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FC1/ResourceCompilerImage/neuquant.cpp
romkazvo 34d6c5d489 123
2023-08-07 19:29:24 +08:00

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// WOUTER
// - made it compile as C++
// - fixed /0 bug with small image sizes
/* 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://www.acm.org/~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.
*/
#include "neuquant.h"
#include <assert.h>
/* Network Definitions
------------------- */
#define maxnetpos (netsize-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 (netsize>>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; /* biased by 10 bits */
/* radbias and alpharadbias used for radpower calculation */
#define radbiasshift 8
#define radbias (((int) 1)<<radbiasshift)
#define alpharadbshift (alphabiasshift+radbiasshift)
#define alpharadbias (((int) 1)<<alpharadbshift)
/* Types and Global Variables
-------------------------- */
static unsigned char *thepicture; /* the input image itself */
static int lengthcount; /* lengthcount = H*W*3 */
static int samplefac; /* sampling factor 1..30 */
typedef int pixel[4]; /* BGRc */
static pixel network[netsize]; /* the network itself */
static int netindex[256]; /* for network lookup - really 256 */
static int bias [netsize]; /* bias and freq arrays for learning */
static int freq [netsize];
static int radpower[initrad]; /* radpower for precomputation */
/* Initialise network in range (0,0,0) to (255,255,255) and set parameters
----------------------------------------------------------------------- */
void initnet(unsigned char *thepic, int len, int sample)
{
register int i;
register int *p;
thepicture = thepic;
lengthcount = len;
samplefac = sample;
for (i=0; i<netsize; i++) {
p = network[i];
p[0] = p[1] = p[2] = (i << (netbiasshift+8))/netsize;
freq[i] = intbias/netsize; /* 1/netsize */
bias[i] = 0;
}
}
/* Unbias network to give byte values 0..255 and record position i to prepare for sort
----------------------------------------------------------------------------------- */
void unbiasnet()
{
int i,j,temp;
for (i=0; i<netsize; i++) {
for (j=0; j<3; j++) {
/* OLD CODE: network[i][j] >>= netbiasshift; */
/* Fix based on bug report by Juergen Weigert jw@suse.de */
temp = (network[i][j] + (1 << (netbiasshift - 1))) >> netbiasshift;
if (temp > 255) temp = 255;
network[i][j] = temp;
}
network[i][3] = i; /* record colour no */
}
}
/* Output colour map
----------------- */
void writecolourmap(unsigned char *p)
{
int i,j;
for (j=0; j<netsize; j++)
for (i=0; i<3; i++)
*p++ = network[j][i];
}
/* Insertion sort of network and building of netindex[0..255] (to do after unbias)
------------------------------------------------------------------------------- */
void inxbuild()
{
register int i,j,smallpos,smallval;
register int *p,*q;
int previouscol,startpos;
previouscol = 0;
startpos = 0;
for (i=0; i<netsize; i++) {
p = network[i];
smallpos = i;
smallval = p[1]; /* index on g */
/* find smallest in i..netsize-1 */
for (j=i+1; j<netsize; j++) {
q = network[j];
if (q[1] < smallval) { /* index on g */
smallpos = j;
smallval = q[1]; /* index on g */
}
}
q = 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;
}
/* smallval entry is now in position i */
if (smallval != previouscol) {
netindex[previouscol] = (startpos+i)>>1;
for (j=previouscol+1; j<smallval; j++) netindex[j] = i;
previouscol = smallval;
startpos = i;
}
}
netindex[previouscol] = (startpos+maxnetpos)>>1;
for (j=previouscol+1; j<256; j++) netindex[j] = maxnetpos; /* really 256 */
}
/* Search for BGR values 0..255 (after net is unbiased) and return colour index
---------------------------------------------------------------------------- */
int inxsearch(int b, int g, int r)
{
register int i,j,dist,a,bestd;
