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