# Exploring Art Data 7

We’ve looked at brightness and contrast, let’s look at colours.

The images we’ve downloaded are stored in traditional computer graphics style as red, green and blue values (RGB values). We can extract the RGB values from the image and create a palette for the image using a standard “clustering” function. We can then sort the colours in the palette in order of brightness in order to make the palette easier to look at when we plot it.

```
## Get the r,g,b colour values for all the pixels in the image as a list
imagePixelsRGBs<-function(bitmap){
## Get flat lists of red, green and blue pixel values
red<-imageData(channel(bitmap, "red"))
dim(red)<-NULL
green<-imageData(channel(bitmap, "green"))
dim(green)<-NULL
blue<-imageData(channel(bitmap, "blue"))
dim(blue)<-NULL
## Combine these lists into a table of pixel r,g,b values
rgbs<-data.frame(red=red, green=green,blue=blue)
}
## Sort a palette's colours in rough order of brightness
sortPaletteColours<-function(palette){
colourValues<-apply(palette, 1, sum)
palette[order(colourValues),]
}
## Quantize the colours (extract a colour palette
quantizeColours<-function(bitmap, count){
rgbs<-imagePixelsRGBs(bitmap)
## Cluster r,g,b values as points in RGB space
clusters<-kmeans(rgbs, count)
## The centre of each cluster is its average colour
averageColours<-clusters\$centers
## Return the colours in brightness order
sortPaletteColours(averageColours)
}
## Get palettes for each painting
colourCount<-8
palettes<-lapply(artworksThumbnails,
function(bitmap){quantizeColours(bitmap, colourCount)})```

Having got the palettes we can sort them in order of total brightness.

```
## Get the palettes in order of brightness
sortPalettes<-function(palettes){
## Sum the pixel values and divide them by the number of pixels
paletteValues<-sapply(palettes,
function(palette){sum(palette) / length(palette)})
palettes[order(paletteValues)]
}
## Sort the colours in order of brightness
sortedPalettes<-sortPalettes(palettes)```

And finally we can convert the colours to yet another format and plot the palettes.

```## Convert the palette colours to R colours
paletteToColours<-function(palette){
apply(palette, 1,
function(colour){rgb(colour[1], colour[2], colour[3])})
}
## Plot palettes
## Get a flat list of colours
palettesColours<-sapply(sortedPalettes, paletteToColours, USE.NAMES=FALSE)
## Plot the colours for each palette
par(mar=c(4, 20, 4, 4))
image(matrix(1:(length(sortedPalettes) * colourCount), colourCount,
length(sortedPalettes)), col=palettesColours, axes=FALSE)
axis(2, at=seq(0.0, 1.0, 1.0 / (length(sortedPalettes) - 1)),
labels=names(sortedPalettes), las=2, tick=0)```

Which looks like this:

Better palette extraction and more perceptual brightness sorting are left as exercises for the reader. 🙂

Posted in Art Open Data