --git a/eyeGestures/eye.py b/eyeGestures/eye.py index 184c4ee..9775132 100644 --- a/eyeGestures/eye.py +++ b/eyeGestures/eye.py @@ -3,7 +3,7 @@ from typing import Optional, Tuple import cv2 -import mediapipe as mp +from mediapipe.tasks.python.vision.face_landmarker import FaceLandmarksConnections import numpy as np import numpy.typing as npt @@ -13,8 +13,24 @@ class Eye: """Class storing data related and representing a eye""" - LEFT_EYE_KEYPOINTS = np.array(list(mp.solutions.face_mesh.FACEMESH_LEFT_EYE))[:, 0] - RIGHT_EYE_KEYPOINTS = np.array(list(mp.solutions.face_mesh.FACEMESH_RIGHT_EYE))[:, 0] + LEFT_EYE_KEYPOINTS = np.array( + sorted( + { + index + for connection in FaceLandmarksConnections.FACE_LANDMARKS_LEFT_EYE + for index in (connection.start, connection.end) + } + ) + ) + RIGHT_EYE_KEYPOINTS = np.array( + sorted( + { + index + for connection in FaceLandmarksConnections.FACE_LANDMARKS_RIGHT_EYE + for index in (connection.start, connection.end) + } + ) + ) LEFT_EYE_PUPIL_KEYPOINT = [473] RIGHT_EYE_PUPIL_KEYPOINT = [468] diff --git a/eyeGestures/face.py b/eyeGestures/face.py index 42575eb..2496ebf 100644 --- a/eyeGestures/face.py +++ b/eyeGestures/face.py @@ -1,37 +1,70 @@ """Module providing finding and extraction of face from image.""" +import os +from pathlib import Path from typing import Any, NamedTuple, Optional, Tuple +import urllib.request import cv2 import mediapipe as mp +from mediapipe.tasks import python +from mediapipe.tasks.python import vision import numpy as np import numpy.typing as npt from eyeGestures.eye import Eye +FACE_LANDMARKER_MODEL_URL = ( + "https://storage.googleapis.com/mediapipe-models/face_landmarker/face_landmarker/float16/1/face_landmarker.task" +) + + +def _resolve_model_asset_path(model_asset_path: Optional[str]) -> str: + """Resolve the FaceLandmarker model path.""" + + if model_asset_path: + return model_asset_path + + env_model_path = os.getenv("EYEGESTURES_FACE_LANDMARKER_MODEL") + if env_model_path: + return env_model_path + + default_model_path = Path(__file__).with_name("face_landmarker_v2_with_blendshapes.task") + if default_model_path.exists(): + return str(default_model_path) + + default_model_path.parent.mkdir(parents=True, exist_ok=True) + urllib.request.urlretrieve(FACE_LANDMARKER_MODEL_URL, default_model_path) + return str(default_model_path) + class FaceFinder: """Class helping finding face""" - def __init__(self) -> None: - self.mp_face_mesh = mp.solutions.face_mesh.FaceMesh( - refine_landmarks=True, - static_image_mode=False, - min_detection_confidence=0.5, - min_tracking_confidence=0.5, + def __init__(self, model_asset_path: Optional[str] = None) -> None: + resolved_model_path = _resolve_model_asset_path(model_asset_path) + base_options = python.BaseOptions(model_asset_path=resolved_model_path) + options = vision.FaceLandmarkerOptions( + base_options=base_options, + output_face_blendshapes=True, + output_facial_transformation_matrixes=True, + num_faces=1, ) + self.face_landmarker = vision.FaceLandmarker.create_from_options(options) def find(self, image: cv2.typing.MatLike) -> Optional[Any]: - """Find face mesh""" + """Find face landmarks.""" assert len(image.shape) > 2 try: - face_mesh = self.mp_face_mesh.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) - if face_mesh.multi_face_landmarks is None: + rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) + mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=rgb_image) + detection_result = self.face_landmarker.detect(mp_image) + if not detection_result.face_landmarks: return None - return face_mesh + return detection_result except Exception as e: print(f"Exception in FaceFinder: {e}") return None @@ -77,15 +110,13 @@ def get_landmarks(self) -> Optional[npt.NDArray[np.float64]]: return self.landmarks def _landmarks(self, face: Any) -> npt.NDArray[np.float64]: + face_landmarks = face.face_landmarks[0] - __complex_landmark_points = face.multi_face_landmarks - __complex_landmarks = __complex_landmark_points[0].landmark - - __face_landmarks = [] - for landmark in __complex_landmarks: - __face_landmarks.append((landmark.x * self.image_w, landmark.y * self.image_h)) + scaled_landmarks = [] + for landmark in face_landmarks: + scaled_landmarks.append((landmark.x * self.image_w, landmark.y * self.image_h)) - return np.array(__face_landmarks) + return np.array(scaled_landmarks) def process(self, image: cv2.typing.MatLike, face: Optional[Any]) -> None: """Process face landmarks on image""" diff --git a/pyproject.toml b/pyproject.toml index db66e9f..259a4a4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -52,13 +52,19 @@ maintainers = [ description = "Package for eye tracking algorithm allowing for development of gaze controlled computer interface" readme = "README.md" license = {file = "LICENSE"} -requires-python = ">=3.8" +requires-python = ">=3.13" keywords = ["eyetracking", "eye", "gaze", "tracking", "gazetracking"] classifiers = [ "Programming Language :: Python :: 3", "License :: OSI Approved :: GNU General Public License v3 (GPLv3)", "Operating System :: OS Independent", ] +dependencies = [ + "maturin>=1.13.3", + "mediapipe>=0.10.35", + "opencv-python>=4.13.0.92", + "pygame>=2.6.1", +] [project.urls] Homepage = "https://github.com/NativeSensors/EyeGestures" @@ -110,4 +116,4 @@ plugins = ["numpy.typing.mypy_plugin"] [[tool.mypy.overrides]] module = ["sklearn.*", "mediapipe.*"] -ignore_missing_imports = true \ No newline at end of file +ignore_missing_imports = true diff --git a/scripts/csv_reader.py b/scripts/csv_reader.py deleted file mode 100644 index 77c378d..0000000 --- a/scripts/csv_reader.py +++ /dev/null @@ -1,48 +0,0 @@ -import pickle -import csv -import ast - -def read_gaze_data_from_csv(filename): - """Reads gaze event data from a CSV file. - - Args: - filename (str): Name of the CSV file to read data from. - - Returns: - list: List of dictionaries containing gaze event data. - """ - - gaze_data = [] - - with open(filename, newline='') as csvfile: - reader = csv.reader(csvfile) - for n,row in enumerate(reader): - if n > 0: - print(n,row[6],type(row[6]),row[6][2]) - print(bytearray(f'{row[6][2:-3]}', 'latin-1')) - # Deserialize the string representation of arrays - - row_data = { - "point_x": row[0], - "point_y": row[1], - "blink": row[2], - "fixation": row[3], - "screen_x": row[4], - "screen_y": row[5], - "l_eye_landmarks": pickle.loads(bytearray(f'{row[6][2:-1]}', 'latin-1').decode('unicode_escape').encode('latin1')), # Deserialize the string representation of the array - "r_eye_landmarks": pickle.loads(bytearray(f'{row[7][2:-1]}', 'latin-1').decode('unicode_escape').encode('latin1')), # Deserialize the string representation of the array - "l_eye_pupil": pickle.loads(bytearray(f'{row[8][2:-1]}', 'latin-1').decode('unicode_escape').encode('latin1')), # Deserialize the string representation of the array - "r_eye_pupil": pickle.loads(bytearray(f'{row[9][2:-1]}', 'latin-1').decode('unicode_escape').encode('latin1')), # Deserialize the string representation of the array - "screen_width": row[10], - "screen_height": row[11], - "rois": row[12] # Deserialize the string representation of the array - } - gaze_data.append(row_data) - - return gaze_data - -# Example usage: -filename = 'collection_91675a1d.csv' -gaze_data = read_gaze_data_from_csv(filename) -for data in gaze_data: - print(data) \ No newline at end of file diff --git a/scripts/heatmap_maker.py b/scripts/heatmap_maker.py deleted file mode 100644 index 4be7384..0000000 --- a/scripts/heatmap_maker.py +++ /dev/null @@ -1,123 +0,0 @@ -import matplotlib.pyplot as plt -import numpy as np -import argparse -import pickle -import csv -import ast -import re -import os - -def read_gaze_data_from_csv(filename): - """Reads gaze event data from a CSV file. - - Args: - filename (str): Name of the CSV file to read data from. - - Returns: - list: List of dictionaries containing gaze event data. - """ - - gaze_data = [] - - with open(filename, newline='') as csvfile: - reader = csv.reader(csvfile) - for n,row in enumerate(reader): - if n > 0: - # print(n,row[6],type(row[6]),row[6][2]) - # print(bytearray(f'{row[6][2:-3]}', 'latin-1')) - # Deserialize the string representation of arrays - - row_data = { - "timestamp": row[0], - "point_x": row[1], - "point_y": row[2], - "blink": row[3], - "fixation": row[4], - "screen_x": row[5], - "screen_y": row[6], - "screen_width": row[7], - "screen_height": row[8], - "rois": row[12] # Deserialize the string representation of the array - } - gaze_data.append(row_data) - - return gaze_data - -def draw_heatmap(data_points,x_dim,y_dim,picture_path = None): - x_range = [0,x_dim] - y_range = [0,y_dim] - heatmap, xedges, yedges = np.histogram2d(data_points[:,0], data_points[:,1], bins = 50, range=[x_range, y_range]) - extent = [xedges[0], xedges[-1], yedges[-1], yedges[0]] - alpha = 1.0 - - plt.clf() - if picture_path: - map_image = plt.imread(picture_path) - map_image = np.flipud(map_image) - alpha = 0.5 - plt.imshow(map_image, extent=extent, aspect='auto') - - - # Plot the image as background - plt.imshow(heatmap.T, extent=extent, origin='lower', alpha=alpha, cmap='hot') - plt.gca().invert_yaxis() - plt.show() - -def main(gaze_data_file,background_file = False, step = None, window_size = None, start = None, stop = None): - - # Example usage: - # filename = 'data/collection_73cbc017/data.csv' - # picture = 'data/collection_73cbc017/recordings/1713258117.949606.png' - - # picture = background_file - - recordings_path = f"{os.path.dirname(gaze_data_file)}/recordings" - - filename = gaze_data_file - gaze_data = read_gaze_data_from_csv(filename) - data_points = [] - timestamps = [] - screen_w = float(gaze_data[0]["screen_width"]) - screen_h = float(gaze_data[0]["screen_height"]) - - for data in gaze_data: - timestamps.append(float(data["timestamp"])) - data_points.append([float(data["screen_x"]),float(data["screen_y"])]) - data_points = np.array(data_points) - - if start: - index = timestamps.index(start) - timestamps = timestamps[index:] - data_points = data_points[index:,:] - - if stop: - index = timestamps.index(stop) - timestamps = timestamps[:index] - data_points = data_points[:index,:] - - if window_size == None or step == None: - picture_path = None - if background_file: - picture_path = f"{recordings_path}/{timestamps[0]}.png" - draw_heatmap(data_points,screen_w,screen_h,picture_path) - else: - for i in range(0,len(timestamps),step): - picture_path = None - if background_file: - print(f"{background_file}") - picture_path = f"{recordings_path}/{timestamps[i]}.png" - data_points_range = data_points[i:i+window_size] - draw_heatmap(data_points_range,screen_w,screen_h,picture_path) - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description='Generate heatmap based on gaze data over an image.') - parser.add_argument('path_to_gaze_data', type=str, help='Path to the gaze data file') - parser.add_argument('--window', type=int, help='Window size in samples') - parser.add_argument('--step', type=int, help='Window size in samples') - parser.add_argument('--background', action='store_true', help='Path to the background file') - parser.add_argument('--start', type=float, help='Window size in samples') - parser.add_argument('--stop', type=float, help='Path to the background file') - - args = parser.parse_args() - - main(args.path_to_gaze_data,background_file = args.background, step = args.step, window_size = args.window, start = args.start, stop = args.stop) diff --git a/tests/__init__.py b/tests/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/tests/data/face_1.jpg b/tests/data/face_1.jpg deleted file mode 100644 index 0d3a0e19bcc67767c2251cccd1a53c2e1f019d0c..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 42883 zcmY&<1ytKj^kou42^30@0;NcxxE6N^?(SOLt!U9winqAC6xZTTixhV)?(Xie>9@Q8 zvpXCJKSIvDnRn;D``$bA{P%eU0F{xDmH+^O000p21$dqZ1OPz)GmsF!A%PJ;k-*5{ ze*^gi@_*yS3ltOx3d##qR5UbHRP_IU0FjW8kip2PFJ7QxprN8+V7@{OOw9lO>HoZ- z|L>>&|8xKI^xOf!f&c?S{zyP900;|+gav%=0&oKWARsdEpRNCQ0AGN>$S4pbR5Zk2 zkq~eFKW_s68T|qU@;nDXM*;#sP$VegV|TT66u4gi!hiq}G#8R9P7aC!#R15uGd-?u z6+35hr@v{OqjN2_5Z^xoB1cnJ$5%gknpW<_$lXqxI^)RS7ND{t+`02vdz`m@tXTFxF*@D1j(AfE-?QCkQDHXGo&G?g*bG`?L 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+ + + made in PL + + +EyeGestures is open source eyetracking software/library using native webcams and phone camers for achieving its goal. The aim of library is to bring accessibility of eye-tracking and eye-driven interfaces without requirement of obtaining expensive hardware. + +Our [Mission](https://github.com/NativeSensors/EyeGestures/blob/main/MISSION.md)! + +# EyeGesturesLite + +EyeGesturesLite is JavaScript implementation of [EyeGestures algoritm](https://github.com/NativeSensors/EyeGestures). If you need python version, check original repository. + +### How does it work? + +It is a gaze tracker that uses machine learning and built-in cameras (such as a webcam) to provide gaze tracking for the user. It includes a built-in calibration mode that displays 20 red circles for the user to focus on, along with a blue cursor that follows the user’s gaze. During the calibration process, the cursor will gradually start following user's gaze more and more. By the end of the 20 points, the cursor should be able to independently follow the user’s gaze. + +### ⚙️ Try: + +[EyeGesturesLite](https://eyegestures.com/tryLite) + +### 🔧 Build your own: + +#### CDN: + +0. External dependencies CDNs: + +```html + + +``` + +1. You need two CDN links: +```html + + +``` + +2. Place `video` element (which can be hidden) somewhere in the page together with status and error divs (can stay invisible): +```html + + + +``` + +3. Then javascript interface code: +```html + +``` + +#### NPM package [WiP]: + +1. Install npm package: +``` +npm install eyegestures +``` + +2. Place `video` element in your DOM (which can be hidden) somewhere in the page together with status and error divs (can stay invisible): +```html + + + +``` + +3. try to import: +```JS +import EyeGestures from 'eyegestures'; + +const gestures = new EyeGestures("video_element_id",(point,calibration)=>{/*point,calibration*/}) +// after this call: gestures.start(); +console.log(gestures); +``` +>[!Warning] +> EyeGestures needs DOM to operate and its constructor expects to receive video element/camera feed id string. +> + +### rules of using + +You can use it free of charge as long as you keep our logo. If you want to remove logo then contact: contact@eyegestures.com. + +### 📇 Find us: +- [discord](https://discord.gg/FvagCX8T4h) +- [twitter](https://twitter.com/PW4ltz) +- [daily.dev](https://dly.to/JEe1Sz6vLey) +- email: contact@eyegestures.com + +### Troubleshooting: + + +### 💻 Contributors + + + + + +### 💵 Support the project + +We will be extremely grateful for your support: it helps to keep server running + fuels our brains with coffee. + +Support project on Polar (in exchange we provide access to alphas versions!): + +Subscribe on Polar + + + + + Star History Chart + diff --git a/web/demo/eyekey.html b/web/demo/eyekey.html new file mode 100644 index 0000000..851edc1 --- /dev/null +++ b/web/demo/eyekey.html @@ -0,0 +1,355 @@ + + + + + + Eye QWERTY Keyboard · EyeGestures + + + + + + + + + + +
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+ + +
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+ Starting… +
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Output
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Complete calibration to unlock keyboard

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+ + Look at a key and hold gaze to select + x: —   y: — +
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a/web/example/eyegestures.css b/web/example/eyegestures.css new file mode 100644 index 0000000..146f2e3 --- /dev/null +++ b/web/example/eyegestures.css @@ -0,0 +1,26 @@ +#cursor { + position: fixed; + width: 50px; + height: 50px; + background: rgba(0, 0, 255, 0.342); + border-radius: 50%; + border: medium solid rgba(0, 0, 255, 0.171); + z-index: 1000; +} +#calib_cursor { + position: fixed; + width: 200px; + height: 200px; + background: #ff575777; + border-radius: 50%; + border: medium solid #ff5757; + z-index: 10000; +} +#logoDivEyeGestures{ +} +#logoDivEyeGestures:hover{ + transform: scale(1.01); +} +#logoDivEyeGestures:active{ + transform: scale(1.0); +} \ No newline at end of file diff --git a/web/example/simple.html b/web/example/simple.html new file mode 100644 index 0000000..85f1f17 --- /dev/null +++ b/web/example/simple.html @@ -0,0 +1,90 @@ + + + + + + EyeGesturesLite + + + + + + +
+
+

EyeGesturesLite

+
Initializing...
