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#ifndef lint
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static const char RCSid[] = "$Id: neuclrtab.c,v 2.10 2003/06/30 14:59:12 schorsch Exp $";
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#endif
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/*
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* Neural-Net quantization algorithm based on work of Anthony Dekker
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*/
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#include "copyright.h"
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#include <string.h>
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#include "standard.h"
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#include "color.h"
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#include "random.h"
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#include "clrtab.h"
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#ifdef COMPAT_MODE
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#define neu_init new_histo
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#define neu_pixel cnt_pixel
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#define neu_colrs cnt_colrs
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#define neu_clrtab new_clrtab
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#define neu_map_pixel map_pixel
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#define neu_map_colrs map_colrs
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#define neu_dith_colrs dith_colrs
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#endif
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/* our color table (global) */
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extern BYTE clrtab[256][3];
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static int clrtabsiz;
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#ifndef DEFSMPFAC
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#ifdef SPEED
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#define DEFSMPFAC (240/SPEED+3)
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#else
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#define DEFSMPFAC 30
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#endif
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#endif
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int samplefac = DEFSMPFAC; /* sampling factor */
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/* Samples array starts off holding spacing between adjacent
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* samples, and ends up holding actual BGR sample values.
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*/
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static BYTE *thesamples;
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static int nsamples;
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static BYTE *cursamp;
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static long skipcount;
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#define MAXSKIP (1<<24-1)
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#define nskip(sp) ((long)(sp)[0]<<16|(long)(sp)[1]<<8|(long)(sp)[2])
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#define setskip(sp,n) ((sp)[0]=(n)>>16,(sp)[1]=((n)>>8)&255,(sp)[2]=(n)&255)
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static void initnet(void);
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static void inxbuild(void);
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static int inxsearch(int b, int g, int r);
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static int contest(int b, int g, int r);
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static void altersingle(int alpha, int i, int b, int g, int r);
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static void alterneigh(int rad, int i, int b, int g, int r);
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static void learn(void);
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static void unbiasnet(void);
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static void cpyclrtab(void);
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extern int
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neu_init( /* initialize our sample array */
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long npixels
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)
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{
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register int nsleft;
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register long sv;
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double rval, cumprob;
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long npleft;
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nsamples = npixels/samplefac;
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if (nsamples < 600)
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return(-1);
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thesamples = (BYTE *)malloc(nsamples*3);
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if (thesamples == NULL)
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return(-1);
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cursamp = thesamples;
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npleft = npixels;
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nsleft = nsamples;
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while (nsleft) {
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rval = frandom(); /* random distance to next sample */
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sv = 0;
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cumprob = 0.;
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while ((cumprob += (1.-cumprob)*nsleft/(npleft-sv)) < rval)
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sv++;
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if (nsleft == nsamples)
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skipcount = sv;
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else {
