Error oculto de NeuQuant.js (cuantización de color de JavaScript) en la conversión JS

NeuQuant.js Funciona bien cuando el ancho y la altura de la imagen son múltiplos de 100:

 300x300

De lo contrario, obviamente hay un error:

 299x300

(Estos fueron hechos conesta aplicación web.)

Estoy 90% seguro de que el error está en NeuQuant.js. He hecho pruebas utilizando conjsgif yomggif, y ambos codificadores tienen el mismo error. Solo es obvio con las imágenes fotográficas (cuantificar a 256 colores) cuando el tamaño de la imagen no es un múltiplo de 100.

Si entiende las redes neuronales, la cuantización del color y / o los problemas con la transferencia de AS3 a JS, eche un vistazo. El portero original ha abandonado el proyecto y está tan cerca de trabajar.

Aquí estámi código que lo implementa en un trabajador con OMGGIF:

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
  });
};

Y todosNeuQuant.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 - [email protected])
* @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;
}

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