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* refactor: Standardize naming conventions * chore: Update the version and re-run experiments * chore: Improve code quality tooling and documentation - Add pre-commit job to CI workflow for automated linting on PRs - Update uniface/__init__.py with copyright header, module docstring, and logically grouped exports - Revise CONTRIBUTING.md to reflect pre-commit handles all formatting - Remove redundant ruff check from CI (now handled by pre-commit) - Update build job Python version to 3.11 (matches requires-python)
105 lines
2.9 KiB
Python
105 lines
2.9 KiB
Python
# Gaze estimation on detected faces
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# Usage: python run_gaze_estimation.py --image path/to/image.jpg
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# python run_gaze_estimation.py --webcam
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import argparse
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import os
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from pathlib import Path
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import cv2
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import numpy as np
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from uniface import RetinaFace
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from uniface.gaze import MobileGaze
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from uniface.visualization import draw_gaze
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def process_image(detector, gaze_estimator, image_path: str, save_dir: str = 'outputs'):
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image = cv2.imread(image_path)
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if image is None:
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print(f"Error: Failed to load image from '{image_path}'")
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return
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faces = detector.detect(image)
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print(f'Detected {len(faces)} face(s)')
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for i, face in enumerate(faces):
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bbox = face.bbox
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x1, y1, x2, y2 = map(int, bbox[:4])
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face_crop = image[y1:y2, x1:x2]
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if face_crop.size == 0:
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continue
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pitch, yaw = gaze_estimator.estimate(face_crop)
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print(f' Face {i + 1}: pitch={np.degrees(pitch):.1f}°, yaw={np.degrees(yaw):.1f}°')
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# Draw both bbox and gaze arrow with angle text
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draw_gaze(image, bbox, pitch, yaw, draw_angles=True)
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os.makedirs(save_dir, exist_ok=True)
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output_path = os.path.join(save_dir, f'{Path(image_path).stem}_gaze.jpg')
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cv2.imwrite(output_path, image)
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print(f'Output saved: {output_path}')
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def run_webcam(detector, gaze_estimator):
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cap = cv2.VideoCapture(0)
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if not cap.isOpened():
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print('Cannot open webcam')
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return
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print("Press 'q' to quit")
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while True:
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ret, frame = cap.read()
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if not ret:
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break
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frame = cv2.flip(frame, 1)
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faces = detector.detect(frame)
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for face in faces:
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bbox = face.bbox
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x1, y1, x2, y2 = map(int, bbox[:4])
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face_crop = frame[y1:y2, x1:x2]
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if face_crop.size == 0:
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continue
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pitch, yaw = gaze_estimator.estimate(face_crop)
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# Draw both bbox and gaze arrow
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draw_gaze(frame, bbox, pitch, yaw)
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cv2.putText(frame, f'Faces: {len(faces)}', (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
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cv2.imshow('Gaze Estimation', frame)
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if cv2.waitKey(1) & 0xFF == ord('q'):
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break
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cap.release()
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cv2.destroyAllWindows()
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def main():
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parser = argparse.ArgumentParser(description='Run gaze estimation')
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parser.add_argument('--image', type=str, help='Path to input image')
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parser.add_argument('--webcam', action='store_true', help='Use webcam')
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parser.add_argument('--save_dir', type=str, default='outputs')
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args = parser.parse_args()
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if not args.image and not args.webcam:
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parser.error('Either --image or --webcam must be specified')
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detector = RetinaFace()
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gaze_estimator = MobileGaze()
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if args.webcam:
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run_webcam(detector, gaze_estimator)
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else:
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process_image(detector, gaze_estimator, args.image, args.save_dir)
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if __name__ == '__main__':
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main()
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