          retval = np.array(new_images)
          logger.debug("Overlayed foreground. Shape: %s", retval.shape)
          return retval
  
      def _get_headers(self, side, width):
          """ Set header row for the final preview frame
  
          Parameters
          ----------
          side: {"a" or "b"}
 
</s> remove         new_images = list()
        for idx, img in enumerate(backgrounds):
            img[offset:offset + foregrounds[idx].shape[0],
                offset:offset + foregrounds[idx].shape[1], :3] = foregrounds[idx]
            new_images.append(img)
        retval = np.array(new_images)
        logger.debug("Overlayed foreground. Shape: %s", retval.shape)
        return retval
 </s> add         for foreground, background in zip(foregrounds, backgrounds):
            background[offset:offset + foreground.shape[0],
                       offset:offset + foreground.shape[1], :3] = foreground
        logger.debug("Overlayed foreground. Shape: %s", backgrounds.shape)
        return backgrounds </s> remove     def _transform_landmarks(self, side, detected_faces):
        """ Transform frame landmarks to their aligned face variant.

        Parameters
        ----------
        side: {"a" or "b"}
            The side currently being processed
        detected_faces: list
            A list of :class:`lib.faces_detect.DetectedFace` objects

        Returns
        -------
        dict
            The face filenames as keys with the aligned landmarks as value.
        """
        landmarks = dict()
        for face in detected_faces.values():
            face.load_aligned(None, size=self._training_size)
            for filename in self._hash_to_filenames(side, face.hash):
                landmarks[filename] = face.aligned_landmarks
        return landmarks

 </s> add  </s> remove             cv2.rectangle(img, (t_l, t_l), (t_l + length, t_l + length), color, 3)
            cv2.rectangle(img, (b_r, t_l), (b_r - length, t_l + length), color, 3)
            cv2.rectangle(img, (b_r, b_r), (b_r - length, b_r - length), color, 3)
            cv2.rectangle(img, (t_l, b_r), (t_l + length, b_r - length), color, 3)
            new_images.append(img)
        retval = np.array(new_images)
        logger.debug("Overlayed background. Shape: %s", retval.shape)
        return retval
 </s> add             cv2.rectangle(img, (t_l, t_l), (t_l + length, t_l + length), color, 2)
            cv2.rectangle(img, (b_r, t_l), (b_r - length, t_l + length), color, 2)
            cv2.rectangle(img, (b_r, b_r), (b_r - length, b_r - length), color, 2)
            cv2.rectangle(img, (t_l, b_r), (t_l + length, b_r - length), color, 2)
        logger.debug("Overlayed background. Shape: %s", images.shape)
        return images </s> remove         """ Patch targets and prediction images into images of training image size.
 </s> add         """ Patch targets and prediction images into images of model output size. </s> remove         alignments: :class:`lib.alignments.Alignments`
 </s> add         alignments: :class:`lib.align.Alignments`
    @classmethod
    def _get_headers(cls, side, width):
          retval = np.array(new_images)
          logger.debug("Overlayed foreground. Shape: %s", retval.shape)
          return retval
  
    @classmethod
    def _get_headers(cls, side, width):
          """ Set header row for the final preview frame
  
          Parameters
          ----------
          side: {"a" or "b"}
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 
plugins/train/trainer/_base.py