application
Opensource software¶
OpenCorr - DIC Opensource Framework (2D/3D)¶
OpenCorr: An open source library for research and development of digital image correlation
OpenCorr is an open source C++ library made for research and development of digital image correlation (DIC) technology. It provides full-function modules of 2D and stereo DIC, as well as digital volume correlation (DVC). In contrast with most of current open source DIC software which is application-oriented, the sophisticated design of OpenCorr makes it developer-friendly and facilitates the method study. Users can create DIC software quickly for specific applications by assembling the modules in this library and realize new algorithms readily by modifying the modules or incorporating additional modules. Three examples are given as the showcases for the capability of OpenCorr. The first one is an image feature guided DIC method, which can accurately measure large strain over 35%. The second is a stereo DIC method combining image feature guided DIC with an epipolar constraint aided algorithm to achieve enhanced robustness. The third is a novel self-adaptive DIC method, which dynamically optimize the size and shape of subset at each point of interest (POI) according to the nearby image features. The computation speed of DIC and DVC modules is evaluated through a few tests, which demonstrate superior running efficiency of the library. OpenCorr has attracted rapidly growing interest from both academic and industrial communities. It is expected to stimulate the advance and spread of DIC technology.
https://github.com/vincentjzy/OpenCorr
Deep Convolutional NN solver for Speckle (DIC)¶
https://github.com/RuYangNU/Deep-Dic-deep-learning-based-digital-image-correlation
Digital image correlation (DIC) has become an industry standard to retrieve accurate displacement and strain measurement in tensile testing and other material characterization. Though traditional DIC offers a high precision estimation of deformation for general tensile testing cases, the prediction becomes unstable at large deformation or when the speckle patterns start to tear. In addition, traditional DIC requires a long computation time and often produces a low spatial resolution output affected by filtering and speckle pattern quality. To address these challenges, we propose a new deep learning-based DIC approach – Deep DIC, in which two convolutional neural networks, DisplacementNet and StrainNet, are designed to work together for end-to-end prediction of displacements and strains. DisplacementNet predicts the displacement field and adaptively tracks the region of interest. StrainNet predicts the strain field directly from the image input without relying on the displacement prediction, which significantly improves the strain prediction accuracy. A new dataset generation method is developed to synthesize a realistic and comprehensive dataset, including generation of speckle patterns and deformation of the speckle image with synthetic displacement field. Though trained on synthetic dataset only, Deep DIC is tested on both simulated and experimental data. Deep DIC gives highly consistent and comparable predictions of displacement and strain with those obtained from commercial DIC software, while it outperforms commercial software with very robust strain prediction even at large and localized deformation and varied pattern qualities. In addition, Deep DIC is capable of real-time prediction of deformation with a calculation time down to milliseconds.
μDIC: An open-source toolkit for digital image correlation (python)¶
A Digital Image Correlation toolkit, formulated as a Python package. This package aims at providing a complete toolkit for performing DIC analysis on experimental data, performing virtual experiments, as well as a framework for further development. A suite of tools for generating synthetic speckle images, modelling of sensor artefacts and deformation of images by displacement fields, are included. The virtual experiments are used as a part of the accuracy assessment of the toolkit as well as for testing during development. B-spline elements are employed for the discretisation of the displacement fields and allow the polynomial order and degree of continuity to be controlled by the user.