Initial face anonymization demo with Gradio interface

- Add InsightFace-based face detection using antelopev2 model
- Implement blur and pixelation anonymization methods
- Create interactive Gradio UI with sample image support
- Add adjustable parameters for blur strength and pixel size

app.py

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+import gradio as gr
+import cv2
+import numpy as np
+from PIL import Image, ImageFilter
+import insightface
+from insightface.app import FaceAnalysis
+import os
+import tempfile
+from huggingface_hub import hf_hub_download
+
+class FaceAnonymizer:
+    def __init__(self):
+        self.app = FaceAnalysis(name='antelopev2', providers=['CPUExecutionProvider'])
+        self.app.prepare(ctx_id=0, det_size=(640, 640))
+    
+    def detect_faces(self, image):
+        """Detect faces in the image using InsightFace"""
+        if isinstance(image, Image.Image):
+            image_cv = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
+        else:
+            image_cv = image
+        
+        faces = self.app.get(image_cv)
+        return faces
+    
+    def blur_faces(self, image, blur_strength=15):
+        """Apply Gaussian blur to detected faces"""
+        if isinstance(image, Image.Image):
+            pil_image = image.copy()
+            image_cv = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
+        else:
+            image_cv = image.copy()
+            pil_image = Image.fromarray(cv2.cvtColor(image_cv, cv2.COLOR_BGR2RGB))
+        
+        faces = self.detect_faces(image_cv)
+        
+        for face in faces:
+            bbox = face.bbox.astype(int)
+            x1, y1, x2, y2 = bbox
+            
+            # Extract face region
+            face_region = pil_image.crop((x1, y1, x2, y2))
+            
+            # Apply blur
+            blurred_face = face_region.filter(ImageFilter.GaussianBlur(radius=blur_strength))
+            
+            # Paste back
+            pil_image.paste(blurred_face, (x1, y1))
+        
+        return pil_image
+    
+    def pixelate_faces(self, image, pixel_size=20):
+        """Apply pixelation to detected faces"""
+        if isinstance(image, Image.Image):
+            pil_image = image.copy()
+            image_cv = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
+        else:
+            image_cv = image.copy()
+            pil_image = Image.fromarray(cv2.cvtColor(image_cv, cv2.COLOR_BGR2RGB))
+        
+        faces = self.detect_faces(image_cv)
+        
+        for face in faces:
+            bbox = face.bbox.astype(int)
+            x1, y1, x2, y2 = bbox
+            
+            # Extract face region
+            face_region = pil_image.crop((x1, y1, x2, y2))
+            
+            # Calculate dimensions for pixelation
+            width, height = face_region.size
+            small_width = max(1, width // pixel_size)
+            small_height = max(1, height // pixel_size)
+            
+            # Resize down and back up for pixelation effect
+            pixelated_face = face_region.resize((small_width, small_height), Image.NEAREST)
+            pixelated_face = pixelated_face.resize((width, height), Image.NEAREST)
+            
+            # Paste back
+            pil_image.paste(pixelated_face, (x1, y1))
+        
+        return pil_image
+
+# Initialize the anonymizer
+anonymizer = FaceAnonymizer()
+
+def anonymize_image(image, anonymization_method, blur_strength, pixel_size):
+    """Main function to anonymize faces in an image"""
+    if image is None:
+        return None
+    
+    try:
+        # Convert to PIL Image if needed
+        if isinstance(image, np.ndarray):
+            image = Image.fromarray(image)
+        
+        if anonymization_method == "Blur":
+            result = anonymizer.blur_faces(image, blur_strength=blur_strength)
+        elif anonymization_method == "Pixelation":
+            result = anonymizer.pixelate_faces(image, pixel_size=pixel_size)
+        else:
+            result = image
+        
+        return result
+    except Exception as e:
+        print(f"Error processing image: {str(e)}")
+        return image
+
+def load_sample_image(sample_name):
+    """Load a sample image from assets folder"""
+    asset_path = f"assets/{sample_name}"
+    if os.path.exists(asset_path):
+        return Image.open(asset_path)
+    return None
+
+# Create assets folder if it doesn't exist
+os.makedirs("assets", exist_ok=True)
+
+# Gradio Interface
+with gr.Blocks(title="Face Anonymization Demo", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("""
+    # 🎭 Face Anonymization Demo
+    
+    Upload an image or select a sample to anonymize faces using different methods.
+    The app uses InsightFace for face detection and provides blur and pixelation options.
+    """)
+    
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown("### Input")
+            
+            # Image input
+            input_image = gr.Image(
+                label="Upload Image",
+                type="pil",
+                height=400
+            )
+            
+            # Sample images
+            gr.Markdown("**Or select a sample:**")
+            with gr.Row():
+                sample_btn1 = gr.Button("Face 1", size="sm")
+                sample_btn2 = gr.Button("Face 2", size="sm")
+                sample_btn3 = gr.Button("Face 3", size="sm")
+            
+            # Anonymization controls
+            gr.Markdown("### Anonymization Settings")
+            
+            anonymization_method = gr.Radio(
+                choices=["Blur", "Pixelation"],
+                label="Anonymization Method",
+                value="Blur"
+            )
+            
+            with gr.Group():
+                blur_strength = gr.Slider(
+                    minimum=5,
+                    maximum=50,
+                    value=15,
+                    step=1,
+                    label="Blur Strength",
+                    visible=True
+                )
+                
+                pixel_size = gr.Slider(
+                    minimum=5,
+                    maximum=50,
+                    value=20,
+                    step=1,
+                    label="Pixel Size",
+                    visible=False
+                )
+            
+            # Process button
+            process_btn = gr.Button("🎭 Anonymize Faces", variant="primary", size="lg")
+        
+        with gr.Column():
+            gr.Markdown("### Result")
+            output_image = gr.Image(
+                label="Anonymized Image",
+                type="pil",
+                height=400
+            )
+    
+    # Event handlers
+    def update_controls(method):
+        if method == "Blur":
+            return gr.update(visible=True), gr.update(visible=False)
+        else:
+            return gr.update(visible=False), gr.update(visible=True)
+    
+    anonymization_method.change(
+        fn=update_controls,
+        inputs=[anonymization_method],
+        outputs=[blur_strength, pixel_size]
+    )
+    
+    # Sample image buttons
+    sample_btn1.click(
+        fn=lambda: load_sample_image("face1.jpg"),
+        outputs=input_image
+    )
+    
+    sample_btn2.click(
+        fn=lambda: load_sample_image("face2.jpg"),
+        outputs=input_image
+    )
+    
+    sample_btn3.click(
+        fn=lambda: load_sample_image("face3.jpg"),
+        outputs=input_image
+    )
+    
+    # Process button
+    process_btn.click(
+        fn=anonymize_image,
+        inputs=[input_image, anonymization_method, blur_strength, pixel_size],
+        outputs=output_image
+    )
+    
+    gr.Markdown("""
+    ---
+    **Note:** This demo uses InsightFace for face detection and applies anonymization techniques locally.
+    No images are stored or transmitted externally.
+    """)
+
+if __name__ == "__main__":
+    demo.launch()

assets/README.md

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+# Assets Folder
+
+Please upload the following sample images to this folder:
+
+- `face1.jpg` - Sample face image 1
+- `face2.jpg` - Sample face image 2  
+- `face3.jpg` - Sample face image 3
+
+These images will be used as sample inputs in the face anonymization demo.

requirements.txt

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+gradio==4.44.0
+insightface==0.7.3
+opencv-python==4.8.1.78
+pillow==10.0.1
+numpy==1.24.3
+onnxruntime==1.16.3
+huggingface-hub==0.19.4