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Your IP : 3.15.187.189
/*
* NeuQuant Neural-Net Quantization Algorithm
* ------------------------------------------
*
* Copyright (c) 1994 Anthony Dekker
*
* NEUQUANT Neural-Net quantization algorithm by Anthony Dekker, 1994. See
* "Kohonen neural networks for optimal colour quantization" in "Network:
* Computation in Neural Systems" Vol. 5 (1994) pp 351-367. for a discussion of
* the algorithm.
*
* Any party obtaining a copy of these files from the author, directly or
* indirectly, is granted, free of charge, a full and unrestricted irrevocable,
* world-wide, paid up, royalty-free, nonexclusive right and license to deal in
* this software and documentation files (the "Software"), including without
* limitation the rights to use, copy, modify, merge, publish, distribute,
* sublicense, and/or sell copies of the Software, and to permit persons who
* receive copies from any such party to do so, with the only requirement being
* that this copyright notice remain intact.
*/
/**
* @preserve TypeScript port:
* Copyright 2015-2018 Igor Bezkrovnyi
* All rights reserved. (MIT Licensed)
*
* neuquant.ts - part of Image Quantization Library
*/
import { Palette } from '../../utils/palette';
import { Point } from '../../utils/point';
import { PointContainer } from '../../utils/pointContainer';
import { AbstractDistanceCalculator } from '../../distance/distanceCalculator';
import { AbstractPaletteQuantizer } from '../paletteQuantizer';
import { PaletteQuantizerYieldValue } from '../paletteQuantizerYieldValue';
import { ProgressTracker } from '../../utils';
// bias for colour values
const networkBiasShift = 3;
class Neuron {
r: number;
g: number;
b: number;
a: number;
constructor(defaultValue: number) {
this.r = this.g = this.b = this.a = defaultValue;
}
/**
* There is a fix in original NEUQUANT by Anthony Dekker (http://members.ozemail.com.au/~dekker/NEUQUANT.HTML)
* @example
* r = Math.min(255, (neuron.r + (1 << (networkBiasShift - 1))) >> networkBiasShift);
*/
toPoint() {
return Point.createByRGBA(
this.r >> networkBiasShift,
this.g >> networkBiasShift,
this.b >> networkBiasShift,
this.a >> networkBiasShift,
);
}
subtract(r: number, g: number, b: number, a: number) {
this.r -= r | 0;
this.g -= g | 0;
this.b -= b | 0;
this.a -= a | 0;
}
/*
public subtract(r : number, g : number, b : number, a : number) : void {
this.r = (-r + this.r) | 0;
this.g = (-g + this.g) | 0;
this.b = (-b + this.b) | 0;
this.a = (-a + this.a) | 0;
this.r -= r;
this.g -= g;
this.b -= b;
this.a -= a;
this.r -= r | 0;
this.g -= g | 0;
this.b -= b | 0;
this.a -= a | 0;
}
*/
}
export class NeuQuant extends AbstractPaletteQuantizer {
/*
four primes near 500 - assume no image has a length so large
that it is divisible by all four primes
*/
private static readonly _prime1 = 499;
private static readonly _prime2 = 491;
private static readonly _prime3 = 487;
private static readonly _prime4 = 503;
private static readonly _minpicturebytes = NeuQuant._prime4;
// no. of learning cycles
private static readonly _nCycles = 100;
// defs for freq and bias
private static readonly _initialBiasShift = 16;
// bias for fractions
private static readonly _initialBias = 1 << NeuQuant._initialBiasShift;
private static readonly _gammaShift = 10;
// gamma = 1024
// TODO: why gamma is never used?
