imgpalr package makes it easy to create color palettes from image
Install the CRAN release of
Install the development version from GitHub with
# install.packages("remotes") remotes::install_github("leonawicz/imgpalr")
The main function is
image_pal. It accepts PNG, JPG, BMP or GIF (first
frame) images either from disk or URL. It returns a vector of colors
defining a palette based on the image and your other function arguments.
You can also set
plot = TRUE to plot a preview of the palette, which
includes the source image thumbnail for visual reference.
The examples below offer some typical considerations to make when deriving a color palette from an arbitrary image.
In this first set of examples, divergent, qualitative and sequential palettes are generated from the same image and while varying some additional settings.
library(imgpalr) set.seed(1) x <- paste0(system.file(package = "imgpalr"), "/", c("blue-yellow", "purples", "colors"), ".jpg") # Three palette types, one image # A divergent palette image_pal(x, type = "div", saturation = c(0.75, 1), brightness = c(0.75, 1), plot = TRUE)
# A qualitative palette image_pal(x, type = "qual", bw = c(0.25, 0.9), plot = TRUE)
# A sequential palette image_pal(x, type = "seq", k = 2, saturation = c(0.75, 1), brightness = c(0.75, 1), seq_by = "hsv", plot = TRUE)
In this test image, hue varies over a narrow range. A sequential palette is sensible here, but not necessarily best sorted by hue. Doing so does still show a perceivable order to the colors, but it is much more difficult to discern. Sorting the palette first by saturation or brightness makes a much better sequential palette in this case.
image_pal(x, type = "seq", seq_by = "hsv", plot = TRUE)
image_pal(x, type = "seq", seq_by = "svh", plot = TRUE)
image_pal(x, type = "seq", seq_by = "vsh", plot = TRUE)
Using an image with several prominent hues, a divergent palette is not sensible here. A sequential is likely best sorted by hue.
Note in the second image below, you can also set
quantize = TRUE to
show a color-quantized reference thumbnail image based on the derived
palette. This makes use of the
image_quantmap function. Rather than
only quantizing the image, it does so while also mapping the colors of
any image to an arbitrary color palette based on nearest distances in
image_pal(x, type = "qual", brightness = c(0.4, 1), plot = TRUE)
image_pal(x, type = "seq", bw = c(0.2, 1), saturation = c(0.2, 1), plot = TRUE, quantize = TRUE)
Palette generation uses k-means clustering; results are different each
time you call
image_pal. If the palette you obtain does not feel
right, even with fixed arguments you can run it again to obtain a
different palette. Depending on the settings and the nature of the
source image, it may change quite a bit. If you need a reproducible
palette, set the
seed argument. In the example above, the seed was set
globally to avoid having to set it in each call to
You can quantize the colors in an image using
Choose any vector of colors. Each pixel has its color mapped to
whichever of these colors it is closest to in RGB space. The RGB array
is returned. You can plot the image with the palette.
x <- system.file("blue-yellow.jpg", package = "imgpalr") pal <- c("black", "navyblue", "dodgerblue", "yellow") a <- image_quantmap(x, pal, k = 7, plot = TRUE)
str(a) #> num [1:317, 1:400, 1:3] 0 0 0 0 0 0 0 0 0 0 ...
This works well if you want to quantize the colors to colors a short
distance away in RGB space, but if you want to also swap them out for
very different colors, this should be a two step process. If you provide
an equal-length vector of colors to the
pal2 argument, these colors
will replace those in
pal after the initial quantization.
pal2 <- c("darkred", "darkgreen", "tomato", "orange") a <- image_quantmap(x, pal, pal2, k = 7, plot = TRUE, show_pal = FALSE)
Note: This function can be very slow for large
k and/or larger images.
There is also the RImagePalette package on CRAN, which uses the median cut algorithm for finding they dominant colors in an image.
imgpalr was originally inspired by the
paletter package on
GitHub. Both packages use k-means clustering to find key image colors,
but take some different approaches in methods for assembling color
The palette preview (without the thumbnail addition) is based off of
scales::show_col, which is a convenient function for plotting
palettes. You can also use
pals::pal.bands to do the same using a
different visual layout.
If you want to directly manipulate the color properties of an image for its own sake rather than derive color palettes for other purposes, you can do so using the magick package, which provides bindings to the ImageMagick library.
Please note that the
imgpalr project is released with a Contributor
By contributing to this project, you agree to abide by its terms.
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