feat: Add face blurring for privacy (#39)

* feat: Add face blurring for privacy

* chore: Revert back the version

CONTRIBUTING.md

@@ -51,6 +51,7 @@ Example notebooks demonstrating library usage:
 | Face Recognition | [face_analyzer.ipynb](examples/face_analyzer.ipynb) |
 | Face Verification | [face_verification.ipynb](examples/face_verification.ipynb) |
 | Face Search | [face_search.ipynb](examples/face_search.ipynb) |
+| Face Anonymization | [face_anonymization.ipynb](examples/face_anonymization.ipynb) |
 
 ## Questions?
 

QUICKSTART.md

@@ -328,7 +328,86 @@ Detected 12 facial components
 
 ---
 
-## 9. Batch Processing (3 minutes)
+## 9. Face Anonymization (2 minutes)
+
+Automatically blur faces for privacy protection:
+
+```python
+from uniface.privacy import anonymize_faces
+import cv2
+
+# One-liner: automatic detection and blurring
+image = cv2.imread("group_photo.jpg")
+anonymized = anonymize_faces(image, method='pixelate')
+cv2.imwrite("anonymized.jpg", anonymized)
+print("Faces anonymized successfully!")
+```
+
+**Manual control with custom parameters:**
+
+```python
+from uniface import RetinaFace
+from uniface.privacy import BlurFace
+
+# Initialize detector and blurrer
+detector = RetinaFace()
+blurrer = BlurFace(method='gaussian', blur_strength=5.0)
+
+# Detect and anonymize
+faces = detector.detect(image)
+anonymized = blurrer.anonymize(image, faces)
+cv2.imwrite("output.jpg", anonymized)
+```
+
+**Available blur methods:**
+
+```python
+# Pixelation (news media standard)
+blurrer = BlurFace(method='pixelate', pixel_blocks=8)
+
+# Gaussian blur (smooth, natural)
+blurrer = BlurFace(method='gaussian', blur_strength=4.0)
+
+# Black boxes (maximum privacy)
+blurrer = BlurFace(method='blackout', color=(0, 0, 0))
+
+# Elliptical blur (natural face shape)
+blurrer = BlurFace(method='elliptical', blur_strength=3.0, margin=30)
+
+# Median blur (edge-preserving)
+blurrer = BlurFace(method='median', blur_strength=3.0)
+```
+
+**Webcam anonymization:**
+
+```python
+import cv2
+from uniface import RetinaFace
+from uniface.privacy import BlurFace
+
+detector = RetinaFace()
+blurrer = BlurFace(method='pixelate')
+cap = cv2.VideoCapture(0)
+
+while True:
+    ret, frame = cap.read()
+    if not ret:
+        break
+
+    faces = detector.detect(frame)
+    frame = blurrer.anonymize(frame, faces, inplace=True)
+
+    cv2.imshow('Anonymized', frame)
+    if cv2.waitKey(1) & 0xFF == ord('q'):
+        break
+
+cap.release()
+cv2.destroyAllWindows()
+```
+
+---
+
+## 10. Batch Processing (3 minutes)
 
 Process multiple images:
 
@@ -361,7 +440,7 @@ print("Done!")
 
 ---
 
-## 10. Model Selection
+## 11. Model Selection
 
 Choose the right model for your use case:
 

