/* 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)<<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
---------------------------------------------------------------------------- */
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<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;
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) {
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;
for (i=0; i<rad; 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<rad; 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;
}