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// @require https://update.greasyfork.org/scripts/428486/1028788/neu-quant.js
/*
* 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.
*/
/*
* This class handles Neural-Net quantization algorithm
* @author Kevin Weiner (original Java version - kweiner@fmsware.com)
* @author Thibault Imbert (AS3 version - bytearray.org)
* @author Kevin Kwok (JavaScript version - https://github.com/antimatter15/jsgif)
* @version 0.1 AS3 implementation
*/
NeuQuant = function() {
var exports = {};
var netsize = 256; /* number of colours used */
/* four primes near 500 - assume no image has a length so large */
/* that it is divisible by all four primes */
var prime1 = 499;
var prime2 = 491;
var prime3 = 487;
var prime4 = 503;
var minpicturebytes = (3 * prime4); /* minimum size for input image */
/*
* Program Skeleton ---------------- [select samplefac in range 1..30] [read
* image from input file] pic = (unsigned char*) malloc(3*width*height);
* initnet(pic,3*width*height,samplefac); learn(); unbiasnet(); [write output
* image header, using writecolourmap(f)] inxbuild(); write output image using
* inxsearch(b,g,r)
*/
/*
* Network Definitions -------------------
*/
var maxnetpos = (netsize - 1);
var netbiasshift = 4; /* bias for colour values */
var ncycles = 100; /* no. of learning cycles */
/* defs for freq and bias */
var intbiasshift = 16; /* bias for fractions */
var intbias = (1 << intbiasshift);
var gammashift = 10; /* gamma = 1024 */
var gamma = (1 << gammashift);
var betashift = 10;
var beta = (intbias >> betashift); /* beta = 1/1024 */
var betagamma = (intbias << (gammashift - betashift));
/* defs for decreasing radius factor */
var initrad = (netsize >> 3); /* for 256 cols, radius starts */
var radiusbiasshift = 6; /* at 32.0 biased by 6 bits */
var radiusbias = (1 << radiusbiasshift);
var initradius = (initrad * radiusbias); /* and decreases by a */
var radiusdec = 30; /* factor of 1/30 each cycle */
/* defs for decreasing alpha factor */
var alphabiasshift = 10; /* alpha starts at 1.0 */
var initalpha = (1 << alphabiasshift);
var alphadec; /* biased by 10 bits */
/* radbias and alpharadbias used for radpower calculation */
var radbiasshift = 8;
var radbias = (1 << radbiasshift);
var alpharadbshift = (alphabiasshift + radbiasshift);
var alpharadbias = (1 << alpharadbshift);
/*
* Types and Global Variables --------------------------
*/
var thepicture; /* the input image itself */
var lengthcount; /* lengthcount = H*W*3 */
var samplefac; /* sampling factor 1..30 */
// typedef int pixel[4]; /* BGRc */
var network; /* the network itself - [netsize][4] */
var netindex = [];
/* for network lookup - really 256 */
var bias = [];
/* bias and freq arrays for learning */
var freq = [];
var radpower = [];
var NeuQuant = exports.NeuQuant = function NeuQuant(thepic, len, sample) {
var i;
var p;
thepicture = thepic;
lengthcount = len;
samplefac = sample;
network = new Array(netsize);
for (i = 0; i < netsize; i++) {
network[i] = new Array(4);
p = network[i];
p[0] = p[1] = p[2] = (i << (netbiasshift + 8)) / netsize;
freq[i] = intbias / netsize; /* 1/netsize */
bias[i] = 0;
}
};
var colorMap = function colorMap() {
var map = [];
var index = new Array(netsize);
for (var i = 0; i < netsize; i++)
index[network[i][3]] = i;
