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feat: Enhace emotion inference speed on ARM and add FaceAnalyzer, Face classes for ease of use. (#25)

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* 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
This commit is contained in:
Yakhyokhuja Valikhujaev
2025-11-30 20:32:07 +09:00
committed by GitHub
parent 779952e3f8
commit 0c93598007
51 changed files with 1605 additions and 966 deletions
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52
scripts/run_recognition.py
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@@ -13,9 +13,9 @@ from uniface.recognition import ArcFace, MobileFace, SphereFace
def get_recognizer(name: str):
if name == "arcface":
if name == 'arcface':
return ArcFace()
elif name == "mobileface":
elif name == 'mobileface':
return MobileFace()
else:
return SphereFace()
@@ -29,18 +29,18 @@ def run_inference(detector, recognizer, image_path: str):
faces = detector.detect(image)
if not faces:
print("No faces detected.")
print('No faces detected.')
return
print(f"Detected {len(faces)} face(s). Extracting embedding for the first face...")
print(f'Detected {len(faces)} face(s). Extracting embedding for the first face...')
landmarks = np.array(faces[0]["landmarks"]) # 5-point landmarks for alignment
landmarks = faces[0]['landmarks'] # 5-point landmarks for alignment (already np.ndarray)
embedding = recognizer.get_embedding(image, landmarks)
norm_embedding = recognizer.get_normalized_embedding(image, landmarks) # L2 normalized
print(f" Embedding shape: {embedding.shape}")
print(f" L2 norm (raw): {np.linalg.norm(embedding):.4f}")
print(f" L2 norm (normalized): {np.linalg.norm(norm_embedding):.4f}")
print(f' Embedding shape: {embedding.shape}')
print(f' L2 norm (raw): {np.linalg.norm(embedding):.4f}')
print(f' L2 norm (normalized): {np.linalg.norm(norm_embedding):.4f}')
def compare_faces(detector, recognizer, image1_path: str, image2_path: str, threshold: float = 0.35):
@@ -48,18 +48,18 @@ def compare_faces(detector, recognizer, image1_path: str, image2_path: str, thre
img2 = cv2.imread(image2_path)
if img1 is None or img2 is None:
print("Error: Failed to load one or both images")
print('Error: Failed to load one or both images')
return
faces1 = detector.detect(img1)
faces2 = detector.detect(img2)
if not faces1 or not faces2:
print("Error: No faces detected in one or both images")
print('Error: No faces detected in one or both images')
return
landmarks1 = np.array(faces1[0]["landmarks"])
landmarks2 = np.array(faces2[0]["landmarks"])
landmarks1 = faces1[0]['landmarks']
landmarks2 = faces2[0]['landmarks']
embedding1 = recognizer.get_normalized_embedding(img1, landmarks1)
embedding2 = recognizer.get_normalized_embedding(img2, landmarks2)
@@ -68,26 +68,26 @@ def compare_faces(detector, recognizer, image1_path: str, image2_path: str, thre
similarity = compute_similarity(embedding1, embedding2, normalized=True)
is_match = similarity > threshold
print(f"Similarity: {similarity:.4f}")
print(f"Result: {'Same person' if is_match else 'Different person'} (threshold: {threshold})")
print(f'Similarity: {similarity:.4f}')
print(f'Result: {"Same person" if is_match else "Different person"} (threshold: {threshold})')
def main():
parser = argparse.ArgumentParser(description="Face recognition and comparison")
parser.add_argument("--image", type=str, help="Single image for embedding extraction")
parser.add_argument("--image1", type=str, help="First image for comparison")
parser.add_argument("--image2", type=str, help="Second image for comparison")
parser.add_argument("--threshold", type=float, default=0.35, help="Similarity threshold")
parser.add_argument("--detector", type=str, default="retinaface", choices=["retinaface", "scrfd"])
parser = argparse.ArgumentParser(description='Face recognition and comparison')
parser.add_argument('--image', type=str, help='Single image for embedding extraction')
parser.add_argument('--image1', type=str, help='First image for comparison')
parser.add_argument('--image2', type=str, help='Second image for comparison')
parser.add_argument('--threshold', type=float, default=0.35, help='Similarity threshold')
parser.add_argument('--detector', type=str, default='retinaface', choices=['retinaface', 'scrfd'])
parser.add_argument(
"--recognizer",
'--recognizer',
type=str,
default="arcface",
choices=["arcface", "mobileface", "sphereface"],
default='arcface',
choices=['arcface', 'mobileface', 'sphereface'],
)
args = parser.parse_args()
detector = RetinaFace() if args.detector == "retinaface" else SCRFD()
detector = RetinaFace() if args.detector == 'retinaface' else SCRFD()
recognizer = get_recognizer(args.recognizer)
if args.image1 and args.image2:
@@ -95,9 +95,9 @@ def main():
elif args.image:
run_inference(detector, recognizer, args.image)
else:
print("Error: Provide --image or both --image1 and --image2")
print('Error: Provide --image or both --image1 and --image2')
parser.print_help()
if __name__ == "__main__":
if __name__ == '__main__':
main()