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I have used a java canny detector from the public source. I wanted to detect edges of fibre in the image from microscope. But the result is kind of dissapointig. If you look at the result you can see that some edges are "doubled", we can see parrallel curves really close to each other in some places and also some false edges. I would like to improve the result of the algorithm. How can I change the parameters beside low/highthreshold to improve effect?
CODE IS RUNNING, just put the source.jpg in the project folder and run.
import java.awt.image.BufferedImage;
import java.io.File;
import java.io.IOException;
import java.util.Arrays;
import javax.imageio.ImageIO;
public class CannyEdgeDetector {
// statics
private final static float GAUSSIAN_CUT_OFF = 0.005f;
private final static float MAGNITUDE_SCALE = 100F;
private final static float MAGNITUDE_LIMIT = 1000F;
private final static int MAGNITUDE_MAX = (int) (MAGNITUDE_SCALE * MAGNITUDE_LIMIT);
// fields
private int height;
private int width;
private int picsize;
private int[] data;
private int[] magnitude;
private BufferedImage sourceImage;
private BufferedImage edgesImage;
private float gaussianKernelRadius;
private float lowThreshold;
private float highThreshold;
private int gaussianKernelWidth;
private boolean contrastNormalized;
private float[] xConv;
private float[] yConv;
private float[] xGradient;
private float[] yGradient;
// constructors
/**
* Constructs a new detector with default parameters.
*/
public CannyEdgeDetector() {
lowThreshold = 2.5f;
highThreshold = 7.5f;
gaussianKernelRadius = 2f;
gaussianKernelWidth = 16;
contrastNormalized = false;
}
// accessors
/**
* The image that provides the luminance data used by this detector to
* generate edges.
*
* #return the source image, or null
*/
public BufferedImage getSourceImage() {
return sourceImage;
}
/**
* Specifies the image that will provide the luminance data in which edges
* will be detected. A source image must be set before the process method
* is called.
*
* #param image a source of luminance data
*/
public void setSourceImage(BufferedImage image) {
sourceImage = image;
}
/**
* Obtains an image containing the edges detected during the last call to
* the process method. The buffered image is an opaque image of type
* BufferedImage.TYPE_INT_ARGB in which edge pixels are white and all other
* pixels are black.
*
* #return an image containing the detected edges, or null if the process
* method has not yet been called.
*/
public BufferedImage getEdgesImage() {
return edgesImage;
}
/**
* Sets the edges image. Calling this method will not change the operation
* of the edge detector in any way. It is intended to provide a means by
* which the memory referenced by the detector object may be reduced.
*
* #param edgesImage expected (though not required) to be null
*/
public void setEdgesImage(BufferedImage edgesImage) {
this.edgesImage = edgesImage;
}
/**
* The low threshold for hysteresis. The default value is 2.5.
*
* #return the low hysteresis threshold
*/
public float getLowThreshold() {
return lowThreshold;
}
/**
* Sets the low threshold for hysteresis. Suitable values for this parameter
* must be determined experimentally for each application. It is nonsensical
* (though not prohibited) for this value to exceed the high threshold value.
*
* #param threshold a low hysteresis threshold
*/
public void setLowThreshold(float threshold) {
if (threshold < 0) throw new IllegalArgumentException();
lowThreshold = threshold;
}
/**
* The high threshold for hysteresis. The default value is 7.5.
*
* #return the high hysteresis threshold
*/
public float getHighThreshold() {
return highThreshold;
}
/**
* Sets the high threshold for hysteresis. Suitable values for this
* parameter must be determined experimentally for each application. It is
* nonsensical (though not prohibited) for this value to be less than the
* low threshold value.
*
* #param threshold a high hysteresis threshold
*/
public void setHighThreshold(float threshold) {
if (threshold < 0) throw new IllegalArgumentException();
highThreshold = threshold;
}
/**
* The number of pixels across which the Gaussian kernel is applied.
* The default value is 16.
*
* #return the radius of the convolution operation in pixels
*/
public int getGaussianKernelWidth() {
return gaussianKernelWidth;
}
/**
* The number of pixels across which the Gaussian kernel is applied.
* This implementation will reduce the radius if the contribution of pixel
* values is deemed negligable, so this is actually a maximum radius.
*
* #param gaussianKernelWidth a radius for the convolution operation in
* pixels, at least 2.
*/
public void setGaussianKernelWidth(int gaussianKernelWidth) {
if (gaussianKernelWidth < 2) throw new IllegalArgumentException();
this.gaussianKernelWidth = gaussianKernelWidth;
}
/**
* The radius of the Gaussian convolution kernel used to smooth the source
* image prior to gradient calculation. The default value is 16.
*
* #return the Gaussian kernel radius in pixels
*/
public float getGaussianKernelRadius() {
return gaussianKernelRadius;
}
/**
* Sets the radius of the Gaussian convolution kernel used to smooth the
* source image prior to gradient calculation.
*
* #return a Gaussian kernel radius in pixels, must exceed 0.1f.
*/
public void setGaussianKernelRadius(float gaussianKernelRadius) {
if (gaussianKernelRadius < 0.1f) throw new IllegalArgumentException();
this.gaussianKernelRadius = gaussianKernelRadius;
}
/**
* Whether the luminance data extracted from the source image is normalized
* by linearizing its histogram prior to edge extraction. The default value
* is false.
*
* #return whether the contrast is normalized
*/
public boolean isContrastNormalized() {
return contrastNormalized;
}
/**
* Sets whether the contrast is normalized
* #param contrastNormalized true if the contrast should be normalized,
* false otherwise
*/
public void setContrastNormalized(boolean contrastNormalized) {
this.contrastNormalized = contrastNormalized;
}
// methods
public void process() {
width = sourceImage.getWidth();
height = sourceImage.getHeight();
picsize = width * height;
initArrays();
readLuminance();
if (contrastNormalized) normalizeContrast();
computeGradients(gaussianKernelRadius, gaussianKernelWidth);
int low = Math.round(lowThreshold * MAGNITUDE_SCALE);
int high = Math.round( highThreshold * MAGNITUDE_SCALE);
performHysteresis(low, high);
thresholdEdges();
writeEdges(data);
}
// private utility methods
private void initArrays() {
if (data == null || picsize != data.length) {
data = new int[picsize];
magnitude = new int[picsize];
xConv = new float[picsize];
yConv = new float[picsize];
xGradient = new float[picsize];
yGradient = new float[picsize];
}
}
//NOTE: The elements of the method below (specifically the technique for
//non-maximal suppression and the technique for gradient computation)
//are derived from an implementation posted in the following forum (with the
//clear intent of others using the code):
// http://forum.java.sun.com/thread.jspa?threadID=546211&start=45&tstart=0
//My code effectively mimics the algorithm exhibited above.
