440 lines
14 KiB
Java
440 lines
14 KiB
Java
package com.wf.captcha;
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/**
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*
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*/
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public class Quant {
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protected static final int netsize = 256; /* number of colours used */
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/* four primes near 500 - assume no image has a length so large */
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/* that it is divisible by all four primes */
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protected static final int prime1 = 499;
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protected static final int prime2 = 491;
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protected static final int prime3 = 487;
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protected static final int prime4 = 503;
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protected static final int minpicturebytes = (3 * prime4);
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/* minimum size for input image */
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/* Program Skeleton
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----------------
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[select samplefac in range 1..30]
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[read image from input file]
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pic = (unsigned char*) malloc(3*width*height);
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initnet(pic,3*width*height,samplefac);
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learn();
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unbiasnet();
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[write output image header, using writecolourmap(f)]
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inxbuild();
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write output image using inxsearch(b,g,r) */
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/* Network Definitions
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------------------- */
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protected static final int maxnetpos = (netsize - 1);
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protected static final int netbiasshift = 4; /* bias for colour values */
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protected static final int ncycles = 100; /* no. of learning cycles */
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/* defs for freq and bias */
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protected static final int intbiasshift = 16; /* bias for fractions */
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protected static final int intbias = (((int) 1) << intbiasshift);
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protected static final int gammashift = 10; /* gamma = 1024 */
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protected static final int gamma = (((int) 1) << gammashift);
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protected static final int betashift = 10;
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protected static final int beta = (intbias >> betashift); /* beta = 1/1024 */
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protected static final int betagamma =
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(intbias << (gammashift - betashift));
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/* defs for decreasing radius factor */
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protected static final int initrad = (netsize >> 3); /* for 256 cols, radius starts */
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protected static final int radiusbiasshift = 6; /* at 32.0 biased by 6 bits */
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protected static final int radiusbias = (((int) 1) << radiusbiasshift);
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protected static final int initradius = (initrad * radiusbias); /* and decreases by a */
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protected static final int radiusdec = 30; /* factor of 1/30 each cycle */
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/* defs for decreasing alpha factor */
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protected static final int alphabiasshift = 10; /* alpha starts at 1.0 */
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protected static final int initalpha = (((int) 1) << alphabiasshift);
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protected int alphadec; /* biased by 10 bits */
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/* radbias and alpharadbias used for radpower calculation */
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protected static final int radbiasshift = 8;
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protected static final int radbias = (((int) 1) << radbiasshift);
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protected static final int alpharadbshift = (alphabiasshift + radbiasshift);
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protected static final int alpharadbias = (((int) 1) << alpharadbshift);
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/* Types and Global Variables
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-------------------------- */
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protected byte[] thepicture; /* the input image itself */
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protected int lengthcount; /* lengthcount = H*W*3 */
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protected int samplefac; /* sampling factor 1..30 */
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// typedef int pixel[4]; /* BGRc */
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protected int[][] network; /* the network itself - [netsize][4] */
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protected int[] netindex = new int[256];
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/* for network lookup - really 256 */
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protected int[] bias = new int[netsize];
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/* bias and freq arrays for learning */
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protected int[] freq = new int[netsize];
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protected int[] radpower = new int[initrad];
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/* radpower for precomputation */
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/* Initialise network in range (0,0,0) to (255,255,255) and set parameters
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----------------------------------------------------------------------- */
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public Quant(byte[] thepic, int len, int sample) {
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int i;
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int[] p;
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thepicture = thepic;
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lengthcount = len;
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samplefac = sample;
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network = new int[netsize][];
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for (i = 0; i < netsize; i++) {
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network[i] = new int[4];
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p = network[i];
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p[0] = p[1] = p[2] = (i << (netbiasshift + 8)) / netsize;
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freq[i] = intbias / netsize; /* 1/netsize */
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bias[i] = 0;
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}
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}
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public byte[] colorMap() {
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byte[] map = new byte[3 * netsize];
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int[] index = new int[netsize];
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for (int i = 0; i < netsize; i++)
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index[network[i][3]] = i;
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int k = 0;
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for (int i = 0; i < netsize; i++) {
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int j = index[i];
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map[k++] = (byte) (network[j][0]);
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map[k++] = (byte) (network[j][1]);
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map[k++] = (byte) (network[j][2]);
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}
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return map;
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}
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/* Insertion sort of network and building of netindex[0..255] (to do after unbias)
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------------------------------------------------------------------------------- */
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public void inxbuild() {
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int i, j, smallpos, smallval;
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int[] p;
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int[] q;
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int previouscol, startpos;
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previouscol = 0;
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startpos = 0;
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for (i = 0; i < netsize; i++) {
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p = network[i];
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smallpos = i;
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smallval = p[1]; /* index on g */
