This repository houses a modular, open-source MATLAB code to perform two dimensional Digital Image Correlation (DIC). This code forms part of the paper titled: "A 117 Line 2D Digital Image Correlation Code Written in MATLAB" which aims to serve as an educational resource to bridge the gap between the theory of DIC and its practical implementation. Furthermore, although the code is designed as an educational resource, its validation combined with its modularity makes it attractive as a starting point to develop the capabilities of DIC.

More specifically, DIC has become a popular tool in many fields to determine the displacements and deformations experienced by an object from images captured of the object. However the DIC process is complicated, being composed of several intricate elements. Although there are papers which explain DIC in its entirety while still catering to newcomers to the concept, these publications neglect to discuss how the theory presented is implemented in practice. This gap in literature makes it difficult to gain a working knowledge of DIC, which is necessary in order to contribute towards its development. The code, named ADIC2D, is a 2D, subset-based DIC framework that is predominantly consistent with current (as of the writing of the paper) state-of-the-art techniques. ADIC2D was validated showing that it performs on par with well-established DIC algorithms and thus is sufficiently reliable for practical use (refer to the paper, referenced below, for more details).

ADIC2D can be used through the command line of MATLAB. However it is advised to create a run file, such as the example file provided which is called runme.m, in order to run ADIC2D. This file (runme.m) runs the ADIC2D code for Samples 1, 2 or 3 of the 2D DIC challenge (version 1). The sample being analysed can be controlled by changing the value assigned to the variable "Sample" one line 4. The function input values on lines 9-18 of runme.m can be changed in order to set up the DIC analysis in the desired way. Once ADIC2D has analysed the images and returns the results (as ProcData on line 21), the displacement error metrics are computed and displayed in a tabulated format.

The correlation aspect of ADIC2D can be run using parallel processing by changing the for loop line 18 of the function SubCorr to a parfor loop. Note however that this does increase memory required by ADIC2D in order to run (exceeding memory limits of your PC will result in the code crashing).

ProcData stores the results in a vector format. In order to display the displacement and position information of the subsets easily a gridded format is necessary. The function AddGridFormat accepts ProcData and adds fields with gridded matrices for the purpose of displaying the results. More specifically, AddGridFormat adds the following fields:

• PD(d).POSX - which for image d stores the x-positions of the subsets (in the world CS) in a gridded format
• PD(d).POSY - which for image d stores the y-positions of the subsets (in the world CS) in a gridded format
• PD(d).UX - which for image d stores the x-displacements of the subsets (in the world CS) in a gridded format
• PD(d).UY - which for image d stores the y-displacements of the subsets (in the world CS) in a gridded format

This function is called as shown on line 30 of runme.m and the displacements can be plotted as shown on lines 31-38.

In the case where subsets fail to be correlated successfully the function CSTrans can crash. In such a case it is advised to use the alternative function CSTransRobust to avoid performing displacement transformation on these subsets which fail correlation. This simply requires changing CSTrans, of line 10 of function ADIC2D, from CSTrans to CSTransRobust.

In order to use this runme.m file Samples 1, 2 and 3 of the DIC challenge must be downloaded from the SEM website https://sem.org/2ddic. Thereafter they need to be unzipped and the unzipped folders of each sample need to be placed in a folder within the working directory named "DIC Challenge 1.0". (Alternatively you can save the Sample images to a location of your choice and modify the variable "ImageLocation", of line 99, to have the appropriate string for the full path to the folder containing the images)

Note that use of this code (ADIC2D and all its sub-functions) falls under the GNU GENERAL PUBLIC LICENSE Version 3. Furthermore, note that in the case of using this code for works related to publications (scientific or otherwise) requires citing of the source paper (citation given below).

Atkinson, D.; Becker, T. A 117 Line 2D Digital Image Correlation Code Written in MATLAB. Remote Sens. 2020, 12, 2906. Paper can be accessed at: https://doi.org/10.3390/rs12182906