          batch = read_image_batch(filenames)
          processed = dict()
  
          # Initialize processing training size on first image
          if not self._processing.initialized:
              self._processing.initialize(batch.shape[1])
  
 
</s> remove         if self._training_opts["warp_to_landmarks"]:
 </s> add         if self._mask_class or to_landmarks: </s> remove             batch[..., :3] = self._processing.color_adjust(batch[..., :3])
 </s> add             batch = self._processing.color_adjust(batch)

        # Add mask to batch prior to transforms and warps
        if self._mask_class:
            batch = np.array([self._mask_class(src_pts, image, channels=4).mask
                              for src_pts, image in zip(batch_src_pts, batch)]) </s> remove         # Color Augmentation of the image only
 </s> add         # Color augmentation before mask is added </s> remove             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)
        else:
            warp_kwargs = dict()
 </s> add  </s> remove         retval = (self.model.training_opts.get("replicate_input_mask", False) or
                  self.model.training_opts.get("penalized_mask_loss", True))
 </s> add         retval = bool(self.model.training_opts.get("mask_type", None))
        to_landmarks = self._training_opts["warp_to_landmarks"]
          batch = read_image_batch(filenames)
          processed = dict()
        to_landmarks = self._training_opts["warp_to_landmarks"]
  
          # Initialize processing training size on first image
          if not self._processing.initialized:
              self._processing.initialize(batch.shape[1])
  
keep add keep keep keep keep keep
 Serialize masks to alignments file

- Add new serializers (npy + compressed)
- Remove Serializer option from cli
- Revert get_aligned call in scripts/extract
- Default alignments to compressed
- Size masks to 128px and compress
- Remove mask thresholding/blur from generation code
- Add Mask class to lib/faces_detect
- Revert debug landmarks to aligned face
- Revert non-extraction code to staging version 
lib/training_data.py