Fast GLCM feature texture computation. This R package includes two GLCM implementations:
More details on the functionality of fastGLCM can be found in the package Documentation, Vignette and blog-post
Installation:To install the package from CRAN use,
install.packages("fastGLCM")
and to download the latest version of the package from Github,
remotes::install_github('mlampros/fastGLCM')
Docker Image
Docker images of the fastGLCM package are available to download from my dockerhub account. The images come with Rstudio and the R-development version (latest) installed. The whole process was tested on Ubuntu 18.04. To pull & run the image do the following,
docker pull mlampros/fastglcm:rstudiodev
docker run -d --name rstudio_dev -e USER=rstudio -e PASSWORD=give_here_your_password --rm -p 8787:8787 mlampros/fastglcm:rstudiodev
The user can also bind a home directory / folder to the image to use its files by specifying the -v command,
docker run -d --name rstudio_dev -e USER=rstudio -e PASSWORD=give_here_your_password --rm -p 8787:8787 -v /home/YOUR_DIR:/home/rstudio/YOUR_DIR mlampros/fastglcm:rstudiodev
The USER defaults to rstudio but you have to give your PASSWORD of preference (see https://rocker-project.org for more information).
Open your web-browser and depending where the docker image was build / run give,
1st. Option on your personal computer,
2nd. Option on a cloud instance,
to access the Rstudio console in order to give your username and password.
Similar Projects:If you use the code of this repository in your paper or research please cite both fastGLCM and the original articles (see CITATION) https://CRAN.R-project.org/package=fastGLCM
:
@Manual{,
title = {{fastGLCM}: Fast Gray Level Co-occurrence Matrix computation (GLCM) using R},
author = {Lampros Mouselimis},
year = {2022},
note = {R package version 1.0.2},
url = {https://CRAN.R-project.org/package=fastGLCM},
}
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