Tabulate hexadecimal colours from RGB image bands in R

This (mostly useless) nugget came out of a sophisticated and ridiculous side-project that a colleague initiated. We needed a tool that would pull a table of hexadecimal colours out of an image (you know, those weird # colours that R and HTML use?). We wanted to know the most common colours in a given image and couldn’t find a pre-built tool that did this. So I made one. Again, probably useless to anyone else, but here it is because, well, the ether isn’t saturated (yet).

The function actually pulls all the images (.jpg files) out of a given directory path and batch processes them, producing a unique output for each. The last piece of code combines the tables for multiple images and outputs a single coloured barplot.

For this example, I have used two images from the iconic 1996 film ‘Pulp Fiction’, both of which are certainly subject to all kinds of copyright that should prevent me from posting them, but whatever. Download them and put them in a directory (within your working directory) called “Pulp Fiction”.

And here’s the code:

# Required packages

# Directory title (must be in the working directory)
name <- "Pulp Fiction"

# Path to images (also the output path for tables & images)
filepath <- paste(name,"/",sep="")

# List of image files (will be used to name outputs)
image.list <- list.files(filepath,pattern=".jpg")
image.list <- substr(image.list,1,nchar(image.list)-4)

## FUNCTION ## <- function(jpeg, round.dig, n.col){
 # Image must have a .jpg extension (NOT .jpeg)
 # jpeg = file name of image (without the .jpg extension)
 # round.dig = number of digits to round RGB values to (more = more unique but similar colours)
 # n.col = number of unique colour to plot 
 # Import image
 img <- readJPEG(paste(filepath,jpeg,".jpg",sep=""))
 # Extract RGB layers
 # Note the rounding of the RGB values
 img.R <- round(img[,,1],1) # RED
 img.G <- round(img[,,2],1) # GREEN
 img.B <- round(img[,,3],1) # BLUE
 # Make unique colours
 rgb <- hex(RGB(c(img.R),c(img.G),c(img.B))) <- <-[order([2]),]$Prop <- round($Freq/sum($Freq),5)[1:10,]; dim(
 cols <- as.character([1:n.col,1])
 # Save hex colour table
 write.csv(, paste(filepath,jpeg,"_RGB_Table.csv",sep=""), row.names=F)
 # Plot; x11(w=12,h=5)
 par(mfrow=c(2,4), mar=c(1,1,1,1))
 image(t(img.R[nrow(img.R):1L,]),axes=F, col=colorRampPalette(c("white","red"))(10))
 image(t(img.G[nrow(img.G):1L,]),axes=F, col=colorRampPalette(c("white","green"))(10))
 image(t(img.B[nrow(img.B):1L,]),axes=F, col=colorRampPalette(c("white","blue"))(10))
 plot(1:100,1:100,type="n",axes=F,ann=F); rasterImage(img,1,1,100,100)
 hist(img.R, breaks=12, col="pink", xlab="RED", ylab="", las=1, main="")
 hist(img.G, breaks=12, col="darkolivegreen1", xlab="GREEN", ylab="", las=1, main="")
 hist(img.B, breaks=12, col="lightblue", xlab="BLUE", ylab="", las=1, main="")
 barplot(rep(n.col,n.col), col=cols, axes=F, names.arg=cols, las=2)

# Run the function for the image list
# Rounds to 1 decimal, 15 colour output
lapply(image.list,, 1, 15)

For each of the images, you should have an output like this…



…as well as a .csv table with the complete list of unique colours, including the number and proportion of pixels in the image of that colour.

   rgb     Freq   Prop
1  #000000 236295 0.30046
2  #DA5900 90143  0.11462
3  #DA5959 40361  0.05132
4  #595900 32629  0.04149

Here is where you’ll notice the effect of rounding the RGB values. If you leave them more precise (i.e. more decimals), you’ll end up with many more unique colours and many nearly identical colours repeated in the image. Do what you like.

With multiple images, the last piece of code creates a combined colour plot (using the proportions, so you need not worry about combining low and high resolution images).

# Combine all the images
for(i in image.list){
 dat <- read.csv(paste(filepath,i,"_RGB_Table.csv",sep=""))
 } else {
 dat <- rbind(dat,read.csv(paste(filepath,i,"_RGB_Table.csv",sep="")))
# Combine duplicate values
dat <- ddply(dat, .(rgb), summarise, Prop=round(sum(Prop)/length(image.list),5))
dat <- dat[order(-dat$Prop),]
head(dat); dim(dat)
# Save the table
write.csv(dat, paste(filepath,name,"_Combined_Hex_Table.csv",sep=""), row.names=F)
# Plot combined colours; x11()
n.cols <- 30
barplot(rep(n.cols,n.cols), axes=F, col=rev(as.character(dat[1:n.cols,"rgb"])), names.arg=rev(as.character(dat[1:n.cols,"rgb"])), las=2, main=name, horiz=T)
# Save the plot

The output plot will look something like this:

Pulp Fiction_Combined_Hex_Plot

Now, you can pull your favourite colours off the plot (with the handy # hex code printed in the margin). Have fun at home with your friends making colour palettes for your favourite internet images! Woohoo!

WEAK CREDIT: the included function uses a .jpg plotting code that I found on StackExchange. I would credit it to the thread, but can’t now seem to relocate it. Sorry to the person who posted it. I hope karma finds you (and not me).


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2 Responses to Tabulate hexadecimal colours from RGB image bands in R

  1. Andrew says:

    This code seems really useful for the project I’m doing but I can’t seem to get it to work. When I copy paste the code, it keeps giving me “unexpected symbol” errors and whatnot. I’ve replaced filepath with the directory, and jpeg with the name of the file and the other little things that were said to be done within the code but still no luck!

    Think you could help?



    • roder1 says:

      Thanks Andrew for the comment. Hard to pinpoint exactly where the issue is with your application of the code. I ran the whole code again and it works for me (RStudio running R 3.2.1). I’m also on OSX, so I have to change the x11() commands to quartz() and the savePlot() to I would suggest, just after the “image.list <- " lines (before the function call) to create all the function objects and run the function line-by-line to spot the problem.

      You can insert these lines just above the function:

      jpeg <- image.list[1]
      round.dig <- 1
      n.col <- 15

      Then, comment out the function call: <- function(jpeg, round.dig, n.col){

      Then proceed to run the function line-by-line to find the problem. Once you find the guilty command, that might help you isolate the problem. But feel free to comment again if you can't sort it out. Happy to help. You can also email me your code and example image if you like.

      We're hoping to package up this function in the near future too, which will make it much less finicky to work with. Stay tuned…


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