  import cv2
  import numpy
  from random import shuffle
  
  from .utils import BackgroundGenerator
  from .umeyama import umeyama
  
  class TrainingDataGenerator():
      def __init__(self, random_transform_args, coverage, scale=5, zoom=1): #TODO thos default should stay in the warp function
          self.random_transform_args = random_transform_args
          self.coverage = coverage
 
</s> remove from lib.utils import get_folder, get_image_paths, set_system_verbosity
 </s> add from lib.utils import get_folder, get_image_paths, set_system_verbosity, Timelapse </s> remove     def minibatchAB(self, images, batchsize):
        batch = BackgroundGenerator(self.minibatch(images, batchsize), 1)
 </s> add     def minibatchAB(self, images, batchsize, doShuffle=True):
        batch = lib.utils.BackgroundGenerator(self.minibatch(images, batchsize, doShuffle), 1)

import lib.utils
from lib.umeyama import umeyama

  import cv2
  import numpy
  from random import shuffle
  
import lib.utils
from lib.umeyama import umeyama
import lib.utils
from lib.umeyama import umeyama
  
  class TrainingDataGenerator():
      def __init__(self, random_transform_args, coverage, scale=5, zoom=1): #TODO thos default should stay in the warp function
          self.random_transform_args = random_transform_args
          self.coverage = coverage
keep keep keep keep replace replace keep keep keep keep keep
 Add timelapse functionality (#512)

* add new cli options

* add timelapse script

* add batch size on image generation

* fix circular dependency

* add attribute check

* refactor to 3 parameters

* fix some typos

* do not shuffle the set

* default to models folder when no timelapse output supplied

* almost forgot this 
lib/training_data.py