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A Particle Imaging Velocimetry (PIV) and Dense PIV Software Laboratory with GPGPU processing
- PIV and Hybrid PIV high performance computing application with GP-GPU OpenCL support (by Aparapi)
- Friendly Graphical User Interface (GUI)
- Supports both dense and sparse Liu-Shen combined with Lucas-Kanade and Lucas-Kanade only Optical Flow methods
- Support classic PIV and PIV with warping modes
- MATLAB file format data export including multi-volume support
- Adaptive PIV support with configurable start and end Interrogation Area window sizes
- Multiple sub-pixel methods including Polynomial Gaussian 1D-1D and Hongwei Guo's Gaussian 1D-1D Robust Linear regression, among others
- Vector validation and substitution including secondary peak substitution
- Single XML configuration file
- Support images sequences and image pairs
- Selectable GPU per CPU threads distribution
Mendes, L.P.N.; Ricardo, A.M.C.; Bernardino, A.J.M.; Ferreira, R.M.L. A Hybrid PIV/Optical Flow Method for Incompressible Turbulent Flows. Water 2024, 16, 1021. https://doi.org/10.3390/w16071021
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Pre-requisites: Java development kit at least version 8, maven, QuickLabPIV-libs, Aparapi
https://github.com/CoreRasurae/QuickLabPIV-libs/releases/tag/QuickLabPIV-libs_v0.1.0
Package generation instructions:
Step 1 - Compile QuickLab-libs
mvn package
Step 2 - Install QuickLab-libs
mvn install:install-file -Dfile=<PATH_TO_JAR>/QuickLabPIV-libs.jar -DlocalRepositoryPath=<BASE_DIR>/mavenLM -DgroupId=pt.quickLabPIV.libs -DartifactId=quickLabPIV-libs -Dversion=0.1.0-LM -Dpackaging=jar
Step 3 - Adjust mavenLM local repository folder in pom.xml, by updating the url to the correct path
<repository>
<id>mavenLM</id>
<url>file:///mavenLM</url>
</repository>
Step 4 - Adjust version for Aparapi if required
<!-- https://mvnrepository.com/artifact/com.aparapi/aparapi -->
<dependency>
<groupId>com.aparapi</groupId>
<artifactId>aparapi</artifactId>
<version>3.0.2</version>
</dependency>
Step 5 - Compile QuickLab
mvn package
optionally skip tests:
mvn package -DskipTests=true
- Liu-Shen combined with dense Lucas-Kanade hybrid PIV:
examples/LiuShenWithLucasKanade_HybridPIV.xml