neuquant.cs

该即时通讯系统系统能够实现像QQ一样的通讯功能

using System;

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

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

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

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

		protected int alphadec; /* biased by 10 bits */

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