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setskip(cursamp, sv);
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cursamp += 3;
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}
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npleft -= sv+1;
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nsleft--;
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}
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setskip(cursamp, npleft); /* tag on end to skip the rest */
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cursamp = thesamples;
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return(0);
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}
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extern void
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neu_pixel( /* add pixel to our samples */
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register BYTE col[]
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)
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{
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if (!skipcount--) {
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skipcount = nskip(cursamp);
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cursamp[0] = col[BLU];
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cursamp[1] = col[GRN];
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cursamp[2] = col[RED];
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cursamp += 3;
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}
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}
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extern void
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neu_colrs( /* add a scanline to our samples */
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register COLR *cs,
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register int n
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)
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{
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while (n > skipcount) {
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cs += skipcount;
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n -= skipcount+1;
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skipcount = nskip(cursamp);
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cursamp[0] = cs[0][BLU];
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cursamp[1] = cs[0][GRN];
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cursamp[2] = cs[0][RED];
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cs++;
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cursamp += 3;
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}
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skipcount -= n;
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}
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extern int
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neu_clrtab( /* make new color table using ncolors */
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int ncolors
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)
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{
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clrtabsiz = ncolors;
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if (clrtabsiz > 256) clrtabsiz = 256;
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initnet();
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learn();
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unbiasnet();
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cpyclrtab();
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inxbuild();
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/* we're done with our samples */
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free((void *)thesamples);
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/* reset dithering function */
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neu_dith_colrs((BYTE *)NULL, (COLR *)NULL, 0);
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/* return new color table size */
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return(clrtabsiz);
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}
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extern int
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neu_map_pixel( /* get pixel for color */
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register BYTE col[]
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)
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{
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return(inxsearch(col[BLU],col[GRN],col[RED]));
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}
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extern void
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neu_map_colrs( /* convert a scanline to color index values */
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register BYTE *bs,
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register COLR *cs,
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register int n
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)
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{
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while (n-- > 0) {
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*bs++ = inxsearch(cs[0][BLU],cs[0][GRN],cs[0][RED]);
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cs++;
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}
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}
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extern void
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neu_dith_colrs( /* convert scanline to dithered index values */
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register BYTE *bs,
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register COLR *cs,
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int n
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)
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{
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static short (*cerr)[3] = NULL;
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static int N = 0;
