63 |
|
nsamples = npixels/samplefac; |
64 |
|
if (nsamples < 600) |
65 |
|
return(-1); |
66 |
< |
thesamples = (BYTE *)malloc((nsamples+1)*3); |
66 |
> |
thesamples = (BYTE *)malloc(nsamples*3); |
67 |
|
if (thesamples == NULL) |
68 |
|
return(-1); |
69 |
|
cursamp = thesamples; |
75 |
|
cumprob = 0.; |
76 |
|
while ((cumprob += (1.-cumprob)*nsleft/(npleft-sv)) < rval) |
77 |
|
sv++; |
78 |
< |
setskip(cursamp, sv); |
79 |
< |
cursamp += 3; |
80 |
< |
npleft -= sv; |
78 |
> |
if (nsleft == nsamples) |
79 |
> |
skipcount = sv; |
80 |
> |
else { |
81 |
> |
setskip(cursamp, sv); |
82 |
> |
cursamp += 3; |
83 |
> |
} |
84 |
> |
npleft -= sv+1; |
85 |
|
nsleft--; |
86 |
|
} |
87 |
< |
setskip(cursamp, 0); /* dummy tagged onto end */ |
87 |
> |
setskip(cursamp, npleft); /* tag on end to skip the rest */ |
88 |
|
cursamp = thesamples; |
85 |
– |
skipcount = nskip(cursamp); |
89 |
|
return(0); |
90 |
|
} |
91 |
|
|
94 |
|
register BYTE col[]; |
95 |
|
{ |
96 |
|
if (!skipcount--) { |
97 |
+ |
skipcount = nskip(cursamp); |
98 |
|
cursamp[0] = col[BLU]; |
99 |
|
cursamp[1] = col[GRN]; |
100 |
|
cursamp[2] = col[RED]; |
101 |
|
cursamp += 3; |
98 |
– |
skipcount = nskip(cursamp); |
102 |
|
} |
103 |
|
} |
104 |
|
|
109 |
|
{ |
110 |
|
while (n > skipcount) { |
111 |
|
cs += skipcount; |
112 |
+ |
n -= skipcount+1; |
113 |
+ |
skipcount = nskip(cursamp); |
114 |
|
cursamp[0] = cs[0][BLU]; |
115 |
|
cursamp[1] = cs[0][GRN]; |
116 |
|
cursamp[2] = cs[0][RED]; |
117 |
|
cs++; |
113 |
– |
n -= skipcount+1; |
118 |
|
cursamp += 3; |
115 |
– |
skipcount = nskip(cursamp); |
119 |
|
} |
120 |
|
skipcount -= n; |
121 |
|
} |
208 |
|
} |
209 |
|
|
210 |
|
/* The following was adapted and modified from the original (GW) */ |
211 |
+ |
|
212 |
+ |
/* cheater definitions (GW) */ |
213 |
+ |
#define thepicture thesamples |
214 |
+ |
#define lengthcount (nsamples*3) |
215 |
+ |
#define samplefac 1 |
216 |
+ |
|
217 |
|
/*----------------------------------------------------------------------*/ |
218 |
|
/* */ |
219 |
|
/* NeuQuant */ |
220 |
|
/* -------- */ |
221 |
|
/* */ |
222 |
< |
/* Copyright: Anthony Dekker, June 1994 */ |
222 |
> |
/* Copyright: Anthony Dekker, November 1994 */ |
223 |
|
/* */ |
224 |
|
/* This program performs colour quantization of graphics images (SUN */ |
225 |
|
/* raster files). It uses a Kohonen Neural Network. It produces */ |
252 |
|
/* Email: [email protected] */ |
253 |
|
/*----------------------------------------------------------------------*/ |
254 |
|
|
255 |
< |
#define bool int |
256 |
< |
#define false 0 |
257 |
< |
#define true 1 |
255 |
> |
#define bool int |
256 |
> |
#define false 0 |
257 |
> |
#define true 1 |
258 |
|
|
259 |
< |
