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greg |
2.1 |
#ifndef lint |
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schorsch |
2.10 |
static const char RCSid[] = "$Id: neuclrtab.c,v 2.9 2003/02/22 02:07:27 greg Exp $"; |
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greg |
2.1 |
#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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schorsch |
2.10 |
#include "copyright.h" |
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#include <string.h> |
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greg |
2.1 |
#include "standard.h" |
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#include "color.h" |
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#include "random.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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53 |
greg |
2.8 |
static cpyclrtab(); |
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greg |
2.1 |
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neu_init(npixels) /* initialize our sample array */ |
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long npixels; |
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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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greg |
2.2 |
thesamples = (BYTE *)malloc(nsamples*3); |
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greg |
2.1 |
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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greg |
2.2 |
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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greg |
2.1 |
nsleft--; |
87 |
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} |
88 |
greg |
2.2 |
setskip(cursamp, npleft); /* tag on end to skip the rest */ |
89 |
greg |
2.1 |
cursamp = thesamples; |
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return(0); |
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} |
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neu_pixel(col) /* add pixel to our samples */ |
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register BYTE col[]; |
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{ |
97 |
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if (!skipcount--) { |
98 |
greg |
2.2 |
skipcount = nskip(cursamp); |
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greg |
2.1 |
cursamp[0] = col[BLU]; |
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cursamp[1] = col[GRN]; |
101 |
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cursamp[2] = col[RED]; |
102 |
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cursamp += 3; |
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} |
104 |
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} |
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neu_colrs(cs, n) /* 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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while (n > skipcount) { |
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cs += skipcount; |
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greg |
2.2 |
n -= skipcount+1; |
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skipcount = nskip(cursamp); |
115 |
greg |
2.1 |
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; |
120 |
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} |
121 |
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skipcount -= n; |
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} |
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neu_clrtab(ncolors) /* make new color table using ncolors */ |
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int ncolors; |
127 |
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{ |
128 |
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clrtabsiz = ncolors; |
129 |
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if (clrtabsiz > 256) clrtabsiz = 256; |
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initnet(); |
131 |
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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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greg |
