NeuQuant.java源码（处理GIF图片）

/* 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

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 = (((int) 1) << intbiasshift);

protected static final int gammashift = 10; /* gamma = 1024 */

protected static final int gamma = (((int) 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 = (((int) 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 = (((int) 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 = (((int) 1) << radbiasshift);

protected static final int alpharadbshift = (alphabiasshift + radbiasshift);

protected static final int alpharadbias = (((int) 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

----------------------------------------------------------------------- */

public 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;

}

}



public 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)

------------------------------------------------------------------------------- */

public 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

------------------ */

public 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

---------------------------------------------------------------------------- */

public 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);

}

public byte[] process() {

learn();

unbiasnet();

inxbuild();

return colorMap();

}



/* Unbias network to give byte values 0..255 and record position i to prepare for sort

----------------------------------------------------------------------------------- */

public void unbiasnet() {

int i, j;

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) {

} // 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) {

}

}

}

}



/* 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 = ~(((int) 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);

}

}