Skip to content
Delineate region of interest and extract time-series data from digital repeat photography images
Branch: master
Clone or download
Latest commit 1a7d3ce Apr 19, 2019
Type Name Latest commit message Commit time
Failed to load latest commit information.
R app dir Apr 17, 2019
data-raw functionalized Mar 20, 2018
inst app dir Apr 17, 2019
man app dir Apr 17, 2019
tests app dir Apr 17, 2019
vignettes upfate vignettes Nov 11, 2018
.Rbuildignore clean Oct 31, 2018
.gitignore clean Oct 31, 2018
.travis.yml YAML fixes Apr 19, 2019
DESCRIPTION app dir Apr 17, 2019
NAMESPACE app dir Apr 17, 2019 cleanup Nov 2, 2018 Update Nov 17, 2018 comments to cran Nov 2, 2018
xROI.Rproj cleanup built Sep 28, 2018

License: AGPL v3 DOI

lifecycle Travis CI Coverage status

CRAN status Downloads Downloads

xROI: Delineate Region of Interests (ROI's) and Extract Time-Series Data from Digital Repeat Photography Images

Here, we present an interactive open-source toolkit, called xROI[^*], that facilitates the process of time-series extraction and improves the quality of the final data. xROI provides a responsive environment for scientists to interactively:

a) delineate regions of interest (ROI), b) handle field of view (FOV) shifts, c) extract and export time series data characterizing color-based metrics.

Using xROI, the user can detect FOV shifts with minimal difficulty. The software gives user the opportunity to re-adjust mask files or redraw new ones every time an FOV shift occurs.

xROI Design

R language and Shiny package were used as the main development tool for xROI, while Markdown, HTML, CSS and JavaScript languages were used to smoothen the interactivity. While Shiny apps are primarily used for web-based applications to be used online, we used Shiny for its graphical user interface capabilities. In other words, both UI and server modules are run locally from the same machine and hence no internet connection is required. The xROI’s UI element presents a side-panel for data entry and three main tab-pages, each responsible for a specific task. The server-side element consists of R and shell scripts. Image processing and geospatial features were performed using the Geospatial Data Abstraction Library (GDAL) and the rgdal and raster R packages.

Install xROI

The xROI R package has been published on The Comprehensive R Archive Network (CRAN). The latest tested xROI package can be installed from the CRAN packages repository by running the following command in an R environment:

utils::install.packages('xROI', repos = "" )

Alternatively, the latest beta release of xROI can be directly downloaded and installed from the GitHub repository:

# install devtools first
utils::install.packages('devtools', repos = "" )


xROI depends on many R packages including: raster, rgdal, sp, jpeg, tiff, shiny, shinyjs, shinyBS, shinyAce, shinyTime, shinyFiles, shinydashboard, shinythemes, colourpicker, rjson, stringr, data.table, lubridate, plotly, moments, and RCurl. All the required libraries and packages will be automatically installed with installation of xROI. The package offers a fully interactive high-level interface as well as a set of low-level functions for ROI processing.

Launch xROI

A comprehensive user manual for low-level image processing using xROI is available from xROI.pdf. While the user manual includes a set of examples for each function; here we explain the graphical interactive mode. The interactive mode can be launched from an interactive R environment by the following command.


or form the command line (e.g., shell in Linux, Terminal in macOS and Command Prompt in Windows machines) where an R engine is already installed by:

Rscript -e “xROI::Launch(Interactive = TRUE)”

Calling the Launch function opens up the app in the system’s default web browser, offering an example dataset to explore different modules or upload a new dataset of images.

Follow the steps below:

  1. Draw an ROI by clicking on the sample image and enter the metadata.

  2. Save the ROI and metadata and explore its content on your computer.

  3. Explore if there is any FOV shift in the dataset using the CLI processer tab.

  4. Go to the Time series extraction tab. Extract the time-series. Save the output and explore the dataset in R.

Loading files from disk

By default, xROI can load images and their timings from the filenames (according to the PhenoCam naming convention: sitename_YYYY_MM_DD_HHMMSS.jpg, where it includes the site name, date and time). However, if the files are not named in this format, the user can select the option "From filelist.csv". In this case, the software looks for a comma separated file named “filelist.csv” in the selected directory,to obtain information about how to properly load the dataset. The filelist.csv file contains a list of images and their associated timing and is formatted in comma-separated-values format as follows. The user is responsible for generating the filelist.csv file. Each row includes 1 column for the filename as character strings and six columns for year, month, day, hour, minute and second of the acquisition date and time, in that order. An example file is presented in Appendix A: filelist.csv. User can explore loaded images using the exploring panel.

Here is an example:


End xROI

When you are done with the xROI interface you can close the tab in your browser and end the session in R by using one of the following opitons

In RStudio: Press the key on your keyboard. In R Terminal: Press <Ctrl + C> on your keyboard.

[^*]: The R package is developed and maintained by Bijan Seyednarollah. Most recent release is available from:

You can’t perform that action at this time.