kuijiang94/S2VD

Semi-supervised Video Deraining with Dynamical Rain Generator (CVPR, 2021, Pytorch)

PythonMIT

S2VD

Semi-supervised Video Deraining with Dynamical Rain Generator (CVPR, 2021)

Requirements and Dependencies

Ubuntu 16.04, cuda 10.0
Python 3.6.10, Pytorch 1.6.0
More detail (See environment.yml)

Training pipelines

Download the NTURain dataset from here or Baidu Cloud(Passwd:dtgv), and prepare the training data as follows:

Labled synthetic data:

    python makedata/preparedata_NTU.py  --ntu_path your_downloaded_synthetic_path --train_path your_saved_train_path

Unlabled real data:

    python makedata/preparedata_NTU_semi.py  --ntu_path_semi your_downloaded_real_path --train_path your_saved_train_path

Note that you should better put the synthetic and real training data sets into two different training folders.

Modify the configured file options_derain.json according to your own training and testing path.
Begin training:
```
    python main_NTURain.py
```

Testing pipelines

You need firstly download the testing dataset of NTURain and MSCSC into the folder testsets.

NTURain synthetic data set:
```
    python test_NTURain_synthetic.py
```
This manuscript will re-produce the paper results in Table 1.
NTURain real data set:
```
    python test_NTURain_real.py
```
MSCSC real data set:
```
    python test_MSCSC_real.py
```

Citation

@incollection{CVPR2021_2429,
title = {Semi-supervised video deraining with dynamical rain generator},
author = {Yue, Zongsheng and Xie, Jianwen and Zhao, Qian and Meng, Deyu},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2021}
}