Create app.py

app.py

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+import gradio as gr
+import matplotlib.pyplot as plt
+import numpy as np
+from PIL import Image
+import cv2
+import torch
+import facer
+from typing import Tuple
+
+def process_image(input_image: np.ndarray) -> np.ndarray:
+    """
+    Process the input image to apply face smoothing effect.
+    
+    Args:
+        input_image (np.ndarray): Input image in numpy array format
+        
+    Returns:
+        np.ndarray: Processed image with smoothing effect applied to face
+    """
+    device = 'cpu'
+    
+    # Convert numpy array to PIL Image and back to ensure correct format
+    input_pil = Image.fromarray(input_image)
+    
+    # Convert image to format expected by facer
+    image = facer.hwc2bchw(np.array(input_pil)).to(device=device)
+    
+    # Initialize face detector
+    face_detector = facer.face_detector('retinaface/mobilenet', device=device)
+    
+    # Detect faces
+    with torch.inference_mode():
+        faces = face_detector(image)
+        
+    if len(faces['bbox']) == 0:
+        raise ValueError("No faces detected in the image!")
+    
+    # Initialize face parser
+    face_parser = facer.face_parser('farl/lapa/448', device=device)
+    
+    # Parse face features
+    with torch.inference_mode():
+        faces = face_parser(image, faces)
+    
+    # Process nose segment
+    nose_array = np.array(faces['seg']['logits'][0][6])
+    nose_array = np.where(nose_array > 0, 1, 0)
+    
+    # Process face segment
+    face_array = np.array(faces['seg']['logits'][0][1])
+    face_array = np.where(face_array > 0, 1, 0)
+    
+    # Combine face and nose arrays
+    face_array = np.clip(face_array + nose_array, 0, 1)
+    
+    # Apply bilateral filter for smoothing
+    smooth_img = cv2.bilateralFilter(input_image, 30, 75, 75)
+    
+    # Apply smoothing only to face region
+    smooth_img[face_array == 0] = input_image[face_array == 0]
+    
+    return smooth_img
+
+def smooth_face(input_img) -> Tuple[np.ndarray, str]:
+    """
+    Gradio interface function to process the image and handle errors.
+    
+    Args:
+        input_img: Input image from Gradio interface
+        
+    Returns:
+        Tuple[np.ndarray, str]: Processed image and status message
+    """
+    try:
+        processed_img = process_image(input_img)
+        return processed_img, "Face smoothing applied successfully!"
+    except ValueError as e:
+        return input_img, str(e)
+    except Exception as e:
+        return input_img, f"Error processing image: {str(e)}"
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=smooth_face,
+    inputs=gr.Image(type="numpy"),
+    outputs=[
+        gr.Image(type="numpy", label="Processed Image"),
+        gr.Textbox(label="Status")
+    ],
+    title="Face Smoothing App",
+    description="Upload an image to apply face smoothing effect. The app will detect faces and apply smoothing only to the face region.",
+    examples=["face-4.jpg"]  # Add example images here if you have any
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()