Update app.py

app.py

@@ -86,29 +86,25 @@ def estimate_depth(image, model_name="depth-anything/Depth-Anything-V2-Small-hf"
     
     return depth_map
 
-def apply_depth_aware_blur(image, max_sigma=15, min_sigma=0):
-    """Apply depth-aware blur with farther objects more blurred."""
-    # Estimate depth (1 = farthest)
-    depth_map = estimate_depth(image)
+def apply_depth_aware_blur(image, depth_map, max_sigma=15, min_sigma=0):
+    """Apply depth-aware blur where farther objects are more blurred."""
     
-    image_array = np.array(image)
-    
-    # Create single blurred version at max sigma for efficiency
-    max_blurred = np.zeros_like(image_array, dtype=np.float32)
+    # Ensure depth map is normalized between 0 (near) and 1 (far)
+    depth_map = (depth_map - depth_map.min()) / (depth_map.max() - depth_map.min())
+
+    # Convert image to NumPy array
+    image_array = np.array(image, dtype=np.float32)
+
+    # Initialize blurred image array
+    blurred_image = np.zeros_like(image_array)
+
+    # Apply pixel-wise Gaussian blur based on depth
     for channel in range(3):
-        max_blurred[:, :, channel] = gaussian_filter(
-            image_array[:, :, channel].astype(np.float32),
-            sigma=max_sigma
-        )
-    
-    # Create 3-channel depth map for blending
-    depth_3d = np.stack([depth_map] * 3, axis=2)
+        sigma_map = min_sigma + depth_map * (max_sigma - min_sigma)  # Map depth to sigma
+        blurred_image[:, :, channel] = gaussian_filter(image_array[:, :, channel], sigma=sigma_map)
     
-    # Blend between original (near) and blurred (far) based on depth
-    # Higher depth values (farther) get more blur
-    result = image_array * (1 - depth_3d) + max_blurred * depth_3d
-    
-    return Image.fromarray(result.astype(np.uint8))
+    # Convert back to image format
+    return Image.fromarray(blurred_image.astype(np.uint8))
 
 def process_image(image, blur_type, sigma=15):
     """Process image based on blur type."""