Color Vision Deficiency Datasets & Recoloring Evaluation using GANs

Setup

Prerequisites

• Linux or OSX
• NVIDIA GPU + CUDA CuDNN (CPU mode and CUDA without CuDNN may work with minimal modification, but untested)

Getting Started

• Install torch and dependencies from https://github.com/torch/distro
• Install torch packages nngraph and display

luarocks install nngraph
luarocks install https://raw.githubusercontent.com/szym/display/master/display-scm-0.rockspec

• Clone this repo:

git clone https://github.com/doubletry/pix2pix.git
cd pix2pix

• Download and unzip the dataset.zip to the datasets folder (https://pan.baidu.com/s/1rCJt8yVkkfunlvU54Qxn_g) or run this:

bash ./scripts/download_dataset.sh

• Download and unzip the pix2pix model to the checkpoints folder (https://pan.baidu.com/s/1AUl2SpSJp5YTjWWZJF7qSA)
• Combine the images:

python scripts/combine_A_and_B.py --fold_A ./datasets/colourblindness/A/ --fold_B ./datasets/colourblindness/B --fold_AB ./datasets/colourblindness/

• Test the model:

bash ./test_model.sh

The test results will be saved to an html file here: ./results/colourblindness/latest_net_G_val/index.html.

Citation

If you use this code for your research, please cite our paper Image-to-Image Translation Using Conditional Adversarial Networks:

@article{pix2pix2017,
  title={Image-to-Image Translation with Conditional Adversarial Networks},
  author={Isola, Phillip and Zhu, Jun-Yan and Zhou, Tinghui and Efros, Alexei A},
  journal={CVPR},
  year={2017}
}

Acknowledgments

Code borrows heavily from DCGAN. The data loader is modified from DCGAN and Context-Encoder.