Nice idea. I confess I'm slightly not too keen on the word "Novelty" -
reminds me of cheap Christmas presents... :) You could consider making the
package a bit more general... For my purposes I've been using a small bit
of code that extracts a selection of colors from images to make a palette.
Basically, it's this:
using Images, Colors, Clustering
function dominant_colors(img, n=10, i=10, tolerance=0.01; resize = 1)
w, h = size(img)
neww = round(Int, w/resize)
newh = round(Int, w/resize)
smaller_image = Images.imresize(img, (neww, newh))
imdata = convert(Array{Float64},
raw(separate(smaller_image).data))/256
w, h, nchannels = size(imdata)
d = transpose(reshape(imdata, w*h, nchannels))
R = kmeans(d, n, maxiter=i, tol=tolerance)
cols = RGB{Float64}[]
for i in 1:nchannels:length(R.centers)
push!(cols, RGB(R.centers[i], R.centers[i+1], R.centers[i+2]))
end
return cols, R.cweights/sum(R.cweights)
end
sorted_palette, wts =
dominant_colors(imread("/tmp/van-gogh-starry-sky.png"), 10, 40, resize=3)
which gives a selection of colors from the image (with weights if needed).
An interesting feature of this is that the results always vary slightly
each time - sometimes I stack them to see the differences:
<https://lh3.googleusercontent.com/-jdRcudzT78o/VlVpwJWQWvI/AAAAAAAAALE/jGOqal2nDA4/s1600/van-gogh-result-1.png>