2.9 |
free((void *)thesamples); |
137 |
greg |
2.1 |
/* 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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int |
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neu_map_pixel(col) /* get pixel for color */ |
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register BYTE col[]; |
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{ |
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return(inxsearch(col[BLU],col[GRN],col[RED])); |
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} |
150 |
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152 |
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neu_map_colrs(bs, cs, n) /* convert a scanline to color index values */ |
153 |
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register BYTE *bs; |
154 |
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register COLR *cs; |
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register int n; |
156 |
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{ |
157 |
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while (n-- > 0) { |
158 |
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*bs++ = inxsearch(cs[0][BLU],cs[0][GRN],cs[0][RED]); |
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cs++; |
160 |
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} |
161 |
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} |
162 |
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neu_dith_colrs(bs, cs, n) /* convert scanline to dithered index values */ |
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register BYTE *bs; |
166 |
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register COLR *cs; |
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int n; |
168 |
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{ |
169 |
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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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174 |
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if (n != N) { /* get error propogation array */ |
175 |
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if (N) { |
176 |
greg |
2.9 |
free((void *)cerr); |
177 |
greg |
2.1 |
cerr = NULL; |
178 |
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} |
179 |
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if (n) |
180 |
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cerr = (short (*)[3])malloc(3*n*sizeof(short)); |
181 |
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if (cerr == NULL) { |
182 |
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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; |
187 |
schorsch |
2.10 |
memset((char *)cerr, '\0', 3*N*sizeof(short)); |
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greg |
2.1 |
} |
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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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} |
209 |
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} |
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/* The following was adapted and modified from the original (GW) */ |
212 |
greg |
2.6 |
|
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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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218 |
greg |
2.1 |
/*----------------------------------------------------------------------*/ |
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/* */ |
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/* NeuQuant */ |
221 |
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/* -------- */ |
222 |
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/* */ |
223 |
greg |
2.6 |
/* Copyright: Anthony Dekker, November 1994 */ |
224 |
greg |
2.1 |
/* */ |
225 |
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/* This program performs colour quantization of graphics images (SUN */ |
226 |
