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PIV LiteFlowNet with PyTorch

This is my PyTorch reimplementation of the PIV-LiteFlowNet [1] model. It's an upgraded LiteFlowNet [2] model with additional layers and trained specifically on Particle Image Velocimetry (PIV) images (please refer to the original paper [1] for the complete dataset details and sources). Also thanks to Sniklaus [3] for the PyTorch reimplementation of the original LiteFlowNet model. If you would like to use this particular implementation, please acknowledge it appropriately [4].

Model

The trained models for both the original LiteFlowNet and PIV-LiteFlowNet are already available in the models/pretrain_torch/. These originate from the original authors, I just converted them to PyTorch.

The correlation layer is implemented in CUDA using CuPy, which is why CuPy is a required dependency. It can be installed using pip install cupy or alternatively using one of the provided binary packages as outlined in the CuPy repository.

Usage

To run it sequentially on 1000 images in your directory, starting from the first image, use the following command. There are 2 model options the are avaialble to run with, piv for the PIV-LiteFlowNet-en and hui for the original LiteFlowNet model. Please check on the CLI --help for more info.

python run.py --model piv -s 0 -n 1000 --input ./images/test --output ./test-output

If you want to visualize the flow results in sequence, or even compile it as video format, you can use the piv-viz package.

References

[1]  @article{piv-liteflownet,
         author = {Shengze Cai and Jiaming Liang and Qi Gao and Chao Xu and Runjie Wei},
         journal = {IEEE Transactions on Instrumentation and Measurement},
         title = {Particle Image Velocimetry Based on a Deep Learning Motion Estimator},
         year = {2020},
         volume = {69},
         number = {6},
         pages = {3538-3554},
         doi = {10.1109/TIM.2019.2932649}
}

[2]  @inproceedings{Hui_CVPR_2018,
         author = {Tak-Wai Hui and Xiaoou Tang and Chen Change Loy},
         title = {{LiteFlowNet}: A Lightweight Convolutional Neural Network for Optical Flow Estimation},
         booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},
         year = {2018}
     }

[3]  @misc{pytorch-liteflownet,
         author = {Simon Niklaus},
         title = {A Reimplementation of {LiteFlowNet} Using {PyTorch}},
         year = {2019},
         howpublished = {\url{https://github.com/sniklaus/pytorch-liteflownet}}
    }

[4]  @misc{piv-liteflownet-pytorch,
         author = {Faber Silitonga},
         title = {A Reimplementation of {PIV-LiteFlowNet-en} Using {PyTorch}},
         year = {2021},
         howpublished = {\url{https://github.com/abrosua/piv_liteflownet-pytorch}}
    }