//Since I don't know the providence of the code that was posted it is a
//possibility (though I think a very remote one) that this code violates
//someone's intellectual property rights. If this concerns you feel free to
//contact me for an alternative, though less efficient, implementation.
private void computeGradients(float kernelRadius, int kernelWidth) {
//generate the gaussian convolution masks
float kernel[] = new float[kernelWidth];
float diffKernel[] = new float[kernelWidth];
int kwidth;
for (kwidth = 0; kwidth < kernelWidth; kwidth++) {
float g1 = gaussian(kwidth, kernelRadius);
if (g1 <= GAUSSIAN_CUT_OFF && kwidth >= 2) break;
float g2 = gaussian(kwidth - 0.5f, kernelRadius);
float g3 = gaussian(kwidth + 0.5f, kernelRadius);
kernel[kwidth] = (g1 + g2 + g3) / 3f / (2f * (float) Math.PI * kernelRadius * kernelRadius);
diffKernel[kwidth] = g3 - g2;
}
int initX = kwidth - 1;
int maxX = width - (kwidth - 1);
int initY = width * (kwidth - 1);
int maxY = width * (height - (kwidth - 1));
//perform convolution in x and y directions
for (int x = initX; x < maxX; x++) {
for (int y = initY; y < maxY; y += width) {
int index = x + y;
float sumX = data[index] * kernel[0];
float sumY = sumX;
int xOffset = 1;
int yOffset = width;
for(; xOffset < kwidth ;) {
sumY += kernel[xOffset] * (data[index - yOffset] + data[index + yOffset]);
sumX += kernel[xOffset] * (data[index - xOffset] + data[index + xOffset]);
yOffset += width;
xOffset++;
}
yConv[index] = sumY;
xConv[index] = sumX;
}
}
for (int x = initX; x < maxX; x++) {
for (int y = initY; y < maxY; y += width) {
float sum = 0f;
int index = x + y;
for (int i = 1; i < kwidth; i++)
sum += diffKernel[i] * (yConv[index - i] - yConv[index + i]);
xGradient[index] = sum;
}
}
for (int x = kwidth; x < width - kwidth; x++) {
for (int y = initY; y < maxY; y += width) {
float sum = 0.0f;
int index = x + y;
int yOffset = width;
for (int i = 1; i < kwidth; i++) {
sum += diffKernel[i] * (xConv[index - yOffset] - xConv[index + yOffset]);
yOffset += width;
}
yGradient[index] = sum;
}
}
initX = kwidth;
maxX = width - kwidth;
initY = width * kwidth;
maxY = width * (height - kwidth);
for (int x = initX; x < maxX; x++) {
for (int y = initY; y < maxY; y += width) {
int index = x + y;
int indexN = index - width;
int indexS = index + width;
int indexW = index - 1;
int indexE = index + 1;
int indexNW = indexN - 1;
int indexNE = indexN + 1;
int indexSW = indexS - 1;
int indexSE = indexS + 1;
float xGrad = xGradient[index];
float yGrad = yGradient[index];
float gradMag = hypot(xGrad, yGrad);
//perform non-maximal supression
float nMag = hypot(xGradient[indexN], yGradient[indexN]);
float sMag = hypot(xGradient[indexS], yGradient[indexS]);
float wMag = hypot(xGradient[indexW], yGradient[indexW]);
float eMag = hypot(xGradient[indexE], yGradient[indexE]);
float neMag = hypot(xGradient[indexNE], yGradient[indexNE]);
float seMag = hypot(xGradient[indexSE], yGradient[indexSE]);
float swMag = hypot(xGradient[indexSW], yGradient[indexSW]);
float nwMag = hypot(xGradient[indexNW], yGradient[indexNW]);
float tmp;
/*
* An explanation of what's happening here, for those who want
* to understand the source: This performs the "non-maximal
* supression" phase of the Canny edge detection in which we
* need to compare the gradient magnitude to that in the
* direction of the gradient; only if the value is a local
* maximum do we consider the point as an edge candidate.
*
* We need to break the comparison into a number of different
* cases depending on the gradient direction so that the
* appropriate values can be used. To avoid computing the
* gradient direction, we use two simple comparisons: first we
* check that the partial derivatives have the same sign (1)
* and then we check which is larger (2). As a consequence, we
* have reduced the problem to one of four identical cases that
* each test the central gradient magnitude against the values at
* two points with 'identical support'; what this means is that
* the geometry required to accurately interpolate the magnitude
* of gradient function at those points has an identical
* geometry (upto right-angled-rotation/reflection).
*
* When comparing the central gradient to the two interpolated
* values, we avoid performing any divisions by multiplying both
* sides of each inequality by the greater of the two partial
* derivatives. The common comparand is stored in a temporary
* variable (3) and reused in the mirror case (4).
*
*/
if (xGrad * yGrad <= (float) 0 /*(1)*/
? Math.abs(xGrad) >= Math.abs(yGrad) /*(2)*/
? (tmp = Math.abs(xGrad * gradMag)) >= Math.abs(yGrad * neMag - (xGrad + yGrad) * eMag) /*(3)*/
&& tmp > Math.abs(yGrad * swMag - (xGrad + yGrad) * wMag) /*(4)*/
: (tmp = Math.abs(yGrad * gradMag)) >= Math.abs(xGrad * neMag - (yGrad + xGrad) * nMag) /*(3)*/
&& tmp > Math.abs(xGrad * swMag - (yGrad + xGrad) * sMag) /*(4)*/
: Math.abs(xGrad) >= Math.abs(yGrad) /*(2)*/
? (tmp = Math.abs(xGrad * gradMag)) >= Math.abs(yGrad * seMag + (xGrad - yGrad) * eMag) /*(3)*/
&& tmp > Math.abs(yGrad * nwMag + (xGrad - yGrad) * wMag) /*(4)*/
: (tmp = Math.abs(yGrad * gradMag)) >= Math.abs(xGrad * seMag + (yGrad - xGrad) * sMag) /*(3)*/
&& tmp > Math.abs(xGrad * nwMag + (yGrad - xGrad) * nMag) /*(4)*/
) {
magnitude[index] = gradMag >= MAGNITUDE_LIMIT ? MAGNITUDE_MAX : (int) (MAGNITUDE_SCALE * gradMag);
//NOTE: The orientation of the edge is not employed by this
//implementation. It is a simple matter to compute it at
//this point as: Math.atan2(yGrad, xGrad);
} else {
magnitude[index] = 0;
}
}
}
}
//NOTE: It is quite feasible to replace the implementation of this method
//with one which only loosely approximates the hypot function. I've tested
//simple approximations such as Math.abs(x) + Math.abs(y) and they work fine.
private float hypot(float x, float y) {
return (float) Math.hypot(x, y);
}
private float gaussian(float x, float sigma) {
return (float) Math.exp(-(x * x) / (2f * sigma * sigma));
}
private void performHysteresis(int low, int high) {
//NOTE: this implementation reuses the data array to store both
//luminance data from the image, and edge intensity from the processing.
//This is done for memory efficiency, other implementations may wish
//to separate these functions.
