  
  import logging
  
  from hashlib import sha1
  from random import shuffle, choice
  
  import cv2
  import numpy as np
  from scipy.interpolate import griddata
  
 
</s> remove         self.samples = batch[0] if do_preview else None
 </s> add  </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         self.target = batch[1] if do_preview else None
 </s> add         self.generate_preview(do_preview) </s> remove     def minibatch(side, is_timelapse, load_process):
 </s> add     def minibatch(side, is_display, load_process):
from random import random, shuffle, choice
  
  import logging
  
  from hashlib import sha1
from random import random, shuffle, choice
  
  import cv2
  import numpy as np
  from scipy.interpolate import griddata
  
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