var k = 0;
for (var l = 0; l < netsize; l++) {
var j = index[l];
map[k++] = (network[j][0]);
map[k++] = (network[j][1]);
map[k++] = (network[j][2]);
}
return map;
};
/*
* Insertion sort of network and building of netindex[0..255] (to do after
* unbias)
* -------------------------------------------------------------------------------
*/
var inxbuild = function inxbuild() {
var i;
var j;
var smallpos;
var smallval;
var p;
var q;
var previouscol;
var startpos;
previouscol = 0;
startpos = 0;
for (i = 0; i < netsize; i++) {
p = network[i];
smallpos = i;
smallval = p[1]; /* index on g */
/* find smallest in i..netsize-1 */
for (j = i + 1; j < netsize; j++) {
q = network[j];
if (q[1] < smallval) { /* index on g */
smallpos = j;
smallval = q[1]; /* index on g */
}
}
q = network[smallpos];
/* swap p (i) and q (smallpos) entries */
if (i != smallpos) {
j = q[0];
q[0] = p[0];
p[0] = j;
j = q[1];
q[1] = p[1];
p[1] = j;
j = q[2];
q[2] = p[2];
p[2] = j;
j = q[3];
q[3] = p[3];
p[3] = j;
}
/* smallval entry is now in position i */
if (smallval != previouscol) {
netindex[previouscol] = (startpos + i) >> 1;
for (j = previouscol + 1; j < smallval; j++) netindex[j] = i;
previouscol = smallval;
startpos = i;
}
}
netindex[previouscol] = (startpos + maxnetpos) >> 1;
for (j = previouscol + 1; j < 256; j++) netindex[j] = maxnetpos; /* really 256 */
};
/*
* Main Learning Loop ------------------
*/
var learn = function learn() {
var i;
var j;
var b;
var g;
var r;
var radius;
var rad;
var alpha;
var step;
var delta;
var samplepixels;
var p;
var pix;
var lim;
if (lengthcount < minpicturebytes) samplefac = 1;
alphadec = 30 + ((samplefac - 1) / 3);
p = thepicture;
pix = 0;
lim = lengthcount;
samplepixels = lengthcount / (3 * samplefac);
delta = (samplepixels / ncycles) | 0;
alpha = initalpha;
radius = initradius;
rad = radius >> radiusbiasshift;
if (rad <= 1) rad = 0;
for (i = 0; i < rad; i++) radpower[i] = alpha * (((rad * rad - i * i) * radbias) / (rad * rad));
if (lengthcount < minpicturebytes) step = 3;
else if ((lengthcount % prime1) !== 0) step = 3 * prime1;
else {
if ((lengthcount % prime2) !== 0) step = 3 * prime2;
else {
if ((lengthcount % prime3) !== 0) step = 3 * prime3;
else step = 3 * prime4;
}
}
i = 0;
while (i < samplepixels) {
b = (p[pix + 0] & 0xff) << netbiasshift;
g = (p[pix + 1] & 0xff) << netbiasshift;
r = (p[pix + 2] & 0xff) << netbiasshift;
j = contest(b, g, r);
altersingle(alpha, j, b, g, r);
if (rad !== 0) alterneigh(rad, j, b, g, r); /* alter neighbours */
pix += step;
if (pix >= lim) pix -= lengthcount;
i++;
if (delta === 0) delta = 1;
if (i % delta === 0) {
alpha -= alpha / alphadec;
radius -= radius / radiusdec;
rad = radius >> radiusbiasshift;
if (rad <= 1) rad = 0;
for (j = 0; j < rad; j++) radpower[j] = alpha * (((rad * rad - j * j) * radbias) / (rad * rad));
}
}
};
/*
** Search for BGR values 0..255 (after net is unbiased) and return colour
* index
* ----------------------------------------------------------------------------
*/
var map = exports.map = function map(b, g, r) {
var i;
var j;
var dist;
var a;
var bestd;
var p;
var best;
bestd = 1000; /* biggest possible dist is 256*3 */
best = -1;
i = netindex[g]; /* index on g */
j = i - 1; /* start at netindex[g] and work outwards */
while ((i < netsize) || (j >= 0)) {
if (i < netsize) {
