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{
"version": 3,
"sources": ["../../src/constants/index.ts", "../../src/constants/bt709.ts", "../../src/conversion/index.ts", "../../src/conversion/rgb2xyz.ts", "../../src/utils/arithmetic.ts", "../../src/conversion/rgb2hsl.ts", "../../src/conversion/xyz2lab.ts", "../../src/conversion/rgb2lab.ts", "../../src/conversion/lab2xyz.ts", "../../src/conversion/xyz2rgb.ts", "../../src/conversion/lab2rgb.ts", "../../src/distance/index.ts", "../../src/distance/distanceCalculator.ts", "../../src/distance/cie94.ts", "../../src/distance/ciede2000.ts", "../../src/distance/cmetric.ts", "../../src/distance/euclidean.ts", "../../src/distance/manhattan.ts", "../../src/distance/pngQuant.ts", "../../src/palette/index.ts", "../../src/palette/paletteQuantizer.ts", "../../src/utils/point.ts", "../../src/utils/pointContainer.ts", "../../src/utils/palette.ts", "../../src/utils/index.ts", "../../src/utils/hueStatistics.ts", "../../src/utils/progressTracker.ts", "../../src/palette/neuquant/neuquant.ts", "../../src/palette/neuquant/neuquantFloat.ts", "../../src/palette/rgbquant/colorHistogram.ts", "../../src/palette/rgbquant/rgbquant.ts", "../../src/palette/wu/wuQuant.ts", "../../src/image/index.ts", "../../src/image/imageQuantizer.ts", "../../src/image/nearestColor.ts", "../../src/image/array.ts", "../../src/image/spaceFillingCurves/hilbertCurve.ts", "../../src/image/riemersma.ts", "../../src/quality/index.ts", "../../src/quality/ssim.ts", "../../src/basicAPI.ts", "../../src/index.ts"],
"sourcesContent": ["/**\n * @preserve\n * Copyright 2015-2018 Igor Bezkrovnyi\n * All rights reserved. (MIT Licensed)\n *\n * constants.ts - part of Image Quantization Library\n */\nimport * as bt709 from './bt709';\n\nexport { bt709 };\n", "/**\n * @preserve\n * Copyright 2015-2018 Igor Bezkrovnyi\n * All rights reserved. (MIT Licensed)\n *\n * constants.ts - part of Image Quantization Library\n */\n\n/**\n * sRGB (based on ITU-R Recommendation BT.709)\n * http://en.wikipedia.org/wiki/SRGB\n */\nexport enum Y {\n RED = 0.2126,\n GREEN = 0.7152,\n BLUE = 0.0722,\n WHITE = 1,\n}\n\nexport enum x {\n RED = 0.64,\n GREEN = 0.3,\n BLUE = 0.15,\n WHITE = 0.3127,\n}\n\nexport enum y {\n RED = 0.33,\n GREEN = 0.6,\n BLUE = 0.06,\n WHITE = 0.329,\n}\n", "/**\n * @preserve\n * Copyright 2015-2018 Igor Bezkrovnyi\n * All rights reserved. (MIT Licensed)\n *\n * iq.ts - Image Quantization Library\n */\nexport { rgb2xyz } from './rgb2xyz';\nexport { rgb2hsl } from './rgb2hsl';\nexport { rgb2lab } from './rgb2lab';\nexport { lab2xyz } from './lab2xyz';\nexport { lab2rgb } from './lab2rgb';\nexport { xyz2lab } from './xyz2lab';\nexport { xyz2rgb } from './xyz2rgb';\n", "/**\n * @preserve\n * Copyright 2015-2018 Igor Bezkrovnyi\n * All rights reserved. (MIT Licensed)\n *\n * rgb2xyz.ts - part of Image Quantization Library\n */\nfunction correctGamma(n: number) {\n return n > 0.04045 ? ((n + 0.055) / 1.055) ** 2.4 : n / 12.92;\n}\n\nexport function rgb2xyz(r: number, g: number, b: number) {\n // gamma correction, see https://en.wikipedia.org/wiki/SRGB#The_reverse_transformation\n r = correctGamma(r / 255);\n g = correctGamma(g / 255);\n b = correctGamma(b / 255);\n\n // Observer. = 2\u00B0, Illuminant = D65\n return {\n x: r * 0.4124 + g * 0.3576 + b * 0.1805,\n y: r * 0.2126 + g * 0.7152 + b * 0.0722,\n z: r * 0.0193 + g * 0.1192 + b * 0.9505,\n };\n}\n", "export function degrees2radians(n: number) {\n return n * (Math.PI / 180);\n}\n\nexport function max3(a: number, b: number, c: number) {\n let m = a;\n if (m < b) m = b;\n if (m < c) m = c;\n return m;\n}\n\nexport function min3(a: number, b: number, c: number) {\n let m = a;\n if (m > b) m = b;\n if (m > c) m = c;\n return m;\n}\n\nexport function intInRange(value: number, low: number, high: number) {\n if (value > high) value = high;\n if (value < low) value = low;\n return value | 0;\n}\n\nexport function inRange0to255Rounded(n: number) {\n n = Math.round(n);\n if (n > 255) n = 255;\n else if (n < 0) n = 0;\n return n;\n}\n\nexport function inRange0to255(n: number) {\n if (n > 255) n = 255;\n else if (n < 0) n = 0;\n return n;\n}\n\nexport function stableSort<T>(\n arrayToSort: T[],\n callback: (a: T, b: T) => number,\n) {\n const type = typeof arrayToSort[0];\n let sorted: T[];\n\n if (type === 'number' || type === 'string') {\n const ord = Object.create(null);\n for (let i = 0, l = arrayToSort.length; i < l; i++) {\n const val = arrayToSort[i] as unknown as string;\n if (ord[val] || ord[val] === 0) continue;\n ord[val] = i;\n }\n\n sorted = arrayToSort.sort((a, b) => callback(a, b) || ord[a] - ord[b]);\n } else {\n const ord2 = arrayToSort.slice(0);\n sorted = arrayToSort.sort(\n (a, b) => callback(a, b) || ord2.indexOf(a) - ord2.indexOf(b),\n );\n }\n\n return sorted;\n}\n", "/**\n * @preserve\n * Copyright 2015-2018 Igor Bezkrovnyi\n * All rights reserved. (MIT Licensed)\n *\n * rgb2hsl.ts - part of Image Quantization Library\n */\nimport { min3, max3 } from '../utils/arithmetic';\n\n/**\n * Calculate HSL from RGB\n * Hue is in degrees [0..360]\n * Lightness: [0..1]\n * Saturation: [0..1]\n * http://web.archive.org/web/20060914040436/http://local.wasp.uwa.edu.au/~pbourke/colour/hsl/\n */\nexport function rgb2hsl(r: number, g: number, b: number) {\n const min = min3(r, g, b);\n const max = max3(r, g, b);\n const delta = max - min;\n const l = (min + max) / 510;\n\n let s = 0;\n if (l > 0 && l < 1) s = delta / (l < 0.5 ? max + min : 510 - max - min);\n\n let h = 0;\n if (delta > 0) {\n if (max === r) {\n h = (g - b) / delta;\n } else if (max === g) {\n h = 2 + (b - r) / delta;\n } else {\n h = 4 + (r - g) / delta;\n }\n\n h *= 60;\n if (h < 0) h += 360;\n }\n return { h, s, l };\n}\n", "/**\n * @preserve\n * Copyright 2015-2018 Igor Bezkrovnyi\n * All rights reserved. (MIT Licensed)\n *\n * xyz2lab.ts - part of Image Quantization Library\n */\nconst refX = 0.95047; // ref_X = 95.047 Observer= 2\u00B0, Illuminant= D65\nconst refY = 1.0; // ref_Y = 100.000\nconst refZ = 1.08883; // ref_Z = 108.883\n\nfunction pivot(n: number) {\n return n > 0.008856 ? n ** (1 / 3) : 7.787 * n + 16 / 116;\n}\n\nexport function xyz2lab(x: number, y: number, z: number) {\n x = pivot(x / refX);\n y = pivot(y / refY);\n z = pivot(z / refZ);\n\n if (116 * y - 16 < 0) throw new Error('xxx');\n return {\n L: Math.max(0, 116 * y - 16),\n a: 500 * (x - y),\n b: 200 * (y - z),\n };\n}\n", "/**\n * @preserve\n * Copyright 2015-2018 Igor Bezkrovnyi\n * All rights reserved. (MIT Licensed)\n *\n * rgb2lab.ts - part of Image Quantization Library\n */\nimport { rgb2xyz } from './rgb2xyz';\nimport { xyz2lab } from './xyz2lab';\n\nexport function rgb2lab(r: number, g: number, b: number) {\n const xyz = rgb2xyz(r, g, b);\n return xyz2lab(xyz.x, xyz.y, xyz.z);\n}\n", "/**\n * @preserve\n * Copyright 2015-2018 Igor Bezkrovnyi\n * All rights reserved. (MIT Licensed)\n *\n * lab2xyz.ts - part of Image Quantization Library\n */\nconst refX = 0.95047; // ref_X = 95.047 Observer= 2\u00B0, Illuminant = D65\nconst refY = 1.0; // ref_Y = 100.000\nconst refZ = 1.08883; // ref_Z = 108.883\n\nfunction pivot(n: number) {\n return n > 0.206893034 ? n ** 3 : (n - 16 / 116) / 7.787;\n}\n\nexport function lab2xyz(L: number, a: number, b: number) {\n const y = (L + 16) / 116;\n const x = a / 500 + y;\n const z = y - b / 200;\n\n return {\n x: refX * pivot(x),\n y: refY * pivot(y),\n z: refZ * pivot(z),\n };\n}\n", "/**\n * @preserve\n * Copyright 2015-2018 Igor Bezkrovnyi\n * All rights reserved. (MIT Licensed)\n *\n * xyz2rgb.ts - part of Image Quantization Library\n */\nimport { inRange0to255Rounded } from '../utils/arithmetic';\n\n// gamma correction, see https://en.wikipedia.org/wiki/SRGB#The_reverse_transformation\nfunction correctGamma(n: number) {\n return n > 0.0031308 ? 1.055 * n ** (1 / 2.4) - 0.055 : 12.92 * n;\n}\n\nexport function xyz2rgb(x: number, y: number, z: number) {\n // Observer. = 2\u00B0, Illuminant = D65\n const r = correctGamma(x * 3.2406 + y * -1.5372 + z * -0.4986);\n const g = correctGamma(x * -0.9689 + y * 1.8758 + z * 0.0415);\n const b = correctGamma(x * 0.0557 + y * -0.204 + z * 1.057);\n\n return {\n r: inRange0to255Rounded(r * 255),\n g: inRange0to255Rounded(g * 255),\n b: inRange0to255Rounded(b * 255),\n };\n}\n", "/**\n * @preserve\n * Copyright 2015-2018 Igor Bezkrovnyi\n * All rights reserved. (MIT Licensed)\n *\n * lab2rgb.ts - part of Image Quantization Library\n */\nimport { lab2xyz } from './lab2xyz';\nimport { xyz2rgb } from './xyz2rgb';\n\nexport function lab2rgb(L: number, a: number, b: number) {\n const xyz = lab2xyz(L, a, b);\n return xyz2rgb(xyz.x, xyz.y, xyz.z);\n}\n", "/**\n * @preserve\n * Copyright 2015-2018 Igor Bezkrovnyi\n * All rights reserved. (MIT Licensed)\n *\n * iq.ts - Image Quantization Library\n */\nexport { AbstractDistanceCalculator } from './distanceCalculator';\nexport { CIE94Textiles, CIE94GraphicArts } from './cie94';\nexport { CIEDE2000 } from './ciede2000';\nexport { CMetric } from './cmetric';\nexport {\n AbstractEuclidean,\n Euclidean,\n EuclideanBT709NoAlpha,\n EuclideanBT709,\n} from './euclidean';\nexport {\n AbstractManhattan,\n Manhattan,\n ManhattanBT709,\n ManhattanNommyde,\n} from './manhattan';\nexport { PNGQuant } from './pngQuant';\n", "/**\n * @preserve\n * Copyright 2015-2018 Igor Bezkrovnyi\n * All rights reserved. (MIT Licensed)\n *\n * common.ts - part of Image Quantization Library\n */\nimport { PointRGBA } from '../utils/point';\n\nexport abstract class AbstractDistanceCalculator {\n protected _maxDistance!: number;\n protected _whitePoint!: {\n r: number;\n g: number;\n b: number;\n a: number;\n };\n\n constructor() {\n this._setDefaults();\n\n // set default maximal color component deltas (255 - 0 = 255)\n this.setWhitePoint(255, 255, 255, 255);\n }\n\n setWhitePoint(r: number, g: number, b: number, a: number) {\n this._whitePoint = {\n r: r > 0 ? 255 / r : 0,\n g: g > 0 ? 255 / g : 0,\n b: b > 0 ? 255 / b : 0,\n a: a > 0 ? 255 / a : 0,\n };\n this._maxDistance = this.calculateRaw(r, g, b, a, 0, 0, 0, 0);\n }\n\n calculateNormalized(colorA: PointRGBA, colorB: PointRGBA) {\n return (\n this.calculateRaw(\n colorA.r,\n colorA.g,\n colorA.b,\n colorA.a,\n colorB.r,\n colorB.g,\n colorB.b,\n colorB.a,\n ) / this._maxDistance\n );\n }\n\n /**\n * Calculate raw distance (non-normalized)\n */\n abstract calculateRaw(\n r1: number,\n g1: number,\n b1: number,\n a1: number,\n r2: number,\n g2: number,\n b2: number,\n a2: number,\n ): number;\n\n protected abstract _setDefaults(): void;\n}\n", "/**\n * @preserve\n * Copyright 2015-2018 Igor Bezkrovnyi\n * All rights reserved. (MIT Licensed)\n *\n * cie94.ts - part of Image Quantization Library\n */\nimport { AbstractDistanceCalculator } from './distanceCalculator';\nimport { rgb2lab } from '../conversion/rgb2lab';\nimport { inRange0to255 } from '../utils/arithmetic';\n\n/**\n * CIE94 method of delta-e\n * http://en.wikipedia.org/wiki/Color_difference#CIE94\n */\nexport abstract class AbstractCIE94 extends AbstractDistanceCalculator {\n /**\n * Weight in distance: 0.25\n * Max DeltaE: 100\n * Max DeltaA: 255\n */\n declare protected _kA: number;\n declare protected _Kl: number;\n declare protected _K1: number;\n declare protected _K2: number;\n\n calculateRaw(\n r1: number,\n g1: number,\n b1: number,\n a1: number,\n r2: number,\n g2: number,\n b2: number,\n a2: number,\n ) {\n const lab1 = rgb2lab(\n inRange0to255(r1 * this._whitePoint.r),\n inRange0to255(g1 * this._whitePoint.g),\n inRange0to255(b1 * this._whitePoint.b),\n );\n const lab2 = rgb2lab(\n inRange0to255(r2 * this._whitePoint.r),\n inRange0to255(g2 * this._whitePoint.g),\n inRange0to255(b2 * this._whitePoint.b),\n );\n\n const dL = lab1.L - lab2.L;\n const dA = lab1.a - lab2.a;\n const dB = lab1.b - lab2.b;\n const c1 = Math.sqrt(lab1.a * lab1.a + lab1.b * lab1.b);\n const c2 = Math.sqrt(lab2.a * lab2.a + lab2.b * lab2.b);\n const dC = c1 - c2;\n\n let deltaH = dA * dA + dB * dB - dC * dC;\n deltaH = deltaH < 0 ? 0 : Math.sqrt(deltaH);\n\n const dAlpha = (a2 - a1) * this._whitePoint.a * this._kA;\n\n // TODO: add alpha channel support\n return Math.sqrt(\n (dL / this._Kl) ** 2 +\n (dC / (1.0 + this._K1 * c1)) ** 2 +\n (deltaH / (1.0 + this._K2 * c1)) ** 2 +\n dAlpha ** 2,\n );\n }\n}\n\nexport class CIE94Textiles extends AbstractCIE94 {\n protected _setDefaults() {\n this._Kl = 2.0;\n this._K1 = 0.048;\n this._K2 = 0.014;\n this._kA = (0.25 * 50) / 255;\n }\n}\n\nexport class CIE94GraphicArts extends AbstractCIE94 {\n protected _setDefaults() {\n this._Kl = 1.0;\n this._K1 = 0.045;\n this._K2 = 0.015;\n this._kA = (0.25 * 100) / 255;\n }\n}\n", "/**\n * @preserve\n * Copyright 2015-2018 Igor Bezkrovnyi\n * All rights reserved. (MIT Licensed)\n *\n * ciede2000.ts - part of Image Quantization Library\n */\nimport { AbstractDistanceCalculator } from './distanceCalculator';\nimport { rgb2lab } from '../conversion/rgb2lab';\nimport { degrees2radians, inRange0to255 } from '../utils/arithmetic';\n\n/**\n * CIEDE2000 algorithm - Adapted from Sharma et al's MATLAB implementation at\n * http://www.ece.rochester.edu/~gsharma/ciede2000/\n */\nexport class CIEDE2000 extends AbstractDistanceCalculator {\n /**\n * Weight in distance: 0.25\n * Max DeltaE: 100\n * Max DeltaA: 255\n */\n private static readonly _kA = (0.25 * 100) / 255;\n private static readonly _pow25to7 = 25 ** 7;\n private static readonly _deg360InRad = degrees2radians(360);\n private static readonly _deg180InRad = degrees2radians(180);\n private static readonly _deg30InRad = degrees2radians(30);\n private static readonly _deg6InRad = degrees2radians(6);\n private static readonly _deg63InRad = degrees2radians(63);\n private static readonly _deg275InRad = degrees2radians(275);\n private static readonly _deg25InRad = degrees2radians(25);\n\n protected _setDefaults() {}\n\n private static _calculatehp(b: number, ap: number) {\n const hp = Math.atan2(b, ap);\n if (hp >= 0) return hp;\n return hp + CIEDE2000._deg360InRad;\n }\n\n private static _calculateRT(ahp: number, aCp: number) {\n const aCp_to_7 = aCp ** 7.0;\n const R_C = 2.0 * Math.sqrt(aCp_to_7 / (aCp_to_7 + CIEDE2000._pow25to7)); // 25^7\n const delta_theta =\n CIEDE2000._deg30InRad *\n Math.exp(\n -(((ahp - CIEDE2000._deg275InRad) / CIEDE2000._deg25InRad) ** 2.0),\n );\n return -Math.sin(2.0 * delta_theta) * R_C;\n }\n\n private static _calculateT(ahp: number) {\n return (\n 1.0 -\n 0.17 * Math.cos(ahp - CIEDE2000._deg30InRad) +\n 0.24 * Math.cos(ahp * 2.0) +\n 0.32 * Math.cos(ahp * 3.0 + CIEDE2000._deg6InRad) -\n 0.2 * Math.cos(ahp * 4.0 - CIEDE2000._deg63InRad)\n );\n }\n\n private static _calculate_ahp(\n C1pC2p: number,\n h_bar: number,\n h1p: number,\n h2p: number,\n ) {\n const hpSum = h1p + h2p;\n if (C1pC2p === 0) return hpSum;\n if (h_bar <= CIEDE2000._deg180InRad) return hpSum / 2.0;\n if (hpSum < CIEDE2000._deg360InRad) {\n return (hpSum + CIEDE2000._deg360InRad) / 2.0;\n }\n return (hpSum - CIEDE2000._deg360InRad) / 2.0;\n }\n\n private static _calculate_dHp(\n C1pC2p: number,\n h_bar: number,\n h2p: number,\n h1p: number,\n ) {\n let dhp;\n if (C1pC2p === 0) {\n dhp = 0;\n } else if (h_bar <= CIEDE2000._deg180InRad) {\n dhp = h2p - h1p;\n } else if (h2p <= h1p) {\n dhp = h2p - h1p + CIEDE2000._deg360InRad;\n } else {\n dhp = h2p - h1p - CIEDE2000._deg360InRad;\n }\n return 2.0 * Math.sqrt(C1pC2p) * Math.sin(dhp / 2.0);\n }\n\n calculateRaw(\n r1: number,\n g1: number,\n b1: number,\n a1: number,\n r2: number,\n g2: number,\n b2: number,\n a2: number,\n ) {\n const lab1 = rgb2lab(\n inRange0to255(r1 * this._whitePoint.r),\n inRange0to255(g1 * this._whitePoint.g),\n inRange0to255(b1 * this._whitePoint.b),\n );\n const lab2 = rgb2lab(\n inRange0to255(r2 * this._whitePoint.r),\n inRange0to255(g2 * this._whitePoint.g),\n inRange0to255(b2 * this._whitePoint.b),\n );\n const dA = (a2 - a1) * this._whitePoint.a * CIEDE2000._kA;\n const dE2 = this.calculateRawInLab(lab1, lab2);\n\n return Math.sqrt(dE2 + dA * dA);\n }\n\n calculateRawInLab(\n Lab1: { L: number; a: number; b: number },\n Lab2: { L: number; a: number; b: number },\n ) {\n // Get L,a,b values for color 1\n const L1 = Lab1.L;\n const a1 = Lab1.a;\n const b1 = Lab1.b;\n\n // Get L,a,b values for color 2\n const L2 = Lab2.L;\n const a2 = Lab2.a;\n const b2 = Lab2.b;\n\n // Calculate Cprime1, Cprime2, Cabbar\n const C1 = Math.sqrt(a1 * a1 + b1 * b1);\n const C2 = Math.sqrt(a2 * a2 + b2 * b2);\n const pow_a_C1_C2_to_7 = ((C1 + C2) / 2.0) ** 7.0;\n\n const G =\n 0.5 *\n (1.0 -\n Math.sqrt(pow_a_C1_C2_to_7 / (pow_a_C1_C2_to_7 + CIEDE2000._pow25to7))); // 25^7\n const a1p = (1.0 + G) * a1;\n const a2p = (1.0 + G) * a2;\n\n const C1p = Math.sqrt(a1p * a1p + b1 * b1);\n const C2p = Math.sqrt(a2p * a2p + b2 * b2);\n const C1pC2p = C1p * C2p;\n\n // Angles in Degree.