imagefluency is an simple R package for image fluency scores. The package allows to get scores for several basic aesthetic principles that facilitate fluent cognitive processing of images. If you want to try it out before installing, you can find an interactive Shiny app here (alpha version).
The main functions are:
img_contrast()to get the visual contrast of an image.
img_complexity()to get the visual complexity of an image (equals 1 minus image simplicity)
img_self_similarity()to get the visual self-similarity of an image
img_simplicity()function to get the visual simplicity of an image (equals 1 minus image complexity).
img_symmetry()to get the vertical and horizontal symmetry of an image.
img_typicality()to get the visual typicality of a list of images relative to each other
Other helpful functions are:
img_read()wrapper function to read images into R using
read.bitmap()from the readbitmap package
rgb2gray()convert images from RGB into grayscale (might speed up computation)
run_imagefluency()to launch a Shiny app locally on your computer for an interactive demo of the main functions
The main author is Stefan Mayer.
You can install the current stable version from CRAN.
To download the latest development version from Github use the
install_github function of the
# install devtools if necessary if (!require("devtools")) install.packages("devtools") # install imagefluency from github devtools::install_github('stm/imagefluency')
Use the following link to report bugs/issues: https://github.com/stm/imagefluency/issues
# visual contrast # # example image file (from package): bike.jpg bike_location <- system.file("example_images", "bike.jpg", package = "imagefluency") # read image from file bike <- img_read(bike_location) # get contrast img_contrast(bike) # visual symmetry # # read image rails <- img_read(system.file("example_images", "rails.jpg", package = "imagefluency")) # get only vertical symmetry img_symmetry(rails, horizontal = FALSE)
See the package vignette for a detailled introduction (or type
vignette("imagefluency", package = "imagefluency") into the R console) and the reference page for details on each function.
If you want to cite this package in a scientific journal or in any other context, run the following code in your
utils::citation(package = "imagefluency")
There is currently a publication in preparation corresponding this package and the citation will be updated once it’s published.
img_complexity function relies on the packages R.utils and magick. The
img_self_similarity function relies on the packages OpenImageR, pracma, and quadprog. The
img_read function relies on the readbitmap package. The
run_imagefluency shiny app depends on shiny.
Mayer, S. & Landwehr, J, R. (2018). Quantifying Visual Aesthetics Based on Processing Fluency Theory: Four Algorithmic Measures for Antecedents of Aesthetic Preferences. Psychology of Aesthetics, Creativity, and the Arts, 12(4), 399–431. doi: 10.1037/aca0000187
Mayer, S. & Landwehr, J. R. (2018). Objective measures of design typicality. Design Studies, 54, 146–161. doi: 10.1016/j.destud.2017.09.004