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https://github.com/mlampros/OpenImageR
Image processing Toolkit in R
https://github.com/mlampros/OpenImageR
filtering gabor-feature-extraction gabor-filters hog-features image image-hashing processing r rcpparmadillo recognition slic slico superpixels
Last synced: 3 months ago
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Image processing Toolkit in R
- Host: GitHub
- URL: https://github.com/mlampros/OpenImageR
- Owner: mlampros
- Created: 2016-07-08T12:01:42.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2023-07-08T07:38:45.000Z (over 1 year ago)
- Last Synced: 2024-07-12T00:05:20.366Z (4 months ago)
- Topics: filtering, gabor-feature-extraction, gabor-filters, hog-features, image, image-hashing, processing, r, rcpparmadillo, recognition, slic, slico, superpixels
- Language: R
- Homepage: https://mlampros.github.io/OpenImageR/
- Size: 48.7 MB
- Stars: 57
- Watchers: 5
- Forks: 10
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
[](https://github.com/mlampros/OpenImageR/actions)
[](https://codecov.io/github/mlampros/OpenImageR?branch=master)
[](http://cran.r-project.org/package=OpenImageR)
[](http://www.r-pkg.org/pkg/OpenImageR)
[](https://hub.docker.com/r/mlampros/openimager)
[](https://cran.r-project.org/package=OpenImageR)
## OpenImageR
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](http://mlampros.github.io/2016/07/08/OpenImageR/), [second](http://mlampros.github.io/2018/08/08/Gabor_Feature_Extraction/) and [third](http://mlampros.github.io/2018/11/09/Image_Segmentation_Superpixels_Clustering/) 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,
```R
install.packages("OpenImageR")
```
or download the latest version from Github using the *remotes* package,
```R
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),
```R
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',
```R
# include
# include
// [[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 :
```R
# include
# include
// [[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,
```R
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](https://github.com/mlampros/OpenImageR/issues)
**UPDATE 29-11-2019**
**Docker images** of the *OpenImageR* package are available to download from my [dockerhub](https://hub.docker.com/r/mlampros/openimager) 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,
```R
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,
```R
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,
```R
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/](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,
```R
http://0.0.0.0:8787
```
**2nd. Option** on a cloud instance,
```R
http://Public DNS:8787
```
to access the Rstudio console in order to give your username and password.
### **Citation:**
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`:
```R
@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},
}
```