OpenCorr: 开源数字图像相关法C++程序开发库

OpenCorr是一个开源的C++程序开发库，旨在提供一套轻量而高效的开发工具，帮助对数字图像相关法（Digital image correlation, DIC）或数字体图像相关法（Digital volume correlation, DVC）感兴趣的用户学习和研究算法的原理及实现，或者根据自己的特定需求，像组装乐高积木一样，开发新的DIC或DVC算法，或制作相关处理软件。OpenCorr GUI是我们在这个开发库基础上制作的一个图形界面软件，展示了这个库中大部分DIC和DVC功能。

大家如有问题或建议，欢迎联系我们。具体可通过以下途径获得帮助：

  • 给我们写Email: zhenyujiang (at) scut.edu.cn
  • 在GitHub的项目讨论区交流
  • 加入OpenCorr的QQ讨论群：597895040

OpenCorr的代码和文档通过GitHub发布和维护，网址为 https://github.com/vincentjzy/OpenCorr

OpenCorr GUI

使用说明

1. 如何使用OpenCorr

2. OpenCorr的架构

3. 数据对象

4. 处理方法

5. GPU加速

6. 实例说明

7. 图形界面软件

致谢

OpenCorr 中提供的 DIC和DVC 算法是我们近年来在该领域一系列探索的成果，这些研究工作得到了多个国家自然科学基金项目的资助。在此，特别感谢一直以来给予我大力支持的两位合作者：新加坡南洋理工大学的钱克矛教授和华南理工大学的董守斌教授。

开发人员

  • 蒋震宇 博士，华南理工大学教授、博士生导师
  • 张铃启 博士，日本理化学研究所博士后
  • 王添翼 博士，美国布鲁克海文国家实验室博士后
  • 陈蔚 博士，美的集团先行研究主任工程师
  • 黄健文，商汤科技软件工程师
  • 杨俊荣，腾讯科技软件工程师
  • 林傲宇，中国南方电网工程师
  • 李睿，华南理工大学博士生
  • 任豪强，华南理工大学博士生

相关论文

用户可参阅我们发表的论文，以便深入了解OpenCorr中算法的原理。如果OpenCorr对你的工作有帮助，请引用以下这篇论文，以便更多朋友了解OpenCorr。

@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)
  12. W. Yin, Y. Ji, J. Chen, R. Li, S. Feng, Q. Chen, B. Pan, Z. Jiang, C. Zuo, Initializing and accelerating Stereo-DIC computation using semi-global matching with geometric constraints. Optics and Lasers in Engineering (2024) 172: 107879. (https://doi.org/10.1016/j.optlaseng.2023.107879)

影响

我们欣喜地发现，一些其他院校的同仁在他们的DIC研究中使用OpenCorr开发新算法或作为测试基准。欢迎大家提供更多以下列表中尚未包括的工作。

  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)
  3. Y Li, L Wei, X Zhang. Measurement of nonuniform strain distribution in CORC cable due to bending process by a segmentation-aided stereo digital image correlation (2023) 63: 813-822. (https://doi.org/10.1007/s11340-023-00953-y)
  4. Yusheng Wang, Zhixiang Huang, Pengfei Zhu, Rui Zhu, Tianci Hu, Dahai Zhang, Dong Jiang. Effects of compressed speckle image on digital image correlation for vibration measurement. Measurement (2023) 217: 113041. (https://doi.org/10.1016/j.measurement.2023.113041)
  5. Yong Su. An analytical study on the low-pass filtering effect of digital image correlation caused by under-matched shape functions. Optics and Lasers in Engineering (2023) 168: 107679. (https://doi.org/10.1016/j.optlaseng.2023.107679)
  6. Chuanguo Xiong , Yuhan Gao, Yuhua huang , Fulong Zhu. Specular surface deformation measurement based on projected-speckle deflectometry with digital image correlation. Optics and Lasers in Engineering (2023) 170: 107776. (https://doi.org/10.1016/j.optlaseng.2023.107776)
  7. Xiao Hong, Li Chengnan, Feng Mingchi. Large deformation measurement method of speckle images based on deep learning. Acta Optica Sinica (2023) 43(14): 1412001. (https://doi.org/10.3788/AOS222084)
  8. Jiaqiu Wang, Hao Wu, Zhengduo Zhu5, Hujin Xie, Han Yu, Qiuxiang Huang, Yuqiao Xiang, Phani Kumari Paritala, Jessica Benitez Mendieta, Haveena Anbananthan, Jorge Alberto Amaya Catano, Runxin Fang, Luping Wang, Zhiyong Li. Impact of Speckle Deformability on Digital Imaging Correlation, IEEE Access (2024) 12:66466-66477. (https://doi.org/10.1109/ACCESS.2024.3398786)
  9. Derui Li, Bin Cheng, Sheng Xiang. Direct cubic B-spline interpolation: A fuzzy interpolating method for weightless, robust and accurate DVC computation. Optics and Lasers in Engineering (2024) 172: 107886. (https://doi.org/10.1016/j.optlaseng.2023.107886)
  10. Datao Li, Xiahui Wei, Yingrong Gao, Jinsong Jiang, Wei Xia, Binhua Wang. Investigations on tensile mechanical properties of rigid insulation tile materials at elevated temperatures based on digital image correlation algorithm. Construction and Building Materials (2024) 413: 134925. (https://doi.org/10.1016/j.conbuildmat.2024.134925)
  11. Jiashuai Yang, Kemao Qian, Lianpo Wang. R3-DICnet: an end-to-end recursive residual refinement DIC network for larger deformation measurement. Optics Express (2024) 32(1): 907-921. (https://doi.org/10.1364/OE.505655)
  12. Longyong Tong, Hang Zhou, Brian Sheil. Multicore CPU-based parallel computing accelerated digital image correlation for large soil deformations measurement. Computers and Geotechnics (2024) 166: 106027. (https://doi.org/10.1016/j.compgeo.2023.106027)
  13. Hengrui Cui, Zhoumo Zeng, Jian Li, Hui Zhang, Fenglong Yang, Shili Chen. The effect of error coefficient matrices and correlation criteria on dic computation errors. Optics and Lasers in Engineering (2024) 174: 107954. (https://doi.org/10.1016/j.optlaseng.2023.107954)
  14. Siqi Wang, Zehui Zhu, Tao Ma, Jianwei Fan. Asphalt concrete characterization using digital image correlation: A systematic review of best practices, applications, and future vision. Journal of Testing and Evaluation (2024) 52(4): 20230485. (https://doi.org/10.1520/JTE20230485)
  15. Yujia Cheng, Quanbao Wang, Lightweight digital image correlation network for stratospheric airship skin deformation measurement, Optical Engineering (2024) 63(7): 073103. (https://doi.org/10.1117/1.OE.63.7.073103)
  16. Yahong Feng, Lianpo Wang. Stereo-DICNet: An efficient and unified speckle matching network for stereo digital image correlation measurement. Optics and Lasers in Engineering (2024) 179: 108267. (https://doi.org/10.1016/j.optlaseng.2024.108267)
  17. Yong Su, Li Lao, Modeling the measurement accuracy of one-dimensional boundary subsets in digital image correlation, Optics and Lasers in Engineering (2024) 181: 108362. (https://doi.org/10.1016/j.optlaseng.2024.108362)