Update app.py

app.py

@@ -46,21 +46,21 @@ def process_image(image, categories_to_hide):
 
     # Expand clothing boundaries if clothes are in `categories_to_hide`
     if "Clothes" in categories_to_hide:
-    clothing_mask = np.isin(mask_data, grouped_mapping["Clothes"]).astype(np.uint8)
-
-    # Determine kernel size (2% of the smaller image dimension)
-    height, width = clothing_mask.shape
-    kernel_size = max(1, int(0.02 * min(height, width)))  # Ensure at least 1 pixel
-    kernel = np.ones((kernel_size, kernel_size), np.uint8)
-
-    # **Step 1: Fill gaps using Morphological Closing (Dilation + Erosion)**
-    closed_clothing_mask = cv2.morphologyEx(clothing_mask, cv2.MORPH_CLOSE, kernel, iterations=1)
-
-    # **Step 2: Expand clothing boundary using Dilation**
-    dilated_clothing_mask = cv2.dilate(closed_clothing_mask, kernel, iterations=1)
-
-    # Update mask_combined with the expanded clothing mask
-    mask_combined |= (dilated_clothing_mask == 1)
+        clothing_mask = np.isin(mask_data, grouped_mapping["Clothes"]).astype(np.uint8)
+    
+        # Determine kernel size (2% of the smaller image dimension)
+        height, width = clothing_mask.shape
+        kernel_size = max(1, int(0.02 * min(height, width)))  # Ensure at least 1 pixel
+        kernel = np.ones((kernel_size, kernel_size), np.uint8)
+    
+        # **Step 1: Fill gaps using Morphological Closing (Dilation + Erosion)**
+        closed_clothing_mask = cv2.morphologyEx(clothing_mask, cv2.MORPH_CLOSE, kernel, iterations=1)
+    
+        # **Step 2: Expand clothing boundary using Dilation**
+        dilated_clothing_mask = cv2.dilate(closed_clothing_mask, kernel, iterations=1)
+    
+        # Update mask_combined with the expanded clothing mask
+        mask_combined |= (dilated_clothing_mask == 1)
 
 
     # Apply the mask (preserve only selected regions)