+
+ +
+
+ + + + + + + \ No newline at end of file diff --git a/web/eyegestures.js b/web/eyegestures.js new file mode 100644 index 0000000..8558d4f --- /dev/null +++ b/web/eyegestures.js @@ -0,0 +1,2 @@ +import EyeGestures from './src/eyegestures.min.js'; +export default EyeGestures \ No newline at end of file diff --git a/web/npm_test.js b/web/npm_test.js new file mode 100644 index 0000000..afcddac --- /dev/null +++ b/web/npm_test.js @@ -0,0 +1,5 @@ +import EyeGestures from 'eyegestures'; + +const gestures = new EyeGestures("video_element_id",(point,calibration)=>{/*point,calibration*/}) +// after this call: gestures.start(); +console.log(gestures); diff --git a/web/package-lock.json b/web/package-lock.json new file mode 100644 index 0000000..abdf653 --- /dev/null +++ b/web/package-lock.json @@ -0,0 +1,141 @@ +{ + "name": "eyegestures", + "version": "1.0.0", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "eyegestures", + "version": "1.0.0", + "license": "SEE 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"node_modules/terser": { + "version": "5.48.0", + "resolved": "https://registry.npmjs.org/terser/-/terser-5.48.0.tgz", + "integrity": "sha512-J/9An6vs9Us6wKRriSFXBWdRZapREHqFzdNUKk0pmu804EMR6dr6winwo7e5JDxN4xahxQsuysyYFwlwj4XN/Q==", + "dev": true, + "license": "BSD-2-Clause", + "dependencies": { + "@jridgewell/source-map": "^0.3.3", + "acorn": "^8.15.0", + "commander": "^2.20.0", + "source-map-support": "~0.5.20" + }, + "bin": { + "terser": "bin/terser" + }, + "engines": { + "node": ">=10" + } + } + } +} diff --git a/web/package.json b/web/package.json new file mode 100644 index 0000000..7a0d4a2 --- /dev/null +++ b/web/package.json @@ -0,0 +1,38 @@ +{ + "name": "eyegestures", + "version": "1.0.0", + "type": "module", + "description": "Gaze tracking algorithm for web.", + "main": "eyegestures.js", + "directories": { + "src": "src" + }, + "scripts": { + "test": "echo \"Error: no test specified\" && exit 1" + }, + "repository": { + "type": "git", + "url": "git+https://github.com/NativeSensors/EyeGesturesLite.git" + }, + "keywords": [ + "eye", + "tracking", + "gaze", + "tracking", + "eyegestures", + "eyetracking", + "gazetracking" + ], + "author": "Piotr Walas", + "license": "SEE LICENSE IN License", + "bugs": { + "url": "https://github.com/NativeSensors/EyeGesturesLite/issues" + }, + "homepage": "https://github.com/NativeSensors/EyeGesturesLite#readme", + "dependencies": { + "eyegestures": "^1.0.0" + }, + "devDependencies": { + "terser": "^5.48.0" + } +} diff --git a/web/src/eyegestures.js b/web/src/eyegestures.js new file mode 100644 index 0000000..de9a295 --- /dev/null +++ b/web/src/eyegestures.js @@ -0,0 +1,455 @@ +const __eyeGesturesScriptUrl = (document.currentScript && document.currentScript.src) || window.location.href; +let __eyeGesturesEngineModulePromise = null; + +function __resolveEngineModuleUrl() { + if (window.EyeGesturesEngineModuleUrl) { + return window.EyeGesturesEngineModuleUrl; + } + console.log("__eyeGesturesScriptUrl: ", __eyeGesturesScriptUrl); + return new URL('EyegesturesEngine.js', __eyeGesturesScriptUrl).href; +} + +function __loadEngineModule() { + if (!__eyeGesturesEngineModulePromise) { + __eyeGesturesEngineModulePromise = import(__resolveEngineModuleUrl()); + } + return __eyeGesturesEngineModulePromise; +} + +VERSION = "4.0.0"; +class EyeGestures{ // strip export default before making cdn/web embeddable version + constructor(videoElement_ID, onGaze) + { + const cursor = document.createElement('div'); + cursor.id = "cursor"; + cursor.style.display = "None"; + document.body.appendChild(cursor); + const calib_cursor = document.createElement('div'); + calib_cursor.id = "calib_cursor"; + calib_cursor.style.display = "None"; + + const logoDiv = document.createElement('div'); + logoDiv.id = "logoDivEyeGestures"; + logoDiv.style.width = "200px"; + logoDiv.style.height = "60px"; + logoDiv.style.position = "fixed"; + logoDiv.style.bottom = "10px"; + logoDiv.style.right = "10px"; + logoDiv.style.zIndex = "9999"; + logoDiv.style.background = "black"; + logoDiv.style.borderRadius = "10px"; + logoDiv.style.display = "none"; + logoDiv.onclick = function() { + window.location.href = "https://eyegestures.com/"; + }; + const logo = document.createElement('div'); + logo.style.margin = "10px"; + logo.innerHTML = 'Logo'; + logoDiv.appendChild(logo); + const canvas = document.createElement('canvas'); + canvas.id = "output_canvas"; + canvas.width = "50"; + canvas.height = "50"; + canvas.style.margin = "5px"; + canvas.style.borderRadius = "10px"; + canvas.style.border = "none"; + canvas.style.background = "#222"; + logoDiv.appendChild(canvas); + document.body.appendChild(logoDiv); + + document.body.appendChild(calib_cursor); + + this.engine = null; + this.onGaze = onGaze; + this.run = false; + this.__invisible = false; + this.screen_width = 0; + this.screen_height = 0; + + this.updateViewportSize(); + + if (window.isSecureContext) { + this.init(videoElement_ID); + } + else { + console.error('This application requires a secure context (HTTPS or localhost)'); + } + + } + + updateViewportSize() { + this.screen_width = Math.max(document.documentElement.clientWidth || 0, window.innerWidth || 0); + this.screen_height = Math.max(document.documentElement.clientHeight || 0, window.innerHeight || 0); + } + + showUpCalibrationInstructions(onRead) { + // Create content container + // const content = document.createElement('div'); + // content.style.textAlign = 'center'; + // content.style.color = '#fff'; + // content.style.fontFamily = 'Arial, sans-serif'; + + // // Create instructional text + // const instructionText1 = document.createElement('h3'); + // instructionText1.textContent = 'EyeGestures Upcalibration:'; + // instructionText1.style.fontSize = '1.5rem'; + // instructionText1.style.marginBottom = '20px'; + + // const instructionText2 = document.createElement('p'); + // instructionText2.innerHTML = 'We need to improve calibration. Focus on 25 red circles.'; + // instructionText2.style.marginBottom = '20px'; + + // const instructionText3 = document.createElement('p'); + // instructionText3.innerHTML = 'The blue circle is your estimated gaze. With every calibration point, the tracker will gradually listen more and more to your gaze.'; + // instructionText3.style.marginBottom = '20px'; + + // // Append elements to content + // content.appendChild(instructionText1); + // content.appendChild(instructionText2); + // content.appendChild(instructionText3); + // content.appendChild(button); + } + + showCalibrationInstructions(onRead) { + // Create overlay + const overlay = document.createElement('div'); + overlay.id = 'calibrationOverlay'; + overlay.style.position = 'fixed'; + overlay.style.top = '0'; + overlay.style.left = '0'; + overlay.style.width = '100vw'; + overlay.style.height = '100vh'; + overlay.style.backgroundColor = 'rgba(0, 0, 0, 0.9)'; + overlay.style.display = 'flex'; + overlay.style.justifyContent = 'center'; + overlay.style.alignItems = 'center'; + overlay.style.zIndex = '1000'; + + // Create content container + const content = document.createElement('div'); + content.style.textAlign = 'center'; + content.style.color = '#fff'; + content.style.fontFamily = 'Arial, sans-serif'; + + // Create instructional text + const instructionText1 = document.createElement('h3'); + instructionText1.textContent = 'EyeGestures Calibration:'; + instructionText1.style.fontSize = '1.5rem'; + instructionText1.style.marginBottom = '20px'; + + const instructionText2 = document.createElement('p'); + instructionText2.innerHTML = 'To calibrate properly you need to gaze on 25 red circles.'; + instructionText2.style.marginBottom = '20px'; + + const instructionText3 = document.createElement('p'); + instructionText3.innerHTML = 'The blue circle is your estimated gaze. With every calibration point, the tracker will gradually listen more and more to your gaze.'; + instructionText3.style.marginBottom = '20px'; + + // Create button + const button = document.createElement('button'); + button.textContent = 'Continue'; + button.style.padding = '10px 20px'; + button.style.fontSize = '1rem'; + button.style.border = 'none'; + button.style.borderRadius = '5px'; + button.style.backgroundColor = '#5e17eb'; + button.style.color = '#fff'; + button.style.cursor = 'pointer'; + + // Button click event to remove overlay + button.addEventListener('click', () => { + document.body.removeChild(overlay); + onRead(); + }); + // Append elements to content + content.appendChild(instructionText1); + content.appendChild(instructionText2); + content.appendChild(instructionText3); + content.appendChild(button); + + // Append content to overlay + overlay.appendChild(content); + + // Append overlay to body + document.body.appendChild(overlay); + + setTimeout(() => { + document.body.removeChild(overlay); + onRead(); + },15000) + } + + + // Status update function + updateStatus(message) { + document.getElementById('status').textContent = message; + } + + // Error display function + showError(message) { + const errorElement = document.getElementById('error'); + errorElement.textContent = message; + errorElement.style.display = 'block'; + } + + // Function to load script with promise + loadScript(url) { + return new Promise((resolve, reject) => { + const script = document.createElement('script'); + script.src = url; + script.onload = resolve; + script.onerror = reject; + document.head.appendChild(script); + }); + } + + // Main initialization + async init(videoElement_id) { + try { + this.updateViewportSize(); + this.updateStatus('Loading EyeGestures engine...'); + const engineModule = await __loadEngineModule(); + await engineModule.default(); + this.engine = new engineModule.EyeGesturesEngineWasm(this.screen_width, this.screen_height); + + this.updateStatus('Loading MediaPipe library...'); + + // Load MediaPipe library + await this.loadScript('https://cdn.jsdelivr.net/npm/@mediapipe/drawing_utils@0.3/drawing_utils.js'); + await this.loadScript('https://cdn.jsdelivr.net/npm/@mediapipe/face_mesh@0.4.1633559619/face_mesh.js'); + + this.updateStatus('MediaPipe library loaded, initializing...'); + + if (typeof FaceMesh === 'undefined') { + throw new Error('FaceMesh is not defined. Library not loaded correctly.'); + } + + await this.setupMediaPipe(videoElement_id); + } catch (error) { + console.error('Initialization error:', error); + this.showError('Initialization error: ' + error.message); + } + } + + async setupMediaPipe(videoElement_id) { + try { + // Initialize FaceMesh solution + const faceMesh = new FaceMesh({ + locateFile: (file) => { + console.log("Loading file:", file); + return `https://cdn.jsdelivr.net/npm/@mediapipe/face_mesh@0.4.1633559619/${file}`; + } + }); + + // Set options for FaceMesh + faceMesh.setOptions({ + maxNumFaces: 1, + refineLandmarks: true, + minDetectionConfidence: 0.5, + minTrackingConfidence: 0.5 + }); + + // Initialize the FaceMesh instance + await faceMesh.initialize(); + this.updateStatus('FaceMesh initialized successfully'); + + // Set callback for the results + faceMesh.onResults(this.onFaceMeshResults.bind(this)); + + // Access video stream + const stream = await navigator.mediaDevices.getUserMedia({ + video: {} + }); + + const videoElement = document.getElementById(videoElement_id); + videoElement.srcObject = stream; + + // Wait for video to be loaded + videoElement.onloadeddata = () => { + this.updateStatus('Video stream started'); + videoElement.play(); + requestAnimationFrame(processFrame); + }; + + async function processFrame() { + const videoElement = document.getElementById("video"); + if (videoElement.readyState !== videoElement.HAVE_ENOUGH_DATA) { + requestAnimationFrame(processFrame); + return; + } + + const canvas = document.getElementById("output_canvas"); + const ctx = canvas.getContext("2d"); + try { + // ctx.drawImage(videoElement, 0, 0, canvas.width, canvas.height); + ctx.save(); + ctx.scale(-1, 1); // Flip horizontally (invert x-axis) + ctx.translate(-canvas.width, 0); // Adjust translation to ensure the image is drawn correctly + faceMesh.send({ image: videoElement }); + ctx.restore(); + } catch (error) { + console.error('Error processing frame:', error); + showError('Error processing frame: ' + error.message); + } + requestAnimationFrame(processFrame); + } + + } catch (error) { + console.error('Error initializing MediaPipe:', error); + showError('Error initializing MediaPipe: ' + error.message); + } + } + + onFaceMeshResults(results) { + if ((!results.multiFaceLandmarks || !results.multiFaceLandmarks.length) || !this.run) { + return; + } + + if (!this.engine) { + return; + } + + this.updateViewportSize(); + + // Define eye keypoints before using them + const LEFT_EYE_KEYPOINTS = [ + 33, 133, 160, 159, 158, 157, 173, 155, 154, 153, 144, 145, 153, 246, 468 + ]; + const RIGHT_EYE_KEYPOINTS = [ + 362, 263, 387, 386, 385, 384, 398, 382, 381, 380, 374, 373, 374, 466, 473 + ]; + + let landmarks = results.multiFaceLandmarks[0]; + const flattened = new Float64Array(landmarks.flatMap(point => [point.x, point.y])); + const output = this.engine.process(flattened); + const x = output[0]; + const y = output[1]; + const calibrating = output[2] === 1; + const x_calib = output[3]; + const y_calib = output[4]; + + // visual layer + let l_landmarks = LEFT_EYE_KEYPOINTS.map(index => landmarks[index]); + let r_landmarks = RIGHT_EYE_KEYPOINTS.map(index => landmarks[index]); + let scale_x = 1; + let scale_y = 1; + let offset_x = 0; + let offset_y = 0; + let width = x - offset_x; + let height = y - offset_y; + const canvas = document.getElementById("output_canvas"); + const ctx = canvas.getContext("2d"); + ctx.clearRect(0, 0, canvas.width, canvas.height); + let left_eye_coordinates = []; + let right_eye_coordinates = []; + + ctx.fillStyle = '#ff5757'; + l_landmarks.forEach(landmark => { + left_eye_coordinates.push( + [ + (((landmark.x- offset_x)/width) * scale_x ), + (((landmark.y- offset_y)/height) * scale_y ) + ] + ); + ctx.beginPath(); + ctx.arc( + landmark.x * canvas.width, + landmark.y * canvas.height, + 3, // radius + 0, + 2 * Math.PI + ); + ctx.fill(); + }); + + // Draw dots for each landmark + ctx.fillStyle = '#5e17eb'; + r_landmarks.forEach(landmark => { + right_eye_coordinates.push( + [ + (((landmark.x- offset_x)/width) * scale_x ), + (((landmark.y- offset_y)/height) * scale_y ) + ] + ); + ctx.beginPath(); + ctx.arc( + landmark.x * canvas.width, + landmark.y * canvas.height, + 3, // radius + 0, + 2 * Math.PI + ); + ctx.fill(); + }); + + let cursor = document.getElementById("cursor"); + let left = Math.min(Math.max(x,0),this.screen_width) + let top = Math.min(Math.max(y,0),this.screen_height) + cursor.style.left = `${left - 25}px`; + cursor.style.top = `${top - 25}px`; + let calib_cursor = document.getElementById("calib_cursor"); + let calib_left = Math.min(Math.max(x_calib,0),this.screen_width); + let calib_top = Math.min(Math.max(y_calib,0),this.screen_height); + calib_cursor.style.left = `${calib_left - 100}px`; + calib_cursor.style.top = `${calib_top - 100}px`; + + if(!calibrating){ + calib_cursor.style.display = "none"; + }else{ + calib_cursor.style.display = "block"; + } + + this.onGaze([x, y], calibrating); + } + + __run(){ + this.run = true; + } + + start(){ + const logoDivEyeGestures = document.getElementById("logoDivEyeGestures"); + logoDivEyeGestures.style.display = "flex"; + + this.showCalibrationInstructions(this.__run.bind(this)); + + if(!this.__invisible){ + let cursor = document.getElementById("cursor"); + cursor.style.display = "block"; + } + + let calib_cursor = document.getElementById("calib_cursor"); + calib_cursor.style.display = "block"; + // this.run = true; + }; + + invisible() + { + this.__invisible = true; + let cursor = document.getElementById("cursor"); + cursor.style.display = "none"; + } + + visible() + { + this.__invisible = false; + let cursor = document.getElementById("cursor"); + cursor.style.display = "block"; + } + + stop(){ + this.run = false; + console.log("stop"); + }; + + recalibrate(){ + if (this.engine) { + this.engine.recalibrate(); + } + }; + + addPointToCalibration(x, y){ + if (this.engine) { + this.engine.add_calibration_point(x, y); + } + }; +} diff --git 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insertions(+), 53 deletions(-) diff --git a/Makefile b/Makefile index 26ac2e5..4188bdb 100644 --- a/Makefile +++ b/Makefile @@ -1,5 +1,4 @@ -.PHONY: format format_check test min wasm python - +.PHONY: format format_check test min wasm python python-help format: isort eyeGestures @@ -28,11 +27,37 @@ wasm: cd ./engine && wasm-pack build --target web $(MINIFY) $(JS_SRC) $(MINIFY_FLAGS) -o engine/pkg/eyegestures.min.js -# Python buil +# Python build PYTHON = uv run +MATURIN_PYTHON_FLAGS = --release --no-default-features --features python +# Space-separated interpreters used by `make python`. +# Example single: `make python PYTHON_INTERPRETERS="python3.13"` +# Example multi: `make python PYTHON_INTERPRETERS="python3.12 python3.13"` +# Default: use whichever `python` is in PATH. +PYTHON_INTERPRETERS ?= python +# Set to 1 to allow builds for newer CPython versions than PyO3 officially supports. +PYO3_USE_ABI3_FORWARD_COMPATIBILITY ?