Arrays.fill(data, 0);
int offset = 0;
for (int y = 0; y < height; y++) {
for (int x = 0; x < width; x++) {
if (data[offset] == 0 && magnitude[offset] >= high) {
follow(x, y, offset, low);
}
offset++;
}
}
}
private void follow(int x1, int y1, int i1, int threshold) {
int x0 = x1 == 0 ? x1 : x1 - 1;
int x2 = x1 == width - 1 ? x1 : x1 + 1;
int y0 = y1 == 0 ? y1 : y1 - 1;
int y2 = y1 == height -1 ? y1 : y1 + 1;
data[i1] = magnitude[i1];
for (int x = x0; x <= x2; x++) {
for (int y = y0; y <= y2; y++) {
int i2 = x + y * width;
if ((y != y1 || x != x1)
&& data[i2] == 0
&& magnitude[i2] >= threshold) {
follow(x, y, i2, threshold);
return;
}
}
}
}
private void thresholdEdges() {
for (int i = 0; i < picsize; i++) {
data[i] = data[i] > 0 ? -1 : 0xff000000;
}
}
private int luminance(float r, float g, float b) {
return Math.round(0.299f * r + 0.587f * g + 0.114f * b);
}
private void readLuminance() {
int type = sourceImage.getType();
if (type == BufferedImage.TYPE_INT_RGB || type == BufferedImage.TYPE_INT_ARGB) {
int[] pixels = (int[]) sourceImage.getData().getDataElements(0, 0, width, height, null);
for (int i = 0; i < picsize; i++) {
int p = pixels[i];
int r = (p & 0xff0000) >> 16;
int g = (p & 0xff00) >> 8;
int b = p & 0xff;
data[i] = luminance(r, g, b);
}
} else if (type == BufferedImage.TYPE_BYTE_GRAY) {
byte[] pixels = (byte[]) sourceImage.getData().getDataElements(0, 0, width, height, null);
for (int i = 0; i < picsize; i++) {
data[i] = (pixels[i] & 0xff);
}
} else if (type == BufferedImage.TYPE_USHORT_GRAY) {
short[] pixels = (short[]) sourceImage.getData().getDataElements(0, 0, width, height, null);
for (int i = 0; i < picsize; i++) {
data[i] = (pixels[i] & 0xffff) / 256;
}
} else if (type == BufferedImage.TYPE_3BYTE_BGR) {
byte[] pixels = (byte[]) sourceImage.getData().getDataElements(0, 0, width, height, null);
int offset = 0;
for (int i = 0; i < picsize; i++) {
int b = pixels[offset++] & 0xff;
int g = pixels[offset++] & 0xff;
int r = pixels[offset++] & 0xff;
data[i] = luminance(r, g, b);
}
} else {
throw new IllegalArgumentException("Unsupported image type: " + type);
}
}
private void normalizeContrast() {
int[] histogram = new int[256];
for (int i = 0; i < data.length; i++) {
histogram[data[i]]++;
}
int[] remap = new int[256];
int sum = 0;
int j = 0;
for (int i = 0; i < histogram.length; i++) {
sum += histogram[i];
int target = sum*255/picsize;
for (int k = j+1; k <=target; k++) {
remap[k] = i;
}
j = target;
}
for (int i = 0; i < data.length; i++) {
data[i] = remap[data[i]];
}
}
private void writeEdges(int pixels[]) {
//NOTE: There is currently no mechanism for obtaining the edge data
//in any other format other than an INT_ARGB type BufferedImage.
//This may be easily remedied by providing alternative accessors.
if (edgesImage == null) {
edgesImage = new BufferedImage(width, height, BufferedImage.TYPE_INT_ARGB);
}
edgesImage.getWritableTile(0, 0).setDataElements(0, 0, width, height, pixels);
}
public static void main(String[] args) {
//create the detector
CannyEdgeDetector detector = new CannyEdgeDetector();
//adjust its parameters as desired
// detector.setLowThreshold(2.5f);
detector.setHighThreshold(4f);
BufferedImage frame = null;
try {
frame = ImageIO.read(new File("source.jpg"));
} catch (IOException e) {
e.printStackTrace();
}
//apply it to an image
detector.setSourceImage(frame);
detector.process();
BufferedImage edges = detector.getEdgesImage();
try {
ImageIO.write(edges, "png", new File("result.png"));
} catch (IOException e) {
e.printStackTrace();
}
}
}
Decreasing the sigma value should result in more edges and links. It also will result in more spurious edges, but the hysteresis process is there to try to alleviate that.
You may also want to look into the connected components to find important edges.
I'm trying to make some shapes with Java, I created two rectangles and they show up normally, but lately I integrated a polygon shape code but it not showing up while running the program. Somebody help in this please!
Here is a screenshot after running:
Here is the code I use:
import java.awt.*;
import java.awt.event.*;
import javax.swing.*;
public class shapes extends JPanel{
int midX = 120;
int midY = 60;
int radius[] = {118,40,90,40};
int nPoints = 16;
int[] X = new int[nPoints];
int[] Y = new int[nPoints];
int i;
double max;
public void paintComponent(Graphics gphcs){
super.paintComponent(gphcs);
this.setBackground(Color.WHITE);
gphcs.setColor(Color.BLUE);
gphcs.fillRect(25,25,100,30);
gphcs.setColor(Color.GRAY);
gphcs.fillRect(25,65,100,30);
gphcs.setColor(new Color(190,81,215));
gphcs.drawString("This is my text", 25, 120);
for (double current=0.0; i<nPoints; i++)
{
double x = Math.cos(current*((2*Math.PI)/max))*radius[i % 4];
double y = Math.sin(current*((2*Math.PI)/max))*radius[i % 4];
X[i] = (int) x+midX;
Y[i] = (int) y+midY;
}
gphcs.setColor(Color.RED);
gphcs.fillPolygon(X, Y, nPoints);
}
}
Acutally I wanted the polygon to show up as a star shape but it even didn't appear at all!
Thank you..