#define initrad 32 |
260 |
< |
#define radiusdec 30 |
261 |
< |
#define alphadec; 30 |
259 |
> |
/* network defs */ |
260 |
> |
#define netsize clrtabsiz /* number of colours - can change this */ |
261 |
> |
#define maxnetpos (netsize-1) |
262 |
> |
#define netbiasshift 4 /* bias for colour values */ |
263 |
> |
#define ncycles 100 /* no. of learning cycles */ |
264 |
|
|
265 |
|
/* defs for freq and bias */ |
266 |
< |
#define gammashift 10 |
267 |
< |
#define betashift gammashift |
268 |
< |
#define intbiasshift 16 |
269 |
< |
#define intbias (1<<intbiasshift) |
270 |
< |
#define gamma (1<<gammashift) |
271 |
< |
#define beta (intbias>>betashift) |
266 |
> |
#define intbiasshift 16 /* bias for fractions */ |
267 |
> |
#define intbias (((int) 1)<<intbiasshift) |
268 |
> |
#define gammashift 10 /* gamma = 1024 */ |
269 |
> |
#define gamma (((int) 1)<<gammashift) |
270 |
> |
#define betashift 10 |
271 |
> |
#define beta (intbias>>betashift) /* beta = 1/1024 */ |
272 |
|
#define betagamma (intbias<<(gammashift-betashift)) |
262 |
– |
#define gammaphi (intbias<<(gammashift-8)) |
273 |
|
|
274 |
< |
/* defs for rad and alpha */ |
275 |
< |
#define maxrad (initrad+1) |
276 |
< |
#define radiusbiasshift 6 |
277 |
< |
#define radiusbias (1<<radiusbiasshift) |
278 |
< |
#define initradius ((int) (initrad*radiusbias)) |
279 |
< |
#define alphabiasshift 10 |
280 |
< |
#define initalpha (1<<alphabiasshift) |
274 |
> |
/* defs for decreasing radius factor */ |
275 |
> |
#define initrad (256>>3) /* for 256 cols, radius starts */ |
276 |
> |
#define radiusbiasshift 6 /* at 32.0 biased by 6 bits */ |
277 |
> |
#define radiusbias (((int) 1)<<radiusbiasshift) |
278 |
> |
#define initradius (initrad*radiusbias) /* and decreases by a */ |
279 |
> |
#define radiusdec 30 /* factor of 1/30 each cycle */ |
280 |
> |
|
281 |
> |
/* defs for decreasing alpha factor */ |
282 |
> |
#define alphabiasshift 10 /* alpha starts at 1.0 */ |
283 |
> |
#define initalpha (((int) 1)<<alphabiasshift) |
284 |
> |
int alphadec; /* biased by 10 bits */ |
285 |
> |
|
286 |
> |
/* radbias and alpharadbias used for radpower calculation */ |
287 |
|
#define radbiasshift 8 |
288 |
< |
#define radbias (1<<radbiasshift) |
288 |
> |
#define radbias (((int) 1)<<radbiasshift) |
289 |
|
#define alpharadbshift (alphabiasshift+radbiasshift) |
290 |
< |
#define alpharadbias (1<<alpharadbshift) |
290 |
> |
#define alpharadbias (((int) 1)<<alpharadbshift) |
291 |
|
|
292 |
< |
/* other defs */ |
293 |
< |
#define netbiasshift 4 |
294 |
< |
#define funnyshift (intbiasshift-netbiasshift) |
295 |
< |
#define maxnetval ((256<<netbiasshift)-1) |
296 |
< |
#define ncycles 100 |
297 |
< |
#define jump1 499 /* prime */ |
282 |
