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Anonymous committed 9d1c2cb

- nothing to do here, was a backup :)

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src/gd_nnquant.c.work

-/* 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.
- * See also  http://members.ozemail.com.au/~dekker/NEUQUANT.HTML
- *
- * 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.
- * 
- *
- * Modified to process 32bit RGBA images.
- * Stuart Coyle 2004-2006
- *
- * Ported to libgd by Pierre A. Joye
- *  - Thread safety (drop global variables)
- */
-
-#include <stdlib.h>
-#include <string.h>
-#include <gd.h>
-#include "gdhelpers.h"
-
-#include "gd_nnquant.h"
-
-/* Network Definitions
-   ------------------- */
-   
-#define maxnetpos	(MAXNETSIZE-1)
-#define netbiasshift	4			/* bias for colour values */
-#define ncycles		100			/* no. of learning cycles */
-
-/* defs for freq and bias */
-#define intbiasshift    16			/* bias for fractions */
-#define intbias		(((int) 1)<<intbiasshift)
-#define gammashift  	10			/* gamma = 1024 */
-#define gamma   	(((int) 1)<<gammashift)
-#define betashift  	10
-#define beta		(intbias>>betashift)	/* beta = 1/1024 */
-#define betagamma	(intbias<<(gammashift-betashift))
-
-/* defs for decreasing radius factor */
-#define initrad		(MAXNETSIZE>>3)		/* for 256 cols, radius starts */
-#define radiusbiasshift	6			/* at 32.0 biased by 6 bits */
-#define radiusbias	(((int) 1)<<radiusbiasshift)
-#define initradius	(initrad*radiusbias)	/* and decreases by a */
-#define radiusdec	30			/* factor of 1/30 each cycle */ 
-
-/* defs for decreasing alpha factor */
-#define alphabiasshift	10			/* alpha starts at 1.0 */
-#define initalpha	(((int) 1)<<alphabiasshift)
-int alphadec;					
-
-/* radbias and alpharadbias used for radpower calculation */
-#define radbiasshift	8
-#define radbias		(((int) 1)<<radbiasshift)
-#define alpharadbshift  (alphabiasshift+radbiasshift)
-#define alpharadbias    (((int) 1)<<alpharadbshift)
-
-#define ALPHA 0
-#define RED 1
-#define BLUE 2
-#define GREEN 3
-
-typedef int nq_pixel[5];				
-
-typedef struct {
-	/* biased by 10 bits */
-	int alphadec;
-
-	/* lengthcount = H*W*3 */
-	int lengthcount;
-
-	/* sampling factor 1..30 */
-	int samplefac;
-
-	/* Number of colours to use. Made a global instead of #define */
-	int netsize;
-
-	/* for network lookup - really 256 */
-	int netindex[256];
-
-	/* ABGRc */
-	/* the network itself */
-	nq_pixel network[MAXNETSIZE];
-
-	/* bias and freq arrays for learning */
-	int bias[MAXNETSIZE];
-	int freq[MAXNETSIZE];
-
-	/* radpower for precomputation */
-	int radpower[initrad];
-
-	/* the input image itself */
-	unsigned char *thepicture;
-} nn_quant;
-
-/* Initialise network in range (0,0,0,0) to (255,255,255,255) and set parameters
-   ----------------------------------------------------------------------- */
-void initnet(nnq, thepic, len, sample, colours)	
-	nn_quant *nnq;
-	unsigned char *thepic;
-	int len;
-	int sample;
-	int colours;
-{
-	register int i;
-	register int *p;
-
-	/* Clear out network from previous runs */
-	/* thanks to Chen Bin for this fix */
-	memset((void*)nnq->network, 0, sizeof(nq_pixel)*MAXNETSIZE);
-
-	nnq->thepicture = thepic;
-	nnq->lengthcount = len;
-	nnq->samplefac = sample;
-	nnq->netsize = colours; 
-
-	for (i=0; i < nnq->netsize; i++) {
-		p = nnq->network[i];
-		p[0] = p[1] = p[2] = p[3] = (i << (netbiasshift+8)) / nnq->netsize;
-		nnq->freq[i] = intbias / nnq->netsize;	/* 1/netsize */
-		nnq->bias[i] = 0;
-	}
-}
-
-/* -------------------------- */