All your polygon co-ordinates are the same. Try
for (int i=0; i < nPoints; i++) {
double x = Math.cos(i * ((2 * Math.PI) / nPoints)) * radius[i % 4];
double y = Math.sin(i * ((2 * Math.PI) / nPoints)) * radius[i % 4];
X[i] = (int) x + midX;
Y[i] = (int) y + midY;
}
I have coded a heightmap but it seems to lag the client. I just don't know how to increase the fps. I get about 3-6fps with the heightmap. Im using a quite large bmp for the heightmap, I think its 1024x1024. When i use a smaller on its fine, maybe im just not using the code effectively. Is there a better way to code this heightmap or did I just code it wrong. It is my first time I have worked on a heightmap. Thanks
public class HeightMap {
private final float xScale, yScale, zScale;
private float[][] heightMap;
private FloatBuffer vertices, normals, texCoords;
private IntBuffer indices;
private Vector3f[] verticesArray, normalsArray;
private int[] indicesArray;
private int width;
private int height;
public float getHeight(int x, int y) {
return heightMap[x][y] * yScale;
}
public HeightMap(String path, int resolution) {
heightMap = loadHeightmap("heightmap.bmp");
xScale = 1000f / resolution;
yScale = 8;
zScale = 1000f / resolution;
verticesArray = new Vector3f[width * height];
vertices = BufferUtils.createFloatBuffer(3 * width * height);
texCoords = BufferUtils.createFloatBuffer(2 * width * height);
for (int x = 0; x < width; x++) {
for (int y = 0; y < height; y++) {
final int pos = height * x + y;
final Vector3f vertex = new Vector3f(xScale * x, yScale * heightMap[x][y], zScale * y);
verticesArray[pos] = vertex;
vertex.store(vertices);
texCoords.put(x / (float) width);
texCoords.put(y / (float) height);
}
}
vertices.flip();
texCoords.flip();
normalsArray = new Vector3f[height * width];
normals = BufferUtils.createFloatBuffer(3 * width * height);
final float xzScale = xScale;
for (int x = 0; x < width; ++x) {
for (int y = 0; y < height; ++y) {
final int nextX = x < width - 1 ? x + 1 : x;
final int prevX = x > 0 ? x - 1 : x;
float sx = heightMap[nextX][y] - heightMap[prevX][y];
if (x == 0 || x == width - 1) {
sx *= 2;
}
final int nextY = y < height - 1 ? y + 1 : y;
final int prevY = y > 0 ? y - 1 : y;
float sy = heightMap[x][nextY] - heightMap[x][prevY];
if (y == 0 || y == height - 1) {
sy *= 2;
}
final Vector3f normal = new Vector3f(-sx * yScale, 2 * xzScale, sy * yScale).normalise(null);
normalsArray[height * x + y] = normal;
normal.store(normals);
}
}
normals.flip();
indicesArray = new int[6 * (height - 1) * (width - 1)];
indices = BufferUtils.createIntBuffer(6 * (width - 1) * (height - 1));
for (int i = 0; i < width - 1; i++) {
for (int j = 0; j < height - 1; j++) {
int pos = (height - 1) * i + j;
indices.put(height * i + j);
indices.put(height * (i + 1) + j);
indices.put(height * (i + 1) + (j + 1));
indicesArray[6 * pos] = height * i + j;
indicesArray[6 * pos + 1] = height * (i + 1) + j;
indicesArray[6 * pos + 2] = height * (i + 1) + (j + 1);
indices.put(height * i + j);
indices.put(height * i + (j + 1));
indices.put(height * (i + 1) + (j + 1));
indicesArray[6 * pos + 3] = height * i + j;
indicesArray[6 * pos + 4] = height * i + (j + 1);
indicesArray[6 * pos + 5] = height * (i + 1) + (j + 1);
}
}
indices.flip();
}
private float[][] loadHeightmap(String fileName) {
try {
BufferedImage img = ImageIO.read(ResourceLoader.getResourceAsStream(fileName));
width = img.getWidth();
height = img.getHeight();
float[][] heightMap = new float[width][height];
for (int x = 0; x < width; x++) {
for (int y = 0; y < height; y++) {
heightMap[x][y] = 0xFF & img.getRGB(x, y);
}
}
return heightMap;
} catch (IOException e) {
System.out.println("Nincs meg a heightmap!");
return null;
}
}
public void render() {
glEnableClientState(GL_NORMAL_ARRAY);
glEnableClientState(GL_VERTEX_ARRAY);
glEnableClientState(GL_TEXTURE_COORD_ARRAY);
glNormalPointer(0, normals);
glVertexPointer(3, 0, vertices);
glTexCoordPointer(2, 0, texCoords);
glDrawElements(GL_TRIANGLE_STRIP, indices);
glDisableClientState(GL_NORMAL_ARRAY);
glDisableClientState(GL_TEXTURE_COORD_ARRAY);
glDisableClientState(GL_VERTEX_ARRAY);
}
}
Sorry to bring up an old topic, however i see a lot of people ask this:
Use a display list, instead of re-making the heightmap every time.
TheCodingUniverse has a good tutorial on how to do this.
Hi I am working a on project that I need to implement an edge detector. I need to do it in VHDL however I am a little better at Java so I am looking at getting a working code in Java first then transfering it over. The code below I found but I can't get it working, I keep getting an error in the main on this line: detector.setSourceImage(frame); error says frame can not be resolved to a variable. I understand why I'm getting the error but not sure how to fix it because I don't know how to get the picture in. I am just looking for a quick fix to make this work so I can get started on the VHDL part. Thanks for any help you can give.
package CannyEdgeDetector;
public class CannyEdgeDetector {
// statics
private final static float GAUSSIAN_CUT_OFF = 0.005f;
private final static float MAGNITUDE_SCALE = 100F;
private final static float MAGNITUDE_LIMIT = 1000F;
private final static int MAGNITUDE_MAX = (int) (MAGNITUDE_SCALE * MAGNITUDE_LIMIT);
// fields
private int height;
private int width;
private int picsize;
private int[] data;
private int[] magnitude;
private BufferedImage sourceImage;
private BufferedImage edgesImage;
private float gaussianKernelRadius;
private float lowThreshold;
private float highThreshold;
private int gaussianKernelWidth;
private boolean contrastNormalized;
private float[] xConv;
private float[] yConv;
private float[] xGradient;
private float[] yGradient;
// constructors
/**
* Constructs a new detector with default parameters.
*/
public CannyEdgeDetector() {
lowThreshold = 2.5f;
highThreshold = 7.5f;
gaussianKernelRadius = 2f;
gaussianKernelWidth = 16;
contrastNormalized = false;
}
// accessors
/**
* The image that provides the luminance data used by this detector to
* generate edges.
*
* #return the source image, or null
*/
public BufferedImage getSourceImage() {
return sourceImage;
}
/**
* Specifies the image that will provide the luminance data in which edges
* will be detected. A source image must be set before the process method
* is called.
*
* #param image a source of luminance data
*/
public void setSourceImage(BufferedImage image) {
sourceImage = image;
}
/**
* Obtains an image containing the edges detected during the last call to
* the process method. The buffered image is an opaque image of type
* BufferedImage.TYPE_INT_ARGB in which edge pixels are white and all other
* pixels are black.
*
* #return an image containing the detected edges, or null if the process
* method has not yet been called.
*/
public BufferedImage getEdgesImage() {
return edgesImage;
}
/**
* Sets the edges image. Calling this method will not change the operation
* of the edge detector in any way. It is intended to provide a means by
* which the memory referenced by the detector object may be reduced.
*
* #param edgesImage expected (though not required) to be null
*/
public void setEdgesImage(BufferedImage edgesImage) {
this.edgesImage = edgesImage;
}
/**
* The low threshold for hysteresis. The default value is 2.5.
*
* #return the low hysteresis threshold
*/
public float getLowThreshold() {
return lowThreshold;
}
/**
* Sets the low threshold for hysteresis. Suitable values for this parameter
* must be determined experimentally for each application. It is nonsensical
* (though not prohibited) for this value to exceed the high threshold value.
*
* #param threshold a low hysteresis threshold
*/
public void setLowThreshold(float threshold) {
if (threshold < 0) throw new IllegalArgumentException();
lowThreshold = threshold;
}
/**
* The high threshold for hysteresis. The default value is 7.5.