< |
#define jump2 491 /* prime */ |
283 |
< |
#define jump3 487 /* any pic whose size was divisible by all */ |
284 |
< |
#define jump4 503 /* four primes would be simply enormous */ |
292 |
> |
/* four primes near 500 - assume no image has a length so large */ |
293 |
> |
/* that it is divisible by all four primes */ |
294 |
> |
#define prime1 499 |
295 |
> |
#define prime2 491 |
296 |
> |
#define prime3 487 |
297 |
> |
#define prime4 503 |
298 |
|
|
286 |
– |
/* cheater definitions (GW) */ |
287 |
– |
#define thepicture thesamples |
288 |
– |
#define lengthcount (nsamples*3) |
289 |
– |
#define samplefac 1 |
290 |
– |
|
299 |
|
typedef int pixel[4]; /* BGRc */ |
300 |
+ |
pixel network[256]; |
301 |
|
|
302 |
< |
static pixel network[256]; |
302 |
> |
int netindex[256]; /* for network lookup - really 256 */ |
303 |
|
|
304 |
< |
static int netindex[256]; |
304 |
> |
int bias [256]; /* bias and freq arrays for learning */ |
305 |
> |
int freq [256]; |
306 |
> |
int radpower[initrad]; /* radpower for precomputation */ |
307 |
|
|
297 |
– |
static int bias [256]; |
298 |
– |
static int freq [256]; |
299 |
– |
static int radpower[256]; /* actually need only go up to maxrad */ |
308 |
|
|
309 |
< |
/* fixed space overhead 256*4+256+256+256+256 words = 256*8 = 8kB */ |
309 |
> |
/* initialise network in range (0,0,0) to (255,255,255) */ |
310 |
|
|
311 |
< |
|
304 |
< |
static |
305 |
< |
initnet() |
311 |
> |
initnet() |
312 |
|
{ |
313 |
|
register int i; |
314 |
|
register int *p; |
315 |
|
|
316 |
< |
for (i=0; i<clrtabsiz; i++) { |
316 |
> |
for (i=0; i<netsize; i++) { |
317 |
|
p = network[i]; |
318 |
< |
p[0] = i << netbiasshift; |
319 |
< |
p[1] = i << netbiasshift; |
314 |
< |
p[2] = i << netbiasshift; |
315 |
< |
freq[i] = intbias >> 8; /* 1/256 */ |
318 |
> |
p[0] = p[1] = p[2] = (i << (netbiasshift+8))/netsize; |
319 |
> |
freq[i] = intbias/netsize; /* 1/netsize */ |
320 |
|
bias[i] = 0; |
321 |
|
} |
322 |
|
} |
323 |
|
|
324 |
|
|
325 |
< |
static |
325 |
> |
/* do after unbias - insertion sort of network and build netindex[0..255] */ |
326 |
> |
|
327 |
|
inxbuild() |
328 |
|
{ |
329 |
|
register int i,j,smallpos,smallval; |
330 |
|
register int *p,*q; |
331 |
< |
int start,previous; |
331 |
> |
int previouscol,startpos; |
332 |
|
|
333 |
< |
previous = 0; |
334 |
< |
start = 0; |
335 |
< |
for (i=0; i<clrtabsiz; i++) { |
333 |
> |
previouscol = 0; |
334 |
> |
startpos = 0; |
335 |
> |
for (i=0; i<netsize; i++) { |
336 |
|
p = network[i]; |
337 |
|
smallpos = i; |
338 |
|
smallval = p[1]; /* index on g */ |
339 |
< |
/* find smallest in i+1..clrtabsiz-1 */ |
340 |
< |
for (j=i+1; j<clrtabsiz; j++) { |
339 |
> |
/* find smallest in i..netsize-1 */ |
