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cbcd89b167
* chore: Rename scripts to tools folder and unify argument parser * refactor: Centralize dataclasses in types.py and add __call__ to all models - Move Face and result dataclasses to uniface/types.py - Add GazeResult, SpoofingResult, EmotionResult (frozen=True) - Add __call__ to BaseDetector, BaseRecognizer, BaseLandmarker - Add __repr__ to all dataclasses - Replace print() with Logger in onnx_utils.py - Update tools and docs to use new dataclass return types - Add test_types.py with comprehensive dataclass testschore: Rename files under tools folder and unitify argument parser for them
106 lines
3.2 KiB
Python
106 lines
3.2 KiB
Python
# Copyright 2025 Yakhyokhuja Valikhujaev
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# Author: Yakhyokhuja Valikhujaev
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# GitHub: https://github.com/yakhyo
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"""Batch face detection on a folder of images.
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Usage:
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python tools/batch_process.py --input images/ --output results/
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"""
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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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