              in_queue=queue_in,
              out_queue=queue_out,
              args=(images, side, is_timelapse, do_shuffle, batchsize))
          load_process.start()
          logger.debug("Batching to queue: (side: '%s', is_timelapse: %s)", side, is_timelapse)
          return self.minibatch(side, is_timelapse, load_process)
  
      @staticmethod
      def make_queues(side, is_timelapse):
          """ Create the buffer token queues for Fixed Producer Dispatcher """
          q_name = "timelapse_{}".format(side) if is_timelapse else "train_{}".format(side)
 
</s> remove     def make_queues(side, is_timelapse):
 </s> add     def make_queues(side, is_preview, is_timelapse): </s> remove             args=(images, side, is_timelapse, do_shuffle, batchsize))
 </s> add             args=(images, side, is_display, do_shuffle, batchsize)) </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) </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     def process_face(self, filename, side, is_timelapse):
 </s> add     def process_face(self, filename, side, is_display):
        logger.debug("Batching to queue: (side: '%s', is_display: %s)", side, is_display)
        return self.minibatch(side, is_display, load_process)
              in_queue=queue_in,
              out_queue=queue_out,
              args=(images, side, is_timelapse, do_shuffle, batchsize))
          load_process.start()
        logger.debug("Batching to queue: (side: '%s', is_display: %s)", side, is_display)
        return self.minibatch(side, is_display, load_process)
        logger.debug("Batching to queue: (side: '%s', is_display: %s)", side, is_display)
        return self.minibatch(side, is_display, load_process)
  
      @staticmethod
      def make_queues(side, is_timelapse):
          """ Create the buffer token queues for Fixed Producer Dispatcher """
          q_name = "timelapse_{}".format(side) if is_timelapse else "train_{}".format(side)
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