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CRAN: Package fastGLCM

fastGLCM: 'GLCM' Texture Features

Two 'Gray Level Co-occurrence Matrix' ('GLCM') implementations are included: The first is a fast 'GLCM' feature texture computation based on 'Python' 'Numpy' arrays ('Github' Repository, <https://github.com/tzm030329/GLCM>). The second is a fast 'GLCM' 'RcppArmadillo' implementation which is parallelized (using 'OpenMP') with the option to return all 'GLCM' features at once. For more information, see "Artifact-Free Thin Cloud Removal Using Gans" by Toizumi Takahiro, Zini Simone, Sagi Kazutoshi, Kaneko Eiji, Tsukada Masato, Schettini Raimondo (2019), IEEE International Conference on Image Processing (ICIP), pp. 3596-3600, <doi:10.1109/ICIP.2019.8803652>.

Version: 1.0.2 Depends: R (≥ 3.2.3) Imports: Rcpp (≥ 1.0.8.3), R6, rlang, OpenImageR, utils LinkingTo: Rcpp, RcppArmadillo, OpenImageR Suggests: reticulate, covr, knitr, rmarkdown, testthat (≥ 3.0.0) Published: 2022-09-25 DOI: 10.32614/CRAN.package.fastGLCM Author: Lampros Mouselimis [aut, cre], Takahiro Toizumi [cph] (Author of the fastGLCM Python code) Maintainer: Lampros Mouselimis <mouselimislampros at gmail.com> BugReports: https://github.com/mlampros/fastGLCM/issues License: GPL-3 Copyright: inst/COPYRIGHTS
fastGLCM copyright details URL: https://github.com/mlampros/fastGLCM NeedsCompilation: yes SystemRequirements: apt-get-pip: apt-get install -y python3-pip (deb), python3-pip: python3 -m pip install -U pip (deb), numpy: pip3 install -U numpy (deb), cv2: pip3 install -U opencv-python (deb), matplotlib: pip3 install -U matplotlib (deb), skimage: pip3 install -U scikit-image (deb), libarmadillo: apt-get install -y libarmadillo-dev (deb), libblas: apt-get install -y libblas-dev (deb), liblapack: apt-get install -y liblapack-dev (deb), libarpack++2: apt-get install -y libarpack++2-dev (deb), gfortran: apt-get install -y gfortran (deb) Citation: fastGLCM citation info Materials: README NEWS CRAN checks: fastGLCM results Documentation: Downloads: Linking:

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