User:Simpsons contributor/Quantizer

package FractalProcessor;

/* * 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 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 = (((int) 1) << intbiasshift);

protected static final int gammashift = 10; /* gamma = 1024 */

protected static final int gamma = (((int) 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 = (((int) 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 = (((int) 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 = (((int) 1) << radbiasshift);

protected static final int alpharadbshift = (alphabiasshift + radbiasshift);

protected static final int alpharadbias = (((int) 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, j;

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 = ~(((int) 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); } }