= 1 + +ifeq ($(OS),Windows_NT) +PYO3_FORWARD_ENV = set PYO3_USE_ABI3_FORWARD_COMPATIBILITY=$(PYO3_USE_ABI3_FORWARD_COMPATIBILITY) && +else +PYO3_FORWARD_ENV = PYO3_USE_ABI3_FORWARD_COMPATIBILITY=$(PYO3_USE_ABI3_FORWARD_COMPATIBILITY) +endif python: - cd ./engine && maturin develop --release --no-default-features --features python + cd ./engine && $(PYO3_FORWARD_ENV) maturin build $(MATURIN_PYTHON_FLAGS) --interpreter $(PYTHON_INTERPRETERS) + +python-help: + @echo "make python" + @echo " - builds wheel using default interpreter: python" + @echo "make python PYTHON_INTERPRETERS=\"python3.13\"" + @echo " - builds wheel for one interpreter" + @echo "make python PYTHON_INTERPRETERS=\"python3.12 python3.13\"" + @echo " - builds wheel files in engine/target/wheels" + @echo "make python PYO3_USE_ABI3_FORWARD_COMPATIBILITY=0" + @echo " - enforces PyO3 max supported Python version" + @echo "Tip (Windows): prefer explicit paths if discovery fails, e.g." + @echo " make python PYTHON_INTERPRETERS=\"C:/Python313/python.exe C:/Python314/python.exe\"" clean: rm -f src/eyegestures.min.js diff --git a/engine/Cargo.lock b/engine/Cargo.lock index 4f97a21..2a6d276 100644 --- a/engine/Cargo.lock +++ b/engine/Cargo.lock @@ -359,7 +359,7 @@ dependencies = [ "serde", "serde_json", "shell-words", - "target-lexicon 0.13.5", + "target-lexicon", "which", ] @@ -1322,15 +1322,6 @@ dependencies = [ "web-time", ] -[[package]] -name = "indoc" -version = "2.0.7" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "79cf5c93f93228cf8efb3ba362535fb11199ac548a09ce117c9b1adc3030d706" -dependencies = [ - "rustversion", -] - [[package]] name = "is-terminal" version = "0.4.17" @@ -1629,7 +1620,7 @@ dependencies = [ "serde_json", "sha2 0.11.0", "tar", - "target-lexicon 0.13.5", + "target-lexicon", "tempfile", "textwrap", "thiserror 2.0.18", @@ -1669,15 +1660,6 @@ version = "0.3.3" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "38d1115007560874e373613744c6fba374c17688327a71c1476d1a5954cc857b" -[[package]] -name = "memoffset" -version = "0.9.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "488016bfae457b036d996092f6cb448677611ce4449e970ceaf42695203f218a" -dependencies = [ - "autocfg", -] - [[package]] name = "mimalloc" version = "0.1.52" @@ -2057,37 +2039,33 @@ dependencies = [ [[package]] name = "pyo3" -version = "0.23.5" +version = "0.28.3" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "7778bffd85cf38175ac1f545509665d0b9b92a198ca7941f131f85f7a4f9a872" +checksum = "91fd8e38a3b50ed1167fb981cd6fd60147e091784c427b8f7183a7ee32c31c12" dependencies = [ - "cfg-if", - "indoc", "libc", - "memoffset", "once_cell", "portable-atomic", "pyo3-build-config", "pyo3-ffi", "pyo3-macros", - "unindent", ] [[package]] name = "pyo3-build-config" -version = "0.23.5" +version = "0.28.3" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "94f6cbe86ef3bf18998d9df6e0f3fc1050a8c5efa409bf712e661a4366e010fb" +checksum = "e368e7ddfdeb98c9bca7f8383be1648fd84ab466bf2bc015e94008db6d35611e" dependencies = [ - "once_cell", - "target-lexicon 0.12.16", + "python3-dll-a", + "target-lexicon", ] [[package]] name = "pyo3-ffi" -version = "0.23.5" +version = "0.28.3" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "e9f1b4c431c0bb1c8fb0a338709859eed0d030ff6daa34368d3b152a63dfdd8d" +checksum = "7f29e10af80b1f7ccaf7f69eace800a03ecd13e883acfacc1e5d0988605f651e" dependencies = [ "libc", "pyo3-build-config", @@ -2107,9 +2085,9 @@ dependencies = [ [[package]] name = "pyo3-macros" -version = "0.23.5" +version = "0.28.3" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "fbc2201328f63c4710f68abdf653c89d8dbc2858b88c5d88b0ff38a75288a9da" +checksum = "df6e520eff47c45997d2fc7dd8214b25dd1310918bbb2642156ef66a67f29813" dependencies = [ "proc-macro2", "pyo3-macros-backend", @@ -2119,9 +2097,9 @@ dependencies = [ [[package]] name = "pyo3-macros-backend" -version = "0.23.5" +version = "0.28.3" source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "fca6726ad0f3da9c9de093d6f116a93c1a38e417ed73bf138472cf4064f72028" +checksum = "c4cdc218d835738f81c2338f822078af45b4afdf8b2e33cbb5916f108b813acb" dependencies = [ "heck", "proc-macro2", @@ -2160,6 +2138,15 @@ dependencies = [ "zip 8.6.0", ] +[[package]] +name = "python3-dll-a" +version = "0.2.15" +source = "registry+https://github.com/rust-lang/crates.io-index" +checksum = "d80ba7540edb18890d444c5aa8e1f1f99b1bdf26fb26ae383135325f4a36042b" +dependencies = [ + "cc", +] + [[package]] name = "quote" version = "1.0.45" @@ -2770,12 +2757,6 @@ dependencies = [ "xattr", ] -[[package]] -name = "target-lexicon" -version = "0.12.16" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "61c41af27dd6d1e27b1b16b489db798443478cef1f06a660c96db617ba5de3b1" - [[package]] name = "target-lexicon" version = "0.13.5" @@ -3161,12 +3142,6 @@ version = "0.2.6" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "ebc1c04c71510c7f702b52b7c350734c9ff1295c464a03335b00bb84fc54f853" -[[package]] -name = "unindent" -version = "0.2.4" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "7264e107f553ccae879d21fbea1d6724ac785e8c3bfc762137959b5802826ef3" - [[package]] name = "unit-prefix" version = "0.5.2" diff --git a/engine/Cargo.toml b/engine/Cargo.toml index e252bec..94bfb07 100644 --- a/engine/Cargo.toml +++ b/engine/Cargo.toml @@ -12,7 +12,9 @@ wasm-bindgen = { version = "0.2", optional = true } web-sys = { version = "0.3.99", features = ["console"], optional = true } # Python dependencies -pyo3 = { version = "0.23", optional = true, features = ["extension-module"] } +# `generate-import-lib` allows Windows builds when maturin uses bundled sysconfig +# for discovered interpreters. +pyo3 = { version = "0.28.2", optional = true, features = ["extension-module", "generate-import-lib"] } maturin = "1.13.3" [features] From b60a14c01996678d4a1f71fb7510f783e1274cd1 Mon Sep 17 00:00:00 2001 From: W4ltz <40773550+PeterWaIIace@users.noreply.github.com> Date: Sat, 30 May 2026 23:21:55 +0200 Subject: [PATCH 04/14] for twine builds --- eyeGestures/__init__.py | 7 ++++-- pybuild.bat | 3 ++- pyproject.toml | 52 ++++++++--------------------------------- uv.lock | 25 +------------------- 4 files changed, 18 insertions(+), 69 deletions(-) diff --git a/eyeGestures/__init__.py b/eyeGestures/__init__.py index 2dcd1bf..30317b7 100644 --- a/eyeGestures/__init__.py +++ b/eyeGestures/__init__.py @@ -11,9 +11,12 @@ from eyeGestures.utils import recoverable try: - from EyegesturesEngine import EyeGesturesEnginePython as RustEyeGesturesEngine + from .EyegesturesEngine import EyeGesturesEnginePython as RustEyeGesturesEngine except ImportError: - RustEyeGesturesEngine = None + try: + from EyegesturesEngine import EyeGesturesEnginePython as RustEyeGesturesEngine + except ImportError: + RustEyeGesturesEngine = None VERSION = "4.0.0" diff --git a/pybuild.bat b/pybuild.bat index 5e8a7c6..f247dd6 100644 --- a/pybuild.bat +++ b/pybuild.bat @@ -2,4 +2,5 @@ @REM pyinstaller ./apps/app_win.spec @REM deactivate; -python -m build +uv run --no-sync --python 3.13 --with build python -m build --wheel +uv run --no-sync --python 3.14 --with build python -m build --wheel \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 259a4a4..bf1c544 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,48 +1,10 @@ [build-system] -requires = ["hatchling"] -build-backend = "hatchling.build" - -[tool.hatch.build.targets.sdist] - -dependencies = [ - "beautifulsoup4>=4.12.3", - "keyboard>=0.13.5", - "matplotlib>=3.8.2", - "mediapipe>=0.10.8", - "numpy>=1.26.4", - "Pillow>=10.3.0", - "PyAutoGUI>=0.9.54", - "pycocotools>=2.0.7", - "pygame>=2.5.2", - "pynput>=1.7.6", - "pytest>=7.4.2", - "scikit_learn>=1.3.2", - "scipy>=1.12.0", - "screeninfo>=0.8.1", - "setuptools>=70.0.0", - "scipy>=1.12.0", - "opencv-contrib-python>=4.9.0.80", - "xlwt>=1.3.0" -] - -include = [ - "eyeGestures/*.py", - "eyeGestures/screenTracker/*.py", - "eyeGestures/eyegestures.py" -] - -exclude = [ - "*.json", - "lab/*", - "app.py", - "test_app.py", - "test.py", - "tools/*" -] +requires = ["maturin>=1.13,<2.0"] +build-backend = "maturin" [project] name = "eyeGestures" -version = "3.2.4" +version = "4.0.0" authors = [ { name="Piotr Walas", email="piotr.walas@eyegestures.com" }, ] @@ -60,12 +22,18 @@ classifiers = [ "Operating System :: OS Independent", ] dependencies = [ - "maturin>=1.13.3", "mediapipe>=0.10.35", "opencv-python>=4.13.0.92", "pygame>=2.6.1", ] +[tool.maturin] +manifest-path = "engine/Cargo.toml" +features = ["python"] +no-default-features = true +python-source = "." +module-name = "eyeGestures.EyegesturesEngine" + [project.urls] Homepage = "https://github.com/NativeSensors/EyeGestures" Issues = "https://github.com/NativeSensors/EyeGestures/Issues" diff --git a/uv.lock b/uv.lock index 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.github/workflows/code_quality.yml | 3 +- Makefile | 17 ++- .../EyegesturesEngine.cp314-win_amd64.pyd | Bin 0 -> 301056 bytes eyeGestures/Legacy/processing.py | 2 +- .../Legacy/screenTracker/screenTracker.py | 12 +- eyeGestures/__init__.py | 14 +- eyeGestures/eye.py | 2 +- eyeGestures/face.py | 6 +- eyeGestures/processing.py | 2 +- pyproject.toml | 3 +- uv.lock | 127 +++++++++++++++--- 11 files changed, 145 insertions(+), 43 deletions(-) create mode 100644 eyeGestures/EyegesturesEngine.cp314-win_amd64.pyd diff --git a/.github/workflows/code_quality.yml b/.github/workflows/code_quality.yml index 4dcb718..4432d3c 100644 --- a/.github/workflows/code_quality.yml +++ b/.github/workflows/code_quality.yml @@ -16,8 +16,7 @@ jobs: python-version: ${{ matrix.python-version }} - name: Install dependencies run: | - python -m pip install --upgrade pip - pip install -r requirements_dev.txt + uv run sync - name: Analysing code with checkers run: | make check diff --git a/Makefile b/Makefile index 4188bdb..4fe0de2 100644 --- a/Makefile +++ b/Makefile @@ -1,17 +1,20 @@ +# Python build +FORMAT_TOOLS = uv run --with isort --with black +CHECK_TOOLS = uv run --with pylint --with flake8 --with mypy --with isort --with black .PHONY: format format_check test min wasm python python-help format: - isort eyeGestures - black eyeGestures + $(FORMAT_TOOLS) python -m isort eyeGestures + $(FORMAT_TOOLS) python -m black eyeGestures format_check: - isort eyeGestures --check - black eyeGestures --check + $(CHECK_TOOLS) python -m isort eyeGestures --check + $(CHECK_TOOLS) python -m black eyeGestures --check check: format_check - pylint eyeGestures - flake8 eyeGestures - mypy eyeGestures + $(CHECK_TOOLS) python -m pylint eyeGestures + $(CHECK_TOOLS) python -m flake8 eyeGestures + $(CHECK_TOOLS) python -m mypy eyeGestures # Web build diff --git a/eyeGestures/EyegesturesEngine.cp314-win_amd64.pyd b/eyeGestures/EyegesturesEngine.cp314-win_amd64.pyd new file mode 100644 index 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It configures and manages entire algorithm""" - - def __init__(self, calibration_radius: int = 1000) -> None: - self.calibration_radius = calibration_radius - - self.clb: Dict[str, Calibrator_v2] = dict() # Calibrator_v2() - self.cap = None - self.calibration: Dict[str, bool] = dict() - self.average_points: Dict[str, npt.NDArray[np.float64]] = dict() - self.filled_points: Dict[str, int] = dict() - self.enable_CN = False - self.calibrate_gestures = False - - self.finder = FaceFinder() - self.face = Face() - - # this has to be contexted - self.prev_timestamp: Dict[str, float] = dict() - self.prev_point: Dict[str, npt.NDArray[np.float64]] = dict() - self.fix: Optional[float] = None - self.velocity_max: Dict[str, int] = dict() - self.velocity_min: Dict[str, int] = dict() - self.fixationTracker: Dict[str, Fixation] = dict() - self.key_points_buffer: Dict[str, List[npt.NDArray[np.float64]]] = dict() - - self.starting_head_position = np.zeros((1, 2)) - self.starting_size = np.zeros((1, 2)) - - def saveModel(self, context: str = "main") -> Optional[bytes]: - if context in self.clb: - return pickle.dumps(self.clb[context]) - return None - - def loadModel(self, model: Any, context: str = "main") -> None: - self.clb[context] = pickle.loads(model) - - def uploadCalibrationMap(self, points: npt.NDArray[np.float64], context: str = "main") -> None: - self.addContext(context) - self.clb[context].updMatrix(np.array(points)) - - def getLandmarks(self, frame: cv2.typing.MatLike) -> Tuple[npt.NDArray[np.float64], bool, cv2.typing.MatLike]: - - frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) - frame = cv2.flip(frame, 1) - - # try: - self.face.process(frame, self.finder.find(frame)) - - face_landmarks = self.face.get_landmarks() - assert face_landmarks is not None - l_eye = self.face.get_left_eye() - r_eye = self.face.get_right_eye() - l_eye_landmarks = l_eye.getLandmarks() - r_eye_landmarks = r_eye.getLandmarks() - blink = l_eye.getBlink() and r_eye.getBlink() - - # get x,y offset - x_offset = np.min(face_landmarks[:, 0]) - y_offset = np.min(face_landmarks[:, 1]) - x_width = np.max(face_landmarks[:, 0]) - x_offset - y_width = np.max(face_landmarks[:, 1]) - y_offset - - # get head position - head_offset = np.zeros((1, 2)) - scale_x = 1 - scale_y = 1 - if np.array_equal(self.starting_head_position, np.zeros((1, 2))): - self.starting_head_position = np.array([[x_offset, y_offset]]) - self.starting_size = np.array([[x_width, y_width]]) - else: - head_offset = np.array([[x_offset, y_offset]]) - self.starting_head_position - scale_x = self.starting_size[0, 0] / x_width - scale_y = self.starting_size[0, 1] / y_width - - # eye_events = np.array([event.blink,event.fixation]).reshape(1, 2) - assert l_eye_landmarks is not None - assert r_eye_landmarks is not None - key_points: npt.NDArray[np.float64] = np.concatenate( - ( - l_eye_landmarks, - r_eye_landmarks, - np.array([[scale_x, scale_y]]), - head_offset, - ) - ) - key_points[:, 0] = key_points[:, 0] - head_offset[:, 0] - key_points[:, 1] = key_points[:, 1] - head_offset[:, 1] - - key_points[:, 0] = key_points[:, 0] * scale_x - key_points[:, 1] = key_points[:, 1] * scale_y - - key_points[-1, 0] = head_offset[0, 0] - key_points[-1, 1] = head_offset[0, 1] - # print(self.starting_size,x_width,y_width) - subframe = frame[ - int(y_offset) : int(y_offset + y_width), - int(x_offset) : int(x_offset + x_width), - ] - return key_points, blink, subframe - - def whichAlgorithm(self, context: str = "main") -> str: - if context in self.clb: - return self.clb[context].whichAlgorithm() - return "None" - - def reset(self, context: str = "main") -> None: - self.filled_points[context] = 0 - if context in self.clb: - self.addContext(context) - - def setFixation(self, fix: float) -> None: - self.fix = fix - - def addContext(self, context: str) -> None: - if context not in self.clb: - self.clb[context] = Calibrator_v2(self.calibration_radius) - self.average_points[context] = np.zeros((20, 2)) - self.filled_points[context] = 0 - self.calibration[context] = False - self.prev_timestamp[context] = time.time() - self.prev_point[context] = np.array((0.0, 0.0)) - self.velocity_max[context] = 0 - self.velocity_min[context] = 100000000 - self.fixationTracker[context] = Fixation(0, 0, 100) - self.key_points_buffer[context] = [] - - @recoverable(ret_error_params=(None, None)) - def step( - self, frame: cv2.typing.MatLike, calibration: bool, width: int, height: int, context: str = "main" - ) -> Tuple[Optional[Gevent], Optional[Cevent]]: - self.addContext(context) - - self.calibration[context] = calibration - - key_points, blink, sub_frame = self.getLandmarks(frame) - - self.key_points_buffer[context].append(key_points) - if len(self.key_points_buffer[context]) > 10: - self.key_points_buffer[context].pop(0) - key_points = low_pass_filter_fourier(key_points, 200) - - y_point = self.clb[context].predict(key_points) - self.average_points[context][1:, :] = self.average_points[context][ - : (self.average_points[context].shape[0] - 1), : - ] - self.average_points[context][0, :] = y_point - - if ( - self.filled_points[context] < self.average_points[context].shape[0] - and (y_point != np.array([0.0, 0.0])).any() - ): - self.filled_points[context] += 1 - if self.filled_points[context] == 0: - self.filled_points[context] = 1 - - averaged_point = np.sum(self.average_points[context][:, :], axis=0) / (self.filled_points[context]) - fixation = self.fixationTracker[context].process(averaged_point[0], averaged_point[1]) - - duration = time.time() - self.prev_timestamp[context] - velocity = abs(averaged_point - self.prev_point[context]) / duration - velocity = np.sqrt(velocity[0] ** 2 + velocity[1] ** 2) - self.prev_point[context] = averaged_point - self.prev_timestamp[context] = time.time() - - self.velocity_max[context] = max(self.velocity_max[context], velocity) - self.velocity_min[context] = min(self.velocity_min[context], velocity) - - saccades = velocity > (self.velocity_max[context]) / 4 - - if self.calibration[context] and ( - self.clb[context].insideClbRadius(averaged_point, width, height) - or self.filled_points[context] < self.average_points[context].shape[0] * 10 - ): - self.clb[context].add(key_points, self.clb[context].getCurrentPoint(width, height)) - else: - self.clb[context].post_fit() - - if self.calibration[context] and self.clb[context].insideAcptcRadius(averaged_point, width, height): - if self.clb[context].isReadyToMove(): - self.clb[context].movePoint() - - gevent = Gevent( - point=averaged_point, - blink=blink, - fixation=fixation, - saccades=saccades, - sub_frame=sub_frame, - ) - cevent = Cevent( - self.clb[context].getCurrentPoint(width, height), - self.clb[context].acceptance_radius, - self.clb[context].calibration_radius, - ) - return (gevent, cevent) - - -class EyeGestures_v2: - """Main class for EyeGesture tracker. It configures and manages entire algorithm""" - - def __init__(self, calibration_radius: int = 1000) -> None: - self.monitor_width = 1 - self.monitor_height = 1 - self.calibration_radius = calibration_radius - - self.clb: Dict[str, Calibrator_v2] = dict() # Calibrator_v2() - self.cap = None - self.gestures = EyeGestures_v1(285, 115, 200, 100) - - self.calibration: Dict[str, bool] = dict() - - self.CN: int = 5 - - self.average_points: Dict[str, npt.NDArray[np.float64]] = dict() - self.filled_points: Dict[str, int] = dict() - self.enable_CN = False - self.calibrate_gestures = False - - self.fix = 0.8 - - def saveModel(self, context: str = "main") -> Optional[bytes]: - if context in self.clb: - return pickle.dumps(self.clb[context]) - return None - - def loadModel(self, model: Any, context: str = "main") -> None: - self.clb[context] = pickle.loads(model) - - def uploadCalibrationMap(self, points: npt.NDArray[np.float64], context="main") -> None: - self.addContext(context) - self.clb[context].updMatrix(np.array(points)) - - def getLandmarks( - self, frame: cv2.typing.MatLike, calibrate: bool = False, context: str = "main" - ) -> Tuple[npt.NDArray[np.float64], npt.NDArray[np.float64], bool, bool, Optional[Cevent]]: - - frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) - frame = cv2.flip(frame, 1) - # frame = cv2.resize(frame, (360, 640)) - - event, cevent = self.gestures.step( - frame, - context, - calibrate, # set calibration - switch to False to stop calibration - self.monitor_width, - self.monitor_height, - 0, - 0, - self.fix, - 100, - ) - - if event is not None or cevent is not None: - cursor_x, cursor_y = event.point[0], event.point[1] - l_eye_landmarks = event.l_eye.getLandmarks() - r_eye_landmarks = event.r_eye.getLandmarks() - - cursors = np.array([cursor_x, cursor_y]).reshape(1, 2) - eye_events = np.array([event.blink, event.fixation]).reshape(1, 2) - key_points = np.concatenate((cursors, l_eye_landmarks, r_eye_landmarks, eye_events)) - return ( - np.array((cursor_x, cursor_y)), - key_points, - event.blink, - event.fixation, - cevent, - ) - return np.array((0.0, 0.0)), np.array([]), False, False, None - - def whichAlgorithm(self, context: str = "main") -> str: - if context in self.clb: - return self.clb[context].whichAlgorithm() - return "None" - - def setClassicImpact(self, impact: int) -> None: - self.CN = impact - - def reset(self, context: str = "main") -> None: - self.filled_points[context] = 0 - if context in self.clb: - self.addContext(context) - - def setFixation(self, fix: float) -> None: - self.fix = fix - - def setClassicalImpact(self, CN: int) -> None: - self.CN = CN - - def enableCNCalib(self) -> None: - self.enable_CN = True - - def disableCNCalib(self) -> None: - self.enable_CN = False - - def addContext(self, context: str) -> None: - if context not in self.clb: - self.clb[context] = Calibrator_v2(self.calibration_radius) - self.average_points[context] = np.zeros((20, 2)) - self.filled_points[context] = 0 - self.calibration[context] = False - - @recoverable(ret_error_params=(None, None)) - def step( - self, frame: cv2.typing.MatLike, calibration: bool, width: int, height: int, context: str = "main" - ) -> Tuple[Optional[Gevent], Optional[Cevent]]: - self.addContext(context) - - self.calibration[context] = calibration - self.monitor_width = width - self.monitor_height = height - - classic_point, key_points, blink, fixation, cevent = self.getLandmarks( - frame, self.calibrate_gestures and self.enable_CN, context=context - ) - - margin = 10 - if cevent is None: - raise ValueError("cevent must not be None") - if (classic_point[0] <= margin) and self.calibration[context]: - self.calibrate_gestures = cevent.calibration - elif (classic_point[0] >= width - margin) and self.calibration[context]: - self.calibrate_gestures = cevent.calibration - elif (cevent.point[1] <= margin) and self.calibration[context]: - self.calibrate_gestures = cevent.calibration - elif (classic_point[1] >= height - margin) and self.calibration[context]: - self.calibrate_gestures = cevent.calibration - else: - self.calibrate_gestures = False - - y_point = self.clb[context].predict(key_points) - self.average_points[context][1:, :] = self.average_points[context][ - : (self.average_points[context].shape[0] - 1), : - ] - if fixation <= self.fix: - self.average_points[context][0, :] = y_point - - if ( - self.filled_points[context] < self.average_points[context].shape[0] - and (y_point != np.array([0.0, 0.0])).any() - ): - self.filled_points[context] += 1 - averaged_point = (np.sum(self.average_points[context][:, :], axis=0) + (classic_point * self.CN)) / ( - self.filled_points[context] + self.CN - ) - - if self.calibration[context] and ( - self.clb[context].insideClbRadius(averaged_point, width, height) - or self.filled_points[context] < self.average_points[context].shape[0] * 10 - ): - self.clb[context].add(key_points, self.clb[context].getCurrentPoint(width, height)) - else: - self.clb[context].post_fit() - - if self.calibration[context] and self.clb[context].insideAcptcRadius(averaged_point, width, height): - if self.clb[context].isReadyToMove(): - self.clb[context].movePoint() - - gevent = Gevent(averaged_point, blink, fixation) - cevent = Cevent( - self.clb[context].getCurrentPoint(width, height), - self.clb[context].acceptance_radius, - self.clb[context].calibration_radius, - ) - return (gevent, cevent) - - -class EyeGestures_v1: - """Main class for EyeGesture tracker. It configures and manages entier algorithm""" - - def __init__(self, roi_x=225, roi_y=105, roi_width=80, roi_height=15): - - screen_width = 500 - screen_height = 500 - height = 250 - width = 250 - - roi_x = roi_x % screen_width - roi_y = roi_y % screen_height - roi_width = roi_width % screen_width - roi_height = roi_height % screen_height - - self.screen_width = screen_width - self.screen_height = screen_height - - self.screen = dp.Screen(screen_width, screen_height) - - self.gaze = GazeTracker( - screen_width, - screen_height, - width, - height, - roi_x, - roi_y, - roi_width, - roi_height, - ) - - self.calibrators = dict() - self.calibrate = False - - def getFeatures(self, image): - """[NOT RECOMMENDED] Function allowing for extraction of gaze features from image""" - - return self.gaze.getFeatures(image) - - # @timeit - # 0.011 - 0.015 s for execution - def step( - self, - image, - context, - calibration, - display_width, - display_height, - display_offset_x=0, - display_offset_y=0, - fixation_freeze=0.7, - freeze_radius=20, - offset_x=0, - offset_y=0, - ): - """Function performing estimation and returning event object""" - - display = dp.Display(display_width, display_height, display_offset_x, display_offset_y) - - # allow for random recalibration shot - event = self.gaze.estimate( - image, - display, - context, - calibration, - fixation_freeze, - freeze_radius, - offset_x, - offset_y, - ) - cevent = None - - if event is not None: - cursor_x, cursor_y = event.point[0], event.point[1] - if context in self.calibrators: - self.calibrate = self.calibrators[context].calibrate(cursor_x, cursor_y, event.fixation) - else: - self.calibrators[context] = Calibrator_v1(display_width, display_height, cursor_x, cursor_y) - self.calibrate = self.calibrators[context].calibrate(cursor_x, cursor_y, event.fixation) - - cpoint = self.calibrators[context].get_current_point() - calibration = self.calibrate and cpoint != (0, 0) - cevent = Cevent(cpoint, 100, 100, calibration) - - return (event, cevent) diff --git a/eyeGestures/Legacy/calibration_v1.py b/eyeGestures/Legacy/calibration_v1.py deleted file mode 100644 index 12d037a..0000000 --- a/eyeGestures/Legacy/calibration_v1.py +++ /dev/null @@ -1,165 +0,0 @@ -import time - - -class CalibrationPositions: - LEFT = "left" - RIGHT = "right" - TOP = "top" - BOTTOM = "bottom" - - -class Calibrator: - - def __init__(self, width, height, start_x, start_y): - self.width = width - self.height = height - self.start_x = start_x - self.start_y = start_y - - self.prev_x = 0 - self.prev_y = 0 - - self.calibration_margin = 200 - self.calibration_steps = [] - self.__set_order() - - self.calibrate_left = False - self.calibrate_right = False - self.calibrate_top = False - self.calibrate_bottom = False - - self.calibration = False - self.drawn = False - self.prev_point = None - self.last_calib = time.time() - - def __add_left(self): - self.calibration_steps.append(CalibrationPositions.LEFT) - return self - - def __add_right(self): - self.calibration_steps.append(CalibrationPositions.RIGHT) - return self - - def __add_top(self): - self.calibration_steps.append(CalibrationPositions.TOP) - return self - - def __add_bottom(self): - self.calibration_steps.append(CalibrationPositions.BOTTOM) - return self - - def __set_order(self): - if self.start_x < self.width / 2 and CalibrationPositions.LEFT not in self.calibration_steps: - self.__add_left().__add_right() - elif self.start_x > self.width / 2 and CalibrationPositions.RIGHT not in self.calibration_steps: - self.__add_right().__add_left() - - if self.start_y < self.height / 2 and CalibrationPositions.TOP not in self.calibration_steps: - self.__add_top().__add_bottom() - elif self.start_y > self.height / 2 and CalibrationPositions.BOTTOM not in self.calibration_steps: - self.__add_bottom().__add_top() - - def add_recalibrate(self, recalibrate_step): - - if recalibrate_step not in self.calibration_steps: - self.calibration_steps.append(recalibrate_step) - - def get_current_point(self): - if len(self.calibration_steps) > 0: - if CalibrationPositions.LEFT == self.calibration_steps[0]: - return (self.calibration_margin, int(self.height / 2)) - if CalibrationPositions.RIGHT == self.calibration_steps[0]: - return (self.width - self.calibration_margin, int(self.height / 2)) - if CalibrationPositions.TOP == self.calibration_steps[0]: - return (int(self.width / 2), self.calibration_margin) - if CalibrationPositions.BOTTOM == self.calibration_steps[0]: - return (int(self.width / 2), self.height - self.calibration_margin) - return (0, 0) - - def calibrate(self, x, y, fix): - - if abs(x - self.width / 2) > 150 and self.prev_point in [ - CalibrationPositions.TOP, - CalibrationPositions.BOTTOM, - ]: - if x - self.width / 2 < 0: - self.add_recalibrate(CalibrationPositions.LEFT) - else: - self.add_recalibrate(CalibrationPositions.RIGHT) - - if abs(y - self.height / 2) > 150 and self.prev_point in [ - CalibrationPositions.LEFT, - CalibrationPositions.RIGHT, - ]: - if y - self.height / 2 < 0: - self.add_recalibrate(CalibrationPositions.TOP) - else: - self.add_recalibrate(CalibrationPositions.BOTTOM) - - self.prev_y = y - self.prev_x = x - if len(self.calibration_steps) <= 0: - self.prev_point = None - return False - - fixation_thresh = 0.3 - if fix > fixation_thresh and (time.time() - self.last_calib) > 5.0: - if CalibrationPositions.LEFT == self.calibration_steps[0] and x < self.calibration_margin: - if CalibrationPositions.LEFT in self.calibration_steps: - self.calibration_steps.remove(CalibrationPositions.LEFT) - self.prev_point = CalibrationPositions.LEFT - self.drawn = False - self.last_calib = time.time() - return True - if CalibrationPositions.RIGHT == self.calibration_steps[0] and x > self.width - self.calibration_margin: - if CalibrationPositions.RIGHT in self.calibration_steps: - self.calibration_steps.remove(CalibrationPositions.RIGHT) - self.prev_point = CalibrationPositions.RIGHT - self.drawn = False - self.last_calib = time.time() - return True - if CalibrationPositions.TOP == self.calibration_steps[0] and y < self.calibration_margin: - if CalibrationPositions.TOP in self.calibration_steps: - self.calibration_steps.remove(CalibrationPositions.TOP) - self.prev_point = CalibrationPositions.TOP - self.drawn = False - self.last_calib = time.time() - return True - if CalibrationPositions.BOTTOM == self.calibration_steps[0] and y > self.height - self.calibration_margin: - if CalibrationPositions.BOTTOM in self.calibration_steps: - self.calibration_steps.remove(CalibrationPositions.BOTTOM) - self.prev_point = CalibrationPositions.BOTTOM - self.drawn = False - self.last_calib = time.time() - return True - # TODO: somewhere here is bug breaking entire program - self.last_calib = time.time() - self.drawn = False - self.prev_point = None - - if self.calibration_steps[0] in [ - CalibrationPositions.RIGHT, - CalibrationPositions.LEFT, - ]: - if x < self.width / 2: - if CalibrationPositions.RIGHT in self.calibration_steps: - self.calibration_steps.remove(CalibrationPositions.RIGHT) - self.calibration_steps.insert(0, CalibrationPositions.RIGHT) - else: - if CalibrationPositions.LEFT in self.calibration_steps: - self.calibration_steps.remove(CalibrationPositions.LEFT) - self.calibration_steps.insert(0, CalibrationPositions.LEFT) - return True - - if self.calibration_steps[0] is CalibrationPositions.TOP: - self.calibration_steps.insert(0, CalibrationPositions.BOTTOM) - return True - self.calibration_steps.insert(0, CalibrationPositions.TOP) - return True - - self.prev_point = None - return False - - def calibrated(self): - return len(self.calibration_steps) <= 0 diff --git a/eyeGestures/Legacy/calibration_v2.py b/eyeGestures/Legacy/calibration_v2.py deleted file mode 100644 index e5ec973..0000000 --- a/eyeGestures/Legacy/calibration_v2.py +++ /dev/null @@ -1,201 +0,0 @@ -import threading -from typing import List - -import numpy as np -import numpy.typing as npt -import sklearn.linear_model as scireg - - -def euclidean_distance(point1: npt.NDArray[np.float64], point2: npt.NDArray[np.float64]) -> np.float64: - return np.linalg.norm(point1 - point2) - - -class Calibrator: - - PRECISION_LIMIT = 50 - PRECISION_STEP = 10 - ACCEPTANCE_RADIUS = 500 - CALIBRATION_RADIUS = 1000 - - def __init__(self, calibration_radius: int = 1000) -> None: - self.X: List[npt.NDArray[np.float64]] = [] - self.Y_y: List[npt.NDArray[np.float64]] = [] - self.Y_x: List[npt.NDArray[np.float64]] = [] - self.__tmp_X: List[npt.NDArray[np.float64]] = [] - self.__tmp_Y_y: List[npt.NDArray[np.float64]] = [] - self.__tmp_Y_x: List[npt.NDArray[np.float64]] = [] - self.reg = None - self.reg_x = scireg.Ridge(alpha=0.5) - self.reg_y = scireg.Ridge(alpha=0.5) - self.current_algorithm = "Ridge" - self.fitted = False - self.cv_not_set = True - self.fixations_x = None - self.fixations_y = None - - self.matrix = CalibrationMatrix() - - self.precision_limit = self.PRECISION_LIMIT - self.precision_step = self.PRECISION_STEP - self.acceptance_radius = int(calibration_radius / 2) - self.calibration_radius = int(calibration_radius) - - self.lock = threading.Lock() - self.calcualtion_coroutine = threading.Thread(target=self.__async_post_fit) - self.fit_coroutines: List[threading.Thread] = [] - - def __launch_fit(self) -> None: - coroutine = threading.Thread(target=self.__async_fit) - self.fit_coroutines.append(coroutine) - coroutine.start() - self.__join_finished() - - def __join_finished(self) -> None: - for coroutine in self.fit_coroutines: - if not coroutine.is_alive(): - coroutine.join() - - def add(self, x: npt.NDArray[np.float64], y: npt.NDArray[np.float64]) -> None: - with self.lock: - self.__tmp_X.append(x.flatten()) - self.__tmp_Y_y.append(y[1]) - self.__tmp_Y_x.append(y[0]) - self.__launch_fit() - - # This coroutine helps to asynchronously recalculate results - def __async_fit(self) -> None: - try: - with self.lock: - __fit_tmp_X = np.array(self.__tmp_X + self.X, dtype=object) - __fit_tmp_Y_y = np.array(self.__tmp_Y_y + self.Y_y) - __fit_tmp_Y_x = np.array(self.__tmp_Y_x + self.Y_x) - self.reg_x.fit(__fit_tmp_X, __fit_tmp_Y_x) - self.reg_y.fit(__fit_tmp_X, __fit_tmp_Y_y) - self.fitted = True - except Exception as e: - print(f"Exception as {e}") - - # This coroutine helps to asynchronously recalculate results - def __async_post_fit(self) -> None: - try: - tmp_fixations_x = scireg.LassoCV(cv=50, max_iter=10000) - tmp_fixations_y = scireg.LassoCV(cv=50, max_iter=10000) - - __tmp_X = np.array(self.X) - __tmp_Y_y = np.array(self.Y_y) - __tmp_Y_x = np.array(self.Y_x) - - tmp_fixations_x.fit(__tmp_X, __tmp_Y_x) - tmp_fixations_y.fit(__tmp_X, __tmp_Y_y) - - with self.lock: - self.fixations_x = tmp_fixations_x - self.fixations_y = tmp_fixations_y - self.fitted = True - self.current_algorithm = "LassoCV" - except Exception as e: - print(f"Exception as {e}") - self.cv_not_set = True - - def post_fit(self) -> None: - if self.cv_not_set: - # self.calcualtion_coroutine.start() - self.cv_not_set = False - - def whichAlgorithm(self) -> str: - with self.lock: - return self.current_algorithm - - def predict(self, x: npt.NDArray[np.float64]) -> npt.NDArray[np.float64]: - with self.lock: - if self.fitted: - x = x.flatten() - x = x.reshape(1, -1) - y_x = self.reg_x.predict(x)[0] - y_y = self.reg_y.predict(x)[0] - return np.array([y_x, y_y]) - return np.array([0.0, 0.0]) - - def movePoint(self) -> None: - with self.lock: - self.X = self.X + self.__tmp_X - self.Y_y = self.Y_y + self.__tmp_Y_y - self.Y_x = self.Y_x + self.__tmp_Y_x - self.matrix.movePoint() - self.__tmp_X = [] - self.__tmp_Y_y = [] - self.__tmp_Y_x = [] - - def isReadyToMove(self) -> int: - return len(self.