                - The mask only
                - The masked face
          """
          mask = detected_face.mask[mask_type]
          mask.set_blur_and_threshold(**self._output["opts"])
          if not self._output["full_frame"] or self._input_is_faces:
              if self._input_is_faces:
                  face = AlignedFace(detected_face.landmarks_xy,
                                     image=detected_face.image,
 
</s> remove             masked = (face.astype("float32") * mask.astype("float32") / 255.).astype("uint8")
            mask = np.tile(mask, 3)
            for img in (face, masked, mask):
 </s> add             masked = (face.astype("float32") * imask.astype("float32") / 255.).astype("uint8")
            imask = np.tile(imask, 3)
            for img in (face, masked, imask): </s> remove                 out_image = np.concatenate((face, masked, mask), axis=1)
 </s> add                 out_image = np.concatenate((face, masked, imask), axis=1) </s> remove             mask = mask.get_full_frame_mask(face.shape[1], face.shape[0])
            mask = np.expand_dims(mask, -1)
 </s> add             imask = mask.get_full_frame_mask(face.shape[1], face.shape[0])
            imask = np.expand_dims(imask, -1) </s> remove     def _input_faces(self, *args):
 </s> add     def _process_face(self,
                      filename: str,
                      image: np.ndarray,
                      metadata: Dict[str, Any]) -> Optional["ExtractMedia"]:
        """ Process a single face when masking from face images

        filename: str
            the filename currently being processed
        image: :class:`numpy.ndarray`
            The current face being processed
        metadata: dict
            The source frame metadata from the PNG header

        Returns
        -------
        :class:`plugins.pipeline.ExtractMedia` or ``None``
            If the update type is 'output' then nothing is returned otherwise the extract media for
            the face is returned
        """
        frame_name = metadata["source"]["source_filename"]
        face_index = metadata["source"]["face_index"]
        alignment = self._alignments.get_faces_in_frame(frame_name)
        if not alignment or face_index > len(alignment) - 1:
            self._counts["skip"] += 1
            logger.warning("Skipping Face not found in alignments file: '%s'", filename)
            return None
        alignment = alignment[face_index]
        self._counts["face"] += 1

        if self._check_for_missing(frame_name, face_index, alignment):
            return None

        detected_face = self._get_detected_face(alignment)
        if self._update_type == "output":
            detected_face.image = image
            self._save(frame_name, face_index, detected_face)
            return None

        media = ExtractMedia(filename, image, detected_faces=[detected_face])
        media.add_frame_metadata(metadata["source"])
        self._counts["update"] += 1
        return media

    def _input_faces(self, *args: Union[tuple, Tuple["EventQueue"]]) -> None:
        assert detected_face.image is not None
                - The mask only
                - The masked face
          """
          mask = detected_face.mask[mask_type]
        assert detected_face.image is not None
          mask.set_blur_and_threshold(**self._output["opts"])
          if not self._output["full_frame"] or self._input_is_faces:
              if self._input_is_faces:
                  face = AlignedFace(detected_face.landmarks_xy,
                                     image=detected_face.image,
keep keep keep add keep keep keep keep keep
 Minor updates and fixups
  - Mask Tool - Typing + BiSeNet mask update fix
  - Alignments Tool - Auto search for alignments file 
tools/mask/mask.py