Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import torch
+from PIL import Image, ImageFilter
+from transformers import pipeline, SegformerFeatureExtractor, SegformerForSemanticSegmentation
+
+# Load the pre-trained segmentation model
+feature_extractor = SegformerFeatureExtractor.from_pretrained("nvidia/segformer-b1-finetuned-cityscapes-1024-1024")
+segmentation_model = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b1-finetuned-cityscapes-1024-1024")
+
+def apply_blur_effect(image, blur_type):
+    """
+    Applies either Gaussian blur to the whole image, 
+    depth-based blur, or background blur while keeping the foreground sharp.
+    """
+    image = image.resize((512, 512))  # Resize input image
+
+    if blur_type == "Gaussian Blur":
+        # Apply a fixed Gaussian blur to the entire image
+        blurred_image = image.filter(ImageFilter.GaussianBlur(radius=15))
+        return blurred_image
+
+    elif blur_type == "Depth-Based Lens Blur":
+        # Use depth estimation model to get depth map
+        depth_estimator = pipeline(task="depth-estimation", model="Intel/zoedepth-nyu-kitti")
+        outputs = depth_estimator(image)
+        depth_map = np.array(outputs["depth"])
+
+        # Normalize depth map
+        depth_map_normalized = (depth_map - np.min(depth_map)) / (np.max(depth_map) - np.min(depth_map))
+        depth_array = np.clip(depth_map_normalized * 5, 0, 5).astype(int)  # Scale depth to select blur levels
+
+        # Generate different levels of Gaussian blur
+        blur_levels = [image.filter(ImageFilter.GaussianBlur(radius=r)) for r in range(6)]
+        
+        # Create depth-based blur image
+        depth_based_blur_image = Image.new("RGB", image.size)
+        for i in range(image.width):
+            for j in range(image.height):
+                blur_level = depth_array[j, i]
+                depth_based_blur_image.putpixel((i, j), blur_levels[blur_level].getpixel((i, j)))
+        
+        return depth_based_blur_image
+
+    elif blur_type == "Background Blur (Zoom Effect)":
+        # Perform segmentation to get foreground and background masks
+        inputs = feature_extractor(images=image, return_tensors="pt")
+        outputs = segmentation_model(**inputs)
+        logits = outputs.logits  # Shape: (batch, num_classes, height, width)
+        predicted_mask = torch.argmax(logits, dim=1).squeeze().cpu().numpy()
+
+        # Create a binary mask (1 = foreground, 0 = background)
+        foreground_mask = (predicted_mask == 24).astype(np.uint8)  # 'Person' class in Cityscapes dataset
+
+        # Convert the mask into a PIL image for processing
+        mask_image = Image.fromarray((foreground_mask * 255).astype(np.uint8))
+
+        # Apply Gaussian blur to the entire image
+        blurred_image = image.filter(ImageFilter.GaussianBlur(radius=15))
+
+        # Blend the sharp foreground and blurred background
+        final_image = Image.composite(image, blurred_image, mask_image)
+
+        return final_image
+
+    return image
+
+# Gradio UI
+interface = gr.Interface(
+    fn=apply_blur_effect,
+    inputs=[
+        gr.Image(type="pil"),  
+        gr.Radio(["Gaussian Blur", "Depth-Based Lens Blur", "Background Blur (Zoom Effect)"], label="Blur Type"),
+    ],
+    outputs="image",
+    title="Image Blur Effects: Gaussian, Depth-Based & Background Blur",
+    description="Upload an image and apply Gaussian blur, depth-based blur, or background blur (Zoom-like effect).",
+)
+
+# Launch the Gradio app
+if __name__ == "__main__":
+    interface.launch()