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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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/*----------------------------------------------------------------------*/ |
255 |
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256 |
greg |
2.6 |
#define bool int |
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#define false 0 |
258 |
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#define true 1 |
259 |
greg |
2.1 |
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260 |
greg |
2.6 |
/* network defs */ |
261 |
greg |
2.7 |
#define netsize clrtabsiz /* number of colours - can change this */ |
262 |
greg |
2.6 |
#define maxnetpos (netsize-1) |
263 |
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#define netbiasshift 4 /* bias for colour values */ |
264 |
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#define ncycles 100 /* no. of learning cycles */ |
265 |
greg |
2.1 |
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266 |
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/* defs for freq and bias */ |
267 |
greg |
2.6 |
#define intbiasshift 16 /* bias for fractions */ |
268 |
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#define intbias (((int) 1)<<intbiasshift) |
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#define gammashift 10 /* gamma = 1024 */ |
270 |
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#define gamma (((int) 1)<<gammashift) |
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#define betashift 10 |
272 |
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#define beta (intbias>>betashift) /* beta = 1/1024 */ |
273 |
greg |
2.1 |
#define betagamma (intbias<<(gammashift-betashift)) |
274 |
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275 |
greg |
2.6 |
/* defs for decreasing radius factor */ |
276 |
greg |
2.7 |
#define initrad (256>>3) /* for 256 cols, radius starts */ |
277 |
greg |
2.6 |
#define radiusbiasshift 6 /* at 32.0 biased by 6 bits */ |
278 |
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#define radiusbias (((int) 1)<<radiusbiasshift) |
279 |
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#define initradius (initrad*radiusbias) /* and decreases by a */ |
280 |
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#define radiusdec 30 /* factor of 1/30 each cycle */ |
281 |
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282 |
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/* defs for decreasing alpha factor */ |
283 |
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#define alphabiasshift 10 /* alpha starts at 1.0 */ |
284 |
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#define initalpha (((int) 1)<<alphabiasshift) |
285 |
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int alphadec; /* biased by 10 bits */ |
286 |
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287 |
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/* radbias and alpharadbias used for radpower calculation */ |
288 |
greg |
2.1 |
#define radbiasshift 8 |
289 |
greg |
2.6 |
#define radbias (((int) 1)<<radbiasshift) |
290 |
greg |
2.1 |
#define alpharadbshift (alphabiasshift+radbiasshift) |
291 |
greg |
2.6 |
#define alpharadbias (((int) 1)<<alpharadbshift) |
292 |
greg |
2.1 |
|
293 |
greg |
2.6 |
/* four primes near 500 - assume no image has a length so large */ |
294 |
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/* that it is divisible by all four primes */ |
295 |
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#define prime1 499 |
296 |
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#define prime2 491 |
297 |
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#define prime3 487 |
298 |
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#define prime4 503 |
299 |
greg |
2.1 |
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300 |
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typedef int pixel[4]; /* BGRc */ |
301 |
greg |
2.7 |
pixel network[256]; |
302 |
greg |
2.1 |
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303 |
greg |
2.6 |
int netindex[256]; /* for network lookup - really 256 */ |
304 |
greg |
2.1 |
|
305 |
greg |
2.7 |
