Update utils_mask.py

utils_mask.py

@@ -60,7 +60,7 @@ def get_mask_location(model_type, category, model_parse: Image.Image, keypoint:
     elif model_type == 'dc':
         arm_width = 45
     else:
-        raise ValueError("model_type must be \'hd\' or \'dc\'!")
+        raise ValueError("model_type must be 'hd' or 'dc'!")
 
     parse_head = (parse_array == 1).astype(np.float32) + \
                  (parse_array == 3).astype(np.float32) + \
@@ -77,17 +77,18 @@ 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 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)
+        parse_mask_upper = (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) + \
+                                        (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 == 13).astype(np.float32) + \
+                          (parse_array == 6).astype(np.float32)
         
         parser_mask_changeable += np.logical_and(parse_array, np.logical_not(parser_mask_fixed))
 
@@ -97,9 +98,8 @@ def get_mask_location(model_type, category, model_parse: Image.Image, keypoint:
         # 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)
-
+        # Final mask without further dilation to avoid affecting the upper body mask
+        parse_mask = combined_mask
 
     elif category == 'upper_body':
         parse_mask = (parse_array == 4).astype(np.float32) + (parse_array == 7).astype(np.float32)
@@ -140,7 +140,6 @@ def get_mask_location(model_type, category, model_parse: Image.Image, keypoint:
         size_right = [shoulder_right[0] - ARM_LINE_WIDTH // 2, shoulder_right[1] - ARM_LINE_WIDTH // 2, shoulder_right[0] + ARM_LINE_WIDTH // 2,
                       shoulder_right[1] + ARM_LINE_WIDTH // 2]
         
-
         if wrist_right[0] <= 1. and wrist_right[1] <= 1.:
             im_arms_right = arms_right
         else: