          """ Performs the augmentation and compiles target images and samples. See
          :func:`minibatch_ab` for more details on the output. """
          logger.trace("Process batch: (filenames: '%s', side: '%s')", filenames, side)
          batch = read_image_batch(filenames)
          batch = self._apply_mask(filenames, batch, side)
          processed = dict()
  
          # Initialize processing training size on first image
          if not self._processing.initialized:
              self._processing.initialize(batch.shape[1])
 
</s> remove         logger.trace("Input batch shape: %s, side: %s", batch.shape, side)
 </s> add         logger.trace("Input filenames: %s, batch shape: %s, side: %s",
                     filenames, batch.shape, side) </s> remove     def _get_landmarks(self, filenames, side):
        """ Obtains the 68 Point Landmarks for the images in this batch. This is only called if
        config :attr:`_warp_to_landmarks` is ``True``. If the landmarks for an image cannot be
        found, then an error is raised. """
        logger.trace("Retrieving landmarks: (filenames: %s, side: '%s')", filenames, side)
        src_points = [self._landmarks[side].get(filename, None) for filename in filenames]
        # Raise error on missing alignments
        if not all(isinstance(pts, np.ndarray) for pts in src_points):
            missing = [filenames[idx] for idx, pts in enumerate(src_points) if pts is None]
            msg = ("Files missing alignments for this batch: {}"
                   "\nAt least one of your images does not have a matching entry in your "
                   "alignments file."
                   "\nIf you are using 'warp to landmarks' then every "
                   "face you intend to train on must exist within the alignments file."
                   "\nThe specific files that caused this failure are listed above."
                   "\nMost likely there will be more than just these files missing from the "
                   "alignments file. You can use the Alignments Tool to help identify missing "
                   "alignments".format(missing))
            raise FaceswapError(msg)

        logger.trace("Returning: (src_points: %s)", [str(src) for src in src_points])
        return np.array(src_points)

 </s> add  </s> remove             batch_src_pts = self._get_landmarks(filenames, side)
            batch_dst_pts = self._get_closest_match(filenames, side, batch_src_pts)
            warp_kwargs = dict(batch_src_points=batch_src_pts,
                               batch_dst_points=batch_dst_pts)
 </s> add             batch_dst_pts = self._get_closest_match(filenames, side, landmarks)
            warp_kwargs = dict(batch_src_points=landmarks, batch_dst_points=batch_dst_pts) </s> remove         retval = dict()
 </s> add  </s> remove             retval[side] = self._to_detected_faces(alignments, side)
        logger.debug("Returning: %s", {k: len(v) for k, v in retval.items()})
        return retval
 </s> add             self._detected_faces[side] = self._to_detected_faces(alignments, side)
            self._alignments_version[side] = alignments.version
        logger.debug("alignments_versions: %s, detected_faces: %s, ", self._alignments_version,
                     {k: len(v) for k, v in self._detected_faces.items()})
        batch, landmarks = self._crop_to_center(filenames, batch, side)
          """ Performs the augmentation and compiles target images and samples. See
          :func:`minibatch_ab` for more details on the output. """
          logger.trace("Process batch: (filenames: '%s', side: '%s')", filenames, side)
          batch = read_image_batch(filenames)
        batch, landmarks = self._crop_to_center(filenames, batch, side)
          batch = self._apply_mask(filenames, batch, side)
          processed = dict()
  
          # Initialize processing training size on first image
          if not self._processing.initialized:
              self._processing.initialize(batch.shape[1])
keep keep keep add keep 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 
lib/training_data.py