\n const h1p = CIEDE2000._calculatehp(b1, a1p);\n const h2p = CIEDE2000._calculatehp(b2, a2p);\n const h_bar = Math.abs(h1p - h2p);\n const dLp = L2 - L1;\n const dCp = C2p - C1p;\n const dHp = CIEDE2000._calculate_dHp(C1pC2p, h_bar, h2p, h1p);\n const ahp = CIEDE2000._calculate_ahp(C1pC2p, h_bar, h1p, h2p);\n\n const T = CIEDE2000._calculateT(ahp);\n\n const aCp = (C1p + C2p) / 2.0;\n const aLp_minus_50_square = ((L1 + L2) / 2.0 - 50.0) ** 2.0;\n const S_L =\n 1.0 +\n (0.015 * aLp_minus_50_square) / Math.sqrt(20.0 + aLp_minus_50_square);\n const S_C = 1.0 + 0.045 * aCp;\n const S_H = 1.0 + 0.015 * T * aCp;\n\n const R_T = CIEDE2000._calculateRT(ahp, aCp);\n\n const dLpSL = dLp / S_L; // S_L * kL, where kL is 1.0\n const dCpSC = dCp / S_C; // S_C * kC, where kC is 1.0\n const dHpSH = dHp / S_H; // S_H * kH, where kH is 1.0\n\n return dLpSL ** 2 + dCpSC ** 2 + dHpSH ** 2 + R_T * dCpSC * dHpSH;\n }\n}\n", "/**\n * @preserve\n * Copyright 2015-2018 Igor Bezkrovnyi\n * All rights reserved. (MIT Licensed)\n *\n * cmetric.ts - part of Image Quantization Library\n */\nimport { AbstractDistanceCalculator } from './distanceCalculator';\n\n/**\n * TODO: Name it: http://www.compuphase.com/cmetric.htm\n */\nexport class CMetric extends AbstractDistanceCalculator {\n calculateRaw(\n r1: number,\n g1: number,\n b1: number,\n a1: number,\n r2: number,\n g2: number,\n b2: number,\n a2: number,\n ) {\n const rmean = ((r1 + r2) / 2) * this._whitePoint.r;\n const r = (r1 - r2) * this._whitePoint.r;\n const g = (g1 - g2) * this._whitePoint.g;\n const b = (b1 - b2) * this._whitePoint.b;\n const dE =\n (((512 + rmean) * r * r) >> 8) +\n 4 * g * g +\n (((767 - rmean) * b * b) >> 8);\n const dA = (a2 - a1) * this._whitePoint.a;\n\n return Math.sqrt(dE + dA * dA);\n }\n\n protected _setDefaults() {}\n}\n", "/**\n * @preserve\n * Copyright 2015-2018 Igor Bezkrovnyi\n * All rights reserved. (MIT Licensed)\n *\n * euclidean.ts - part of Image Quantization Library\n */\nimport { AbstractDistanceCalculator } from './distanceCalculator';\nimport { Y } from '../constants/bt709';\n\n/**\n * Euclidean color distance\n */\nexport abstract class AbstractEuclidean extends AbstractDistanceCalculator {\n declare protected _kR: number;\n declare protected _kG: number;\n declare protected _kB: number;\n declare protected _kA: number;\n\n calculateRaw(\n r1: number,\n g1: number,\n b1: number,\n a1: number,\n r2: number,\n g2: number,\n b2: number,\n a2: number,\n ) {\n const dR = r2 - r1;\n const dG = g2 - g1;\n const dB = b2 - b1;\n const dA = a2 - a1;\n return Math.sqrt(\n this._kR * dR * dR +\n this._kG * dG * dG +\n this._kB * dB * dB +\n this._kA * dA * dA,\n );\n }\n}\n\nexport class Euclidean extends AbstractEuclidean {\n protected _setDefaults() {\n this._kR = 1;\n this._kG = 1;\n this._kB = 1;\n this._kA = 1;\n }\n}\n\n/**\n * Euclidean color distance (RGBQuant modification w Alpha)\n */\nexport class EuclideanBT709 extends AbstractEuclidean {\n protected _setDefaults() {\n this._kR = Y.RED;\n this._kG = Y.GREEN;\n this._kB = Y.BLUE;\n // TODO: what is the best coefficient below?\n this._kA = 1;\n }\n}\n\n/**\n * Euclidean color distance (RGBQuant modification w/o Alpha)\n */\nexport class EuclideanBT709NoAlpha extends AbstractEuclidean {\n protected _setDefaults() {\n this._kR = Y.RED;\n this._kG = Y.GREEN;\n this._kB = Y.BLUE;\n this._kA = 0;\n }\n}\n", "/**\n * @preserve\n * Copyright 2015-2018 Igor Bezkrovnyi\n * All rights reserved. (MIT Licensed)\n *\n * manhattanNeuQuant.ts - part of Image Quantization Library\n */\nimport { AbstractDistanceCalculator } from './distanceCalculator';\nimport { Y } from '../constants/bt709';\n\n/**\n * Manhattan distance (NeuQuant modification) - w/o sRGB coefficients\n */\nexport abstract class AbstractManhattan extends AbstractDistanceCalculator {\n declare protected _kR: number;\n declare protected _kG: number;\n declare protected _kB: number;\n declare protected _kA: number;\n\n calculateRaw(\n r1: number,\n g1: number,\n b1: number,\n a1: number,\n r2: number,\n g2: number,\n b2: number,\n a2: number,\n ) {\n let dR = r2 - r1;\n let dG = g2 - g1;\n let dB = b2 - b1;\n let dA = a2 - a1;\n if (dR < 0) dR = 0 - dR;\n if (dG < 0) dG = 0 - dG;\n if (dB < 0) dB = 0 - dB;\n if (dA < 0) dA = 0 - dA;\n\n return this._kR * dR + this._kG * dG + this._kB * dB + this._kA * dA;\n }\n}\n\nexport class Manhattan extends AbstractManhattan {\n protected _setDefaults() {\n this._kR = 1;\n this._kG = 1;\n this._kB = 1;\n this._kA = 1;\n }\n}\n\n/**\n * Manhattan distance (Nommyde modification)\n * https://github.com/igor-bezkrovny/image-quantization/issues/4#issuecomment-235155320\n */\nexport class ManhattanNommyde extends AbstractManhattan {\n protected _setDefaults() {\n this._kR = 0.4984;\n this._kG = 0.8625;\n this._kB = 0.2979;\n // TODO: what is the best coefficient below?\n this._kA = 1;\n }\n}\n\n/**\n * Manhattan distance (sRGB coefficients)\n */\nexport class ManhattanBT709 extends AbstractManhattan {\n protected _setDefaults() {\n this._kR = Y.RED;\n this._kG = Y.GREEN;\n this._kB = Y.BLUE;\n // TODO: what is the best coefficient below?\n this._kA = 1;\n }\n}\n", "/**\n * @preserve\n * Copyright 2015-2018 Igor Bezkrovnyi\n * All rights reserved. (MIT Licensed)\n *\n * pngQuant.ts - part of Image Quantization Library\n */\nimport { AbstractDistanceCalculator } from './distanceCalculator';\n\n/**\n * TODO: check quality of this distance equation\n * TODO: ask author for usage rights\n * taken from:\n * {@link http://stackoverflow.com/questions/4754506/color-similarity-distance-in-rgba-color-space/8796867#8796867}\n * {@link https://github.com/pornel/pngquant/blob/cc39b47799a7ff2ef17b529f9415ff6e6b213b8f/lib/pam.h#L148}\n */\nexport class PNGQuant extends AbstractDistanceCalculator {\n /**\n * Author's comments\n * px_b.rgb = px.rgb + 0*(1-px.a) // blend px on black\n * px_b.a = px.a + 1*(1-px.a)\n * px_w.rgb = px.rgb + 1*(1-px.a) // blend px on white\n * px_w.a = px.a + 1*(1-px.a)\n *\n * px_b.rgb = px.rgb // difference same as in opaque RGB\n * px_b.a = 1\n * px_w.rgb = px.rgb - px.a // difference simplifies to formula below\n * px_w.a = 1\n *\n * (px.rgb - px.a) - (py.rgb - py.a)\n * (px.rgb - py.rgb) + (py.a - px.a)\n *\n */\n calculateRaw(\n r1: number,\n g1: number,\n b1: number,\n a1: number,\n r2: number,\n g2: number,\n b2: number,\n a2: number,\n ) {\n const alphas = (a2 - a1) * this._whitePoint.a;\n return (\n this._colordifferenceCh(\n r1 * this._whitePoint.r,\n r2 * this._whitePoint.r,\n alphas,\n ) +\n this._colordifferenceCh(\n g1 * this._whitePoint.g,\n g2 * this._whitePoint.g,\n alphas,\n ) +\n this._colordifferenceCh(\n b1 * this._whitePoint.b,\n b2 * this._whitePoint.b,\n alphas,\n )\n );\n }\n\n private _colordifferenceCh(x: number, y: number, alphas: number) {\n // maximum of channel blended on white, and blended on black\n // premultiplied alpha and backgrounds 0/1 shorten the formula\n const black = x - y;\n const white = black + alphas;\n\n return black * black + white * white;\n }\n\n protected _setDefaults() {}\n}\n", "/**\n * @preserve\n * Copyright 2015-2018 Igor Bezkrovnyi\n * All rights reserved. (MIT Licensed)\n *\n * iq.ts - Image Quantization Library\n */\nexport { AbstractPaletteQuantizer } from './paletteQuantizer';\nexport { NeuQuant } from './neuquant/neuquant';\nexport { NeuQuantFloat } from './neuquant/neuquantFloat';\nexport { RGBQuant } from './rgbquant/rgbquant';\nexport { ColorHistogram } from './rgbquant/colorHistogram';\nexport { WuQuant, WuColorCube } from './wu/wuQuant';\nexport { PaletteQuantizerYieldValue } from './paletteQuantizerYieldValue';\n", "/**\n * @preserve\n * Copyright 2015-2018 Igor Bezkrovnyi\n * All rights reserved. (MIT Licensed)\n *\n * common.ts - part of Image Quantization Library\n */\nimport { PointContainer } from '../utils/pointContainer';\nimport { PaletteQuantizerYieldValue } from './paletteQuantizerYieldValue';\n\nexport abstract class AbstractPaletteQuantizer {\n abstract sample(pointContainer: PointContainer): void;\n abstract quantize(): IterableIterator<PaletteQuantizerYieldValue>;\n\n quantizeSync() {\n for (const value of this.quantize()) {\n if (value.palette) {\n return value.palette;\n }\n }\n\n throw new Error('unreachable');\n }\n}\n", "/**\n * @preserve\n * Copyright 2015-2018 Igor Bezkrovnyi\n * All rights reserved. (MIT Licensed)\n *\n * point.ts - part of Image Quantization Library\n */\nimport { Y } from '../constants/bt709';\n\nexport interface PointRGBA {\n r: number;\n g: number;\n b: number;\n a: number;\n}\n\n/**\n * v8 optimized class\n * 1) \"constructor\" should have initialization with worst types\n * 2) \"set\" should have |0 / >>> 0\n */\nexport class Point implements PointRGBA {\n r: number;\n g: number;\n b: number;\n a: number;\n uint32: number;\n rgba: number[]; // TODO: better name is quadruplet or quad may be?\n // Lab : { L : number; a : number; b : number };\n\n static createByQuadruplet(quadruplet: number[]) {\n const point = new Point();\n\n point.r = quadruplet[0] | 0;\n point.g = quadruplet[1] | 0;\n point.b = quadruplet[2] | 0;\n point.a = quadruplet[3] | 0;\n point._loadUINT32();\n point._loadQuadruplet();\n // point._loadLab();\n return point;\n }\n\n static createByRGBA(red: number, green: number, blue: number, alpha: number) {\n const point = new Point();\n\n point.r = red | 0;\n point.g = green | 0;\n point.b = blue | 0;\n point.a = alpha | 0;\n point._loadUINT32();\n point._loadQuadruplet();\n // point._loadLab();\n return point;\n }\n\n static createByUint32(uint32: number) {\n const point = new Point();\n\n point.uint32 = uint32 >>> 0;\n point._loadRGBA();\n point._loadQuadruplet();\n // point._loadLab();\n return point;\n }\n\n constructor() {\n this.uint32 = -1 >>> 0;\n this.r = this.g = this.b = this.a = 0;\n this.rgba = new Array(4);\n this.rgba[0] = 0;\n this.rgba[1] = 0;\n this.rgba[2] = 0;\n this.rgba[3] = 0;\n /*\n this.Lab = {\n L : 0.0,\n a : 0.0,\n b : 0.0\n };\n */\n }\n\n from(point: Point) {\n this.r = point.r;\n this.g = point.g;\n this.b = point.b;\n this.a = point.a;\n this.uint32 = point.uint32;\n this.rgba[0] = point.r;\n this.rgba[1] = point.g;\n this.rgba[2] = point.b;\n this.rgba[3] = point.a;\n\n /*\n this.Lab.L = point.Lab.L;\n this.Lab.a = point.Lab.a;\n this.Lab.b = point.Lab.b;\n */\n }\n\n /*\n * TODO:\n Luminance from RGB:\n\n Luminance (standard for certain colour spaces): (0.2126*R + 0.7152*G + 0.0722*B) [1]\n Luminance (perceived option 1): (0.299*R + 0.587*G + 0.114*B) [2]\n Luminance (perceived option 2, slower to calculate): sqrt( 0.241*R^2 + 0.691*G^2 + 0.068*B^2 ) ? sqrt( 0.299*R^2 + 0.587*G^2 + 0.114*B^2 ) (thanks to @MatthewHerbst) [http://alienryderflex.com/hsp.html]\n */\n getLuminosity(useAlphaChannel: boolean) {\n let r = this.r;\n let g = this.g;\n let b = this.b;\n\n if (useAlphaChannel) {\n r = Math.min(255, 255 - this.a + (this.a * r) / 255);\n g = Math.min(255, 255 - this.a + (this.a * g) / 255);\n b = Math.min(255, 255 - this.a + (this.a * b) / 255);\n }\n\n // var luma = this.r * Point._RED_COEFFICIENT + this.g * Point._GREEN_COEFFICIENT + this.b * Point._BLUE_COEFFICIENT;\n\n /*\n if(useAlphaChannel) {\n luma = (luma * (255 - this.a)) / 255;\n }\n */\n\n return r * Y.RED + g * Y.GREEN + b * Y.BLUE;\n }\n\n private _loadUINT32() {\n this.uint32 =\n ((this.a << 24) | (this.b << 16) | (this.g << 8) | this.r) >>> 0;\n }\n\n private _loadRGBA() {\n this.r = this.uint32 & 0xff;\n this.g = (this.uint32 >>> 8) & 0xff;\n this.b = (this.uint32 >>> 16) & 0xff;\n this.a = (this.uint32 >>> 24) & 0xff;\n }\n\n private _loadQuadruplet() {\n this.rgba[0] = this.r;\n this.rgba[1] = this.g;\n this.rgba[2] = this.b;\n this.rgba[3] = this.a;\n\n /*\n var xyz = rgb2xyz(this.r, this.g, this.b);\n var lab = xyz2lab(xyz.x, xyz.y, xyz.z);\n this.lab.l = lab.l;\n this.lab.a = lab.a;\n this.lab.b = lab.b;\n */\n }\n\n /*\n private _loadLab() : void {\n var Lab = Color.Conversion.rgb2lab(this.r, this.g, this.b);\n this.Lab.L = Lab.L;\n this.Lab.a = Lab.a;\n this.Lab.b = Lab.b;\n }\n */\n}\n", "/**\n * @preserve\n * Copyright 2015-2018 Igor Bezkrovnyi\n * All rights reserved. (MIT Licensed)\n *\n * pointContainer.ts - part of Image Quantization Library\n */\nimport { Point } from './point';\n\n/**\n * v8 optimizations done.\n * fromXXX methods are static to move out polymorphic code from class instance itself.\n */\nexport class PointContainer {\n private readonly _pointArray: Point[];\n private _width: number;\n private _height: number;\n\n constructor() {\n this._width = 0;\n this._height = 0;\n this._pointArray = [];\n }\n\n getWidth() {\n return this._width;\n }\n\n getHeight() {\n return this._height;\n }\n\n setWidth(width: number) {\n this._width = width;\n }\n\n setHeight(height: number) {\n this._height = height;\n }\n\n getPointArray() {\n return this._pointArray;\n }\n\n clone() {\n const clone = new PointContainer();\n clone._width = this._width;\n clone._height = this._height;\n\n for (let i = 0, l = this._pointArray.length; i < l; i++) {\n clone._pointArray[i] = Point.createByUint32(\n this._pointArray[i].uint32 | 0,\n ); // \"| 0\" is added for v8 optimization\n }\n\n return clone;\n }\n\n toUint32Array() {\n const l = this._pointArray.length;\n const uint32Array = new Uint32Array(l);\n\n for (let i = 0; i < l; i++) {\n uint32Array[i] = this._pointArray[i].uint32;\n }\n\n return uint32Array;\n }\n\n toUint8Array() {\n return new Uint8Array(this.toUint32Array().buffer);\n }\n\n static fromHTMLImageElement(img: HTMLImageElement) {\n const width = img.naturalWidth;\n const height = img.naturalHeight;\n\n const canvas = document.createElement('canvas');\n canvas.width = width;\n canvas.height = height;\n\n const ctx = canvas.getContext('2d')!;\n ctx.drawImage(img, 0, 0, width, height, 0, 0, width, height);\n\n return PointContainer.fromHTMLCanvasElement(canvas);\n }\n\n static fromHTMLCanvasElement(canvas: HTMLCanvasElement) {\n const width = canvas.width;\n const height = canvas.height;\n\n const ctx = canvas.getContext('2d')!;\n const imgData = ctx.getImageData(0, 0, width, height);\n\n return PointContainer.fromImageData(imgData);\n }\n\n static fromImageData(imageData: ImageData) {\n const width = imageData.width;\n const height = imageData.height;\n\n return PointContainer.fromUint8Array(imageData.data, width, height);\n }\n\n static fromUint8Array(\n uint8Array: number[] | Uint8Array | Uint8ClampedArray,\n width: number,\n height: number,\n ) {\n switch (Object.prototype.toString.call(uint8Array)) {\n case '[object Uint8ClampedArray]':\n case '[object Uint8Array]':\n break;\n\n default:\n uint8Array = new Uint8Array(uint8Array);\n }\n\n const uint32Array = new Uint32Array((uint8Array as Uint8Array).buffer);\n return PointContainer.fromUint32Array(uint32Array, width, height);\n }\n\n static fromUint32Array(\n uint32Array: Uint32Array,\n width: number,\n height: number,\n ) {\n const container = new PointContainer();\n\n container._width = width;\n container._height = height;\n\n for (let i = 0, l = uint32Array.length; i < l; i++) {\n container._pointArray[i] = Point.createByUint32(uint32Array[i] | 0); // \"| 0\" is added for v8 optimization\n }\n\n return container;\n }\n\n static fromBuffer(buffer: Buffer, width: number, height: number) {\n const uint32Array = new Uint32Array(\n buffer.buffer,\n buffer.byteOffset,\n buffer.byteLength / Uint32Array.BYTES_PER_ELEMENT,\n );\n return PointContainer.fromUint32Array(uint32Array, width, height);\n }\n}\n", "/**\n * @preserve\n * Copyright 2015-2018 Igor Bezkrovnyi\n * All rights reserved. (MIT Licensed)\n *\n * palette.ts - part of Image Quantization Library\n */\n\nimport { Point } from './point';\nimport { PointContainer } from './pointContainer';\nimport { AbstractDistanceCalculator } from '../distance/distanceCalculator';\nimport { rgb2hsl } from '../conversion/rgb2hsl';\n\n// TODO: make paletteArray via pointContainer, so, export will be available via pointContainer.exportXXX\n\nconst hueGroups = 10;\n\nexport function hueGroup(hue: number, segmentsNumber: number) {\n const maxHue = 360;\n const seg = maxHue / segmentsNumber;\n const half = seg / 2;\n\n for (let i = 1, mid = seg - half; i < segmentsNumber; i++, mid += seg) {\n if (hue >= mid && hue < mid + seg) return i;\n }\n return 0;\n}\n\nexport class Palette {\n private readonly _pointContainer: PointContainer;\n private readonly _pointArray: Point[] = [];\n private _i32idx: { [key: string]: number } = {};\n\n constructor() {\n this._pointContainer = new PointContainer();\n this._pointContainer.setHeight(1);\n this._pointArray = this._pointContainer.getPointArray();\n }\n\n add(color: Point) {\n this._pointArray.push(color);\n this._pointContainer.setWidth(this._pointArray.length);\n }\n\n has(color: Point) {\n for (let i = this._pointArray.length - 1; i >= 0; i--) {\n if (color.uint32 === this._pointArray[i].uint32) return true;\n }\n\n return false;\n }\n\n // TOTRY: use HUSL - http://boronine.com/husl/ http://www.husl-colors.org/ https://github.com/husl-colors/husl\n getNearestColor(\n colorDistanceCalculator: AbstractDistanceCalculator,\n color: Point,\n ) {\n return this._pointArray[\n this._getNearestIndex(colorDistanceCalculator, color) | 0\n ];\n }\n\n getPointContainer() {\n return this._pointContainer;\n }\n\n // TOTRY: use HUSL - http://boronine.com/husl/\n /*\n public nearestIndexByUint32(i32) {\n var idx : number = this._nearestPointFromCache(\"\" + i32);\n if (idx >= 0) return idx;\n\n var min = 1000,\n rgb = [\n (i32 & 0xff),\n (i32 >>> 8) & 0xff,\n (i32 >>> 16) & 0xff,\n (i32 >>> 24) & 0xff\n ],\n len = this._pointArray.length;\n\n idx = 0;\n for (var i = 0; i < len; i++) {\n var dist = Utils.distEuclidean(rgb, this._pointArray[i].rgba);\n\n if (dist < min) {\n min = dist;\n idx = i;\n }\n }\n\n this._i32idx[i32] = idx;\n return idx;\n }\n */\n\n private _nearestPointFromCache(key: string) {\n return typeof this._i32idx[key] === 'number' ? this._i32idx[key] : -1;\n }\n\n private _getNearestIndex(\n colorDistanceCalculator: AbstractDistanceCalculator,\n point: Point,\n ) {\n let idx = this._nearestPointFromCache('' + point.uint32);\n if (idx >= 0) return idx;\n\n let minimalDistance = Number.MAX_VALUE;\n\n idx = 0;\n for (let i = 0, l = this._pointArray.length; i < l; i++) {\n const p = this._pointArray[i];\n const distance = colorDistanceCalculator.calculateRaw(\n point.r,\n point.g,\n point.b,\n point.a,\n p.r,\n p.g,\n p.b,\n p.a,\n );\n\n if (distance < minimalDistance) {\n minimalDistance = distance;\n idx = i;\n }\n }\n\n this._i32idx[point.uint32] = idx;\n return idx;\n }\n\n /*\n public reduce(histogram : ColorHistogram, colors : number) {\n if (this._pointArray.length > colors) {\n var idxi32 = histogram.getImportanceSortedColorsIDXI32();\n\n // quantize histogram to existing palette\n var keep = [], uniqueColors = 0, idx, pruned = false;\n\n for (var i = 0, len = idxi32.length; i < len; i++) {\n // palette length reached, unset all remaining colors (sparse palette)\n if (uniqueColors >= colors) {\n this.prunePal(keep);\n pruned = true;\n break;\n } else {\n idx = this.nearestIndexByUint32(idxi32[i]);\n if (keep.indexOf(idx) < 0) {\n keep.push(idx);\n uniqueColors++;\n }\n }\n }\n\n if (!pruned) {\n this.prunePal(keep);\n }\n }\n }\n\n // TODO: check usage, not tested!\n public prunePal(keep : number[]) {\n var colors = this._pointArray.length;\n for (var colorIndex = colors - 1; colorIndex >= 0; colorIndex--) {\n if (keep.indexOf(colorIndex) < 0) {\n\n if(colorIndex + 1 < colors) {\n this._pointArray[ colorIndex ] = this._pointArray [ colors - 1 ];\n }\n --colors;\n //this._pointArray[colorIndex] = null;\n }\n }\n console.log(\"colors pruned: \" + (this._pointArray.length - colors));\n this._pointArray.length = colors;\n this._i32idx = {};\n }\n */\n\n // TODO: group very low lum and very high lum colors\n // TODO: pass custom sort order\n // TODO: sort criteria function should be placed to HueStats class\n sort() {\n this._i32idx = {};\n this._pointArray.sort((a: Point, b: Point) => {\n const hslA = rgb2hsl(a.r, a.g, a.b);\n const hslB = rgb2hsl(b.r, b.g, b.b);\n\n // sort all grays + whites together\n const hueA =\n a.r === a.g && a.g === a.b ? 0 : 1 + hueGroup(hslA.h, hueGroups);\n const hueB =\n b.r === b.g && b.g === b.b ? 0 : 1 + hueGroup(hslB.h, hueGroups);\n /*\n var hueA = (a.r === a.g && a.g === a.b) ? 0 : 1 + Utils.hueGroup(hslA.h, hueGroups);\n var hueB = (b.r === b.g && b.g === b.b) ? 0 : 1 + Utils.hueGroup(hslB.h, hueGroups);\n */\n\n const hueDiff = hueB - hueA;\n if (hueDiff) return -hueDiff;\n\n /*\n var lumDiff = Utils.lumGroup(+hslB.l.toFixed(2)) - Utils.lumGroup(+hslA.l.toFixed(2));\n if (lumDiff) return -lumDiff;\n */\n const lA = a.getLuminosity(true);\n const lB = b.getLuminosity(true);\n\n if (lB - lA !== 0) return lB - lA;\n\n const satDiff = ((hslB.s * 100) | 0) - ((hslA.s * 100) | 0);\n if (satDiff) return -satDiff;\n\n return 0;\n });\n }\n}\n", "/**\n * @preserve\n * Copyright 2015-2018 Igor Bezkrovnyi\n * All rights reserved. (MIT Licensed)\n *\n * iq.ts - Image Quantization Library\n */\nimport * as arithmetic from './arithmetic';\nimport { HueStatistics } from './hueStatistics';\nimport { Palette } from './palette';\nimport { Point } from './point';\nimport { PointContainer } from './pointContainer';\nimport { ProgressTracker } from './progressTracker';\n\nexport {\n Point,\n PointContainer,\n Palette,\n HueStatistics,\n ProgressTracker,\n arithmetic,\n};\n", "/**\n * @preserve\n * Copyright 2015-2018 Igor Bezkrovnyi\n * All rights reserved. (MIT Licensed)\n *\n * hueStatistics.ts - part of Image Quantization Library\n */\nimport { rgb2hsl } from '../conversion/rgb2hsl';\nimport { hueGroup } from './palette';\n\nclass HueGroup {\n num = 0;\n cols: number[] = [];\n}\n\nexport class HueStatistics {\n private _numGroups: number;\n private _minCols: number;\n private _stats: HueGroup[];\n private _groupsFull: number;\n\n constructor(numGroups: number, minCols: number) {\n this._numGroups = numGroups;\n this._minCols = minCols;\n this._stats = [];\n\n for (let i = 0; i <= numGroups; i++) {\n this._stats[i] = new HueGroup();\n }\n\n this._groupsFull = 0;\n }\n\n check(i32: number) {\n if (this._groupsFull === this._numGroups + 1) {\n this.check = () => {};\n }\n\n const r = i32 & 0xff;\n const g = (i32 >>> 8) & 0xff;\n const b = (i32 >>> 16) & 0xff;\n const hg =\n r === g && g === b\n ? 0\n : 1 + hueGroup(rgb2hsl(r, g, b).h, this._numGroups);\n const gr = this._stats[hg];\n const min = this._minCols;\n\n gr.num++;\n\n if (gr.num > min) {\n return;\n }\n if (gr.num === min) {\n this._groupsFull++;\n }\n\n if (gr.num <= min) {\n this._stats[hg].cols.push(i32);\n }\n }\n\n injectIntoDictionary(histG: Record<string, number>) {\n for (let i = 0; i <= this._numGroups; i++) {\n if (this._stats[i].num <= this._minCols) {\n this._stats[i].cols.forEach((col: number) => {\n if (!histG[col]) {\n histG[col] = 1;\n } else {\n histG[col]++;\n }\n });\n }\n }\n }\n\n injectIntoArray(histG: string[]) {\n for (let i = 0; i <= this._numGroups; i++) {\n if (this._stats[i].num <= this._minCols) {\n this._stats[i].cols.forEach((col: unknown) => {\n if (histG.indexOf(col as string) === -1) {\n histG.push(col as string);\n }\n });\n }\n }\n }\n}\n", "export class ProgressTracker {\n static readonly steps = 100;\n\n progress: number;\n\n private _step: number;\n private _range: number;\n private _last: number;\n private _progressRange: number;\n\n constructor(valueRange: number, progressRange: number) {\n this._range = valueRange;\n this._progressRange = progressRange;\n this._step = Math.max(1, (this._range / (ProgressTracker.steps + 1)) | 0);\n this._last = -this._step;\n this.progress = 0;\n }\n\n shouldNotify(current: number) {\n if (current - this._last >= this._step) {\n this._last = current;\n this.progress = Math.min(\n (this._progressRange * this._last) / this._range,\n this._progressRange,\n );\n return true;\n }\n\n return false;\n }\n}\n", "/*\n * NeuQuant Neural-Net Quantization Algorithm\n * ------------------------------------------\n *\n * Copyright (c) 1994 Anthony Dekker\n *\n * NEUQUANT Neural-Net quantization algorithm by Anthony Dekker, 1994. See\n * \"Kohonen neural networks for optimal colour quantization\" in \"Network:\n * Computation in Neural Systems\" Vol. 5 (1994) pp 351-367. for a discussion of\n * the algorithm.\n *\n * Any party obtaining a copy of these files from the author, directly or\n * indirectly, is granted, free of charge, a full and unrestricted irrevocable,\n * world-wide, paid up, royalty-free, nonexclusive right and license to deal in\n * this software and documentation files (the \"Software\"), including without\n * limitation the rights to use, copy, modify, merge, publish, distribute,\n * sublicense, and/or sell copies of the Software, and to permit persons who\n * receive copies from any such party to do so, with the only requirement being\n * that this copyright notice remain intact.\n */\n\n/**\n * @preserve TypeScript port:\n * Copyright 2015-2018 Igor Bezkrovnyi\n * All rights reserved. (MIT Licensed)\n *\n * neuquant.ts - part of Image Quantization Library\n */\n\nimport { Palette } from '../../utils/palette';\nimport { Point } from '../../utils/point';\nimport { PointContainer } from '../../utils/pointContainer';\nimport { AbstractDistanceCalculator } from '../../distance/distanceCalculator';\nimport { AbstractPaletteQuantizer } from '../paletteQuantizer';\nimport { PaletteQuantizerYieldValue } from '../paletteQuantizerYieldValue';\nimport { ProgressTracker } from '../../utils';\n\n// bias for colour values\nconst networkBiasShift = 3;\n\nclass Neuron {\n r: number;\n g: number;\n b: number;\n a: number;\n\n constructor(defaultValue: number) {\n this.r = this.g = this.b = this.a = defaultValue;\n }\n\n /**\n * There is a fix in original NEUQUANT by Anthony Dekker (http://members.ozemail.com.au/~dekker/NEUQUANT.HTML)\n * @example\n * r = Math.min(255, (neuron.r + (1 << (networkBiasShift - 1))) >> networkBiasShift);\n */\n toPoint() {\n return Point.createByRGBA(\n this.r >> networkBiasShift,\n this.g >> networkBiasShift,\n this.b >> networkBiasShift,\n this.a >> networkBiasShift,\n );\n }\n\n subtract(r: number, g: number, b: number, a: number) {\n this.r -= r | 0;\n this.g -= g | 0;\n this.b -= b | 0;\n this.a -= a | 0;\n }\n\n /*\n public subtract(r : number, g : number, b : number, a : number) : void {\n this.r = (-r + this.r) | 0;\n this.g = (-g + this.g) | 0;\n this.b = (-b + this.b) | 0;\n this.a = (-a + this.a) | 0;\n\n this.r -= r;\n this.g -= g;\n this.b -= b;\n this.a -= a;\n\n this.r -= r | 0;\n this.g -= g | 0;\n this.b -= b | 0;\n this.a -= a | 0;\n }\n */\n}\n\nexport class NeuQuant extends AbstractPaletteQuantizer {\n /*\n four primes near 500 - assume no image has a length so large\n that it is divisible by all four primes\n */\n private static readonly _prime1 = 499;\n private static readonly _prime2 = 491;\n private static readonly _prime3 = 487;\n private static readonly _prime4 = 503;\n private static readonly _minpicturebytes = NeuQuant._prime4;\n\n // no. of learning cycles\n private static readonly _nCycles = 100;\n\n // defs for freq and bias\n private static readonly _initialBiasShift = 16;\n\n // bias for fractions\n private static readonly _initialBias = 1 << NeuQuant._initialBiasShift;\n private static readonly _gammaShift = 10;\n\n // gamma = 1024\n // TODO: why gamma is never used?\n // private static _gamma : number = (1 << NeuQuant._gammaShift);\n private static readonly _betaShift = 10;\n private static readonly _beta = NeuQuant._initialBias >> NeuQuant._betaShift;\n\n // beta = 1/1024\n private static readonly _betaGamma =\n NeuQuant._initialBias << (NeuQuant._gammaShift - NeuQuant._betaShift);\n\n /*\n * for 256 cols, radius starts\n */\n private static readonly _radiusBiasShift = 6;\n\n // at 32.0 biased by 6 bits\n private static readonly _radiusBias = 1 << NeuQuant._radiusBiasShift;\n\n // and decreases by a factor of 1/30 each cycle\n private static readonly _radiusDecrease = 30;\n\n /* defs for decreasing alpha factor */\n\n // alpha starts at 1.0\n private static readonly _alphaBiasShift = 10;\n\n // biased by 10 bits\n private static readonly _initAlpha = 1 << NeuQuant._alphaBiasShift;\n\n /* radBias and alphaRadBias used for radpower calculation */\n private static readonly _radBiasShift = 8;\n private static readonly _radBias = 1 << NeuQuant._radBiasShift;\n private static readonly _alphaRadBiasShift =\n NeuQuant._alphaBiasShift + NeuQuant._radBiasShift;\n private static readonly _alphaRadBias = 1 << NeuQuant._alphaRadBiasShift;\n\n private _pointArray: Point[];\n private readonly _networkSize: number;\n private _network!: Neuron[];\n\n /** sampling factor 1..30 */\n private readonly _sampleFactor!: number;\n private _radPower!: number[];\n\n // bias and freq arrays for learning\n private _freq!: number[];\n\n /* for network lookup - really 256 */\n private _bias!: number[];\n private readonly _distance: AbstractDistanceCalculator;\n\n constructor(\n colorDistanceCalculator: AbstractDistanceCalculator,\n colors = 256,\n ) {\n super();\n this._distance = colorDistanceCalculator;\n this._pointArray = [];\n this._sampleFactor = 1;\n this._networkSize = colors;\n\n this._distance.setWhitePoint(\n 255 << networkBiasShift,\n 255 << networkBiasShift,\n 255 << networkBiasShift,\n 255 << networkBiasShift,\n );\n }\n\n sample(pointContainer: PointContainer) {\n this._pointArray = this._pointArray.concat(pointContainer.getPointArray());\n }\n\n *quantize(): IterableIterator<PaletteQuantizerYieldValue> {\n this._init();\n\n yield* this._learn();\n\n yield {\n palette: this._buildPalette(),\n progress: 100,\n };\n }\n\n private _init() {\n this._freq = [];\n this._bias = [];\n this._radPower = [];\n this._network = [];\n for (let i = 0; i < this._networkSize; i++) {\n this._network[i] = new Neuron(\n ((i << (networkBiasShift + 8)) / this._networkSize) | 0,\n );\n\n // 1/this._networkSize\n this._freq[i] = (NeuQuant._initialBias / this._networkSize) | 0;\n this._bias[i] = 0;\n }\n }\n\n /**\n * Main Learning Loop\n */\n private *_learn() {\n let sampleFactor = this._sampleFactor;\n const pointsNumber = this._pointArray.length;\n if (pointsNumber < NeuQuant._minpicturebytes) sampleFactor = 1;\n\n const alphadec = (30 + (sampleFactor - 1) / 3) | 0;\n const pointsToSample = (pointsNumber / sampleFactor) | 0;\n\n let delta = (pointsToSample / NeuQuant._nCycles) | 0;\n let alpha = NeuQuant._initAlpha;\n let radius = (this._networkSize >> 3) * NeuQuant._radiusBias;\n\n let rad = radius >> NeuQuant._radiusBiasShift;\n if (rad <= 1) rad = 0;\n\n for (let i = 0; i < rad; i++) {\n this._radPower[i] =\n (alpha * (((rad * rad - i * i) * NeuQuant._radBias) / (rad * rad))) >>>\n 0;\n }\n\n let step;\n if (pointsNumber < NeuQuant._minpicturebytes) {\n step = 1;\n } else if (pointsNumber % NeuQuant._prime1 !== 0) {\n step = NeuQuant._prime1;\n } else if (pointsNumber % NeuQuant._prime2 !== 0) {\n step = NeuQuant._prime2;\n } else if (pointsNumber % NeuQuant._prime3 !== 0) {\n step = NeuQuant._prime3;\n } else {\n step = NeuQuant._prime4;\n }\n\n const tracker = new ProgressTracker(pointsToSample, 99);\n for (let i = 0, pointIndex = 0; i < pointsToSample; ) {\n if (tracker.shouldNotify(i)) {\n yield {\n progress: tracker.progress,\n };\n }\n\n const point = this._pointArray[pointIndex];\n const b = point.b << networkBiasShift;\n const g = point.g << networkBiasShift;\n const r = point.r << networkBiasShift;\n const a = point.a << networkBiasShift;\n const neuronIndex = this._contest(b, g, r, a);\n\n this._alterSingle(alpha, neuronIndex, b, g, r, a);\n if (rad !