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bower_components/gif.js/src/TypedNeuQuant.js

Index: bower_components/gif.js/src/TypedNeuQuant.js
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-/* 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.
- * See also  http://members.ozemail.com.au/~dekker/NEUQUANT.HTML
- *
- * 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.
- *
- * (JavaScript port 2012 by Johan Nordberg)
- */
-
-var ncycles = 100; // number of learning cycles
-var netsize = 256; // number of colors used
-var maxnetpos = netsize - 1;
-
-// defs for freq and bias
-var netbiasshift = 4; // bias for colour values
-var intbiasshift = 16; // bias for fractions
-var intbias = (1 << intbiasshift);
-var gammashift = 10;
-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);
-
-// 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);
-
-/*
-  Constructor: NeuQuant
-
-  Arguments:
-
-  pixels - array of pixels in RGB format
-  samplefac - sampling factor 1 to 30 where lower is better quality
-
-  >
-  > pixels = [r, g, b, r, g, b, r, g, b, ..]
-  >
-*/
-function NeuQuant(pixels, samplefac) {
-  var network; // int[netsize][4]
-  var netindex; // for network lookup - really 256
-
-  // bias and freq arrays for learning
-  var bias;
-  var freq;
-  var radpower;
-
-  /*
-    Private Method: init
-
-    sets up arrays
-  */
-  function init() {
-    network = [];
-    netindex = new Int32Array(256);
-    bias = new Int32Array(netsize);
-    freq = new Int32Array(netsize);
-    radpower = new Int32Array(netsize >> 3);
-
-    var i, v;
-    for (i = 0; i < netsize; i++) {
-      v = (i << (netbiasshift + 8)) / netsize;
-      network[i] = new Float64Array([v, v, v, 0]);
-      //network[i] = [v, v, v, 0]
-      freq[i] = intbias / netsize;
-      bias[i] = 0;
-    }
-  }
-
-  /*
-    Private Method: unbiasnet
-
-    unbiases network to give byte values 0..255 and record position i to prepare for sort
-  */
-  function unbiasnet() {
-    for (var i = 0; i < netsize; i++) {
-      network[i][0] >>= netbiasshift;
-      network[i][1] >>= netbiasshift;
-      network[i][2] >>= netbiasshift;
-      network[i][3] = i; // record color number
-    }
-  }
-
-  /*
-    Private Method: altersingle
-
-    moves neuron *i* towards biased (b,g,r) by factor *alpha*
-  */
-  function altersingle(alpha, i, b, g, r) {
-    network[i][0] -= (alpha * (network[i][0] - b)) / initalpha;
-    network[i][1] -= (alpha * (network[i][1] - g)) / initalpha;
-    network[i][2] -= (alpha * (network[i][2] - r)) / initalpha;
-  }
-
-  /*
-    Private Method: alterneigh
-
-    moves neurons in *radius* around index *i* towards biased (b,g,r) by factor *alpha*
-  */
-  function alterneigh(radius, i, b, g, r) {
-    var lo = Math.abs(i - radius);
-    var hi = Math.min(i + radius, netsize);
-
-    var j = i + 1;
-    var k = i - 1;
-    var m = 1;
-
-    var p, a;
-    while ((j < hi) || (k > lo)) {
-      a = radpower[m++];
-
-      if (j < hi) {
-        p = network[j++];
-        p[0] -= (a * (p[0] - b)) / alpharadbias;
-        p[1] -= (a * (p[1] - g)) / alpharadbias;
-        p[2] -= (a * (p[2] - r)) / alpharadbias;
-      }
-
-      if (k > lo) {
-        p = network[k--];
-        p[0] -= (a * (p[0] - b)) / alpharadbias;
-        p[1] -= (a * (p[1] - g)) / alpharadbias;
-        p[2] -= (a * (p[2] - r)) / alpharadbias;
-      }
-    }
-  }
-
-  /*
-    Private Method: contest
-
-    searches for biased BGR values
-  */
-  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 bestd = ~(1 << 31);
-    var bestbiasd = bestd;
-    var bestpos = -1;
-    var bestbiaspos = bestpos;
-
-    var i, n, dist, biasdist, betafreq;
-    for (i = 0; i < netsize; i++) {
-      n = network[i];
-
-      dist = Math.abs(n[0] - b) + Math.abs(n[1] - g) + Math.abs(n[2] - r);
-      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;
-  }
-
-  /*
-    Private Method: inxbuild
-
-    sorts network and builds netindex[0..255]
-  */
-  function inxbuild() {
-    var i, j, p, q, smallpos, smallval, 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
-  }
-
-  /*
-    Private Method: inxsearch
-
-    searches for BGR values 0..255 and returns a color index
-  */
-  function inxsearch(b, g, r) {
-    var a, p, dist;
-
-    var bestd = 1000; // biggest possible dist is 256*3
-    var best = -1;
-
-    var i = netindex[g]; // index on g
-    var 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;
-  }
-
-  /*
-    Private Method: learn
-
-    "Main Learning Loop"
-  */
-  function learn() {
-    var i;
-
-    var lengthcount = pixels.length;
-    var alphadec = 30 + ((samplefac - 1) / 3);
-    var samplepixels = lengthcount / (3 * samplefac);
-    var delta = ~~(samplepixels / ncycles);
-    var alpha = initalpha;
-    var radius = initradius;
-
-    var rad = radius >> radiusbiasshift;
-
-    if (rad <= 1) rad = 0;
-    for (i = 0; i < rad; i++)
-      radpower[i] = alpha * (((rad * rad - i * i) * radbias) / (rad * rad));
-
-    var step;
-    if (lengthcount < minpicturebytes) {
-      samplefac = 1;
-      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;
-    }
-
-    var b, g, r, j;
-    var pix = 0; // current pixel
-
-    i = 0;
-    while (i < samplepixels) {
-      b = (pixels[pix] & 0xff) << netbiasshift;
-      g = (pixels[pix + 1] & 0xff) << netbiasshift;
-      r = (pixels[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 >= lengthcount) 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));
-      }
-    }
-  }
-
-  /*
-    Method: buildColormap
-
-    1. initializes network
-    2. trains it
-    3. removes misconceptions
-    4. builds colorindex
-  */
-  function buildColormap() {
-    init();
-    learn();
-    unbiasnet();
-    inxbuild();
-  }
-  this.buildColormap = buildColormap;
-
-  /*
-    Method: getColormap
-
-    builds colormap from the index
-
-    returns array in the format:
-
-    >
-    > [r, g, b, r, g, b, r, g, b, ..]
-    >
-  */
-  function getColormap() {
-    var map = [];
-    var index = [];
-
-    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;
-  }
-  this.getColormap = getColormap;
-
-  /*
-    Method: lookupRGB
-
-    looks for the closest *r*, *g*, *b* color in the map and
-    returns its index
-  */
-  this.lookupRGB = inxsearch;
-}
-
-module.exports = NeuQuant;