Code to extract colors from an image based on k-means

ExtractPaletteColors.R

library(jpeg)

# Custom function similar to print.palette(...)
RenderPal <- function(x,name){
  
  if ((missing(x)) || (missing(name))){
    stop("Internal error, please troubleshoot")
  }
  n <- length(x)
  old <- graphics::par(mar = c(0.5, 0.5, 0.5, 0.5))
  on.exit(graphics::par(old))
  
  graphics::image(1:n, 1, as.matrix(1:n), col = x,
                  ylab = "", xaxt = "n", yaxt = "n", bty = "n")
  graphics::rect(0, 0.9, n + 1, 1.1, col = grDevices::rgb(1, 1, 1, 0.8), border = NA)
  graphics::text((n + 1) / 2, 1, labels = name, cex = 2, family = "serif")
}
# -------------------------------------------------------------------------
# Read in image
img=readJPEG(...)

# find dimensions of your image
imgDm=dim(img)

# Convert image to rgb data.frame
imgRGB <- data.frame(
  x = rep(1:imgDm[2], each = imgDm[1]),
  y = rep(imgDm[1]:1, imgDm[2]),
  R = as.vector(img[,,1]),
  G = as.vector(img[,,2]),
  B = as.vector(img[,,3])
)

# How many clusters? 
kClusters=10

# Determine kmean cluster based on the number of clusters provided
kMean=kmeans(imgRGB[, c("R", "G", "B")], centers = kClusters)

#  Extract the kmean colors
kColours <- rgb(kMean$centers[kMean$cluster,])

# Summary of colors
cols = unique(kColours)

# How are the colors distributed 
sort(table(kColours)/sum(table(kColours))*100)

# What is your final palette
RenderPal(cols,"My Palette")