*
* #return the high hysteresis threshold
*/
public float getHighThreshold() {
return highThreshold;
}
/**
* Sets the high threshold for hysteresis. Suitable values for this
* parameter must be determined experimentally for each application. It is
* nonsensical (though not prohibited) for this value to be less than the
* low threshold value.
*
* #param threshold a high hysteresis threshold
*/
public void setHighThreshold(float threshold) {
if (threshold < 0) throw new IllegalArgumentException();
highThreshold = threshold;
}
/**
* The number of pixels across which the Gaussian kernel is applied.
* The default value is 16.
*
* #return the radius of the convolution operation in pixels
*/
public int getGaussianKernelWidth() {
return gaussianKernelWidth;
}
/**
* The number of pixels across which the Gaussian kernel is applied.
* This implementation will reduce the radius if the contribution of pixel
* values is deemed negligable, so this is actually a maximum radius.
*
* #param gaussianKernelWidth a radius for the convolution operation in
* pixels, at least 2.
*/
public void setGaussianKernelWidth(int gaussianKernelWidth) {
if (gaussianKernelWidth < 2) throw new IllegalArgumentException();
this.gaussianKernelWidth = gaussianKernelWidth;
}
/**
* The radius of the Gaussian convolution kernel used to smooth the source
* image prior to gradient calculation. The default value is 16.
*
* #return the Gaussian kernel radius in pixels
*/
public float getGaussianKernelRadius() {
return gaussianKernelRadius;
}
/**
* Sets the radius of the Gaussian convolution kernel used to smooth the
* source image prior to gradient calculation.
*
* #return a Gaussian kernel radius in pixels, must exceed 0.1f.
*/
public void setGaussianKernelRadius(float gaussianKernelRadius) {
if (gaussianKernelRadius < 0.1f) throw new IllegalArgumentException();
this.gaussianKernelRadius = gaussianKernelRadius;
}
/**
* Whether the luminance data extracted from the source image is normalized
* by linearizing its histogram prior to edge extraction. The default value
* is false.
*
* #return whether the contrast is normalized
*/
public boolean isContrastNormalized() {
return contrastNormalized;
}
/**
* Sets whether the contrast is normalized
* #param contrastNormalized true if the contrast should be normalized,
* false otherwise
*/
public void setContrastNormalized(boolean contrastNormalized) {
this.contrastNormalized = contrastNormalized;
}
// methods
public void process() {
width = sourceImage.getWidth();
height = sourceImage.getHeight();
picsize = width * height;
initArrays();
readLuminance();
if (contrastNormalized) normalizeContrast();
computeGradients(gaussianKernelRadius, gaussianKernelWidth);
int low = Math.round(lowThreshold * MAGNITUDE_SCALE);
int high = Math.round( highThreshold * MAGNITUDE_SCALE);
performHysteresis(low, high);
thresholdEdges();
writeEdges(data);
}
// private utility methods
private void initArrays() {
if (data == null || picsize != data.length) {
data = new int[picsize];
magnitude = new int[picsize];
xConv = new float[picsize];
yConv = new float[picsize];
xGradient = new float[picsize];
yGradient = new float[picsize];
}
}
//NOTE: The elements of the method below (specifically the technique for
//non-maximal suppression and the technique for gradient computation)
//are derived from an implementation posted in the following forum (with the
//clear intent of others using the code):
// http://forum.java.sun.com/thread.jspa?threadID=546211&start=45&tstart=0
//My code effectively mimics the algorithm exhibited above.
//Since I don't know the providence of the code that was posted it is a
//possibility (though I think a very remote one) that this code violates
//someone's intellectual property rights. If this concerns you feel free to
//contact me for an alternative, though less efficient, implementation.
private void computeGradients(float kernelRadius, int kernelWidth) {
//generate the gaussian convolution masks
float kernel[] = new float[kernelWidth];
float diffKernel[] = new float[kernelWidth];
int kwidth;
for (kwidth = 0; kwidth < kernelWidth; kwidth++) {
float g1 = gaussian(kwidth, kernelRadius);
if (g1 <= GAUSSIAN_CUT_OFF && kwidth >= 2) break;
float g2 = gaussian(kwidth - 0.5f, kernelRadius);
float g3 = gaussian(kwidth + 0.5f, kernelRadius);
kernel[kwidth] = (g1 + g2 + g3) / 3f / (2f * (float) Math.PI * kernelRadius * kernelRadius);
diffKernel[kwidth] = g3 - g2;
}
int initX = kwidth - 1;
int maxX = width - (kwidth - 1);
int initY = width * (kwidth - 1);
int maxY = width * (height - (kwidth - 1));
//perform convolution in x and y directions
for (int x = initX; x < maxX; x++) {
for (int y = initY; y < maxY; y += width) {
int index = x + y;
float sumX = data[index] * kernel[0];
float sumY = sumX;
int xOffset = 1;
int yOffset = width;
for(; xOffset < kwidth ;) {
sumY += kernel[xOffset] * (data[index - yOffset] + data[index + yOffset]);
sumX += kernel[xOffset] * (data[index - xOffset] + data[index + xOffset]);
yOffset += width;
xOffset++;
}
yConv[index] = sumY;
xConv[index] = sumX;
}
}
for (int x = initX; x < maxX; x++) {
for (int y = initY; y < maxY; y += width) {
float sum = 0f;
int index = x + y;
for (int i = 1; i < kwidth; i++)
sum += diffKernel[i] * (yConv[index - i] - yConv[index + i]);
xGradient[index] = sum;
}
}
for (int x = kwidth; x < width - kwidth; x++) {
for (int y = initY; y < maxY; y += width) {
float sum = 0.0f;
int index = x + y;
int yOffset = width;
for (int i = 1; i < kwidth; i++) {
sum += diffKernel[i] * (xConv[index - yOffset] - xConv[index + yOffset]);
yOffset += width;
}
yGradient[index] = sum;
}
}
initX = kwidth;
maxX = width - kwidth;
initY = width * kwidth;
maxY = width * (height - kwidth);
for (int x = initX; x < maxX; x++) {
for (int y = initY; y < maxY; y += width) {
int index = x + y;
int indexN = index - width;
int indexS = index + width;
int indexW = index - 1;
int indexE = index + 1;
int indexNW = indexN - 1;
int indexNE = indexN + 1;
int indexSW = indexS - 1;
int indexSE = indexS + 1;
float xGrad = xGradient[index];
float yGrad = yGradient[index];
float gradMag = hypot(xGrad, yGrad);
//perform non-maximal supression
float nMag = hypot(xGradient[indexN], yGradient[indexN]);
float sMag = hypot(xGradient[indexS], yGradient[indexS]);
float wMag = hypot(xGradient[indexW], yGradient[indexW]);
float eMag = hypot(xGradient[indexE], yGradient[indexE]);
float neMag = hypot(xGradient[indexNE], yGradient[indexNE]);
float seMag = hypot(xGradient[indexSE], yGradient[indexSE]);
float swMag = hypot(xGradient[indexSW], yGradient[indexSW]);
float nwMag = hypot(xGradient[indexNW], yGradient[indexNW]);
float tmp;
/*
* An explanation of what's happening here, for those who want
* to understand the source: This performs the "non-maximal
* supression" phase of the Canny edge detection in which we
* need to compare the gradient magnitude to that in the
* direction of the gradient; only if the value is a local
* maximum do we consider the point as an edge candidate.