340 |
> |
for (j=i+1; j<netsize; j++) { |
341 |
|
q = network[j]; |
342 |
|
if (q[1] < smallval) { /* index on g */ |
343 |
|
smallpos = j; |
345 |
|
} |
346 |
|
} |
347 |
|
q = network[smallpos]; |
348 |
+ |
/* swap p (i) and q (smallpos) entries */ |
349 |
|
if (i != smallpos) { |
350 |
|
j = q[0]; q[0] = p[0]; p[0] = j; |
351 |
|
j = q[1]; q[1] = p[1]; p[1] = j; |
353 |
|
j = q[3]; q[3] = p[3]; p[3] = j; |
354 |
|
} |
355 |
|
/* smallval entry is now in position i */ |
356 |
< |
if (smallval != previous) { |
357 |
< |
netindex[previous] = (start+i)>>1; |
358 |
< |
for (j=previous+1; j<smallval; j++) netindex[j] = i; |
359 |
< |
previous = smallval; |
360 |
< |
start = i; |
356 |
> |
if (smallval != previouscol) { |
357 |
> |
netindex[previouscol] = (startpos+i)>>1; |
358 |
> |
for (j=previouscol+1; j<smallval; j++) netindex[j] = i; |
359 |
> |
previouscol = smallval; |
360 |
> |
startpos = i; |
361 |
|
} |
362 |
|
} |
363 |
< |
netindex[previous] = (start+clrtabsiz-1)>>1; |
364 |
< |
for (j=previous+1; j<clrtabsiz; j++) netindex[j] = clrtabsiz-1; |
363 |
> |
netindex[previouscol] = (startpos+maxnetpos)>>1; |
364 |
> |
for (j=previouscol+1; j<256; j++) netindex[j] = maxnetpos; /* really 256 */ |
365 |
|
} |
366 |
|
|
367 |
|
|
368 |
< |
static int |
363 |
< |
inxsearch(b,g,r) /* accepts real BGR values after net is unbiased */ |
368 |
> |
int inxsearch(b,g,r) /* accepts real BGR values after net is unbiased */ |
369 |
|
register int b,g,r; |
370 |
|
{ |
371 |
< |
register int i,j,best,x,y,bestd; |
371 |
> |
register int i,j,dist,a,bestd; |
372 |
|
register int *p; |
373 |
+ |
int best; |
374 |
|
|
375 |
|
bestd = 1000; /* biggest possible dist is 256*3 */ |
376 |
|
best = -1; |
377 |
|
i = netindex[g]; /* index on g */ |
378 |
< |
j = i-1; |
378 |
> |
j = i-1; /* start at netindex[g] and work outwards */ |
379 |
|
|
380 |
< |
while ((i<clrtabsiz) || (j>=0)) { |
381 |
< |
if (i<clrtabsiz) { |
380 |
> |
while ((i<netsize) || (j>=0)) { |
381 |
> |
if (i<netsize) { |
382 |
|
p = network[i]; |
383 |
< |
x = p[1] - g; /* inx key */ |
384 |
< |
if (x >= bestd) i = clrtabsiz; /* stop iter */ |
383 |
> |
dist = p[1] - g; /* inx key */ |
384 |
> |
if (dist >= bestd) i = netsize; /* stop iter */ |
385 |
|
else { |
386 |
|
i++; |
387 |
< |
if (x<0) x = -x; |
388 |
< |
y = p[0] - b; |
389 |
< |
if (y<0) y = -y; |
390 |
< |
x += y; |
391 |
< |
if (x<bestd) { |
392 |
< |
y = p[2] - r; |
393 |
< |
if (y<0) y = -y; |
388 |
< |
x += y; /* x holds distance */ |
389 |
< |
if (x<bestd) {bestd=x; best=p[3];} |
387 |
> |
if (dist<0) dist = -dist; |
388 |
> |
a = p[0] - b; if (a<0) a = -a; |
389 |
> |
dist += a; |
390 |
> |
if (dist<bestd) { |
391 |
> |