README.md

@@ -23,6 +23,7 @@
 - **Face Parsing**: BiSeNet-based semantic segmentation with 19 facial component classes
 - **Gaze Estimation**: Real-time gaze direction prediction with MobileGaze
 - **Attribute Analysis**: Age, gender, and emotion detection
+- **Face Anonymization**: Privacy-preserving face blurring with multiple methods
 - **Face Alignment**: Precise alignment for downstream tasks
 - **Hardware Acceleration**: ARM64 optimizations (Apple Silicon), CUDA (NVIDIA), CPU fallback
 - **Simple API**: Intuitive factory functions and clean interfaces
@@ -198,6 +199,34 @@ vis_result = vis_parsing_maps(face_rgb, mask, save_image=False)
 print(f"Unique classes: {len(np.unique(mask))}")
 ```
 
+### Face Anonymization
+
+Blur or pixelate faces for privacy protection:
+
+```python
+from uniface import RetinaFace
+from uniface.privacy import BlurFace, anonymize_faces
+
+# Method 1: One-liner with automatic detection
+anonymized = anonymize_faces(image, method='pixelate')
+
+# Method 2: Manual control with custom parameters
+detector = RetinaFace()
+blurrer = BlurFace(method='gaussian', blur_strength=4.0)
+
+faces = detector.detect(image)
+anonymized = blurrer.anonymize(image, faces)
+
+# Available methods with examples
+methods = {
+    'gaussian': BlurFace(method='gaussian', blur_strength=3.0),    # Smooth, natural blur
+    'pixelate': BlurFace(method='pixelate', pixel_blocks=10),      # Blocky effect (news media)
+    'blackout': BlurFace(method='blackout', color=(0, 0, 0)),      # Solid color (max privacy)
+    'elliptical': BlurFace(method='elliptical', margin=20),        # Soft oval blur
+    'median': BlurFace(method='median', blur_strength=3.0)         # Edge-preserving
+}
+```
+
 ---
 
 ## Documentation
@@ -216,6 +245,7 @@ print(f"Unique classes: {len(np.unique(mask))}")
 from uniface.detection import RetinaFace, SCRFD
 from uniface.recognition import ArcFace
 from uniface.landmark import Landmark106
+from uniface.privacy import BlurFace, anonymize_faces
 
 from uniface.constants import SCRFDWeights
 

scripts/README.md

@@ -7,6 +7,7 @@ Scripts for testing UniFace features.
 | Script | Description |
 |--------|-------------|
 | `run_detection.py` | Face detection on image or webcam |
+| `run_anonymization.py` | Face anonymization/blurring for privacy |
 | `run_age_gender.py` | Age and gender prediction |
 | `run_emotion.py` | Emotion detection (7 or 8 emotions) |
 | `run_gaze_estimation.py` | Gaze direction estimation |
@@ -26,6 +27,11 @@ Scripts for testing UniFace features.
 python scripts/run_detection.py --image assets/test.jpg
 python scripts/run_detection.py --webcam
 
+# Face anonymization
+python scripts/run_anonymization.py --image assets/test.jpg --method pixelate
+python scripts/run_anonymization.py --webcam --method gaussian
+python scripts/run_anonymization.py --image photo.jpg --method pixelate --pixel-blocks 5
+
 # Age and gender
 python scripts/run_age_gender.py --image assets/test.jpg
 python scripts/run_age_gender.py --webcam

scripts/run_anonymization.py

@@ -0,0 +1,207 @@
+# Face anonymization/blurring for privacy
+# Usage: python run_anonymization.py --image path/to/image.jpg --method pixelate
+#        python run_anonymization.py --webcam --method gaussian
+
+import argparse
+import os
+
+import cv2
+
+from uniface import RetinaFace
+from uniface.privacy import BlurFace
+
+
+def process_image(
+    detector,
+    blurrer: BlurFace,
+    image_path: str,
+    save_dir: str = 'outputs',
+    show_detections: bool = False,
+):
+    """Process a single image."""
+    image = cv2.imread(image_path)
+    if image is None:
+        print(f"Error: Failed to load image from '{image_path}'")
+        return
+
+    # Detect faces
+    faces = detector.detect(image)
+    print(f'Detected {len(faces)} face(s)')
+
+    # Optionally draw detection boxes before blurring
