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https://github.com/yakhyo/uniface.git
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98 lines
3.1 KiB
Python
98 lines
3.1 KiB
Python
# Real-time face search: match webcam faces against a reference image
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# Usage: python run_face_search.py --image reference.jpg
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import argparse
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import cv2
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import numpy as np
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from uniface.detection import SCRFD, RetinaFace
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from uniface.face_utils import compute_similarity
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from uniface.recognition import ArcFace, MobileFace, SphereFace
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def get_recognizer(name: str):
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if name == "arcface":
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return ArcFace()
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elif name == "mobileface":
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return MobileFace()
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else:
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return SphereFace()
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def extract_reference_embedding(detector, recognizer, image_path: str) -> np.ndarray:
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image = cv2.imread(image_path)
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if image is None:
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raise RuntimeError(f"Failed to load image: {image_path}")
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faces = detector.detect(image)
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if not faces:
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raise RuntimeError("No faces found in reference image.")
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landmarks = np.array(faces[0]["landmarks"])
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return recognizer.get_normalized_embedding(image, landmarks)
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def run_webcam(detector, recognizer, ref_embedding: np.ndarray, threshold: float = 0.4):
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cap = cv2.VideoCapture(0) # 0 = default webcam
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if not cap.isOpened():
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raise RuntimeError("Webcam could not be opened.")
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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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landmarks = np.array(face["landmarks"])
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x1, y1, x2, y2 = map(int, bbox)
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embedding = recognizer.get_normalized_embedding(frame, landmarks)
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sim = compute_similarity(ref_embedding, embedding) # compare with reference
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# green = match, red = unknown
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label = f"Match ({sim:.2f})" if sim > threshold else f"Unknown ({sim:.2f})"
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color = (0, 255, 0) if sim > threshold else (0, 0, 255)
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cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2)
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cv2.putText(frame, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 2)
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cv2.imshow("Face Recognition", 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="Face search using a reference image")
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parser.add_argument("--image", type=str, required=True, help="Reference face image")
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parser.add_argument("--threshold", type=float, default=0.4, help="Match threshold")
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parser.add_argument("--detector", type=str, default="scrfd", choices=["retinaface", "scrfd"])
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parser.add_argument(
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"--recognizer",
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type=str,
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default="arcface",
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choices=["arcface", "mobileface", "sphereface"],
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)
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args = parser.parse_args()
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detector = RetinaFace() if args.detector == "retinaface" else SCRFD()
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recognizer = get_recognizer(args.recognizer)
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print(f"Loading reference: {args.image}")
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ref_embedding = extract_reference_embedding(detector, recognizer, args.image)
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run_webcam(detector, recognizer, ref_embedding, args.threshold)
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if __name__ == "__main__":
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main()
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