This is the source code of PIV-DCNN

We submit it to Experiments in Fluids. Some useful information is also provided here.

Directory structure
Installation guidance
More experimental results
Some tips about our method
Links to data generaton,experiment arrangement

1. Directory structure

├── PIV-DCNN20170710
│   ├── readme.md
│   ├── install.m
│   ├── data
│   │   ├── ImagesForDataset
│   │   │   ├── readme.md
│   │   │   └── A1-10.bmp
│   │   ├── NetF1~F4-3
│   │   │   ├── imdb.mat
│   │   │   ├── net-epoch-1.mat
│   │   │   └── net-train.pdf
│   │   └── TestImages
│   │       ├── readme.md
│   │       └── *.tif, *bmp, *jpg
│   ├── PIV_DNN
│   │   ├── readme.md
│   │   ├── ChangesForMatConvNet
│   │   │   └── some files 
│   │   ├── cnn_pivdnn_init.m 
│   │   ├── cnn_pivdnn.m
│   │   ├── genPIVImgDB_F4.m
│   │   ├── genPIVImgDB.m
│   │   ├── ImgData.mat
│   │   ├── Nets.mat
│   │   ├── pivdnn.m
│   │   └── train.m
│   ├── experiments
│   │   ├── readme.md
│   │   └── exp0-7.m
│   ├── matconvnet
│   │   └── MatConvNet files
│   ├── OpticalFlow
│   │   └── OpticalFlow files
│   ├── PIVCC
│   │   ├── dctn.m
│   │   ├── idctn.m
│   │   ├── PIV_analysis.m
│   │   └── smoothn.m
│   └── pivlab
│   │   └──PIVlab files

2. Installation

Install GPU driver, CUDA and CuDNN.
Run install.m in the main folder. It will download the MatConvNet, compile files, and copy some customized files to the MatConvNet automatically.
Cautions: You need to specify the configure terms (enable GPU, CUDA root, ...) in install.m if the default values do not work. (Note, the slow CPU version without GPU support is also ok)
Enjoy it. cd '../PIV_DNN'; run('PIVdnn.m'); or cd '../experiments';run('exp1.m').

Information about the packages

PIVlab website
MatConvNet website

3. More experimental results

3.1. Vortex Pair flow

Raw Images
Vector field (color contour reprensents corresponding magnitude)
Vector component histogram

3.2. Turbulent Jet

Raw Images
Vector field (color contour reprensents corresponding magnitude)
Smoothed vector field
Vector component histogram

3.3 Jet flow

Raw Images
Vector field (color contour reprensents corresponding magnitude)
Vector component histogram

4. Tips:

System requirement(8G RAM, Hard disk space > 50G, GPU with computation ability >3.0 is promotional)
CuDNN is highly recommended
Training 6 DCNN nets(optional, about 2 days with GPU accelaration):cd '../PIV_DNN';run('train.m')
The figures in our manuscript (about 2 day to run massive Monte - Carlo simulation):cd '../experiments'; run('exp0.m')

~~Yong Lee (Email: leeyong@hust.edu.cn) @2017-07-10~~ Yong Lee (Email:yongli.cv@gmail.com) @2019-12-19

yongleex / PIV-DCNN