The OpenImageR package is an image processing library. It includes functions for image preprocessing, filtering and image recognition. More details on the functionality of OpenImageR can be found in the first, second and third blog-posts, and in the package Documentation ( scroll down for information on how to use the docker image )
UPDATE 06-11-2018
As of version 1.1.2 the OpenImageR package allows R package maintainers to perform linking between packages at a C++ code (Rcpp) level. This means that the Rcpp functions of the OpenImageR package can be called in the C++ files of another package. In the next lines I'll give detailed explanations on how this can be done:
Assumming that an R package ('PackageA') calls one of the OpenImageR Rcpp functions. Then the maintainer of 'PackageA' has to :
- 1st. install the OpenImageR package to take advantage of the new functionality either from CRAN using,
install.packages("OpenImageR")
or download the latest version from Github using the remotes package,
remotes::install_github('mlampros/OpenImageR')
- 2nd. update the DESCRIPTION file of 'PackageA' and especially the LinkingTo field by adding the OpenImageR package (besides any other packages),
LinkingTo: OpenImageR
- 3rd. open a new C++ file (for instance in Rstudio) and at the top of the file add the following 'headers', 'depends' and 'plugins',
# include <RcppArmadillo.h>
# include <OpenImageRheader.h>
// [[Rcpp::depends("RcppArmadillo")]]
// [[Rcpp::depends(OpenImageR)]]
The available C++ classes (Utility_functions, Gabor_Features, Gabor_Features_Rcpp, HoG_features, Image_Hashing) can be found in the inst/include/OpenImageRheader.h file.
A complete minimal example would be :
# include <RcppArmadillo.h>
# include <OpenImageRheader.h>
// [[Rcpp::depends("RcppArmadillo")]]
// [[Rcpp::depends(OpenImageR)]]
// [[Rcpp::export]]
arma::mat rgb_2gray_exp(arma::cube RGB_image) {
oimageR::Utility_functions UTLF;
return UTLF.rgb_2gray_rcpp(RGB_image);
}
Then, by opening an R file a user can call the rgb_2gray_exp function using,
Rcpp::sourceCpp('example.cpp') # assuming that the previous Rcpp code is included in 'example.cpp'
set.seed(1)
im_rgb = array(runif(30000), c(100, 100, 3))
im_grey = rgb_2gray_exp(im_rgb)
str(im_grey)
Use the following link to report bugs/issues,
https://github.com/mlampros/OpenImageR/issues
UPDATE 29-11-2019
Docker images of the OpenImageR 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/openimager:rstudiodev
docker run -d --name rstudio_dev -e USER=rstudio -e PASSWORD=give_here_your_password --rm -p 8787:8787 mlampros/openimager: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/openimager:rstudiodev
In the latter case you might have first give permission privileges for write access to YOUR_DIR directory (not necessarily) using,
chmod -R 777 /home/YOUR_DIR
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,
http://0.0.0.0:8787
2nd. Option on a cloud instance,
http://Public DNS:8787
to access the Rstudio console in order to give your username and password.
If you use the code of this repository in your paper or research please cite both OpenImageR and the original articles / software https://CRAN.R-project.org/package=OpenImageR:
@Manual{,
title = {{OpenImageR}: An Image Processing Toolkit},
author = {Lampros Mouselimis},
year = {2023},
note = {R package version 1.3.0},
url = {https://CRAN.R-project.org/package=OpenImageR},
}
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