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int err[3], errp[3];
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register int x, i;
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if (n != N) { /* get error propogation array */
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if (N) {
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free((void *)cerr);
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cerr = NULL;
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}
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if (n)
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cerr = (short (*)[3])malloc(3*n*sizeof(short));
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if (cerr == NULL) {
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N = 0;
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map_colrs(bs, cs, n);
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return;
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}
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N = n;
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memset((char *)cerr, '\0', 3*N*sizeof(short));
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}
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err[0] = err[1] = err[2] = 0;
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for (x = 0; x < n; x++) {
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for (i = 0; i < 3; i++) { /* dither value */
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errp[i] = err[i];
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err[i] += cerr[x][i];
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#ifdef MAXERR
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if (err[i] > MAXERR) err[i] = MAXERR;
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else if (err[i] < -MAXERR) err[i] = -MAXERR;
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#endif
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err[i] += cs[x][i];
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if (err[i] < 0) err[i] = 0;
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else if (err[i] > 255) err[i] = 255;
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}
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bs[x] = inxsearch(err[BLU],err[GRN],err[RED]);
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for (i = 0; i < 3; i++) { /* propagate error */
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err[i] -= clrtab[bs[x]][i];
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err[i] /= 3;
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cerr[x][i] = err[i] + errp[i];
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}
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}
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}
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/* The following was adapted and modified from the original (GW) */
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/* cheater definitions (GW) */
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#define thepicture thesamples
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#define lengthcount (nsamples*3)
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#define samplefac 1
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/*----------------------------------------------------------------------*/
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/* */
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/* NeuQuant */
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/* -------- */
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/* */
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/* Copyright: Anthony Dekker, November 1994 */
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/* */
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/* This program performs colour quantization of graphics images (SUN */
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/* raster files). It uses a Kohonen Neural Network. It produces */
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/* better results than existing methods and runs faster, using minimal */
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/* space (8kB plus the image itself). The algorithm is described in */
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/* the paper "Kohonen Neural Networks for Optimal Colour Quantization" */
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/* to appear in the journal "Network: Computation in Neural Systems". */
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/* It is a significant improvement of an earlier algorithm. */
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/* */
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/* This program is distributed free for academic use or for evaluation */
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/* by commercial organizations. */
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/* */
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/* Usage: NeuQuant -n inputfile > outputfile */
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/* */
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/* where n is a sampling factor for neural learning. */
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/* */
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/* Program performance compared with other methods is as follows: */
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/* */
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/* Algorithm | Av. CPU Time | Quantization Error */
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/* ------------------------------------------------------------- */
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/* NeuQuant -3 | 314 | 5.55 */
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/* NeuQuant -10 | 119 | 5.97 */
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/* NeuQuant -30 | 65 | 6.53 */
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/* Oct-Trees | 141 | 8.96 */
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/* Median Cut (XV -best) | 420 | 9.28 */