== 0) this._alterNeighbour(rad, neuronIndex, b, g, r, a);\n\n /* alter neighbours */\n pointIndex += step;\n if (pointIndex >= pointsNumber) pointIndex -= pointsNumber;\n i++;\n\n if (delta === 0) delta = 1;\n\n if (i % delta === 0) {\n alpha -= (alpha / alphadec) | 0;\n radius -= (radius / NeuQuant._radiusDecrease) | 0;\n rad = radius >> NeuQuant._radiusBiasShift;\n\n if (rad <= 1) rad = 0;\n for (let j = 0; j < rad; j++) {\n this._radPower[j] =\n (alpha *\n (((rad * rad - j * j) * NeuQuant._radBias) / (rad * rad))) >>>\n 0;\n }\n }\n }\n }\n\n private _buildPalette() {\n const palette = new Palette();\n\n this._network.forEach((neuron) => {\n palette.add(neuron.toPoint());\n });\n\n palette.sort();\n return palette;\n }\n\n /**\n * Move adjacent neurons by precomputed alpha*(1-((i-j)^2/[r]^2)) in radpower[|i-j|]\n */\n private _alterNeighbour(\n rad: number,\n i: number,\n b: number,\n g: number,\n r: number,\n al: number,\n ) {\n let lo = i - rad;\n if (lo < -1) lo = -1;\n\n let hi = i + rad;\n if (hi > this._networkSize) hi = this._networkSize;\n\n let j = i + 1;\n let k = i - 1;\n let m = 1;\n\n while (j < hi || k > lo) {\n const a = this._radPower[m++] / NeuQuant._alphaRadBias;\n if (j < hi) {\n const p = this._network[j++];\n p.subtract(a * (p.r - r), a * (p.g - g), a * (p.b - b), a * (p.a - al));\n }\n\n if (k > lo) {\n const p = this._network[k--];\n p.subtract(a * (p.r - r), a * (p.g - g), a * (p.b - b), a * (p.a - al));\n }\n }\n }\n\n /**\n * Move neuron i towards biased (b,g,r) by factor alpha\n */\n private _alterSingle(\n alpha: number,\n i: number,\n b: number,\n g: number,\n r: number,\n a: number,\n ) {\n alpha /= NeuQuant._initAlpha;\n\n /* alter hit neuron */\n const n = this._network[i];\n n.subtract(\n alpha * (n.r - r),\n alpha * (n.g - g),\n alpha * (n.b - b),\n alpha * (n.a - a),\n );\n }\n\n /**\n * Search for biased BGR values\n * description:\n * finds closest neuron (min dist) and updates freq\n * finds best neuron (min dist-bias) and returns position\n * for frequently chosen neurons, freq[i] is high and bias[i] is negative\n * bias[i] = _gamma*((1/this._networkSize)-freq[i])\n *\n * Original distance equation:\n * dist = abs(dR) + abs(dG) + abs(dB)\n */\n private _contest(b: number, g: number, r: number, a: number) {\n const multiplier = (255 * 4) << networkBiasShift;\n\n let bestd = ~(1 << 31);\n let bestbiasd = bestd;\n let bestpos = -1;\n let bestbiaspos = bestpos;\n\n for (let i = 0; i < this._networkSize; i++) {\n const n = this._network[i];\n const dist =\n (this._distance.calculateNormalized(n, { r, g, b, a }) * multiplier) |\n 0;\n\n if (dist < bestd) {\n bestd = dist;\n bestpos = i;\n }\n\n const biasdist =\n dist -\n (this._bias[i] >> (NeuQuant._initialBiasShift - networkBiasShift));\n if (biasdist < bestbiasd) {\n bestbiasd = biasdist;\n bestbiaspos = i;\n }\n const betafreq = this._freq[i] >> NeuQuant._betaShift;\n this._freq[i] -= betafreq;\n this._bias[i] += betafreq << NeuQuant._gammaShift;\n }\n this._freq[bestpos] += NeuQuant._beta;\n this._bias[bestpos] -= NeuQuant._betaGamma;\n return bestbiaspos;\n }\n}\n", "/*\n * NeuQuantFloat Neural-Net Quantization Algorithm\n * ------------------------------------------\n *\n * Copyright (c) 1994 Anthony Dekker\n *\n * NEUQUANT Neural-Net quantization algorithm by Anthony Dekker, 1994. See\n * \"Kohonen neural networks for optimal colour quantization\" in \"Network:\n * Computation in Neural Systems\" Vol. 5 (1994) pp 351-367. for a discussion of\n * the algorithm.\n *\n * Any party obtaining a copy of these files from the author, directly or\n * indirectly, is granted, free of charge, a full and unrestricted irrevocable,\n * world-wide, paid up, royalty-free, nonexclusive right and license to deal in\n * this software and documentation files (the \"Software\"), including without\n * limitation the rights to use, copy, modify, merge, publish, distribute,\n * sublicense, and/or sell copies of the Software, and to permit persons who\n * receive copies from any such party to do so, with the only requirement being\n * that this copyright notice remain intact.\n */\n/**\n * @preserve TypeScript port:\n * Copyright 2015-2018 Igor Bezkrovnyi\n * All rights reserved. (MIT Licensed)\n *\n * neuquant.ts - part of Image Quantization Library\n */\nimport { Palette } from '../../utils/palette';\nimport { Point } from '../../utils/point';\nimport { PointContainer } from '../../utils/pointContainer';\nimport { AbstractDistanceCalculator } from '../../distance/distanceCalculator';\nimport { AbstractPaletteQuantizer } from '../paletteQuantizer';\nimport { PaletteQuantizerYieldValue } from '../paletteQuantizerYieldValue';\nimport { ProgressTracker } from '../../utils';\n\n// bias for colour values\nconst networkBiasShift = 3;\n\nclass NeuronFloat {\n r: number;\n g: number;\n b: number;\n a: number;\n\n constructor(defaultValue: number) {\n this.r = this.g = this.b = this.a = defaultValue;\n }\n\n /**\n * There is a fix in original NEUQUANT by Anthony Dekker (http://members.ozemail.com.au/~dekker/NEUQUANT.HTML)\n * @example\n * r = Math.min(255, (neuron.r + (1 << (networkBiasShift - 1))) >> networkBiasShift);\n */\n toPoint() {\n return Point.createByRGBA(\n this.r >> networkBiasShift,\n this.g >> networkBiasShift,\n this.b >> networkBiasShift,\n this.a >> networkBiasShift,\n );\n }\n\n subtract(r: number, g: number, b: number, a: number) {\n this.r -= r;\n this.g -= g;\n this.b -= b;\n this.a -= a;\n }\n}\n\nexport class NeuQuantFloat extends AbstractPaletteQuantizer {\n /*\n four primes near 500 - assume no image has a length so large\n that it is divisible by all four primes\n */\n private static readonly _prime1 = 499;\n private static readonly _prime2 = 491;\n private static readonly _prime3 = 487;\n private static readonly _prime4 = 503;\n private static readonly _minpicturebytes = NeuQuantFloat._prime4;\n\n // no. of learning cycles\n private static readonly _nCycles = 100;\n\n // defs for freq and bias\n private static readonly _initialBiasShift = 16;\n\n // bias for fractions\n private static readonly _initialBias = 1 << NeuQuantFloat._initialBiasShift;\n private static readonly _gammaShift = 10;\n\n // gamma = 1024\n // TODO: why gamma is never used?\n // private static _gamma : number = (1 << NeuQuantFloat._gammaShift);\n private static readonly _betaShift = 10;\n private static readonly _beta =\n NeuQuantFloat._initialBias >> NeuQuantFloat._betaShift;\n\n // beta = 1/1024\n private static readonly _betaGamma =\n NeuQuantFloat._initialBias <<\n (NeuQuantFloat._gammaShift - NeuQuantFloat._betaShift);\n\n /*\n * for 256 cols, radius starts\n */\n private static readonly _radiusBiasShift = 6;\n\n // at 32.0 biased by 6 bits\n private static readonly _radiusBias = 1 << NeuQuantFloat._radiusBiasShift;\n\n // and decreases by a factor of 1/30 each cycle\n private static readonly _radiusDecrease = 30;\n\n /* defs for decreasing alpha factor */\n\n // alpha starts at 1.0\n private static readonly _alphaBiasShift = 10;\n\n // biased by 10 bits\n private static readonly _initAlpha = 1 << NeuQuantFloat._alphaBiasShift;\n\n /* radBias and alphaRadBias used for radpower calculation */\n private static readonly _radBiasShift = 8;\n private static readonly _radBias = 1 << NeuQuantFloat._radBiasShift;\n private static readonly _alphaRadBiasShift =\n NeuQuantFloat._alphaBiasShift + NeuQuantFloat._radBiasShift;\n private static readonly _alphaRadBias = 1 << NeuQuantFloat._alphaRadBiasShift;\n\n private _pointArray!: Point[];\n private readonly _networkSize!: number;\n private _network!: NeuronFloat[];\n\n /** sampling factor 1..30 */\n private readonly _sampleFactor!: number;\n private _radPower!: number[];\n\n // bias and freq arrays for learning\n private _freq!: number[];\n\n /* for network lookup - really 256 */\n private _bias!: number[];\n private readonly _distance: AbstractDistanceCalculator;\n\n constructor(\n colorDistanceCalculator: AbstractDistanceCalculator,\n colors = 256,\n ) {\n super();\n this._distance = colorDistanceCalculator;\n this._pointArray = [];\n this._sampleFactor = 1;\n this._networkSize = colors;\n\n this._distance.setWhitePoint(\n 255 << networkBiasShift,\n 255 << networkBiasShift,\n 255 << networkBiasShift,\n 255 << networkBiasShift,\n );\n }\n\n sample(pointContainer: PointContainer) {\n this._pointArray = this._pointArray.concat(pointContainer.getPointArray());\n }\n\n *quantize() {\n this._init();\n yield* this._learn();\n\n yield {\n palette: this._buildPalette(),\n progress: 100,\n };\n }\n\n private _init() {\n this._freq = [];\n this._bias = [];\n this._radPower = [];\n this._network = [];\n for (let i = 0; i < this._networkSize; i++) {\n this._network[i] = new NeuronFloat(\n (i << (networkBiasShift + 8)) / this._networkSize,\n );\n\n // 1/this._networkSize\n this._freq[i] = NeuQuantFloat._initialBias / this._networkSize;\n this._bias[i] = 0;\n }\n }\n\n /**\n * Main Learning Loop\n */\n private *_learn(): IterableIterator<PaletteQuantizerYieldValue> {\n let sampleFactor = this._sampleFactor;\n\n const pointsNumber = this._pointArray.length;\n if (pointsNumber < NeuQuantFloat._minpicturebytes) sampleFactor = 1;\n\n const alphadec = 30 + (sampleFactor - 1) / 3;\n const pointsToSample = pointsNumber / sampleFactor;\n\n let delta = (pointsToSample / NeuQuantFloat._nCycles) | 0;\n let alpha = NeuQuantFloat._initAlpha;\n let radius = (this._networkSize >> 3) * NeuQuantFloat._radiusBias;\n\n let rad = radius >> NeuQuantFloat._radiusBiasShift;\n if (rad <= 1) rad = 0;\n\n for (let i = 0; i < rad; i++) {\n this._radPower[i] =\n alpha * (((rad * rad - i * i) * NeuQuantFloat._radBias) / (rad * rad));\n }\n\n let step;\n if (pointsNumber < NeuQuantFloat._minpicturebytes) {\n step = 1;\n } else if (pointsNumber % NeuQuantFloat._prime1 !== 0) {\n step = NeuQuantFloat._prime1;\n } else if (pointsNumber % NeuQuantFloat._prime2 !== 0) {\n step = NeuQuantFloat._prime2;\n } else if (pointsNumber % NeuQuantFloat._prime3 !== 0) {\n step = NeuQuantFloat._prime3;\n } else {\n step = NeuQuantFloat._prime4;\n }\n\n const tracker = new ProgressTracker(pointsToSample, 99);\n for (let i = 0, pointIndex = 0; i < pointsToSample; ) {\n if (tracker.shouldNotify(i)) {\n yield {\n progress: tracker.progress,\n };\n }\n\n const point = this._pointArray[pointIndex];\n const b = point.b << networkBiasShift;\n const g = point.g << networkBiasShift;\n const r = point.r << networkBiasShift;\n const a = point.a << networkBiasShift;\n const neuronIndex = this._contest(b, g, r, a);\n\n this._alterSingle(alpha, neuronIndex, b, g, r, a);\n if (rad !== 0) this._alterNeighbour(rad, neuronIndex, b, g, r, a);\n\n /* alter neighbours */\n pointIndex += step;\n if (pointIndex >= pointsNumber) pointIndex -= pointsNumber;\n i++;\n\n if (delta === 0) delta = 1;\n\n if (i % delta === 0) {\n alpha -= alpha / alphadec;\n radius -= radius / NeuQuantFloat._radiusDecrease;\n rad = radius >> NeuQuantFloat._radiusBiasShift;\n\n if (rad <= 1) rad = 0;\n for (let j = 0; j < rad; j++) {\n this._radPower[j] =\n alpha *\n (((rad * rad - j * j) * NeuQuantFloat._radBias) / (rad * rad));\n }\n }\n }\n }\n\n private _buildPalette() {\n const palette = new Palette();\n\n this._network.forEach((neuron) => {\n palette.add(neuron.toPoint());\n });\n\n palette.sort();\n return palette;\n }\n\n /**\n * Move adjacent neurons by precomputed alpha*(1-((i-j)^2/[r]^2)) in radpower[|i-j|]\n */\n private _alterNeighbour(\n rad: number,\n i: number,\n b: number,\n g: number,\n r: number,\n al: number,\n ) {\n let lo = i - rad;\n if (lo < -1) lo = -1;\n\n let hi = i + rad;\n if (hi > this._networkSize) hi = this._networkSize;\n\n let j = i + 1;\n let k = i - 1;\n let m = 1;\n\n while (j < hi || k > lo) {\n const a = this._radPower[m++] / NeuQuantFloat._alphaRadBias;\n if (j < hi) {\n const p = this._network[j++];\n p.subtract(a * (p.r - r), a * (p.g - g), a * (p.b - b), a * (p.a - al));\n }\n\n if (k > lo) {\n const p = this._network[k--];\n p.subtract(a * (p.r - r), a * (p.g - g), a * (p.b - b), a * (p.a - al));\n }\n }\n }\n\n /**\n * Move neuron i towards biased (b,g,r) by factor alpha\n */\n private _alterSingle(\n alpha: number,\n i: number,\n b: number,\n g: number,\n r: number,\n a: number,\n ) {\n alpha /= NeuQuantFloat._initAlpha;\n\n /* alter hit neuron */\n const n = this._network[i];\n n.subtract(\n alpha * (n.r - r),\n alpha * (n.g - g),\n alpha * (n.b - b),\n alpha * (n.a - a),\n );\n }\n\n /**\n * Search for biased BGR values\n * description:\n * finds closest neuron (min dist) and updates freq\n * finds best neuron (min dist-bias) and returns position\n * for frequently chosen neurons, freq[i] is high and bias[i] is negative\n * bias[i] = _gamma*((1/this._networkSize)-freq[i])\n *\n * Original distance equation:\n * dist = abs(dR) + abs(dG) + abs(dB)\n */\n private _contest(b: number, g: number, r: number, al: number) {\n const multiplier = (255 * 4) << networkBiasShift;\n\n let bestd = ~(1 << 31);\n let bestbiasd = bestd;\n let bestpos = -1;\n let bestbiaspos = bestpos;\n\n for (let i = 0; i < this._networkSize; i++) {\n const n = this._network[i];\n const dist =\n this._distance.calculateNormalized(n, { r, g, b, a: al }) * multiplier;\n\n if (dist < bestd) {\n bestd = dist;\n bestpos = i;\n }\n\n const biasdist =\n dist -\n (this._bias[i] >> (NeuQuantFloat._initialBiasShift - networkBiasShift));\n if (biasdist < bestbiasd) {\n bestbiasd = biasdist;\n bestbiaspos = i;\n }\n const betafreq = this._freq[i] >> NeuQuantFloat._betaShift;\n this._freq[i] -= betafreq;\n this._bias[i] += betafreq << NeuQuantFloat._gammaShift;\n }\n this._freq[bestpos] += NeuQuantFloat._beta;\n this._bias[bestpos] -= NeuQuantFloat._betaGamma;\n return bestbiaspos;\n }\n}\n", "/*\n * Copyright (c) 2015, Leon Sorokin\n * All rights reserved. (MIT Licensed)\n *\n * ColorHistogram.js - an image quantization lib\n */\n\n/**\n * @preserve TypeScript port:\n * Copyright 2015-2018 Igor Bezkrovnyi\n * All rights reserved. (MIT Licensed)\n *\n * colorHistogram.ts - part of Image Quantization Library\n */\nimport { HueStatistics } from '../../utils/hueStatistics';\nimport { PointContainer } from '../../utils/pointContainer';\nimport { stableSort } from '../../utils/arithmetic';\n\ninterface Box {\n x: number;\n y: number;\n h: number;\n w: number;\n}\n\nexport class ColorHistogram {\n private static _boxSize = [64, 64];\n private static _boxPixels = 2;\n private static _hueGroups = 10;\n\n // 1 = by global population, 2 = subregion population threshold\n private _method: number;\n\n // HueStatistics instance\n private _hueStats: HueStatistics;\n\n private _histogram: { [color: string]: number };\n\n // # of highest-frequency colors to start with for palette reduction\n private _initColors: number;\n\n // if > 0, enables hues stats and min-color retention per group\n private _minHueCols: number;\n\n constructor(method: number, colors: number) {\n // 1 = by global population, 2 = subregion population threshold\n this._method = method;\n\n // if > 0, enables hues stats and min-color retention per group\n this._minHueCols = colors << 2; // opts.minHueCols || 0;\n\n // # of highest-frequency colors to start with for palette reduction\n this._initColors = colors << 2;\n\n // HueStatistics instance\n this._hueStats = new HueStatistics(\n ColorHistogram._hueGroups,\n this._minHueCols,\n );\n\n this._histogram = Object.create(null);\n }\n\n sample(pointContainer: PointContainer) {\n switch (this._method) {\n case 1:\n this._colorStats1D(pointContainer);\n break;\n case 2:\n this._colorStats2D(pointContainer);\n break;\n }\n }\n\n getImportanceSortedColorsIDXI32() {\n // TODO: fix typing issue in stableSort func\n const sorted = stableSort(\n Object.keys(this._histogram),\n (a, b) => this._histogram[b] - this._histogram[a],\n );\n if (sorted.length === 0) {\n return [];\n }\n\n let idxi32;\n switch (this._method) {\n case 1:\n const initialColorsLimit = Math.min(sorted.length, this._initColors);\n const last = sorted[initialColorsLimit - 1];\n const freq = this._histogram[last];\n\n idxi32 = sorted.slice(0, initialColorsLimit);\n\n // add any cut off colors with same freq as last\n let pos = initialColorsLimit;\n const len = sorted.length;\n while (pos < len && this._histogram[sorted[pos]] === freq) {\n idxi32.push(sorted[pos++]);\n }\n\n // inject min huegroup colors\n this._hueStats.injectIntoArray(idxi32);\n break;\n\n case 2:\n idxi32 = sorted;\n break;\n\n default:\n // TODO: rethink errors\n throw new Error('Incorrect method');\n }\n\n // int32-ify values\n return idxi32.map((v) => +v);\n }\n\n // global top-population\n private _colorStats1D(pointContainer: PointContainer) {\n const histG = this._histogram;\n const pointArray = pointContainer.getPointArray();\n const len = pointArray.length;\n\n for (let i = 0; i < len; i++) {\n const col = pointArray[i].uint32;\n\n // collect hue stats\n this._hueStats.check(col);\n\n if (col in histG) {\n histG[col]++;\n } else {\n histG[col] = 1;\n }\n }\n }\n\n // population threshold within subregions\n // FIXME: this can over-reduce (few/no colors same?), need a way to keep\n // important colors that dont ever reach local thresholds (gradients?)