mirror of
https://github.com/yakhyo/uniface.git
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0c93598007
* feat: Update linting and type annotations, return types in detect * feat: add face analyzer and face classes * chore: Update the format and clean up some docstrings * docs: Update usage documentation * feat: Change AgeGender model output to 0, 1 instead of string (Female, Male) * test: Update testing code * feat: Add Apple silicon backend for torchscript inference * feat: Add face analyzer example and add run emotion for testing
118 lines
3.3 KiB
Python
118 lines
3.3 KiB
Python
# 106-point facial landmark detection
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# Usage: python run_landmarks.py --image path/to/image.jpg
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# python run_landmarks.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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from uniface import SCRFD, Landmark106, RetinaFace
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def process_image(detector, landmarker, 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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if not faces:
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return
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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)
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cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
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landmarks = landmarker.get_landmarks(image, bbox)
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print(f' Face {i + 1}: {len(landmarks)} landmarks')
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for x, y in landmarks.astype(int):
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cv2.circle(image, (x, y), 1, (0, 255, 0), -1)
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cv2.putText(
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image,
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f'Face {i + 1}',
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(x1, y1 - 10),
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cv2.FONT_HERSHEY_SIMPLEX,
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0.5,
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(0, 255, 0),
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2,
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)
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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}_landmarks.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, landmarker):
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cap = cv2.VideoCapture(0) # 0 = default webcam
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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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frame = cv2.flip(frame, 1) # mirror for natural interaction
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if not ret:
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break
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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)
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cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
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landmarks = landmarker.get_landmarks(frame, bbox) # 106 points
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for x, y in landmarks.astype(int):
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cv2.circle(frame, (x, y), 1, (0, 255, 0), -1)
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cv2.putText(
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frame,
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f'Faces: {len(faces)}',
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(10, 30),
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cv2.FONT_HERSHEY_SIMPLEX,
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1,
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(0, 255, 0),
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2,
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)
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cv2.imshow('106-Point Landmarks', 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 facial landmark detection')
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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('--detector', type=str, default='retinaface', choices=['retinaface', 'scrfd'])
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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() if args.detector == 'retinaface' else SCRFD()
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landmarker = Landmark106()
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if args.webcam:
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run_webcam(detector, landmarker)
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else:
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process_image(detector, landmarker, args.image, args.save_dir)
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if __name__ == '__main__':
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main()
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