/*
* Open Source Physics software is free software as described near the bottom of this code file.
*
* For additional information and documentation on Open Source Physics please see:
* <http://www.opensourcephysics.org/>
*/
/*
* The org.opensourcephysics.media.gif package provides animated gif
* implementations of the Video and VideoRecorder interfaces.
*
* Copyright (c) 2014 Douglas Brown and Wolfgang Christian.
*
* This is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 2 of the License, or
* (at your option) any later version.
*
* This software is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this; if not, write to the Free Software
* Foundation, Inc., 59 Temple Place, Suite 330, Boston MA 02111-1307 USA
* or view the license online at http://www.gnu.org/copyleft/gpl.html
*
* For additional information and documentation on Open Source Physics,
* please see <http://www.opensourcephysics.org/>.
*/
package org.opensourcephysics.media.gif;
/* 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.
*
* 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.
*/
// Ported to Java 12/00 K Weiner
/** A class to provide color quantization for GIF */
public class NeuQuant {
protected static final int netsize = 256; /* number of colours used */
/* four primes near 500 - assume no image has a length so large */
/* that it is divisible by all four primes */
protected static final int prime1 = 499;
protected static final int prime2 = 491;
protected static final int prime3 = 487;
protected static final int prime4 = 503;
protected static final int minpicturebytes = (3*prime4);
/* minimum size for input image */
/* Program Skeleton
----------------
[select samplefac in range 1..30]
[read image from input file]
pic = (unsigned char*) malloc(3*width*height);
initnet(pic,3*width*height,samplefac);
learn();
unbiasnet();
[write output image header, using writecolourmap(f)]
inxbuild();
write output image using inxsearch(b,g,r) */
/* Network Definitions
------------------- */
protected static final int maxnetpos = (netsize-1);
protected static final int netbiasshift = 4; /* bias for colour values */
protected static final int ncycles = 100; /* no. of learning cycles */
/* defs for freq and bias */
protected static final int intbiasshift = 16; /* bias for fractions */
protected static final int intbias = (1<<intbiasshift);
protected static final int gammashift = 10; /* gamma = 1024 */
protected static final int gamma = (1<<gammashift);
protected static final int betashift = 10;
protected static final int beta = (intbias>>betashift); /* beta = 1/1024 */
protected static final int betagamma = (intbias<<(gammashift-betashift));
/* defs for decreasing radius factor */
protected static final int initrad = (netsize>>3);
/* for 256 cols, radius starts */
protected static final int radiusbiasshift = 6;
/* at 32.0 biased by 6 bits */
protected static final int radiusbias = (1<<radiusbiasshift);
protected static final int initradius = (initrad*radiusbias);
/* and decreases by a */
protected static final int radiusdec = 30; /* factor of 1/30 each cycle */
/* defs for decreasing alpha factor */
protected static final int alphabiasshift = 10; /* alpha starts at 1.0 */
protected static final int initalpha = (1<<alphabiasshift);
protected int alphadec; /* biased by 10 bits */
/* radbias and alpharadbias used for radpower calculation */
protected static final int radbiasshift = 8;
protected static final int radbias = (1<<radbiasshift);
protected static final int alpharadbshift = (alphabiasshift+radbiasshift);
protected static final int alpharadbias = (1<<alpharadbshift);
/* Types and Global Variables
-------------------------- */
protected byte[] thepicture; /* the input image itself */
protected int lengthcount; /* lengthcount = H*W*3 */
protected int samplefac; /* sampling factor 1..30 */
// typedef int pixel[4]; /* BGRc */
protected int[][] network; /* the network itself - [netsize][4] */
protected int[] netindex = new int[256];
/* for network lookup - really 256 */
protected int[] bias = new int[netsize];
/* bias and freq arrays for learning */
protected int[] freq = new int[netsize];
protected int[] radpower = new int[initrad];
/* radpower for precomputation */
/* Initialise network in range (0,0,0) to (255,255,255) and set parameters
----------------------------------------------------------------------- */
NeuQuant(byte[] thepic, int len, int sample) {
int i;
int[] p;
thepicture = thepic;
lengthcount = len;
samplefac = sample;
network = new int[netsize][];
for(i = 0; i<netsize; i++) {
network[i] = new int[4];
p = network[i];
p[0] = p[1] = p[2] = (i<<(netbiasshift+8))/netsize;
freq[i] = intbias/netsize; /* 1/netsize */
bias[i] = 0;
}
}
byte[] colorMap() {
byte[] map = new byte[3*netsize];
