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* refactor: Standardize naming conventions * chore: Update the version and re-run experiments * chore: Improve code quality tooling and documentation - Add pre-commit job to CI workflow for automated linting on PRs - Update uniface/__init__.py with copyright header, module docstring, and logically grouped exports - Revise CONTRIBUTING.md to reflect pre-commit handles all formatting - Remove redundant ruff check from CI (now handled by pre-commit) - Update build job Python version to 3.11 (matches requires-python)
99 lines
3.0 KiB
Python
99 lines
3.0 KiB
Python
# Batch face detection on a folder of images
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# Usage: python batch_process.py --input images/ --output results/
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import argparse
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from pathlib import Path
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import cv2
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from tqdm import tqdm
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from uniface import SCRFD, RetinaFace
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from uniface.visualization import draw_detections
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def get_image_files(input_dir: Path, extensions: tuple) -> list:
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files = []
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for ext in extensions:
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files.extend(input_dir.glob(f'*.{ext}'))
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files.extend(input_dir.glob(f'*.{ext.upper()}'))
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return sorted(files)
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def process_image(detector, image_path: Path, output_path: Path, threshold: float) -> int:
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"""Process single image. Returns face count or -1 on error."""
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image = cv2.imread(str(image_path))
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if image is None:
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return -1
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faces = detector.detect(image)
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# unpack face data for visualization
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bboxes = [f.bbox for f in faces]
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scores = [f.confidence for f in faces]
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landmarks = [f.landmarks for f in faces]
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draw_detections(
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image=image, bboxes=bboxes, scores=scores, landmarks=landmarks, vis_threshold=threshold, fancy_bbox=True
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)
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cv2.putText(
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image,
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f'Faces: {len(faces)}',
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(10, 30),
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cv2.FONT_HERSHEY_SIMPLEX,
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1,
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(0, 255, 0),
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2,
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)
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cv2.imwrite(str(output_path), image)
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return len(faces)
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def main():
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parser = argparse.ArgumentParser(description='Batch process images with face detection')
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parser.add_argument('--input', type=str, required=True, help='Input directory')
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parser.add_argument('--output', type=str, required=True, help='Output directory')
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parser.add_argument('--detector', type=str, default='retinaface', choices=['retinaface', 'scrfd'])
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parser.add_argument('--threshold', type=float, default=0.6, help='Visualization threshold')
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parser.add_argument('--extensions', type=str, default='jpg,jpeg,png,bmp', help='Image extensions')
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args = parser.parse_args()
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input_path = Path(args.input)
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output_path = Path(args.output)
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if not input_path.exists():
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print(f"Error: Input directory '{args.input}' does not exist")
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return
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output_path.mkdir(parents=True, exist_ok=True)
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extensions = tuple(ext.strip() for ext in args.extensions.split(','))
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image_files = get_image_files(input_path, extensions)
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if not image_files:
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print(f'No images found with extensions {extensions}')
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return
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print(f'Found {len(image_files)} images')
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detector = RetinaFace() if args.detector == 'retinaface' else SCRFD()
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success, errors, total_faces = 0, 0, 0
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for img_path in tqdm(image_files, desc='Processing', unit='img'):
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out_path = output_path / f'{img_path.stem}_detected{img_path.suffix}'
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result = process_image(detector, img_path, out_path, args.threshold)
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if result >= 0:
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success += 1
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total_faces += result
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else:
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errors += 1
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print(f'\nFailed: {img_path.name}')
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print(f'\nDone! {success} processed, {errors} errors, {total_faces} faces total')
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if __name__ == '__main__':
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main()
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