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Functionality of the fastGLCM R package

This blog post is a slight modification of the R package Vignette and shows how to use the Rcpp Armadillo version of the fastGLCM R package. The fastGLCM R package is an RcppArmadillo implementation of the Python Code for Fast Gray-Level Co-Occurrence Matrix by numpy,

The python version works similarly and is included as an R6 class (see the documentation of fastglcm). However, it requires a python configuration in the user’s operating system and additionally the installation of the reticulate R package.

For the theoretical background of the Gray-Level Co-Occurrence Matrix Textures the user can consult an existing Tutorial of the University of Calgary.


Sample Satellite Imagery


The fastGLCM R package includes an ALOS-3 simulation image from JAXA (Japan Aerospace Exploration Agency) in compressed format (.zip) around Joso City, Ibaraki Prefecture from September 11, 2015, that will be used in this blog-post for illustration purposes.

Both fastGLCM versions of the R package take a 2-dimensional object as input (numeric matrix) and it is required that the range of pixel values are between 0 and 255,

require(fastGLCM)
#> Loading required package: fastGLCM
require(OpenImageR)
#> Loading required package: OpenImageR
require(utils)

temp_dir = tempdir(check = FALSE)
# temp_dir

zip_file = system.file('images', 'JAXA_Joso-City2_PAN.tif.zip', package = "fastGLCM")
utils::unzip(zip_file, exdir = temp_dir)
path_extracted = file.path(temp_dir, 'JAXA_Joso-City2_PAN.tif')

im = readImage(path = path_extracted)
dim(im)
#> [1] 1555 1414


imageShow(im)

Alt Text


To decrease the computation time the initial width and height will be reduced to 500,

#....................................................
# the pixel values will be adjusted between 0 and 255
#....................................................

im = resizeImage(im, 500, 500, 'nearest')
im = OpenImageR::norm_matrix_range(im, 0, 255)

#---------------------------------
# computation of all GLCM features
#---------------------------------

methods = c('mean',
            'std',
            'contrast',
            'dissimilarity',
            'homogeneity',
            'ASM',
            'energy',
            'max',
            'entropy')

res_glcm = fastGLCM_Rcpp(data = im,
                         methods = methods,
                         levels = 8,
                         kernel_size = 5,
                         distance = 1.0,
                         angle = 0.0,
                         threads = 1,
                         verbose = TRUE)
#> Elapsed time: 0 hours and 0 minutes and 1 seconds.

if (file.exists(path_extracted)) file.remove(path_extracted)
#> [1] TRUE

str(res_glcm)
#> List of 9
#>  $ mean         : num [1:500, 1:500] 0.578 0.766 0.953 0.938 0.938 ...
#>  $ std          : num [1:500, 1:500] 28.3 40 51.8 59.5 59.5 ...
#>  $ contrast     : num [1:500, 1:500] 2 2 2 0 0 1 2 4 4 4 ...
#>  $ dissimilarity: num [1:500, 1:500] 2 2 2 0 0 1 2 4 4 4 ...
#>  $ homogeneity  : num [1:500, 1:500] 8 11 14 15 15 14.5 14 13 13 13 ...
#>  $ ASM          : num [1:500, 1:500] 51 102 171 225 225 147 107 73 73 73 ...
#>  $ energy       : num [1:500, 1:500] 7.14 10.1 13.08 15 15 ...
#>  $ max          : num [1:500, 1:500] 7 10 13 15 15 12 10 8 8 8 ...
#>  $ entropy      : num [1:500, 1:500] 8.59 8.49 8.42 8.07 8.07 ...


The output matrices based on the selected methods (mean, std, contrast, dissimilarity, homogeneity, ASM, energy, max, entropy) can be visualized in a multi-plot,

plot_multi_images(list_images = res_glcm,
                  par_ROWS = 2,
                  par_COLS = 5,
                  titles = methods)


Credits:


Package Installation & Citation:


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')


If you use the fastGLCM R package in your paper or research please cite both fastGLCM and the original articles / software https://cran.r-project.org/web/packages/fastGLCM/citation.html:


@Manual{,
  title = {fastGLCM: Fast Gray Level Co-occurrence Matrix computation (GLCM) using R},
  author = {Lampros Mouselimis},
  year = {2022},
  note = {R package version 1.0.0},
  url = {https://CRAN.R-project.org/package=fastGLCM},
}


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