          """ Run model to get predictions """
          for mask, face in zip(batch["feed"], batch["detected_faces"]):
              parts = self.parse_parts(np.array(face.feed_landmarks))
              for item in parts:
                  item = np.concatenate(item)
                  hull = cv2.convexHull(item).astype("int32")  # pylint: disable=no-member
                  cv2.fillConvexPoly(mask, hull, 1.0, lineType=cv2.LINE_AA)
          batch["prediction"] = batch["feed"]
          return batch
  
      def process_output(self, batch):
 
</s>
                item = np.rint(np.concatenate(item)).astype("int32")
                hull = cv2.convexHull(item)
          """ Run model to get predictions """
          for mask, face in zip(batch["feed"], batch["detected_faces"]):
              parts = self.parse_parts(np.array(face.feed_landmarks))
              for item in parts:
                item = np.rint(np.concatenate(item)).astype("int32")
                hull = cv2.convexHull(item)
                item = np.rint(np.concatenate(item)).astype("int32")
                hull = cv2.convexHull(item)
                  cv2.fillConvexPoly(mask, hull, 1.0, lineType=cv2.LINE_AA)
          batch["prediction"] = batch["feed"]
          return batch
  
      def process_output(self, batch):
keep keep keep keep replace replace keep keep keep keep keep
 Landmarks stored and used as floating point numbers (#928)

* remove and fix int adjustments
* masking rounding
plugins/extract/mask/extended.py