*
* We need to break the comparison into a number of different
* cases depending on the gradient direction so that the
* appropriate values can be used. To avoid computing the
* gradient direction, we use two simple comparisons: first we
* check that the partial derivatives have the same sign (1)
* and then we check which is larger (2). As a consequence, we
* have reduced the problem to one of four identical cases that
* each test the central gradient magnitude against the values at
* two points with 'identical support'; what this means is that
* the geometry required to accurately interpolate the magnitude
* of gradient function at those points has an identical
* geometry (upto right-angled-rotation/reflection).
*
* When comparing the central gradient to the two interpolated
* values, we avoid performing any divisions by multiplying both
* sides of each inequality by the greater of the two partial
* derivatives. The common comparand is stored in a temporary
* variable (3) and reused in the mirror case (4).
*
*/
if (xGrad * yGrad <= (float) 0 /*(1)*/
? Math.abs(xGrad) >= Math.abs(yGrad) /*(2)*/
? (tmp = Math.abs(xGrad * gradMag)) >= Math.abs(yGrad * neMag - (xGrad + yGrad) * eMag) /*(3)*/
&& tmp > Math.abs(yGrad * swMag - (xGrad + yGrad) * wMag) /*(4)*/
: (tmp = Math.abs(yGrad * gradMag)) >= Math.abs(xGrad * neMag - (yGrad + xGrad) * nMag) /*(3)*/
&& tmp > Math.abs(xGrad * swMag - (yGrad + xGrad) * sMag) /*(4)*/
: Math.abs(xGrad) >= Math.abs(yGrad) /*(2)*/
? (tmp = Math.abs(xGrad * gradMag)) >= Math.abs(yGrad * seMag + (xGrad - yGrad) * eMag) /*(3)*/
&& tmp > Math.abs(yGrad * nwMag + (xGrad - yGrad) * wMag) /*(4)*/
: (tmp = Math.abs(yGrad * gradMag)) >= Math.abs(xGrad * seMag + (yGrad - xGrad) * sMag) /*(3)*/
&& tmp > Math.abs(xGrad * nwMag + (yGrad - xGrad) * nMag) /*(4)*/
) {
magnitude[index] = gradMag >= MAGNITUDE_LIMIT ? MAGNITUDE_MAX : (int) (MAGNITUDE_SCALE * gradMag);
//NOTE: The orientation of the edge is not employed by this
//implementation. It is a simple matter to compute it at
//this point as: Math.atan2(yGrad, xGrad);
} else {
magnitude[index] = 0;
}
}
}
}
//NOTE: It is quite feasible to replace the implementation of this method
//with one which only loosely approximates the hypot function. I've tested
//simple approximations such as Math.abs(x) + Math.abs(y) and they work fine.
private float hypot(float x, float y) {
return (float) Math.hypot(x, y);
}
private float gaussian(float x, float sigma) {
return (float) Math.exp(-(x * x) / (2f * sigma * sigma));
}
private void performHysteresis(int low, int high) {
//NOTE: this implementation reuses the data array to store both
//luminance data from the image, and edge intensity from the processing.
//This is done for memory efficiency, other implementations may wish
//to separate these functions.
Arrays.fill(data, 0);
int offset = 0;
for (int y = 0; y < height; y++) {
for (int x = 0; x < width; x++) {
if (data[offset] == 0 && magnitude[offset] >= high) {
follow(x, y, offset, low);
}
offset++;
}
}
}
private void follow(int x1, int y1, int i1, int threshold) {
int x0 = x1 == 0 ? x1 : x1 - 1;
int x2 = x1 == width - 1 ? x1 : x1 + 1;
int y0 = y1 == 0 ? y1 : y1 - 1;
int y2 = y1 == height -1 ? y1 : y1 + 1;
data[i1] = magnitude[i1];
for (int x = x0; x <= x2; x++) {
for (int y = y0; y <= y2; y++) {
int i2 = x + y * width;
if ((y != y1 || x != x1)
&& data[i2] == 0
&& magnitude[i2] >= threshold) {
follow(x, y, i2, threshold);
return;
}
}
}
}
private void thresholdEdges() {
for (int i = 0; i < picsize; i++) {
data[i] = data[i] > 0 ? -1 : 0xff000000;
}
}
private int luminance(float r, float g, float b) {
return Math.round(0.299f * r + 0.587f * g + 0.114f * b);
}
private void readLuminance() {
int type = sourceImage.getType();
if (type == BufferedImage.TYPE_INT_RGB || type == BufferedImage.TYPE_INT_ARGB) {
int[] pixels = (int[]) sourceImage.getData().getDataElements(0, 0, width, height, null);
for (int i = 0; i < picsize; i++) {
int p = pixels[i];
int r = (p & 0xff0000) >> 16;
int g = (p & 0xff00) >> 8;
int b = p & 0xff;
data[i] = luminance(r, g, b);
}
} else if (type == BufferedImage.TYPE_BYTE_GRAY) {
byte[] pixels = (byte[]) sourceImage.getData().getDataElements(0, 0, width, height, null);
for (int i = 0; i < picsize; i++) {
data[i] = (pixels[i] & 0xff);
}
} else if (type == BufferedImage.TYPE_USHORT_GRAY) {
short[] pixels = (short[]) sourceImage.getData().getDataElements(0, 0, width, height, null);
for (int i = 0; i < picsize; i++) {
data[i] = (pixels[i] & 0xffff) / 256;
}
} else if (type == BufferedImage.TYPE_3BYTE_BGR) {
byte[] pixels = (byte[]) sourceImage.getData().getDataElements(0, 0, width, height, null);
int offset = 0;
for (int i = 0; i < picsize; i++) {
int b = pixels[offset++] & 0xff;
int g = pixels[offset++] & 0xff;
int r = pixels[offset++] & 0xff;
data[i] = luminance(r, g, b);
}
} else {
throw new IllegalArgumentException("Unsupported image type: " + type);
}
}
private void normalizeContrast() {
int[] histogram = new int[256];
for (int i = 0; i < data.length; i++) {
histogram[data[i]]++;
}
int[] remap = new int[256];
int sum = 0;
int j = 0;
for (int i = 0; i < histogram.length; i++) {
sum += histogram[i];
int target = sum*255/picsize;
for (int k = j+1; k <=target; k++) {
remap[k] = i;
}
j = target;
}
for (int i = 0; i < data.length; i++) {
data[i] = remap[data[i]];
}
}
private void writeEdges(int pixels[]) {
//NOTE: There is currently no mechanism for obtaining the edge data
//in any other format other than an INT_ARGB type BufferedImage.
