Blog post: https://towardsdatascience.com/gangogh-creating-art-with-gans-8d087d8f74a1

Note: Code heavily inspired and built off of the improved wasserstein GAN training code available and found at: https://github.com/igul222/improved_wgan_training

@rodrigobdz has created an updated version of the codebase with improved documentation, please find it at https://github.com/rodrigobdz/GANGogh.

We used training data from wikiart.org, but any training data will do. It's prefered to download this training data from this torrent or the Google Drive file. If both of those fail, consider using scape_wiki.py as a last resort.

Use picStuff.py to create image data set of 64x64 pieces of art scraped from wikiart. Take note of the root and PATH variables and modify accordingly.

Update the path to the dataset in wikiartGenre.py. Also, update the styles variable dictating the number of training images per genre. If using the traning data set linked, above, use the following:

styles = {'abstract': 14999,
          'animal-painting': 1798,
          'cityscape': 6598,
          'figurative': 4500,
          'flower-painting': 1800,
          'genre-painting': 14997,
          'landscape': 15000,
          'marina': 1800,
          'mythological-painting': 2099,
          'nude-painting-nu': 3000,
          'portrait': 14999,
          'religious-painting': 8400,
          'still-life': 2996,
          'symbolic-painting': 2999}

Run GANGogh.py