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/* Median Cut (XV -slow) | 72 | 12.15 */
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/* */
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/* Author's address: Dept of ISCS, National University of Singapore */
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/* Kent Ridge, Singapore 0511 */
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/* Email: [email protected] */
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/*----------------------------------------------------------------------*/
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#define bool int
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#define false 0
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#define true 1
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/* network defs */
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#define netsize clrtabsiz /* number of colours - can change this */
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#define maxnetpos (netsize-1)
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#define netbiasshift 4 /* bias for colour values */
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#define ncycles 100 /* no. of learning cycles */
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/* defs for freq and bias */
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#define intbiasshift 16 /* bias for fractions */
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#define intbias (((int) 1)<<intbiasshift)
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#define gammashift 10 /* gamma = 1024 */
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#define gamma (((int) 1)<<gammashift)
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#define betashift 10
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#define beta (intbias>>betashift) /* beta = 1/1024 */
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#define betagamma (intbias<<(gammashift-betashift))
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/* defs for decreasing radius factor */
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#define initrad (256>>3) /* for 256 cols, radius starts */
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#define radiusbiasshift 6 /* at 32.0 biased by 6 bits */
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#define radiusbias (((int) 1)<<radiusbiasshift)
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#define initradius (initrad*radiusbias) /* and decreases by a */
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#define radiusdec 30 /* factor of 1/30 each cycle */
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/* defs for decreasing alpha factor */
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#define alphabiasshift 10 /* alpha starts at 1.0 */
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#define initalpha (((int) 1)<<alphabiasshift)
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int alphadec; /* biased by 10 bits */
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/* radbias and alpharadbias used for radpower calculation */
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#define radbiasshift 8
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#define radbias (((int) 1)<<radbiasshift)
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#define alpharadbshift (alphabiasshift+radbiasshift)
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#define alpharadbias (((int) 1)<<alpharadbshift)
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/* four primes near 500 - assume no image has a length so large */
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/* that it is divisible by all four primes */
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#define prime1 499
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#define prime2 491
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#define prime3 487
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#define prime4 503
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typedef int pixel[4]; /* BGRc */
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pixel network[256];
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int netindex[256]; /* for network lookup - really 256 */
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int bias [256]; /* bias and freq arrays for learning */
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int freq [256];
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int radpower[initrad]; /* radpower for precomputation */
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/* initialise network in range (0,0,0) to (255,255,255) */
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static void
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initnet(void)
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{
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register int i;
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register int *p;
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for (i=0; i<netsize; i++) {
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p = network[i];
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p[0] = p[1] = p[2] = (i << (netbiasshift+8))/netsize;
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freq[i] = intbias/netsize; /* 1/netsize */
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bias[i] = 0;
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}
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}
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/* do after unbias - insertion sort of network and build netindex[0..255] */
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static void
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inxbuild(void)
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{
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register int i,j,smallpos,smallval;
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register int *p,*q;
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int previouscol,startpos;
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previouscol = 0;