-	
-/* Unbias network to give byte values 0..255 and record
- * position i to prepare for sort
- */
-/* -------------------------- */
-
-void unbiasnet(nn_quant *nnq)
-{
-	int i,j,temp;
-
-	for (i=0; i < nnq->netsize; i++) {
-		for (j=0; j<4; j++) {
-			/* OLD CODE: network[i][j] >>= netbiasshift; */
-			/* Fix based on bug report by Juergen Weigert jw@suse.de */
-			temp = (nnq->network[i][j] + (1 << (netbiasshift - 1))) >> netbiasshift;
-			if (temp > 255) temp = 255;
-			nnq->network[i][j] = temp;
-		}
-		nnq->network[i][4] = i;			/* record colour no */
-	}
-}
-
-/* Output colour map
-	 ----------------- */
-void writecolourmap(nnq, f)
-	nn_quant *nnq;
-	FILE *f;
-{
-	int i,j;
-
-	for (i=3; i>=0; i--) 
-		for (j=0; j < nnq->netsize; j++) 
-			putc(nnq->network[j][i], f);
-}	          
-
-/* Output colormap to unsigned char ptr in RGBA format */
-void getcolormap(nnq, map)
-	nn_quant *nnq;
-	unsigned char *map;
-{
-	int i,j;
-	for(j=0; j < nnq->netsize; j++){
-		for (i=3; i>=0; i--){
-			*map = nnq->network[j][i];
-			map++;
-		}
-	}
-}
-
-/* Insertion sort of network and building of netindex[0..255] (to do after unbias)
-   ------------------------------------------------------------------------------- */
-void inxbuild(nn_quant *nnq)
-{
-	register int i,j,smallpos,smallval;
-	register int *p,*q;
-	int previouscol,startpos;
-
-	previouscol = 0;
-	startpos = 0;
-	for (i=0; i < nnq->netsize; i++) {
-		p = nnq->network[i];
-		smallpos = i;
-		smallval = p[2];			/* index on g */
-		/* find smallest in i..netsize-1 */
-		for (j=i+1; j < nnq->netsize; j++) {
-			q = nnq->network[j];
-			if (q[2] < smallval) {		/* index on g */
-				smallpos = j;
-				smallval = q[2];	/* index on g */
-			}
-		}
-		q = nnq->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;
-			j = q[4];   q[4] = p[4];   p[4] = j;
-		}
-		/* smallval entry is now in position i */
-		if (smallval != previouscol) {
-			nnq->netindex[previouscol] = (startpos+i)>>1;
-			for (j=previouscol+1; j<smallval; j++) nnq->netindex[j] = i;
-			previouscol = smallval;
-			startpos = i;
-		}
-	}
-	nnq->netindex[previouscol] = (startpos+maxnetpos)>>1;
-	for (j=previouscol+1; j<256; j++) nnq->netindex[j] = maxnetpos; /* really 256 */
-}
-
-
-/* Search for ABGR values 0..255 (after net is unbiased) and return colour index
-	 ---------------------------------------------------------------------------- */
-int inxsearch(nnq, al,b,g,r)
-	nn_quant *nnq;
-	register int al, b, g, r;
-{
-	register int i, j, dist, a, bestd;
-	register int *p;
-	int best;
-
-	bestd = 1000;		/* biggest possible dist is 256*3 */
-	best = -1;
-	i = nnq->netindex[g];	/* index on g */
-	j = i-1;		/* start at netindex[g] and work outwards */
-
-	while ((i<nnq->netsize) || (j>=0)) {
-		if (i< nnq->netsize) {
-			p = nnq->network[i];
-			dist = p[2] - g;		/* inx key */
-			if (dist >= bestd) i = nnq->netsize;	/* stop iter */
-			else {
-				i++;
-				if (dist<0) dist = -dist;
-				a = p[1] - b;   if (a<0) a = -a;
-				dist += a;
-				if (dist<bestd) {
-					a = p[3] - r;   if (a<0) a = -a;
-					dist += a;
-				}
-				if(dist<bestd) {
-					a = p[0] - al; if (a<0) a = -a;
-					dist += a;
-				}
-				if (dist<bestd) {bestd=dist; best=p[4];}
-			}
-		}
-
-		if (j>=0) {
-			p = nnq->network[j];
-			dist = g - p[2]; /* inx key - reverse dif */
-			if (dist >= bestd) j = -1; /* stop iter */
-			else {
-				j--;
-				if (dist<0) dist = -dist;
-				a = p[1] - b;   if (a<0) a = -a;
-				dist += a;
-				if (dist<bestd) {