// private static _gamma : number = (1 << NeuQuant._gammaShift);
private static readonly _betaShift = 10;
private static readonly _beta = NeuQuant._initialBias >> NeuQuant._betaShift;
// beta = 1/1024
private static readonly _betaGamma =
NeuQuant._initialBias << (NeuQuant._gammaShift - NeuQuant._betaShift);
/*
* for 256 cols, radius starts
*/
private static readonly _radiusBiasShift = 6;
// at 32.0 biased by 6 bits
private static readonly _radiusBias = 1 << NeuQuant._radiusBiasShift;
// and decreases by a factor of 1/30 each cycle
private static readonly _radiusDecrease = 30;
/* defs for decreasing alpha factor */
// alpha starts at 1.0
private static readonly _alphaBiasShift = 10;
// biased by 10 bits
private static readonly _initAlpha = 1 << NeuQuant._alphaBiasShift;
/* radBias and alphaRadBias used for radpower calculation */
private static readonly _radBiasShift = 8;
private static readonly _radBias = 1 << NeuQuant._radBiasShift;
private static readonly _alphaRadBiasShift =
NeuQuant._alphaBiasShift + NeuQuant._radBiasShift;
private static readonly _alphaRadBias = 1 << NeuQuant._alphaRadBiasShift;
private _pointArray: Point[];
private readonly _networkSize: number;
private _network!: Neuron[];
/** sampling factor 1..30 */
private readonly _sampleFactor!: number;
private _radPower!: number[];
// bias and freq arrays for learning
private _freq!: number[];
/* for network lookup - really 256 */
private _bias!: number[];
private readonly _distance: AbstractDistanceCalculator;
constructor(
colorDistanceCalculator: AbstractDistanceCalculator,
colors = 256,
) {
super();
this._distance = colorDistanceCalculator;
this._pointArray = [];
this._sampleFactor = 1;
this._networkSize = colors;
this._distance.setWhitePoint(
255 << networkBiasShift,
255 << networkBiasShift,
255 << networkBiasShift,
255 << networkBiasShift,
);
}
sample(pointContainer: PointContainer) {
this._pointArray = this._pointArray.concat(pointContainer.getPointArray());
}
*quantize(): IterableIterator<PaletteQuantizerYieldValue> {
this._init();
yield* this._learn();
yield {
palette: this._buildPalette(),
progress: 100,
};
}
private _init() {
this._freq = [];
this._bias = [];
this._radPower = [];
this._network = [];
for (let i = 0; i < this._networkSize; i++) {
this._network[i] = new Neuron(
((i << (networkBiasShift + 8)) / this._networkSize) | 0,
);
// 1/this._networkSize
this._freq[i] = (NeuQuant._initialBias / this._networkSize) | 0;
this._bias[i] = 0;
}
}
/**
* Main Learning Loop
*/
private *_learn() {
let sampleFactor = this._sampleFactor;
const pointsNumber = this._pointArray.length;
if (pointsNumber < NeuQuant._minpicturebytes) sampleFactor = 1;
const alphadec = (30 + (sampleFactor - 1) / 3) | 0;
const pointsToSample = (pointsNumber / sampleFactor) | 0;
let delta = (pointsToSample / NeuQuant._nCycles) | 0;
let alpha = NeuQuant._initAlpha;
let radius = (this._networkSize >> 3) * NeuQuant._radiusBias;
let rad = radius >> NeuQuant._radiusBiasShift;
if (rad <= 1) rad = 0;
for (let i = 0; i < rad; i++) {
this._radPower[i] =
(alpha * (((rad * rad - i * i) * NeuQuant._radBias) / (rad * rad))) >>>
0;
}
let step;
if (pointsNumber < NeuQuant._minpicturebytes) {
step = 1;
} else if (pointsNumber % NeuQuant._prime1 !== 0) {
step = NeuQuant._prime1;
} else if (pointsNumber % NeuQuant._prime2 !== 0) {
step = NeuQuant._prime2;
} else if (pointsNumber % NeuQuant._prime3 !== 0) {
