          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)
  
          # Color augmentation before mask is added
          if self._training_opts["augment_color"]:
              batch = self._processing.color_adjust(batch)
 
</s> remove         # Color augmentation before mask is added
 </s> add         # Color Augmentation of the image only </s> remove             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> add             batch[..., :3] = self._processing.color_adjust(batch[..., :3]) </s> remove         to_landmarks = self._training_opts["warp_to_landmarks"]
 </s> add  </s> remove         # 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> add         # 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)]
        if self._training_opts["warp_to_landmarks"]:
          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)
  
          # Color augmentation before mask is added
          if self._training_opts["augment_color"]:
              batch = self._processing.color_adjust(batch)
keep keep keep keep replace keep keep keep keep keep
 Smart Mask Exposure for Extraction & Training (#831)

Smart Masks - Initial Commit 
lib/training_data.py