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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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42
scripts/batch_process.py
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@@ -14,8 +14,8 @@ from uniface.visualization import draw_detections
def get_image_files(input_dir: Path, extensions: tuple) -> list:
files = []
for ext in extensions:
files.extend(input_dir.glob(f"*.{ext}"))
files.extend(input_dir.glob(f"*.{ext.upper()}"))
files.extend(input_dir.glob(f'*.{ext}'))
files.extend(input_dir.glob(f'*.{ext.upper()}'))
return sorted(files)
@@ -28,14 +28,14 @@ def process_image(detector, image_path: Path, output_path: Path, threshold: floa
faces = detector.detect(image)
# unpack face data for visualization
bboxes = [f["bbox"] for f in faces]
scores = [f["confidence"] for f in faces]
landmarks = [f["landmarks"] for f in faces]
bboxes = [f['bbox'] for f in faces]
scores = [f['confidence'] for f in faces]
landmarks = [f['landmarks'] for f in faces]
draw_detections(image, bboxes, scores, landmarks, vis_threshold=threshold)
cv2.putText(
image,
f"Faces: {len(faces)}",
f'Faces: {len(faces)}',
(10, 30),
cv2.FONT_HERSHEY_SIMPLEX,
1,
@@ -48,12 +48,12 @@ def process_image(detector, image_path: Path, output_path: Path, threshold: floa
def main():
parser = argparse.ArgumentParser(description="Batch process images with face detection")
parser.add_argument("--input", type=str, required=True, help="Input directory")
parser.add_argument("--output", type=str, required=True, help="Output directory")
parser.add_argument("--detector", type=str, default="retinaface", choices=["retinaface", "scrfd"])
parser.add_argument("--threshold", type=float, default=0.6, help="Visualization threshold")
parser.add_argument("--extensions", type=str, default="jpg,jpeg,png,bmp", help="Image extensions")
parser = argparse.ArgumentParser(description='Batch process images with face detection')
parser.add_argument('--input', type=str, required=True, help='Input directory')
parser.add_argument('--output', type=str, required=True, help='Output directory')
parser.add_argument('--detector', type=str, default='retinaface', choices=['retinaface', 'scrfd'])
parser.add_argument('--threshold', type=float, default=0.6, help='Visualization threshold')
parser.add_argument('--extensions', type=str, default='jpg,jpeg,png,bmp', help='Image extensions')
args = parser.parse_args()
input_path = Path(args.input)
@@ -65,21 +65,21 @@ def main():
output_path.mkdir(parents=True, exist_ok=True)
extensions = tuple(ext.strip() for ext in args.extensions.split(","))
extensions = tuple(ext.strip() for ext in args.extensions.split(','))
image_files = get_image_files(input_path, extensions)
if not image_files:
print(f"No images found with extensions {extensions}")
print(f'No images found with extensions {extensions}')
return
print(f"Found {len(image_files)} images")
print(f'Found {len(image_files)} images')
detector = RetinaFace() if args.detector == "retinaface" else SCRFD()
detector = RetinaFace() if args.detector == 'retinaface' else SCRFD()
success, errors, total_faces = 0, 0, 0
for img_path in tqdm(image_files, desc="Processing", unit="img"):
out_path = output_path / f"{img_path.stem}_detected{img_path.suffix}"
for img_path in tqdm(image_files, desc='Processing', unit='img'):
out_path = output_path / f'{img_path.stem}_detected{img_path.suffix}'
result = process_image(detector, img_path, out_path, args.threshold)
if result >= 0:
@@ -87,10 +87,10 @@ def main():
total_faces += result
else:
errors += 1
print(f"\nFailed: {img_path.name}")
print(f'\nFailed: {img_path.name}')
print(f"\nDone! {success} processed, {errors} errors, {total_faces} faces total")
print(f'\nDone! {success} processed, {errors} errors, {total_faces} faces total')
if __name__ == "__main__":
if __name__ == '__main__':
main()