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
## Get flat lists of red, green and blue pixel values
red<-imageData(channel(bitmap, "red"))
green<-imageData(channel(bitmap, "green"))
blue<-imageData(channel(bitmap, "blue"))
## 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
colourValues<-apply(palette, 1, sum)
## Quantize the colours (extract a colour palette
quantizeColours<-function(bitmap, count){
## Cluster r,g,b values as points in RGB space
clusters<-kmeans(rgbs, count)
## The centre of each cluster is its average colour
## Return the colours in brightness order
## Get palettes for each painting
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
## Sum the pixel values and divide them by the number of pixels
function(palette){sum(palette) / length(palette)})
## Sort the colours in order of brightness

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:

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

Posted in Art Open Data