Java tutorial
// Constructors: // public NeuQuant (Image im, ImageObserver obs) throws IOException -- default sample = 1 // public NeuQuant (int sample, Image im, ImageObserver obs) throws IOException // public NeuQuant (Image im, int w, int h) throws IOException -- default sample = 1 // public NeuQuant (int sample, Image im, int w, int h) throws IOException // Initialisation method: call this first // public void init () // Methods to look up pixels (use in a loop) // public int convert (int pixel) // public int lookup (int pixel) // public int lookup (Color c) // public int lookup (boolean rgb, int x, int g, int y) // Method to write out colour map (used for GIFs, with "true" parameter) // public int writeColourMap (boolean rgb, OutputStream out) throws IOException // Other methods to interrogate colour map // public int getColorCount () // public Color getColor (int i) import java.io.*; import java.awt.Image; import java.awt.Color; import java.awt.image.*; import org.opencv.core.CvType; import org.opencv.core.Mat; /* 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. * See also http://www.acm.org/~dekker/NEUQUANT.HTML * * 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. */ public class NeuQuant { public static final int ncycles = 100; // no. of learning cycles public int netsize = 256; // number of colours used public static final int specials = 3; // number of reserved colours used public static final int bgColour = specials - 1; // reserved background colour public int cutnetsize = netsize - specials; public int maxnetpos = netsize - 1; public int initrad = netsize / 8; // for 256 cols, radius starts at 32 public static final int radiusbiasshift = 6; public static final int radiusbias = 1 << radiusbiasshift; public int initBiasRadius = initrad * radiusbias; public static final int radiusdec = 30; // factor of 1/30 each cycle public static final int alphabiasshift = 10; // alpha starts at 1 public static final int initalpha = 1 << alphabiasshift; // biased by 10 bits public static final double gamma = 1024.0; public static final double beta = 1.0 / 1024.0; public static final double betagamma = beta * gamma; private double[][] network = new double[netsize][3]; // the network itself protected int[][] colormap = new int[netsize][4]; // the network itself private int[] netindex = new int[256]; // for network lookup - really 256 private double[] bias = new double[netsize]; // bias and freq arrays for learning private double[] freq = new double[netsize]; // four primes near 500 - assume no image has a length so large // that it is divisible by all four primes public static final int prime1 = 499; public static final int prime2 = 491; public static final int prime3 = 487; public static final int prime4 = 503; public static final int maxprime = prime4; protected int[] pixels = null; private int samplefac = 0; public NeuQuant(Image im, int w, int h, int size) throws IOException { this(1); netsize = size; cutnetsize = netsize - specials; maxnetpos = netsize - 1; initrad = netsize / 8; // for 256 cols, radius starts at 32 initBiasRadius = initrad * radiusbias; setPixels(im, w, h); setUpArrays(); } public NeuQuant(int sample, Image im, int w, int h) throws IOException { this(sample); setPixels(im, w, h); setUpArrays(); } public NeuQuant(Image im, ImageObserver obs) throws IOException { this(1); setPixels(im, obs); setUpArrays(); } protected NeuQuant(int sample) throws IOException { if (sample < 1) throw new IOException("Sample must be 1..30"); if (sample > 30) throw new IOException("Sample must be 1..30"); samplefac = sample; // rest later } public NeuQuant(int sample, Image im, ImageObserver obs) throws IOException { this(sample); setPixels(im, obs); setUpArrays(); } public int getColorCount() { return netsize; } public Color getColor(int i) { if (i < 0 || i >= netsize) return null; int bb = colormap[i][0]; int gg = colormap[i][1]; int rr = colormap[i][2]; return new Color(rr, gg, bb); } public Mat createPalette() throws IOException { Mat palette = Mat.zeros(netsize, 1, CvType.CV_8UC3); for (int i = 0; i < netsize; i++) { int bb = colormap[i][0]; int gg = colormap[i][1]; int rr = colormap[i][2]; palette.put(i, 0, new double[] { bb, gg, rr }); } return palette; } public int writeColourMap(boolean rgb) throws IOException { for (int i = 0; i < netsize; i++) { int bb = colormap[i][0]; int gg = colormap[i][1]; int rr = colormap[i][2]; System.out.print(rgb ? rr : bb); System.out.print(gg); System.out.println(rgb ? bb : rr); } return netsize; } protected void setUpArrays() { network[0][0] = 0.0; // black network[0][1] = 0.0; network[0][2] = 0.0; network[1][0] = 255.0; // white network[1][1] = 255.0; network[1][2] = 255.0; // RESERVED bgColour // background for (int i = 0; i < specials; i++) { freq[i] = 1.0 / netsize; bias[i] = 0.0; } for (int i = specials; i < netsize; i++) { double[] p = network[i]; p[0] = (255.0 * (i - specials)) / cutnetsize; p[1] = (255.0 * (i - specials)) / cutnetsize; p[2] = (255.0 * (i - specials)) / cutnetsize; freq[i] = 1.0 / netsize; bias[i] = 0.0; } } private void setPixels(Image im, ImageObserver obs) throws IOException { if (im == null) throw new IOException("Image is null"); int w = im.getWidth(obs); int h = im.getHeight(obs); setPixels(im, w, h); } private void setPixels(Image im, int w, int h) throws IOException { if (w * h < maxprime) throw new IOException("Image is too small"); pixels = new int[w * h]; java.awt.image.PixelGrabber pg = new java.awt.image.PixelGrabber(im, 0, 0, w, h, pixels, 0, w); try { pg.grabPixels(); } catch (InterruptedException e) { } if ((pg.getStatus() & java.awt.image.ImageObserver.ABORT) != 0) { throw new IOException("Image pixel grab aborted or errored"); } } public void init() { learn(); fix(); inxbuild(); } private void altersingle(double alpha, int i, double b, double g, double r) { // Move neuron i towards biased (b,g,r) by factor alpha double[] n = network[i]; // alter hit neuron n[0] -= (alpha * (n[0] - b)); n[1] -= (alpha * (n[1] - g)); n[2] -= (alpha * (n[2] - r)); } private void alterneigh(double alpha, int rad, int i, double b, double g, double r) { int lo = i - rad; if (lo < specials - 1) lo = specials - 1; int hi = i + rad; if (hi > netsize) hi = netsize; int j = i + 1; int k = i - 1; int q = 0; while ((j < hi) || (k > lo)) { double a = (alpha * (rad * rad - q * q)) / (rad * rad); q++; if (j < hi) { double[] p = network[j]; p[0] -= (a * (p[0] - b)); p[1] -= (a * (p[1] - g)); p[2] -= (a * (p[2] - r)); j++; } if (k > lo) { double[] p = network[k]; p[0] -= (a * (p[0] - b)); p[1] -= (a * (p[1] - g)); p[2] -= (a * (p[2] - r)); k--; } } } private int contest(double b, double g, double r) { // Search for biased BGR values // 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]) double bestd = Float.MAX_VALUE; double bestbiasd = bestd; int bestpos = -1; int bestbiaspos = bestpos; for (int i = specials; i < netsize; i++) { double[] n = network[i]; double dist = n[0] - b; if (dist < 0) dist = -dist; double 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; } double biasdist = dist - bias[i]; if (biasdist < bestbiasd) { bestbiasd = biasdist; bestbiaspos = i; } freq[i] -= beta * freq[i]; bias[i] += betagamma * freq[i]; } freq[bestpos] += beta; bias[bestpos] -= betagamma; return bestbiaspos; } private int specialFind(double b, double g, double r) { for (int i = 0; i < specials; i++) { double[] n = network[i]; if (n[0] == b && n[1] == g && n[2] == r) return i; } return -1; } private void learn() { int biasRadius = initBiasRadius; int alphadec = 30 + ((samplefac - 1) / 3); int lengthcount = pixels.length; int samplepixels = lengthcount / samplefac; int delta = samplepixels / ncycles; int alpha = initalpha; int i = 0; int rad = biasRadius >> radiusbiasshift; if (rad <= 1) rad = 0; System.err.println("beginning 1D learning: samplepixels=" + samplepixels + " rad=" + rad); int step = 0; int pos = 0; if ((lengthcount % prime1) != 0) step = prime1; else { if ((lengthcount % prime2) != 0) step = prime2; else { if ((lengthcount % prime3) != 0) step = prime3; else step = prime4; } } i = 0; while (i < samplepixels) { int p = pixels[pos]; int red = (p >> 16) & 0xff; int green = (p >> 8) & 0xff; int blue = (p) & 0xff; double b = blue; double g = green; double r = red; if (i == 0) { // remember background colour network[bgColour][0] = b; network[bgColour][1] = g; network[bgColour][2] = r; } int j = specialFind(b, g, r); j = j < 0 ? contest(b, g, r) : j; if (j >= specials) { // don't learn for specials double a = (1.0 * alpha) / initalpha; altersingle(a, j, b, g, r); if (rad > 0) alterneigh(a, rad, j, b, g, r); // alter neighbours } pos += step; while (pos >= lengthcount) pos -= lengthcount; i++; if (i % delta == 0) { alpha -= alpha / alphadec; biasRadius -= biasRadius / radiusdec; rad = biasRadius >> radiusbiasshift; if (rad <= 1) rad = 0; } } System.err.println("finished 1D learning: final alpha=" + (1.0 * alpha) / initalpha + "!"); } private void fix() { for (int i = 0; i < netsize; i++) { for (int j = 0; j < 3; j++) { int x = (int) (0.5 + network[i][j]); if (x < 0) x = 0; if (x > 255) x = 255; colormap[i][j] = x; } colormap[i][3] = i; } } private void inxbuild() { // Insertion sort of network and building of netindex[0..255] int previouscol = 0; int startpos = 0; for (int i = 0; i < netsize; i++) { int[] p = colormap[i]; int[] q = null; int smallpos = i; int smallval = p[1]; // index on g // find smallest in i..netsize-1 for (int j = i + 1; j < netsize; j++) { q = colormap[j]; if (q[1] < smallval) { // index on g smallpos = j; smallval = q[1]; // index on g } } q = colormap[smallpos]; // swap p (i) and q (smallpos) entries if (i != smallpos) { int 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 (int j = previouscol + 1; j < smallval; j++) netindex[j] = i; previouscol = smallval; startpos = i; } } netindex[previouscol] = (startpos + maxnetpos) >> 1; for (int j = previouscol + 1; j < 256; j++) netindex[j] = maxnetpos; // really 256 } public int convert(int pixel) { int alfa = (pixel >> 24) & 0xff; int r = (pixel >> 16) & 0xff; int g = (pixel >> 8) & 0xff; int b = (pixel) & 0xff; int i = inxsearch(b, g, r); int bb = colormap[i][0]; int gg = colormap[i][1]; int rr = colormap[i][2]; return (alfa << 24) | (rr << 16) | (gg << 8) | (bb); } public int lookup(int pixel) { int r = (pixel >> 16) & 0xff; int g = (pixel >> 8) & 0xff; int b = (pixel) & 0xff; int i = inxsearch(b, g, r); return i; } public int lookup(Color c) { int r = c.getRed(); int g = c.getGreen(); int b = c.getBlue(); int i = inxsearch(b, g, r); return i; } public int lookup(boolean rgb, int x, int g, int y) { int i = rgb ? inxsearch(y, g, x) : inxsearch(x, g, y); return i; } protected int inxsearch(int b, int g, int r) { // Search for BGR values 0..255 and return colour index int bestd = 1000; // biggest possible dist is 256*3 int best = -1; int i = netindex[g]; // index on g int j = i - 1; // start at netindex[g] and work outwards while ((i < netsize) || (j >= 0)) { if (i < netsize) { int[] p = colormap[i]; int dist = p[1] - g; // inx key if (dist >= bestd) i = netsize; // stop iter else { if (dist < 0) dist = -dist; int 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 = i; } } i++; } } if (j >= 0) { int[] p = colormap[j]; int dist = g - p[1]; // inx key - reverse dif if (dist >= bestd) j = -1; // stop iter else { if (dist < 0) dist = -dist; int 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 = j; } } j--; } } } return best; } }