code: /* 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
  
  package UI;
  
  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 = (((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);
  	}
  }
  