register int *p;
int best;
bestd = 1000; /* biggest possible dist is 256*3 */
best = -1;
i = netindex[g]; /* index on g */
j = i-1; /* start at netindex[g] and work outwards */
while ((i<netsize) || (j>=0)) {
if (i<netsize) {
p = network[i];
dist = p[1] - g; /* inx key */
if (dist >= bestd) i = netsize; /* stop iter */
else {
i++;
if (dist<0) dist = -dist;
a = p[0] - b; if (a<0) a = -a;
dist += a;
if (dist<bestd) {
a = p[2] - r; if (a<0) a = -a;
dist += a;
if (dist<bestd) {bestd=dist; best=p[3];}
}
}
}
if (j>=0) {
p = network[j];
dist = g - p[1]; /* inx key - reverse dif */
if (dist >= bestd) j = -1; /* stop iter */
else {
j--;
if (dist<0) dist = -dist;
a = p[0] - b; if (a<0) a = -a;
dist += a;
if (dist<bestd) {
a = p[2] - r; if (a<0) a = -a;
dist += a;
if (dist<bestd) {bestd=dist; best=p[3];}
}
}
}
}
return(best);
}
/* Search for biased BGR values
---------------------------- */
int contest(int b, int g, int 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;
int bestpos,bestbiaspos,bestd,bestbiasd;
register int *p,*f, *n;
bestd = ~(((int) 1)<<31);
bestbiasd = bestd;
bestpos = -1;
bestbiaspos = bestpos;
p = bias;
f = freq;
for (i=0; i<netsize; i++) {
n = network[i];
dist = n[0] - b; if (dist<0) dist = -dist;
a = n[1] - g; if (a<0) a = -a;
dist += a;
a = n[2] - 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);
}
freq[bestpos] += beta;
bias[bestpos] -= betagamma;
return(bestbiaspos);
}
/* Move neuron i towards biased (b,g,r) by factor alpha
---------------------------------------------------- */
void altersingle(int alpha, int i, int b, int g, int r)
{
register int *n;
n = network[i]; /* alter hit neuron */
*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(int rad, int i, int b, int g, int 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>netsize) hi=netsize;
j = i+1;
k = i-1;
q = radpower;
while ((j<hi) || (k>lo)) {
a = (*(++q));
if (j<hi) {
p = network[j];
*p -= (a*(*p - b)) / alpharadbias;
p++;
*p -= (a*(*p - g)) / alpharadbias;
p++;
*p -= (a*(*p - r)) / alpharadbias;
j++;
}
if (k>lo) {
p = network[k];
*p -= (a*(*p - b)) / alpharadbias;
p++;
*p -= (a*(*p - g)) / alpharadbias;
p++;
*p -= (a*(*p - r)) / alpharadbias;
k--;
}
}
}
/* Main Learning Loop
------------------ */
void learn()
{
register int i,j,b,g,r;
int radius,rad,alpha,step,delta,samplepixels;
register unsigned char *p;
unsigned char *lim;
alphadec = 30 + ((samplefac-1)/3);
p = thepicture;
lim = thepicture + lengthcount;
samplepixels = lengthcount/(3*samplefac);
//if(samplepixels<ncycles) samplepixels = ncycles;
//assert(lengthcount>=ncycles);
delta = samplepixels/ncycles;
if(!delta) delta = 1; // WOUTER: I guess noone ever tested with small images (mipmaps)
alpha = initalpha;
radius = initradius;
rad = radius >> radiusbiasshift;
if (rad <= 1) rad = 0;
for (i=0; i<rad; i++)
radpower[i] = alpha*(((rad*rad - i*i)*radbias)/(rad*rad));
//fprintf(stderr,"beginning 1D learning: initial radius=%d\n", rad);
if ((lengthcount%prime1) != 0) step = 3*prime1;
else {
if ((lengthcount%prime2) !=0) step = 3*prime2;
else {
if ((lengthcount%prime3) !=0) step = 3*prime3;
else step = 3*prime4;
}
}
i = 0;
while (i < samplepixels) {
b = p[0] << netbiasshift;
g = p[1] << netbiasshift;
r = p[2] << netbiasshift;
j = contest(b,g,r);
altersingle(alpha,j,b,g,r);
if (rad) alterneigh(rad,j,b,g,r); /* alter neighbours */
p += step;
while (p >= lim) p -= lengthcount; // wouter: was: if (p >= lim)
i++;
if (i%delta == 0) {
alpha -= alpha / alphadec;
radius -= radius / radiusdec;
rad = radius >> radiusbiasshift;
if (rad <= 1) rad = 0;
for (j=0; j<rad; j++)
radpower[j] = alpha*(((rad*rad - j*j)*radbias)/(rad*rad));
}
}
//fprintf(stderr,"finished 1D learning: final alpha=%f !\n",((float)alpha)/initalpha);
}
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