Create app.py

app.py

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+import cv2
+import numpy as np
+import gradio as gr
+from PIL import Image
+
+
+def resize_to_512(img: Image.Image) -> Image.Image:
+    if img.size != (512, 512):
+        return img.resize((512, 512))
+    return img
+
+
+def gaussian_blur(img: Image.Image, kernel_size: int):
+    img = resize_to_512(img)
+    img_cv = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
+    blurred = cv2.GaussianBlur(img_cv, (kernel_size | 1, kernel_size | 1), 0)
+    return cv2.cvtColor(blurred, cv2.COLOR_BGR2RGB)
+
+
+def lens_blur(img: Image.Image, max_blur_radius: int):
+    img = resize_to_512(img)
+    original = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
+    original_rgb = cv2.cvtColor(original, cv2.COLOR_BGR2RGB)
+
+    # Create synthetic depth map
+    depth_norm = np.zeros((original.shape[0], original.shape[1]), dtype=np.float32)
+    cv2.circle(depth_norm, (original.shape[1] // 2, original.shape[0] // 2), 100, 1, -1)
+    depth_norm = cv2.GaussianBlur(depth_norm, (21, 21), 0)
+
+    blurred_image = np.zeros_like(original_rgb)
+
+    for i in range(original.shape[0]):
+        for j in range(original.shape[1]):
+            blur_radius = int(depth_norm[i, j] * max_blur_radius)
+            if blur_radius % 2 == 0:
+                blur_radius += 1
+
+            x_min = max(j - blur_radius, 0)
+            x_max = min(j + blur_radius, original.shape[1])
+            y_min = max(i - blur_radius, 0)
+            y_max = min(i + blur_radius, original.shape[0])
+
+            roi = original_rgb[y_min:y_max, x_min:x_max]
+
+            if blur_radius > 1:
+                blurred_roi = cv2.GaussianBlur(roi, (blur_radius, blur_radius), 0)
+                try:
+                    blurred_image[i, j] = blurred_roi[
+                        blur_radius // 2, blur_radius // 2
+                    ]
+                except:
+                    blurred_image[i, j] = original_rgb[i, j]
+            else:
+                blurred_image[i, j] = original_rgb[i, j]
+
+    return blurred_image
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("## Gaussian and Lens Blur App")
+    with gr.Row():
+        image_input = gr.Image(type="pil", label="Upload an Image")
+    with gr.Row():
+        kernel_slider = gr.Slider(1, 49, value=11, step=2, label="Gaussian Kernel Size")
+        max_blur_slider = gr.Slider(
+            1, 50, value=15, step=1, label="Max Lens Blur Radius"
+        )
+    with gr.Row():
+        gaussian_output = gr.Image(label="Gaussian Blurred Image")
+        lens_output = gr.Image(label="Depth-Based Lens Blurred Image")
+    with gr.Row():
+        blur_btn = gr.Button("Apply Blur")
+
+    blur_btn.click(
+        fn=gaussian_blur, inputs=[image_input, kernel_slider], outputs=gaussian_output
+    )
+    blur_btn.click(
+        fn=lens_blur, inputs=[image_input, max_blur_slider], outputs=lens_output
+    )
+
+demo.launch()