-					a = p[3] - r;   if (a<0) a = -a;
-					dist += a;
-				}
-				if(dist<bestd) {
-					a = p[0] - al; if (a<0) a = -a;
-					dist += a;
-				}			
-				if (dist<bestd) {bestd=dist; best=p[4];}
-			}
-		}
-	}
-
-	return(best);
-}
-
-/* Search for biased ABGR values
-   ---------------------------- */
-int contest(nnq, al,b,g,r)
-	nn_quant *nnq;
-	register int al,b,g,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]) */
-
-	register int i,dist,a,biasdist,betafreq;
-	int bestpos,bestbiaspos,bestd,bestbiasd;
-	register int *p,*f, *n;
-
-	bestd = ~(((int) 1)<<31);
-	bestbiasd = bestd;
-	bestpos = -1;
-	bestbiaspos = bestpos;
-	p = nnq->bias;
-	f = nnq->freq;
-
-	for (i=0; i< nnq->netsize; i++) {
-		n = nnq->network[i];
-		dist = n[0] - al;   if (dist<0) dist = -dist;
-		a = n[1] - b;   if (a<0) a = -a;
-		dist += a;
-		a = n[2] - g;   if (a<0) a = -a;
-		dist += a;
-		a = n[3] - r;   if (a<0) a = -a;
-		dist += a;
-		if (dist<bestd) {bestd=dist; bestpos=i;}
-		biasdist = dist - ((*p)>>(intbiasshift-netbiasshift));
-		if (biasdist<bestbiasd) {bestbiasd=biasdist; bestbiaspos=i;}
-		betafreq = (*f >> betashift);
-		*f++ -= betafreq;
-		*p++ += (betafreq<<gammashift);
-	}
-	nnq->freq[bestpos] += beta;
-	nnq->bias[bestpos] -= betagamma;
-	return(bestbiaspos);
-}
-
-
-/* Move neuron i towards biased (a,b,g,r) by factor alpha
-	 ---------------------------------------------------- */
-
-void altersingle(nnq, alpha,i,al,b,g,r)
-	nn_quant *nnq;
-	register int alpha,i,al,b,g,r;
-{
-	register int *n;
-
-	n = nnq->network[i];	/* alter hit neuron */
-	*n -= (alpha*(*n - al)) / initalpha;
-	n++;
-	*n -= (alpha*(*n - b)) / initalpha;
-	n++;
-	*n -= (alpha*(*n - g)) / initalpha;
-	n++;
-	*n -= (alpha*(*n - r)) / initalpha;
-}
-
-
-/* Move adjacent neurons by precomputed alpha*(1-((i-j)^2/[r]^2)) in radpower[|i-j|]
-	 --------------------------------------------------------------------------------- */
-
-void alterneigh(nnq, rad,i,al,b,g,r)
-	nn_quant *nnq;
-	int rad,i;
-	register int al,b,g,r;
-{
-	register int j,k,lo,hi,a;
-	register int *p, *q;
-
-	lo = i-rad;   if (lo<-1) lo=-1;
-	hi = i+rad;   if (hi>nnq->netsize) hi=nnq->netsize;
-
-	j = i+1;
-	k = i-1;
-	q = nnq->radpower;
-	while ((j<hi) || (k>lo)) {
-		a = (*(++q));
-		if (j<hi) {
-			p = nnq->network[j];
-			*p -= (a*(*p - al)) / alpharadbias;
-			p++;
-			*p -= (a*(*p - b)) / alpharadbias;
-			p++;
-			*p -= (a*(*p - g)) / alpharadbias;
-			p++;
-			*p -= (a*(*p - r)) / alpharadbias;
-			j++;
-		}
-		if (k>lo) {
-			p = nnq->network[k];
-			*p -= (a*(*p - al)) / alpharadbias;
-			p++;
-			*p -= (a*(*p - b)) / alpharadbias;
-			p++;
-			*p -= (a*(*p - g)) / alpharadbias;
-			p++;
-			*p -= (a*(*p - r)) / alpharadbias;
-			k--;
-		}
-	}
-}
-
-
-/* Main Learning Loop
-   ------------------ */
-
-void learn(nnq, verbose) /* Stu: N.B. added parameter so that main() could control verbosity. */
-	nn_quant *nnq;
-	int verbose;
-{
-	register int i,j,al,b,g,r;
-	int radius,rad,alpha,step,delta,samplepixels;
-	register unsigned char *p;
-	unsigned char *lim;
-
-	nnq->alphadec = 30 + ((nnq->samplefac-1)/3);
-	p = nnq->thepicture;
-	lim = nnq->thepicture + nnq->lengthcount;
-	samplepixels = nnq->lengthcount/(4 * nnq->samplefac); 
-	/* here's a problem with small images: samplepixels < ncycles => delta = 0 */
-	delta = samplepixels/ncycles; 
-	/* kludge to fix */
-	if(delta==0) delta = 1; 
-	alpha = initalpha;
-	radius = initradius;
-
-	rad = radius >> radiusbiasshift;
-	if (rad <= 1) rad = 0;
-	for (i=0; i<rad; i++) 
