                                self._meta["affine_matrix"][face_index],
                                frame_dims,
                                frame,
                                flags=cv2.WARP_INVERSE_MAP | interpolator,
                                borderMode=cv2.BORDER_CONSTANT)[slices[0], slices[1]][..., None]
          rgb = np.tile(rgb_color, mask.shape).astype("uint8")
          rgba = np.concatenate((rgb, np.minimum(mask, self._meta["roi_mask"][face_index])), axis=2)
          return Image.fromarray(rgba)
  
      def _update_roi_box(self, mask, face_index, color):
 
</s> remove                            detected_face.reference_matrix,
 </s> add                            reference_face.adjusted_matrix, </s> remove         mask: :class:`~lib.faces_detect.Mask`
 </s> add         mask: :class:`~lib.align.Mask` </s> remove     @classmethod
    def _frame_overlay(cls, images, target_size, color):
 </s> add     def _process_full(self, side, images, prediction_size, color): </s> remove         for mask, face in zip(batch["feed"], batch["detected_faces"]):
            parts = self.parse_parts(np.array(face.feed_landmarks))
 </s> add         for mask, face in zip(batch["feed"], batch["feed_faces"]):
            parts = self.parse_parts(np.array(face.landmarks)) </s> remove         for mask, face in zip(batch["feed"], batch["detected_faces"]):
            parts = self.parse_parts(np.array(face.feed_landmarks))
 </s> add         for mask, face in zip(batch["feed"], batch["feed_faces"]):
            parts = self.parse_parts(np.array(face.landmarks))
                              borderMode=cv2.BORDER_CONSTANT)[slices[0], slices[1]]
        mask = mask[..., None] if mask.ndim == 2 else mask
                                self._meta["affine_matrix"][face_index],
                                frame_dims,
                                frame,
                                flags=cv2.WARP_INVERSE_MAP | interpolator,
                              borderMode=cv2.BORDER_CONSTANT)[slices[0], slices[1]]
        mask = mask[..., None] if mask.ndim == 2 else mask
          rgb = np.tile(rgb_color, mask.shape).astype("uint8")
          rgba = np.concatenate((rgb, np.minimum(mask, self._meta["roi_mask"][face_index])), axis=2)
          return Image.fromarray(rgba)
  
      def _update_roi_box(self, mask, face_index, color):
keep keep keep keep replace keep keep keep keep keep
 Extract - Increase area and move centering (#1095)

* Extract
  - Implement aligner re-feeding
  - Add extract type to pipeline.ExtractMedia
  - Add pose annotation to debug
* Convert
  - implement centering
  - remove usage of feed and reference face properties
  - Remove distributed option from convert
  - Force update of alignments file on legacy receive
* Train
  - Resize preview image to model output size
  - Force legacy centering if centering does not exist in model's state file
  - Enable training on legacy face sets

* Alignments Tool
  - Update draw to include head/pose
  - Remove DFL drop + linting
  - Remove remove-frames job
  - remove align-eyes option
  - Update legacy masks to new extract type
  - Exit if attempting to merge version 1.0 alignments files with version 2.0 alignments files
  - Re-generate thumbnails on legacy upgrade
* Mask Tool
  - Update for new extract + bugfix full frame
* Manual Tool
  - Update to new extraction method
   - Disable legacy alignments,
   - extract box bugfix
   - extract faces - size to 512 and center on head
* Preview Tool
  - Display based on model centering
* Sort Tool
  - Use alignments for sort by face

* lib.aligner
  - Add Pose Class
  - Add AlignedFace Class
  - center _MEAN_FACE on x
  - Add meta information with versioning to alignments file
  - lib.aligner.get_align_matrix to use landmarks not face
  - Refactor aligned faces in lib.faces_detect
* lib.logger
  - larger file log padding
* lib.config
  - Fix global changeable_items
* lib.face_filter
  - Use new extracted face images
* lib.image
  - bump thumbnail default size to 96px 
tools/manual/frameviewer/editor/mask.py