\n private _colorStats2D(pointContainer: PointContainer) {\n const width = pointContainer.getWidth();\n const height = pointContainer.getHeight();\n const pointArray = pointContainer.getPointArray();\n\n const boxW = ColorHistogram._boxSize[0];\n const boxH = ColorHistogram._boxSize[1];\n const area = boxW * boxH;\n const boxes = this._makeBoxes(width, height, boxW, boxH);\n const histG = this._histogram;\n\n boxes.forEach((box) => {\n let effc = Math.round((box.w * box.h) / area) * ColorHistogram._boxPixels;\n if (effc < 2) effc = 2;\n\n const histL: Record<string, number> = {};\n this._iterateBox(box, width, (i) => {\n const col = pointArray[i].uint32;\n\n // collect hue stats\n this._hueStats.check(col);\n\n if (col in histG) {\n histG[col]++;\n } else if (col in histL) {\n if (++histL[col] >= effc) {\n histG[col] = histL[col];\n }\n } else {\n histL[col] = 1;\n }\n });\n });\n\n // inject min huegroup colors\n this._hueStats.injectIntoDictionary(histG);\n }\n\n // iterates @bbox within a parent rect of width @wid; calls @fn, passing index within parent\n private _iterateBox(bbox: Box, wid: number, fn: (i: number) => void) {\n const b = bbox;\n const i0 = b.y * wid + b.x;\n const i1 = (b.y + b.h - 1) * wid + (b.x + b.w - 1);\n const incr = wid - b.w + 1;\n\n let cnt = 0;\n let i = i0;\n\n do {\n fn.call(this, i);\n i += ++cnt % b.w === 0 ? incr : 1;\n } while (i <= i1);\n }\n\n /**\n * partitions a rectangle of width x height into\n * array of boxes stepX x stepY (or less)\n */\n private _makeBoxes(\n width: number,\n height: number,\n stepX: number,\n stepY: number,\n ) {\n const wrem = width % stepX;\n const hrem = height % stepY;\n const xend = width - wrem;\n const yend = height - hrem;\n const boxesArray = [];\n\n for (let y = 0; y < height; y += stepY) {\n for (let x = 0; x < width; x += stepX) {\n boxesArray.push({\n x,\n y,\n w: x === xend ? wrem : stepX,\n h: y === yend ? hrem : stepY,\n });\n }\n }\n\n return boxesArray;\n }\n}\n", "/*\n * Copyright (c) 2015, Leon Sorokin\n * All rights reserved. (MIT Licensed)\n *\n * RGBQuant.js - an image quantization lib\n */\n\n/**\n * @preserve TypeScript port:\n * Copyright 2015-2018 Igor Bezkrovnyi\n * All rights reserved. (MIT Licensed)\n *\n * rgbquant.ts - part of Image Quantization Library\n */\n\nimport { Palette } from '../../utils/palette';\nimport { Point } from '../../utils/point';\nimport { PointContainer } from '../../utils/pointContainer';\nimport { AbstractDistanceCalculator } from '../../distance/distanceCalculator';\nimport { ColorHistogram } from './colorHistogram';\nimport { AbstractPaletteQuantizer } from '../paletteQuantizer';\nimport { PaletteQuantizerYieldValue } from '../paletteQuantizerYieldValue';\nimport { stableSort } from '../../utils/arithmetic';\nimport { ProgressTracker } from '../../utils';\n\nclass RemovedColor {\n readonly index: number;\n readonly color: Point;\n readonly distance: number;\n\n constructor(index: number, color: Point, distance: number) {\n this.index = index;\n this.color = color;\n this.distance = distance;\n }\n}\n\n// TODO: make input/output image and input/output palettes with instances of class Point only!\nexport class RGBQuant extends AbstractPaletteQuantizer {\n // desired final palette size\n private readonly _colors: number;\n\n // color-distance threshold for initial reduction pass\n private readonly _initialDistance: number;\n\n // subsequent passes threshold\n private readonly _distanceIncrement: number;\n\n // accumulated histogram\n private readonly _histogram: ColorHistogram;\n private readonly _distance: AbstractDistanceCalculator;\n\n constructor(\n colorDistanceCalculator: AbstractDistanceCalculator,\n colors = 256,\n method = 2,\n ) {\n super();\n this._distance = colorDistanceCalculator;\n // desired final palette size\n this._colors = colors;\n\n // histogram to accumulate\n this._histogram = new ColorHistogram(method, colors);\n\n this._initialDistance = 0.01;\n this._distanceIncrement = 0.005;\n }\n\n // gathers histogram info\n sample(image: PointContainer) {\n /*\n var pointArray = image.getPointArray(), max = [0, 0, 0, 0], min = [255, 255, 255, 255];\n\n for (var i = 0, l = pointArray.length; i < l; i++) {\n var color = pointArray[i];\n for (var componentIndex = 0; componentIndex < 4; componentIndex++) {\n if (max[componentIndex] < color.rgba[componentIndex]) max[componentIndex] = color.rgba[componentIndex];\n if (min[componentIndex] > color.rgba[componentIndex]) min[componentIndex] = color.rgba[componentIndex];\n }\n }\n var rd = max[0] - min[0], gd = max[1] - min[1], bd = max[2] - min[2], ad = max[3] - min[3];\n this._distance.setWhitePoint(rd, gd, bd, ad);\n\n this._initialDistance = (Math.sqrt(rd * rd + gd * gd + bd * bd + ad * ad) / Math.sqrt(255 * 255 + 255 * 255 + 255 * 255)) * 0.01;\n */\n\n this._histogram.sample(image);\n }\n\n // reduces histogram to palette, remaps & memoizes reduced colors\n *quantize() {\n const idxi32 = this._histogram.getImportanceSortedColorsIDXI32();\n if (idxi32.length === 0) {\n throw new Error('No colors in image');\n }\n\n yield* this._buildPalette(idxi32);\n }\n\n // reduces similar colors from an importance-sorted Uint32 rgba array\n private *_buildPalette(\n idxi32: number[],\n ): IterableIterator<PaletteQuantizerYieldValue> {\n // reduce histogram to create initial palette\n // build full rgb palette\n const palette = new Palette();\n const colorArray = palette.getPointContainer().getPointArray();\n const usageArray = new Array(idxi32.length);\n\n for (let i = 0; i < idxi32.length; i++) {\n colorArray.push(Point.createByUint32(idxi32[i]));\n usageArray[i] = 1;\n }\n\n const len = colorArray.length;\n const memDist = [];\n\n let palLen = len;\n let thold = this._initialDistance;\n\n // palette already at or below desired length\n const tracker = new ProgressTracker(palLen - this._colors, 99);\n while (palLen > this._colors) {\n memDist.length = 0;\n\n // iterate palette\n for (let i = 0; i < len; i++) {\n if (tracker.shouldNotify(len - palLen)) {\n yield {\n progress: tracker.progress,\n };\n }\n\n if (usageArray[i] === 0) continue;\n const pxi = colorArray[i];\n // if (!pxi) continue;\n\n for (let j = i + 1; j < len; j++) {\n if (usageArray[j] === 0) continue;\n const pxj = colorArray[j];\n // if (!pxj) continue;\n\n const dist = this._distance.calculateNormalized(pxi, pxj);\n if (dist < thold) {\n // store index,rgb,dist\n memDist.push(new RemovedColor(j, pxj, dist));\n usageArray[j] = 0;\n palLen--;\n }\n }\n }\n // palette reduction pass\n // console.log(\"palette length: \" + palLen);\n\n // if palette is still much larger than target, increment by larger initDist\n thold +=\n palLen > this._colors * 3\n ? this._initialDistance\n : this._distanceIncrement;\n }\n\n // if palette is over-reduced, re-add removed colors with largest distances from last round\n if (palLen < this._colors) {\n // sort descending\n stableSort(memDist, (a, b) => b.distance - a.distance);\n\n let k = 0;\n while (palLen < this._colors && k < memDist.length) {\n const removedColor = memDist[k];\n // re-inject rgb into final palette\n usageArray[removedColor.index] = 1;\n palLen++;\n k++;\n }\n }\n\n let colors = colorArray.length;\n for (let colorIndex = colors - 1; colorIndex >= 0; colorIndex--) {\n if (usageArray[colorIndex] === 0) {\n if (colorIndex !== colors - 1) {\n colorArray[colorIndex] = colorArray[colors - 1];\n }\n --colors;\n }\n }\n colorArray.length = colors;\n\n palette.sort();\n\n yield {\n palette,\n progress: 100,\n };\n }\n}\n", "/**\n * @preserve\n * Copyright 2015-2018 Igor Bezkrovnyi\n * All rights reserved. (MIT Licensed)\n *\n * wuQuant.ts - part of Image Quantization Library\n */\nimport { Palette } from '../../utils/palette';\nimport { Point } from '../../utils/point';\nimport { PointContainer } from '../../utils/pointContainer';\nimport { AbstractDistanceCalculator } from '../../distance/distanceCalculator';\nimport { AbstractPaletteQuantizer } from '../paletteQuantizer';\nimport { PaletteQuantizerYieldValue } from '../paletteQuantizerYieldValue';\nimport { ProgressTracker } from '../../utils';\n\nfunction createArray1D(dimension1: number) {\n const a = [];\n for (let k = 0; k < dimension1; k++) {\n a[k] = 0;\n }\n return a;\n}\n\nfunction createArray4D(\n dimension1: number,\n dimension2: number,\n dimension3: number,\n dimension4: number,\n): number[][][][] {\n const a = new Array(dimension1);\n for (let i = 0; i < dimension1; i++) {\n a[i] = new Array(dimension2);\n for (let j = 0; j < dimension2; j++) {\n a[i][j] = new Array(dimension3);\n for (let k = 0; k < dimension3; k++) {\n a[i][j][k] = new Array(dimension4);\n for (let l = 0; l < dimension4; l++) {\n a[i][j][k][l] = 0;\n }\n }\n }\n }\n return a;\n}\n\nfunction createArray3D(\n dimension1: number,\n dimension2: number,\n dimension3: number,\n): number[][][] {\n const a = new Array(dimension1);\n for (let i = 0; i < dimension1; i++) {\n a[i] = new Array(dimension2);\n for (let j = 0; j < dimension2; j++) {\n a[i][j] = new Array(dimension3);\n for (let k = 0; k < dimension3; k++) {\n a[i][j][k] = 0;\n }\n }\n }\n return a;\n}\n\nfunction fillArray3D<T>(\n a: T[][][],\n dimension1: number,\n dimension2: number,\n dimension3: number,\n value: T,\n) {\n for (let i = 0; i < dimension1; i++) {\n a[i] = [];\n for (let j = 0; j < dimension2; j++) {\n a[i][j] = [];\n for (let k = 0; k < dimension3; k++) {\n a[i][j][k] = value;\n }\n }\n }\n}\n\nfunction fillArray1D<T>(a: T[], dimension1: number, value: T) {\n for (let i = 0; i < dimension1; i++) {\n a[i] = value;\n }\n}\n\nexport class WuColorCube {\n redMinimum!: number;\n redMaximum!: number;\n greenMinimum!: number;\n greenMaximum!: number;\n blueMinimum!: number;\n blueMaximum!: number;\n volume!: number;\n alphaMinimum!: number;\n alphaMaximum!: number;\n}\n\nexport class WuQuant extends AbstractPaletteQuantizer {\n private static readonly _alpha = 3;\n private static readonly _red = 2;\n private static readonly _green = 1;\n private static readonly _blue = 0;\n\n private _reds!: number[];\n private _greens!: number[];\n private _blues!: number[];\n private _alphas!: number[];\n private _sums!: number[];\n\n private _weights!: number[][][][];\n private _momentsRed!: number[][][][];\n private _momentsGreen!: number[][][][];\n private _momentsBlue!: number[][][][];\n private _momentsAlpha!: number[][][][];\n private _moments!: number[][][][];\n private _table!: number[];\n private _pixels!: Point[];\n\n private _cubes!: WuColorCube[];\n private _colors!: number;\n\n private _significantBitsPerChannel!: number;\n private _maxSideIndex!: number;\n private _alphaMaxSideIndex!: number;\n private _sideSize!: number;\n private _alphaSideSize!: number;\n\n private readonly _distance: AbstractDistanceCalculator;\n\n constructor(\n colorDistanceCalculator: AbstractDistanceCalculator,\n colors = 256,\n significantBitsPerChannel = 5,\n ) {\n super();\n this._distance = colorDistanceCalculator;\n this._setQuality(significantBitsPerChannel);\n this._initialize(colors);\n }\n\n sample(image: PointContainer) {\n const pointArray = image.getPointArray();\n\n for (let i = 0, l = pointArray.length; i < l; i++) {\n this._addColor(pointArray[i]);\n }\n\n this._pixels = this._pixels.concat(pointArray);\n }\n\n *quantize() {\n yield* this._preparePalette();\n\n const palette = new Palette();\n\n // generates palette\n for (let paletteIndex = 0; paletteIndex < this._colors; paletteIndex++) {\n if (this._sums[paletteIndex] > 0) {\n const sum = this._sums[paletteIndex];\n const r = this._reds[paletteIndex] / sum;\n const g = this._greens[paletteIndex] / sum;\n const b = this._blues[paletteIndex] / sum;\n const a = this._alphas[paletteIndex] / sum;\n\n const color = Point.createByRGBA(r | 0, g | 0, b | 0, a | 0);\n palette.add(color);\n }\n }\n\n palette.sort();\n\n yield {\n palette,\n progress: 100,\n };\n }\n\n private *_preparePalette() {\n // preprocess the colors\n yield* this._calculateMoments();\n\n let next = 0;\n const volumeVariance = createArray1D(this._colors);\n\n // processes the cubes\n for (let cubeIndex = 1; cubeIndex < this._colors; ++cubeIndex) {\n // if cut is possible; make it\n if (this._cut(this._cubes[next], this._cubes[cubeIndex])) {\n volumeVariance[next] =\n this._cubes[next].volume > 1\n ? this._calculateVariance(this._cubes[next])\n : 0.0;\n volumeVariance[cubeIndex] =\n this._cubes[cubeIndex].volume > 1\n ? this._calculateVariance(this._cubes[cubeIndex])\n : 0.0;\n } else {\n // the cut was not possible, revert the index\n volumeVariance[next] = 0.0;\n cubeIndex--;\n }\n\n next = 0;\n let temp = volumeVariance[0];\n\n for (let index = 1; index <= cubeIndex; ++index) {\n if (volumeVariance[index] > temp) {\n temp = volumeVariance[index];\n next = index;\n }\n }\n\n if (temp <= 0.0) {\n this._colors = cubeIndex + 1;\n break;\n }\n }\n\n const lookupRed = [];\n const lookupGreen = [];\n const lookupBlue = [];\n const lookupAlpha = [];\n\n // precalculates lookup tables\n for (let k = 0; k < this._colors; ++k) {\n const weight = WuQuant._volume(this._cubes[k], this._weights);\n\n if (weight > 0) {\n lookupRed[k] =\n (WuQuant._volume(this._cubes[k], this._momentsRed) / weight) | 0;\n lookupGreen[k] =\n (WuQuant._volume(this._cubes[k], this._momentsGreen) / weight) | 0;\n lookupBlue[k] =\n (WuQuant._volume(this._cubes[k], this._momentsBlue) / weight) | 0;\n lookupAlpha[k] =\n (WuQuant._volume(this._cubes[k], this._momentsAlpha) / weight) | 0;\n } else {\n lookupRed[k] = 0;\n lookupGreen[k] = 0;\n lookupBlue[k] = 0;\n lookupAlpha[k] = 0;\n }\n }\n\n this._reds = createArray1D(this._colors + 1);\n this._greens = createArray1D(this._colors + 1);\n this._blues = createArray1D(this._colors + 1);\n this._alphas = createArray1D(this._colors + 1);\n this._sums = createArray1D(this._colors + 1);\n\n // scans and adds colors\n for (let index = 0, l = this._pixels.length; index < l; index++) {\n const color = this._pixels[index];\n\n const match = -1;\n\n let bestMatch = match;\n let bestDistance = Number.MAX_VALUE;\n\n for (let lookup = 0; lookup < this._colors; lookup++) {\n const foundRed = lookupRed[lookup];\n const foundGreen = lookupGreen[lookup];\n const foundBlue = lookupBlue[lookup];\n const foundAlpha = lookupAlpha[lookup];\n\n const distance = this._distance.calculateRaw(\n foundRed,\n foundGreen,\n foundBlue,\n foundAlpha,\n color.r,\n color.g,\n color.b,\n color.a,\n );\n\n if (distance < bestDistance) {\n bestDistance = distance;\n bestMatch = lookup;\n }\n }\n\n this._reds[bestMatch] += color.r;\n this._greens[bestMatch] += color.g;\n this._blues[bestMatch] += color.b;\n this._alphas[bestMatch] += color.a;\n this._sums[bestMatch]++;\n }\n }\n\n private _addColor(color: Point) {\n const bitsToRemove = 8 - this._significantBitsPerChannel;\n const indexRed = (color.r >> bitsToRemove) + 1;\n const indexGreen = (color.g >> bitsToRemove) + 1;\n const indexBlue = (color.b >> bitsToRemove) + 1;\n const indexAlpha = (color.a >> bitsToRemove) + 1;\n\n // if(color.a > 10) {\n this._weights[indexAlpha][indexRed][indexGreen][indexBlue]++;\n this._momentsRed[indexAlpha][indexRed][indexGreen][indexBlue] += color.r;\n this._momentsGreen[indexAlpha][indexRed][indexGreen][indexBlue] += color.g;\n this._momentsBlue[indexAlpha][indexRed][indexGreen][indexBlue] += color.b;\n this._momentsAlpha[indexAlpha][indexRed][indexGreen][indexBlue] += color.a;\n this._moments[indexAlpha][indexRed][indexGreen][indexBlue] +=\n this._table[color.r] +\n this._table[color.g] +\n this._table[color.b] +\n this._table[color.a];\n // }\n }\n\n /**\n * Converts the histogram to a series of _moments.