-		nnq->radpower[i] = alpha*(((rad*rad - i*i)*radbias)/(rad*rad));
-
-	if(verbose) fprintf(stderr,"beginning 1D learning: initial radius=%d\n", rad);
-
-	if ((nnq->lengthcount%prime1) != 0) step = 4*prime1;
-	else {
-		if ((nnq->lengthcount%prime2) !=0) step = 4*prime2;
-		else {
-			if ((nnq->lengthcount%prime3) !=0) step = 4*prime3;
-			else step = 4*prime4;
-		}
-	}
-
-	i = 0;
-	while (i < samplepixels) {
-		al = p[3] << netbiasshift;
-		b = p[2] << netbiasshift;
-		g = p[1] << netbiasshift;
-		r = p[0] << netbiasshift;
-		j = contest(nnq, al,b,g,r);
-
-		altersingle(nnq, alpha,j,al,b,g,r);
-		if (rad) alterneigh(nnq, rad,j,al,b,g,r);   /* alter neighbours */
-
-		p += step;
-		while (p >= lim) p -= nnq->lengthcount;
-
-		i++;
-		if (i%delta == 0) {                    /* FPE here if delta=0*/	
-			alpha -= alpha / nnq->alphadec;
-			radius -= radius / radiusdec;
-			rad = radius >> radiusbiasshift;
-			if (rad <= 1) rad = 0;
-			for (j=0; j<rad; j++) 
-				nnq->radpower[j] = alpha*(((rad*rad - j*j)*radbias)/(rad*rad));
-		}
-	}
-	if(verbose) fprintf(stderr,"finished 1D learning: final alpha=%f !\n",((float)alpha)/initalpha);
-}
-
-gdImagePtr gdImageNeuQuant(gdImagePtr im, const int max_color, int sample_factor)
-{
-	sample_factor = 3;
-	const int newcolors = max_color;
-	const int verbose = 1;
-
-  int bot_idx, top_idx; /* for remapping of indices */
-  int remap[MAXNETSIZE];
-  int i,x;
-
-	unsigned char map[MAXNETSIZE][4];
-	unsigned char *d;
-
-	nn_quant *nnq = NULL;
-
-	int row;
-	unsigned char *rgba;
-	gdImagePtr dst;
-
-	/* Start neuquant */
-
-	if (!im->trueColor) {
-		rgba = (unsigned char *) gdMalloc(gdImageSX(im) * gdImageSY(im) * 4);
-		if (!rgba) {
-			return NULL;
-		}
-		d = rgba;
-		for (row = 0; row < gdImageSY(im); row++) {
-			int *p = im->tpixels[row];
-
-			for (i = 0; i < gdImageSX(im); i++) {
-				*d++ = gdImageRed(im, *p);
-				*d++ = gdImageGreen(im, (*p));
-				*d++ = gdImageBlue(im, (*p));
-				*d++ = gdImageAlpha(im, (*p++));
-			}
-		}
-
-			if (!nnq) {
-			return NULL;
-		}
-	} else {
-		rgba = (unsigned char *)im->tpixels;
-	}
-
-	nnq = (nn_quant *) gdMalloc(sizeof(nn_quant));
-
-	initnet(nnq, rgba, gdImageSY(im) * gdImageSX(im) * 4, sample_factor, newcolors);
-
-	learn(nnq, verbose);
-	unbiasnet(nnq);
-	getcolormap(nnq, (unsigned char*)map);
-	inxbuild(nnq); 
-	/* remapping colormap to eliminate opaque tRNS-chunk entries... */
-	for (top_idx = newcolors-1, bot_idx = x = 0;  x < newcolors;  ++x) {
-		if (map[x][3] == 255) /* maxval */
-			remap[x] = top_idx--;
-		else
-			remap[x] = bot_idx++;
-	}
-	if (bot_idx != top_idx + 1) {
-		fprintf(stderr,
-				"  internal logic error: remapped bot_idx = %d, top_idx = %d\n",
-				bot_idx, top_idx);
-		fflush(stderr);
-		return NULL;
-	}
-
-	dst = gdImageCreate(gdImageSX(im), gdImageSY(im));
-	if (!dst) {
-		return NULL;
-	}
-
-	for (x = 0; x < newcolors; ++x) {
-		dst->red[remap[x]] = map[x][0];
-		dst->green[remap[x]] = map[x][1];
-		dst->blue[remap[x]] = map[x][2];
-		dst->alpha[remap[x]] = map[x][3];
-		dst->open[remap[x]] = 0;
-		dst->colorsTotal++;
-	}
-
-	/* Do each image row */
-	for ( row = 0; row < gdImageSY(im); ++row ) {
-		int offset;
-		unsigned char *p = dst->pixels[row];
-
-		/* Assign the new colors */
-		offset = row * gdImageSX(im) * 4;
-		for(i=0; i < gdImageSX(im); i++){
-			p[i] = remap[
-						inxsearch(nnq, rgba[i * 4 + offset + 3],
-						rgba[i * 4 + offset + 2],
-						rgba[i * 4 + offset + 1],
-						rgba[i * 4 + offset + 0])
-					];
-		}
-	}
-	return dst;
-}