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88 lines
2.8 KiB
Python
88 lines
2.8 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
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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(frame, f"Faces: {len(faces)}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
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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"])
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parser.add_argument("--threshold", type=float, default=0.6, 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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detector = RetinaFace() if args.method == "retinaface" else SCRFD()
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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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