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/* find smallest in i..netsize-1 */
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for (j = i + 1; j < netsize; j++) {
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q = network[j];
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if (q[1] < smallval) { /* index on g */
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smallpos = j;
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smallval = q[1]; /* index on g */
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}
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}
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q = network[smallpos];
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/* swap p (i) and q (smallpos) entries */
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if (i != smallpos) {
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j = q[0];
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q[0] = p[0];
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p[0] = j;
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j = q[1];
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q[1] = p[1];
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p[1] = j;
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j = q[2];
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q[2] = p[2];
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p[2] = j;
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j = q[3];
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q[3] = p[3];
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p[3] = j;
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}
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/* smallval entry is now in position i */
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if (smallval != previouscol) {
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netindex[previouscol] = (startpos + i) >> 1;
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for (j = previouscol + 1; j < smallval; j++)
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netindex[j] = i;
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previouscol = smallval;
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startpos = i;
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}
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}
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netindex[previouscol] = (startpos + maxnetpos) >> 1;
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for (j = previouscol + 1; j < 256; j++)
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netindex[j] = maxnetpos; /* really 256 */
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}
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/* Main Learning Loop
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------------------ */
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public void learn() {
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int i, j, b, g, r;
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int radius, rad, alpha, step, delta, samplepixels;
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byte[] p;
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int pix, lim;
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if (lengthcount < minpicturebytes)
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samplefac = 1;
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alphadec = 30 + ((samplefac - 1) / 3);
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p = thepicture;
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pix = 0;
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lim = lengthcount;
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samplepixels = lengthcount / (3 * samplefac);
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delta = samplepixels / ncycles;
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alpha = initalpha;
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radius = initradius;
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rad = radius >> radiusbiasshift;
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if (rad <= 1)
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rad = 0;
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for (i = 0; i < rad; i++)
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radpower[i] =
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alpha * (((rad * rad - i * i) * radbias) / (rad * rad));
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//fprintf(stderr,"beginning 1D learning: initial radius=%d\n", rad);
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if (lengthcount < minpicturebytes)
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step = 3;
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else if ((lengthcount % prime1) != 0)
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step = 3 * prime1;
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else {
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if ((lengthcount % prime2) != 0)
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step = 3 * prime2;
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else {
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if ((lengthcount % prime3) != 0)
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step = 3 * prime3;
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else
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step = 3 * prime4;
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}
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}
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i = 0;
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while (i < samplepixels) {
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b = (p[pix + 0] & 0xff) << netbiasshift;
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g = (p[pix + 1] & 0xff) << netbiasshift;
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r = (p[pix + 2] & 0xff) << netbiasshift;
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j = contest(b, g, r);
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altersingle(alpha, j, b, g, r);
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if (rad != 0)
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alterneigh(rad, j, b, g, r); /* alter neighbours */
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pix += step;
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if (pix >= lim)
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pix -= lengthcount;
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i++;
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if (delta == 0)
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delta = 1;
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if (i % delta == 0) {
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alpha -= alpha / alphadec;
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radius -= radius / radiusdec;
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rad = radius >> radiusbiasshift;
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if (rad <= 1)
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rad = 0;
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for (j = 0; j < rad; j++)
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radpower[j] =
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alpha * (((rad * rad - j * j) * radbias) / (rad * rad));
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}
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}
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//fprintf(stderr,"finished 1D learning: final alpha=%f !\n",((float)alpha)/initalpha);
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}
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/* Search for BGR values 0..255 (after net is unbiased) and return colour index
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---------------------------------------------------------------------------- */
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public int map(int b, int g, int r) {
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int i, j, dist, a, bestd;
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int[] p;
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int best;
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bestd = 1000; /* biggest possible dist is 256*3 */
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best = -1;
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i = netindex[g]; /* index on g */
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j = i - 1; /* start at netindex[g] and work outwards */
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while ((i < netsize) || (j >= 0)) {
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if (i < netsize) {
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p = network[i];
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dist = p[1] - g; /* inx key */
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if (dist >= bestd)
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i = netsize; /* stop iter */
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else {
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i++;
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if (dist < 0)
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dist = -dist;
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a = p[0] - b;
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if (a < 0)
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a = -a;
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dist += a;
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if (dist < bestd) {
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a = p[2] - r;
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if (a < 0)
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a = -a;
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dist += a;
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if (dist < bestd) {
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bestd = dist;
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best = p[3];
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}
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}
