                  epoch += 1
                  if i == batchsize - 1:
                      break
              memory_wrapper.ready()
          logger.debug("Finished batching: (epoch: %s, side: '%s', is_timelapse: %s)",
                       epoch, side, is_timelapse)
  
      def validate_samples(self, data):
          """ Check the total number of images against batchsize and return
              the total number of images """
          length = len(data)
 
</s> remove                 imgs = self.process_face(img_path, side, is_timelapse)
 </s> add                 imgs = self.process_face(img_path, side, is_display) </s> remove     def minibatch(side, is_timelapse, load_process):
 </s> add     def minibatch(side, is_display, load_process): </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     def load_batches(self, mem_gen, images, side, is_timelapse,
 </s> add     def load_batches(self, mem_gen, images, side, is_display, </s> remove         logger.debug("Loading batch: (image_count: %s, side: '%s', is_timelapse: %s, "
                     "do_shuffle: %s)", len(images), side, is_timelapse, do_shuffle)
 </s> add         logger.debug("Loading batch: (image_count: %s, side: '%s', is_display: %s, "
                     "do_shuffle: %s)", len(images), side, is_display, do_shuffle)
        logger.debug("Finished batching: (epoch: %s, side: '%s', is_display: %s)",
                     epoch, side, is_display)
                  epoch += 1
                  if i == batchsize - 1:
                      break
              memory_wrapper.ready()
        logger.debug("Finished batching: (epoch: %s, side: '%s', is_display: %s)",
                     epoch, side, is_display)
        logger.debug("Finished batching: (epoch: %s, side: '%s', is_display: %s)",
                     epoch, side, is_display)
  
      def validate_samples(self, data):
          """ Check the total number of images against batchsize and return
              the total number of images """
          length = len(data)
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