step = NeuQuant._prime3;
} else {
step = NeuQuant._prime4;
}
const tracker = new ProgressTracker(pointsToSample, 99);
for (let i = 0, pointIndex = 0; i < pointsToSample; ) {
if (tracker.shouldNotify(i)) {
yield {
progress: tracker.progress,
};
}
const point = this._pointArray[pointIndex];
const b = point.b << networkBiasShift;
const g = point.g << networkBiasShift;
const r = point.r << networkBiasShift;
const a = point.a << networkBiasShift;
const neuronIndex = this._contest(b, g, r, a);
this._alterSingle(alpha, neuronIndex, b, g, r, a);
if (rad !== 0) this._alterNeighbour(rad, neuronIndex, b, g, r, a);
/* alter neighbours */
pointIndex += step;
if (pointIndex >= pointsNumber) pointIndex -= pointsNumber;
i++;
if (delta === 0) delta = 1;
if (i % delta === 0) {
alpha -= (alpha / alphadec) | 0;
radius -= (radius / NeuQuant._radiusDecrease) | 0;
rad = radius >> NeuQuant._radiusBiasShift;
if (rad <= 1) rad = 0;
for (let j = 0; j < rad; j++) {
this._radPower[j] =
(alpha *
(((rad * rad - j * j) * NeuQuant._radBias) / (rad * rad))) >>>
0;
}
}
}
}
private _buildPalette() {
const palette = new Palette();
this._network.forEach((neuron) => {
palette.add(neuron.toPoint());
});
palette.sort();
return palette;
}
/**
* Move adjacent neurons by precomputed alpha*(1-((i-j)^2/[r]^2)) in radpower[|i-j|]
*/
private _alterNeighbour(
rad: number,
i: number,
b: number,
g: number,
r: number,
al: number,
) {
let lo = i - rad;
if (lo < -1) lo = -1;
let hi = i + rad;
if (hi > this._networkSize) hi = this._networkSize;
let j = i + 1;
let k = i - 1;
let m = 1;
while (j < hi || k > lo) {
const a = this._radPower[m++] / NeuQuant._alphaRadBias;
if (j < hi) {
const p = this._network[j++];
p.subtract(a * (p.r - r), a * (p.g - g), a * (p.b - b), a * (p.a - al));
}
if (k > lo) {
const p = this._network[k--];
p.subtract(a * (p.r - r), a * (p.g - g), a * (p.b - b), a * (p.a - al));
}
}
}
/**
* Move neuron i towards biased (b,g,r) by factor alpha
*/
private _alterSingle(
alpha: number,
i: number,
b: number,
g: number,
r: number,
a: number,
) {
alpha /= NeuQuant._initAlpha;
/* alter hit neuron */
const n = this._network[i];
n.subtract(
alpha * (n.r - r),
alpha * (n.g - g),
alpha * (n.b - b),
alpha * (n.a - a),
);
}
/**
* Search for biased BGR values
* description:
* finds closest neuron (min dist) and updates freq
* finds best neuron (min dist-bias) and returns position
* for frequently chosen neurons, freq[i] is high and bias[i] is negative
* bias[i] = _gamma*((1/this._networkSize)-freq[i])
*
* Original distance equation:
* dist = abs(dR) + abs(dG) + abs(dB)
*/
private _contest(b: number, g: number, r: number, a: number) {
const multiplier = (255 * 4) << networkBiasShift;
let bestd = ~(1 << 31);
let bestbiasd = bestd;
let bestpos = -1;
let bestbiaspos = bestpos;
for (let i = 0; i < this._networkSize; i++) {
const n = this._network[i];
const dist =
(this._distance.calculateNormalized(n, { r, g, b, a }) * multiplier) |
0;
if (dist < bestd) {
bestd = dist;
bestpos = i;
}
const biasdist =
dist -
(this._bias[i] >> (NeuQuant._initialBiasShift - networkBiasShift));
if (biasdist < bestbiasd) {
bestbiasd = biasdist;
bestbiaspos = i;
}
const betafreq = this._freq[i] >> NeuQuant._betaShift;
this._freq[i] -= betafreq;
this._bias[i] += betafreq << NeuQuant._gammaShift;
}
this._freq[bestpos] += NeuQuant._beta;
this._bias[bestpos] -= NeuQuant._betaGamma;
return bestbiaspos;
}
}