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}
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}
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if (j >= 0) {
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p = network[j];
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dist = g - p[1]; /* inx key - reverse dif */
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if (dist >= bestd)
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j = -1; /* stop iter */
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else {
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j--;
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if (dist < 0)
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dist = -dist;
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a = p[0] - b;
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if (a < 0)
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a = -a;
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dist += a;
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if (dist < bestd) {
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a = p[2] - r;
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if (a < 0)
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a = -a;
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dist += a;
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if (dist < bestd) {
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bestd = dist;
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best = p[3];
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}
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}
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}
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}
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}
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return (best);
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}
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public byte[] process() {
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learn();
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unbiasnet();
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inxbuild();
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return colorMap();
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}
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/* Unbias network to give byte values 0..255 and record position i to prepare for sort
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----------------------------------------------------------------------------------- */
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public void unbiasnet() {
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int i, j;
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for (i = 0; i < netsize; i++) {
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network[i][0] >>= netbiasshift;
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network[i][1] >>= netbiasshift;
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network[i][2] >>= netbiasshift;
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network[i][3] = i; /* record colour no */
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}
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}
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/* Move adjacent neurons by precomputed alpha*(1-((i-j)^2/[r]^2)) in radpower[|i-j|]
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--------------------------------------------------------------------------------- */
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protected void alterneigh(int rad, int i, int b, int g, int r) {
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int j, k, lo, hi, a, m;
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int[] p;
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lo = i - rad;
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if (lo < -1)
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lo = -1;
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hi = i + rad;
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if (hi > netsize)
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hi = netsize;
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j = i + 1;
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k = i - 1;
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m = 1;
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while ((j < hi) || (k > lo)) {
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a = radpower[m++];
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if (j < hi) {
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p = network[j++];
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try {
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p[0] -= (a * (p[0] - b)) / alpharadbias;
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p[1] -= (a * (p[1] - g)) / alpharadbias;
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p[2] -= (a * (p[2] - r)) / alpharadbias;
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} catch (Exception e) {
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} // prevents 1.3 miscompilation
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}
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if (k > lo) {
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p = network[k--];
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try {
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p[0] -= (a * (p[0] - b)) / alpharadbias;
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p[1] -= (a * (p[1] - g)) / alpharadbias;
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p[2] -= (a * (p[2] - r)) / alpharadbias;
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} catch (Exception e) {
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}
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}
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}
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}
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/* Move neuron i towards biased (b,g,r) by factor alpha
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---------------------------------------------------- */
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protected void altersingle(int alpha, int i, int b, int g, int r) {
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/* alter hit neuron */
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int[] n = network[i];
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n[0] -= (alpha * (n[0] - b)) / initalpha;
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n[1] -= (alpha * (n[1] - g)) / initalpha;
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n[2] -= (alpha * (n[2] - r)) / initalpha;
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}
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/* Search for biased BGR values
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---------------------------- */
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protected int contest(int b, int g, int r) {
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/* finds closest neuron (min dist) and updates freq */
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/* finds best neuron (min dist-bias) and returns position */
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/* for frequently chosen neurons, freq[i] is high and bias[i] is negative */
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/* bias[i] = gamma*((1/netsize)-freq[i]) */
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int i, dist, a, biasdist, betafreq;
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int bestpos, bestbiaspos, bestd, bestbiasd;
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int[] n;
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bestd = ~(((int) 1) << 31);
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bestbiasd = bestd;
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bestpos = -1;
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bestbiaspos = bestpos;
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for (i = 0; i < netsize; i++) {
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n = network[i];
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dist = n[0] - b;
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if (dist < 0)
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dist = -dist;
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a = n[1] - g;
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if (a < 0)
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a = -a;
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dist += a;
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a = n[2] - r;
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if (a < 0)
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a = -a;
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dist += a;
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if (dist < bestd) {
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bestd = dist;
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bestpos = i;
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}
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biasdist = dist - ((bias[i]) >> (intbiasshift - netbiasshift));
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if (biasdist < bestbiasd) {
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bestbiasd = biasdist;
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bestbiaspos = i;
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}
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betafreq = (freq[i] >> betashift);
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freq[i] -= betafreq;
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bias[i] += (betafreq << gammashift);
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}
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freq[bestpos] += beta;
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bias[bestpos] -= betagamma;
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return (bestbiaspos);
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}
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} |