Play with an interactive version here!
The RImagePalette
package is a pure R implementation of the median cut algorithm for extracting the dominant colors from an image (in-depth explanation here). This package lets you use the colors from an image you like to create pretty plots, or to swap colors from one image to another.
Install from CRAN using:
install.packages("RImagePalette")
Or from github, using:
devtools::install_github("joelcarlson/RImagePalette")
It's simple to create palettes from an image using the image_palette()
function:
library(RImagePalette)
#Load an image
lifeAquatic <- jpeg::readJPEG("figs/LifeAquatic.jpg")
display_image(lifeAquatic)
#Create a palette of 9 colors
lifeAquaticPalette <- image_palette(lifeAquatic, n=9)
scales::show_col(lifeAquaticPalette)
Not happy with the results? We can tweak some settings until the scale is to our liking:
lifeAquaticPalette <- image_palette(lifeAquatic, n=9, choice=median, volume=TRUE)
scales::show_col(lifeAquaticPalette)
If it contains colors we like, we can pick and choose, and use them as a scale:
library(ggplot2)
#Create plot
p <- ggplot(data = iris, aes(x=Species, y=Sepal.Width, fill=Species)) + geom_bar(stat="identity")
#Apply scale
p + theme_bw() + scale_fill_manual(values=lifeAquaticPalette[c(2,3,6)])
RImagePalette
can create both discrete and continuous scales from images for use with ggplot2
using the new scale_color_image
(or for plots requiring fills, the scale_fill_image()
) function:
#Load an image
desert <- jpeg::readJPEG("figs/Desert.jpg")
display_image(desert)
#Create plot
p <- ggplot(data = iris, aes(x=Sepal.Length, y=Sepal.Width, col=Species)) + geom_point(size=3)
#Add discrete scale from image
p + theme_bw() + scale_color_image(image=desert)
#Create plot
p <- ggplot(data = iris, aes(x=Sepal.Length, y=Sepal.Width, col=Sepal.Length)) + geom_point(size=3)
#Use discrete=FALSE for a continuous scale
p + theme_bw() + scale_color_image(image=desert, discrete=FALSE)
Note: This feature is experimental at the moment, and as such is non-optimized, and slow. You must install from github to access the quantize_image
function
We can also quantize images into a discrete number of colors using the quantize_image
function:
#Load the famous mandrill
mandrill <- png::readPNG("figs/mandrill.png")
#Quantize using 7 colors
quant_mandrill <- quantize_image(mandrill, n=7)
When displayed closely reproduces the original image:
Another method for doing so is to use the kmeans approach, as discussed in this blog post by Ryan Walker. Here is the comparison between kmeans (on the left) and median cut (on the right) using 4 colors:
We can swap colors across images using the switch_colors()
function:
celery <- jpeg::readJPEG("figs/CeleryLunch.jpg")
billMurray <- jpeg::readJPEG("figs/BillMurray.jpg")
switch_colors(billMurray, celery, source_colors = 10)
There is an element of randomness in the median cut algorithm, so set your seeds carefully, and try running the algorithm a few times if you aren't happy with the results. Other ways to alter the palette: try using choice = median
, volume = TRUE
or change the value of n
.
There are a number of projects that inspired or helped this project along, and they deserve some recognition:
color-thief.js by Lokesh Dhakar.
Wes Anderson Palettes by Karthik Ram.
this blog post from Jo Fai Chow.
and this blog post by Ryan Walker
Thank you all for your great work!