          logger.debug("Finished minibatch generator for queue: (side: '%s', is_timelapse: %s)",
                       side, is_timelapse)
          load_process.join()
  
      def process_face(self, filename, side, is_timelapse):
          """ Load an image and perform transformation and warping """
          logger.trace("Process face: (filename: '%s', side: '%s', is_timelapse: %s)",
                       filename, side, is_timelapse)
          image = cv2_read_img(filename, raise_error=True)
          if self.mask_class or self.training_opts["warp_to_landmarks"]:
 
</s> remove         logger.trace("Process face: (filename: '%s', side: '%s', is_timelapse: %s)",
                     filename, side, is_timelapse)
 </s> add         logger.trace("Process face: (filename: '%s', side: '%s', is_display: %s)",
                     filename, side, is_display) </s> remove         logger.debug("Finished minibatch generator for queue: (side: '%s', is_timelapse: %s)",
                     side, is_timelapse)
 </s> add         logger.debug("Finished minibatch generator for queue: (side: '%s', is_display: %s)",
                     side, is_display) </s> remove                              "is_timelapse: %s)",
                             len(batch), [item.shape for item in batch], side, is_timelapse)
 </s> add                              "is_display: %s)",
                             len(batch), [item.shape for item in batch], side, is_display) </s> remove         logger.debug("Launching minibatch generator for queue (side: '%s', is_timelapse: %s)",
                     side, is_timelapse)
 </s> add         logger.debug("Launching minibatch generator for queue (side: '%s', is_display: %s)",
                     side, is_display) </s> remove         logger.debug("Finished batching: (epoch: %s, side: '%s', is_timelapse: %s)",
                     epoch, side, is_timelapse)
 </s> add         logger.debug("Finished batching: (epoch: %s, side: '%s', is_display: %s)",
                     epoch, side, is_display)
    def process_face(self, filename, side, is_display):
          logger.debug("Finished minibatch generator for queue: (side: '%s', is_timelapse: %s)",
                       side, is_timelapse)
          load_process.join()
  
    def process_face(self, filename, side, is_display):
          """ Load an image and perform transformation and warping """
          logger.trace("Process face: (filename: '%s', side: '%s', is_timelapse: %s)",
                       filename, side, is_timelapse)
          image = cv2_read_img(filename, raise_error=True)
          if self.mask_class or self.training_opts["warp_to_landmarks"]:
keep keep keep keep replace keep keep keep keep keep
 Color Augmentation method (#752)

* Color Augmentation: Implement testing code

* Tweak Contrast and Lighting augmentation amounts

* Prevent color augmentation from entering timelapse

* Remove all augmentations from preview images 
lib/training_data.py