a = p[2] - r; if (a<0) a = -a; |
392 |
> |
dist += a; |
393 |
> |
if (dist<bestd) {bestd=dist; best=p[3];} |
394 |
|
} |
395 |
|
} |
396 |
|
} |
397 |
|
if (j>=0) { |
398 |
|
p = network[j]; |
399 |
< |
x = g - p[1]; /* inx key - reverse dif */ |
400 |
< |
if (x >= bestd) j = -1; /* stop iter */ |
399 |
> |
dist = g - p[1]; /* inx key - reverse dif */ |
400 |
> |
if (dist >= bestd) j = -1; /* stop iter */ |
401 |
|
else { |
402 |
|
j--; |
403 |
< |
if (x<0) x = -x; |
404 |
< |
y = p[0] - b; |
405 |
< |
if (y<0) y = -y; |
406 |
< |
x += y; |
407 |
< |
if (x<bestd) { |
408 |
< |
y = p[2] - r; |
409 |
< |
if (y<0) y = -y; |
406 |
< |
x += y; /* x holds distance */ |
407 |
< |
if (x<bestd) {bestd=x; best=p[3];} |
403 |
> |
if (dist<0) dist = -dist; |
404 |
> |
a = p[0] - b; if (a<0) a = -a; |
405 |
> |
dist += a; |
406 |
> |
if (dist<bestd) { |
407 |
> |
a = p[2] - r; if (a<0) a = -a; |
408 |
> |
dist += a; |
409 |
> |
if (dist<bestd) {bestd=dist; best=p[3];} |
410 |
|
} |
411 |
|
} |
412 |
|
} |
415 |
|
} |
416 |
|
|
417 |
|
|
418 |
< |
static int |
419 |
< |
contest(b,g,r) /* accepts biased BGR values */ |
418 |
> |
/* finds closest neuron (min dist) and updates freq */ |
419 |
> |
/* finds best neuron (min dist-bias) and returns position */ |
420 |
> |
/* for frequently chosen neurons, freq[i] is high and bias[i] is negative */ |
421 |
> |
/* bias[i] = gamma*((1/netsize)-freq[i]) */ |
422 |
> |
|
423 |
> |
int contest(b,g,r) /* accepts biased BGR values */ |
424 |
|
register int b,g,r; |
425 |
|
{ |
426 |
< |
register int i,best,bestbias,x,y,bestd,bestbiasd; |
427 |
< |
register int *p,*q, *pp; |
426 |
> |
register int i,dist,a,biasdist,betafreq; |
427 |
> |
int bestpos,bestbiaspos,bestd,bestbiasd; |
428 |
> |
register int *p,*f, *n; |
429 |
|
|
430 |
< |
bestd = ~(1<<31); |
430 |
> |
bestd = ~(((int) 1)<<31); |
431 |
|
bestbiasd = bestd; |
432 |
< |
best = -1; |
433 |
< |
bestbias = best; |
434 |
< |
q = bias; |
435 |
< |
p = freq; |
436 |
< |
for (i=0; i<clrtabsiz; i++) { |
437 |
< |
pp = network[i]; |
438 |
< |
x = pp[0] - b; |
439 |
< |
if (x<0) x = -x; |
440 |
< |
y = pp[1] - g; |
441 |
< |
if (y<0) y = -y; |
442 |
< |
x += y; |
443 |
< |
y = pp[2] - r; |
444 |
< |
if (y<0) y = -y; |
445 |
< |
x += y; /* x holds distance */ |
446 |
< |
/* >> netbiasshift not needed if funnyshift used */ |
447 |
< |
if (x<bestd) {bestd=x; best=i;} |
448 |
< |
y = x - ((*q)>>funnyshift); /* y holds biasd */ |
449 |
< |
if (y<bestbiasd) {bestbiasd=y; bestbias=i;} |
443 |
< |
y = (*p >> betashift); /* y holds beta*freq */ |
444 |
< |
*p -= y; |
445 |
< |
*q += (y<<gammashift); |
446 |
< |
p++; |
447 |
< |
q++; |
432 |
> |
bestpos = -1; |
433 |
> |
bestbiaspos = bestpos; |
434 |
> |
