Extract colours from a JPG file to use as a (divergent) colour palette.

palettebuildr.R

palettebuildr <- function(pathToJPEG = "logo.jpg", ncols = 3, dist.method = "euclidian", clust.method = "complete"){
  require(jpeg)
  
  #Read in the jpeg file
  img <- readJPEG(pathToJPEG)
  
  #Using the whole image is overkill, especially for large files.
  #Therefore, create a grid from which extract the colors
  xgrid <- ceiling(seq(1, dim(img)[1], length.out = 50))
  ygrid <- ceiling(seq(1, dim(img)[2], length.out = 50))
  pixels <- expand.grid(xgrid, ygrid)
  
  #Get the red/green/blue values for each pixel
  pixels$redVals <- apply(pixels, 1, function(x){return(img[x[1], x[2], ][1])})
  pixels$greenVals <- apply(pixels, 1, function(x){return(img[x[1], x[2], ][2])})
  pixels$blueVals <- apply(pixels, 1, function(x){return(img[x[1] ,x[2], ][3])})
  
  #Get the euclidian distances of the colour values.
  #This creates a distance matrix showing how similar the pixels
  #are to eachother.
  distMat <- dist(pixels[, 3:5], method = dist.method)
  
  #Use hierarchical clustering to group the colours into n groups
  x <- hclust(distMat,method = clust.method)
  
  #Assign each pixel to one of k-groups based on clustering
  pixels$groups <- cutree(x, k = ncols)
  
  #Take average red/green/blue values for the whole group
  redGroup <- tapply(pixels$redVals, pixels$groups, mean)
  greenGroup <- tapply(pixels$greenVals, pixels$groups, mean)
  blueGroup <- tapply(pixels$blueVals, pixels$groups, mean)
  
  #Create a dataframe of colours to return
  groupCols <- data.frame(gp = 1:ncols, red = redGroup, green = greenGroup, blue=blueGroup)
  groupCols$hexCode <- rgb(red = groupCols$red, green = groupCols$green, blue = groupCols$blue, alpha = 1, maxColorValue = 1)
  
  return(groupCols)
}

Use like this:

colorPalette = palettebuildr("starrynight.jpg", ncols = 5)
pie(rep(1, nrow(colorPalette)), col = colorPalette$hexCode,
    labels = substr(colorPalette$hexCode, 1, 7), border = "white")