__tmp_X) > 30 # magic number - collect 30 points - - def getCurrentPoint(self, width: int, heigth: int) -> npt.NDArray[np.float64]: - return self.matrix.getCurrentPoint(width, heigth) - - def updMatrix(self, points: npt.NDArray[np.float64]) -> None: - self.matrix.updMatrix(points) - - def unfit(self) -> None: - self.acceptance_radius = self.ACCEPTANCE_RADIUS - self.calibration_radius = self.CALIBRATION_RADIUS - self.fitted = False - - def increase_precision(self) -> None: - if self.acceptance_radius > self.precision_limit: - self.acceptance_radius -= self.precision_step - if self.calibration_radius > self.precision_limit and self.acceptance_radius < self.calibration_radius: - self.calibration_radius -= self.precision_step - - def insideClbRadius(self, point: npt.NDArray[np.float64], width: int, height: int) -> np.bool_: - return euclidean_distance(point, self.getCurrentPoint(width, height)) < self.calibration_radius - - def insideAcptcRadius(self, point: npt.NDArray[np.float64], width: int, height: int) -> np.bool_: - return euclidean_distance(point, self.getCurrentPoint(width, height)) < self.acceptance_radius - - -class CalibrationMatrix: - - def __init__(self) -> None: - - self.iterator = 0 - self.points: npt.NDArray[np.float64] = np.array( - [ - [1, 0.5], - [0.75, 0.5], - [0.5, 0.5], - [0.25, 0.5], - [0.0, 0.5], - [1.0, 1.0], - [0.75, 1.0], - [0.5, 1.0], - [0.25, 1.0], - [0.0, 1.0], - [1.0, 0.0], - [0.75, 0.0], - [0.5, 0.0], - [0.25, 0.0], - [0.0, 0.0], - [0.0, 0.75], - [0.75, 0.75], - [0.5, 0.75], - [0.25, 0.75], - [0.0, 0.75], - [1.0, 0.25], - [0.75, 0.25], - [0.5, 0.25], - [0.25, 0.25], - [0.0, 0.25], - ] - ) - - def updMatrix(self, points: npt.NDArray[np.float64]) -> None: - self.points = points - self.iterator = 0 - - def movePoint(self) -> None: - self.iterator += 1 - self.iterator %= len(self.points) - - def getCurrentPoint(self, width: int = 1, height: int = 1) -> npt.NDArray[np.float64]: - it = self.iterator - return np.array([self.points[it, 0] * width, self.points[it, 1] * height]) diff --git a/eyeGestures/Legacy/eye.py b/eyeGestures/Legacy/eye.py deleted file mode 100644 index 184c4ee..0000000 --- a/eyeGestures/Legacy/eye.py +++ /dev/null @@ -1,188 +0,0 @@ -"""Module providing a extraction of eye from face object.""" - -from typing import Optional, Tuple - -import cv2 -import mediapipe as mp -import numpy as np -import numpy.typing as npt - -from eyeGestures.utils import Buffor - - -class Eye: - """Class storing data related and representing a eye""" - - LEFT_EYE_KEYPOINTS = np.array(list(mp.solutions.face_mesh.FACEMESH_LEFT_EYE))[:, 0] - RIGHT_EYE_KEYPOINTS = np.array(list(mp.solutions.face_mesh.FACEMESH_RIGHT_EYE))[:, 0] - LEFT_EYE_PUPIL_KEYPOINT = [473] - RIGHT_EYE_PUPIL_KEYPOINT = [468] - - # LEFT_EYE_KEYPOINTS = [36, 37, 38, 39, 40, 41] # keypoint indices for left eye - # RIGHT_EYE_KEYPOINTS = [42, 43, 44, 45, 46, 47] # keypoint indices for right eye - - scale = (150, 100) - - def __init__(self, side: int) -> None: - - # check if eye is left or right - if side == 1: - self.side = "right" - self.pupil_index = self.RIGHT_EYE_PUPIL_KEYPOINT - elif side == 0: - self.side = "left" - self.pupil_index = self.LEFT_EYE_PUPIL_KEYPOINT - - self.x = 0 - self.y = 0 - self.width = 0 - self.height = 0 - self.center_x = 0 - self.center_y = 0 - self.image: Optional[cv2.typing.MatLike] = None - self.pupil: Optional[npt.NDArray[np.float64]] = None - self.offset: Optional[npt.NDArray[np.float64]] = None - self.region: Optional[npt.NDArray[np.float64]] = None - self.cut_image: Optional[cv2.typing.MatLike] = None - self.landmarks: Optional[npt.NDArray[np.float64]] = None - - # self._process(self.image,self.region) - - def update( - self, image: cv2.typing.MatLike, landmarks: npt.NDArray[np.float64], offset: npt.NDArray[np.float64] - ) -> None: - """function updating data stored inside eye object""" - - self.image = image - self.offset = offset - self.landmarks = landmarks - # check if eye is left or right - if self.side == "right": - self.region = np.array(landmarks[self.RIGHT_EYE_KEYPOINTS]) - # landmarks[self.RIGHT_EYE_KEYPOINTS], dtype=np.int32) - - elif self.side == "left": - self.region = np.array(landmarks[self.LEFT_EYE_KEYPOINTS]) - # landmarks[self.LEFT_EYE_KEYPOINTS], dtype=np.int32) - - self.pupil = landmarks[self.pupil_index][0] - assert self.region is not None - self._process(self.image, self.region) - - def getCenter(self) -> Tuple[float, float]: - """function returning center of eye""" - - return (float(self.center_x), float(self.center_y)) - - def getPos(self) -> Tuple[int, int]: - """function returning position of eye in the image""" - - return (int(self.x), int(self.y)) - - def getPupil(self) -> Optional[npt.NDArray[np.float64]]: - """function returning pupil object""" - - # return self.pupil.getCoords() - return self.pupil - - def getBlink(self) -> bool: - """function returning blink event""" - # return self.pupil.getCoords() - return (self.height) <= 3 # 2x margin - - def getImage(self) -> Optional[cv2.typing.MatLike]: - """function returning image of the eye cut from the entire face image""" - - # TODO: draw additional parameters - return self.cut_image - - def getGaze(self, gaze_buffor: Buffor, y_correction: int = 0, x_correction: int = 0) -> npt.NDArray[np.float64]: - """function returning gaze position""" - - # pupilCoords = self.pupil.getCoords() - assert self.offset is not None - assert self.region is not None - assert self.pupil is not None - center = np.array((self.center_x, self.center_y)) - self.offset - - region_corrected = self.region - self.offset - pupil_corrected = self.pupil - self.offset - - vectors = region_corrected - center - pupil = pupil_corrected - center - - vectors = vectors - pupil - gaze_vector = np.zeros((2)) - - gaze_vector[1] = np.sum(vectors, axis=0)[1] * 10 - y_correction - gaze_vector[0] = -np.sum(vectors, axis=0)[0] * 10 - x_correction - - # print("gaze_vector: ",gaze_vector) - gaze_buffor.add(gaze_vector) - return gaze_buffor.getAvg() - - def getOpenness(self) -> float: - """function returning eye openness""" - - return self.height / 2 - - def getLandmarks(self) -> Optional[npt.NDArray[np.float64]]: - """function returning eye landmarks""" - - return self.region - - def getBoundingBox(self) -> Tuple[int, int, int, int]: - return (int(self.x), int(self.y), int(self.width), int(self.height)) - - def _process(self, image: cv2.typing.MatLike, region: npt.NDArray[np.float64]) -> None: - h, w, _ = image.shape - - mask = np.full((h, w), 0, dtype=np.uint8) - background = np.zeros((h, w), dtype=np.uint8) - - region_int = np.array(region, dtype=np.int32) - cv2.fillPoly(mask, [region_int], (0,)) - - masked_image = cv2.bitwise_not(background, cv2.cvtColor(image.copy(), cv2.COLOR_BGR2GRAY), mask=mask) - - margin = 2 - min_x = np.min(region_int[:, 0]) - margin - max_x = np.max(region_int[:, 0]) + margin - min_y = np.min(region_int[:, 1]) - margin - max_y = np.max(region_int[:, 1]) + margin - - self.x = min_x - self.y = min_y - - self.width = np.max(region_int[:, 0]) - np.min(region_int[:, 0]) - self.height = np.max(region_int[:, 1]) - np.min(region_int[:, 1]) - - self.center_x = (min_x + max_x) / 2 - self.center_y = (min_y + max_y) / 2 - - # HACKETY_HACK: - assert self.pupil is not None - self.pupil[1] = np.min(region[:, 1]) - - self.cut_image = masked_image[min_y:max_y, min_x:max_x] - # print(f"here: {self.cut_image.shape,min_y,max_y,min_x,max_x}") - # self.cut_image = cv2.cvtColor(self.cut_image, cv2.COLOR_GRAY2BGR) - - # for point in self.region: - # point = point - (min_x, min_y) - # cv2.circle(self.cut_image, point.astype( - # int), 1, (255, 0, 0, 150), 1) - - # pupil = self.pupil - (min_x, min_y) - - # cv2.circle(self.cut_image, pupil.astype(int), 1, (0, 255, 0, 150), 1) - - # self.cut_image = cv2.resize(self.cut_image, self.scale) - - # save cut_image to buffor and get avg from previous buffors - # self.eyeBuffer.add(self.cut_image) - # self.cut_image = np.array(self.eyeBuffer.getAvg(), dtype=np.uint8) - - # LEGACY - # org_scale = (max_x - min_x,max_y - min_y) - # self.pupil = pupil.Pupil(self.cut_image, min_x, min_y, self.scale, org_scale) diff --git a/eyeGestures/Legacy/face.py b/eyeGestures/Legacy/face.py deleted file mode 100644 index 42575eb..0000000 --- a/eyeGestures/Legacy/face.py +++ /dev/null @@ -1,106 +0,0 @@ -"""Module providing finding and extraction of face from image.""" - -from typing import Any, NamedTuple, Optional, Tuple - -import cv2 -import mediapipe as mp -import numpy as np -import numpy.typing as npt - -from eyeGestures.eye import Eye - - -class FaceFinder: - """Class helping finding face""" - - def __init__(self) -> None: - self.mp_face_mesh = mp.solutions.face_mesh.FaceMesh( - refine_landmarks=True, - static_image_mode=False, - min_detection_confidence=0.5, - min_tracking_confidence=0.5, - ) - - def find(self, image: cv2.typing.MatLike) -> Optional[Any]: - """Find face mesh""" - - assert len(image.shape) > 2 - - try: - face_mesh = self.mp_face_mesh.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) - if face_mesh.multi_face_landmarks is None: - return None - - return face_mesh - except Exception as e: - print(f"Exception in FaceFinder: {e}") - return None - - -class Face: - """Class keeping and processing face landmarks""" - - def __init__(self) -> None: - self.eye_left = Eye(0) - self.eye_right = Eye(1) - self.landmarks: Optional[npt.NDArray[np.float64]] = None - self.image_h: Optional[int] = None - self.image_w: Optional[int] = None - self.face: Optional[NamedTuple] = None - - def get_bounding_box(self) -> Tuple[int, int, int, int]: - """Get bounding box of face""" - if self.landmarks is not None: - margin = 0 - min_x = np.min(self.landmarks[:, 0]) - margin - max_x = np.max(self.landmarks[:, 0]) + margin - min_y = np.min(self.landmarks[:, 1]) - margin - max_y = np.max(self.landmarks[:, 1]) + margin - - width = int((max_x - min_x)) - height = int((max_y - min_y)) - x = int(min_x) - y = int(min_y) - return (x, y, width, height) - return (0, 0, 0, 0) - - def get_left_eye(self) -> Eye: - """Get left eye""" - return self.eye_left - - def get_right_eye(self) -> Eye: - """Get right eye""" - return self.eye_right - - def get_landmarks(self) -> Optional[npt.NDArray[np.float64]]: - """Get landmarks""" - return self.landmarks - - def _landmarks(self, face: Any) -> npt.NDArray[np.float64]: - - __complex_landmark_points = face.multi_face_landmarks - __complex_landmarks = __complex_landmark_points[0].landmark - - __face_landmarks = [] - for landmark in __complex_landmarks: - __face_landmarks.append((landmark.x * self.image_w, landmark.y * self.image_h)) - - return np.array(__face_landmarks) - - def process(self, image: cv2.typing.MatLike, face: Optional[Any]) -> None: - """Process face landmarks on image""" - # try: - self.face = face - self.image_h, self.image_w, _ = image.shape - self.landmarks = self._landmarks(self.face) - # self.nose = nose.Nose(image,self.landmarks,self.getBoundingBox()) - - x, y, _, _ = self.get_bounding_box() - offset = np.array((x, y)) - # offset = offset - self.nose.getHeadTiltOffset() - - self.eye_left.update(image, self.landmarks, offset) - self.eye_right.update(image, self.landmarks, offset) - # except Exception as e: - # print(f"Caught exception: {e}") - # return None diff --git a/eyeGestures/Legacy/gazeEstimator.py b/eyeGestures/Legacy/gazeEstimator.py deleted file mode 100644 index fb31df9..0000000 --- a/eyeGestures/Legacy/gazeEstimator.py +++ /dev/null @@ -1,273 +0,0 @@ -import numpy as np - -import eyeGestures.screenTracker.dataPoints as dp -from eyeGestures.face import Face, FaceFinder -from eyeGestures.Fixation import Fixation -from eyeGestures.gaze_contexter import GazeContext -from eyeGestures.gevent import Gevent -from eyeGestures.processing import EyeProcessor -from eyeGestures.screenTracker.screenTracker import ScreenManager -from eyeGestures.utils import Buffor - - -def isInside(circle_x, circle_y, r, x, y): - """Function checking if points are inside the circle""" - - # Compare radius of circle - # with distance of its center - # from given point - if (x - circle_x) * (x - circle_x) + (y - circle_y) * (y - circle_y) <= r * r: - return True - return False - - -class GazeTracker: - """Class processing images and returning gaze tracker""" - - N_FEATURES = 16 - - def __init__( - self, - screen_width, - screen_heigth, - eye_screen_w, - eye_screen_h, - roi_x, - roi_y, - roi_width, - roi_height, - ): - self.screen = dp.Screen(screen_width, screen_heigth) - - self.offset_x = 0 - self.offset_y = 0 - - self.roi_x = roi_x - self.roi_y = roi_y - self.roi_width = roi_width - self.roi_height = roi_height - - self.eye_screen_w = eye_screen_w - self.eye_screen_h = eye_screen_h - - self.eyeProcessorLeft = EyeProcessor(eye_screen_w, eye_screen_h) - self.eyeProcessorRight = EyeProcessor(eye_screen_w, eye_screen_h) - - self.screen_man = ScreenManager() - - self.finder = FaceFinder() - self.face = Face() - - # those are used for analysis - - self.point_screen = [0.0, 0.0] - self.freezed_point = [0.0, 0.0] - - self.GContext = GazeContext() - - # self.calibration = False - - def __gaze_intersection(self, l_eye, r_eye, l_buff, r_buff): - l_pupil = l_eye.getPupil() - l_gaze = l_eye.getGaze(l_buff) - - r_pupil = r_eye.getPupil() - r_gaze = r_eye.getGaze(r_buff) - - l_end = l_gaze + l_pupil - r_end = r_gaze + r_pupil - - l_m = (l_end[1] - l_pupil[1]) / (l_end[0] - l_pupil[0]) - r_m = (r_end[1] - r_pupil[1]) / (r_end[0] - r_pupil[0]) - - l_b = l_end[1] - l_m * l_end[0] - r_b = r_end[1] - r_m * r_end[0] - - i_x = (r_b - l_b) / (l_m - r_m) - i_y = r_m * i_x + r_b - return (i_x, i_y) - - def __pupil(self, eye, eyeProcessor, intersection_x, buffor): - eyeProcessor.append(eye.getPupil(), eye.getLandmarks(), buffor) - point = eyeProcessor.getAvgPupil(self.eye_screen_w, self.eye_screen_h, buffor) - point = np.array((int(intersection_x), point[1])) - - return point, buffor - - def estimate( - self, - image, - display, - context_id, - calibration, - fixation_freeze=0.7, - freeze_radius=20, - offset_x=0, - offset_y=0, - ): - """Function estimating gaze and returning gaze event based on image""" - - event = None - face_mesh = self.getFeatures(image) - if not face_mesh: - return None - - if face_mesh.multi_face_landmarks: - self.face.process(image, face_mesh) - - context = self.GContext.get( - context_id, - display, - face=None, - roi=dp.ScreenROI(self.roi_x, self.roi_y, self.roi_width, self.roi_height), - edges=dp.ScreenROI(285, 105, 80, 15), - cluster_boundaries=dp.ScreenROI(225, 125, 20, 20), - buffor=Buffor(200), - l_pupil=Buffor(20), - r_pupil=Buffor(20), - l_eye_buff=Buffor(20), - r_eye_buff=Buffor(20), - fixation=Fixation(0, 0, 100), - ) - context.calibration = calibration - - if self.face is not None: - if self.face.landmarks is None: - return event - - if context.face is None: - x, y, w, h = self.face.get_bounding_box() - i_w = self.face.image_w - i_h = self.face.image_h - context.face = (x, y, w, h, i_w, i_h) - - l_eye = self.face.get_left_eye() - r_eye = self.face.get_right_eye() - - # TODO: check what happens here before with l_pupil - intersection_x, _ = self.__gaze_intersection(l_eye, r_eye, context.l_eye_buff, context.r_eye_buff) - l_point, l_buffor = self.__pupil(l_eye, self.eyeProcessorLeft, intersection_x, context.l_pupil) - r_point, r_buffor = self.__pupil(r_eye, self.eyeProcessorRight, intersection_x, context.r_pupil) - - context.l_pupil = l_buffor - context.r_pupil = r_buffor - - compound_point = np.array(((l_point + r_point) / 2), dtype=np.uint32) - - blink = l_eye.getBlink() or r_eye.getBlink() - if blink != True: - context.gaze_buffor.add(compound_point) - # current face radius - _, _, face_w, face_h = self.face.get_bounding_box() - image_w = self.face.image_w - image_h = self.face.image_h - - face_w_perc = face_w / image_w - face_h_perc = face_h / image_h - - # # prev current face radius - _, _, c_face_w, c_face_h, c_image_w, c_image_h = context.face - - c_face_w_perc = c_face_w / c_image_w - c_face_h_perc = c_face_h / c_image_h - - x, y, w, h = self.face.get_bounding_box() - i_w = self.face.image_w - i_h = self.face.image_h - context.face = (x, y, w, h, i_w, i_h) - - # roi update - - # context.roi.x -= diff_face_x * 500 # current size of virtual display - # context.roi.y -= diff_face_y * 500 # current size of virtual display - if abs(face_w_perc / c_face_w_perc - 1.0) > 0.02: - context.roi.width = context.roi.width * abs(face_w_perc / c_face_w_perc) - # context.edges.width= context.edges.width * abs(face_w_perc/c_face_w_perc) - context.gaze_buffor.flush() - # context.calibration = True - if abs(face_h_perc / c_face_h_perc - 1.0) > 0.02: - context.roi.height = context.roi.height * abs(face_h_perc / c_face_h_perc) - # context.edges.height= context.edges.height * abs(face_w_perc/c_face_w_perc) - context.gaze_buffor.flush() - # context.calibration = True - - self.point_screen, roi, cluster = self.screen_man.process( - context.gaze_buffor, - context.roi, - context.edges, - self.screen, - context.display, - context.calibration, - (offset_x, offset_y), - ) - - context.roi = roi - if cluster: - x, y, width, height = cluster.getBoundaries() - context.cluster_boundaries.x = x - context.cluster_boundaries.y = y - context.cluster_boundaries.width = width - context.cluster_boundaries.height = height - - self.GContext.update(context_id, context) - - ########################################################### - - fix = context.fixation.process(self.point_screen[0], self.point_screen[1]) - # this should prevent of sudden movement down when blinking - not perfect yet - - if fix > fixation_freeze: - r = freeze_radius - if not isInside( - self.freezed_point[0], - self.freezed_point[1], - r, - self.point_screen[0], - self.point_screen[1], - ): - self.freezed_point = self.point_screen - - event = Gevent( - self.freezed_point, - blink, - fix, - l_eye, - r_eye, - display, - context.roi, - context.edges, - context.cluster_boundaries, - context_id, - ) - else: - self.freezed_point = self.point_screen - event = Gevent( - self.point_screen, - blink, - fix, - l_eye, - r_eye, - display, - context.roi, - context.edges, - context.cluster_boundaries, - context_id, - ) - - return event - - # def get_contextes(self): - # """Function