p = bias; |
435 |
> |
f = freq; |
436 |
> |
|
437 |
> |
for (i=0; i<netsize; i++) { |
438 |
> |
n = network[i]; |
439 |
> |
dist = n[0] - b; if (dist<0) dist = -dist; |
440 |
> |
a = n[1] - g; if (a<0) a = -a; |
441 |
> |
dist += a; |
442 |
> |
a = n[2] - r; if (a<0) a = -a; |
443 |
> |
dist += a; |
444 |
> |
if (dist<bestd) {bestd=dist; bestpos=i;} |
445 |
> |
biasdist = dist - ((*p)>>(intbiasshift-netbiasshift)); |
446 |
> |
if (biasdist<bestbiasd) {bestbiasd=biasdist; bestbiaspos=i;} |
447 |
> |
betafreq = (*f >> betashift); |
448 |
> |
*f++ -= betafreq; |
449 |
> |
*p++ += (betafreq<<gammashift); |
450 |
|
} |
451 |
< |
freq[best] += beta; |
452 |
< |
bias[best] -= betagamma; |
453 |
< |
return(bestbias); |
451 |
> |
freq[bestpos] += beta; |
452 |
> |
bias[bestpos] -= betagamma; |
453 |
> |
return(bestbiaspos); |
454 |
|
} |
455 |
|
|
456 |
|
|
457 |
< |
static |
458 |
< |
alterneigh(rad,i,b,g,r) /* accepts biased BGR values */ |
457 |
> |
/* move neuron i towards (b,g,r) by factor alpha */ |
458 |
> |
|
459 |
> |
altersingle(alpha,i,b,g,r) /* accepts biased BGR values */ |
460 |
> |
register int alpha,i,b,g,r; |
461 |
> |
{ |
462 |
> |
register int *n; |
463 |
> |
|
464 |
> |
n = network[i]; /* alter hit neuron */ |
465 |
> |
*n -= (alpha*(*n - b)) / initalpha; |
466 |
> |
n++; |
467 |
> |
*n -= (alpha*(*n - g)) / initalpha; |
468 |
> |
n++; |
469 |
> |
*n -= (alpha*(*n - r)) / initalpha; |
470 |
> |
} |
471 |
> |
|
472 |
> |
|
473 |
> |
/* move neurons adjacent to i towards (b,g,r) by factor */ |
474 |
> |
/* alpha*(1-((i-j)^2/[r]^2)) precomputed as radpower[|i-j|]*/ |
475 |
> |
|
476 |
> |
alterneigh(rad,i,b,g,r) /* accents biased BGR values */ |
477 |
|
int rad,i; |
478 |
|
register int b,g,r; |
479 |
|
{ |
480 |
|
register int j,k,lo,hi,a; |
481 |
|
register int *p, *q; |
482 |
|
|
483 |
< |
lo = i-rad; |
484 |
< |
if (lo<-1) lo= -1; |
465 |
< |
hi = i+rad; |
466 |
< |
if (hi>clrtabsiz) hi=clrtabsiz; |
483 |
> |
lo = i-rad; if (lo<-1) lo= -1; |
484 |
> |
hi = i+rad; if (hi>netsize) hi=netsize; |
485 |
|
|
486 |
|
j = i+1; |
487 |
|
k = i-1; |
510 |
|
} |
511 |
|
|
512 |
|
|
495 |
– |
static |
496 |
– |
altersingle(alpha,j,b,g,r) /* accepts biased BGR values */ |
497 |
– |
register int alpha,j,b,g,r; |
498 |
– |
{ |
499 |
– |
register int *q; |
500 |
– |
|
501 |
– |
q = network[j]; /* alter hit neuron */ |
502 |
– |
*q -= (alpha*(*q - b)) / initalpha; |
503 |
– |
q++; |
504 |
– |
*q -= (alpha*(*q - g)) / initalpha; |
505 |
– |
q++; |
506 |
– |
*q -= (alpha*(*q - r)) / initalpha; |
507 |
– |
} |
508 |
– |
|
509 |
– |
|
510 |
– |
static |
513 |
|
learn() |
514 |
|
{ |
515 |
|
register int i,j,b,g,r; |
516 |
< |
int radius,rad,alpha,step,delta,upto; |
516 |
