Commonly available digital image correlation (DIC) tools are designed to evaluate displacement/deformation fields using well-optimized correlation functions and computational procedures. However, real-time monitoring at high sampling rates or tracking motion at predefined locations from large sequence images can be hardly accomplished due to extreme computational costs. As shown in this paper, a lightweight approach based on the registration of subpixel subset shifts using discrete Fourier transform (DFT) can easily tackle large sets of images if the measurement is limited to discrete locations. A simple open-source python software designed for evaluation of displacements using virtual extensometers is described in this paper, along with the analysis of its performance. It is shown that the DFT-based algorithms outperform a commonly used DIC package in terms of computational efficiency and required user interactions, without sacrificing precision and accuracy.

常用的数字图像相关 (DIC) 工具旨在使用优化良好的相关函数和计算程序来评估位移/变形场。然而，由于极端的计算成本，很难实现高采样率的实时监控或跟踪来自大序列图像的预定义位置的运动。如本文所示，如果测量仅限于离散位置，则基于使用离散傅立叶变换 (DFT) 的子像素子集偏移配准的轻量级方法可以轻松处理大型图像集。本文描述了一个简单的开源 python 软件，其设计用于使用虚拟引伸计评估位移，并对其性能进行分析。