gemini-browser

soft_palettegen.go (5797B)

---

 // 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
 }