Multispectral conditional Generative Adversarial Nets
This repository is an implementation of "Filmy Cloud Removal on Satellite Imagery with Multispectral Conditional Generative Adversarial Nets".
Requirements
I recommend Anaconda to manage your Python libraries.
Because it is easy to install some of the libraries necessary to prepare the data.
- Python3 (tested with 3.5.4)
- Chainer (tested with 5.0.0)
- cupy (tested with 5.0.0)
- matplotlib (tested with 2.2.2)
- OpenCV (tested with 3.3.1)
- tqdm (tested with 4.15.0)
- PyYAML (tested with 3.12)
- mpi4py (tested with 3.0.0)
Preparing the data
Please refer to make_dataset/README.md.
Training examples
You need set each parameters in a config file.
CUDA_VISIBLE_DEVICES=0 python train_pix2pix.py --config_path configs/config_nirrgb2rgbcloud.yml --results_dir results/pix2pix
If you want to resume the training from snapshot, use --snapshot option.
- pretrained model (WIP)
Evaluation examples
CUDA_VISIBLE_DEVICES=0 python test.py --dir_nir <path to nir dir> --dir_rgb <path to rgb dir> --imlist_nir <path to nir list file> --imlist_rgb <path to rgb list file> --results_dir results/test_pix2pix --config_path results/pix2pix/config_nirrgb2rgbcloud.yml --gen_model results/pix2pix/Generator_<iterations>.npz
License
Academic use only.