Update utils_mask.py

utils_mask.py

@@ -77,8 +77,9 @@ def get_mask_location(model_type, category, model_parse: Image.Image, keypoint:
     arms_left = (parse_array == 14).astype(np.float32)
     arms_right = (parse_array == 15).astype(np.float32)
 
+  
     if category == 'dresses':
-        # Initial dress mask without legs
+        # Initial dress mask for the upper body
         parse_mask_upper = (parse_array == 7).astype(np.float32) + \
                         (parse_array == 4).astype(np.float32) + \
                         (parse_array == 5).astype(np.float32)
@@ -87,18 +88,17 @@ def get_mask_location(model_type, category, model_parse: Image.Image, keypoint:
         parse_mask_legs = (parse_array == 12).astype(np.float32) + \
                         (parse_array == 13).astype(np.float32) + \
                         (parse_array == 6).astype(np.float32)
-
+        
         parser_mask_changeable += np.logical_and(parse_array, np.logical_not(parser_mask_fixed))
 
         # Fill gaps between legs
         leg_mask_filled = cv2.dilate(parse_mask_legs.astype(np.uint8), np.ones((6, 6), np.uint8), iterations=6)
-    
 
-        # Combine filled leg mask with upper body mask
-        parse_mask = np.maximum(parse_mask_upper, leg_mask_filled)
-
-  
+        # Combine the filled leg mask with the upper body mask
+        combined_mask = np.maximum(parse_mask_upper, leg_mask_filled)
 
+        # Final dilation to ensure there are no small gaps
+        parse_mask = cv2.dilate(combined_mask.astype(np.uint8), np.ones((5, 5), np.uint8), iterations=5)
 
 
     elif category == 'upper_body':