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startpos = 0;
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for (i=0; i<netsize; i++) {
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p = network[i];
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smallpos = i;
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smallval = p[1]; /* index on g */
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/* find smallest in i..netsize-1 */
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for (j=i+1; j<netsize; j++) {
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q = network[j];
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if (q[1] < smallval) { /* index on g */
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smallpos = j;
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smallval = q[1]; /* index on g */
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}
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}
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q = network[smallpos];
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/* swap p (i) and q (smallpos) entries */
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if (i != smallpos) {
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j = q[0]; q[0] = p[0]; p[0] = j;
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j = q[1]; q[1] = p[1]; p[1] = j;
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j = q[2]; q[2] = p[2]; p[2] = j;
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j = q[3]; q[3] = p[3]; p[3] = j;
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}
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/* smallval entry is now in position i */
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if (smallval != previouscol) {
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netindex[previouscol] = (startpos+i)>>1;
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for (j=previouscol+1; j<smallval; j++) netindex[j] = i;
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previouscol = smallval;
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startpos = i;
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}
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}
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netindex[previouscol] = (startpos+maxnetpos)>>1;
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for (j=previouscol+1; j<256; j++) netindex[j] = maxnetpos; /* really 256 */
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}
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static int
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inxsearch( /* accepts real BGR values after net is unbiased */
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register int b,
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register int g,
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| 397 |
register int r
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| 398 |
)
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| 399 |
{
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| 400 |
register int i,j,dist,a,bestd;
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| 401 |
register int *p;
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| 402 |
int best;
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| 403 |
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| 404 |
bestd = 1000; /* biggest possible dist is 256*3 */
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| 405 |
best = -1;
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| 406 |
i = netindex[g]; /* index on g */
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| 407 |
j = i-1; /* start at netindex[g] and work outwards */
|
| 408 |
|
| 409 |
while ((i<netsize) || (j>=0)) {
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| 410 |
if (i<netsize) {
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| 411 |
p = network[i];
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| 412 |
dist = p[1] - g; /* inx key */
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| 413 |
if (dist >= bestd) i = netsize; /* stop iter */
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| 414 |
else {
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| 415 |
i++;
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| 416 |
if (dist<0) dist = -dist;
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| 417 |
a = p[0] - b; if (a<0) a = -a;
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| 418 |
dist += a;
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| 419 |
if (dist<bestd) {
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| 420 |
a = p[2] - r; if (a<0) a = -a;
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| 421 |
dist += a;
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| 422 |
if (dist<bestd) {bestd=dist; best=p[3];}
|
| 423 |
}
|
| 424 |
}
|
| 425 |
}
|
| 426 |
if (j>=0) {
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| 427 |
p = network[j];
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| 428 |
dist = g - p[1]; /* inx key - reverse dif */
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| 429 |
if (dist >= bestd) j = -1; /* stop iter */
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| 430 |
else {
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| 431 |
j--;
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| 432 |
if (dist<0) dist = -dist;
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| 433 |
a = p[0] - b; if (a<0) a = -a;
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| 434 |
dist += a;
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| 435 |
if (dist<bestd) {
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| 436 |
a = p[2] - r; if (a<0) a = -a;
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| 437 |
dist += a;
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| 438 |
if (dist<bestd) {bestd=dist; best=p[3];}
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| 439 |
}
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| 440 |
}
|
| 441 |
}
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| 442 |
}
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| 443 |
