                                               cv2.INTER_AREA)
                                    for image in batch], dtype='float32') / 255.
                          for size in self._output_sizes]
          logger.trace("Target image shapes: %s",
                       [tgt_images.shape[1:] for tgt_images in target_batch])
  
          retval = self._separate_target_mask(target_batch)
          logger.trace("Final targets: %s",
                       {k: v.shape if isinstance(v, np.ndarray) else [img.shape for img in v]
                        for k, v in retval.items()})
 
</s> remove                                   for image in batch], dtype='float32') / 255.
 </s> add                                   for image in batch]) </s> remove                      "landmarks_xy: %s, filename: %s)",
 </s> add                      "landmarks_xy: %s, mask: %s, filename: %s)", </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         targets = [batch[..., :3] for batch in size_list_of_batches]
        if size_list_of_batches[-1].shape[-1] == 4:
            masks = [size_list_of_batches[-1][..., 3:]]
 </s> add         batch = [tgt.astype("float32") / 255.0 for tgt in batch]
        if all(tgt.shape[-1] == 4 for tgt in batch):
            logger.trace("Batch contains mask")
            sizes = [item.shape[1] for item in batch]
            mask_batch = np.expand_dims(batch[sizes.index(max(sizes))][..., -1], axis=-1)
            batch = [item[..., :3] for item in batch]
            logger.trace("batch shapes: %s, mask_batch shape: %s",
                         [tgt.shape for tgt in batch], mask_batch.shape)
            retval = dict(targets=batch, masks=mask_batch) </s> remove             images = np.array([cv2.addWeighted(img,  # pylint: disable=no-member
                                               1.0, masks3[idx], 0.3, 0)
 </s> add             images = np.array([cv2.addWeighted(img, 1.0,  # pylint: disable=no-member
                                               masks3[idx], 0.3,
                                               0)
                     [tgt.shape for tgt_images in target_batch for tgt in tgt_images])
                                               cv2.INTER_AREA)
                                    for image in batch], dtype='float32') / 255.
                          for size in self._output_sizes]
          logger.trace("Target image shapes: %s",
                     [tgt.shape for tgt_images in target_batch for tgt in tgt_images])
  
          retval = self._separate_target_mask(target_batch)
          logger.trace("Final targets: %s",
                       {k: v.shape if isinstance(v, np.ndarray) else [img.shape for img in v]
                        for k, v in retval.items()})
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