//This may be easily remedied by providing alternative accessors.
if (edgesImage == null) {
edgesImage = new BufferedImage(width, height, BufferedImage.TYPE_INT_ARGB);
}
edgesImage.getWritableTile(0, 0).setDataElements(0, 0, width, height, pixels);
}
}
public static void main(String []Args){
//create the detector
CannyEdgeDetector detector = new CannyEdgeDetector();
//adjust its parameters as desired
detector.setLowThreshold(0.5f);
detector.setHighThreshold(1f);
//apply it to an image
detector.setSourceImage(frame);
detector.process();
BufferedImage edges = detector.getEdgesImage();
}
}
Why don't you just read a test image from a file? That way, you can verify that it's working properly before transferring.
I'm trying to convert the following code (from Wikipedia) from Java to JavaScript:
/*
* 3 June 2003, [[:en:User:Cyp]]:
* Maze, generated by my algorithm
* 24 October 2006, [[:en:User:quin]]:
* Source edited for clarity
* 25 January 2009, [[:en:User:DebateG]]:
* Source edited again for clarity and reusability
* 1 June 2009, [[:en:User:Nandhp]]:
* Source edited to produce SVG file when run from the command-line
*
* This program was originally written by [[:en:User:Cyp]], who
* attached it to the image description page for an image generated by
* it on en.wikipedia. The image was licensed under CC-BY-SA-3.0/GFDL.
*/
import java.awt.*;
import java.applet.*;
import java.util.Random;
/* Define the bit masks */
class Constants {
public static final int WALL_ABOVE = 1;
public static final int WALL_BELOW = 2;
public static final int WALL_LEFT = 4;
public static final int WALL_RIGHT = 8;
public static final int QUEUED = 16;
public static final int IN_MAZE = 32;
}
public class Maze extends java.applet.Applet {
/* The width and height (in cells) of the maze */
private int width;
private int height;
private int maze[][];
private static final Random rnd = new Random();
/* The width in pixels of each cell */
private int cell_width;
/* Construct a Maze with the default width, height, and cell_width */
public Maze() {
this(20,20,10);
}
/* Construct a Maze with specified width, height, and cell_width */
public Maze(int width, int height, int cell_width) {
this.width = width;
this.height = height;
this.cell_width = cell_width;
}
/* Initialization method that will be called when the program is
* run from the command-line. Maze will be written as SVG file. */
public static void main(String[] args) {
Maze m = new Maze();
m.createMaze();
m.printSVG();
}
/* Initialization method that will be called when the program is
* run as an applet. Maze will be displayed on-screen. */
public void init() {
createMaze();
}
/* The maze generation algorithm. */
private void createMaze(){
int x, y, n, d;
int dx[] = { 0, 0, -1, 1 };
int dy[] = { -1, 1, 0, 0 };
int todo[] = new int[height * width], todonum = 0;
/* We want to create a maze on a grid. */
maze = new int[width][height];
/* We start with a grid full of walls. */
for (x = 0; x < width; ++x) {
for (y = 0; y < height; ++y) {
if (x == 0 || x == width - 1 || y == 0 || y == height - 1) {
maze[x][y] = Constants.IN_MAZE;
} else {
maze[x][y] = 63;
}
}
}
/* Select any square of the grid, to start with. */
x = 1 + rnd.nextInt (width - 2);
y = 1 + rnd.nextInt (height - 2);
/* Mark this square as connected to the maze. */
maze[x][y] &= ~48;
/* Remember the surrounding squares, as we will */
for (d = 0; d < 4; ++d) {
if ((maze[][d][][d] & Constants.QUEUED) != 0) {
/* want to connect them to the maze. */
todo[todonum++] = ((x + dx[d]) << Constants.QUEUED) | (y + dy[d]);
maze[][d][][d] &= ~Constants.QUEUED;
}
}
/* We won't be finished until all is connected. */
while (todonum > 0) {
/* We select one of the squares next to the maze. */
n = rnd.nextInt (todonum);
x = todo[n] >> 16; /* the top 2 bytes of the data */
y = todo[n] & 65535; /* the bottom 2 bytes of the data */
/* We will connect it, so remove it from the queue. */
todo[n] = todo[--todonum];
/* Select a direction, which leads to the maze. */
do {
d = rnd.nextInt (4);
}
while ((maze[][d][][d] & Constants.IN_MAZE) != 0);
/* Connect this square to the maze. */
maze[x][y] &= ~((1 << d) | Constants.IN_MAZE);
maze[][d][][d] &= ~(1 << (d ^ 1));
/* Remember the surrounding squares, which aren't */
for (d = 0; d < 4; ++d) {
if ((maze[][d][][d] & Constants.QUEUED) != 0) {
/* connected to the maze, and aren't yet queued to be. */
todo[todonum++] = ((x + dx[d]) << Constants.QUEUED) | (y + dy[d]);
maze[][d][][d] &= ~Constants.QUEUED;
}
}
/* Repeat until finished. */
}
/* Add an entrance and exit. */
maze[1][1] &= ~Constants.WALL_ABOVE;
maze[width - 2][height - 2] &= ~Constants.WALL_BELOW;
}
/* Called by the applet infrastructure to display the maze on-screen. */
public void paint(Graphics g) {
drawMaze(g);
}
/* Called to write the maze to an SVG file. */
public void printSVG() {
System.out.format("<svg width=\"%d\" height=\"%d\" version=\"1.1\""
+ " xmlns=\"http://www.w3.org/2000/svg\">\n",
width*cell_width, height*cell_width);
System.out.println(" <g stroke=\"black\" stroke-width=\"1\""
+ " stroke-linecap=\"round\">");
drawMaze(null);
System.out.println(" </g>\n</svg>");
}
/* Main maze-drawing loop. */
public void drawMaze(Graphics g) {
int x, y;
for (x = 1; x < width - 1; ++x) {
for (y = 1; y < height - 1; ++y) {
if ((maze[x][y] & Constants.WALL_ABOVE) != 0)
drawLine( x * cell_width, y * cell_width,
(x + 1) * cell_width, y * cell_width, g);
if ((maze[x][y] & Constants.WALL_BELOW) != 0)
drawLine( x * cell_width, (y + 1) * cell_width,
(x + 1) * cell_width, (y + 1) * cell_width, g);
if ((maze[x][y] & Constants.WALL_LEFT) != 0)
drawLine( x * cell_width, y * cell_width,
x * cell_width, (y + 1) * cell_width, g);
if ((maze[x][y] & Constants.WALL_RIGHT) != 0)
drawLine((x + 1) * cell_width, y * cell_width,
(x + 1) * cell_width, (y + 1) * cell_width, g);
}
}
}
/* Draw a line, either in the SVG file or on the screen. */
public void drawLine(int x1, int y1, int x2, int y2, Graphics g) {
if ( g != null ) g.drawLine(x1, y1, x2, y2);
else System.out.format(" <line x1=\"%d\" y1=\"%d\""
+ " x2=\"%d\" y2=\"%d\" />\n", x1, y1, x2, y2);
}
}
Anyway, I was chugging along fairly quickly when I came to a bit that I just don't understand:
/* Remember the surrounding squares, as we will */
for (var d = 0; d < 4; ++d) {
if ((maze[][d][][d] & Constants.QUEUED) != 0) {
/* want to connect them to the maze. */
todo[todonum++] = ((x + dx[d]) << Constants.QUEUED) | (y + dy[d]);
maze[][d][][d] &= ~Constants.QUEUED;
}
}
What I don't get is why there are four sets of brackets following the "maze" parameter instead of just two, since "maze" is a two dimensional array, not a four dimensional array.