+    if show_detections and faces:
+        from uniface.visualization import draw_detections
+
+        preview = image.copy()
+        bboxes = [face['bbox'] for face in faces]
+        scores = [face['confidence'] for face in faces]
+        landmarks = [face['landmarks'] for face in faces]
+        draw_detections(preview, bboxes, scores, landmarks)
+
+        # Show preview
+        cv2.imshow('Detections (Press any key to continue)', preview)
+        cv2.waitKey(0)
+        cv2.destroyAllWindows()
+
+    # Anonymize faces
+    if faces:
+        anonymized = blurrer.anonymize(image, faces)
+    else:
+        anonymized = image
+
+    # Save output
+    os.makedirs(save_dir, exist_ok=True)
+    basename = os.path.splitext(os.path.basename(image_path))[0]
+    output_path = os.path.join(save_dir, f'{basename}_anonymized.jpg')
+    cv2.imwrite(output_path, anonymized)
+    print(f'Output saved: {output_path}')
+
+
+def run_webcam(detector, blurrer: BlurFace):
+    """Run real-time anonymization on webcam."""
+    cap = cv2.VideoCapture(0)
+    if not cap.isOpened():
+        print('Cannot open webcam')
+        return
+
+    print("Press 'q' to quit")
+
+    while True:
+        ret, frame = cap.read()
+        frame = cv2.flip(frame, 1)  # mirror for natural interaction
+        if not ret:
+            break
+
+        # Detect and anonymize
+        faces = detector.detect(frame)
+        if faces:
+            frame = blurrer.anonymize(frame, faces, inplace=True)
+
+        # Display info
+        cv2.putText(
+            frame,
+            f'Faces blurred: {len(faces)} | Method: {blurrer.method}',
+            (10, 30),
+            cv2.FONT_HERSHEY_SIMPLEX,
+            0.7,
+            (0, 255, 0),
+            2,
+        )
+
+        cv2.imshow('Face Anonymization (Press q to quit)', frame)
+
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            break
+
+    cap.release()
+    cv2.destroyAllWindows()
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description='Face anonymization using various blur methods',
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+Examples:
+  # Anonymize image with pixelation (default)
+  python run_anonymization.py --image photo.jpg
+
+  # Use Gaussian blur with custom strength
+  python run_anonymization.py --image photo.jpg --method gaussian --blur-strength 5.0
+
+  # Real-time webcam anonymization
+  python run_anonymization.py --webcam --method pixelate
+
+  # Black boxes for maximum privacy
+  python run_anonymization.py --image photo.jpg --method blackout
+
+  # Custom pixelation intensity
+  python run_anonymization.py --image photo.jpg --method pixelate --pixel-blocks 5
+        """,
+    )
+
+    # Input/output
+    parser.add_argument('--image', type=str, help='Path to input image')
+    parser.add_argument('--webcam', action='store_true', help='Use webcam for real-time anonymization')
+    parser.add_argument('--save-dir', type=str, default='outputs', help='Output directory (default: outputs)')
+
+    # Blur method
+    parser.add_argument(
+        '--method',
+        type=str,
+        default='pixelate',
+        choices=['gaussian', 'pixelate', 'blackout', 'elliptical', 'median'],
+        help='Blur method (default: pixelate)',
+    )
+
+    # Method-specific parameters
+    parser.add_argument(
+        '--blur-strength',
+        type=float,
+        default=3.0,
+        help='Blur strength for gaussian/elliptical/median (default: 3.0)',
+    )
+    parser.add_argument(
+        '--pixel-blocks',
+        type=int,
+        default=20,
+        help='Number of pixel blocks for pixelate (default: 10, lower=more pixelated)',
+    )
+    parser.add_argument(
+        '--color',
+        type=str,
+        default='0,0,0',
+        help='Fill color for blackout as R,G,B (default: 0,0,0 for black)',
