mirror of
https://github.com/boxgaming/qbjs.git
synced 2024-09-20 04:24:45 +00:00
529 lines
12 KiB
JavaScript
529 lines
12 KiB
JavaScript
/*
|
|
* 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;
|
|
};
|