Update utils_mask.py

utils_mask.py

@@ -76,22 +76,22 @@ def get_mask_location(model_type, category, model_parse: Image.Image, keypoint:
 
     arms_left = (parse_array == label_map["left_arm"]).astype(np.float32)
     arms_right = (parse_array == label_map["right_arm"]).astype(np.float32)
-
+    
     if category == 'dresses':
-        parse_mask_upper = (parse_array == label_map["upper_clothes"]).astype(np.float32) + \
-                           (parse_array == label_map["dress"]).astype(np.float32)
-
-        parse_mask_lower = (parse_array == label_map["skirt"]).astype(np.float32) + \
-                           (parse_array == label_map["pants"]).astype(np.float32)
-
-        # Dilate the leg parts to fill gaps
-        parse_mask_legs = (parse_array == label_map["left_leg"]).astype(np.float32) + \
-                          (parse_array == label_map["right_leg"]).astype(np.float32)
+        # Initial dress mask for the upper body (excluding head)
+        parse_mask_upper = np.logical_or((parse_array == label_map["upper_clothes"]), (parse_array == label_map["dress"])).astype(np.float32)
+        
+        # Create a mask for the legs (including skirts and pants)
+        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_dilated = cv2.dilate(parse_mask_legs.astype(np.uint8), np.ones((6, 6), np.uint8), iterations=6)
 
-        # Combine upper body and dilated lower body masks
-        parse_mask_lower = np.maximum(parse_mask_lower, parse_mask_legs_dilated)
-        parse_mask = np.logical_or(parse_mask_upper, parse_mask_lower)
+        # Combine the upper body mask with the dilated leg mask
+        parse_mask = np.maximum(parse_mask_upper, parse_mask_legs_dilated)
 
     elif category == 'upper_body':
         parse_mask = (parse_array == label_map["upper_clothes"]).astype(np.float32)