/* 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 2004-2007

 * From: http://pngnq.sourceforge.net/

 *

 * Ported to libgd by Pierre A. Joye

 * (and make it thread safety by droping static and  global variables)

 */

#ifdef HAVE_CONFIG_H

#include "config.h"

#endif /* HAVE_CONFIG_H */

#include <stdlib.h>

#include <string.h>

#include "gd.h"

#include "gdhelpers.h"

#include "gd_errors.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)<

#define gammashift  	10			/* gamma = 1024 */

#define gamma   	(((int) 1)<

#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)<

#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)<

int alphadec;

/* radbias and alpharadbias used for radpower calculation */

#define radbiasshift	8

#define radbias		(((int) 1)<

#define alpharadbshift  (alphabiasshift+radbiasshift)

#define alpharadbias    (((int) 1)<

#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

	 ---------------------------------------------------------------------------- */

unsigned 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;

	unsigned int best;

	bestd = 1000;		/* biggest possible dist is 256*3 */

	best = 0;

	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

					a = p[3] - r;

					if (a<0) a = -a;

					dist += a;

				}

				if(dist

					a = p[0] - al;

					if (a<0) a = -a;

					dist += a;

				}

				if (dist

					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

					a = p[3] - r;

					if (a<0) a = -a;

					dist += a;

				}

				if(dist

					a = p[0] - al;

					if (a<0) a = -a;

					dist += a;

				}

				if (dist

					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;

	unsigned int bestpos,bestbiaspos;

	double bestd,bestbiasd;

	register int *p,*f, *n;

	bestd = ~(((int) 1)<<31);

	bestbiasd = bestd;

	bestpos = 0;

	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=dist;

			bestpos=i;

		}

		biasdist = dist - ((*p)>>(intbiasshift-netbiasshift));

		if (biasdist

			bestbiasd=biasdist;

			bestbiaspos=i;

		}

		betafreq = (*f >> betashift);

		*f++ -= betafreq;

		*p++ += (betafreq<

	}

	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

			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;

	for (i=0; i

		nnq->radpower[i] = alpha*(((rad*rad - i*i)*radbias)/(rad*rad));

	if (verbose) gd_error_ex(GD_NOTICE, "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[ALPHA] << netbiasshift;

		b = p[BLUE] << netbiasshift;

		g = p[GREEN] << netbiasshift;

		r = p[RED] << 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

				nnq->radpower[j] = alpha*(((rad*rad - j*j)*radbias)/(rad*rad));

		}

	}

	if (verbose) gd_error_ex(GD_NOTICE, "finished 1D learning: final alpha=%f !\n",((float)alpha)/initalpha);

}

/**

 * Function: gdImageNeuQuant

 *

 * Creates a new palette image from a truecolor image

 *

 * This is the same as calling <gdImageCreatePaletteFromTrueColor> with the

 * quantization method <GD_QUANT_NEUQUANT>.

 *

 * Parameters:

 *   im            - The image.

 *   max_color     - The number of desired palette entries.

 *   sample_factor - The quantization precision between 1 (highest quality) and

 *                   10 (fastest).

 *

 * Returns:

 *   A newly create palette image; NULL on failure.

 */

BGD_DECLARE(gdImagePtr) gdImageNeuQuant(gdImagePtr im, const int max_color, int sample_factor)

{

	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 = NULL;

	gdImagePtr dst = NULL;

	/* Default it to 3 */

	if (sample_factor < 1) {

		sample_factor = 3;

	}

	/* Start neuquant */

	/* Pierre:

	 * This implementation works with aligned contiguous buffer only

	 * Upcoming new buffers are contiguous and will be much faster.

	 * let don't bloat this code to support our good "old" 31bit format.

	 * It alos lets us convert palette image, if one likes to reduce

	 * a palette

	 */

	if (overflow2(gdImageSX(im), gdImageSY(im))

	        || overflow2(gdImageSX(im) * gdImageSY(im), 4)) {

		goto done;

	}

	rgba = (unsigned char *) gdMalloc(gdImageSX(im) * gdImageSY(im) * 4);

	if (!rgba) {

		goto done;

	}

	d = rgba;

	for (row = 0; row < gdImageSY(im); row++) {

		int *p = im->tpixels[row];

		register int c;

		for (i = 0; i < gdImageSX(im); i++) {

			c = *p;

			*d++ = gdImageAlpha(im, c);

			*d++ = gdImageRed(im, c);

			*d++ = gdImageBlue(im, c);

			*d++ = gdImageGreen(im, c);

			p++;

		}

	}

	nnq = (nn_quant *) gdMalloc(sizeof(nn_quant));

	if (!nnq) {

		goto done;

	}

	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) {

		gd_error("  internal logic error: remapped bot_idx = %d, top_idx = %d\n",

			 bot_idx, top_idx);

		goto done;

	}

	dst = gdImageCreate(gdImageSX(im), gdImageSY(im));

	if (!dst) {

		goto done;

	}

	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 + ALPHA],

			                     rgba[i * 4 + offset + BLUE],

			                     rgba[i * 4 + offset + GREEN],

			                     rgba[i * 4 + offset + RED])

			       ];

		}

	}

done:

	if (rgba) {

		gdFree(rgba);

	}

	if (nnq) {

		gdFree(nnq);

	}

	return dst;

}