p = network[i];
dist = p[1] - g; /* inx key */
if (dist >= bestd) i = netsize; /* stop iter */
else {
i++;
if (dist < 0) dist = -dist;
a = p[0] - b;
if (a < 0) a = -a;
dist += a;
if (dist < bestd) {
a = p[2] - r;
if (a < 0) a = -a;
dist += a;
if (dist < bestd) {
bestd = dist;
best = p[3];
}
}
}
}
if (j >= 0) {
p = network[j];
dist = g - p[1]; /* inx key - reverse dif */
if (dist >= bestd) j = -1; /* stop iter */
else {
j--;
if (dist < 0) dist = -dist;
a = p[0] - b;
if (a < 0) a = -a;
dist += a;
if (dist < bestd) {
a = p[2] - r;
if (a < 0) a = -a;
dist += a;
if (dist < bestd) {
bestd = dist;
best = p[3];
}
}
}
}
}
return (best);
};
var process = exports.process = function process() {
learn();
unbiasnet();
inxbuild();
return colorMap();
};
/*
* Unbias network to give byte values 0..255 and record position i to prepare
* for sort
* -----------------------------------------------------------------------------------
*/
var unbiasnet = function unbiasnet() {
var i;
var j;
for (i = 0; i < netsize; i++) {
network[i][0] >>= netbiasshift;
network[i][1] >>= netbiasshift;
network[i][2] >>= netbiasshift;
network[i][3] = i; /* record colour no */
}
};
/*
* Move adjacent neurons by precomputed alpha*(1-((i-j)^2/[r]^2)) in
* radpower[|i-j|]
* ---------------------------------------------------------------------------------
*/
var alterneigh = function alterneigh(rad, i, b, g, r) {
var j;
var k;
var lo;
var hi;
var a;
var m;
var p;
lo = i - rad;
if (lo < -1) lo = -1;
hi = i + rad;
if (hi > netsize) hi = netsize;
j = i + 1;
k = i - 1;
m = 1;
while ((j < hi) || (k > lo)) {
a = radpower[m++];
if (j < hi) {
p = network[j++];
try {
p[0] -= (a * (p[0] - b)) / alpharadbias;
p[1] -= (a * (p[1] - g)) / alpharadbias;
p[2] -= (a * (p[2] - r)) / alpharadbias;
} catch (e) {} // prevents 1.3 miscompilation
}
if (k > lo) {
p = network[k--];
try {
p[0] -= (a * (p[0] - b)) / alpharadbias;
p[1] -= (a * (p[1] - g)) / alpharadbias;
p[2] -= (a * (p[2] - r)) / alpharadbias;
} catch (e) {}
}
}
};
/*
* Move neuron i towards biased (b,g,r) by factor alpha
* ----------------------------------------------------
*/
var altersingle = function altersingle(alpha, i, b, g, r) {
/* alter hit neuron */
var n = network[i];
n[0] -= (alpha * (n[0] - b)) / initalpha;
n[1] -= (alpha * (n[1] - g)) / initalpha;
n[2] -= (alpha * (n[2] - r)) / initalpha;
};
/*
* Search for biased BGR values ----------------------------
*/
var contest = function contest(b, g, r) {
/* 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/netsize)-freq[i]) */
var i;
var dist;
var a;
var biasdist;
var betafreq;
var bestpos;
var bestbiaspos;
var bestd;
var bestbiasd;
var n;
bestd = ~ (1 << 31);
bestbiasd = bestd;
bestpos = -1;
bestbiaspos = bestpos;
for (i = 0; i < netsize; i++) {
n = network[i];
dist = n[0] - b;
if (dist < 0) dist = -dist;
a = n[1] - g;
if (a < 0) a = -a;
dist += a;
a = n[2] - r;
if (a < 0) a = -a;
dist += a;
if (dist < bestd) {
bestd = dist;
bestpos = i;
}
biasdist = dist - ((bias[i]) >> (intbiasshift - netbiasshift));
if (biasdist < bestbiasd) {
bestbiasd = biasdist;
bestbiaspos = i;
}
betafreq = (freq[i] >> betashift);
freq[i] -= betafreq;
bias[i] += (betafreq << gammashift);
}
freq[bestpos] += beta;
bias[bestpos] -= betagamma;
return (bestbiaspos);
};
NeuQuant.apply(this, arguments);
return exports;
};
