Package: openlsa
OpenLSAOpenLSA is a Python Library developed for processing images of periodic patterns to retrieve the displacement that warps them.
Full-field displacement maps are thus deduced from a pair of images.
Designed for the Experimental Mechanics community, LSA is able to process optimized patterns in terms of metrological performance, such as the checkerboard one [1].An illustration of a wooden specimen, subjected to a compression load, is detailed within the file "Script_example.py". An interested reader can refer to the LSA seminal paper [2].[1] Increasing accuracy and precision of Digital Image Correlation through pattern optimization. Bomarito, G. F. and Hochhalter, J. D. and Ruggles, T. J. & Cannon, A. H.; Optics and Lasers in Engineering; 2017 (doi: 10.1016/j.optlaseng.2016.11.005)[2] Extracting displacement and strain fields from checkerboard images with the Localized Spectrum Analysis M. Grédiac, B. Blaysat & F. Sur; Experimental Mechanics, 2019. (doi: 10.1007/s11340-018-00439-2)Getting startedOpenLSA sources are available at therepositoryor the library can be installed with pip:pip install OpenLSARecommendationsOpenLSA requires numerous Python libraries: numpy, scipy, scikit-image, opencv-python, pillow, matplotlib, and boto3.
All dependencies are correctly managed when using the pip installation.UsageA set of images and the minimal script example is provided in therepository.
Nevertheless, the main steps for retrieving a displacement field from a reference ref_im.tif to a current cur_im.tif are given in what follows.Requiered librariesimport numpy as np
from PIL import Image
from openlsa import OpenLSALoading imagesimg_0 = np.array(Image.open("ref_im.tif"))
img_t = np.array(Image.open("cur_im.tif"))Images are loaded and formatted as numpy arrays.Initializing LSA & kernellsa = OpenLSA(img_0, verbose=True, display=True)
kernel = my_lsa.compute_kernel(std=my_lsa.pitch().max())Thelsaclass constructor directly analyses the reference image to build up LSA parameters such as the two orthogonal wave vectors characterizing its pattern.
Theverbose(respectively,display) option is set toTrue, so the program, during execution, displays information in the terminal (respectively, separate figures).
The LSA kernel is also defined in the proposed script.
The smallest width is chosen here, with the standard deviation of the Gaussian kernel being set to the pattern period.Computing phasesphi_0, __ = my_lsa.compute_phases_mod(img_0, kernel)
phi_t, __ = my_lsa.compute_phases_mod(img_t, kernel)Thecompute_phases_modmethod is called to compute the phase modulations associated with each image.Solving the temporal unwrapingphi_t, __ = my_lsa.temporal_unwrap(img_0, img_t, phi_0, phi_t)The pairing of the current phase modulationsphi_ttophi_0is performed by following a specific point from the reference image to the current one.
Point selection is automatically defined.Computing of the displacementu_xy = my_lsa.compute_displacement(phi_0, phi_t, min_iter=6)Finally, a fixed point algorithm is called for computing the displacement field from both phase modulationsphi_0andphi_t.
Displacementu_xyis formatted as a complex number, its real (respectively, imaginary) part corresponding to the component in the row (respectively, column) direction.SupportTell people where they can go for help.
It can be any combination of an issue tracker, a chat room, an email address, etc.RoadmapShort term developments will consist of:Extending the displacement calculation to take into account camera model.Adding the deconvolution algorithm.LicenseThese python codes can be used for non-profit academic research only.
They are distributed under the terms of the GNU General Public License v3.