> |
int radius,rad,alpha,step,delta,samplepixels; |
517 |
|
register unsigned char *p; |
518 |
|
unsigned char *lim; |
519 |
|
|
520 |
< |
upto = lengthcount/(3*samplefac); |
519 |
< |
delta = upto/ncycles; |
520 |
< |
lim = thepicture + lengthcount; |
520 |
> |
alphadec = 30 + ((samplefac-1)/3); |
521 |
|
p = thepicture; |
522 |
+ |
lim = thepicture + lengthcount; |
523 |
+ |
samplepixels = lengthcount/(3*samplefac); |
524 |
+ |
delta = samplepixels/ncycles; |
525 |
|
alpha = initalpha; |
526 |
|
radius = initradius; |
527 |
+ |
|
528 |
|
rad = radius >> radiusbiasshift; |
529 |
|
if (rad <= 1) rad = 0; |
530 |
|
for (i=0; i<rad; i++) |
531 |
|
radpower[i] = alpha*(((rad*rad - i*i)*radbias)/(rad*rad)); |
532 |
< |
|
533 |
< |
if ((lengthcount%jump1) != 0) step = 3*jump1; |
532 |
> |
|
533 |
> |
if ((lengthcount%prime1) != 0) step = 3*prime1; |
534 |
|
else { |
535 |
< |
if ((lengthcount%jump2) !=0) step = 3*jump2; |
535 |
> |
if ((lengthcount%prime2) !=0) step = 3*prime2; |
536 |
|
else { |
537 |
< |
if ((lengthcount%jump3) !=0) step = 3*jump3; |
538 |
< |
else step = 3*jump4; |
537 |
> |
if ((lengthcount%prime3) !=0) step = 3*prime3; |
538 |
> |
else step = 3*prime4; |
539 |
|
} |
540 |
|
} |
541 |
+ |
|
542 |
|
i = 0; |
543 |
< |
while (i < upto) { |
543 |
> |
while (i < samplepixels) { |
544 |
|
b = p[0] << netbiasshift; |
545 |
|
g = p[1] << netbiasshift; |
546 |
|
r = p[2] << netbiasshift; |
547 |
|
j = contest(b,g,r); |
548 |
|
|
549 |
|
altersingle(alpha,j,b,g,r); |
550 |
< |
if (rad) alterneigh(rad,j,b,g,r); |
546 |
< |
/* alter neighbours */ |
550 |
> |
if (rad) alterneigh(rad,j,b,g,r); /* alter neighbours */ |
551 |
|
|
552 |
|
p += step; |
553 |
|
if (p >= lim) p -= lengthcount; |
564 |
|
} |
565 |
|
} |
566 |
|
|
567 |
< |
static |
567 |
> |
/* unbias network to give 0..255 entries */ |
568 |
> |
/* which can then be used for colour map */ |
569 |
> |
/* and record position i to prepare for sort */ |
570 |
> |
|
571 |
|
unbiasnet() |
572 |
|
{ |
573 |
|
int i,j; |
574 |
|
|
575 |
< |
for (i=0; i<clrtabsiz; i++) { |
575 |
> |
for (i=0; i<netsize; i++) { |
576 |
|
for (j=0; j<3; j++) |
577 |
|
network[i][j] >>= netbiasshift; |
578 |
|
network[i][3] = i; /* record colour no */ |
579 |
|
} |
580 |
|
} |
581 |
|
|
582 |
< |
/* Don't do this until the network has been unbiased */ |
582 |
> |
|
583 |
> |
/* Don't do this until the network has been unbiased (GW) */ |
584 |
|
|
585 |
|
static |
586 |
|
cpyclrtab() |
587 |
|
{ |
588 |
|
register int i,j,k; |
589 |
|
|
590 |
< |
for (j=0; j<clrtabsiz; j++) { |
590 |
> |
for (j=0; j<netsize; j++) { |
591 |
|
k = network[j][3]; |
592 |
|
for (i = 0; i < 3; i++) |
593 |
|
clrtab[k][i] = network[j][2-i]; |