one.empty3.feature.HoughTransform Maven / Gradle / Ivy
Go to download
Show more of this group Show more artifacts with this name
Show all versions of empty3-library-3d Show documentation
Show all versions of empty3-library-3d Show documentation
3D rendering engine. Plus modeling. Expected glsl textures 3d and 2d rendering
/*
* Copyright (c) 2022-2023. Manuel Daniel Dahmen
*
*
* Copyright 2012-2023 Manuel Daniel Dahmen
*
* 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 one.empty3.feature;
import one.empty3.io.ProcessFile;
import javax.imageio.ImageIO;
import java.awt.*;
import java.awt.image.BufferedImage;
import java.io.File;
import java.io.IOException;
import java.util.Collections;
import java.util.Vector;
// je crois que le principe c'est
// checker le vote dans l'espace des
//solutions.
/*
*
* Java Implementation of the Hough Transform.
* Used for finding straight lines in an image.
* by Olly Oechsle
*
*
* Note: This class is based on original code from:
* http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm
*
*
* If you represent a line as:
* x cos(theta) + y sin (theta) = r
*
*
* ... 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.
*
*
* 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.
*
*
* 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.
*
*
* 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.
*
*
* @author Olly Oechsle, University of Essex
*/
public class HoughTransform extends ProcessFile {
public static final int NUM_LINES = 30;
private int maxDrawn = 30;
public boolean process(File in, File out) {
// load the file using Java's imageIO library
BufferedImage image = null;
try {
image = ImageIO.read(in);
// create a hough transform object with the right dimensions
width = image.getWidth();
height = image.getHeight();
initialise();
// add the points from the image (or call the addPoint method separately if your points are not in an image
addPoints(image);
// get the lines out
Vector lines = getLines(NUM_LINES);
lines.sort(Collections.reverseOrder());
// draw the lines back onto the image
for (int j = 0; j < maxDrawn && j < lines.size(); j++) {
HoughLine line = lines.elementAt(j);
line.draw(image, Color.RED.getRGB());
}
ImageIO.write(image, "jpg", out);
return true;
} catch (IOException e) {
e.printStackTrace();
return false;
}
}
// 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 initia
*/
/*
* 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 threshold The percentage threshold above which lines are determined from the hough array
*/
public Vector getLines(int threshold) {
// Initialise the vector of lines that we'll return
Vector lines = new Vector(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, houghArray[t][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_RGB);
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;
}
}