+    )
+    parser.add_argument('--margin', type=int, default=20, help='Margin for elliptical blur (default: 20)')
+
+    # Detection
+    parser.add_argument(
+        '--conf-thresh',
+        type=float,
+        default=0.5,
+        help='Detection confidence threshold (default: 0.5)',
+    )
+
+    # Visualization
+    parser.add_argument(
+        '--show-detections',
+        action='store_true',
+        help='Show detection boxes before blurring (image mode only)',
+    )
+
+    args = parser.parse_args()
+
+    # Validate input
+    if not args.image and not args.webcam:
+        parser.error('Either --image or --webcam must be specified')
+
+    # Parse color
+    color_values = [int(x) for x in args.color.split(',')]
+    if len(color_values) != 3:
+        parser.error('--color must be in format R,G,B (e.g., 0,0,0)')
+    color = tuple(color_values)
+
+    # Initialize detector
+    print(f'Initializing face detector (conf_thresh={args.conf_thresh})...')
+    detector = RetinaFace(conf_thresh=args.conf_thresh)
+
+    # Initialize blurrer
+    print(f'Initializing blur method: {args.method}')
+    blurrer = BlurFace(
+        method=args.method,
+        blur_strength=args.blur_strength,
+        pixel_blocks=args.pixel_blocks,
+        color=color,
+        margin=args.margin,
+    )
+
+    # Run
+    if args.webcam:
+        run_webcam(detector, blurrer)
+    else:
+        process_image(detector, blurrer, args.image, args.save_dir, args.show_detections)
+
+
+if __name__ == '__main__':
+    main()

uniface/__init__.py

@@ -40,6 +40,7 @@ from .detection import (
 from .gaze import MobileGaze, create_gaze_estimator
 from .landmark import Landmark106, create_landmarker
 from .parsing import BiSeNet, create_face_parser
+from .privacy import BlurFace, anonymize_faces
 from .recognition import ArcFace, MobileFace, SphereFace, create_recognizer
 
 __all__ = [
@@ -74,6 +75,9 @@ __all__ = [
     # Attribute models
     'AgeGender',
     'Emotion',
+    # Privacy
+    'BlurFace',
+    'anonymize_faces',
     # Utilities
     'compute_similarity',
     'draw_detections',

uniface/gaze/__init__.py

@@ -51,8 +51,4 @@ def create_gaze_estimator(method: str = 'mobilegaze', **kwargs) -> BaseGazeEstim
         raise ValueError(f"Unsupported gaze estimation method: '{method}'. Available: {available}")
 
 
-__all__ = [
+__all__ = ['create_gaze_estimator', 'MobileGaze', 'BaseGazeEstimator']
-    'create_gaze_estimator',
-    'MobileGaze',
-    'BaseGazeEstimator',
-]

uniface/privacy/__init__.py

@@ -0,0 +1,52 @@
+# Copyright 2025 Yakhyokhuja Valikhujaev
+# Author: Yakhyokhuja Valikhujaev
+# GitHub: https://github.com/yakhyo
+
+from typing import Optional
+
+import numpy as np
+
+from .blur import BlurFace
+
+
+def anonymize_faces(
+    image: np.ndarray,
+    detector: Optional[object] = None,
+    method: str = 'pixelate',
+    blur_strength: float = 3.0,
+    pixel_blocks: int = 10,
+    conf_thresh: float = 0.5,
+    **kwargs,
+) -> np.ndarray:
+    """One-line face anonymization with automatic detection.
+
+    Args:
+        image (np.ndarray): Input image (BGR format).
+        detector: Face detector instance. Creates RetinaFace if None.
+        method (str): Blur method name. Defaults to 'pixelate'.
+        blur_strength (float): Blur intensity. Defaults to 3.0.
+        pixel_blocks (int): Block count for pixelate. Defaults to 10.
+        conf_thresh (float): Detection confidence threshold. Defaults to 0.5.
+        **kwargs: Additional detector arguments.
+
+    Returns:
+        np.ndarray: Anonymized image.
+
+    Example:
+        >>> from uniface.privacy import anonymize_faces
+        >>> anonymized = anonymize_faces(image, method='pixelate')
+    """
+    if detector is None:
+        try:
+            from uniface import RetinaFace
+
