enomotokenji / mcgan-cvprw2017-pytorch

This is an implementation of our CVPRW2017 paper "Filmy Cloud Removal on Satellite Imagery with Multispectral Conditional Generative Adversarial Nets".

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)
• PyTorch (tested with 0.4.1)
• TorchVision (tested with 0.2.1)
• Numpy (tested with 1.14.2)
• OpenCV (tested with 3.3.1)
• Pillow (tested with 5.0.0)
• tqdm (tested with 4.15.0)
• PyYAML (tested with 3.12)

Preparing the data

Please refer to make_dataset/README.md.

How to train

You need set each parameters in config.yml.
When you run train.py, config.yml is automatically copied to a directory out_dir defined at config.yml.

python train.py

How to test

python predict.py --config <path_to_config.yml_in_the_out_dir> --test_dir <path_to_a_directory_stored_test_data> --out_dir <path_to_an_output_directory> --pretrained <path_to_a_pretrained_model> --cuda

Pre-trained model

You can download a pre-trained model from here. (200MB)

License

Academic use only.