#!/usr/bin/env python3

from helperFunctions import *
from nstFunctions import *

def main():
    content_image = sys.argv[1:][0]
    style_image = sys.argv[1:][1]


    model_nn(content_image, style_image)

    return


if __name__== "__main__":
  main()

# Invoke like
# parameters are content image path, style image path, output directory, number of training iterations, alpha for cost, beta for cost
# python3 CreateArt.py images/content/ventana-circle_300_400.jpg images/style/TheScream_300_400.jpg ~/CreateArt/output 300 10 40

59f95713e20e97bf13cbbdc5a0f91c50d9e331b5
<mask token>

<mask token>


def main():
    content_image = sys.argv[1:][0]
    style_image = sys.argv[1:][1]
    model_nn(content_image, style_image)
    return


<mask token>

<mask token>


def main():
    content_image = sys.argv[1:][0]
    style_image = sys.argv[1:][1]
    model_nn(content_image, style_image)
    return


if __name__ == '__main__':
    main()

from helperFunctions import *
from nstFunctions import *


def main():
    content_image = sys.argv[1:][0]
    style_image = sys.argv[1:][1]
    model_nn(content_image, style_image)
    return


if __name__ == '__main__':
    main()

#!/usr/bin/env python3

from helperFunctions import *
from nstFunctions import *

def main():
    content_image = sys.argv[1:][0]
    style_image = sys.argv[1:][1]


    model_nn(content_image, style_image)

    return


if __name__== "__main__":
  main()

# Invoke like
# parameters are content image path, style image path, output directory, number of training iterations, alpha for cost, beta for cost
# python3 CreateArt.py images/content/ventana-circle_300_400.jpg images/style/TheScream_300_400.jpg ~/CreateArt/output 300 10 40
