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
104 lines
3.5 KiB
Python
104 lines
3.5 KiB
Python
# Face recognition: extract embeddings or compare two faces
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# Usage: python run_recognition.py --image path/to/image.jpg
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# python run_recognition.py --image1 face1.jpg --image2 face2.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 run_inference(detector, recognizer, image_path: str):
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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 not faces:
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print('No faces detected.')
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return
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print(f'Detected {len(faces)} face(s). Extracting embedding for the first face...')
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landmarks = faces[0]['landmarks'] # 5-point landmarks for alignment (already np.ndarray)
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embedding = recognizer.get_embedding(image, landmarks)
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norm_embedding = recognizer.get_normalized_embedding(image, landmarks) # L2 normalized
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print(f' Embedding shape: {embedding.shape}')
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print(f' L2 norm (raw): {np.linalg.norm(embedding):.4f}')
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print(f' L2 norm (normalized): {np.linalg.norm(norm_embedding):.4f}')
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def compare_faces(detector, recognizer, image1_path: str, image2_path: str, threshold: float = 0.35):
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img1 = cv2.imread(image1_path)
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img2 = cv2.imread(image2_path)
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if img1 is None or img2 is None:
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print('Error: Failed to load one or both images')
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return
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faces1 = detector.detect(img1)
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faces2 = detector.detect(img2)
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if not faces1 or not faces2:
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print('Error: No faces detected in one or both images')
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return
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landmarks1 = faces1[0]['landmarks']
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landmarks2 = faces2[0]['landmarks']
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embedding1 = recognizer.get_normalized_embedding(img1, landmarks1)
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embedding2 = recognizer.get_normalized_embedding(img2, landmarks2)
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# cosine similarity for normalized embeddings
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similarity = compute_similarity(embedding1, embedding2, normalized=True)
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is_match = similarity > threshold
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print(f'Similarity: {similarity:.4f}')
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print(f'Result: {"Same person" if is_match else "Different person"} (threshold: {threshold})')
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def main():
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parser = argparse.ArgumentParser(description='Face recognition and comparison')
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parser.add_argument('--image', type=str, help='Single image for embedding extraction')
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parser.add_argument('--image1', type=str, help='First image for comparison')
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parser.add_argument('--image2', type=str, help='Second image for comparison')
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parser.add_argument('--threshold', type=float, default=0.35, help='Similarity threshold')
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parser.add_argument('--detector', type=str, default='retinaface', 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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if args.image1 and args.image2:
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compare_faces(detector, recognizer, args.image1, args.image2, args.threshold)
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elif args.image:
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run_inference(detector, recognizer, args.image)
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else:
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print('Error: Provide --image or both --image1 and --image2')
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parser.print_help()
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if __name__ == '__main__':
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main()
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