int[] index = new int[netsize];
for(int i = 0; i<netsize; i++) {
index[network[i][3]] = i;
}
int k = 0;
for(int i = 0; i<netsize; i++) {
int j = index[i];
map[k++] = (byte) (network[j][0]);
map[k++] = (byte) (network[j][1]);
map[k++] = (byte) (network[j][2]);
}
return map;
}
/* Insertion sort of network and building of netindex[0..255] (to do after unbias)
------------------------------------------------------------------------------- */
void inxbuild() {
int i, j, smallpos, smallval;
int[] p;
int[] 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 */
}
}
/* Main Learning Loop
------------------ */
void learn() {
int i, j, b, g, r;
int radius, rad, alpha, step, delta, samplepixels;
byte[] p;
int pix, lim;
if(lengthcount<minpicturebytes) {
samplefac = 1;
}
alphadec = 30+((samplefac-1)/3);
p = thepicture;
pix = 0;
lim = lengthcount;
samplepixels = lengthcount/(3*samplefac);
delta = samplepixels/ncycles;
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<minpicturebytes) {
step = 3;
} else 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[pix+0]&0xff)<<netbiasshift;
g = (p[pix+1]&0xff)<<netbiasshift;
r = (p[pix+2]&0xff)<<netbiasshift;
j = contest(b, g, r);
altersingle(alpha, j, b, g, r);
if(rad!=0) {
alterneigh(rad, j, b, g, r); /* alter neighbours */
}
pix += step;
if(pix>=lim) {
pix -= lengthcount;
}
i++;
if(delta==0) {
delta = 1;
}
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);
}
/* Search for BGR values 0..255 (after net is unbiased) and return colour index
---------------------------------------------------------------------------- */
int map(int b, int g, int r) {
int i, j, dist, a, bestd;
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);
}
byte[] process() {
learn();
unbiasnet();
inxbuild();
return colorMap();
}
/* Unbias network to give byte values 0..255 and record position i to prepare for sort
----------------------------------------------------------------------------------- */
void unbiasnet() {
int i;
for(i = 0; i<netsize; i++) {
network[i][0] >>= netbiasshift;
network[i][1] >>= netbiasshift;
network[i][2] >>= netbiasshift;
network[i][3] = i; /* record colour no */
}
}
/* Move adjacent neurons by precomputed alpha*(1-((i-j)^2/[r]^2)) in radpower[|i-j|]
--------------------------------------------------------------------------------- */
protected void alterneigh(int rad, int i, int b, int g, int r) {
int j, k, lo, hi, a, m;
int[] p;
lo = i-rad;
if(lo<-1) {
lo = -1;
}
hi = i+rad;
if(hi>netsize) {
hi = netsize;
}
j = i+1;
k = i-1;
m = 1;
while((j<hi)||(k>lo)) {
a = radpower[m++];
if(j<hi) {
p = network[j++];
try {
p[0] -= (a*(p[0]-b))/alpharadbias;
p[1] -= (a*(p[1]-g))/alpharadbias;
p[2] -= (a*(p[2]-r))/alpharadbias;
} catch(Exception e) {
/** empty block */
} // prevents 1.3 miscompilation
}
if(k>lo) {
p = network[k--];
try {
p[0] -= (a*(p[0]-b))/alpharadbias;
p[1] -= (a*(p[1]-g))/alpharadbias;
p[2] -= (a*(p[2]-r))/alpharadbias;
} catch(Exception e) {
/** empty block */
}
}
}
}
/* Move neuron i towards biased (b,g,r) by factor alpha
---------------------------------------------------- */
protected void altersingle(int alpha, int i, int b, int g, int r) {
/* alter hit neuron */
int[] n = network[i];
n[0] -= (alpha*(n[0]-b))/initalpha;
n[1] -= (alpha*(n[1]-g))/initalpha;
n[2] -= (alpha*(n[2]-r))/initalpha;
}
/* Search for biased BGR values
---------------------------- */
protected 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]) */
int i, dist, a, biasdist, betafreq;
int bestpos, bestbiaspos, bestd, bestbiasd;
int[] n;
bestd = ~(1<<31);
bestbiasd = bestd;
bestpos = -1;
bestbiaspos = bestpos;
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-((bias[i])>>(intbiasshift-netbiasshift));
if(biasdist<bestbiasd) {
bestbiasd = biasdist;
bestbiaspos = i;
}
betafreq = (freq[i]>>betashift);
freq[i] -= betafreq;
bias[i] += (betafreq<<gammashift);
}
freq[bestpos] += beta;
bias[bestpos] -= betagamma;
return(bestbiaspos);
}
}
/*
* Open Source Physics software is free software; you can redistribute
* it and/or modify it under the terms of the GNU General Public License (GPL) as
* published by the Free Software Foundation; either version 2 of the License,
* or(at your option) any later version.
* Code that uses any portion of the code in the org.opensourcephysics package
* or any subpackage (subdirectory) of this package must must also be be released
* under the GNU GPL license.
*
* This software is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this; if not, write to the Free Software
* Foundation, Inc., 59 Temple Place, Suite 330, Boston MA 02111-1307 USA
* or view the license online at http://www.gnu.org/copyleft/gpl.html
*
* Copyright (c) 2007 The Open Source Physics project
* http://www.opensourcephysics.org
*/