https://github.com/amenalahassa/unliteflownet-piv

Unsupervised learning of Particle Image Velocimetry. (ISC 2020)

https://github.com/amenalahassa/unliteflownet-piv

Repository

Unsupervised learning of Particle Image Velocimetry. (ISC 2020)

https://github.com/amenalahassa/UnLiteFlowNet-PIV/blob/master/

# Unsupervised learning of Particle Image Velocimetry
This repository contains materials for ISC 2020 workshop paper [Unsupervised learning of Particle Image Velocimetry](https://arxiv.org/pdf/2007.14487.pdf).

## Introduction
Particle Image Velocimetry (PIV) is a classical flow estimation problem which is widely considered and utilised, especially as a diagnostic tool in experimental fluid dynamics and the remote sensing of environmental flows. We present here what we believe to be the first work which takes an unsupervised learning based approach to tackle PIV problems. The proposed approach is inspired by classic optical flow methods. Instead of using ground truth data, we make use of photometric loss between two consecutive image frames, consistency loss in bidirectional flow estimates and spatial smoothness loss to construct the total unsupervised loss function. The approach shows significant potential and advantages for fluid flow estimation. Results presented here demonstrate that is outputs competitive results compared with classical PIV methods as well as supervised learning based methods for a broad PIV dataset, and even outperforms these existing approaches in some difficult flow cases.

## Sample results
#### Syethetic data: samples from PIV dataset

- Backstep flow


  
  



- Surface Quasi Geostrophic (SQG) flow


  
  




#### Real experimental data: particle Images from [PIV challenge](http://www.pivchallenge.org/)

- Jet Flow


  

  From left to right: Particle images, UnLiteFlowNet-PIV(trained by full integrated loss) output, PIV-LiteNetFlow output


## Unsupervised Loss


  



## Dataset
The dataset used in this work is obtained from the work below:

- [PIV dataset](https://doi.org/10.1007/s00348-019-2717-2) (9GB)
```
Shengze Cai, Shichao Zhou, Chao Xu, Qi Gao. 
Dense motion estimation of particle images via a convolutional neural network, Exp Fluids, 2019
```
- [JHTDB](http://turbulence.pha.jhu.edu)
```
Y. Li, E. Perlman, M. Wan, Y. Yang, R. Burns, C. Meneveau, R. Burns, S. Chen, A. Szalay & G. Eyink. 
A public turbulence database cluster and applications to study Lagrangian evolution of velocity increments in turbulence. Journal of Turbulence 9, No. 31, 2008.
```

## Prerequisite

- cuda (v10.1)

- pytorch (v1.5.0)

- sklearn (v0.22.2)

- livelossplot

  ```pip install livelossplot```

- flowiz

  Library to visualize .flo files
  
  ```pip install flowiz -U```
  
- GPUtil

  ```pip install GPUtil```

## Training
To train from scratch:

1. Download the PIV dataset, remove the current data in the folder ```sample_data``` and extract new data into it.

2. Run the scripts with ```--train``` argument:

    ```python main.py --train```

3. Trained model will be saved in the same folder. (A checkpoint is generated every 5 epochs in default during training)

## Trained model
The trained model ```UnsupervisedLiteFlowNet_pretrained.pt``` is available in the folder ```models```.

## Testing
The data samples for test use are in the folder ```sample_data```.

Test and visualize the sample data results with the pretrained model using:

```python main.py --test```


## Citation

In BibTeX format:
```
@article{zhang2020unsupervised,
  title={Unsupervised Learning of Particle Image Velocimetry},
  author={Mingrui Zhang and Matthew D. Piggott},
  journal={arXiv preprint arXiv:2007.14487},
  year={2020}
}
```

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