int bias [256]; /* bias and freq arrays for learning */ |
306 |
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int freq [256]; |
307 |
greg |
2.6 |
int radpower[initrad]; /* radpower for precomputation */ |
308 |
greg |
2.1 |
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309 |
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310 |
greg |
2.6 |
/* initialise network in range (0,0,0) to (255,255,255) */ |
311 |
greg |
2.1 |
|
312 |
greg |
2.6 |
initnet() |
313 |
greg |
2.1 |
{ |
314 |
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register int i; |
315 |
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register int *p; |
316 |
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317 |
greg |
2.7 |
for (i=0; i<netsize; i++) { |
318 |
greg |
2.1 |
p = network[i]; |
319 |
greg |
2.7 |
p[0] = p[1] = p[2] = (i << (netbiasshift+8))/netsize; |
320 |
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freq[i] = intbias/netsize; /* 1/netsize */ |
321 |
greg |
2.1 |
bias[i] = 0; |
322 |
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} |
323 |
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} |
324 |
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325 |
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326 |
greg |
2.6 |
/* do after unbias - insertion sort of network and build netindex[0..255] */ |
327 |
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328 |
greg |
2.1 |
inxbuild() |
329 |
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{ |
330 |
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register int i,j,smallpos,smallval; |
331 |
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register int *p,*q; |
332 |
greg |
2.6 |
int previouscol,startpos; |
333 |
greg |
2.1 |
|
334 |
greg |
2.6 |
previouscol = 0; |
335 |
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startpos = 0; |
336 |
greg |
2.7 |
for (i=0; i<netsize; i++) { |
337 |
greg |
2.1 |
p = network[i]; |
338 |
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smallpos = i; |
339 |
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smallval = p[1]; /* index on g */ |
340 |
greg |
2.7 |
/* find smallest in i..netsize-1 */ |
341 |
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for (j=i+1; j<netsize; j++) { |
342 |
greg |
2.1 |
q = network[j]; |
343 |
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if (q[1] < smallval) { /* index on g */ |
344 |
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smallpos = j; |
345 |
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smallval = q[1]; /* index on g */ |
346 |
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} |
347 |
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} |
348 |
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q = network[smallpos]; |
349 |
greg |
2.6 |
/* swap p (i) and q (smallpos) entries */ |
350 |
greg |
2.1 |
if (i != smallpos) { |
351 |
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j = q[0]; q[0] = p[0]; p[0] = j; |
352 |
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j = q[1]; q[1] = p[1]; p[1] = j; |
353 |
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j = q[2]; q[2] = p[2]; p[2] = j; |
354 |
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j = q[3]; q[3] = p[3]; p[3] = j; |
355 |
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} |
356 |
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/* smallval entry is now in position i */ |
357 |
greg |
2.6 |
if (smallval != previouscol) { |
358 |
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netindex[previouscol] = (startpos+i)>>1; |
359 |
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for (j=previouscol+1; j<smallval; j++) netindex[j] = i; |
360 |
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previouscol = smallval; |
361 |
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startpos = i; |
362 |
greg |
2.1 |
} |
363 |
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} |
364 |
greg |
2.6 |
netindex[previouscol] = (startpos+maxnetpos)>>1; |
365 |
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for (j=previouscol+1; j<256; j++) netindex[j] = maxnetpos; /* really 256 */ |
