Update utils_mask.py

utils_mask.py

@@ -85,10 +85,10 @@ def get_mask_location(model_type, category, model_parse: Image.Image, keypoint:
         parser_mask_fixed += parser_mask_fixed_lower_cloth
 
         # 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)
+        parse_mask_legs = np.logical_or.reduce((parse_array == label_map["skirt"],
+                                                parse_array == label_map["pants"],
+                                                parse_array == label_map["left_leg"],
+                                                parse_array == label_map["right_leg"])).astype(np.float32)
 
         # 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)
@@ -96,6 +96,7 @@ def get_mask_location(model_type, category, model_parse: Image.Image, keypoint:
         # 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)
         parser_mask_fixed_lower_cloth = (parse_array == label_map["skirt"]).astype(np.float32) + \