          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"]:
              src_pts = self.get_landmarks(filename, image, side)
          if self.mask_class:
              image = self.mask_class(src_pts, image, channels=4).mask
 
</s> remove     def process_face(self, filename, side, is_timelapse):
 </s> add     def process_face(self, 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         image = self.processing.color_adjust(image)
 </s> add         image = self.processing.color_adjust(image,
                                             self.training_opts["augment_color"],
                                             is_display) </s> remove         if not is_timelapse:
 </s> add         if not is_display:
        logger.trace("Process face: (filename: '%s', side: '%s', is_display: %s)",
                     filename, side, is_display)
          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_display: %s)",
                     filename, side, is_display)
        logger.trace("Process face: (filename: '%s', side: '%s', is_display: %s)",
                     filename, side, is_display)
          image = cv2_read_img(filename, raise_error=True)
          if self.mask_class or self.training_opts["warp_to_landmarks"]:
              src_pts = self.get_landmarks(filename, image, side)
          if self.mask_class:
              image = self.mask_class(src_pts, image, channels=4).mask
keep keep keep keep replace 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