\n */\n private *_calculateMoments(): IterableIterator<PaletteQuantizerYieldValue> {\n const area: number[] = [];\n const areaRed: number[] = [];\n const areaGreen: number[] = [];\n const areaBlue: number[] = [];\n const areaAlpha: number[] = [];\n const area2: number[] = [];\n\n const xarea = createArray3D(this._sideSize, this._sideSize, this._sideSize);\n const xareaRed = createArray3D(\n this._sideSize,\n this._sideSize,\n this._sideSize,\n );\n const xareaGreen = createArray3D(\n this._sideSize,\n this._sideSize,\n this._sideSize,\n );\n const xareaBlue = createArray3D(\n this._sideSize,\n this._sideSize,\n this._sideSize,\n );\n const xareaAlpha = createArray3D(\n this._sideSize,\n this._sideSize,\n this._sideSize,\n );\n const xarea2 = createArray3D(\n this._sideSize,\n this._sideSize,\n this._sideSize,\n );\n\n let trackerProgress = 0;\n const tracker = new ProgressTracker(\n this._alphaMaxSideIndex * this._maxSideIndex,\n 99,\n );\n\n for (\n let alphaIndex = 1;\n alphaIndex <= this._alphaMaxSideIndex;\n ++alphaIndex\n ) {\n fillArray3D<number>(\n xarea,\n this._sideSize,\n this._sideSize,\n this._sideSize,\n 0,\n );\n fillArray3D<number>(\n xareaRed,\n this._sideSize,\n this._sideSize,\n this._sideSize,\n 0,\n );\n fillArray3D<number>(\n xareaGreen,\n this._sideSize,\n this._sideSize,\n this._sideSize,\n 0,\n );\n fillArray3D<number>(\n xareaBlue,\n this._sideSize,\n this._sideSize,\n this._sideSize,\n 0,\n );\n fillArray3D<number>(\n xareaAlpha,\n this._sideSize,\n this._sideSize,\n this._sideSize,\n 0,\n );\n fillArray3D<number>(\n xarea2,\n this._sideSize,\n this._sideSize,\n this._sideSize,\n 0,\n );\n\n for (\n let redIndex = 1;\n redIndex <= this._maxSideIndex;\n ++redIndex, ++trackerProgress\n ) {\n if (tracker.shouldNotify(trackerProgress)) {\n yield {\n progress: tracker.progress,\n };\n }\n\n fillArray1D<number>(area, this._sideSize, 0);\n fillArray1D<number>(areaRed, this._sideSize, 0);\n fillArray1D<number>(areaGreen, this._sideSize, 0);\n fillArray1D<number>(areaBlue, this._sideSize, 0);\n fillArray1D<number>(areaAlpha, this._sideSize, 0);\n fillArray1D<number>(area2, this._sideSize, 0);\n\n for (\n let greenIndex = 1;\n greenIndex <= this._maxSideIndex;\n ++greenIndex\n ) {\n let line = 0;\n let lineRed = 0;\n let lineGreen = 0;\n let lineBlue = 0;\n let lineAlpha = 0;\n let line2 = 0.0;\n\n for (\n let blueIndex = 1;\n blueIndex <= this._maxSideIndex;\n ++blueIndex\n ) {\n line += this._weights[alphaIndex][redIndex][greenIndex][blueIndex];\n lineRed +=\n this._momentsRed[alphaIndex][redIndex][greenIndex][blueIndex];\n lineGreen +=\n this._momentsGreen[alphaIndex][redIndex][greenIndex][blueIndex];\n lineBlue +=\n this._momentsBlue[alphaIndex][redIndex][greenIndex][blueIndex];\n lineAlpha +=\n this._momentsAlpha[alphaIndex][redIndex][greenIndex][blueIndex];\n line2 += this._moments[alphaIndex][redIndex][greenIndex][blueIndex];\n\n area[blueIndex] += line;\n areaRed[blueIndex] += lineRed;\n areaGreen[blueIndex] += lineGreen;\n areaBlue[blueIndex] += lineBlue;\n areaAlpha[blueIndex] += lineAlpha;\n area2[blueIndex] += line2;\n\n xarea[redIndex][greenIndex][blueIndex] =\n xarea[redIndex - 1][greenIndex][blueIndex] + area[blueIndex];\n xareaRed[redIndex][greenIndex][blueIndex] =\n xareaRed[redIndex - 1][greenIndex][blueIndex] +\n areaRed[blueIndex];\n xareaGreen[redIndex][greenIndex][blueIndex] =\n xareaGreen[redIndex - 1][greenIndex][blueIndex] +\n areaGreen[blueIndex];\n xareaBlue[redIndex][greenIndex][blueIndex] =\n xareaBlue[redIndex - 1][greenIndex][blueIndex] +\n areaBlue[blueIndex];\n xareaAlpha[redIndex][greenIndex][blueIndex] =\n xareaAlpha[redIndex - 1][greenIndex][blueIndex] +\n areaAlpha[blueIndex];\n xarea2[redIndex][greenIndex][blueIndex] =\n xarea2[redIndex - 1][greenIndex][blueIndex] + area2[blueIndex];\n\n this._weights[alphaIndex][redIndex][greenIndex][blueIndex] =\n this._weights[alphaIndex - 1][redIndex][greenIndex][blueIndex] +\n xarea[redIndex][greenIndex][blueIndex];\n this._momentsRed[alphaIndex][redIndex][greenIndex][blueIndex] =\n this._momentsRed[alphaIndex - 1][redIndex][greenIndex][\n blueIndex\n ] + xareaRed[redIndex][greenIndex][blueIndex];\n this._momentsGreen[alphaIndex][redIndex][greenIndex][blueIndex] =\n this._momentsGreen[alphaIndex - 1][redIndex][greenIndex][\n blueIndex\n ] + xareaGreen[redIndex][greenIndex][blueIndex];\n this._momentsBlue[alphaIndex][redIndex][greenIndex][blueIndex] =\n this._momentsBlue[alphaIndex - 1][redIndex][greenIndex][\n blueIndex\n ] + xareaBlue[redIndex][greenIndex][blueIndex];\n this._momentsAlpha[alphaIndex][redIndex][greenIndex][blueIndex] =\n this._momentsAlpha[alphaIndex - 1][redIndex][greenIndex][\n blueIndex\n ] + xareaAlpha[redIndex][greenIndex][blueIndex];\n this._moments[alphaIndex][redIndex][greenIndex][blueIndex] =\n this._moments[alphaIndex - 1][redIndex][greenIndex][blueIndex] +\n xarea2[redIndex][greenIndex][blueIndex];\n }\n }\n }\n }\n }\n\n /**\n * Computes the volume of the cube in a specific moment.\n */\n private static _volumeFloat(cube: WuColorCube, moment: number[][][][]) {\n return (\n moment[cube.alphaMaximum][cube.redMaximum][cube.greenMaximum][\n cube.blueMaximum\n ] -\n moment[cube.alphaMaximum][cube.redMaximum][cube.greenMinimum][\n cube.blueMaximum\n ] -\n moment[cube.alphaMaximum][cube.redMinimum][cube.greenMaximum][\n cube.blueMaximum\n ] +\n moment[cube.alphaMaximum][cube.redMinimum][cube.greenMinimum][\n cube.blueMaximum\n ] -\n moment[cube.alphaMinimum][cube.redMaximum][cube.greenMaximum][\n cube.blueMaximum\n ] +\n moment[cube.alphaMinimum][cube.redMaximum][cube.greenMinimum][\n cube.blueMaximum\n ] +\n moment[cube.alphaMinimum][cube.redMinimum][cube.greenMaximum][\n cube.blueMaximum\n ] -\n moment[cube.alphaMinimum][cube.redMinimum][cube.greenMinimum][\n cube.blueMaximum\n ] -\n (moment[cube.alphaMaximum][cube.redMaximum][cube.greenMaximum][\n cube.blueMinimum\n ] -\n moment[cube.alphaMinimum][cube.redMaximum][cube.greenMaximum][\n cube.blueMinimum\n ] -\n moment[cube.alphaMaximum][cube.redMaximum][cube.greenMinimum][\n cube.blueMinimum\n ] +\n moment[cube.alphaMinimum][cube.redMaximum][cube.greenMinimum][\n cube.blueMinimum\n ] -\n moment[cube.alphaMaximum][cube.redMinimum][cube.greenMaximum][\n cube.blueMinimum\n ] +\n moment[cube.alphaMinimum][cube.redMinimum][cube.greenMaximum][\n cube.blueMinimum\n ] +\n moment[cube.alphaMaximum][cube.redMinimum][cube.greenMinimum][\n cube.blueMinimum\n ] -\n moment[cube.alphaMinimum][cube.redMinimum][cube.greenMinimum][\n cube.blueMinimum\n ])\n );\n }\n\n /**\n * Computes the volume of the cube in a specific moment.\n */\n private static _volume(cube: WuColorCube, moment: number[][][][]) {\n return WuQuant._volumeFloat(cube, moment) | 0;\n }\n\n /**\n * Splits the cube in given position][and color direction.\n */\n private static _top(\n cube: WuColorCube,\n direction: number,\n position: number,\n moment: number[][][][],\n ) {\n let result;\n switch (direction) {\n case WuQuant._alpha:\n result =\n moment[position][cube.redMaximum][cube.greenMaximum][\n cube.blueMaximum\n ] -\n moment[position][cube.redMaximum][cube.greenMinimum][\n cube.blueMaximum\n ] -\n moment[position][cube.redMinimum][cube.greenMaximum][\n cube.blueMaximum\n ] +\n moment[position][cube.redMinimum][cube.greenMinimum][\n cube.blueMaximum\n ] -\n (moment[position][cube.redMaximum][cube.greenMaximum][\n cube.blueMinimum\n ] -\n moment[position][cube.redMaximum][cube.greenMinimum][\n cube.blueMinimum\n ] -\n moment[position][cube.redMinimum][cube.greenMaximum][\n cube.blueMinimum\n ] +\n moment[position][cube.redMinimum][cube.greenMinimum][\n cube.blueMinimum\n ]);\n break;\n\n case WuQuant._red:\n result =\n moment[cube.alphaMaximum][position][cube.greenMaximum][\n cube.blueMaximum\n ] -\n moment[cube.alphaMaximum][position][cube.greenMinimum][\n cube.blueMaximum\n ] -\n moment[cube.alphaMinimum][position][cube.greenMaximum][\n cube.blueMaximum\n ] +\n moment[cube.alphaMinimum][position][cube.greenMinimum][\n cube.blueMaximum\n ] -\n (moment[cube.alphaMaximum][position][cube.greenMaximum][\n cube.blueMinimum\n ] -\n moment[cube.alphaMaximum][position][cube.greenMinimum][\n cube.blueMinimum\n ] -\n moment[cube.alphaMinimum][position][cube.greenMaximum][\n cube.blueMinimum\n ] +\n moment[cube.alphaMinimum][position][cube.greenMinimum][\n cube.blueMinimum\n ]);\n break;\n\n case WuQuant._green:\n result =\n moment[cube.alphaMaximum][cube.redMaximum][position][\n cube.blueMaximum\n ] -\n moment[cube.alphaMaximum][cube.redMinimum][position][\n cube.blueMaximum\n ] -\n moment[cube.alphaMinimum][cube.redMaximum][position][\n cube.blueMaximum\n ] +\n moment[cube.alphaMinimum][cube.redMinimum][position][\n cube.blueMaximum\n ] -\n (moment[cube.alphaMaximum][cube.redMaximum][position][\n cube.blueMinimum\n ] -\n moment[cube.alphaMaximum][cube.redMinimum][position][\n cube.blueMinimum\n ] -\n moment[cube.alphaMinimum][cube.redMaximum][position][\n cube.blueMinimum\n ] +\n moment[cube.alphaMinimum][cube.redMinimum][position][\n cube.blueMinimum\n ]);\n break;\n\n case WuQuant._blue:\n result =\n moment[cube.alphaMaximum][cube.redMaximum][cube.greenMaximum][\n position\n ] -\n moment[cube.alphaMaximum][cube.redMaximum][cube.greenMinimum][\n position\n ] -\n moment[cube.alphaMaximum][cube.redMinimum][cube.greenMaximum][\n position\n ] +\n moment[cube.alphaMaximum][cube.redMinimum][cube.greenMinimum][\n position\n ] -\n (moment[cube.alphaMinimum][cube.redMaximum][cube.greenMaximum][\n position\n ] -\n moment[cube.alphaMinimum][cube.redMaximum][cube.greenMinimum][\n position\n ] -\n moment[cube.alphaMinimum][cube.redMinimum][cube.greenMaximum][\n position\n ] +\n moment[cube.alphaMinimum][cube.redMinimum][cube.greenMinimum][\n position\n ]);\n break;\n default:\n throw new Error('impossible');\n }\n\n return result | 0;\n }\n\n /**\n * Splits the cube in a given color direction at its minimum.\n */\n private static _bottom(\n cube: WuColorCube,\n direction: number,\n moment: number[][][][],\n ) {\n switch (direction) {\n case WuQuant._alpha:\n return (\n -moment[cube.alphaMinimum][cube.redMaximum][cube.greenMaximum][\n cube.blueMaximum\n ] +\n moment[cube.alphaMinimum][cube.redMaximum][cube.greenMinimum][\n cube.blueMaximum\n ] +\n moment[cube.alphaMinimum][cube.redMinimum][cube.greenMaximum][\n cube.blueMaximum\n ] -\n moment[cube.alphaMinimum][cube.redMinimum][cube.greenMinimum][\n cube.blueMaximum\n ] -\n (-moment[cube.alphaMinimum][cube.redMaximum][cube.greenMaximum][\n cube.blueMinimum\n ] +\n moment[cube.alphaMinimum][cube.redMaximum][cube.greenMinimum][\n cube.blueMinimum\n ] +\n moment[cube.alphaMinimum][cube.redMinimum][cube.greenMaximum][\n cube.blueMinimum\n ] -\n moment[cube.alphaMinimum][cube.redMinimum][cube.greenMinimum][\n cube.blueMinimum\n ])\n );\n\n case WuQuant._red:\n return (\n -moment[cube.alphaMaximum][cube.redMinimum][cube.greenMaximum][\n cube.blueMaximum\n ] +\n moment[cube.alphaMaximum][cube.redMinimum][cube.greenMinimum][\n cube.blueMaximum\n ] +\n moment[cube.alphaMinimum][cube.redMinimum][cube.greenMaximum][\n cube.blueMaximum\n ] -\n moment[cube.alphaMinimum][cube.redMinimum][cube.greenMinimum][\n cube.blueMaximum\n ] -\n (-moment[cube.alphaMaximum][cube.redMinimum][cube.greenMaximum][\n cube.blueMinimum\n ] +\n moment[cube.alphaMaximum][cube.redMinimum][cube.greenMinimum][\n cube.blueMinimum\n ] +\n moment[cube.alphaMinimum][cube.redMinimum][cube.greenMaximum][\n cube.blueMinimum\n ] -\n moment[cube.alphaMinimum][cube.redMinimum][cube.greenMinimum][\n cube.blueMinimum\n ])\n );\n\n case WuQuant._green:\n return (\n -moment[cube.alphaMaximum][cube.redMaximum][cube.greenMinimum][\n cube.blueMaximum\n ] +\n moment[cube.alphaMaximum][cube.redMinimum][cube.greenMinimum][\n cube.blueMaximum\n ] +\n moment[cube.alphaMinimum][cube.redMaximum][cube.greenMinimum][\n cube.blueMaximum\n ] -\n moment[cube.alphaMinimum][cube.redMinimum][cube.greenMinimum][\n cube.blueMaximum\n ] -\n (-moment[cube.alphaMaximum][cube.redMaximum][cube.greenMinimum][\n cube.blueMinimum\n ] +\n moment[cube.alphaMaximum][cube.redMinimum][cube.greenMinimum][\n cube.blueMinimum\n ] +\n moment[cube.alphaMinimum][cube.redMaximum][cube.greenMinimum][\n cube.blueMinimum\n ] -\n moment[cube.alphaMinimum][cube.redMinimum][cube.greenMinimum][\n cube.blueMinimum\n ])\n );\n\n case WuQuant._blue:\n return (\n -moment[cube.alphaMaximum][cube.redMaximum][cube.greenMaximum][\n cube.blueMinimum\n ] +\n moment[cube.alphaMaximum][cube.redMaximum][cube.greenMinimum][\n cube.blueMinimum\n ] +\n moment[cube.alphaMaximum][cube.redMinimum][cube.greenMaximum][\n cube.blueMinimum\n ] -\n moment[cube.alphaMaximum][cube.redMinimum][cube.greenMinimum][\n cube.blueMinimum\n ] -\n (-moment[cube.alphaMinimum][cube.redMaximum][cube.greenMaximum][\n cube.blueMinimum\n ] +\n moment[cube.alphaMinimum][cube.redMaximum][cube.greenMinimum][\n cube.blueMinimum\n ] +\n moment[cube.alphaMinimum][cube.redMinimum][cube.greenMaximum][\n cube.blueMinimum\n ] -\n moment[cube.alphaMinimum][cube.redMinimum][cube.greenMinimum][\n cube.blueMinimum\n ])\n );\n\n default:\n // TODO: why here is return 0, and in this._top there is no default at all (now it is throw error)?\n return 0;\n }\n }\n\n /**\n * Calculates statistical variance for a given cube.\n */\n private _calculateVariance(cube: WuColorCube) {\n const volumeRed = WuQuant._volume(cube, this._momentsRed);\n const volumeGreen = WuQuant._volume(cube, this._momentsGreen);\n const volumeBlue = WuQuant._volume(cube, this._momentsBlue);\n const volumeAlpha = WuQuant._volume(cube, this._momentsAlpha);\n const volumeMoment = WuQuant._volumeFloat(cube, this._moments);\n const volumeWeight = WuQuant._volume(cube, this._weights);\n const distance =\n volumeRed * volumeRed +\n volumeGreen * volumeGreen +\n volumeBlue * volumeBlue +\n volumeAlpha * volumeAlpha;\n\n return volumeMoment - distance / volumeWeight;\n }\n\n /**\n * Finds the optimal (maximal) position for the cut.\n */\n private _maximize(\n cube: WuColorCube,\n direction: number,\n first: number,\n last: number,\n wholeRed: number,\n wholeGreen: number,\n wholeBlue: number,\n wholeAlpha: number,\n wholeWeight: number,\n ) {\n const bottomRed = WuQuant._bottom(cube, direction, this._momentsRed) | 0;\n const bottomGreen =\n WuQuant._bottom(cube, direction, this._momentsGreen) | 0;\n const bottomBlue = WuQuant._bottom(cube, direction, this._momentsBlue) | 0;\n const bottomAlpha =\n WuQuant._bottom(cube, direction, this._momentsAlpha) | 0;\n const bottomWeight = WuQuant._bottom(cube, direction, this._weights) | 0;\n\n let result = 0.0;\n let cutPosition = -1;\n\n for (let position = first; position < last; ++position) {\n // determines the cube cut at a certain position\n let halfRed =\n bottomRed + WuQuant._top(cube, direction, position, this._momentsRed);\n let halfGreen =\n bottomGreen +\n WuQuant._top(cube, direction, position, this._momentsGreen);\n let halfBlue =\n bottomBlue + WuQuant._top(cube, direction, position, this._momentsBlue);\n let halfAlpha =\n bottomAlpha +\n WuQuant._top(cube, direction, position, this._momentsAlpha);\n let halfWeight =\n bottomWeight + WuQuant._top(cube, direction, position, this._weights);\n\n // the cube cannot be cut at bottom (this would lead to empty cube)\n if (halfWeight !== 0) {\n let halfDistance =\n halfRed * halfRed +\n halfGreen * halfGreen +\n halfBlue * halfBlue +\n halfAlpha * halfAlpha;\n let temp = halfDistance / halfWeight;\n\n halfRed = wholeRed - halfRed;\n halfGreen = wholeGreen - halfGreen;\n halfBlue = wholeBlue - halfBlue;\n halfAlpha = wholeAlpha - halfAlpha;\n halfWeight = wholeWeight - halfWeight;\n\n if (halfWeight !== 0) {\n halfDistance =\n halfRed * halfRed +\n halfGreen * halfGreen +\n halfBlue * halfBlue +\n halfAlpha * halfAlpha;\n temp += halfDistance / halfWeight;\n\n if (temp > result) {\n result = temp;\n cutPosition = position;\n }\n }\n }\n }\n\n return { max: result, position: cutPosition };\n }\n\n // Cuts a cube with another one.