当图像宽度和高度是 100 的倍数时，NeuQuant.js效果很好：
300x300
否则，显然有一个错误：
299x300
（这些是用这个网络应用程序制作的。）
我有 90% 的把握这个错误在 NeuQuant.js 中。我已经使用jsgif和omggif进行了测试，两个编码器都有相同的错误。只有当图像大小不是 100 的倍数时，摄影图像（量化为 256 色）才明显。
如果您了解神经网络、颜色量化和/或将 AS3 移植到 JS 的问题，请查看。原来的搬运工已经放弃了这个项目，离工作这么近！
这是我在使用 OMGGIF 的工作人员中实现它的代码：
importScripts('omggif.js', 'NeuQuant.js');
var rgba2rgb = function (data) {
var pixels = [];
var count = 0;
var len = data.length;
for ( var i=0; i<len; i+=4 ) {
pixels[count++] = data[i];
pixels[count++] = data[i+1];
pixels[count++] = data[i+2];
}
return pixels;
}
var rgb2num = function(palette) {
var colors = [];
var count = 0;
var len = palette.length;
for ( var i=0; i<len; i+=3 ) {
colors[count++] = palette[i+2] | (palette[i+1] << 8) | (palette[i] << 16);
}
return colors;
}
self.onmessage = function(event) {
var frames = event.data.frames;
var framesLength = frames.length;
var delay = event.data.delay / 10;
var startTime = Date.now();
var buffer = new Uint8Array( frames[0].width * frames[0].height * framesLength * 5 );
var gif = new GifWriter( buffer, frames[0].width, frames[0].height, { loop: 0 } );
// var pixels = new Uint8Array( frames[0].width * frames[0].height );
var addFrame = function (frame) {
var data = frame.data;
// Make palette with NeuQuant.js
var nqInPixels = rgba2rgb(data);
var len = nqInPixels.length;
var nPix = len / 3;
var map = [];
var nq = new NeuQuant(nqInPixels, len, 10);
// initialize quantizer
var paletteRGB = nq.process(); // create reduced palette
var palette = rgb2num(paletteRGB);
// map image pixels to new palette
var k = 0;
for (var j = 0; j < nPix; j++) {
var index = nq.map(nqInPixels[k++] & 0xff, nqInPixels[k++] & 0xff, nqInPixels[k++] & 0xff);
// usedEntry[index] = true;
map[j] = index;
}
gif.addFrame( 0, 0, frame.width, frame.height, new Uint8Array( map ), { palette: new Uint32Array( palette ), delay: delay } );
}
// Add all frames
for (var i = 0; i<framesLength; i++) {
addFrame( frames[i] );
self.postMessage({
type: "progress",
data: Math.round( (i+1)/framesLength*100 )
});
}
// Finish
var string = '';
for ( var i = 0, l = gif.end(); i < l; i ++ ) {
string += String.fromCharCode( buffer[ i ] );
}
self.postMessage({
type: "gif",
data: string,
frameCount: framesLength,
encodeTime: Math.round( (Date.now()-startTime)/10 ) / 100
});
};
以及所有NeuQuant.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)
* @version 0.1 AS3 implementation
*/
//import flash.utils.ByteArray;
NeuQuant = function()
{
var exports = {};
/*private_static*/ var netsize/*int*/ = 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 */
/*private_static*/ var prime1/*int*/ = 499;
/*private_static*/ var prime2/*int*/ = 491;
/*private_static*/ var prime3/*int*/ = 487;
/*private_static*/ var prime4/*int*/ = 503;
/*private_static*/ var minpicturebytes/*int*/ = (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 -------------------
*/
/*private_static*/ var maxnetpos/*int*/ = (netsize - 1);
/*private_static*/ var netbiasshift/*int*/ = 4; /* bias for colour values */
/*private_static*/ var ncycles/*int*/ = 100; /* no. of learning cycles */
/* defs for freq and bias */
/*private_static*/ var intbiasshift/*int*/ = 16; /* bias for fractions */
/*private_static*/ var intbias/*int*/ = (1 << intbiasshift);
/*private_static*/ var gammashift/*int*/ = 10; /* gamma = 1024 */
/*private_static*/ var gamma/*int*/ = (1 << gammashift);
/*private_static*/ var betashift/*int*/ = 10;
/*private_static*/ var beta/*int*/ = (intbias >> betashift); /* beta = 1/1024 */
/*private_static*/ var betagamma/*int*/ = (intbias << (gammashift - betashift));
/* defs for decreasing radius factor */
/*private_static*/ var initrad/*int*/ = (netsize >> 3); /*
* for 256 cols, radius
* starts
*/
/*private_static*/ var radiusbiasshift/*int*/ = 6; /* at 32.0 biased by 6 bits */
/*private_static*/ var radiusbias/*int*/ = (1 << radiusbiasshift);
/*private_static*/ var initradius/*int*/ = (initrad * radiusbias); /*
* and
* decreases
* by a
*/
/*private_static*/ var radiusdec/*int*/ = 30; /* factor of 1/30 each cycle */
/* defs for decreasing alpha factor */
/*private_static*/ var alphabiasshift/*int*/ = 10; /* alpha starts at 1.0 */
/*private_static*/ var initalpha/*int*/ = (1 << alphabiasshift);
/*private*/ var alphadec/*int*/ /* biased by 10 bits */
/* radbias and alpharadbias used for radpower calculation */
/*private_static*/ var radbiasshift/*int*/ = 8;
/*private_static*/ var radbias/*int*/ = (1 << radbiasshift);
/*private_static*/ var alpharadbshift/*int*/ = (alphabiasshift + radbiasshift);
/*private_static*/ var alpharadbias/*int*/ = (1 << alpharadbshift);
/*
* Types and Global Variables --------------------------
*/
/*private*/ var thepicture/*ByteArray*//* the input image itself */
/*private*/ var lengthcount/*int*/; /* lengthcount = H*W*3 */
/*private*/ var samplefac/*int*/; /* sampling factor 1..30 */
// typedef int pixel[4]; /* BGRc */
/*private*/ var network/*Array*/; /* the network itself - [netsize][4] */
/*protected*/ var netindex/*Array*/ = new Array();
/* for network lookup - really 256 */
/*private*/ var bias/*Array*/ = new Array();
/* bias and freq arrays for learning */
/*private*/ var freq/*Array*/ = new Array();
/*private*/ var radpower/*Array*/ = new Array();
var NeuQuant = exports.NeuQuant = function NeuQuant(thepic/*ByteArray*/, len/*int*/, sample/*int*/)
{
var i/*int*/;
var p/*Array*/;
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()/*ByteArray*/
{
var map/*ByteArray*/ = [];
var index/*Array*/ = new Array(netsize);
for (var i/*int*/ = 0; i < netsize; i++)
index[network[i][3]] = i;
var k/*int*/ = 0;
for (var l/*int*/ = 0; l < netsize; l++) {
var j/*int*/ = 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()/*void*/
{
var i/*int*/;
var j/*int*/;
var smallpos/*int*/;
var smallval/*int*/;
var p/*Array*/;
var q/*Array*/;
var previouscol/*int*/
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 */
}
}
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()/*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*/;
var step/*int*/;
var delta/*int*/;
var samplepixels/*int*/;
var p/*ByteArray*/;
var pix/*int*/;
var lim/*int*/;
if (lengthcount < minpicturebytes) samplefac = 1;
alphadec = 30 + ((samplefac - 1) / 3);
p = thepicture;
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;
}