return(best);
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| 444 |
}
|
| 445 |
|
| 446 |
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| 447 |
/* finds closest neuron (min dist) and updates freq */
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| 448 |
/* finds best neuron (min dist-bias) and returns position */
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| 449 |
/* for frequently chosen neurons, freq[i] is high and bias[i] is negative */
|
| 450 |
/* bias[i] = gamma*((1/netsize)-freq[i]) */
|
| 451 |
|
| 452 |
static int
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| 453 |
contest( /* accepts biased BGR values */
|
| 454 |
register int b,
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| 455 |
register int g,
|
| 456 |
register int r
|
| 457 |
)
|
| 458 |
{
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| 459 |
register int i,dist,a,biasdist,betafreq;
|
| 460 |
int bestpos,bestbiaspos,bestd,bestbiasd;
|
| 461 |
register int *p,*f, *n;
|
| 462 |
|
| 463 |
bestd = ~(((int) 1)<<31);
|
| 464 |
bestbiasd = bestd;
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| 465 |
bestpos = -1;
|
| 466 |
bestbiaspos = bestpos;
|
| 467 |
p = bias;
|
| 468 |
f = freq;
|
| 469 |
|
| 470 |
for (i=0; i<netsize; i++) {
|
| 471 |
n = network[i];
|
| 472 |
dist = n[0] - b; if (dist<0) dist = -dist;
|
| 473 |
a = n[1] - g; if (a<0) a = -a;
|
| 474 |
dist += a;
|
| 475 |
a = n[2] - r; if (a<0) a = -a;
|
| 476 |
dist += a;
|
| 477 |
if (dist<bestd) {bestd=dist; bestpos=i;}
|
| 478 |
biasdist = dist - ((*p)>>(intbiasshift-netbiasshift));
|
| 479 |
if (biasdist<bestbiasd) {bestbiasd=biasdist; bestbiaspos=i;}
|
| 480 |
betafreq = (*f >> betashift);
|
| 481 |
*f++ -= betafreq;
|
| 482 |
*p++ += (betafreq<<gammashift);
|
| 483 |
}
|
| 484 |
freq[bestpos] += beta;
|
| 485 |
bias[bestpos] -= betagamma;
|
| 486 |
return(bestbiaspos);
|
| 487 |
}
|
| 488 |
|
| 489 |
|
| 490 |
/* move neuron i towards (b,g,r) by factor alpha */
|
| 491 |
|
| 492 |
static void
|
| 493 |
altersingle( /* accepts biased BGR values */
|
| 494 |
register int alpha,
|
| 495 |
register int i,
|
| 496 |
register int b,
|
| 497 |
register int g,
|
| 498 |
register int r
|
| 499 |
)
|
| 500 |
{
|
| 501 |
register int *n;
|
| 502 |
|
| 503 |
n = network[i]; /* alter hit neuron */
|
| 504 |
*n -= (alpha*(*n - b)) / initalpha;
|
| 505 |
n++;
|
| 506 |
*n -= (alpha*(*n - g)) / initalpha;
|
| 507 |
n++;
|
| 508 |
*n -= (alpha*(*n - r)) / initalpha;
|
| 509 |
}
|
| 510 |
|
| 511 |
|
| 512 |
/* move neurons adjacent to i towards (b,g,r) by factor */
|
| 513 |
/* alpha*(1-((i-j)^2/[r]^2)) precomputed as radpower[|i-j|]*/
|
| 514 |
|
| 515 |
static void
|
| 516 |
alterneigh( /* accents biased BGR values */
|
| 517 |
int rad,
|
| 518 |
int i,
|
| 519 |
register int b,
|
| 520 |
register int g,
|
| 521 |
register int r
|
| 522 |
)
|
| 523 |
{
|
| 524 |
register int j,k,lo,hi,a;
|
| 525 |
register int *p, *q;
|
| 526 |
|
| 527 |
lo = i-rad; if (lo<-1) lo= -1;
|
| 528 |
hi = i+rad; if (hi>netsize) hi=netsize;
|
| 529 |
|
| 530 |
j = i+1;
|
| 531 |
k = i-1;
|
| 532 |
q = radpower;
|
| 533 |
while ((j<hi) || (k>lo)) {
|
| 534 |
a = (*(++q));
|
| 535 |
if (j<hi) {
|
| 536 |
p = network[j];
|
| 537 |
*p -= (a*(*p - b)) / alpharadbias;
|
| 538 |
p++;
|
| 539 |
*p -= (a*(*p - g)) / alpharadbias;
|
| 540 |
p++;
|
| 541 |
*p -= (a*(*p - r)) / alpharadbias;
|
| 542 |
j++;
|
| 543 |
}
|
| 544 |
if (k>lo) {
|
| 545 |
p = network[k];
|
| 546 |
*p -= (a*(*p - b)) / alpharadbias;
|
| 547 |
p++;
|
| 548 |
*p -= (a*(*p - g)) / alpharadbias;
|
| 549 |
p++;
|
| 550 |
*p -= (a*(*p - r)) / alpharadbias;
|
| 551 |
k--;
|
| 552 |
}
|
| 553 |
}
|
| 554 |
}
|
| 555 |
|
| 556 |
|
| 557 |
static void
|
| 558 |
learn(void)
|
| 559 |
{
|
| 560 |
register int i,j,b,g,r;
|
| 561 |
int radius,rad,alpha,step,delta,samplepixels;
|
| 562 |
register unsigned char *p;
|
| 563 |
unsigned char *lim;
|
| 564 |
|
| 565 |
alphadec = 30 + ((samplefac-1)/3);
|
| 566 |
p = thepicture;
|
| 567 |
lim = thepicture + lengthcount;
|
| 568 |
samplepixels = lengthcount/(3*samplefac);
|
| 569 |
delta = samplepixels/ncycles;
|
| 570 |
alpha = initalpha;
|
| 571 |
radius = initradius;
|
| 572 |
|
| 573 |
rad = radius >> radiusbiasshift;
|
| 574 |
if (rad <= 1) rad = 0;
|
| 575 |
for (i=0; i<rad; i++)
|
| 576 |
radpower[i] = alpha*(((rad*rad - i*i)*radbias)/(rad*rad));
|
| 577 |
|
| 578 |
if ((lengthcount%prime1) != 0) step = 3*prime1;
|
| 579 |
else {
|
| 580 |
if ((lengthcount%prime2) !=0) step = 3*prime2;
|
| 581 |
else {
|
| 582 |
if ((lengthcount%prime3) !=0) step = 3*prime3;
|
| 583 |
else step = 3*prime4;
|
| 584 |
}
|
| 585 |
}
|
| 586 |
|
| 587 |
i = 0;
|
| 588 |
while (i < samplepixels) {
|
| 589 |
b = p[0] << netbiasshift;
|
| 590 |
g = p[1] << netbiasshift;
|
| 591 |
r = p[2] << netbiasshift;
|
| 592 |
j = contest(b,g,r);
|
| 593 |
|
| 594 |
altersingle(alpha,j,b,g,r);
|
| 595 |
if (rad) alterneigh(rad,j,b,g,r); /* alter neighbours */
|
| 596 |
|
| 597 |
p += step;
|
| 598 |
if (p >= lim) p -= lengthcount;
|
| 599 |
|
| 600 |
i++;
|
| 601 |
if (i%delta == 0) {
|
| 602 |
alpha -= alpha / alphadec;
|
| 603 |
radius -= radius / radiusdec;
|
| 604 |
rad = radius >> radiusbiasshift;
|
| 605 |
if (rad <= 1) rad = 0;
|
| 606 |
for (j=0; j<rad; j++)
|
| 607 |
radpower[j] = alpha*(((rad*rad - j*j)*radbias)/(rad*rad));
|
| 608 |
}
|
| 609 |
}
|
| 610 |
}
|
| 611 |
|
| 612 |
/* unbias network to give 0..255 entries */
|
| 613 |
/* which can then be used for colour map */
|
| 614 |
/* and record position i to prepare for sort */
|
| 615 |
|
| 616 |
static void
|
| 617 |
unbiasnet(void)
|
| 618 |
{
|
| 619 |
int i,j;
|
| 620 |
|
| 621 |
for (i=0; i<netsize; i++) {
|
| 622 |
for (j=0; j<3; j++)
|
| 623 |
network[i][j] >>= netbiasshift;
|
| 624 |
network[i][3] = i; /* record colour no */
|
| 625 |
}
|
| 626 |
}
|
| 627 |
|
| 628 |
|
| 629 |
/* Don't do this until the network has been unbiased (GW) */
|
| 630 |
|
| 631 |
static void
|
| 632 |
cpyclrtab(void)
|
| 633 |
{
|
| 634 |
register int i,j,k;
|
| 635 |
|
| 636 |
for (j=0; j<netsize; j++) {
|
| 637 |
k = network[j][3];
|
| 638 |
for (i = 0; i < 3; i++)
|
| 639 |
clrtab[k][i] = network[j][2-i];
|
| 640 |
}
|
| 641 |
}
|
