          if not self._processing.initialized:
              self._processing.initialize(batch.shape[1])
  
          # Get Landmarks prior to manipulating the image
          if self._training_opts["warp_to_landmarks"]:
              batch_src_pts = self._get_landmarks(filenames, batch, 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)
          else:
 
</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         # Random Warp # TODO change masks to have a input mask and a warped target mask
        processed["feed"] = [self._processing.warp(batch[..., :3],
                                                   self._training_opts["warp_to_landmarks"],
                                                   **warp_kwargs)]
 </s> add         # Random Warp
        if to_landmarks:
            warp_kwargs = dict(batch_src_points=batch_src_pts,
                               batch_dst_points=self._get_closest_match(filenames,
                                                                        side,
                                                                        batch_src_pts))
        else:
            warp_kwargs = dict()
        processed["feed"] = self._processing.warp(batch[..., :3], to_landmarks, **warp_kwargs) </s> remove         # Color Augmentation of the image only
 </s> add         # Color augmentation before mask is added </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)])
        if self._mask_class or to_landmarks:
          if not self._processing.initialized:
              self._processing.initialize(batch.shape[1])
  
          # Get Landmarks prior to manipulating the image
        if self._mask_class or to_landmarks:
              batch_src_pts = self._get_landmarks(filenames, batch, 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)
          else:
keep keep keep keep replace 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