From Shadow Generation to Shadow Removal.

This code uses the following libraries

• python 3.7+
• pytorch 1.1+ & tochvision
• scikit-image

Generate the training data using the original ISTD dataset and the code gettraindata.m

or download the training data from: GoogleDrive: ISTD | BaiduNetdisk: ISTD (Access code: 1111)

Your ~/PAISTD8/ folder should look like this

PAISTD8
├── train/
    ├── train_A/
    │   └── 90-1.png
    │   └── ...
    ├── train_B/
    │   └── ...
    └── ...

GoogleDrive: ISTD

BaiduNetdisk: ISTD (Access code: 1111)

Train

1. Set the path of the dataset in train.py
2. Run train.py

Test

1. Set the paths of the dataset and saved models (netG_1.pth) and (netG_2.pth) in test.py
2. Run test.py

1. Set the paths of the shadow removal results and the dataset in evaluate.m
2. Run evaluate.m

GoogleDrive: ISTD

BaiduNetdisk: ISTD (Access code: 1111)

GoogleDrive: ISTD

BaiduNetdisk: ISTD (Access code: 1111)

GoogleDrive: ISTD

BaiduNetdisk: ISTD (Access code: 1111)

Results in shadow and non-shadow regions are computed on each image first and then compute the average of all images in terms of RMSE.

Code is implemented based on Mask-ShadowGAN and LG-ShadowNet.

All codes will be released to public soon.

@inproceedings{liu2021from,
  title={From Shadow Generation to Shadow Removal},
  author={Liu, Zhihao and Yin, Hui and Wu, Xinyi and Wu, Zhenyao and Mi, Yang and Wang, Song},
  booktitle={CVPR},
  year={2021}
}