This repo contains the training & testing code for our sketch generator. We also provide a [pre-trained model].
For technical details and the dataset, please refer to the [paper] and the [project page].
The code is now updated to use PyTorch 0.4 and runs on Windows, Mac and Linux. For the obsolete version with PyTorch 0.3 (Linux only), please check out the branch pytorch-0.3-obsolete.
Windows users should find the corresponding .cmd
files instead of .sh
files mentioned below.
conda env create -f environment.yml
Then activate the environment (sketch) and you are ready to go!
See here for more information about conda environments.
See environment.yml
for a list of dependencies.
- Download the pre-trained model
- Modify the path in
scripts/test_pretrained.sh
- From the repo's root directory, run
scripts/test_pretrained.sh
It supports a folder of images as input.
- Download the images and the rendered sketch from the project page
- Unzip and organize them into the following structure:
- Modify the path in
scripts/train.sh
andscripts/test.sh
- From the repo's root directory, run
scripts/train.sh
to train the model - From the repo's root directory, run
scripts/test.sh
to test on the val set or the test set (specified by the phase flag)
If you use the code or the data for your research, please cite the paper:
@article{LIPS2019,
title={Photo-Sketching: Inferring Contour Drawings from Images},
author={Li, Mengtian and Lin, Zhe and M\v ech, Radom\'ir and and Yumer, Ersin and Ramanan, Deva},
journal={WACV},
year={2019}
}
This code is based on an old version of pix2pix.