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668
js/lib/gif/NeuQuant.js
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668
js/lib/gif/NeuQuant.js
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@@ -0,0 +1,668 @@
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/*
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* NeuQuant Neural-Net Quantization Algorithm
|
||||
* ------------------------------------------
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||||
*
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||||
* Copyright (c) 1994 Anthony Dekker
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*
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||||
* 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
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* that this copyright notice remain intact.
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*/
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||||
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/*
|
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* This class handles Neural-Net quantization algorithm
|
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* @author Kevin Weiner (original Java version - kweiner@fmsware.com)
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* @author Thibault Imbert (AS3 version - bytearray.org)
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* @version 0.1 AS3 implementation
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*/
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//import flash.utils.ByteArray;
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NeuQuant = function()
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{
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var exports = {};
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/*private_static*/ var netsize/*int*/ = 256; /* number of colours used */
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||||
/* four primes near 500 - assume no image has a length so large */
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/* that it is divisible by all four primes */
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||||
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||||
/*private_static*/ var prime1/*int*/ = 499;
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||||
/*private_static*/ var prime2/*int*/ = 491;
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/*private_static*/ var prime3/*int*/ = 487;
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/*private_static*/ var prime4/*int*/ = 503;
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/*private_static*/ var minpicturebytes/*int*/ = (3 * prime4);
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||||
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/* minimum size for input image */
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/*
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* Program Skeleton ---------------- [select samplefac in range 1..30] [read
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* image from input file] pic = (unsigned char*) malloc(3*width*height);
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* initnet(pic,3*width*height,samplefac); learn(); unbiasnet(); [write output
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* image header, using writecolourmap(f)] inxbuild(); write output image using
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* inxsearch(b,g,r)
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||||
*/
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||||
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/*
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* Network Definitions -------------------
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*/
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||||
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/*private_static*/ var maxnetpos/*int*/ = (netsize - 1);
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/*private_static*/ var netbiasshift/*int*/ = 4; /* bias for colour values */
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/*private_static*/ var ncycles/*int*/ = 100; /* no. of learning cycles */
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/* defs for freq and bias */
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/*private_static*/ var intbiasshift/*int*/ = 16; /* bias for fractions */
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/*private_static*/ var intbias/*int*/ = (1 << intbiasshift);
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/*private_static*/ var gammashift/*int*/ = 10; /* gamma = 1024 */
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/*private_static*/ var gamma/*int*/ = (1 << gammashift);
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/*private_static*/ var betashift/*int*/ = 10;
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/*private_static*/ var beta/*int*/ = (intbias >> betashift); /* beta = 1/1024 */
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/*private_static*/ var betagamma/*int*/ = (intbias << (gammashift - betashift));
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/* defs for decreasing radius factor */
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/*private_static*/ var initrad/*int*/ = (netsize >> 3); /*
|
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* for 256 cols, radius
|
||||
* starts
|
||||
*/
|
||||
|
||||
/*private_static*/ var radiusbiasshift/*int*/ = 6; /* at 32.0 biased by 6 bits */
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||||
/*private_static*/ var radiusbias/*int*/ = (1 << radiusbiasshift);
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/*private_static*/ var initradius/*int*/ = (initrad * radiusbias); /*
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* and
|
||||
* decreases
|
||||
* by a
|
||||
*/
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||||
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/*private_static*/ var radiusdec/*int*/ = 30; /* factor of 1/30 each cycle */
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/* defs for decreasing alpha factor */
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/*private_static*/ var alphabiasshift/*int*/ = 10; /* alpha starts at 1.0 */
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||||
/*private_static*/ var initalpha/*int*/ = (1 << alphabiasshift);
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/*private*/ var alphadec/*int*/ /* biased by 10 bits */
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||||
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||||
/* radbias and alpharadbias used for radpower calculation */
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||||
/*private_static*/ var radbiasshift/*int*/ = 8;
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/*private_static*/ var radbias/*int*/ = (1 << radbiasshift);
|
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/*private_static*/ var alpharadbshift/*int*/ = (alphabiasshift + radbiasshift);
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||||
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||||
/*private_static*/ var alpharadbias/*int*/ = (1 << alpharadbshift);
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|
||||
/*