366 |
greg |
2.1 |
} |
367 |
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368 |
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369 |
greg |
2.6 |
int inxsearch(b,g,r) /* accepts real BGR values after net is unbiased */ |
370 |
greg |
2.1 |
register int b,g,r; |
371 |
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{ |
372 |
greg |
2.6 |
register int i,j,dist,a,bestd; |
373 |
greg |
2.1 |
register int *p; |
374 |
greg |
2.6 |
int best; |
375 |
greg |
2.1 |
|
376 |
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bestd = 1000; /* biggest possible dist is 256*3 */ |
377 |
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best = -1; |
378 |
|
|
i = netindex[g]; /* index on g */ |
379 |
greg |
2.6 |
j = i-1; /* start at netindex[g] and work outwards */ |
380 |
greg |
2.1 |
|
381 |
greg |
2.7 |
while ((i<netsize) || (j>=0)) { |
382 |
|
|
if (i<netsize) { |
383 |
greg |
2.1 |
p = network[i]; |
384 |
greg |
2.6 |
dist = p[1] - g; /* inx key */ |
385 |
greg |
2.7 |
if (dist >= bestd) i = netsize; /* stop iter */ |
386 |
greg |
2.1 |
else { |
387 |
|
|
i++; |
388 |
greg |
2.6 |
if (dist<0) dist = -dist; |
389 |
|
|
a = p[0] - b; if (a<0) a = -a; |
390 |
|
|
dist += a; |
391 |
|
|
if (dist<bestd) { |
392 |
|
|
a = p[2] - r; if (a<0) a = -a; |
393 |
|
|
dist += a; |
394 |
|
|
if (dist<bestd) {bestd=dist; best=p[3];} |
395 |
greg |
2.1 |
} |
396 |
|
|
} |
397 |
|
|
} |
398 |
|
|
if (j>=0) { |
399 |
|
|
p = network[j]; |
400 |
greg |
2.6 |
dist = g - p[1]; /* inx key - reverse dif */ |
401 |
|
|
if (dist >= bestd) j = -1; /* stop iter */ |
402 |
greg |
2.1 |
else { |
403 |
|
|
j--; |
404 |
greg |
2.6 |
if (dist<0) dist = -dist; |
405 |
|
|
a = p[0] - b; if (a<0) a = -a; |
406 |
|
|
dist += a; |
407 |
|
|
if (dist<bestd) { |
408 |
|
|
a = p[2] - r; if (a<0) a = -a; |
409 |
|
|
dist += a; |
410 |
|
|
if (dist<bestd) {bestd=dist; best=p[3];} |
411 |
greg |
2.1 |
} |
412 |
|
|
} |
413 |
|
|
} |
414 |
|
|
} |
415 |
|
|
return(best); |
416 |
|
|
} |
417 |
|
|
|
418 |
|
|
|
419 |
greg |
2.6 |
/* finds closest neuron (min dist) and updates freq */ |
420 |
|
|
/* finds best neuron (min dist-bias) and returns position */ |
421 |
|
|
/* for frequently chosen neurons, freq[i] is high and bias[i] is negative */ |
422 |
greg |
2.7 |
/* bias[i] = gamma*((1/netsize)-freq[i]) */ |
423 |
greg |
2.6 |
|
424 |
|
|
int contest(b,g,r) /* accepts biased BGR values */ |
425 |
greg |
2.1 |
register int b,g,r; |
426 |
|
|
{ |
427 |
greg |
2.6 |
register int i,dist,a,biasdist,betafreq; |
428 |
|
|
int bestpos,bestbiaspos,bestd,bestbiasd; |
429 |
|
|
register int *p,*f, *n; |
430 |
greg |
2.1 |
|
431 |
greg |
2.6 |
bestd = ~(((int) 1)<<31); |
432 |
greg |
2.1 |
bestbiasd = bestd; |
433 |
greg |
2.6 |
bestpos = -1; |
434 |
|
|
bestbiaspos = bestpos; |
435 |
|
|
p = bias; |
436 |
|
|
f = freq; |
437 |
|
|
|
438 |
greg |
2.7 |
for (i=0; i<netsize; i++) { |
439 |
greg |
2.6 |
n = network[i]; |
440 |
|
|
dist = n[0] - b; if (dist<0) dist = -dist; |
441 |
|
|
a = n[1] - g; if (a<0) a = -a; |
442 |
|
|
dist += a; |
443 |
|
|
a = n[2] - r; if (a<0) a = -a; |
444 |
|
|
dist += a; |
445 |
|
|
if (dist<bestd) {bestd=dist; bestpos=i;} |
446 |
|
|
biasdist = dist - ((*p)>>(intbiasshift-netbiasshift)); |
447 |
|
|
if (biasdist<bestbiasd) {bestbiasd=biasdist; bestbiaspos=i;} |
448 |
|
|
betafreq = (*f >> betashift); |
449 |
|
|
*f++ -= betafreq; |
450 |
|
|
*p++ += (betafreq<<gammashift); |
451 |
greg |
2.1 |
} |
452 |
greg |
2.6 |
freq[bestpos] += beta; |
453 |
|
|
bias[bestpos] -= betagamma; |
454 |
|
|
return(bestbiaspos); |
455 |
greg |
2.1 |
} |
456 |
|
|
|
457 |
|
|
|
458 |
greg |
2.6 |
/* move neuron i towards (b,g,r) by factor alpha */ |
459 |
|
|
|
460 |
|
|
altersingle(alpha,i,b,g,r) /* accepts biased BGR values */ |
461 |
|
|
register int alpha,i,b,g,r; |
462 |
|
|
{ |
463 |
|
|
register int *n; |
464 |
|
|
|
465 |
|
|
n = network[i]; /* alter hit neuron */ |
466 |
|
|
*n -= (alpha*(*n - b)) / initalpha; |
467 |
|
|
n++; |
468 |
|
|
*n -= (alpha*(*n - g)) / initalpha; |
469 |
|
|
n++; |
470 |
|
|
*n -= (alpha*(*n - r)) / initalpha; |
