| package com.bumptech.glide.gifencoder; |
| |
| /* |
| * 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 |
| 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); |
| } |
| } |