+            detector = RetinaFace(conf_thresh=conf_thresh, **kwargs)
+        except ImportError as err:
+            raise ImportError('Could not import RetinaFace. Please ensure UniFace is properly installed.') from err
+
+    faces = detector.detect(image)
+    blurrer = BlurFace(method=method, blur_strength=blur_strength, pixel_blocks=pixel_blocks)
+    return blurrer.anonymize(image, faces)
+
+
+__all__ = ['BlurFace', 'anonymize_faces']

uniface/privacy/blur.py

@@ -0,0 +1,193 @@
+# Copyright 2025 Yakhyokhuja Valikhujaev
+# Author: Yakhyokhuja Valikhujaev
+# GitHub: https://github.com/yakhyo
+
+from typing import Dict, List, Tuple, Union
+
+import cv2
+import numpy as np
+
+__all__ = ['BlurFace']
+
+
+def _gaussian_blur(region: np.ndarray, strength: float = 3.0) -> np.ndarray:
+    """Apply Gaussian blur to a region."""
+    h, w = region.shape[:2]
+    kernel_size = max(3, int((min(h, w) / 7) * strength)) | 1
+    return cv2.GaussianBlur(region, (kernel_size, kernel_size), 0)
+
+
+def _median_blur(region: np.ndarray, strength: float = 3.0) -> np.ndarray:
+    """Apply median blur to a region."""
+    h, w = region.shape[:2]
+    kernel_size = max(3, int((min(h, w) / 7) * strength)) | 1
+    return cv2.medianBlur(region, kernel_size)
+
+
+def _pixelate_blur(region: np.ndarray, blocks: int = 10) -> np.ndarray:
+    """Apply pixelation to a region."""
+    h, w = region.shape[:2]
+    temp_h, temp_w = max(1, h // blocks), max(1, w // blocks)
+    temp = cv2.resize(region, (temp_w, temp_h), interpolation=cv2.INTER_LINEAR)
+    return cv2.resize(temp, (w, h), interpolation=cv2.INTER_NEAREST)
+
+
+def _blackout_blur(region: np.ndarray, color: Tuple[int, int, int] = (0, 0, 0)) -> np.ndarray:
+    """Replace region with solid color."""
+    return np.full_like(region, color)
+
+
+class EllipticalBlur:
+    """Elliptical blur with soft, feathered edges.
+
+    This blur applies Gaussian blur within an elliptical mask that follows
+    the natural oval shape of faces, requiring full image context for proper blending.
+
+    Args:
+        blur_strength (float): Blur intensity multiplier. Defaults to 3.0.
+        margin (int): Extra pixels to extend ellipse beyond bbox. Defaults to 20.
+    """
+
+    def __init__(self, blur_strength: float = 3.0, margin: int = 20):
+        self.blur_strength = blur_strength
+        self.margin = margin
+
+    def __call__(
+        self,
+        image: np.ndarray,
+        bboxes: List[Union[Tuple, List]],
+        inplace: bool = False,
+    ) -> np.ndarray:
+        if not inplace:
+            image = image.copy()
+
+        h, w = image.shape[:2]
+
+        for bbox in bboxes:
+            x1, y1, x2, y2 = map(int, bbox)
+            center_x, center_y = (x1 + x2) // 2, (y1 + y2) // 2
+            axes_x = (x2 - x1) // 2 + self.margin
+            axes_y = (y2 - y1) // 2 + self.margin
+
+            # Create soft elliptical mask
+            mask = np.zeros((h, w), dtype=np.float32)
+            cv2.ellipse(mask, (center_x, center_y), (axes_x, axes_y), 0, 0, 360, 255, -1)
+            mask = cv2.GaussianBlur(mask, (51, 51), 0) / 255.0
+            mask = mask[:, :, np.newaxis]
+
+            kernel_size = max(3, int((min(axes_y, axes_x) * 2 / 7) * self.blur_strength)) | 1
+            blurred = cv2.GaussianBlur(image, (kernel_size, kernel_size), 0)
+            image = (blurred * mask + image * (1 - mask)).astype(np.uint8)
+
+        return image
+
+
+class BlurFace:
+    """Face blurring with multiple anonymization methods.
+
+    Args:
+        method (str): Blur method - 'gaussian', 'pixelate', 'blackout', 'elliptical', or 'median'.
+            Defaults to 'pixelate'.
+        blur_strength (float): Intensity for gaussian/elliptical/median. Defaults to 3.0.
+        pixel_blocks (int): Block count for pixelate. Defaults to 10.
