OpenCorr: An open source C++ library for digital image correlation

OpenCorr is an open source C++ library for development of 2D, 3D/stereo, and volumetric digital image correlation (DIC). It aims to provide a developer-friendly, lightweight, and efficient kit to the users who are willing to study the state-of-the-art algorithms of DIC and DVC (digital volume correlation), or to create DIC/DVC programs for their specific applications.

Comments and suggestions are most welcome. You may reach us via

  1. Email: zhenyujiang (at) scut.edu.cn;

  2. Discussion in GitHub repository;

  3. Tencent QQ group: 597895040

The codes and documentation are released and continuously updated on GitHub (https://github.com/vincentjzy/OpenCorr).

Instructions

1. Get started

2. Framework

3. Data structures

4. Processing methods

5. GPU acceleration

6. Examples

Developers

  • Dr JIANG Zhenyu, Professor, South China University of Technology
  • Mr ZHANG Lingqi, PhD candidate, Tokyo Institute of Technology
  • Dr WANG Tianyi, PostDoc, BrookHaven Natinoal Lab
  • Dr CHEN Wei, Chief research engineer, Midea
  • Mr HUANG Jianwen, Software engineer, SenseTime
  • Mr YANG Junrong, Software engineer, Tencent
  • Mr LIN Aoyu, Engineer, China Southern Power Grid

Acknowledgements

OpenCorr demonstrates our exploration of DIC and DVC methods in recent years, which got financial support from National Natural Science Foundation of China. I would like to give my special thanks to two collaborators for their continuous and strong support: Professor QIAN Kemao at Nanyang Technological University and Professor DONG Shoubin at South China University of Technology.

Related publication

Users may refer to our papers for more information about the detailed principles and implementations of the algorithms in OpenCorr. If you feel OpenCorr helps, please cite the following paper to make it known by more people.

@article{jiang2023opencorr,
title={OpenCorr: An open source library for research and development of digital image correlation},
author={Jiang, Zhenyu},
journal={Optics and Lasers in Engineering},
volume={165},
pages={107566},
year={2023},
publisher={Elsevier}
}
  1. Z. Jiang, Q. Kemao, H. Miao, J. Yang, L. Tang, Path-independent digital image correlation with high accuracy, speed and robustness, Optics and Lasers in Engineering (2015) 65: 93-102. (https://doi.org/10.1016/j.optlaseng.2014.06.011)
  2. L. Zhang, T. Wang, Z. Jiang, Q. Kemao, Y. Liu, Z. Liu, L. Tang, S. Dong, High accuracy digital image correlation powered by GPU-based parallel computing, Optics and Lasers in Engineering (2015) 69: 7-12. (https://doi.org/10.1016/j.optlaseng.2015.01.012)
  3. T. Wang, Z. Jiang, Q. Kemao, F. Lin, S.H. Soon, GPU accelerated digital volume correlation, Experimental Mechanics (2016) 56(2): 297-309. (https://doi.org/10.1007/s11340-015-0091-4)
  4. Z. Pan, W. Chen, Z. Jiang, L. Tang, Y. Liu, Z. Liu, Performance of global look-up table strategy in digital image correlation with cubic B-spline interpolation and bicubic interpolation, Theoretical and Applied Mechanics Letters (2016) 6(3): 126-130. (https://doi.org/10.1016/j.taml.2016.04.003)
  5. W. Chen, Z. Jiang, L. Tang, Y. Liu, Z. Liu, Equal noise resistance of two mainstream iterative sub-pixel registration algorithms in digital image correlation, Experimental Mechanics (2017) 57(6): 979-996. (https://doi.org/10.1007/s11340-017-0294-y)
  6. J. Huang, L. Zhang, Z. Jiang, S. Dong, W. Chen, Y. Liu, Z. Liu, L. Zhou, L. Tang, Heterogeneous parallel computing accelerated iterative subpixel digital image correlation, Science China Technological Sciences (2018) 61(1):74-85. (https://doi.org/10.1007/s11431-017-9168-0)
  7. J. Yang, J. Huang, Z. Jiang, S. Dong, L. Tang, Y. Liu, Z. Liu, L. Zhou, SIFT-aided path-independent digital image correlation accelerated by parallel computing, Optics and Lasers in Engineering (2020) 127: 105964. (https://doi.org/10.1016/j.optlaseng.2019.105964)
  8. J. Yang, J. Huang, Z. Jiang, S. Dong, L. Tang, Y. Liu, Z. Liu, L. Zhou, 3D SIFT aided path independent digital volume correlation and its GPU acceleration, Optics and Lasers in Engineering (2021) 136: 106323. (https://doi.org/10.1016/j.optlaseng.2020.106323)
  9. L. Cai, J. Yang, S. Dong, Z. Jiang. GPU accelerated parallel reliability-guided digital volume correlation with automatic seed selection based on 3D SIFT. Parallel Computing (2021) 108: 102824. (https://doi.org/10.1016/j.parco.2021.102824)
  10. A. Lin, R. Li, Z. Jiang, S. Dong, Y. Liu, Z. Liu, L. Zhou, L. Tang, Path independent stereo digital image correlation with high speed and analysis resolution, Optics and Lasers in Engineering (2022) 149: 106812. (https://doi.org/10.1016/j.optlaseng.2021.106812)
  11. Z. Jiang, OpenCorr: An open source library for research and development of digital image correlation. Optics and Lasers in Engineering (2023) 165: 107566. (https://doi.org/10.1016/j.optlaseng.2023.107566)

Impact

We are jubilant that OpenCorr helps other colleagues in their study as a benchmark. We would appreciate it If anyone could let us know the work not yet included in this list.

  1. Yuxi Chi, Bing Pan. Accelerating parallel digital image correlation computation with feature mesh interpolation. Measurement (2022) 199: 111554. (https://doi.org/10.1016/j.measurement.2022.111554)
  2. Wang Lianpo. Super-robust digital image correlation based on learning template. Optics and Lasers in Engineering (2022) 158: 107164. (https://doi.org/10.1016/j.optlaseng.2022.107164)