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Diffstat (limited to 'vendor/github.com/lucasb-eyer/go-colorful/soft_palettegen.go')
| -rw-r--r-- | vendor/github.com/lucasb-eyer/go-colorful/soft_palettegen.go | 185 |
1 files changed, 185 insertions, 0 deletions
diff --git a/vendor/github.com/lucasb-eyer/go-colorful/soft_palettegen.go b/vendor/github.com/lucasb-eyer/go-colorful/soft_palettegen.go new file mode 100644 index 0000000..507f2db --- /dev/null +++ b/vendor/github.com/lucasb-eyer/go-colorful/soft_palettegen.go @@ -0,0 +1,185 @@ +// 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 +} + |