Update utils_mask.py

utils_mask.py

@@ -77,29 +77,25 @@ 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 = np.logical_or((parse_array == label_map["upper_clothes"]), (parse_array == label_map["dress"])).astype(np.float32)
-       
+        parse_mask_upper = (parse_array == 4).astype(np.float32) + (parse_array == 7).astype(np.float32)
+        parse_mask_lower = (parse_array == 6).astype(np.float32) + \
+                           (parse_array == 12).astype(np.float32) + \
+                           (parse_array == 13).astype(np.float32) + \
+                           (parse_array == 5).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
 
-        # 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)
+        parse_mask = np.logical_or(parse_mask_upper, parse_mask_lower)
 
         # Dilate the leg mask to ensure coverage and fill gaps
+        parse_mask_legs = np.logical_or.reduce((parse_array == label_map["left_leg"],
+                                                parse_array == label_map["right_leg"],
+                                                parse_array == label_map["skirt"],
+                                                parse_array == label_map["pants"])).astype(np.float32)
         parse_mask_legs_dilated = cv2.dilate(parse_mask_legs.astype(np.uint8), np.ones((6, 6), np.uint8), iterations=6)
-
-        # Combine the upper body mask with the dilated leg mask
-        parse_mask = np.maximum(parse_mask_upper, parse_mask_legs_dilated)
-
-       
-
-    
-
-
+        parse_mask = np.maximum(parse_mask, parse_mask_legs_dilated)
 
     elif category == 'upper_body':
         parse_mask = (parse_array == 4).astype(np.float32) + (parse_array == 7).astype(np.float32)
@@ -139,7 +135,7 @@ def get_mask_location(model_type, category, model_parse: Image.Image, keypoint:
         size_left = [shoulder_left[0] - ARM_LINE_WIDTH // 2, shoulder_left[1] - ARM_LINE_WIDTH // 2, shoulder_left[0] + ARM_LINE_WIDTH // 2, shoulder_left[1] + ARM_LINE_WIDTH // 2]
         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:
@@ -180,5 +176,3 @@ def get_mask_location(model_type, category, model_parse: Image.Image, keypoint:
     mask_gray = Image.fromarray(inpaint_mask.astype(np.uint8) * 127)
 
     return mask, mask_gray
-
-