package org.catacomb.movie.gif;
   
   
   
   /* 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.
   */
  
  // Ported to Java 12/00 K Weiner
  
  public class NeuQuant {
  
      protected static final int 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 */
      protected static final int prime1 = 499;
      protected static final int prime2 = 491;
      protected static final int prime3 = 487;
      protected static final int prime4 = 503;
  
      protected static final int 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
         ------------------- */
  
      protected static final int maxnetpos = (netsize - 1);
      protected static final int netbiasshift = 4; /* bias for colour values */
      protected static final int ncycles = 100; /* no. of learning cycles */
  
      /* defs for freq and bias */
      protected static final int intbiasshift = 16; /* bias for fractions */
      protected static final int intbias = (1 << intbiasshift);
      protected static final int gammashift = 10; /* gamma = 1024 */
      protected static final int gamma = (1 << gammashift);
      protected static final int betashift = 10;
      protected static final int beta = (intbias >> betashift); /* beta = 1/1024 */
      protected static final int betagamma =
          (intbias << (gammashift - betashift));
  
      /* defs for decreasing radius factor */
      protected static final int initrad = (netsize >> 3); /* for 256 cols, radius starts */
      protected static final int radiusbiasshift = 6; /* at 32.0 biased by 6 bits */
      protected static final int radiusbias = (1 << radiusbiasshift);
      protected static final int initradius = (initrad * radiusbias); /* and decreases by a */
      protected static final int radiusdec = 30; /* factor of 1/30 each cycle */
  
      /* defs for decreasing alpha factor */
      protected static final int alphabiasshift = 10; /* alpha starts at 1.0 */
      protected static final int initalpha = (1 << alphabiasshift);
  
      protected int alphadec; /* biased by 10 bits */
  
      /* radbias and alpharadbias used for radpower calculation */
      protected static final int radbiasshift = 8;
      protected static final int radbias = (1 << radbiasshift);
      protected static final int alpharadbshift = (alphabiasshift + radbiasshift);
      protected static final int alpharadbias = (1 << alpharadbshift);
  
      /* Types and Global Variables
      -------------------------- */
  
      protected byte[] thepicture; /* the input image itself */
      protected int lengthcount; /* lengthcount = H*W*3 */
  
      protected int samplefac; /* sampling factor 1..30 */
  
      //   typedef int pixel[4];                /* BGRc */
      protected int[][] network; /* the network itself - [netsize][4] */
  
     protected int[] netindex = new int[256];
     /* for network lookup - really 256 */
 
     protected int[] bias = new int[netsize];
     /* bias and freq arrays for learning */
     protected int[] freq = new int[netsize];
     protected int[] radpower = new int[initrad];
     /* radpower for precomputation */
 
     /* Initialise network in range (0,0,0) to (255,255,255) and set parameters
        ----------------------------------------------------------------------- */
     public NeuQuant(byte[] thepic, int len, int sample) {
 
         int i;
         int[] p;
 
         thepicture = thepic;
         lengthcount = len;
         samplefac = sample;
 
         network = new int[netsize][];
         for (i = 0; i < netsize; i++) {
             network[i] = new int[4];
             p = network[i];
             p[0] = p[1] = p[2] = (i << (netbiasshift + 8)) / netsize;
             freq[i] = intbias / netsize; /* 1/netsize */
             bias[i] = 0;
         }
     }
 
     public byte[] colorMap() {
         byte[] map = new byte[3 * netsize];
         int[] index = new int[netsize];
         for (int i = 0; i < netsize; i++) {
             index[network[i][3]] = i;
         }
         int k = 0;
         for (int i = 0; i < netsize; i++) {
             int j = index[i];
             map[k++] = (byte)(network[j][0]);
             map[k++] = (byte)(network[j][1]);
             map[k++] = (byte)(network[j][2]);
         }
         return map;
     }
 
     /* Insertion sort of network and building of netindex[0..255] (to do after unbias)
        ------------------------------------------------------------------------------- */
     public void inxbuild() {
 
         int i, j, smallpos, smallval;
         int[] p;
         int[] q;
         int previouscol, 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
        ------------------ */
     public void learn() {
 
         int i, j, b, g, r;
         int radius, rad, alpha, step, delta, samplepixels;
         byte[] p;
         int pix, lim;
 
         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));
         }
 
         //fprintf(stderr,"beginning 1D learning: initial radius=%d\n", 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));
                 }
             }
         }
         //fprintf(stderr,"finished 1D learning: final alpha=%f !\n",((float)alpha)/initalpha);
     }
 
     /* Search for BGR values 0..255 (after net is unbiased) and return colour index
        ---------------------------------------------------------------------------- */
     public int map(int b, int g, int r) {
 
         int i, j, dist, a, bestd;
         int[] p;
         int 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);
     }
     public byte[] process() {
         learn();
         unbiasnet();
         inxbuild();
         return colorMap();
     }
 
     /* Unbias network to give byte values 0..255 and record position i to prepare for sort
        ----------------------------------------------------------------------------------- */
     public void unbiasnet() {
 
         int i;
 
         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|]
        --------------------------------------------------------------------------------- */
     protected void alterneigh(int rad, int i, int b, int g, int r) {
 
         int j, k, lo, hi, a, m;
         int[] 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 (Exception 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 (Exception e) {
                 }
             }
         }
     }
 
     /* Move neuron i towards biased (b,g,r) by factor alpha
        ---------------------------------------------------- */
     protected void altersingle(int alpha, int i, int b, int g, int r) {
 
         /* alter hit neuron */
         int[] 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
        ---------------------------- */
     protected int contest(int b, int g, int 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]) */
 
         int i, dist, a, biasdist, betafreq;
         int bestpos, bestbiaspos, bestd, bestbiasd;
         int[] 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);
     }
 }