471 |
|
|
} |
472 |
|
|
|
473 |
|
|
|
474 |
|
|
/* move neurons adjacent to i towards (b,g,r) by factor */ |
475 |
|
|
/* alpha*(1-((i-j)^2/[r]^2)) precomputed as radpower[|i-j|]*/ |
476 |
|
|
|
477 |
|
|
alterneigh(rad,i,b,g,r) /* accents biased BGR values */ |
478 |
greg |
2.1 |
int rad,i; |
479 |
|
|
register int b,g,r; |
480 |
|
|
{ |
481 |
|
|
register int j,k,lo,hi,a; |
482 |
|
|
register int *p, *q; |
483 |
|
|
|
484 |
greg |
2.6 |
lo = i-rad; if (lo<-1) lo= -1; |
485 |
greg |
2.7 |
hi = i+rad; if (hi>netsize) hi=netsize; |
486 |
greg |
2.1 |
|
487 |
|
|
j = i+1; |
488 |
|
|
k = i-1; |
489 |
|
|
q = radpower; |
490 |
|
|
while ((j<hi) || (k>lo)) { |
491 |
|
|
a = (*(++q)); |
492 |
|
|
if (j<hi) { |
493 |
|
|
p = network[j]; |
494 |
|
|
*p -= (a*(*p - b)) / alpharadbias; |
495 |
|
|
p++; |
496 |
|
|
*p -= (a*(*p - g)) / alpharadbias; |
497 |
|
|
p++; |
498 |
|
|
*p -= (a*(*p - r)) / alpharadbias; |
499 |
|
|
j++; |
500 |
|
|
} |
501 |
|
|
if (k>lo) { |
502 |
|
|
p = network[k]; |
503 |
|
|
*p -= (a*(*p - b)) / alpharadbias; |
504 |
|
|
p++; |
505 |
|
|
*p -= (a*(*p - g)) / alpharadbias; |
506 |
|
|
p++; |
507 |
|
|
*p -= (a*(*p - r)) / alpharadbias; |
508 |
|
|
k--; |
509 |
|
|
} |
510 |
|
|
} |
511 |
|
|
} |
512 |
|
|
|
513 |
|
|
|
514 |
|
|
learn() |
515 |
|
|
{ |
516 |
|
|
register int i,j,b,g,r; |
517 |
greg |
2.6 |
int radius,rad,alpha,step,delta,samplepixels; |
518 |
greg |
2.1 |
register unsigned char *p; |
519 |
|
|
unsigned char *lim; |
520 |
|
|
|
521 |
greg |
2.6 |
alphadec = 30 + ((samplefac-1)/3); |
522 |
|
|
p = thepicture; |
523 |
greg |
2.1 |
lim = thepicture + lengthcount; |
524 |
greg |
2.6 |
samplepixels = lengthcount/(3*samplefac); |
525 |
|
|
delta = samplepixels/ncycles; |
526 |
greg |
2.1 |
alpha = initalpha; |
527 |
|
|
radius = initradius; |
528 |
greg |
2.6 |
|
529 |
greg |
2.1 |
rad = radius >> radiusbiasshift; |
530 |
|
|
if (rad <= 1) rad = 0; |
531 |
|
|
for (i=0; i<rad; i++) |
532 |
|
|
radpower[i] = alpha*(((rad*rad - i*i)*radbias)/(rad*rad)); |
533 |
greg |
2.6 |
|
534 |
|
|
if ((lengthcount%prime1) != 0) step = 3*prime1; |
535 |
greg |
2.1 |
else { |
536 |
greg |
2.6 |
if ((lengthcount%prime2) !=0) step = 3*prime2; |
537 |
greg |
2.1 |
else { |
538 |
greg |
2.6 |
if ((lengthcount%prime3) !=0) step = 3*prime3; |
539 |
|
|
else step = 3*prime4; |
540 |
greg |
2.1 |
} |
541 |
|
|
} |
542 |
greg |
2.6 |
|
543 |
greg |
2.1 |
i = 0; |
544 |
greg |
2.6 |
while (i < samplepixels) { |
545 |
greg |
2.1 |
b = p[0] << netbiasshift; |
546 |
|
|
g = p[1] << netbiasshift; |
547 |
|
|
r = p[2] << netbiasshift; |
548 |
|
|
j = contest(b,g,r); |
549 |
|
|
|
550 |
|
|
altersingle(alpha,j,b,g,r); |
551 |
greg |
2.6 |
if (rad) alterneigh(rad,j,b,g,r); /* alter neighbours */ |
552 |
greg |
2.1 |
|
553 |
|
|
p += step; |
554 |
|
|
if (p >= lim) p -= lengthcount; |
555 |
|
|
|
556 |
|
|
i++; |
557 |
|
|
if (i%delta == 0) { |
558 |
|
|
alpha -= alpha / alphadec; |
559 |
|
|
radius -= radius / radiusdec; |
560 |
|
|
rad = radius >> radiusbiasshift; |
561 |
|
|
if (rad <= 1) rad = 0; |
562 |
|
|
for (j=0; j<rad; j++) |
563 |
|
|
radpower[j] = alpha*(((rad*rad - j*j)*radbias)/(rad*rad)); |
564 |
|
|
} |
565 |
|
|
} |
566 |
|
|
} |
567 |
|
|
|
568 |
greg |
2.6 |
/* unbias network to give 0..255 entries */ |
569 |
|
|
/* which can then be used for colour map */ |
570 |
|
|
/* and record position i to prepare for sort */ |
571 |
|
|
|
572 |
greg |
2.1 |
unbiasnet() |
573 |
|
|
{ |
574 |
|
|
int i,j; |
575 |
|
|
|
576 |
greg |
2.7 |
for (i=0; i<netsize; i++) { |
577 |
greg |
2.1 |
for (j=0; j<3; j++) |
578 |
|
|
network[i][j] >>= netbiasshift; |
579 |
|
|
network[i][3] = i; /* record colour no */ |
580 |
|
|
} |
581 |
|
|
} |
582 |
|
|
|
583 |
greg |
2.6 |
|
584 |
|
|
/* Don't do this until the network has been unbiased (GW) */ |
585 |
greg |
2.1 |
|
586 |
|
|
static |
587 |
|
|
cpyclrtab() |
588 |
|
|
{ |
589 |
|
|
register int i,j,k; |
590 |
|
|
|
591 |
greg |
2.7 |
for (j=0; j<netsize; j++) { |
592 |
greg |
2.1 |
k = network[j][3]; |
593 |
|
|
for (i = 0; i < 3; i++) |
594 |
|
|
clrtab[k][i] = network[j][2-i]; |
595 |
|
|
} |
596 |
|
|
} |