              memory = memory_wrapper.get()
              logger.trace("Putting to batch queue: (side: '%s', is_timelapse: %s)",
                           side, is_timelapse)
              for i, img_path in enumerate(img_iter):
                  imgs = self.process_face(img_path, side, is_timelapse)
                  for j, img in enumerate(imgs):
                      memory[j][i][:] = img
                  epoch += 1
                  if i == batchsize - 1:
                      break
 
</s> remove             logger.trace("Putting to batch queue: (side: '%s', is_timelapse: %s)",
                         side, is_timelapse)
 </s> add             logger.trace("Putting to batch 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) </s> remove     def make_queues(side, is_timelapse):
 </s> add     def make_queues(side, is_preview, is_timelapse): </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("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)
                imgs = self.process_face(img_path, side, is_display)
              memory = memory_wrapper.get()
              logger.trace("Putting to batch queue: (side: '%s', is_timelapse: %s)",
                           side, is_timelapse)
              for i, img_path in enumerate(img_iter):
                imgs = self.process_face(img_path, side, is_display)
                  for j, img in enumerate(imgs):
                      memory[j][i][:] = img
                  epoch += 1
                  if i == batchsize - 1:
                      break
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