package ac.essex.ooechs.imaging.commons.edge.hough; import java.awt.image.BufferedImage; import java.awt.*; import java.util.Vector; import java.io.File; /** * <p/> * Java Implementation of the Hough Transform.<br /> * Used for finding straight lines in an image.<br /> * by Olly Oechsle * </p> * <p/> * Note: This class is based on original code from:<br /> * <a href="http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm">http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm</a> * </p> * <p/> * If you represent a line as:<br /> * x cos(theta) + y sin (theta) = r * </p> * <p/> * ... and you know values of x and y, you can calculate all the values of r by going through * all the possible values of theta. If you plot the values of r on a graph for every value of * theta you get a sinusoidal curve. This is the Hough transformation. * </p> * <p/> * The hough tranform works by looking at a number of such x,y coordinates, which are usually * found by some kind of edge detection. Each of these coordinates is transformed into * an r, theta curve. This curve is discretised so we actually only look at a certain discrete * number of theta values. "Accumulator" cells in a hough array along this curve are incremented * for X and Y coordinate. * </p> * <p/> * The accumulator space is plotted rectangularly with theta on one axis and r on the other. * Each point in the array represents an (r, theta) value which can be used to represent a line * using the formula above. * </p> * <p/> * Once all the points have been added should be full of curves. The algorithm then searches for * local peaks in the array. The higher the peak the more values of x and y crossed along that curve, * so high peaks give good indications of a line. * </p> * * @author Olly Oechsle, University of Essex */ public class HoughTransform extends Thread { public static void main(String[] args) throws Exception { String filename = "/home/ooechs/Desktop/vase.png"; // load the file using Java's imageIO library BufferedImage image = javax.imageio.ImageIO.read(new File(filename)); // create a hough transform object with the right dimensions HoughTransform h = new HoughTransform(image.getWidth(), image.getHeight()); // add the points from the image (or call the addPoint method separately if your points are not in an image h.addPoints(image); // get the lines out Vector<HoughLine> lines = h.getLines(30); // draw the lines back onto the image for (int j = 0; j < lines.size(); j++) { HoughLine line = lines.elementAt(j); line.draw(image, Color.RED.getRGB()); } } // The size of the neighbourhood in which to search for other local maxima final int neighbourhoodSize = 4; // How many discrete values of theta shall we check? final int maxTheta = 180; // Using maxTheta, work out the step final double thetaStep = Math.PI / maxTheta; // the width and height of the image protected int width, height; // the hough array protected int[][] houghArray; // the coordinates of the centre of the image protected float centerX, centerY; // the height of the hough array protected int houghHeight; // double the hough height (allows for negative numbers) protected int doubleHeight; // the number of points that have been added protected int numPoints; // cache of values of sin and cos for different theta values. Has a significant performance improvement. private double[] sinCache; private double[] cosCache; /** * Initialises the hough transform. The dimensions of the input image are needed * in order to initialise the hough array. * * @param width The width of the input image * @param height The height of the input image */ public HoughTransform(int width, int height) { this.width = width; this.height = height; initialise(); } /** * Initialises the hough array. Called by the constructor so you don't need to call it * yourself, however you can use it to reset the transform if you want to plug in another * image (although that image must have the same width and height) */ public void initialise() { // Calculate the maximum height the hough array needs to have houghHeight = (int) (Math.sqrt(2) * Math.max(height, width)) / 2; // Double the height of the hough array to cope with negative r values doubleHeight = 2 * houghHeight; // Create the hough array houghArray = new int[maxTheta][doubleHeight]; // Find edge points and vote in array centerX = width / 2; centerY = height / 2; // Count how many points there are numPoints = 0; // cache the values of sin and cos for faster processing sinCache = new double[maxTheta]; cosCache = sinCache.clone(); for (int t = 0; t < maxTheta; t++) { double realTheta = t * thetaStep; sinCache[t] = Math.sin(realTheta); cosCache[t] = Math.cos(realTheta); } } /** * Adds points from an image. The image is assumed to be greyscale black and white, so all pixels that are * not black are counted as edges. The image should have the same dimensions as the one passed to the constructor. */ public void addPoints(BufferedImage image) { // Now find edge points and update the hough array for (int x = 0; x < image.getWidth(); x++) { for (int y = 0; y < image.getHeight(); y++) { // Find non-black pixels if ((image.getRGB(x, y) & 0x000000ff) != 0) { addPoint(x, y); } } } } /** * Adds a single point to the hough transform. You can use this method directly * if your data isn't represented as a buffered image. */ public void addPoint(int x, int y) { // Go through each value of theta for (int t = 0; t < maxTheta; t++) { //Work out the r values for each theta step int r = (int) (((x - centerX) * cosCache[t]) + ((y - centerY) * sinCache[t])); // this copes with negative values of r r += houghHeight; if (r < 0 || r >= doubleHeight) continue; // Increment the hough array houghArray[t][r]++; } numPoints++; } /** * Once points have been added in some way this method extracts the lines and returns them as a Vector * of HoughLine objects, which can be used to draw on the * * @param percentageThreshold The percentage threshold above which lines are determined from the hough array */ public Vector<HoughLine> getLines(int threshold) { // Initialise the vector of lines that we'll return Vector<HoughLine> lines = new Vector<HoughLine>(20); // Only proceed if the hough array is not empty if (numPoints == 0) return lines; // Search for local peaks above threshold to draw for (int t = 0; t < maxTheta; t++) { loop: for (int r = neighbourhoodSize; r < doubleHeight - neighbourhoodSize; r++) { // Only consider points above threshold if (houghArray[t][r] > threshold) { int peak = houghArray[t][r]; // Check that this peak is indeed the local maxima for (int dx = -neighbourhoodSize; dx <= neighbourhoodSize; dx++) { for (int dy = -neighbourhoodSize; dy <= neighbourhoodSize; dy++) { int dt = t + dx; int dr = r + dy; if (dt < 0) dt = dt + maxTheta; else if (dt >= maxTheta) dt = dt - maxTheta; if (houghArray[dt][dr] > peak) { // found a bigger point nearby, skip continue loop; } } } // calculate the true value of theta double theta = t * thetaStep; // add the line to the vector lines.add(new HoughLine(theta, r)); } } } return lines; } /** * Gets the highest value in the hough array */ public int getHighestValue() { int max = 0; for (int t = 0; t < maxTheta; t++) { for (int r = 0; r < doubleHeight; r++) { if (houghArray[t][r] > max) { max = houghArray[t][r]; } } } return max; } /** * Gets the hough array as an image, in case you want to have a look at it. */ public BufferedImage getHoughArrayImage() { int max = getHighestValue(); BufferedImage image = new BufferedImage(maxTheta, doubleHeight, BufferedImage.TYPE_INT_ARGB); for (int t = 0; t < maxTheta; t++) { for (int r = 0; r < doubleHeight; r++) { double value = 255 * ((double) houghArray[t][r]) / max; int v = 255 - (int) value; int c = new Color(v, v, v).getRGB(); image.setRGB(t, r, c); } } return image; } }