A project that trains a convolutional neural network over a dataset to repaint an novel image in the style of a given painting. This is the implementation of Neural Style Transfer from the paper A Neural Algorithm of Artistic Style in Keras 1.0.2. This is also the code for 'Build an AI Artist' on Youtube
- Numpy (http://www.numpy.org/)
- Keras (http://keras.io/#installation)
- Scipy (https://www.scipy.org/install.html)
- Theano (http://deeplearning.net/software/theano/install.html#install)
- h5py (http://docs.h5py.org/en/latest/build.html)
- sklearn (http://scikit-learn.org/stable/install.html)
- Pillow (https://pillow.readthedocs.io/en/latest/installation.html)
- CUDA (GPU) (http://docs.nvidia.com/cuda/cuda-getting-started-guide-for-mac-os-x/)
- CUDNN (GPU) (https://developer.nvidia.com/cudnn)
- VGG16 file https://drive.google.com/file/d/0Bz7KyqmuGsilT0J5dmRCM0ROVHc/view?usp=sharing
Use pip to install any missing dependencies
If you have dependency version issues, use virtualenv
There are 3 images to identify when we run the script
- Your base image (to artify)
- Your reference image (the art to learn from)
- Your generated image
Run the following comand to generate an image in your chosen style
python network.py --base_image_path /path/to/your/image --style_reference_image_path /path/to/your/painting --result_prefix /path/to/generated/file/you/create
Other optional commands are
--image_size: Size of your output image--content_weight: How much to weigh the content--style_weight: How much to weigh the style-style_scale: How much to scale the style--total_variation_weight: Uniformity of the generated image--num_iter: Nmber of iterations--rescale_image: to rescale or not to rescale--rescale_method: rescale algorithm--maintain_aspect_ratio: to maintain aspect ratio or not--content_layer: which layer to focus on for content generation
I'd run this on AWS, but you can run this locally too if you have a GPU. On a 980M GPU, the time required for each epoch depends on mainly image size (gram matrix size) :
- For a 400x400 gram matrix, each epoch takes approximately 11-13 seconds.
- For a 512x512 gram matrix, each epoch takes approximately 18-22 seconds.
- For a 600x600 gram matrix, each epoch takes approximately 28-30 seconds.
Credit for the vast majority of code here goes to Somsubra Majumdar. I've merely created a wrapper around all of the important functions to get people started.