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* feat: Add YOLOv5 face detection model * docs: Update docs, add new model information * feat: Add YOLOv5 face detection model * test: Add testing and running
102 lines
3.1 KiB
Python
102 lines
3.1 KiB
Python
# Face detection on image or webcam
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# Usage: python run_detection.py --image path/to/image.jpg
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# python run_detection.py --webcam
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import argparse
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import os
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import cv2
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from uniface.detection import SCRFD, RetinaFace, YOLOv5Face
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from uniface.visualization import draw_detections
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def process_image(detector, image_path: str, threshold: float = 0.6, 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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if faces:
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bboxes = [face['bbox'] for face in faces]
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scores = [face['confidence'] for face in faces]
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landmarks = [face['landmarks'] for face in faces]
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draw_detections(image, bboxes, scores, landmarks, vis_threshold=threshold)
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os.makedirs(save_dir, exist_ok=True)
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output_path = os.path.join(save_dir, f'{os.path.splitext(os.path.basename(image_path))[0]}_out.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, threshold: float = 0.6):
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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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# unpack face data for visualization
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bboxes = [f['bbox'] for f in faces]
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scores = [f['confidence'] for f in faces]
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landmarks = [f['landmarks'] for f in faces]
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draw_detections(frame, bboxes, scores, landmarks, vis_threshold=threshold)
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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('Face Detection', 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 face 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('--method', type=str, default='retinaface', choices=['retinaface', 'scrfd', 'yolov5face'])
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parser.add_argument('--threshold', type=float, default=0.25, help='Visualization threshold')
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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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if args.method == 'retinaface':
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detector = RetinaFace()
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elif args.method == 'scrfd':
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detector = SCRFD()
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else:
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from uniface.constants import YOLOv5FaceWeights
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detector = YOLOv5Face(model_name=YOLOv5FaceWeights.YOLOV5M)
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if args.webcam:
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run_webcam(detector, args.threshold)
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else:
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process_image(detector, args.image, args.threshold, args.save_dir)
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if __name__ == '__main__':
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main()
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