com.github.zh9131101.utils.NeuQuant Maven / Gradle / Ivy
/*
* Copyright 2021-2039 ZH9131101.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package com.github.zh9131101.utils;
/**
*
* 处理GIF图片
*
*
* @author zh9131101
* @version V1.0.0
* @date 2021-01-07 22:07
* @since 1.0
*/
public class NeuQuant {
/**
* number of colours used
*/
protected static final int netsize = 256;
/* 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);
/**
* bias for colour values
*/
protected static final int netbiasshift = 4;
/**
* no. of learning cycles
*/
protected static final int ncycles = 100;
/* defs for freq and bias */
/**
* bias for fractions
*/
protected static final int intbiasshift = 16;
protected static final int intbias = (1 << intbiasshift);
/**
* gamma = 1024
*/
protected static final int gammashift = 10;
protected static final int gamma = (1 << gammashift);
protected static final int betashift = 10;
/**
* beta = 1/1024
*/
protected static final int beta = (intbias >> betashift);
protected static final int betagamma =
(intbias << (gammashift - betashift));
/* defs for decreasing radius factor */
/**
* for 256 cols, radius starts
*/
protected static final int initrad = (netsize >> 3);
/**
* at 32.0 biased by 6 bits
*/
protected static final int radiusbiasshift = 6;
protected static final int radiusbias = (1 << radiusbiasshift);
/**
* and decreases by a
*/
protected static final int initradius = (initrad * radiusbias);
/**
* factor of 1/30 each cycle
*/
protected static final int radiusdec = 30;
/* defs for decreasing randomChar factor */
/**
* randomChar starts at 1.0
*/
protected static final int alphabiasshift = 10;
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
-------------------------- */
/**
* the input image itself
*/
protected byte[] thepicture;
/**
* lengthcount = H*W*3
*/
protected int lengthcount;
/**
* sampling factor 1..30
*/
protected int samplefac;
// typedef int pixel[4]; /* BGRc */
/**
* the network itself - [netsize][4]
*/
protected int[][] network;
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;
// 1/netsize
freq[i] = intbias / 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;
int j;
int smallpos;
int smallval;
int[] p;
int[] q;
int previouscol = 0;
int startpos = 0;
for (i = 0; i < netsize; i++) {
p = network[i];
smallpos = i;
// index on g
smallval = p[1];
/* find smallest in i..netsize-1 */
for (j = i + 1; j < netsize; j++) {
q = network[j];
// index on g
if (q[1] < smallval) {
smallpos = j;
// index on g
smallval = q[1];
}
}
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++) {
// really 256
netindex[j] = maxnetpos;
}
}
/* Main Learning Loop
------------------ */
public void learn() {
int i;
int j;
int b;
int g;
int r;
int radius;
int rad;
int alpha;
int step;
int delta;
int samplepixels;
byte[] p;
int pix;
int 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] & 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) {
// alter neighbours
alterneigh(rad, j, b, g, r);
}
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 randomChar=%f !\n",((float)randomChar)/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;
// biggest possible dist is 256*3
bestd = 1000;
best = -1;
// index on g
i = netindex[g];
// start at netindex[g] and work outwards
j = i - 1;
while ((i < netsize) || (j >= 0)) {
if (i < netsize) {
p = network[i];
// inx key
dist = p[1] - g;
if (dist >= bestd) {
// stop iter
i = netsize;
} 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];
// inx key - reverse dif
dist = g - p[1];
if (dist >= bestd) {
// stop iter
j = -1;
} 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 randomChar*(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 randomChar
---------------------------------------------------- */
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);
}
}
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