  import cv2
  import numpy
  
  from .utils import BackgroundGenerator
  from .umeyama import umeyama
  
  coverage = 220 # Coverage of the face for training. Larger value will cover more features. @shaoanlu recommends 220. Original is 160
  
 
</s> remove import cv2
import numpy
 </s> add import argparse
 </s> remove from lib.training_data import get_training_data, stack_images
from lib.utils import get_image_paths, get_folder, load_images

from lib.model import autoencoder_A, autoencoder_B
from lib.model import encoder, decoder_A, decoder_B
 </s> add from lib.utils import get_image_paths
from random import shuffle

  import cv2
  import numpy
from random import shuffle
  
  from .utils import BackgroundGenerator
  from .umeyama import umeyama
  
  coverage = 220 # Coverage of the face for training. Larger value will cover more features. @shaoanlu recommends 220. Original is 160
  
keep add keep keep keep keep keep keep
 Adding models as plugins + Face filtering (#53) + #39 + #43 + #44 + #49 (#61)

* Making Models as plugins

* Do not reload model on each image #39 + Adding FaceFilter #53

* Adding @lukaville PR for #43 and #44 (possibly)

* Training done in a separate thread

* Better log for plugin load

* Adding a prefetch to train.py #49
(Note that we prefetch 2 batches of images, due to the queue behavior)
+ More compact logging with verbose info included

* correction of DirectoryProcessor signature

* adding missing import

* Convert with parallel preprocessing of files

* Added coverage var for trainer

Added a var with comment. Feel free to add it as argument

* corrections

* Modifying preview and normalization of image + correction

* Cleanup 
lib/training_data.py