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feat: Common result dataclasses and refactoring several methods. (#50)
* 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
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Yakhyokhuja Valikhujaev
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GitHub
GitHub
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cbcd89b167
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# Face anonymization/blurring for privacy
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# Usage: python run_anonymization.py --image path/to/image.jpg --method pixelate
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# python run_anonymization.py --webcam --method gaussian
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import argparse
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import os
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import cv2
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from uniface import RetinaFace
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from uniface.privacy import BlurFace
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def process_image(
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detector,
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blurrer: BlurFace,
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image_path: str,
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save_dir: str = 'outputs',
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show_detections: bool = False,
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):
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"""Process a single image."""
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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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# Detect faces
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faces = detector.detect(image)
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print(f'Detected {len(faces)} face(s)')
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# Optionally draw detection boxes before blurring
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if show_detections and faces:
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from uniface.visualization import draw_detections
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preview = image.copy()
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bboxes = [face.bbox for face in faces]
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scores = [face.confidence for face in faces]
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landmarks = [face.landmarks for face in faces]
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draw_detections(preview, bboxes, scores, landmarks)
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# Show preview
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cv2.imshow('Detections (Press any key to continue)', preview)
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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# Anonymize faces
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if faces:
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anonymized = blurrer.anonymize(image, faces)
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else:
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anonymized = image
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# Save output
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os.makedirs(save_dir, exist_ok=True)
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basename = os.path.splitext(os.path.basename(image_path))[0]
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output_path = os.path.join(save_dir, f'{basename}_anonymized.jpg')
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cv2.imwrite(output_path, anonymized)
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print(f'Output saved: {output_path}')
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def run_webcam(detector, blurrer: BlurFace):
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"""Run real-time anonymization on webcam."""
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cap = cv2.VideoCapture(0)
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if not cap.isOpened():
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print('Cannot open webcam')
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return
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print("Press 'q' to quit")
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while True:
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ret, frame = cap.read()
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frame = cv2.flip(frame, 1) # mirror for natural interaction
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if not ret:
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break
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# Detect and anonymize
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faces = detector.detect(frame)
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if faces:
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frame = blurrer.anonymize(frame, faces, inplace=True)
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# Display info
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cv2.putText(
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frame,
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f'Faces blurred: {len(faces)} | Method: {blurrer.method}',
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(10, 30),
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cv2.FONT_HERSHEY_SIMPLEX,
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0.7,
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(0, 255, 0),
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2,
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)
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cv2.imshow('Face Anonymization (Press q to quit)', frame)
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if cv2.waitKey(1) & 0xFF == ord('q'):
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break
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cap.release()
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cv2.destroyAllWindows()
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def main():
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parser = argparse.ArgumentParser(
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description='Face anonymization using various blur methods',
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formatter_class=argparse.RawDescriptionHelpFormatter,
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epilog="""
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Examples:
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# Anonymize image with pixelation (default)
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python run_anonymization.py --image photo.jpg
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# Use Gaussian blur with custom strength
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python run_anonymization.py --image photo.jpg --method gaussian --blur-strength 5.0
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# Real-time webcam anonymization
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python run_anonymization.py --webcam --method pixelate
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# Black boxes for maximum privacy
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python run_anonymization.py --image photo.jpg --method blackout
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# Custom pixelation intensity
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python run_anonymization.py --image photo.jpg --method pixelate --pixel-blocks 5
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""",
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)
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# Input/output
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parser.add_argument('--image', type=str, help='Path to input image')
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parser.add_argument('--webcam', action='store_true', help='Use webcam for real-time anonymization')
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parser.add_argument('--save-dir', type=str, default='outputs', help='Output directory (default: outputs)')
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# Blur method
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parser.add_argument(
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'--method',
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type=str,
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default='pixelate',
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choices=['gaussian', 'pixelate', 'blackout', 'elliptical', 'median'],
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help='Blur method (default: pixelate)',
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)
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# Method-specific parameters
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parser.add_argument(
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'--blur-strength',
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type=float,
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default=3.0,
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help='Blur strength for gaussian/elliptical/median (default: 3.0)',
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)
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parser.add_argument(
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'--pixel-blocks',
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type=int,
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default=20,
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help='Number of pixel blocks for pixelate (default: 10, lower=more pixelated)',
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)
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parser.add_argument(
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'--color',
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type=str,
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default='0,0,0',
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help='Fill color for blackout as R,G,B (default: 0,0,0 for black)',
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)
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parser.add_argument('--margin', type=int, default=20, help='Margin for elliptical blur (default: 20)')
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# Detection
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parser.add_argument(
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'--confidence-threshold',
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type=float,
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default=0.5,
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help='Detection confidence threshold (default: 0.5)',
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)
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# Visualization
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parser.add_argument(
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'--show-detections',
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action='store_true',
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help='Show detection boxes before blurring (image mode only)',
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)
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args = parser.parse_args()
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# Validate input
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if not args.image and not args.webcam:
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parser.error('Either --image or --webcam must be specified')
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# Parse color
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color_values = [int(x) for x in args.color.split(',')]
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if len(color_values) != 3:
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parser.error('--color must be in format R,G,B (e.g., 0,0,0)')
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color = tuple(color_values)
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# Initialize detector
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print(f'Initializing face detector (confidence_threshold={args.confidence_threshold})...')
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detector = RetinaFace(confidence_threshold=args.confidence_threshold)
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# Initialize blurrer
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print(f'Initializing blur method: {args.method}')
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blurrer = BlurFace(
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method=args.method,
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blur_strength=args.blur_strength,
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pixel_blocks=args.pixel_blocks,
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color=color,
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margin=args.margin,
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)
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# Run
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if args.webcam:
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run_webcam(detector, blurrer)
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else:
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process_image(detector, blurrer, args.image, args.save_dir, args.show_detections)
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if __name__ == '__main__':
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main()
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