GCPU_OpticalFlow: A GPU accelerated Python software for strain measurement
Ahmed Chabib,
Jean-François Witz,
Pierre Gosselet,
Vincent Magnier
Affiliations
Ahmed Chabib
Corresponding author.; Univ. Lille, CNRS, Centrale Lille, UMR 9013 - LaMcube - Laboratoire de Mécanique, Multiphysique, Multi-échelle, F-59000 Lille, France
Jean-François Witz
Univ. Lille, CNRS, Centrale Lille, UMR 9013 - LaMcube - Laboratoire de Mécanique, Multiphysique, Multi-échelle, F-59000 Lille, France
Pierre Gosselet
Univ. Lille, CNRS, Centrale Lille, UMR 9013 - LaMcube - Laboratoire de Mécanique, Multiphysique, Multi-échelle, F-59000 Lille, France
Vincent Magnier
Univ. Lille, CNRS, Centrale Lille, UMR 9013 - LaMcube - Laboratoire de Mécanique, Multiphysique, Multi-échelle, F-59000 Lille, France
This paper introduces an open-source pixel-wise Digital Image Correlation tool written in Python and targeting graphics processing units (GPUs) with the help of Cupy and Rapids-cuCim libraries. It is capable of computing the kinematic fields that transform an image into another in an efficient and quick way and it allows to treat large images in the GPU. Even if GCPU_OpticalFlow can be easily used by communities concerned by the estimation of displacement, it is particularly tuned to estimate consistent strain (gradient) field. The detection of a crack in a material is presented in this work as a demonstration.