ThermalGAN

This is the PyTorch implementation of the color-to-thermal image translation presented on ECCV 2018 in the paper .

The code is based on the PyTorch implementation of the pix2pix and CycleGAN.

ThermalGAN: [Project] [Paper]

If you use this code for your research, please cite:

@InProceedings{Kniaz2018,
author={Kniaz, Vladimir V. and
Knyaz, Vladimir A. and
Hlad\r{u}vka, Ji{\v r}{\'{\i}}  and Kropatsch, Walter G. and Mizginov, Vladimir A.},
title={{ThermalGAN: Multimodal Color-to-Thermal Image Translation for Person Re-Identification in Multispectral Dataset}},
booktitle={{Computer Vision -- ECCV 2018 Workshops}},
year={2018},
publisher="Springer International Publishing",
}

Prerequisites

• Linux or macOS
• Python 2 or 3
• CPU or NVIDIA GPU + CUDA CuDNN

Getting Started

Installation

• Install PyTorch and dependencies from http://pytorch.org
• Install Torch vision from the source.

git clone https://github.com/pytorch/vision
cd vision
python setup.py install

• Install python libraries visdom and dominate.

pip install visdom
pip install dominate

• Clone this repo:

git clone https://github.com/vlkniaz/ThermalGAN.git

ThermalGAN train/test

• Download a ThermalGAN dataset:

bash ./datasets/download_thermalgan_dataset.sh thermalgan

• Train a model:

#!./scripts/train_thermalgan_rel.sh
python train.py --dataroot ./datasets/thermal_gan --name thermal_gan_rel --model thermal_gan_rel --which_model_netG unet_512 --which_direction AtoB --input_nc 4 --output_nc 1 --lambda_A 100 --dataset_mode thermal_rel --no_lsgan --norm batch --pool_size 0

• To view training results and loss plots, run python -m visdom.server and click the URL http://localhost:8097. To see more intermediate results, check out ./checkpoints/thermal_gan_rel/web/index.html
• Test the model:

#!./scripts/test_thermalgan_rel.sh
python test.py --dataroot ./datasets/thermal_gan --name thermal_gan_rel --model thermal_gan_rel --which_model_netG unet_512 --which_direction AtoB --input_nc 4 --output_nc 1 --loadSize 512 --fineSize 512 --dataset_mode thermal_rel --how_many 352 --gpu_ids -1 --norm batch

The test results will be saved to a html file here: ./results/thermal_gan_rel/latest_test/index.html.

Apply a pre-trained model (ThermalGAN)

Download a pre-trained model with ./pretrained_models/download_thermalgan_dataset.sh.

• For example, if you would like to download ThermalGAN model on the ThermalWorld dataset,

bash pretrained_models/download_thermalgan_model.sh ThermalGAN

• Download the ThermalWorld dataset

bash ./datasets/download_thermalworld_dataset.sh ThermalWorld

• Then generate the results using

bash scripts/test_thermalgan_rel_pretrained.sh