Create app.py

app.py

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+import cv2
+import numpy as np
+import gradio as gr
+from PIL import Image
+
+# Dummy segmentation function: replace with your actual segmentation model inference if available.
+def segment_foreground(img):
+    # Convert input image to a NumPy array
+    np_img = np.array(img.convert("RGB"))
+    h, w, _ = np_img.shape
+    # Create a circular mask as a dummy example
+    mask = np.zeros((h, w), dtype=np.uint8)
+    center = (w // 2, h // 2)
+    radius = min(center) - 10
+    cv2.circle(mask, center, radius, (255), thickness=-1)
+    return mask
+
+# Function to apply Gaussian blur to the background using the segmentation mask.
+def gaussian_blur_background(img, sigma=15):
+    mask = segment_foreground(img)
+    np_img = np.array(img.convert("RGB"))
+    # Apply Gaussian blur to the entire image
+    blurred = cv2.GaussianBlur(np_img, (0, 0), sigma)
+    # Prepare the mask in 3 channels
+    mask_3d = np.stack([mask] * 3, axis=-1) / 255.0
+    # Combine the original (foreground) with the blurred (background)
+    combined = np_img * mask_3d + blurred * (1 - mask_3d)
+    return Image.fromarray(combined.astype(np.uint8))
+
+# Dummy depth estimation function: replace with your actual depth estimation inference.
+def estimate_depth(img):
+    np_img = np.array(img.convert("RGB"))
+    h, w, _ = np_img.shape
+    # Create a gradient depth map: top of the image is close (0), bottom is far (1)
+    depth = np.tile(np.linspace(0, 1, h)[:, None], (1, w))
+    return depth
+
+# Function to apply depth-based lens blur.
+def depth_based_blur(img, max_sigma=20):
+    depth = estimate_depth(img)
+    np_img = np.array(img.convert("RGB"))
+    output = np.zeros_like(np_img)
+    
+    # Normalize the depth map to [0, 1]
+    depth_norm = (depth - depth.min()) / (depth.max() - depth.min() + 1e-8)
+    
+    # Apply a variable Gaussian blur to each row based on the depth value (using the first column as representative)
+    for i in range(np_img.shape[0]):
+        sigma = max_sigma * depth_norm[i, 0]
+        row = cv2.GaussianBlur(np_img[i:i+1, :, :], (0, 0), sigma)
+        output[i, :, :] = row
+    return Image.fromarray(output.astype(np.uint8))
+
+# Function that dispatches the processing based on user selection.
+def process_image(img, effect):
+    if effect == "Gaussian Blur Background":
+        return gaussian_blur_background(img)
+    elif effect == "Depth-based Lens Blur":
+        return depth_based_blur(img)
+    else:
+        return img
+
+# Create the Gradio interface with an image input and a radio button to select the effect.
+iface = gr.Interface(
+    fn=process_image,
+    inputs=[
+        gr.inputs.Image(type="pil", label="Input Image"),
+        gr.inputs.Radio(["Gaussian Blur Background", "Depth-based Lens Blur"], label="Select Effect")
+    ],
+    outputs=gr.outputs.Image(type="pil", label="Output Image"),
+    title="Blur Effects Demo",
+    description="Upload an image and choose an effect to apply either a Gaussian Blur to the background or a Depth-based Lens Blur."
+)
+
+if __name__ == "__main__":
+    iface.launch()