Create app.py

app.py

@@ -0,0 +1,55 @@
+import numpy as np
+import torch
+import gradio as gr
+from transformers import DPTForDepthEstimation, DPTImageProcessor
+from PIL import Image
+import cv2
+
+# Load the model and processor only once (faster performance)
+model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large")
+processor = DPTImageProcessor.from_pretrained("Intel/dpt-large")
+
+def apply_depth_blur(image, max_blur=31, threshold=0.2):
+    """Applies depth-based Gaussian blur using the DPT model."""
+    image_np = np.array(image)  # Convert to numpy array
+    inputs = processor(images=image, return_tensors="pt")
+
+    with torch.no_grad():
+        depth_map = model(**inputs).predicted_depth
+        depth_map = torch.nn.functional.interpolate(
+            depth_map.unsqueeze(1),
+            size=image.size[::-1],
+            mode="bicubic",
+            align_corners=False,
+        ).squeeze().cpu().numpy()
+    
+    # Normalize the depth map
+    depth_map = (depth_map - depth_map.min()) / (depth_map.max() - depth_map.min())
+    depth_map = cv2.GaussianBlur(depth_map, (9, 9), 0)
+
+    # Apply different blur levels based on depth
+    output = image_np.copy()
+    unique_blur_values = np.unique((depth_map * max_blur).astype(int) // 2 * 2 + 1)
+    
+    for ksize in unique_blur_values:
+        if ksize > 1:
+            mask = (depth_map * max_blur).astype(int) == ksize
+            output[mask] = cv2.GaussianBlur(image_np, (ksize, ksize), 0)[mask]
+    
+    return output
+
+# Gradio UI
+with gr.Blocks() as app:
+    gr.Markdown("## 🔥 Depth-Based Gaussian Blur Effect 🔥")
+    with gr.Row():
+        with gr.Column():
+            img_input = gr.Image(label="Upload Image", type="pil")
+            max_blur = gr.Slider(1, 101, 31, step=2, label="Max Blur Intensity")
+            threshold = gr.Slider(0, 1, 0.2, step=0.1, label="Blur Threshold")
+            btn = gr.Button("Apply Blur")
+        with gr.Column():
+            img_output = gr.Image(label="Blurred Image")
+    
+    btn.click(fn=apply_depth_blur, inputs=[img_input, max_blur, threshold], outputs=img_output)
+
+app.launch()