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* Types and Global Variables --------------------------
|
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*/
|
||||
|
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/*private*/ var thepicture/*ByteArray*//* the input image itself */
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/*private*/ var lengthcount/*int*/; /* lengthcount = H*W*3 */
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/*private*/ var samplefac/*int*/; /* sampling factor 1..30 */
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||||
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// typedef int pixel[4]; /* BGRc */
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/*private*/ var network/*Array*/; /* the network itself - [netsize][4] */
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/*protected*/ var netindex/*Array*/ = new Array();
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/* for network lookup - really 256 */
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/*private*/ var bias/*Array*/ = new Array();
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||||
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/* bias and freq arrays for learning */
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/*private*/ var freq/*Array*/ = new Array();
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/*private*/ var radpower/*Array*/ = new Array();
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var NeuQuant = exports.NeuQuant = function NeuQuant(thepic/*ByteArray*/, len/*int*/, sample/*int*/)
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{
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var i/*int*/;
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var p/*Array*/;
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thepicture = thepic;
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lengthcount = len;
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samplefac = sample;
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network = new Array(netsize);
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|
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for (i = 0; i < netsize; i++)
|
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{
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||||
|
||||
network[i] = new Array(4);
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p = network[i];
|
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p[0] = p[1] = p[2] = (i << (netbiasshift + 8)) / netsize;
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freq[i] = intbias / netsize; /* 1/netsize */
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bias[i] = 0;
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}
|
||||
|
||||
}
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var colorMap = function colorMap()/*ByteArray*/
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{
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var map/*ByteArray*/ = [];
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var index/*Array*/ = new Array(netsize);
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for (var i/*int*/ = 0; i < netsize; i++)
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index[network[i][3]] = i;
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var k/*int*/ = 0;
|
||||
for (var l/*int*/ = 0; l < netsize; l++) {
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var j/*int*/ = index[l];
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map[k++] = (network[j][0]);
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||||
map[k++] = (network[j][1]);
|
||||
map[k++] = (network[j][2]);
|
||||
}
|
||||
return map;
|
||||
|
||||
}
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||||
|
||||
/*
|
||||
* Insertion sort of network and building of netindex[0..255] (to do after
|
||||
* unbias)
|
||||
* -------------------------------------------------------------------------------
|
||||
*/
|
||||
|
||||
var inxbuild = function inxbuild()/*void*/
|
||||
{
|
||||
|
||||
var i/*int*/;
|
||||
var j/*int*/;
|
||||
var smallpos/*int*/;
|
||||
var smallval/*int*/;
|
||||
var p/*Array*/;
|
||||
var q/*Array*/;
|
||||
var previouscol/*int*/
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||||
var startpos/*int*/
|
||||
|
||||
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 */
|
||||
}
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||||
}
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||||
|
||||
q = network[smallpos];
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||||
/* 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()/*void*/
|
||||
|
||||
{
|
||||
|
||||
var i/*int*/;
|
||||
var j/*int*/;
|
||||
var b/*int*/;
|
||||
var g/*int*/
|
||||
var r/*int*/;
|
||||
var radius/*int*/;
|
||||
var rad/*int*/;
|
||||
var alpha/*int*/;
|
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var step/*int*/;
|
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var delta/*int*/;
|
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var samplepixels/*int*/;
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var p/*ByteArray*/;
|
||||
var pix/*int*/;
|
||||
var lim/*int*/;
|
||||
|
||||
if (lengthcount < minpicturebytes) samplefac = 1;
|
||||
|
||||
alphadec = 30 + ((samplefac - 1) / 3);
|
||||
p = thepicture;
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||||
pix = 0;
|
||||
lim = lengthcount;
|
||||
samplepixels = lengthcount / (3 * samplefac);
|
||||
delta = samplepixels / ncycles;
|
||||
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/*int*/, g/*int*/, r/*int*/)/*int*/
|
||||
|
||||
{
|
||||
|
||||
var i/*int*/;
|
||||
var j/*int*/;
|
||||
var dist/*int*/
|
||||
var a/*int*/;
|
||||
var bestd/*int*/;
|
||||
var p/*Array*/;
|
||||
var best/*int*/;
|
||||
|
||||
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()/*ByteArray*/
|
||||
{
|
||||
|
||||
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()/*void*/
|
||||
|
||||
{
|
||||
|
||||
var i/*int*/;
|
||||
var j/*int*/;
|
||||
|
||||
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/*int*/, i/*int*/, b/*int*/, g/*int*/, r/*int*/)/*void*/
|
||||
|
||||
{
|
||||
|
||||
var j/*int*/;
|
||||
var k/*int*/;
|
||||
var lo/*int*/;
|
||||
var hi/*int*/;
|
||||
var a/*int*/;
|
||||
var m/*int*/;
|
||||
|
||||
var p/*Array*/;
|
||||
|
||||
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/*Error*/) {} // 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/*Error*/) {}
|
||||
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
/*
|
||||
* Move neuron i towards biased (b,g,r) by factor alpha
|
||||
* ----------------------------------------------------
|
||||
*/
|
||||
|
||||
var altersingle = function altersingle(alpha/*int*/, i/*int*/, b/*int*/, g/*int*/, r/*int*/)/*void*/
|
||||
{
|
||||
|
||||
/* alter hit neuron */
|
||||
var n/*Array*/ = 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/*int*/, g/*int*/, r/*int*/)/*int*/
|
||||
{
|
||||
|
||||
/* 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/*int*/;
|
||||
var dist/*int*/;
|
||||
var a/*int*/;
|
||||
var biasdist/*int*/;
|
||||
var betafreq/*int*/;
|
||||
var bestpos/*int*/;
|
||||
var bestbiaspos/*int*/;
|
||||
var bestd/*int*/;
|
||||
var bestbiasd/*int*/;
|
||||
var n/*Array*/;
|
||||
|
||||
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;
|
||||
}
|
||||
Reference in New Issue
Block a user