returning contextes""" - - # return self.GContext.get_contextes() - - # def add_offset(self,x,y): - # """Function adding offsets to tracker""" - - # self.screen_man.push_window(x,y) - - def getFeatures(self, image): - """Function returning face landmarks""" - - face_mesh = self.finder.find(image) - return face_mesh diff --git a/eyeGestures/Legacy/gaze_contexter.py b/eyeGestures/Legacy/gaze_contexter.py deleted file mode 100644 index 312c78c..0000000 --- a/eyeGestures/Legacy/gaze_contexter.py +++ /dev/null @@ -1,124 +0,0 @@ -"""Module providing a core tracking features.""" - -import eyeGestures.screenTracker.dataPoints as dp -from eyeGestures.Fixation import Fixation -from eyeGestures.utils import Buffor - - -class Contexter: - """Class storing contextes""" - - def __init__(self): - self.context = {} - - def add_context(self, context_id, context): - """Function allowing to add new context to contexter""" - - if context_id not in self.context: - self.context[context_id] = context - return True - return False - - def remove_context(self, context_id): - """Function removing context""" - return self.context.pop(context_id, None) is not None - - def get_context(self, context_id): - """Function returning context based on id""" - return self.context.get(context_id, None) - - def update_context(self, context_id, data): - """Function updating context with new data""" - - if context_id in self.context: - self.context[context_id] = data - else: - self.add_context(context_id, data) - return True - - def get_number_contexts(self): - """Function returning number of contexts""" - - return len(self.context) - - -class Gcontext: - """Helper class for Gcontext""" - - def __init__( - self, - display, - face, - roi, - edges, - cluster_boundaries, - gaze_buffor, - l_pupil, - r_pupil, - l_eye_buff, - r_eye_buff, - fixation, - calibration, - ): - - self.roi = roi - self.face = face - self.edges = edges - self.cluster_boundaries = cluster_boundaries - self.gaze_buffor = gaze_buffor - self.l_pupil = l_pupil - self.r_pupil = r_pupil - self.l_eye_buff = l_eye_buff - self.r_eye_buff = r_eye_buff - self.display = display - self.fixation = fixation - self.calibration = calibration - - -class GazeContext: - """Context wrapper for gaze tracker application""" - - def __init__(self): - self.contexter = Contexter() - - def get( - self, - context_id, - display, - face=None, - roi=dp.ScreenROI(285, 105, 80, 15), - edges=dp.ScreenROI(285, 105, 80, 15), - cluster_boundaries=dp.ScreenROI(225, 125, 20, 20), - buffor=Buffor(200), - l_pupil=Buffor(20), - r_pupil=Buffor(20), - l_eye_buff=Buffor(20), - r_eye_buff=Buffor(20), - fixation=Fixation(0, 0, 100), - calibration=False, - ): - """Function creating new context or returning if id already exists""" - - context = Gcontext( - display=display, - face=face, - roi=roi, - edges=edges, - cluster_boundaries=cluster_boundaries, - gaze_buffor=buffor, - l_pupil=l_pupil, - r_pupil=r_pupil, - l_eye_buff=l_eye_buff, - r_eye_buff=r_eye_buff, - fixation=fixation, - calibration=calibration, - ) - - if self.contexter.add_context(context_id, context): - return context - return self.contexter.get_context(context_id) - - def update(self, context_id, context): - """Function updating existing context""" - - self.contexter.update_context(context_id, context) diff --git a/eyeGestures/Legacy/gevent.py b/eyeGestures/Legacy/gevent.py deleted file mode 100644 index 7bc0181..0000000 --- a/eyeGestures/Legacy/gevent.py +++ /dev/null @@ -1,57 +0,0 @@ -"""Module providing a Gaze Events.""" - -from typing import Any, Optional - -import cv2 -import numpy as np -import numpy.typing as npt - -from eyeGestures.eye import Eye - - -class Gevent: - """Class representing gaze event, with tracked points scaled to screen, blink and fixation.""" - - def __init__( - self, - point: npt.NDArray[np.float64], - blink: bool, - fixation: float, - l_eye: Optional[Eye] = None, - r_eye: Optional[Eye] = None, - screen_man: Optional[Any] = None, - roi: Optional[Any] = None, - edges: Optional[Any] = None, - cluster: Optional[Any] = None, - context: Optional[Any] = None, - saccades: bool = False, - sub_frame: Optional[cv2.typing.MatLike] = None, - ): - - self.point = point - self.blink = blink - self.fixation = fixation - self.saccades = saccades - - # ALL DEBUG DATA - self.roi = roi - self.edges = edges - self.l_eye = l_eye - self.r_eye = r_eye - self.cluster = cluster - self.context = context - self.screen_man = screen_man - self.sub_frame = sub_frame - - -class Cevent: - """Class representing gaze event, with tracked points scaled to screen, blink and fixation.""" - - def __init__( - self, point: npt.NDArray[np.float64], acceptance_radius: int, calibration_radius: int, calibration: bool = False - ) -> None: - - self.point = point - self.acceptance_radius = acceptance_radius - self.calibration_radius = calibration_radius - self.calibration = calibration diff --git a/eyeGestures/Legacy/processing.py b/eyeGestures/Legacy/processing.py deleted file mode 100644 index 4c1bc69..0000000 --- a/eyeGestures/Legacy/processing.py +++ /dev/null @@ -1,72 +0,0 @@ -"""Module providing a processing pupil position to gaze direction.""" - -from typing import Tuple - -import numpy as np -import numpy.typing as npt - - -class EyeProcessor: - """Class providing processing for eye to obtain gaze direction.""" - - def __init__(self, scale_w=250, scale_h=250): - self.scale_w = scale_w - self.scale_h = scale_h - self.min_x = 0 - self.max_x = 0 - self.min_y = 0 - self.max_y = 0 - self.pupil = None - self.landmarks = None - - def append(self, pupil: Tuple[int, int], landmarks: npt.NDArray[np.float64], pupilBuffor): - """Function appending new pupil point to tracker.""" - - self.pupil = pupil - self.landmarks = landmarks - - # get center: - margin = 5 - self.min_x = np.min(self.landmarks[:, 0]) - margin - self.max_x = np.max(self.landmarks[:, 0]) + margin - self.min_y = np.min(self.landmarks[:, 1]) - margin - self.max_y = np.max(self.landmarks[:, 1]) + margin - - assert self.pupil[0] > self.min_x - assert self.pupil[1] > self.min_y - - width = self.max_x - self.min_x - height = (self.max_y - self.min_y) / 2 - - pupilBuffor.add( - self.__convertPoint( - self.pupil, - width=self.scale_w, - height=self.scale_h, - scale_w=width, - scale_h=height, - offset=(self.min_x, self.min_y), - ) - ) - - def __convertPoint(self, point, width=1.0, height=1.0, scale_w=1.0, scale_h=1.0, offset=(0.0, 0.0)): - min_x, min_y = offset - x = int(((point[0] - min_x) / scale_w) * width) - y = int(((point[1] - min_y) / scale_h) * height) - return (x, y) - - def getAvgPupil(self, width, height, pupilBuffor): - """Function returning average point based on tracked.""" - - if width is not None and height is not None: - _retPupil = self.__convertPoint( - pupilBuffor.getAvg(), - width=width, - height=height, - scale_w=self.scale_w, - scale_h=self.scale_h, - ) - else: - _retPupil = pupilBuffor.getAvg() - - return _retPupil diff --git a/eyeGestures/Legacy/requirements.txt b/eyeGestures/Legacy/requirements.txt deleted file mode 100644 index 4436618..0000000 --- a/eyeGestures/Legacy/requirements.txt +++ /dev/null @@ -1,7 +0,0 @@ -mediapipe==0.10.8 -numpy==1.23.4 -opencv_contrib_python==4.8.1.78 -opencv_python==4.8.1.78 -opencv_python_headless==4.8.1.78 -scikit_learn==1.5.0 -scipy==1.11.4 diff --git a/eyeGestures/Legacy/scalling_test.py b/eyeGestures/Legacy/scalling_test.py deleted file mode 100644 index b39cdf3..0000000 --- a/eyeGestures/Legacy/scalling_test.py +++ /dev/null @@ -1,60 +0,0 @@ -import eyeGestures.screenTracker.dataPoints as dp -import eyeGestures.screenTracker.screenTracker as scrtr - -# region of interest inside the virtual screen -ROI_WIDTH = 30 -ROI_HEIGHT = 20 - -####################### screen 2 display Tests ################################# - - -def test_scaleUpTest(): - pos = (50, 50) - BIGGER = 10 - - roi = dp.ScreenROI(pos[0], pos[1], ROI_WIDTH, ROI_HEIGHT) - roi2 = dp.ScreenROI(pos[0], pos[1], ROI_WIDTH + BIGGER, ROI_HEIGHT + BIGGER) - - bigger_roi = scrtr.scaleUp(roi, roi2, 0.1) - - assert bigger_roi.width > roi.width - assert bigger_roi.height > roi.height - - -def test_scaleUpTest_known(): - pos = (50, 50) - BIGGER = 10 - - roi = dp.ScreenROI(pos[0], pos[1], ROI_WIDTH, ROI_HEIGHT) - roi2 = dp.ScreenROI(pos[0], pos[1], ROI_WIDTH + BIGGER, ROI_HEIGHT + BIGGER) - - bigger_roi = scrtr.scaleUp(roi, roi2, 0.1) - - assert int(bigger_roi.width) == int(ROI_WIDTH * 1.1) - assert int(bigger_roi.height) == int(ROI_HEIGHT * 1.1) - - -def test_scaleDownTest(): - pos = (50, 50) - SMALLER = 10 - - roi = dp.ScreenROI(pos[0], pos[1], ROI_WIDTH, ROI_HEIGHT) - roi2 = dp.ScreenROI(pos[0], pos[1], ROI_WIDTH - SMALLER, ROI_HEIGHT - SMALLER) - - smaller_roi = scrtr.scaleDown(roi, roi2, -0.1) - - assert smaller_roi.width < roi.width - assert smaller_roi.height < roi.height - - -def test_scaleDownTest_known(): - pos = (50, 50) - SMALLER = 10 - - roi = dp.ScreenROI(pos[0], pos[1], ROI_WIDTH, ROI_HEIGHT) - roi2 = dp.ScreenROI(pos[0], pos[1], ROI_WIDTH - SMALLER, ROI_HEIGHT - SMALLER) - - smaller_roi = scrtr.scaleDown(roi, roi2, -0.1) - - assert int(smaller_roi.width) == int(ROI_WIDTH * 0.9) - assert int(smaller_roi.height) == int(ROI_HEIGHT * 0.9) diff --git a/eyeGestures/Legacy/screenTracker/__init__.py b/eyeGestures/Legacy/screenTracker/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/eyeGestures/Legacy/screenTracker/clusters.py b/eyeGestures/Legacy/screenTracker/clusters.py deleted file mode 100644 index 38d4d53..0000000 --- a/eyeGestures/Legacy/screenTracker/clusters.py +++ /dev/null @@ -1,91 +0,0 @@ -import numpy as np -from sklearn.cluster import DBSCAN - - -class Cluster: - """Class representing one ROI cluster""" - - def __init__(self, label, points): - - self.label = label - self.points = np.array(points) - self.weight = len(self.points) - self.__centroid = self.centroid(points) - - x, y, w, h = self.boundaries(points) - - self.x = x - self.y = y - self.w = w - self.h = h - - def centroid(self, points): - """Function calculating and returning center of ROI""" - - x, y, w, h = self.boundaries(points) - - c_x, c_y = (x + w / 2, y + h / 2) - - x, y = sum(points) / len(points) - - c_x, c_y = (c_x + x) / 2, (c_y + y) / 2 - return (c_x, c_y) - - def boundaries(self, points): - """Function calculating and returning boundaries of cluster""" - - x = np.min(points[:, 0]) - width = abs(np.max(points[:, 0]) - x) - - y = np.min(points[:, 1]) - height = abs(np.max(points[:, 1]) - y) - - return (x, y, width, height) - - def getBoundaries(self): - """Function returning boundaries of cluster""" - return (self.x, self.y, self.w, self.h) - - def getCenter(self): - """Function returning center of ROI""" - return self.__centroid - - -class Clusters: - """Algorithm dividing tracked points into clusters so it can be estimated were is center of ROI""" - - def __init__(self, buffer): - buffer = np.array(buffer) - - self.head = len(buffer) - 1 - self.main_cluster = None - self.clusters = [] - - dbScan = DBSCAN(eps=12, min_samples=3) - clustering = dbScan.fit(buffer) - - labels = clustering.labels_ - unique_labels = set(labels) - {-1} - - for u_label in unique_labels: - core_sample_indices = clustering.core_sample_indices_ - cluster_points = buffer[core_sample_indices][labels[clustering.core_sample_indices_] == u_label] - self.clusters.append(Cluster(u_label, cluster_points)) - - # return sorted(self.clusters, key=lambda cluster: cluster.weight)[-1] - if len(self.clusters) > 0: - self.main_cluster = self.clusters[labels[self.head]] - else: - self.main_cluster = None - - def clearPoints(self): - """[Possibly dead code] Function reseting cluster""" - self.head = 0 - - def getClusters(self): - """Function returning all clusters""" - return self.clusters - - def getMainCluster(self): - """Function returning main clusters""" - return self.main_cluster diff --git a/eyeGestures/Legacy/screenTracker/dataPoints.py b/eyeGestures/Legacy/screenTracker/dataPoints.py deleted file mode 100644 index fe1c296..0000000 --- a/eyeGestures/Legacy/screenTracker/dataPoints.py +++ /dev/null @@ -1,61 +0,0 @@ -class Center: - """Helper representing Center of object with x,y,width,height""" - - def __init__(self, x, y, width, height): - self.x = (x + width) / 2 - self.y = (y + height) / 2 - - -class Screen: - """Helper representing screen by making box of width,height""" - - def __init__(self, width, height): - self.x = 0 - self.y = 0 - self.width = width - self.height = height - - def getCenter(self): - """Function returnig center of the screen""" - return Center(self.x, self.y, self.width, self.height) - - -class ScreenROI: - """Helper representing ROI by making box of width,height with x and y offset""" - - def __init__(self, x, y, width, height): - self.x = x - self.y = y - self.width = width - self.height = height - - def setCenter(self, x, y): - """Function allowing to move ROI by changing its center""" - self.x = x - self.width / 2 - self.y = y - self.height / 2 - - def getCenter(self): - """Function returnig center of the ROI""" - return Center(self.x, self.y, self.width, self.height) - - def getBoundaries(self): - """Function returnig boundaries of the ROI""" - return (self.x, self.y, self.width, self.height) - - -class Display: - """Helper representing Display by making box of width,height with x and y offset""" - - def __init__(self, width, height, offset_x, offset_y): - self.width = width - self.height = height - self.offset_x = offset_x - self.offset_y = offset_y - - # self.buffor = Buffor(20) - - def getCenter(self): - """Function returnig center of the Display""" - center_x = (self.offset_x + self.width) / 2 - center_y = (self.offset_y + self.height) / 2 - return (center_x, center_y) diff --git a/eyeGestures/Legacy/screenTracker/heatmap.py b/eyeGestures/Legacy/screenTracker/heatmap.py deleted file mode 100644 index 7a6a87a..0000000 --- a/eyeGestures/Legacy/screenTracker/heatmap.py +++ /dev/null @@ -1,68 +0,0 @@ -import numpy as np - - -class Heatmap: - """Helper representing Heatmap of tracked points""" - - def __init__(self, width, height, buffor): - self.inc_step = 10 - self.step = 10 - bars = self.step - - self.width = width - self.height = height - - bars_x = int(width / self.step) - bars_y = int(height / self.step) - - self.axis_x = np.zeros((bars_x)) - self.axis_y = np.zeros((bars_y)) - - for point in buffor: - x = point[0] - y = point[1] - - self.axis_x[min(abs(int(x / bars)), bars_x - 1)] += self.inc_step - self.axis_y[min(abs(int(y / bars)), bars_x - 1)] += self.inc_step - - self.min_x = self.__getParam((self.axis_x > self.inc_step * 4), last=False) - self.max_x = self.__getParam((self.axis_x > self.inc_step * 4), last=True) - self.min_y = self.__getParam((self.axis_y > self.inc_step * 4), last=False) - self.max_y = self.__getParam((self.axis_y > self.inc_step * 4), last=True) - - def __getParam(self, param, last: bool = False): - ret = 0 - retArray = np.where(param) - - if len(retArray[0]) > 0: - ret = retArray[0][-int(last)] * self.step - - if not ret == np.nan: - ret = int(ret) - - return ret - - def getBoundaries(self): - """Function returning boundaries of heatmap""" - x = self.min_x - y = self.min_y - w = self.max_x - self.min_x - h = self.max_y - self.min_y - return (x, y, w, h) - - def getCenter(self): - """Function returning center of heatmap""" - center_x = int((self.max_x - self.min_x) / 2 + self.min_x) - center_y = int((self.max_y - self.min_y) / 2 + self.min_y) - return (center_x, center_y) - - def getPeak(self): - """Function returning peak of heatmap""" - x = int(np.argmax(self.axis_x) * self.inc_step) - y = int(np.argmax(self.axis_y) * self.inc_step) - return (x, y) - - def getHist(self): - """Function returning histogram of the heatmap. - This heatmap is simply 2d histogram, so this function returns values for each axis.""" - return (self.axis_x / self.inc_step, self.axis_y / self.inc_step) diff --git a/eyeGestures/Legacy/screenTracker/screenTracker.py b/eyeGestures/Legacy/screenTracker/screenTracker.py deleted file mode 100644 index a759ec2..0000000 --- a/eyeGestures/Legacy/screenTracker/screenTracker.py +++ /dev/null @@ -1,221 +0,0 @@ -import eyeGestures.screenTracker.dataPoints as dp -from eyeGestures.screenTracker.clusters import Clusters -from eyeGestures.screenTracker.heatmap import Heatmap - -# THIS FILE IS SLOWLY BECOMING BLACK MAGIC - - -def detect_if_inside(point, rect): - px = point[0] - py = point[1] - x, y, width, height = rect.getBoundaries() - - x_in = x < px < x + width - y_in = y < py < y + height - - return x_in and y_in - - -def detect_edges(roi, display, point_on_screen, point_on_display): - """Function performing edge detection based on point, screen and display sizes""" - s_x, s_y = point_on_screen - d_x, d_y = point_on_display - - x, y, width, height = roi.getBoundaries() - new_roi = dp.ScreenROI(x, y, width, height) - - # new_w = screen.width - # margin = 5 - # TODO: fix this to estimate w and h - if d_x <= 0: - new_roi.width = new_roi.width + abs(new_roi.x - s_x) - new_roi.x = s_x - - if d_x >= display.width: - new_roi.width = abs(new_roi.x - s_x) - - if d_y <= 0: - new_roi.height = new_roi.height + abs(new_roi.y - s_y) - new_roi.y = s_y - - if d_y >= display.height: - new_roi.height = abs(new_roi.y - s_y) - - return new_roi - - -def rescale_h(roi, scale_h, change=0.5): - """change size on height""" - scale_diff_h = abs(1.0 - scale_h) - - ret_heigth = roi.height - - if scale_diff_h > change: - ret_heigth = roi.height / 1.0 * scale_h - - return ret_heigth - - -def rescale_w(roi, scale_w, change=0.5): - """change