+        color (Tuple[int, int, int]): Fill color (BGR) for blackout. Defaults to (0, 0, 0).
+        margin (int): Edge margin for elliptical. Defaults to 20.
+
+    Example:
+        >>> blurrer = BlurFace(method='pixelate')
+        >>> anonymized = blurrer.anonymize(image, faces)
+    """
+
+    VALID_METHODS = {'gaussian', 'pixelate', 'blackout', 'elliptical', 'median'}
+
+    def __init__(
+        self,
+        method: str = 'pixelate',
+        blur_strength: float = 3.0,
+        pixel_blocks: int = 15,
+        color: Tuple[int, int, int] = (0, 0, 0),
+        margin: int = 20,
+    ):
+        self.method = method.lower()
+        self._blur_strength = blur_strength
+        self._pixel_blocks = pixel_blocks
+        self._color = color
+        self._margin = margin
+
+        if self.method not in self.VALID_METHODS:
+            raise ValueError(f"Invalid blur method: '{method}'. Choose from: {sorted(self.VALID_METHODS)}")
+
+        if self.method == 'elliptical':
+            self._elliptical = EllipticalBlur(blur_strength, margin)
+
+    def _blur_region(self, region: np.ndarray) -> np.ndarray:
+        if self.method == 'gaussian':
+            return _gaussian_blur(region, self._blur_strength)
+        elif self.method == 'median':
+            return _median_blur(region, self._blur_strength)
+        elif self.method == 'pixelate':
+            return _pixelate_blur(region, self._pixel_blocks)
+        elif self.method == 'blackout':
+            return _blackout_blur(region, self._color)
+
+    def anonymize(
+        self,
+        image: np.ndarray,
+        faces: List[Dict],
+        inplace: bool = False,
+    ) -> np.ndarray:
+        """Anonymize faces in an image.
+
+        Args:
+            image (np.ndarray): Input image (BGR format).
+            faces (List[Dict]): Face detections with 'bbox' key containing [x1, y1, x2, y2].
+            inplace (bool): Modify image in-place if True. Defaults to False.
+
+        Returns:
+            np.ndarray: Image with anonymized faces.
+        """
+        if not faces:
+            return image if inplace else image.copy()
+
+        bboxes = [face['bbox'] for face in faces]
+        return self.blur_regions(image, bboxes, inplace)
+
+    def blur_regions(
+        self,
+        image: np.ndarray,
+        bboxes: List[Union[Tuple, List]],
+        inplace: bool = False,
+    ) -> np.ndarray:
+        """Blur specific rectangular regions in an image.
+
+        Args:
+            image (np.ndarray): Input image (BGR format).
+            bboxes (List): Bounding boxes as [x1, y1, x2, y2].
+            inplace (bool): Modify image in-place if True. Defaults to False.
+
+        Returns:
+            np.ndarray: Image with blurred regions.
+        """
+        if not bboxes:
+            return image if inplace else image.copy()
+
+        if self.method == 'elliptical':
+            return self._elliptical(image, bboxes, inplace)
+
+        if not inplace:
+            image = image.copy()
+
+        h, w = image.shape[:2]
+
+        for bbox in bboxes:
+            x1, y1, x2, y2 = map(int, bbox)
+            x1, y1 = max(0, x1), max(0, y1)
+            x2, y2 = min(w, x2), min(h, y2)
+
+            if x2 > x1 and y2 > y1:
+                image[y1:y2, x1:x2] = self._blur_region(image[y1:y2, x1:x2])
+
+        return image
+
+    def __repr__(self) -> str:
+        return f"BlurFace(method='{self.method}')"

uniface/recognition/__init__.py

@@ -55,10 +55,4 @@ def create_recognizer(method: str = 'arcface', **kwargs) -> BaseRecognizer:
         raise ValueError(f"Unsupported method: '{method}'. Available: {available}")
 
 
-__all__ = [
+__all__ = ['create_recognizer', 'BaseRecognizer', 'ArcFace', 'MobileFace', 'SphereFace']
-    'create_recognizer',
-    'ArcFace',
-    'MobileFace',
-    'SphereFace',
-    'BaseRecognizer',
-]