Update utils_mask.py

utils_mask.py

@@ -80,20 +80,20 @@ def get_mask_location(model_type, category, model_parse: Image.Image, keypoint:
     if category == 'dresses':
         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)
+                        (parse_array == 12).astype(np.float32) + \
+                        (parse_array == 13).astype(np.float32) + \
+                        (parse_array == 5).astype(np.float32)
 
-        # Fill only the gaps between the legs
+        # Fill gaps between the legs
         parse_mask_legs = (parse_array == label_map["left_leg"]).astype(np.float32) + \
-                          (parse_array == label_map["right_leg"]).astype(np.float32)
+                        (parse_array == label_map["right_leg"]).astype(np.float32)
         parse_mask_legs_dilated = cv2.dilate(parse_mask_legs.astype(np.uint8), np.ones((5, 5), np.uint8), iterations=6)
         parse_mask_lower = np.maximum(parse_mask_lower, parse_mask_legs_dilated)
 
-        # Close the gap between the legs
-        fill_gap_between_legs = cv2.morphologyEx(parse_mask_lower.astype(np.uint8), cv2.MORPH_CLOSE, np.ones((15, 15), np.uint8))
+        # Combine upper and filled lower body masks
+        parse_mask = np.logical_or(parse_mask_upper, parse_mask_lower)
 
-        parse_mask = np.logical_or(parse_mask_upper, fill_gap_between_legs)
+        
 
     elif category == 'upper_body':
         parse_mask = (parse_array == 4).astype(np.float32) + (parse_array == 7).astype(np.float32)