size on width""" - scale_diff_w = abs(1.0 - scale_w) - - ret_width = roi.width - - if scale_diff_w > change: - ret_width = roi.width / 1.0 * scale_w - - return ret_width - - -def scaleDown(roi, edge, scale): - """Function scalling down one roi till edges are met""" - - _, _, cluster_w, cluster_h = edge.getBoundaries() - - new_roi = dp.ScreenROI(roi.x, roi.y, roi.width, roi.height) - if cluster_w < roi.width: - new_scale_w = 1.0 + scale - new_w = rescale_w(roi, new_scale_w, scale) - new_roi.width = new_w - - if cluster_h < roi.height: - new_scale_h = 1.0 + scale - new_h = rescale_h(roi, new_scale_h, scale) - new_roi.height = new_h - - return new_roi - - -def scaleUp(roi, roi2, scale): - """Function scalling up one roi into another roi, - second roi is for limiting scaling operation and scale is telling how much you can scale""" - - _, _, roi2_w, roi2_h = roi2.getBoundaries() - - new_roi = dp.ScreenROI(roi.x, roi.y, roi.width, roi.height) - - if roi2_w > roi.width: - new_scale_w = 1.0 + scale - new_w = rescale_w(roi, new_scale_w, scale) - new_roi.width = new_w - - if roi2_h > roi.height: - new_scale_h = 1.0 + scale - new_h = rescale_h(roi, new_scale_h, scale) - new_roi.height = new_h - - return new_roi - - -class ScreenProcessor: - """Class for doing tracking and converting screen to display and display to screen""" - - def __init__(self): - pass - - # RUN FEATURE - def process(self, point, point_offset, buffor_length, roi, edges, screen, display, heatmap): - """Function performing main processing for screen processor. Tons of parameters doing brrrr""" - - s_point_offset = self.display2screen(point_offset, screen, display) - p_on_display = self.screen2display([point[0] + s_point_offset[0], point[1] + s_point_offset[1]], roi, display) - # p_on_display = self.screen2display(point,roi,display) - - if buffor_length > 20: - new_edges = detect_edges(roi, display, point, p_on_display) - edges.x = new_edges.x - edges.y = new_edges.y - edges.width = new_edges.width - edges.height = new_edges.height - - p_on_display = ( - p_on_display[0] + display.offset_x, - p_on_display[1] + display.offset_y, - ) - - # return how close that is hist region - _, _, roi_w, roi_h = heatmap.getBoundaries() - closeness_percentage = (roi_w * roi_h) / (screen.width * screen.height) - return (p_on_display, closeness_percentage) - - # CALIBRATION FEATURE - # it should not affect objects but produce new ones - def update(self, roi, edges, cluster, heatmap): - """Function to update screen processor with new clusters and heatmaps""" - - x, y = heatmap.getCenter() - # ===================================== - # ---------histogram obtained---------- - # ===================================== - new_roi = dp.ScreenROI(roi.x, roi.y, roi.width, roi.height) - - new_roi.setCenter(x, y) - edges.setCenter(x, y) - # self.eyeScreen.setCenter(x,y) - new_roi = scaleUp(new_roi, edges, scale=0.1) - - # ===================================== - # if screen is bigger than edges make it smaller - # ===================================== - new_roi = scaleDown(new_roi, cluster, scale=-0.1) - return new_roi - - def screen2display(self, screen_point, screen, display): - """Function converting screen points to display points""" - - s_x, s_y = screen_point[0], screen_point[1] - - d_x = int((s_x - screen.x) / screen.width * display.width) - d_y = int((s_y - screen.y) / screen.height * display.height) - - d_x = max(d_x, 0) - d_y = max(d_y, 0) - d_x = min(d_x, display.width) - d_y = min(d_y, display.height) - - return (d_x, d_y) - - def display2screen(self, display_point, screen, display): - """Function converting display points to screen points""" - - d_x, d_y = display_point[0], display_point[1] - - s_x = int((d_x) / display.width * screen.width) - s_y = int((d_y) / display.height * screen.height) - - return (s_x, s_y) - - -class ScreenManager: - """Class performing most of tracking after estimating position of gaze""" - - def __init__( - self, - ): - self.screen_processor = ScreenProcessor() - - def process(self, buffor, roi, edges, screen, display, calibration, offset): - """Function doing processing and tracking and calibration of tracker""" - - heatmap = Heatmap(screen.width, screen.height, buffor.getBuffor()) - cluster = Clusters(buffor.getBuffor()).getMainCluster() - - if cluster is not None: - - if calibration: - roi = self.screen_processor.update(roi, edges, cluster, heatmap) - - p, _ = self.screen_processor.process( - buffor.getAvg(20), - (offset[0], offset[1]), - len(buffor.getBuffor()), - roi, - edges, - screen, - display, - heatmap, - ) - - return (p, roi, cluster) - return ([0, 0], roi, cluster) diff --git a/eyeGestures/Legacy/screenTracker_test.py b/eyeGestures/Legacy/screenTracker_test.py deleted file mode 100644 index 6030ae2..0000000 --- a/eyeGestures/Legacy/screenTracker_test.py +++ /dev/null @@ -1,116 +0,0 @@ -import eyeGestures.screenTracker.dataPoints as dp -import eyeGestures.screenTracker.screenTracker as scrtr - -# virtual tracking ScreenProcessor -SCREEN_WIDTH = 500 -SCREEN_HEIGHT = 500 - -# region of interest inside the virtual ScreenProcessor -ROI_WIDTH = 50 -ROI_HEIGHT = 50 - -# size of real display -DISPLAY_WIDTH = 1200 -DISPLAY_HEIGHT = 1800 - -####################### ScreenProcessor 2 display Tests ################################# - - -def test_screen2display_1_1(): - """[TEST]""" - point = (1, 1) - - roi = dp.ScreenROI(0, 0, ROI_WIDTH, ROI_HEIGHT) - display = dp.Display(DISPLAY_WIDTH, DISPLAY_HEIGHT, 0, 0) - - tracker = scrtr.ScreenProcessor() - p = tracker.screen2display(point, roi, display) - - assert p == ( - point[0] / ROI_WIDTH * DISPLAY_WIDTH, - point[1] / ROI_HEIGHT * DISPLAY_HEIGHT, - ) - - -def test_screen2display_55_1(): - """[TEST]""" - point = (55, 1) - - roi = dp.ScreenROI(0, 0, ROI_WIDTH, ROI_HEIGHT) - display = dp.Display(DISPLAY_WIDTH, DISPLAY_HEIGHT, 0, 0) - - tracker = scrtr.ScreenProcessor() - p = tracker.screen2display(point, roi, display) - - assert p == (DISPLAY_WIDTH, point[1] / ROI_HEIGHT * DISPLAY_HEIGHT) - - -def test_screen2display_55_55(): - """[TEST]""" - point = (55, 55) - - roi = dp.ScreenROI(0, 0, ROI_WIDTH, ROI_HEIGHT) - display = dp.Display(DISPLAY_WIDTH, DISPLAY_HEIGHT, 0, 0) - - tracker = scrtr.ScreenProcessor() - p = tracker.screen2display(point, roi, display) - - assert p == (DISPLAY_WIDTH, DISPLAY_HEIGHT) - - -def test_screen2display_0_0(): - """[TEST]""" - point = (0, 0) - - roi = dp.ScreenROI(0, 0, ROI_WIDTH, ROI_HEIGHT) - display = dp.Display(DISPLAY_WIDTH, DISPLAY_HEIGHT, 0, 0) - - tracker = scrtr.ScreenProcessor() - p = tracker.screen2display(point, roi, display) - - assert p == (0, 0) - - -def test_screen2display_1_1_pos_50_50(): - """[TEST]""" - point = (51, 51) - pos = (50, 50) - - roi = dp.ScreenROI(pos[0], pos[1], ROI_WIDTH, ROI_HEIGHT) - display = dp.Display(DISPLAY_WIDTH, DISPLAY_HEIGHT, 0, 0) - - tracker = scrtr.ScreenProcessor() - p = tracker.screen2display(point, roi, display) - - assert p == ( - (point[0] - pos[0]) / ROI_WIDTH * DISPLAY_WIDTH, - (point[1] - pos[1]) / ROI_HEIGHT * DISPLAY_HEIGHT, - ) - - -def test_screen2display_55_55_pos_50_50(): - """[TEST]""" - point = (105, 105) - pos = (50, 50) - - roi = dp.ScreenROI(pos[0], pos[1], ROI_WIDTH, ROI_HEIGHT) - display = dp.Display(DISPLAY_WIDTH, DISPLAY_HEIGHT, 0, 0) - - tracker = scrtr.ScreenProcessor() - p = tracker.screen2display(point, roi, display) - - assert p == (DISPLAY_WIDTH, DISPLAY_HEIGHT) - - -def test_screen2display_0_0_pos_50_50(): - """[TEST]""" - point = (0, 0) - pos = (50, 50) - - roi = dp.ScreenROI(pos[0], pos[1], ROI_WIDTH, ROI_HEIGHT) - display = dp.Display(DISPLAY_WIDTH, DISPLAY_HEIGHT, 0, 0) - - tracker = scrtr.ScreenProcessor() - p = tracker.screen2display(point, roi, display) - - assert p == (0, 0) diff --git a/eyeGestures/Legacy/utils.py b/eyeGestures/Legacy/utils.py deleted file mode 100644 index aba78ab..0000000 --- a/eyeGestures/Legacy/utils.py +++ /dev/null @@ -1,240 +0,0 @@ -import pickle -import platform -import queue -import threading -import time - -import cv2 -import numpy as np -import numpy.typing as npt - -# Make predictions for new data points - - -def recoverable(ret_error_params=()): - def decorator(func): - """ - timeit - """ - - def inner(*args, **kwargs): - """ - inner - """ - try: - return func(*args, **kwargs) - except Exception as e: - print(f"Caugh error: {e}") - return ret_error_params - - return inner - - return decorator - - -def timeit(func): - """ - timeit - """ - - def inner(*args, **kwargs): - """ - inner - """ - start = time.time() - ret = func(*args, **kwargs) - print(f"Elapsed time: {time.time() - start}") - return ret - - return inner - - -def low_pass_filter_fourier(data: npt.NDArray[np.float64], cutoff_frequency: int) -> npt.NDArray[np.float64]: - # Apply Fourier Transform-based filter column-wise - filtered_data = np.zeros_like(data, dtype=float) - for col in range(data.shape[1]): # Iterate over each column - fft_data = np.fft.fft(data[:, col]) - frequencies = np.fft.fftfreq(len(data[:, col])) - # Apply the low-pass filter - fft_data[np.abs(frequencies) > cutoff_frequency] = 0 - # Perform Inverse Fourier Transform - filtered_data[:, col] = np.fft.ifft(fft_data).real - return filtered_data - - -def shape_to_np(shape, dtype="int"): - """ - shape_to_np - """ - coords = np.zeros((68, 2), dtype=dtype) - for i in range(0, 68): - coords[i] = (shape.part(i).x, shape.part(i).y) - return coords - - -def make_image_grid(images, rows, cols): - """ - Make a grid of images. - - Parameters: - - images: list of images to form the grid (all images must be of the same size and type) - - rows, cols: number of rows and columns in the grid - - Returns: - - grid_image: image grid as a single image - """ - # Check if the list of images is not empty and that we have enough to fill the grid - assert images, "List of images is empty" - # assert len(images) >= rows * cols, "Not enough images to fill the grid" - - # Get image dimensions - img_h, img_w = images[0].shape[:2] - - if len(images[0].shape) > 2: - # Create a black canvas to draw the grid on - grid_image = np.zeros((img_h * rows, img_w * cols, images[0].shape[2]), dtype=np.uint8) - else: - grid_image = np.zeros((img_h * rows, img_w * cols), dtype=np.uint8) - - # Copy images to the grid - for i, img in enumerate(images): - if i >= rows * cols: - break # Stop if we have filled the grid - row = i // cols - col = i % cols - grid_image[row * img_h : (row + 1) * img_h, col * img_w : (col + 1) * img_w] = img - - return grid_image - - -class var: - - def __init__(self, _var): - self.__var = _var - - def set(self, _var): - self.__var = _var - - def get(self): - return self.__var - - -class Buffor: - - def __init__(self, length): - self.length = length - self.__buffor = [] - - def add(self, _var): - if len(self.__buffor) >= self.length: - self.__buffor.pop(0) - - self.__buffor.append(_var) - - def getAvg(self, lenght=0): - return np.sum(self.__buffor[-lenght:], axis=0) / len(self.__buffor[-lenght:]) - - def getBuffor(self): - return self.__buffor - - def loadBuffor(self, buffor): - self.__buffor = buffor - - def getLast(self): - return self.__buffor[0] - - def getFirst(self): - return self.__buffor[len(self.__buffor) - 1] - - def getLen(self): - return len(self.__buffor) - - def isFull(self): - return len(self.__buffor) >= self.length - - def flush(self): - tmp = self.__buffor[-1] - self.__buffor = [] - self.__buffor.append(tmp) - - def clear(self): - self.__buffor = [] - - -# Bufforless - - -class VideoCapture: - """Wrapper on openCV2 stream making it bufforless and adding camera search""" - - def __init__(self, name, bufforless=True): - self.bufforless = bufforless - self.run = True - - if isinstance(name, str): - if ".pkl" in name: - self.stream = False - else: - self.stream = True - else: - self.stream = True - - if self.stream: - self.prev_frame = None - - self.__openCam(name) - - self.q = queue.Queue() - self.t = threading.Thread(target=self.__reader) - self.t.start() - else: - self.frames = [] - with open(name, "rb") as file: - self.frames = pickle.load(file) - - def __openCam(self, name): - if isinstance(name, int): - if "Windows" in platform.system(): - self.cap = cv2.VideoCapture(name, cv2.CAP_DSHOW) - else: - self.cap = cv2.VideoCapture(name) - - if self.cap is None or not self.cap.isOpened(): - print(f"Was unable to open camera: {name}.") - print(f"Trying to open camera: {name}.") - if name + 1 < 10: - self.__openCam(name + 1) - else: - self.cap = cv2.VideoCapture(name) - - def __reader(self): - while self.run: - ret, frame = self.cap.read() - if not ret: - break - if not self.q.empty() and self.bufforless: - try: - self.q.get_nowait() - except queue.Empty: - pass - self.q.put((ret, frame)) - - self.flush() - - def flush(self): - while not self.q.empty(): - self.q.get() - - def read(self): - """Function returning latest frame""" - if self.stream: - return self.q.get() - frame = self.frames.pop(0) - self.frames.pop(0) - return ((len(self.frames) >= 1), frame) - - def close(self): - """Function closing stream""" - self.run = False - self.t.join() - self.cap.release() diff --git a/eyeGestures/__init__.py b/eyeGestures/__init__.py index a1cc674..e657c9f 100644 --- a/eyeGestures/__init__.py +++ b/eyeGestures/__init__.py @@ -42,9 +42,6 @@ def __init__(self, calibration_radius: int = 1000) -> None: self.starting_head_position = np.zeros((1, 2)) self.starting_size = np.zeros((1, 2)) - def uploadCalibrationMap(self, points: npt.NDArray[np.float64], context: str = "main") -> None: - self.clb.updMatrix(np.array(points)) - def getLandmarks(self, frame: cv2.typing.MatLike) -> Tuple[npt.NDArray[np.float64], bool, cv2.typing.MatLike]: frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) @@ -104,10 +101,10 @@ def getLandmarks(self, frame: cv2.typing.MatLike) -> Tuple[npt.NDArray[np.float6 ] return key_points, blink, subframe - def whichAlgorithm(self, context: str = "main") -> str: + def whichAlgorithm(self) -> str: return "rust engine" - def reset(self, context: str = "main") -> None: + def reset(self) -> None: self.filled_points = 0 def setFixation(self, fix: float) -> None: From f248a20d4d694af3d3e935ab18300db0118e1ebb Mon Sep 17 00:00:00 2001 From: PeterWaIIace Date: Mon, 8 Jun 2026 22:48:27 +0200 Subject: [PATCH 07/14] updating makefile --- Makefile | 32 ++++++++------------------------ 1 file changed, 8 insertions(+), 24 deletions(-) diff --git a/Makefile b/Makefile index 4fe0de2..a582296 100644 --- a/Makefile +++ b/Makefile @@ -1,6 +1,12 @@ # Python build FORMAT_TOOLS = uv run --with isort --with black CHECK_TOOLS = uv run --with pylint --with flake8 --with mypy --with isort --with black + +MINIFY = npx terser +MINIFY_FLAGS = --compress --mangle +JS_SRC = web/src/eyegestures.js +MIN = $(JS_SRC:.js=.min.js) + .PHONY: format format_check test min wasm python python-help format: @@ -17,12 +23,6 @@ check: format_check $(CHECK_TOOLS) python -m mypy eyeGestures # Web build - -MINIFY = npx terser -MINIFY_FLAGS = --compress --mangle -JS_SRC = web/src/eyegestures.js -MIN = $(JS_SRC:.js=.min.js) - min: $(MINIFY) $(JS_SRC) $(MINIFY_FLAGS) -o engine/pkg/eyegestures.min.js @@ -33,13 +33,9 @@ wasm: # Python build PYTHON = uv run MATURIN_PYTHON_FLAGS = --release --no-default-features --features python -# Space-separated interpreters used by `make python`. -# Example single: `make python PYTHON_INTERPRETERS="python3.13"` -# Example multi: `make python PYTHON_INTERPRETERS="python3.12 python3.13"` -# Default: use whichever `python` is in PATH. -PYTHON_INTERPRETERS ?= python # Set to 1 to allow builds for newer CPython versions than PyO3 officially supports. PYO3_USE_ABI3_FORWARD_COMPATIBILITY ?= 1 +PYTHON_INTERPRETERS=python3.12 python3.13 ifeq ($(OS),Windows_NT) PYO3_FORWARD_ENV = set PYO3_USE_ABI3_FORWARD_COMPATIBILITY=$(PYO3_USE_ABI3_FORWARD_COMPATIBILITY) && @@ -48,19 +44,7 @@ PYO3_FORWARD_ENV = PYO3_USE_ABI3_FORWARD_COMPATIBILITY=$(PYO3_USE_ABI3_FORWARD_C endif python: - cd ./engine && $(PYO3_FORWARD_ENV) maturin build $(MATURIN_PYTHON_FLAGS) --interpreter $(PYTHON_INTERPRETERS) - -python-help: - @echo "make python" - @echo " - builds wheel using default interpreter: python" - @echo "make python PYTHON_INTERPRETERS=\"python3.13\"" - @echo " - builds wheel for one interpreter" - @echo "make python PYTHON_INTERPRETERS=\"python3.12 python3.13\"" - @echo " - builds wheel files in engine/target/wheels" - @echo "make python PYO3_USE_ABI3_FORWARD_COMPATIBILITY=0" - @echo " - enforces PyO3 max supported Python version" - @echo "Tip (Windows): prefer explicit paths if discovery fails, e.g." - @echo " make python PYTHON_INTERPRETERS=\"C:/Python313/python.exe C:/Python314/python.exe\"" + $(PYO3_FORWARD_ENV) maturin build $(MATURIN_PYTHON_FLAGS) --interpreter $(PYTHON_INTERPRETERS) clean: rm -f src/eyegestures.min.js From 63be0227d2eff6a71583d57629adcf26cf913c88 Mon Sep 17 00:00:00 2001 From: W4ltz <40773550+PeterWaIIace@users.noreply.github.com> Date: Mon, 8 Jun 2026 23:22:10 +0200 Subject: [PATCH 08/14] Adding windows builds for 64 and 32 bit systems --- Makefile | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/Makefile b/Makefile index a582296..2f69530 100644 --- a/Makefile +++ b/Makefile @@ -33,6 +33,7 @@ wasm: # Python build PYTHON = uv run MATURIN_PYTHON_FLAGS = --release --no-default-features --features python + # Set to 1 to allow builds for newer CPython versions than PyO3 officially supports. PYO3_USE_ABI3_FORWARD_COMPATIBILITY ?