                  self._faces_per_filename[item["filename"]] = len(item["detected_faces"])
          return item
  
      def _predict(self, batch):
          """ Just return the aligner's predict function """
          return self.predict(batch)
  
      def finalize(self, batch):
          """ Finalize the output from Aligner
  
 
</s> remove         """ Finalize the output from Aligner
 </s> add         """ Finalize the output from Masker </s> remove         batch["prediction"] = np.full(batch["feed"].shape[:-1] + (1,),
                                      fill_value=255,
                                      dtype='uint8')
 </s> add         batch["prediction"] = np.ones_like(batch["feed"], dtype="float32") </s> remove                 hull = cv2.convexHull(item).astype('int32')  # pylint: disable=no-member
                cv2.fillConvexPoly(mask, hull, 255, lineType=cv2.LINE_AA)
        batch["prediction"] = masks
 </s> add                 hull = cv2.convexHull(item).astype("int32")  # pylint: disable=no-member
                cv2.fillConvexPoly(mask, hull, 1.0, lineType=cv2.LINE_AA)
        batch["prediction"] = batch["feed"] </s> remove                 hull = cv2.convexHull(item).astype('int32')  # pylint: disable=no-member
                cv2.fillConvexPoly(mask, hull, 255, lineType=cv2.LINE_AA)
        batch["prediction"] = masks
 </s> add                 hull = cv2.convexHull(item).astype("int32")  # pylint: disable=no-member
                cv2.fillConvexPoly(mask, hull, 1.0, lineType=cv2.LINE_AA)
        batch["prediction"] = batch["feed"] </s> remove         predictions = self.model.predict(batch["feed"])
        batch["prediction"] = predictions * 255.
 </s> add         batch["prediction"] = self.model.predict(batch["feed"])
        """ Just return the masker's predict function """
                  self._faces_per_filename[item["filename"]] = len(item["detected_faces"])
          return item
  
      def _predict(self, batch):
        """ Just return the masker's predict function """
          return self.predict(batch)
  
      def finalize(self, batch):
          """ Finalize the output from Aligner
  
keep keep keep keep replace keep keep keep keep keep
 Mask plugin cleanup

- PEP8 Fixes
- Remove config for non NN Masks
- Tidy up defaults helptext
- cli.py fix typos
- Remove unused imports and functions _base.py
- Standardize input_size param
- Enable and update documentation
- Change references from `aligner` to `masker`
- Change  input_size, output_size and coverage_ratio from kwargs to params
- Move load_aligned to batch input iterator
- Remove unnecessary self.input param
- Add softmax layer append function to KSession
- Remove references to KSession protected objects
- Standardize plugin output into finalize method
- Make masks full frame and add to lib.faces_detect
- Add masks to alignments.json (temporary zipped base64 solution) 
plugins/extract/mask/_base.py