  
          Returns
          -------
          :class:`numpy,ndarray`
              The samples with the frame overlay applied
          """
          logger.debug("full_size: %s, target_size: %s, color: %s",
                       images.shape[1], target_size, color)
          new_images = list()
          full_size = images.shape[1]
 
</s> remove         logger.debug("full_size: %s, target_size: %s, color: %s",
                     images.shape[1], target_size, color)
        new_images = list()
        full_size = images.shape[1]
        padding = (full_size - target_size) // 2
        length = target_size // 4
        t_l, b_r = (padding, full_size - padding)
 </s> add         logger.debug("full_size: %s, prediction_size: %s, color: %s",
                     images.shape[1], prediction_size, color)

        display_size = int(prediction_size * (1 + (1 - self._coverage_ratio)))
        images = self._resize_sample(side, images, display_size)  # Resize targets to display size
        padding = (display_size - prediction_size) // 2
        if padding == 0:
            logger.debug("Resized background. Shape: %s", images.shape)
            return images

        length = display_size // 4
        t_l, b_r = (padding, display_size - padding) </s> remove             The samples to apply the frame to
        target_size: int
            The size of the sample within the full size frame
 </s> add             The input training images to to process
        prediction_size: int
            The size of the predicted output from the model </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         mask = self._get_mask(detected_face, predicted_mask)
 </s> add         mask = self._get_mask(detected_face, predicted_mask, sub_crop_offset) </s> remove         :class:`~lib.alignments.Alignments`
 </s> add         :class:`~lib.align.Alignments`
            The input training images, sized for output and annotated for coverage
  
          Returns
          -------
          :class:`numpy,ndarray`
            The input training images, sized for output and annotated for coverage
          """
          logger.debug("full_size: %s, target_size: %s, color: %s",
                       images.shape[1], target_size, color)
          new_images = list()
          full_size = images.shape[1]
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