= 1 PYTHON_INTERPRETERS=python3.12 python3.13 @@ -43,8 +44,14 @@ else PYO3_FORWARD_ENV = PYO3_USE_ABI3_FORWARD_COMPATIBILITY=$(PYO3_USE_ABI3_FORWARD_COMPATIBILITY) endif +ifeq ($(OS),Windows_NT) +python: + $(PYO3_FORWARD_ENV) maturin build $(MATURIN_PYTHON_FLAGS) --target i686-pc-windows-msvc --interpreter $(PYTHON_INTERPRETERS) + $(PYO3_FORWARD_ENV) maturin build $(MATURIN_PYTHON_FLAGS) --target x86_64-pc-windows-msvc --interpreter $(PYTHON_INTERPRETERS) +else python: $(PYO3_FORWARD_ENV) maturin build $(MATURIN_PYTHON_FLAGS) --interpreter $(PYTHON_INTERPRETERS) +endif clean: rm -f src/eyegestures.min.js From 60d2c6386f02aa9d96f7e12942c503dedb295c7f Mon Sep 17 00:00:00 2001 From: PeterWaIIace Date: Mon, 8 Jun 2026 23:38:57 +0200 Subject: [PATCH 09/14] updating format and lint --- .flake8 | 3 +++ Makefile | 4 ++-- ...uresEngine.cpython-313-x86_64-linux-gnu.so | Bin 0 -> 680840 bytes eyeGestures/__init__.py | 20 +++++++++--------- eyeGestures/eye.py | 20 +++++++++--------- 5 files changed, 25 insertions(+), 22 deletions(-) create mode 100644 .flake8 create mode 100755 eyeGestures/EyegesturesEngine.cpython-313-x86_64-linux-gnu.so diff --git a/.flake8 b/.flake8 new file mode 100644 index 0000000..e658540 --- /dev/null +++ b/.flake8 @@ -0,0 +1,3 @@ +[flake8] +max-line-length = 120 +extend-ignore = E203, E266, E712, W503 diff --git a/Makefile b/Makefile index 2f69530..e772576 100644 --- a/Makefile +++ b/Makefile @@ -1,3 +1,5 @@ +.PHONY: format format_check test min wasm python python-help + # Python build FORMAT_TOOLS = uv run --with isort --with black CHECK_TOOLS = uv run --with pylint --with flake8 --with mypy --with isort --with black @@ -7,8 +9,6 @@ MINIFY_FLAGS = --compress --mangle JS_SRC = web/src/eyegestures.js MIN = $(JS_SRC:.js=.min.js) -.PHONY: format format_check test min wasm python python-help - format: $(FORMAT_TOOLS) python -m isort eyeGestures $(FORMAT_TOOLS) python -m black eyeGestures diff --git 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b/eyeGestures/__init__.py @@ -1,5 +1,3 @@ -import pickle -import time from typing import Any, Dict, List, Optional, Tuple import cv2 @@ -11,10 +9,10 @@ from eyeGestures.utils import recoverable try: - from .EyegesturesEngine import EyeGesturesEnginePython as RustEyeGesturesEngine + from .EyegesturesEngine import EyeGesturesEnginePython as RustEyeGesturesEngine # type: ignore[import-untyped] except ImportError: try: - from EyegesturesEngine import EyeGesturesEnginePython as RustEyeGesturesEngine + from EyegesturesEngine import EyeGesturesEnginePython as RustEyeGesturesEngine # type: ignore[import-untyped] except ImportError: RustEyeGesturesEngine = None @@ -33,7 +31,7 @@ def __init__(self, calibration_radius: int = 1000) -> None: self.finder = FaceFinder() self.face = Face() - self.engine = None + self.engine: Optional[Any] = None self.fix: Optional[float] = None self.fixationTracker: Dict[str, Fixation] = dict() @@ -121,11 +119,13 @@ def step( if self.engine is None: self.engine = RustEyeGesturesEngine(width, height) - landmarks = self.finder.find(frame).face_landmarks[0] - scaled_landmarks = [] - for landmark in landmarks: - scaled_landmarks.append((landmark.x, landmark.y)) - scaled_landmarks = np.array(scaled_landmarks) + find_result = self.finder.find(frame) + if find_result is None or not find_result.face_landmarks: + raise ValueError("No face landmarks detected") + + landmarks = find_result.face_landmarks[0] + scaled_landmarks = np.array([(landmark.x, landmark.y) for landmark in landmarks], dtype=np.float64) + assert self.engine is not None result = self.engine.process(scaled_landmarks.flatten().tolist()) x, y = result[0], result[1] diff --git a/eyeGestures/eye.py b/eyeGestures/eye.py index 81ddfad..490f838 100644 --- a/eyeGestures/eye.py +++ b/eyeGestures/eye.py @@ -53,8 +53,8 @@ def __init__(self, side: int) -> None: self.y = 0 self.width = 0 self.height = 0 - self.center_x = 0 - self.center_y = 0 + self.center_x: float = 0.0 + self.center_y: float = 0.0 self.image: Optional[cv2.typing.MatLike] = None self.pupil: Optional[npt.NDArray[np.float64]] = None self.offset: Optional[npt.NDArray[np.float64]] = None @@ -162,19 +162,19 @@ def _process(self, image: cv2.typing.MatLike, region: npt.NDArray[np.float64]) - masked_image = cv2.bitwise_not(background, cv2.cvtColor(image.copy(), cv2.COLOR_BGR2GRAY), mask=mask) margin = 2 - min_x = np.min(region_int[:, 0]) - margin - max_x = np.max(region_int[:, 0]) + margin - min_y = np.min(region_int[:, 1]) - margin - max_y = np.max(region_int[:, 1]) + margin + min_x = int(np.min(region_int[:, 0]) - margin) + max_x = int(np.max(region_int[:, 0]) + margin) + min_y = int(np.min(region_int[:, 1]) - margin) + max_y = int(np.max(region_int[:, 1]) + margin) self.x = min_x self.y = min_y - self.width = np.max(region_int[:, 0]) - np.min(region_int[:, 0]) - self.height = np.max(region_int[:, 1]) - np.min(region_int[:, 1]) + self.width = int(np.max(region_int[:, 0]) - np.min(region_int[:, 0])) + self.height = int(np.max(region_int[:, 1]) - np.min(region_int[:, 1])) - self.center_x = (min_x + max_x) / 2 - self.center_y = (min_y + max_y) / 2 + self.center_x = float((min_x + max_x) / 2) + self.center_y = float((min_y + max_y) / 2) # HACKETY_HACK: assert self.pupil is not None From 91060a9d492a919346c16b760485e381bcb8b2da Mon Sep 17 00:00:00 2001 From: PeterWaIIace Date: Mon, 8 Jun 2026 23:45:25 +0200 Subject: [PATCH 10/14] adding new readme --- README.md | 121 ++++-------------------------------------------------- 1 file changed, 8 insertions(+), 113 deletions(-) diff --git a/README.md b/README.md index f6578b2..5c954af 100644 --- a/README.md +++ b/README.md @@ -19,10 +19,6 @@ EyeGestures is open source eyetracking software/library using native webcams and Our [Mission](https://github.com/NativeSensors/EyeGestures/blob/main/MISSION.md)! ### 💜 Sponsors: -> [!NOTE] -> Check JS port: [EyeGesturesLite](https://github.com/NativeSensors/EyeGesturesLite)! - - > [!IMPORTANT] > EyeGestures is a fully volunteer-based project and exists thanks to your donations and support. > @@ -30,11 +26,7 @@ Our [Mission](https://github.com/NativeSensors/EyeGestures/blob/main/MISSION.md) > > 📢📢 **If you are bussiness and would like to collaborate, reach us: contact@eyegestures.com** 📢📢 > -> We can provide custom comercial license and invoices if needed. -> -> -> -> +> We are happy to be involved in your project! > --------- ### 🔨 Projects build with EyeGestures: @@ -48,23 +40,11 @@ Our [Mission](https://github.com/NativeSensors/EyeGestures/blob/main/MISSION.md)

-- [EyePilot](https://polar.sh/NativeSensors/products/5fce104c-46ec-4203-892b-a26e0e0ead18) +- [EyePilot](https://polar.sh/NativeSensors/products/5fce104c-46ec-4203-892b-a26e0e0ead18) - waiting to be restored - [EyePather](https://polar.sh/NativeSensors/posts/eyepather-new-tool-in-eyegestures-ecosystem) - discontinued -- [EyeFocus](https://polar.sh/NativeSensors/products/3756d557-134e-4e7e-ac50-d850126aa325?ref=producthunt) <- 4th best product on PH! +- [EyeFocus](https://polar.sh/NativeSensors/products/3756d557-134e-4e7e-ac50-d850126aa325?ref=producthunt) - Add your project! contact@eyegestures.com or PR -Subscribe and get access to our software: -

- Subscribe on Polar - --------- -

- -

- -### ⚓ Ports: -- JavaScript version: [EyeGesturesLite](https://github.com/NativeSensors/EyeGesturesLite)! - ### 💻 Install ``` python3 -m pip install eyeGestures @@ -78,20 +58,16 @@ python3 -m pip install eyeGestures Tracker works best when your camera or laptop is at arm's length, similar to how you would typically use it. If you are further away, it may be less responsive for now - currently working on solving this issue. ``` -python3 examples/simple_example_v2.py -``` - -``` -python3 examples/simple_example.py [legacy tracker, will become obsolete] +python3 examples/simple_example_v4.py ``` ### 🔧 Build your own: -#### Using EyeGesture Engine V3 - Faster smaller better: +#### Engine V4 - Rust based enginee: ```python from eyeGestures.utils import VideoCapture -from eyeGestures import EyeGestures_v3 +from eyeGestures import EyeGestures_v4 # Initialize gesture engine and video capture gestures = EyeGestures_v3() @@ -119,90 +95,9 @@ while True: -#### Using EyeGesture Engine V2 - [Legacy]: - -```python -from eyeGestures.utils import VideoCapture -from eyeGestures import EyeGestures_v2 - -# Initialize gesture engine and video capture -gestures = EyeGestures_v2() -cap = VideoCapture(0) -calibrate = True -screen_width = 500 -screen_height= 500 - -# Process each frame -while True: - ret, frame = cap.read() - event, cevent = gestures.step(frame, - calibrate, - screen_width, - screen_height, - context="my_context") - - if event: - cursor_x, cursor_y = event.point[0], event.point[1] - fixation = event.fixation - # calibration_radius: radius for data collection during calibration -``` - - - - -#### Customize [only V1/V2]: - -You can customize your calibration points/map to fit your solutions. Simple copy snippet below, and place your calibration poitns on x,y planes from 0.0 to 1.0. It will be then automatically scaled to your display. - -```python -gestures = EyeGestures_v2() -gestures.uploadCalibrationMap([[0,0],[0,1],[1,0],[1,1]]) -``` - -V2 is two stage tracker. It runs V1 under the hood but then uses it as feature extractor for V2 machine learning component, and combines both outputs to generate new gaze point. It is possible to control how much V1 affects V2 by: - -```python -gestures.setClassicImpact(N) # setting N = 2 is working best for my testing -``` -This makes that sample obtained from V2 is averaged with N times sample from V1 (same sample copied that many times). In outcome having V2 impacting output in `1/N+1` and V1 `N/N+1`. - -It is also worth to know that you can enable hidden calibration for V1 (same calibration when using only V1, but now it is invisible to user): -```python -gestures.enableCNCalib() -``` - -#### Using EyeGesture Engine V1 - Model-Based Approach [not recommended]: - -```python -from eyeGestures.utils import VideoCapture -from eyeGestures import EyeGestures_v1 - -# Initialize gesture engine with RoI parameters -gestures = EyeGestures_v1() - -cap = VideoCapture(0) -ret, frame = cap.read() -calibrate = True -screen_width = 500 -screen_height= 500 - -# Obtain estimations from camera frames -event, cevent = gestures.estimate( - frame, - "main", - calibrate, # set calibration - switch to False to stop calibration - screen_width, - screen_height, - 0, 0, 0.8, 10 -) - -if event: - cursor_x, cursor_y = event.point[0], event.point[1] - fixation = event.fixation - # calibration_radius: radius for data collection during calibration -``` +#### Legacy Enginees: -Feel free to copy and paste the relevant code snippets for your project. +With release of V4 rust based engine shared betweeen all eyegestures versions (web and desktop), fully pythonic enginees become deprecated. This decision was driven by trying to achieve single algorithm powering eyegestures application. Keeping multiple different enginees was beyond ability to maintain it. Source code can still be found in `src/Legacy`. ### 🔥 Web Demos: From 58940005b5e5a2dbf988ba2736994ff996d92731 Mon Sep 17 00:00:00 2001 From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com> Date: Mon, 8 Jun 2026 21:51:09 +0000 Subject: [PATCH 11/14] Fix CI: install uv and correct uv sync command in workflow --- .github/workflows/code_quality.yml | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/.github/workflows/code_quality.yml b/.github/workflows/code_quality.yml index 4432d3c..835d21e 100644 --- a/.github/workflows/code_quality.yml +++ b/.github/workflows/code_quality.yml @@ -14,9 +14,11 @@ jobs: uses: actions/setup-python@v3 with: python-version: ${{ matrix.python-version }} + - name: Install uv + run: pip install uv - name: Install dependencies run: | - uv run sync + uv sync - name: Analysing code with checkers run: | make check From ab77c0c160f0601644c4127bcb9ff1c7f9ca92b4 Mon Sep 17 00:00:00 2001 From: PeterWaIIace Date: Tue, 9 Jun 2026 00:06:37 +0200 Subject: [PATCH 12/14] import fixes --- eyeGestures/__init__.py | 4 +++- pyproject.toml | 1 - 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/eyeGestures/__init__.py b/eyeGestures/__init__.py index 1745e40..68d2f88 100644 --- a/eyeGestures/__init__.py +++ b/eyeGestures/__init__.py @@ -12,7 +12,9 @@ from .EyegesturesEngine import EyeGesturesEnginePython as RustEyeGesturesEngine # type: ignore[import-untyped] except ImportError: try: - from EyegesturesEngine import EyeGesturesEnginePython as RustEyeGesturesEngine # type: ignore[import-untyped] + from EyegesturesEngine import ( # type: ignore[import-not-found, import-untyped] + EyeGesturesEnginePython as RustEyeGesturesEngine, + ) except ImportError: RustEyeGesturesEngine = None diff --git a/pyproject.toml b/pyproject.toml index 3d0d5ac..d19a666 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -81,7 +81,6 @@ ignore = "DAR101, DAR201, E203, E266, E712, W503" [tool.mypy] strict = false -plugins = ["numpy.typing.mypy_plugin"] [[tool.mypy.overrides]] module = ["sklearn.*", "mediapipe.*"] From d390fe35fb2ed924f1860d32417bacb36378a367 Mon Sep 17 00:00:00 2001 From: PeterWaIIace Date: Tue, 9 Jun 2026 00:22:23 +0200 Subject: [PATCH 13/14] [Fix] imports --- eyeGestures/__init__.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/eyeGestures/__init__.py b/eyeGestures/__init__.py index 68d2f88..d550605 100644 --- a/eyeGestures/__init__.py +++ b/eyeGestures/__init__.py @@ -12,8 +12,8 @@ from .EyegesturesEngine import EyeGesturesEnginePython as RustEyeGesturesEngine # type: ignore[import-untyped] except ImportError: try: - from EyegesturesEngine import ( # type: ignore[import-not-found, import-untyped] - EyeGesturesEnginePython as RustEyeGesturesEngine, + from EyegesturesEngine import ( + EyeGesturesEnginePython as RustEyeGesturesEngine, # type: ignore[import-not-found, import-untyped] ) except ImportError: RustEyeGesturesEngine = None From 81d4197d340f1afbe1ec0525ca205be32404a1d8 Mon Sep 17 00:00:00 2001 From: PeterWaIIace Date: Tue, 9 Jun 2026 00:30:46 +0200 Subject: [PATCH 14/14] [Fix] mypy eyegesture engine --- pyproject.toml | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/pyproject.toml b/pyproject.toml index d19a666..2aa061a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -85,3 +85,7 @@ strict = false [[tool.mypy.overrides]] module = ["sklearn.*", "mediapipe.*"] ignore_missing_imports = true + +[[tool.mypy.overrides]] +module = ["EyegesturesEngine", "eyeGestures.EyegesturesEngine"] +ignore_missing_imports = true

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It configures and manages entire algorithm""" @@ -40,7 +41,7 @@ def __init__(self, calibration_radius: int = 1000) -> None: self.starting_head_position = np.zeros((1, 2)) self.starting_size = np.zeros((1, 2)) - + def uploadCalibrationMap(self, points: npt.NDArray[np.float64], context: str = "main") -> None: self.clb.updMatrix(np.array(points)) @@ -112,10 +113,13 @@ def reset(self, context: str = "main") -> None: def setFixation(self, fix: float) -> None: self.fix = fix - @recoverable(ret_error_params=(None, None, None)) def step( - self, frame: cv2.typing.MatLike, width: int, height: int,): + self, + frame: cv2.typing.MatLike, + width: int, + height: int, + ): if self.engine is None: self.engine = RustEyeGesturesEngine(width, height) @@ -127,9 +131,9 @@ def step( scaled_landmarks = np.array(scaled_landmarks) result = self.engine.process(scaled_landmarks.flatten().tolist()) - x, y = result[0], result[1] + x, y = result[0], result[1] is_calibrating = result[2] == 1.0 calib_x, calib_y = result[3], result[4] - calib_x = min(max(calib_x, 0), width) + calib_x = min(max(calib_x, 0), width) calib_y = min(max(calib_y, 0), height) return [x, y], is_calibrating, [calib_x, calib_y] diff --git a/eyeGestures/eye.py b/eyeGestures/eye.py index 9775132..81ddfad 100644 --- a/eyeGestures/eye.py +++ b/eyeGestures/eye.py @@ -3,9 +3,9 @@ from typing import Optional, Tuple import cv2 -from mediapipe.tasks.python.vision.face_landmarker import FaceLandmarksConnections import numpy as np import numpy.typing as npt +from mediapipe.tasks.python.vision.face_landmarker import FaceLandmarksConnections from eyeGestures.utils import Buffor diff --git a/eyeGestures/face.py b/eyeGestures/face.py index 2496ebf..ef481ec 100644 --- a/eyeGestures/face.py +++ b/eyeGestures/face.py @@ -1,16 +1,16 @@ """Module providing finding and extraction of face from image.""" import os +import urllib.request from pathlib import Path from typing import Any, NamedTuple, Optional, Tuple -import urllib.request import cv2 import mediapipe as mp -from mediapipe.tasks import python -from mediapipe.tasks.python import vision import numpy as np import numpy.typing as npt +from mediapipe.tasks import python +from mediapipe.tasks.python import vision from eyeGestures.eye import Eye diff --git a/eyeGestures/processing.py b/eyeGestures/processing.py index 9a5f8e5..4c1bc69 100644 --- a/eyeGestures/processing.py +++ b/eyeGestures/processing.py @@ -50,7 +50,7 @@ def append(self, pupil: Tuple[int, int], landmarks: npt.NDArray[np.float64], pup ) def __convertPoint(self, point, width=1.0, height=1.0, scale_w=1.0, scale_h=1.0, offset=(0.0, 0.0)): - (min_x, min_y) = offset + min_x, min_y = offset x = int(((point[0] - min_x) / scale_w) * width) y = int(((point[1] - min_y) / scale_h) * height) return (x, y) diff --git a/pyproject.toml b/pyproject.toml index bf1c544..3d0d5ac 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -22,9 +22,10 @@ classifiers = [ "Operating System :: OS Independent", ] dependencies = [ + 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