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186 lines
5.7 KiB
Go
186 lines
5.7 KiB
Go
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// Largely inspired by the descriptions in http://lab.medialab.sciences-po.fr/iwanthue/
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// but written from scratch.
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package colorful
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import (
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"fmt"
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"math"
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"math/rand"
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)
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// The algorithm works in L*a*b* color space and converts to RGB in the end.
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// L* in [0..1], a* and b* in [-1..1]
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type lab_t struct {
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L, A, B float64
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}
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type SoftPaletteSettings struct {
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// A function which can be used to restrict the allowed color-space.
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CheckColor func(l, a, b float64) bool
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// The higher, the better quality but the slower. Usually two figures.
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Iterations int
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// Use up to 160000 or 8000 samples of the L*a*b* space (and thus calls to CheckColor).
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// Set this to true only if your CheckColor shapes the Lab space weirdly.
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ManySamples bool
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}
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// Yeah, windows-stype Foo, FooEx, screw you golang...
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// Uses K-means to cluster the color-space and return the means of the clusters
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// as a new palette of distinctive colors. Falls back to K-medoid if the mean
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// happens to fall outside of the color-space, which can only happen if you
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// specify a CheckColor function.
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func SoftPaletteEx(colorsCount int, settings SoftPaletteSettings) ([]Color, error) {
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// Checks whether it's a valid RGB and also fulfills the potentially provided constraint.
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check := func(col lab_t) bool {
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c := Lab(col.L, col.A, col.B)
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return c.IsValid() && (settings.CheckColor == nil || settings.CheckColor(col.L, col.A, col.B))
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}
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// Sample the color space. These will be the points k-means is run on.
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dl := 0.05
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dab := 0.1
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if settings.ManySamples {
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dl = 0.01
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dab = 0.05
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}
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samples := make([]lab_t, 0, int(1.0/dl*2.0/dab*2.0/dab))
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for l := 0.0; l <= 1.0; l += dl {
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for a := -1.0; a <= 1.0; a += dab {
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for b := -1.0; b <= 1.0; b += dab {
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if check(lab_t{l, a, b}) {
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samples = append(samples, lab_t{l, a, b})
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}
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}
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}
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}
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// That would cause some infinite loops down there...
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if len(samples) < colorsCount {
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return nil, fmt.Errorf("palettegen: more colors requested (%v) than samples available (%v). Your requested color count may be wrong, you might want to use many samples or your constraint function makes the valid color space too small.", colorsCount, len(samples))
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} else if len(samples) == colorsCount {
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return labs2cols(samples), nil // Oops?
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}
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// We take the initial means out of the samples, so they are in fact medoids.
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// This helps us avoid infinite loops or arbitrary cutoffs with too restrictive constraints.
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means := make([]lab_t, colorsCount)
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for i := 0; i < colorsCount; i++ {
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for means[i] = samples[rand.Intn(len(samples))]; in(means, i, means[i]); means[i] = samples[rand.Intn(len(samples))] {
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}
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}
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clusters := make([]int, len(samples))
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samples_used := make([]bool, len(samples))
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// The actual k-means/medoid iterations
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for i := 0; i < settings.Iterations; i++ {
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// Reassing the samples to clusters, i.e. to their closest mean.
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// By the way, also check if any sample is used as a medoid and if so, mark that.
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for isample, sample := range samples {
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samples_used[isample] = false
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mindist := math.Inf(+1)
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for imean, mean := range means {
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dist := lab_dist(sample, mean)
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if dist < mindist {
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mindist = dist
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clusters[isample] = imean
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}
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// Mark samples which are used as a medoid.
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if lab_eq(sample, mean) {
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samples_used[isample] = true
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}
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}
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}
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// Compute new means according to the samples.
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for imean := range means {
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// The new mean is the average of all samples belonging to it..
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nsamples := 0
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newmean := lab_t{0.0, 0.0, 0.0}
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for isample, sample := range samples {
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if clusters[isample] == imean {
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nsamples++
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newmean.L += sample.L
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newmean.A += sample.A
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newmean.B += sample.B
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}
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}
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if nsamples > 0 {
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newmean.L /= float64(nsamples)
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newmean.A /= float64(nsamples)
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newmean.B /= float64(nsamples)
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} else {
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// That mean doesn't have any samples? Get a new mean from the sample list!
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var inewmean int
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for inewmean = rand.Intn(len(samples_used)); samples_used[inewmean]; inewmean = rand.Intn(len(samples_used)) {
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}
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newmean = samples[inewmean]
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samples_used[inewmean] = true
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}
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// But now we still need to check whether the new mean is an allowed color.
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if nsamples > 0 && check(newmean) {
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// It does, life's good (TM)
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means[imean] = newmean
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} else {
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// New mean isn't an allowed color or doesn't have any samples!
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// Switch to medoid mode and pick the closest (unused) sample.
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// This should always find something thanks to len(samples) >= colorsCount
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mindist := math.Inf(+1)
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for isample, sample := range samples {
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if !samples_used[isample] {
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dist := lab_dist(sample, newmean)
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if dist < mindist {
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mindist = dist
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newmean = sample
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}
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}
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}
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}
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}
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}
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return labs2cols(means), nil
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}
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// A wrapper which uses common parameters.
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func SoftPalette(colorsCount int) ([]Color, error) {
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return SoftPaletteEx(colorsCount, SoftPaletteSettings{nil, 50, false})
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}
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func in(haystack []lab_t, upto int, needle lab_t) bool {
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for i := 0; i < upto && i < len(haystack); i++ {
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if haystack[i] == needle {
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return true
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}
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}
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return false
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}
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const LAB_DELTA = 1e-6
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func lab_eq(lab1, lab2 lab_t) bool {
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return math.Abs(lab1.L-lab2.L) < LAB_DELTA &&
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math.Abs(lab1.A-lab2.A) < LAB_DELTA &&
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math.Abs(lab1.B-lab2.B) < LAB_DELTA
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}
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// That's faster than using colorful's DistanceLab since we would have to
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// convert back and forth for that. Here is no conversion.
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func lab_dist(lab1, lab2 lab_t) float64 {
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return math.Sqrt(sq(lab1.L-lab2.L) + sq(lab1.A-lab2.A) + sq(lab1.B-lab2.B))
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}
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func labs2cols(labs []lab_t) (cols []Color) {
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cols = make([]Color, len(labs))
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for k, v := range labs {
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cols[k] = Lab(v.L, v.A, v.B)
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}
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return cols
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}
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