\n private _cut(first: WuColorCube, second: WuColorCube) {\n let direction;\n\n const wholeRed = WuQuant._volume(first, this._momentsRed);\n const wholeGreen = WuQuant._volume(first, this._momentsGreen);\n const wholeBlue = WuQuant._volume(first, this._momentsBlue);\n const wholeAlpha = WuQuant._volume(first, this._momentsAlpha);\n const wholeWeight = WuQuant._volume(first, this._weights);\n\n const red = this._maximize(\n first,\n WuQuant._red,\n first.redMinimum + 1,\n first.redMaximum,\n wholeRed,\n wholeGreen,\n wholeBlue,\n wholeAlpha,\n wholeWeight,\n );\n const green = this._maximize(\n first,\n WuQuant._green,\n first.greenMinimum + 1,\n first.greenMaximum,\n wholeRed,\n wholeGreen,\n wholeBlue,\n wholeAlpha,\n wholeWeight,\n );\n const blue = this._maximize(\n first,\n WuQuant._blue,\n first.blueMinimum + 1,\n first.blueMaximum,\n wholeRed,\n wholeGreen,\n wholeBlue,\n wholeAlpha,\n wholeWeight,\n );\n const alpha = this._maximize(\n first,\n WuQuant._alpha,\n first.alphaMinimum + 1,\n first.alphaMaximum,\n wholeRed,\n wholeGreen,\n wholeBlue,\n wholeAlpha,\n wholeWeight,\n );\n\n if (\n alpha.max >= red.max &&\n alpha.max >= green.max &&\n alpha.max >= blue.max\n ) {\n direction = WuQuant._alpha;\n\n // cannot split empty cube\n if (alpha.position < 0) return false;\n } else if (\n red.max >= alpha.max &&\n red.max >= green.max &&\n red.max >= blue.max\n ) {\n direction = WuQuant._red;\n } else if (\n green.max >= alpha.max &&\n green.max >= red.max &&\n green.max >= blue.max\n ) {\n direction = WuQuant._green;\n } else {\n direction = WuQuant._blue;\n }\n\n second.redMaximum = first.redMaximum;\n second.greenMaximum = first.greenMaximum;\n second.blueMaximum = first.blueMaximum;\n second.alphaMaximum = first.alphaMaximum;\n\n // cuts in a certain direction\n switch (direction) {\n case WuQuant._red:\n second.redMinimum = first.redMaximum = red.position;\n second.greenMinimum = first.greenMinimum;\n second.blueMinimum = first.blueMinimum;\n second.alphaMinimum = first.alphaMinimum;\n break;\n\n case WuQuant._green:\n second.greenMinimum = first.greenMaximum = green.position;\n second.redMinimum = first.redMinimum;\n second.blueMinimum = first.blueMinimum;\n second.alphaMinimum = first.alphaMinimum;\n break;\n\n case WuQuant._blue:\n second.blueMinimum = first.blueMaximum = blue.position;\n second.redMinimum = first.redMinimum;\n second.greenMinimum = first.greenMinimum;\n second.alphaMinimum = first.alphaMinimum;\n break;\n\n case WuQuant._alpha:\n second.alphaMinimum = first.alphaMaximum = alpha.position;\n second.blueMinimum = first.blueMinimum;\n second.redMinimum = first.redMinimum;\n second.greenMinimum = first.greenMinimum;\n break;\n }\n\n // determines the volumes after cut\n first.volume =\n (first.redMaximum - first.redMinimum) *\n (first.greenMaximum - first.greenMinimum) *\n (first.blueMaximum - first.blueMinimum) *\n (first.alphaMaximum - first.alphaMinimum);\n second.volume =\n (second.redMaximum - second.redMinimum) *\n (second.greenMaximum - second.greenMinimum) *\n (second.blueMaximum - second.blueMinimum) *\n (second.alphaMaximum - second.alphaMinimum);\n\n // the cut was successful\n return true;\n }\n\n private _initialize(colors: number) {\n this._colors = colors;\n\n // creates all the _cubes\n this._cubes = [];\n\n // initializes all the _cubes\n for (let cubeIndex = 0; cubeIndex < colors; cubeIndex++) {\n this._cubes[cubeIndex] = new WuColorCube();\n }\n\n // resets the reference minimums\n this._cubes[0].redMinimum = 0;\n this._cubes[0].greenMinimum = 0;\n this._cubes[0].blueMinimum = 0;\n this._cubes[0].alphaMinimum = 0;\n\n // resets the reference maximums\n this._cubes[0].redMaximum = this._maxSideIndex;\n this._cubes[0].greenMaximum = this._maxSideIndex;\n this._cubes[0].blueMaximum = this._maxSideIndex;\n this._cubes[0].alphaMaximum = this._alphaMaxSideIndex;\n\n this._weights = createArray4D(\n this._alphaSideSize,\n this._sideSize,\n this._sideSize,\n this._sideSize,\n );\n this._momentsRed = createArray4D(\n this._alphaSideSize,\n this._sideSize,\n this._sideSize,\n this._sideSize,\n );\n this._momentsGreen = createArray4D(\n this._alphaSideSize,\n this._sideSize,\n this._sideSize,\n this._sideSize,\n );\n this._momentsBlue = createArray4D(\n this._alphaSideSize,\n this._sideSize,\n this._sideSize,\n this._sideSize,\n );\n this._momentsAlpha = createArray4D(\n this._alphaSideSize,\n this._sideSize,\n this._sideSize,\n this._sideSize,\n );\n this._moments = createArray4D(\n this._alphaSideSize,\n this._sideSize,\n this._sideSize,\n this._sideSize,\n );\n\n this._table = [];\n for (let tableIndex = 0; tableIndex < 256; ++tableIndex) {\n this._table[tableIndex] = tableIndex * tableIndex;\n }\n\n this._pixels = [];\n }\n\n private _setQuality(significantBitsPerChannel = 5) {\n this._significantBitsPerChannel = significantBitsPerChannel;\n this._maxSideIndex = 1 << this._significantBitsPerChannel;\n this._alphaMaxSideIndex = this._maxSideIndex;\n\n this._sideSize = this._maxSideIndex + 1;\n this._alphaSideSize = this._alphaMaxSideIndex + 1;\n }\n}\n", "/**\n * @preserve\n * Copyright 2015-2018 Igor Bezkrovnyi\n * All rights reserved. (MIT Licensed)\n *\n * iq.ts - Image Quantization Library\n */\n\nexport { AbstractImageQuantizer } from './imageQuantizer';\nexport { ImageQuantizerYieldValue } from './imageQuantizerYieldValue';\nexport { NearestColor } from './nearestColor';\nexport { ErrorDiffusionArray, ErrorDiffusionArrayKernel } from './array';\nexport { ErrorDiffusionRiemersma } from './riemersma';\n", "/**\n * @preserve\n * Copyright 2015-2018 Igor Bezkrovnyi\n * All rights reserved. (MIT Licensed)\n *\n * common.ts - part of Image Quantization Library\n */\nimport { PointContainer } from '../utils/pointContainer';\nimport { Palette } from '../utils/palette';\nimport { ImageQuantizerYieldValue } from './imageQuantizerYieldValue';\n\nexport abstract class AbstractImageQuantizer {\n abstract quantize(\n pointContainer: PointContainer,\n palette: Palette,\n ): IterableIterator<ImageQuantizerYieldValue>;\n\n quantizeSync(pointContainer: PointContainer, palette: Palette) {\n for (const value of this.quantize(pointContainer, palette)) {\n if (value.pointContainer) {\n return value.pointContainer;\n }\n }\n\n throw new Error('unreachable');\n }\n}\n", "/**\n * @preserve\n * Copyright 2015-2018 Igor Bezkrovnyi\n * All rights reserved. (MIT Licensed)\n *\n * nearestColor.ts - part of Image Quantization Library\n */\nimport { AbstractImageQuantizer } from './imageQuantizer';\nimport { AbstractDistanceCalculator } from '../distance/distanceCalculator';\nimport { PointContainer } from '../utils/pointContainer';\nimport { Palette } from '../utils/palette';\nimport { ImageQuantizerYieldValue } from './imageQuantizerYieldValue';\nimport { ProgressTracker } from '../utils/progressTracker';\n\nexport class NearestColor extends AbstractImageQuantizer {\n private _distance: AbstractDistanceCalculator;\n\n constructor(colorDistanceCalculator: AbstractDistanceCalculator) {\n super();\n this._distance = colorDistanceCalculator;\n }\n\n /**\n * Mutates pointContainer\n */\n *quantize(\n pointContainer: PointContainer,\n palette: Palette,\n ): IterableIterator<ImageQuantizerYieldValue> {\n const pointArray = pointContainer.getPointArray();\n const width = pointContainer.getWidth();\n const height = pointContainer.getHeight();\n\n const tracker = new ProgressTracker(height, 99);\n for (let y = 0; y < height; y++) {\n if (tracker.shouldNotify(y)) {\n yield {\n progress: tracker.progress,\n };\n }\n for (let x = 0, idx = y * width; x < width; x++, idx++) {\n // Image pixel\n const point = pointArray[idx];\n // Reduced pixel\n point.from(palette.getNearestColor(this._distance, point));\n }\n }\n\n yield {\n pointContainer,\n progress: 100,\n };\n }\n}\n", "/**\n * @preserve\n * Copyright 2015-2018 Igor Bezkrovnyi\n * All rights reserved. (MIT Licensed)\n *\n * ditherErrorDiffusionArray.ts - part of Image Quantization Library\n */\nimport { AbstractImageQuantizer } from './imageQuantizer';\nimport { AbstractDistanceCalculator } from '../distance/distanceCalculator';\nimport { PointContainer } from '../utils/pointContainer';\nimport { Palette } from '../utils/palette';\nimport { Point } from '../utils/point';\nimport { inRange0to255Rounded } from '../utils/arithmetic';\nimport { ImageQuantizerYieldValue } from './imageQuantizerYieldValue';\nimport { ProgressTracker } from '../utils/progressTracker';\n\n// TODO: is it the best name for this enum \"kernel\"?\nexport enum ErrorDiffusionArrayKernel {\n FloydSteinberg = 0,\n FalseFloydSteinberg,\n Stucki,\n Atkinson,\n Jarvis,\n Burkes,\n Sierra,\n TwoSierra,\n SierraLite,\n}\n\n// http://www.tannerhelland.com/4660/dithering-eleven-algorithms-source-code/\nexport class ErrorDiffusionArray extends AbstractImageQuantizer {\n private _minColorDistance: number;\n private _serpentine: boolean;\n private _kernel!: number[][];\n /** true = GIMP, false = XNVIEW */\n private _calculateErrorLikeGIMP: boolean;\n\n private _distance: AbstractDistanceCalculator;\n\n constructor(\n colorDistanceCalculator: AbstractDistanceCalculator,\n kernel: ErrorDiffusionArrayKernel,\n serpentine = true,\n minimumColorDistanceToDither = 0,\n calculateErrorLikeGIMP = false,\n ) {\n super();\n this._setKernel(kernel);\n\n this._distance = colorDistanceCalculator;\n this._minColorDistance = minimumColorDistanceToDither;\n this._serpentine = serpentine;\n this._calculateErrorLikeGIMP = calculateErrorLikeGIMP;\n }\n\n /**\n * adapted from http://jsbin.com/iXofIji/2/edit by PAEz\n * fixed version. it doesn't use image pixels as error storage, also it doesn't have 0.3 + 0.3 + 0.3 + 0.3 = 0 error\n * Mutates pointContainer\n */\n *quantize(\n pointContainer: PointContainer,\n palette: Palette,\n ): IterableIterator<ImageQuantizerYieldValue> {\n const pointArray = pointContainer.getPointArray();\n const originalPoint = new Point();\n const width = pointContainer.getWidth();\n const height = pointContainer.getHeight();\n const errorLines: number[][][] = [];\n\n let dir = 1;\n let maxErrorLines = 1;\n\n // initial error lines (number is taken from dithering kernel)\n for (const kernel of this._kernel) {\n const kernelErrorLines = kernel[2] + 1;\n if (maxErrorLines < kernelErrorLines) maxErrorLines = kernelErrorLines;\n }\n for (let i = 0; i < maxErrorLines; i++) {\n this._fillErrorLine((errorLines[i] = []), width);\n }\n\n const tracker = new ProgressTracker(height, 99);\n for (let y = 0; y < height; y++) {\n if (tracker.shouldNotify(y)) {\n yield {\n progress: tracker.progress,\n };\n }\n\n // always serpentine\n if (this._serpentine) dir *= -1;\n\n const lni = y * width;\n const xStart = dir === 1 ? 0 : width - 1;\n const xEnd = dir === 1 ? width : -1;\n\n // cyclic shift with erasing\n this._fillErrorLine(errorLines[0], width);\n // TODO: why it is needed to cast types here?\n errorLines.push(errorLines.shift() as number[][]);\n\n const errorLine = errorLines[0];\n for (\n let x = xStart, idx = lni + xStart;\n x !== xEnd;\n x += dir, idx += dir\n ) {\n // Image pixel\n const point = pointArray[idx];\n // originalPoint = new Utils.Point(),\n const error = errorLine[x];\n\n originalPoint.from(point);\n\n const correctedPoint = Point.createByRGBA(\n inRange0to255Rounded(point.r + error[0]),\n inRange0to255Rounded(point.g + error[1]),\n inRange0to255Rounded(point.b + error[2]),\n inRange0to255Rounded(point.a + error[3]),\n );\n\n // Reduced pixel\n const palettePoint = palette.getNearestColor(\n this._distance,\n correctedPoint,\n );\n point.from(palettePoint);\n\n // dithering strength\n if (this._minColorDistance) {\n const dist = this._distance.calculateNormalized(\n originalPoint,\n palettePoint,\n );\n if (dist < this._minColorDistance) continue;\n }\n\n // Component distance\n let er;\n let eg;\n let eb;\n let ea;\n if (this._calculateErrorLikeGIMP) {\n er = correctedPoint.r - palettePoint.r;\n eg = correctedPoint.g - palettePoint.g;\n eb = correctedPoint.b - palettePoint.b;\n ea = correctedPoint.a - palettePoint.a;\n } else {\n er = originalPoint.r - palettePoint.r;\n eg = originalPoint.g - palettePoint.g;\n eb = originalPoint.b - palettePoint.b;\n ea = originalPoint.a - palettePoint.a;\n }\n\n const dStart = dir === 1 ? 0 : this._kernel.length - 1;\n const dEnd = dir === 1 ? this._kernel.length : -1;\n\n for (let i = dStart; i !== dEnd; i += dir) {\n const x1 = this._kernel[i][1] * dir;\n const y1 = this._kernel[i][2];\n\n if (x1 + x >= 0 && x1 + x < width && y1 + y >= 0 && y1 + y < height) {\n const d = this._kernel[i][0];\n const e = errorLines[y1][x1 + x];\n\n e[0] += er * d;\n e[1] += eg * d;\n e[2] += eb * d;\n e[3] += ea * d;\n }\n }\n }\n }\n\n yield {\n pointContainer,\n progress: 100,\n };\n }\n\n private _fillErrorLine(errorLine: number[][], width: number) {\n // shrink\n if (errorLine.length > width) {\n errorLine.length = width;\n }\n\n // reuse existing arrays\n const l = errorLine.length;\n for (let i = 0; i < l; i++) {\n const error = errorLine[i];\n error[0] = error[1] = error[2] = error[3] = 0;\n }\n\n // create missing arrays\n for (let i = l; i < width; i++) {\n errorLine[i] = [0.0, 0.0, 0.0, 0.0];\n }\n }\n\n private _setKernel(kernel: ErrorDiffusionArrayKernel) {\n switch (kernel) {\n case ErrorDiffusionArrayKernel.FloydSteinberg:\n this._kernel = [\n [7 / 16, 1, 0],\n [3 / 16, -1, 1],\n [5 / 16, 0, 1],\n [1 / 16, 1, 1],\n ];\n break;\n\n case ErrorDiffusionArrayKernel.FalseFloydSteinberg:\n this._kernel = [\n [3 / 8, 1, 0],\n [3 / 8, 0, 1],\n [2 / 8, 1, 1],\n ];\n break;\n\n case ErrorDiffusionArrayKernel.Stucki:\n this._kernel = [\n [8 / 42, 1, 0],\n [4 / 42, 2, 0],\n [2 / 42, -2, 1],\n [4 / 42, -1, 1],\n [8 / 42, 0, 1],\n [4 / 42, 1, 1],\n [2 / 42, 2, 1],\n [1 / 42, -2, 2],\n [2 / 42, -1, 2],\n [4 / 42, 0, 2],\n [2 / 42, 1, 2],\n [1 / 42, 2, 2],\n ];\n break;\n\n case ErrorDiffusionArrayKernel.Atkinson:\n this._kernel = [\n [1 / 8, 1, 0],\n [1 / 8, 2, 0],\n [1 / 8, -1, 1],\n [1 / 8, 0, 1],\n [1 / 8, 1, 1],\n [1 / 8, 0, 2],\n ];\n break;\n\n case ErrorDiffusionArrayKernel.Jarvis:\n this._kernel = [\n // Jarvis, Judice, and Ninke / JJN?\n [7 / 48, 1, 0],\n [5 / 48, 2, 0],\n [3 / 48, -2, 1],\n [5 / 48, -1, 1],\n [7 / 48, 0, 1],\n [5 / 48, 1, 1],\n [3 / 48, 2, 1],\n [1 / 48, -2, 2],\n [3 / 48, -1, 2],\n [5 / 48, 0, 2],\n [3 / 48, 1, 2],\n [1 / 48, 2, 2],\n ];\n break;\n\n case ErrorDiffusionArrayKernel.Burkes:\n this._kernel = [\n [8 / 32, 1, 0],\n [4 / 32, 2, 0],\n [2 / 32, -2, 1],\n [4 / 32, -1, 1],\n [8 / 32, 0, 1],\n [4 / 32, 1, 1],\n [2 / 32, 2, 1],\n ];\n break;\n\n case ErrorDiffusionArrayKernel.Sierra:\n this._kernel = [\n [5 / 32, 1, 0],\n [3 / 32, 2, 0],\n [2 / 32, -2, 1],\n [4 / 32, -1, 1],\n [5 / 32, 0, 1],\n [4 / 32, 1, 1],\n [2 / 32, 2, 1],\n [2 / 32, -1, 2],\n [3 / 32, 0, 2],\n [2 / 32, 1, 2],\n ];\n break;\n\n case ErrorDiffusionArrayKernel.TwoSierra:\n this._kernel = [\n [4 / 16, 1, 0],\n [3 / 16, 2, 0],\n [1 / 16, -2, 1],\n [2 / 16, -1, 1],\n [3 / 16, 0, 1],\n [2 / 16, 1, 1],\n [1 / 16, 2, 1],\n ];\n break;\n\n case ErrorDiffusionArrayKernel.SierraLite:\n this._kernel = [\n [2 / 4, 1, 0],\n [1 / 4, -1, 1],\n [1 / 4, 0, 1],\n ];\n break;\n\n default:\n throw new Error(`ErrorDiffusionArray: unknown kernel = ${kernel}`);\n }\n }\n}\n", "import { ProgressTracker } from '../../utils/progressTracker';\nimport { ImageQuantizerYieldValue } from '../imageQuantizerYieldValue';\n\nenum Direction {\n NONE = 0,\n UP,\n LEFT,\n RIGHT,\n DOWN,\n}\n\ninterface Data {\n x: number;\n y: number;\n width: number;\n height: number;\n level: number;\n index: number;\n tracker: ProgressTracker;\n callback(x: number, y: number): void;\n}\n\nexport function* hilbertCurve(\n width: number,\n height: number,\n callback: (x: number, y: number) => void,\n) {\n const maxBound = Math.max(width, height);\n const level = Math.floor(Math.log(maxBound) / Math.log(2) + 1);\n const tracker = new ProgressTracker(width * height, 99);\n const data = {\n width,\n height,\n level,\n callback,\n tracker,\n index: 0,\n x: 0,\n y: 0,\n };\n yield* walkHilbert(data, Direction.UP);\n visit(data, Direction.NONE);\n}\n\nfunction* walkHilbert(\n data: Data,\n direction: Direction,\n): IterableIterator<ImageQuantizerYieldValue> {\n if (data.level < 1) return;\n\n if (data.tracker.shouldNotify(data.index)) {\n yield { progress: data.tracker.progress };\n }\n data.level--;\n switch (direction) {\n case Direction.LEFT:\n yield* walkHilbert(data, Direction.UP);\n visit(data, Direction.RIGHT);\n yield* walkHilbert(data, Direction.LEFT);\n visit(data, Direction.DOWN);\n yield* walkHilbert(data, Direction.LEFT);\n visit(data, Direction.LEFT);\n yield* walkHilbert(data, Direction.DOWN);\n break;\n\n case Direction.RIGHT:\n yield* walkHilbert(data, Direction.DOWN);\n visit(data, Direction.LEFT);\n yield* walkHilbert(data, Direction.RIGHT);\n visit(data, Direction.UP);\n yield* walkHilbert(data, Direction.RIGHT);\n