Update utils_mask.py

utils_mask.py

@@ -83,22 +83,18 @@ def get_mask_location(model_type, category, model_parse: Image.Image, keypoint:
         parser_mask_fixed_lower_cloth = (parse_array == label_map["skirt"]).astype(np.float32) + \
                                         (parse_array == label_map["pants"]).astype(np.float32)
         parser_mask_fixed += parser_mask_fixed_lower_cloth
-        parser_mask_changeable += np.logical_and(parse_array, np.logical_not(parser_mask_fixed))
-
-        # Create a separate mask for the legs
-        parse_mask_legs = (parse_array == 12).astype(np.float32) + \
-                          (parse_array == 13).astype(np.float32) + \
-                          (parse_array == 6).astype(np.float32) +\
-                          (parse_array == 5).astype(np.float32)
 
-        # Fill gaps between legs
-        leg_mask_filled = cv2.dilate(parse_mask_legs.astype(np.uint8), np.ones((6, 6), np.uint8), iterations=6)
+        # Create a mask for the legs (including skirts and pants)
+        parse_mask_legs = (parse_array == label_map["skirt"]).astype(np.float32) + \
+                          (parse_array == label_map["pants"]).astype(np.float32) + \
+                          (parse_array == label_map["left_leg"]).astype(np.float32) + \
+                          (parse_array == label_map["right_leg"]).astype(np.float32)
 
-        # Combine the filled leg mask with the upper body mask
-        combined_mask = np.maximum(parse_mask_upper, leg_mask_filled)
+        # Dilate the leg mask to ensure coverage and fill gaps
+        parse_mask_legs = cv2.dilate(parse_mask_legs.astype(np.uint8), np.ones((6, 6), np.uint8), iterations=6)
 
-        # Final mask without further dilation to avoid affecting the upper body mask
-        parse_mask = combined_mask
+        # Combine the upper body mask with the leg mask
+        parse_mask = np.maximum(parse_mask_upper, parse_mask_legs)
 
     elif category == 'upper_body':
         parse_mask = (parse_array == 4).astype(np.float32) + (parse_array == 7).astype(np.float32)