          logger.debug(q_names)
          queues = [queue_manager.get_queue(queue) for queue in q_names]
          return queues
  
      def load_batches(self, mem_gen, images, side, is_timelapse,
                       do_shuffle=True, batchsize=0):
          """ Load the warped images and target images to queue """
          logger.debug("Loading batch: (image_count: %s, side: '%s', is_timelapse: %s, "
                       "do_shuffle: %s)", len(images), side, is_timelapse, do_shuffle)
          self.validate_samples(images)
 
</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) </s> remove     def make_queues(side, is_timelapse):
 </s> add     def make_queues(side, is_preview, is_timelapse): </s> remove                      "is_timelapse: %s)", len(images), batchsize, side, do_shuffle, is_timelapse)
 </s> add                      "is_preview, %s, is_timelapse: %s)", len(images), batchsize, side, do_shuffle,
                     is_preview, is_timelapse) </s> remove     def minibatch_ab(self, images, batchsize, side, do_shuffle=True, is_timelapse=False):
 </s> add     def minibatch_ab(self, images, batchsize, side,
                     do_shuffle=True, is_preview=False, is_timelapse=False): </s> remove         q_name = "timelapse_{}".format(side) if is_timelapse else "train_{}".format(side)
 </s> add         q_name = "_{}".format(side)
        if is_preview:
            q_name = "{}{}".format("preview", q_name)
        elif is_timelapse:
            q_name = "{}{}".format("timelapse", q_name)
        else:
            q_name = "{}{}".format("train", q_name)
    def load_batches(self, mem_gen, images, side, is_display,
          logger.debug(q_names)
          queues = [queue_manager.get_queue(queue) for queue in q_names]
          return queues
  
    def load_batches(self, mem_gen, images, side, is_display,
                       do_shuffle=True, batchsize=0):
          """ Load the warped images and target images to queue """
          logger.debug("Loading batch: (image_count: %s, side: '%s', is_timelapse: %s, "
                       "do_shuffle: %s)", len(images), side, is_timelapse, do_shuffle)
          self.validate_samples(images)
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