mirror of
https://github.com/yakhyo/uniface.git
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71 lines
2.4 KiB
Python
71 lines
2.4 KiB
Python
import cv2
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import argparse
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import numpy as np
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from uniface.detection import RetinaFace
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from uniface.constants import RetinaFaceWeights
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from uniface.recognition import ArcFace
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from uniface.face_utils import compute_similarity
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def extract_reference_embedding(detector, recognizer, image_path):
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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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boxes, landmarks = detector.detect(image)
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if len(boxes) == 0:
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raise RuntimeError("No faces found in reference image.")
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embedding = recognizer.get_embedding(image, landmarks[0])
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print(f"Reference embedding extracted (L2 norm = {np.linalg.norm(embedding):.4f})")
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return embedding
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def run_video(detector, recognizer, ref_embedding, threshold=0.45):
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cap = cv2.VideoCapture(0)
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if not cap.isOpened():
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raise RuntimeError("Webcam could not be opened.")
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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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boxes, landmarks = detector.detect(frame)
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for box, lm in zip(boxes, landmarks):
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x1, y1, x2, y2 = map(int, box[:4])
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embedding = recognizer.get_embedding(frame, lm)
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sim = compute_similarity(ref_embedding, embedding)
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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),
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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 recognition using a reference image.")
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parser.add_argument("--image", type=str, required=True, help="Path to the reference face image.")
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parser.add_argument("--model", type=str, default="MNET_V2",
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choices=[m.name for m in RetinaFaceWeights], help="Face detector model.")
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args = parser.parse_args()
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detector = RetinaFace(model_name=RetinaFaceWeights[args.model])
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recognizer = ArcFace()
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ref_embedding = extract_reference_embedding(detector, recognizer, args.image)
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run_video(detector, recognizer, ref_embedding)
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if __name__ == "__main__":
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main()
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