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9bf54f5f78
* refactor: Split recognition models into separate files * feat: Add EdgeFace recognition model * release: Bump version to v3.4.0
118 lines
3.8 KiB
Python
118 lines
3.8 KiB
Python
# Copyright 2025-2026 Yakhyokhuja Valikhujaev
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# Author: Yakhyokhuja Valikhujaev
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# GitHub: https://github.com/yakhyo
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"""Face recognition: extract embeddings or compare two faces.
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Usage:
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python tools/recognize.py --image path/to/image.jpg
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python tools/recognize.py --image1 face1.jpg --image2 face2.jpg
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"""
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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 AdaFace, ArcFace, EdgeFace, MobileFace, SphereFace
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RECOGNIZERS = {
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'arcface': ArcFace,
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'adaface': AdaFace,
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'edgeface': EdgeFace,
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'mobileface': MobileFace,
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'sphereface': SphereFace,
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}
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def get_recognizer(name: str):
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cls = RECOGNIZERS.get(name)
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if cls is None:
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raise ValueError(f"Unknown recognizer: '{name}'. Available: {list(RECOGNIZERS)}")
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return cls()
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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
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embedding = recognizer.get_embedding(image, landmarks)
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raw_norm = np.linalg.norm(embedding)
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norm_embedding = embedding.ravel() / raw_norm if raw_norm > 0 else embedding.ravel()
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print(f' Embedding shape: {embedding.shape}')
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print(f' L2 norm (raw): {raw_norm:.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=list(RECOGNIZERS),
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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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