visit(data, Direction.RIGHT);\n yield* walkHilbert(data, Direction.UP);\n break;\n\n case Direction.UP:\n yield* walkHilbert(data, Direction.LEFT);\n visit(data, Direction.DOWN);\n yield* walkHilbert(data, Direction.UP);\n visit(data, Direction.RIGHT);\n yield* walkHilbert(data, Direction.UP);\n visit(data, Direction.UP);\n yield* walkHilbert(data, Direction.RIGHT);\n break;\n\n case Direction.DOWN:\n yield* walkHilbert(data, Direction.RIGHT);\n visit(data, Direction.UP);\n yield* walkHilbert(data, Direction.DOWN);\n visit(data, Direction.LEFT);\n yield* walkHilbert(data, Direction.DOWN);\n visit(data, Direction.DOWN);\n yield* walkHilbert(data, Direction.LEFT);\n break;\n\n default:\n break;\n }\n data.level++;\n}\n\nfunction visit(data: Data, direction: Direction) {\n if (\n data.x >= 0 &&\n data.x < data.width &&\n data.y >= 0 &&\n data.y < data.height\n ) {\n data.callback(data.x, data.y);\n data.index++;\n }\n switch (direction) {\n case Direction.LEFT:\n data.x--;\n break;\n case Direction.RIGHT:\n data.x++;\n break;\n case Direction.UP:\n data.y--;\n break;\n case Direction.DOWN:\n data.y++;\n break;\n }\n}\n", "/**\n * @preserve\n * MIT License\n *\n * Copyright 2015-2018 Igor Bezkrovnyi\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n * THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n *\n * riemersma.ts - part of Image Quantization Library\n */\nimport { AbstractImageQuantizer } from './imageQuantizer';\nimport { hilbertCurve } from './spaceFillingCurves/hilbertCurve';\nimport { AbstractDistanceCalculator } from '../distance/distanceCalculator';\nimport { PointContainer } from '../utils/pointContainer';\nimport { Palette } from '../utils/palette';\nimport { Point } from '../utils/point';\nimport { inRange0to255Rounded } from '../utils/arithmetic';\n\nexport class ErrorDiffusionRiemersma extends AbstractImageQuantizer {\n private _distance: AbstractDistanceCalculator;\n private _weights: number[];\n private _errorQueueSize: number;\n\n constructor(\n colorDistanceCalculator: AbstractDistanceCalculator,\n errorQueueSize = 16,\n errorPropagation = 1,\n ) {\n super();\n this._distance = colorDistanceCalculator;\n this._errorQueueSize = errorQueueSize;\n this._weights = ErrorDiffusionRiemersma._createWeights(\n errorPropagation,\n errorQueueSize,\n );\n }\n\n /**\n * Mutates pointContainer\n */\n *quantize(pointContainer: PointContainer, palette: Palette) {\n const pointArray = pointContainer.getPointArray();\n const width = pointContainer.getWidth();\n const height = pointContainer.getHeight();\n const errorQueue: Array<{\n r: number;\n g: number;\n b: number;\n a: number;\n }> = [];\n\n let head = 0;\n\n for (let i = 0; i < this._errorQueueSize; i++) {\n errorQueue[i] = { r: 0, g: 0, b: 0, a: 0 };\n }\n\n yield* hilbertCurve(width, height, (x, y) => {\n const p = pointArray[x + y * width];\n let { r, g, b, a } = p;\n for (let i = 0; i < this._errorQueueSize; i++) {\n const weight = this._weights[i];\n const e = errorQueue[(i + head) % this._errorQueueSize];\n\n r += e.r * weight;\n g += e.g * weight;\n b += e.b * weight;\n a += e.a * weight;\n }\n\n const correctedPoint = Point.createByRGBA(\n inRange0to255Rounded(r),\n inRange0to255Rounded(g),\n inRange0to255Rounded(b),\n inRange0to255Rounded(a),\n );\n\n const quantizedPoint = palette.getNearestColor(\n this._distance,\n correctedPoint,\n );\n\n // update head and calculate tail\n head = (head + 1) % this._errorQueueSize;\n const tail = (head + this._errorQueueSize - 1) % this._errorQueueSize;\n\n // update error with new value\n errorQueue[tail].r = p.r - quantizedPoint.r;\n errorQueue[tail].g = p.g - quantizedPoint.g;\n errorQueue[tail].b = p.b - quantizedPoint.b;\n errorQueue[tail].a = p.a - quantizedPoint.a;\n\n // update point\n p.from(quantizedPoint);\n });\n\n yield {\n pointContainer,\n progress: 100,\n };\n }\n\n private static _createWeights(\n errorPropagation: number,\n errorQueueSize: number,\n ) {\n const weights = [];\n\n const multiplier = Math.exp(\n Math.log(errorQueueSize) / (errorQueueSize - 1),\n );\n for (let i = 0, next = 1; i < errorQueueSize; i++) {\n weights[i] = (((next + 0.5) | 0) / errorQueueSize) * errorPropagation;\n next *= multiplier;\n }\n\n return weights;\n }\n}\n", "/**\n * @preserve\n * Copyright 2015-2018 Igor Bezkrovnyi\n * All rights reserved. (MIT Licensed)\n *\n * iq.ts - Image Quantization Library\n */\nexport { ssim } from './ssim';\n", "/**\n * @preserve\n * Copyright 2015-2018 Igor Bezkrovnyi\n * All rights reserved. (MIT Licensed)\n *\n * ssim.ts - part of Image Quantization Library\n */\nimport { PointContainer } from '../utils/pointContainer';\nimport { Y } from '../constants/bt709';\n\n// based on https://github.com/rhys-e/structural-similarity\n// http://en.wikipedia.org/wiki/Structural_similarity\nconst K1 = 0.01;\nconst K2 = 0.03;\n\nexport function ssim(image1: PointContainer, image2: PointContainer) {\n if (\n image1.getHeight() !== image2.getHeight() ||\n image1.getWidth() !== image2.getWidth()\n ) {\n throw new Error('Images have different sizes!');\n }\n\n const bitsPerComponent = 8;\n const L = (1 << bitsPerComponent) - 1;\n const c1 = (K1 * L) ** 2;\n const c2 = (K2 * L) ** 2;\n\n let numWindows = 0;\n let mssim = 0.0;\n\n // calculate ssim for each window\n iterate(\n image1,\n image2,\n (lumaValues1, lumaValues2, averageLumaValue1, averageLumaValue2) => {\n // calculate variance and covariance\n let sigxy = 0.0;\n let sigsqx = 0.0;\n let sigsqy = 0.0;\n\n for (let i = 0; i < lumaValues1.length; i++) {\n sigsqx += (lumaValues1[i] - averageLumaValue1) ** 2;\n sigsqy += (lumaValues2[i] - averageLumaValue2) ** 2;\n\n sigxy +=\n (lumaValues1[i] - averageLumaValue1) *\n (lumaValues2[i] - averageLumaValue2);\n }\n\n const numPixelsInWin = lumaValues1.length - 1;\n sigsqx /= numPixelsInWin;\n sigsqy /= numPixelsInWin;\n sigxy /= numPixelsInWin;\n\n // perform ssim calculation on window\n const numerator =\n (2 * averageLumaValue1 * averageLumaValue2 + c1) * (2 * sigxy + c2);\n const denominator =\n (averageLumaValue1 ** 2 + averageLumaValue2 ** 2 + c1) *\n (sigsqx + sigsqy + c2);\n const ssim = numerator / denominator;\n\n mssim += ssim;\n numWindows++;\n },\n );\n return mssim / numWindows;\n}\n\nfunction iterate(\n image1: PointContainer,\n image2: PointContainer,\n callback: (\n lumaValues1: number[],\n lumaValues2: number[],\n averageLumaValue1: number,\n averageLumaValue2: number,\n ) => void,\n) {\n const windowSize = 8;\n const width = image1.getWidth();\n const height = image1.getHeight();\n\n for (let y = 0; y < height; y += windowSize) {\n for (let x = 0; x < width; x += windowSize) {\n // avoid out-of-width/height\n const windowWidth = Math.min(windowSize, width - x);\n const windowHeight = Math.min(windowSize, height - y);\n\n const lumaValues1 = calculateLumaValuesForWindow(\n image1,\n x,\n y,\n windowWidth,\n windowHeight,\n );\n const lumaValues2 = calculateLumaValuesForWindow(\n image2,\n x,\n y,\n windowWidth,\n windowHeight,\n );\n const averageLuma1 = calculateAverageLuma(lumaValues1);\n const averageLuma2 = calculateAverageLuma(lumaValues2);\n\n callback(lumaValues1, lumaValues2, averageLuma1, averageLuma2);\n }\n }\n}\n\nfunction calculateLumaValuesForWindow(\n image: PointContainer,\n x: number,\n y: number,\n width: number,\n height: number,\n) {\n const pointArray = image.getPointArray();\n const lumaValues = [];\n\n let counter = 0;\n\n for (let j = y; j < y + height; j++) {\n const offset = j * image.getWidth();\n for (let i = x; i < x + width; i++) {\n const point = pointArray[offset + i];\n lumaValues[counter] =\n point.r * Y.RED + point.g * Y.GREEN + point.b * Y.BLUE;\n counter++;\n }\n }\n\n return lumaValues;\n}\n\nfunction calculateAverageLuma(lumaValues: number[]) {\n let sumLuma = 0.0;\n for (const luma of lumaValues) {\n sumLuma += luma;\n }\n\n return sumLuma / lumaValues.length;\n}\n", "/**\n * @preserve\n * Copyright 2015-2018 Igor Bezkrovnyi\n * All rights reserved. (MIT Licensed)\n *\n * helper.ts - part of Image Quantization Library\n */\nimport * as distance from './distance';\nimport * as image from './image';\nimport * as palette from './palette';\nimport { AbstractDistanceCalculator } from './distance/distanceCalculator';\nimport { PointContainer } from './utils/pointContainer';\nimport { Palette } from './utils/palette';\n\nconst setImmediateImpl =\n typeof setImmediate === 'function'\n ? setImmediate\n : typeof process !== 'undefined' && typeof process?.nextTick === 'function'\n ? (callback: () => void) => process.nextTick(callback)\n : (callback: () => void) => setTimeout(callback, 0);\n\nexport type ColorDistanceFormula =\n | 'cie94-textiles'\n | 'cie94-graphic-arts'\n | 'ciede2000'\n | 'color-metric'\n | 'euclidean'\n | 'euclidean-bt709-noalpha'\n | 'euclidean-bt709'\n | 'manhattan'\n | 'manhattan-bt709'\n | 'manhattan-nommyde'\n | 'pngquant';\n\nexport type PaletteQuantization =\n | 'neuquant'\n | 'neuquant-float'\n | 'rgbquant'\n | 'wuquant';\n\nexport type ImageQuantization =\n | 'nearest'\n | 'riemersma'\n | 'floyd-steinberg'\n | 'false-floyd-steinberg'\n | 'stucki'\n | 'atkinson'\n | 'jarvis'\n | 'burkes'\n | 'sierra'\n | 'two-sierra'\n | 'sierra-lite';\n\nexport interface ProgressOptions {\n onProgress?: (progress: number) => void;\n}\n\nexport interface ApplyPaletteOptions {\n colorDistanceFormula?: ColorDistanceFormula;\n imageQuantization?: ImageQuantization;\n}\n\nexport interface BuildPaletteOptions {\n colorDistanceFormula?: ColorDistanceFormula;\n paletteQuantization?: PaletteQuantization;\n colors?: number;\n}\n\nexport function buildPaletteSync(\n images: PointContainer[],\n {\n colorDistanceFormula,\n paletteQuantization,\n colors,\n }: BuildPaletteOptions = {},\n) {\n const distanceCalculator =\n colorDistanceFormulaToColorDistance(colorDistanceFormula);\n const paletteQuantizer = paletteQuantizationToPaletteQuantizer(\n distanceCalculator,\n paletteQuantization,\n colors,\n );\n images.forEach((image) => paletteQuantizer.sample(image));\n return paletteQuantizer.quantizeSync();\n}\n\nexport async function buildPalette(\n images: PointContainer[],\n {\n colorDistanceFormula,\n paletteQuantization,\n colors,\n onProgress,\n }: BuildPaletteOptions & ProgressOptions = {},\n) {\n return new Promise<Palette>((resolve, reject) => {\n const distanceCalculator =\n colorDistanceFormulaToColorDistance(colorDistanceFormula);\n const paletteQuantizer = paletteQuantizationToPaletteQuantizer(\n distanceCalculator,\n paletteQuantization,\n colors,\n );\n images.forEach((image) => paletteQuantizer.sample(image));\n\n let palette: Palette;\n const iterator = paletteQuantizer.quantize();\n const next = () => {\n try {\n const result = iterator.next();\n if (result.done) {\n resolve(palette);\n } else {\n if (result.value.palette) palette = result.value.palette;\n if (onProgress) onProgress(result.value.progress);\n setImmediateImpl(next);\n }\n } catch (error) {\n reject(error);\n }\n };\n setImmediateImpl(next);\n });\n}\n\nexport function applyPaletteSync(\n image: PointContainer,\n palette: Palette,\n { colorDistanceFormula, imageQuantization }: ApplyPaletteOptions = {},\n) {\n const distanceCalculator =\n colorDistanceFormulaToColorDistance(colorDistanceFormula);\n const imageQuantizer = imageQuantizationToImageQuantizer(\n distanceCalculator,\n imageQuantization,\n );\n return imageQuantizer.quantizeSync(image, palette);\n}\n\nexport async function applyPalette(\n image: PointContainer,\n palette: Palette,\n {\n colorDistanceFormula,\n imageQuantization,\n onProgress,\n }: ApplyPaletteOptions & ProgressOptions = {},\n) {\n return new Promise<PointContainer>((resolve, reject) => {\n const distanceCalculator =\n colorDistanceFormulaToColorDistance(colorDistanceFormula);\n const imageQuantizer = imageQuantizationToImageQuantizer(\n distanceCalculator,\n imageQuantization,\n );\n\n let outPointContainer: PointContainer;\n const iterator = imageQuantizer.quantize(image, palette);\n const next = () => {\n try {\n const result = iterator.next();\n if (result.done) {\n resolve(outPointContainer);\n } else {\n if (result.value.pointContainer) {\n outPointContainer = result.value.pointContainer;\n }\n if (onProgress) onProgress(result.value.progress);\n setImmediateImpl(next);\n }\n } catch (error) {\n reject(error);\n }\n };\n setImmediateImpl(next);\n });\n}\n\nfunction colorDistanceFormulaToColorDistance(\n colorDistanceFormula: ColorDistanceFormula = 'euclidean-bt709',\n) {\n switch (colorDistanceFormula) {\n case 'cie94-graphic-arts':\n return new distance.CIE94GraphicArts();\n case 'cie94-textiles':\n return new distance.CIE94Textiles();\n case 'ciede2000':\n return new distance.CIEDE2000();\n case 'color-metric':\n return new distance.CMetric();\n case 'euclidean':\n return new distance.Euclidean();\n case 'euclidean-bt709':\n return new distance.EuclideanBT709();\n case 'euclidean-bt709-noalpha':\n return new distance.EuclideanBT709NoAlpha();\n case 'manhattan':\n return new distance.Manhattan();\n case 'manhattan-bt709':\n return new distance.ManhattanBT709();\n case 'manhattan-nommyde':\n return new distance.ManhattanNommyde();\n case 'pngquant':\n return new distance.PNGQuant();\n default:\n throw new Error(`Unknown colorDistanceFormula ${colorDistanceFormula}`);\n }\n}\n\nfunction imageQuantizationToImageQuantizer(\n distanceCalculator: AbstractDistanceCalculator,\n imageQuantization: ImageQuantization = 'floyd-steinberg',\n) {\n switch (imageQuantization) {\n case 'nearest':\n return new image.NearestColor(distanceCalculator);\n case 'riemersma':\n return new image.ErrorDiffusionRiemersma(distanceCalculator);\n case 'floyd-steinberg':\n return new image.ErrorDiffusionArray(\n distanceCalculator,\n image.ErrorDiffusionArrayKernel.FloydSteinberg,\n );\n case 'false-floyd-steinberg':\n return new image.ErrorDiffusionArray(\n distanceCalculator,\n image.ErrorDiffusionArrayKernel.FalseFloydSteinberg,\n );\n case 'stucki':\n return new image.ErrorDiffusionArray(\n distanceCalculator,\n image.ErrorDiffusionArrayKernel.Stucki,\n );\n case 'atkinson':\n return new image.ErrorDiffusionArray(\n distanceCalculator,\n image.ErrorDiffusionArrayKernel.Atkinson,\n );\n case 'jarvis':\n return new image.ErrorDiffusionArray(\n distanceCalculator,\n image.ErrorDiffusionArrayKernel.Jarvis,\n );\n case 'burkes':\n return new image.ErrorDiffusionArray(\n distanceCalculator,\n image.ErrorDiffusionArrayKernel.Burkes,\n );\n case 'sierra':\n return new image.ErrorDiffusionArray(\n distanceCalculator,\n image.ErrorDiffusionArrayKernel.Sierra,\n );\n case 'two-sierra':\n return new image.ErrorDiffusionArray(\n distanceCalculator,\n image.ErrorDiffusionArrayKernel.TwoSierra,\n );\n case 'sierra-lite':\n return new image.ErrorDiffusionArray(\n distanceCalculator,\n image.ErrorDiffusionArrayKernel.SierraLite,\n );\n default:\n throw new Error(`Unknown imageQuantization ${imageQuantization}`);\n }\n}\n\nfunction paletteQuantizationToPaletteQuantizer(\n distanceCalculator: AbstractDistanceCalculator,\n paletteQuantization: PaletteQuantization = 'wuquant',\n colors = 256,\n) {\n switch (paletteQuantization) {\n case 'neuquant':\n return new palette.NeuQuant(distanceCalculator, colors);\n case 'rgbquant':\n return new palette.RGBQuant(distanceCalculator, colors);\n case 'wuquant':\n return new palette.WuQuant(distanceCalculator, colors);\n case 'neuquant-float':\n return new palette.NeuQuantFloat(distanceCalculator, colors);\n default:\n throw new Error(`Unknown paletteQuantization ${paletteQuantization}`);\n }\n}\n", "/**\n * @preserve\n * Copyright 2015-2018 Igor Bezkrovnyi\n * All rights reserved. (MIT Licensed)\n *\n * iq.ts - Image Quantization Library\n */\nimport * as constants from './constants';\nimport * as conversion from './conversion';\nimport * as distance from './distance';\nimport * as palette from './palette';\nimport * as image from './image';\nimport * as quality from './quality';\nimport * as utils from './utils';\n\nexport {\n buildPalette,\n buildPaletteSync,\n applyPalette,\n applyPaletteSync,\n} from './basicAPI';\n\nexport type {\n ImageQuantization,\n PaletteQuantization,\n ColorDistanceFormula,\n} from './basicAPI';\n\nexport { constants, conversion, distance, palette, image, quality, utils };\n"],
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