UniversalViewer/Assets/Scripts/uGIF/NeuQuant.cs

/* 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
using System;
using UnityEngine;

namespace uGIF
{
	public class NeuQuant
	{
		static readonly 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 */
		static readonly int prime1 = 499;
		static readonly int prime2 = 491;
		static readonly int prime3 = 487;
		static readonly int prime4 = 503;
		static readonly 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
		   ------------------- */
		static readonly int maxnetpos = (netsize - 1);
		static readonly int netbiasshift = 4; /* bias for colour values */
		static readonly int ncycles = 100; /* no. of learning cycles */

		/* defs for freq and bias */
		static readonly int intbiasshift = 16; /* bias for fractions */
		static readonly int intbias = (((int)1) << intbiasshift);
		static readonly int gammashift = 10; /* gamma = 1024 */
		static readonly int gamma = (((int)1) << gammashift);
		static readonly int betashift = 10;
		static readonly int beta = (intbias >> betashift); /* beta = 1/1024 */
		static readonly int betagamma = (intbias << (gammashift - betashift));

		/* defs for decreasing radius factor */
		static readonly int initrad = (netsize >> 3); /* for 256 cols, radius starts */
		static readonly int radiusbiasshift = 6; /* at 32.0 biased by 6 bits */
		static readonly int radiusbias = (((int)1) << radiusbiasshift);
		static readonly int initradius = (initrad * radiusbias); /* and decreases by a */
		static readonly int radiusdec = 30; /* factor of 1/30 each cycle */

		/* defs for decreasing alpha factor */
		static readonly int alphabiasshift = 10; /* alpha starts at 1.0 */
		static readonly int initalpha = (((int)1) << alphabiasshift);
		int alphadec; /* biased by 10 bits */

		/* radbias and alpharadbias used for radpower calculation */
		static readonly int radbiasshift = 8;
		static readonly int radbias = (((int)1) << radbiasshift);
		static readonly int alpharadbshift = (alphabiasshift + radbiasshift);
		static readonly int alpharadbias = (((int)1) << alpharadbshift);

		/* Types and Global Variables
		-------------------------- */

		Color32[] thepicture; /* the input image itself */
		int lengthcount; /* lengthcount = H*W*3 */

		int samplefac; /* sampling factor 1..30 */

		//   typedef int pixel[4];                /* BGRc */
		int[][] network; /* the network itself - [netsize][4] */

		int[] netindex = new int[256];
		/* for network lookup - really 256 */

		int[] bias = new int[netsize];
		/* bias and freq arrays for learning */
		int[] freq = new int[netsize];
		int[] radpower = new int[initrad];
		/* radpower for precomputation */

		/* Initialise network in range (0,0,0) to (255,255,255) and set parameters
		   ----------------------------------------------------------------------- */
		public NeuQuant (Color32[] 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;
			}
		}
	
		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)
		   ------------------------------------------------------------------------------- */
		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
		   ------------------ */
		void Learn ()
		{

			int i, j, b, g, r;
			int radius, rad, alpha, step, delta, samplepixels;

			int pix, lim;

			if (lengthcount < minpicturebytes)
				samplefac = 1;
			alphadec = 30 + ((samplefac - 1) / 3);
			var 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].r & 0xff) << netbiasshift;
				g = (p [pix].g & 0xff) << netbiasshift;
				r = (p [pix].b & 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
		   ---------------------------------------------------------------------------- */
		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
		   ----------------------------------------------------------------------------------- */
		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|]
		   --------------------------------------------------------------------------------- */
		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++];
					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;
					 

				}
			}
		}
	
		/* Move neuron i towards biased (b,g,r) by factor alpha
		   ---------------------------------------------------- */
		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
		   ---------------------------- */
		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);
		}
	}
}