I'm sure there's a good reason for this. Problem is, I just don't get it.
Thanks!
To simply correct the syntax, this answer is as good as any. However to fix the algorithm, Leon's has more insight there.
Looks to me like the code is wrong having two too many sets of square brackets. maze is declared and initialized as a 2-dimensional int array. There's no reason it should have more than that. Perhaps the result of a misinformed or buggy code generator?
Just remove the preceding, empty square brackets [] (or ][) from the array accesses and you'll be set.
My educated guess would be that there is some code missing. Removing the extra [] makes the code compile but it does not generate a maze but is stuck in an infinite loop
I think [][d][][d] are supposed to be maze[x+dx[d]][y+dy[d]].
It is clear that d is supposed to index into dx and dy arrays, which are offsets into the neighbours of the current cell, as is is always iterated 4 times. Also todo[todonum++] = ((x + dx[d]) << Constants.QUEUED) | (y + dy[d]); indexes the maze array using dx[d] and dy[y] as offsets to the neighbours.
I am not sure whether this is the best way thou, as when x=0 and dx offset is -1 you will have and OutOfBoundsException. You may have to handle those cases explicitly.
OK, here's the JavaScript version:
/*
* 3 June 2003, [[:en:User:Cyp]]:
* Maze, generated by my algorithm
* 24 October 2006, [[:en:User:quin]]:
* Source edited for clarity
* 25 January 2009, [[:en:User:DebateG]]:
* Source edited again for clarity and reusability
* 1 June 2009, [[:en:User:Nandhp]]:
* Source edited to produce SVG file when run from the command-line
* 7 January, 2011 [[:en:User:SharkD]]:
* Source converted to JavaScript
*
* This program was originally written by [[:en:User:Cyp]], who
* attached it to the image description page for an image generated by
* it on en.wikipedia. The image was licensed under CC-BY-SA-3.0/GFDL.
*/
/* Recreate a math function that exists in Java but not JavaScript. */
Math.nextInt = function (number) {
return Math.floor(Math.random() * number)
}
/* Recreate a system function that exists in Java but not JavaScript.
* Uncomment either WScript.Echo() or alert() depending on whether you are
* running the script from the Windows command-line or a Web page.
*/
function println(string)
{
// if inside Windows Scripting Host
// WScript.Echo(string)
// if inside a Web page
alert(string)
}
/* Define the bit masks */
var Constants =
{
WALL_ABOVE : 1,
WALL_BELOW : 2,
WALL_LEFT : 4,
WALL_RIGHT : 8,
QUEUED : 16,
IN_MAZE : 32
}
/* Construct a Maze with specified width, height, and cell_width */
function Maze(width, height, cell_width) {
if (width)
this.width = width;
else
this.width = 20;
if (height)
this.height = height;
else
this.height = 20;
if (cell_width)
this.cell_width = cell_width;
else
this.cell_width = 10;
this.maze = []
/* The maze generation algorithm. */
this.createMaze = function() {
var width = this.width
var height = this.height
var maze = this.maze
var x, y, n, d;
var dx = [ 0, 0, -1, 1 ];
var dy = [ -1, 1, 0, 0 ];
var todo = new Array(height * width);
var todonum = 0;
/* We want to create a maze on a grid. */
/* We start with a grid full of walls. */
for (x = 0; x < width; ++x) {
maze[x] = []
for (y = 0; y < height; ++y) {
if (x == 0 || x == width - 1 || y == 0 || y == height - 1) {
maze[x][y] = Constants.IN_MAZE;
}
else {
maze[x][y] = 63;
}
}
}
/* Select any square of the grid, to start with. */
x = 1 + Math.nextInt(width - 2);
y = 1 + Math.nextInt(height - 2);
/* Mark this square as connected to the maze. */
maze[x][y] &= ~48;
/* Remember the surrounding squares, as we will */
for (d = 0; d < 4; ++d) {
if ((maze[x + dx[d]][y + dy[d]] & Constants.QUEUED) != 0) {
/* want to connect them to the maze. */
todo[todonum++] = ((x + dx[d]) << Constants.QUEUED) | (y + dy[d]);
maze[x + dx[d]][y + dy[d]] &= ~Constants.QUEUED;
}
}
/* We won't be finished until all is connected. */
while (todonum > 0) {
/* We select one of the squares next to the maze. */
n = Math.nextInt(todonum);
x = todo[n] >> 16; /* the top 2 bytes of the data */
y = todo[n] & 65535; /* the bottom 2 bytes of the data */
/* We will connect it, so remove it from the queue. */
todo[n] = todo[--todonum];
/* Select a direction, which leads to the maze. */
do {
d = Math.nextInt(4);
}
while ((maze[x + dx[d]][y + dy[d]] & Constants.IN_MAZE) != 0);
/* Connect this square to the maze. */
maze[x][y] &= ~((1 << d) | Constants.IN_MAZE);
maze[x + dx[d]][y + dy[d]] &= ~(1 << (d ^ 1));
/* Remember the surrounding squares, which aren't */
for (d = 0; d < 4; ++d) {
if ((maze[x + dx[d]][y + dy[d]] & Constants.QUEUED) != 0) {
/* connected to the maze, and aren't yet queued to be. */
todo[todonum++] = ((x + dx[d]) << Constants.QUEUED) | (y + dy[d]);
maze[x + dx[d]][y + dy[d]] &= ~Constants.QUEUED;
}
}
/* Repeat until finished. */
}
/* Add an entrance and exit. */
maze[1][1] &= ~Constants.WALL_ABOVE;
maze[width - 2][height - 2] &= ~Constants.WALL_BELOW;
}
/* Called to write the maze to an SVG file. */
this.printSVG = function () {
var outstring = "<svg width=\"" + (width * cell_width) + "\" height=\"" + (height*cell_width) + "\" version=\"1.1\" xmlns=\"http://www.w3.org/2000/svg\">\n"
+ " <g stroke=\"black\" stroke-width=\"1\" stroke-linecap=\"round\">\n"
+ this.drawMaze()
+ " </g>\n</svg>\n";
println(outstring)
}
/* Main maze-drawing loop. */
this.drawMaze = function () {
var x, y;
var width = this.width;
var height = this.height;
var cell_width = this.cell_width
var outstring = ""
for (x = 1; x < width - 1; ++x) {
for (y = 1; y < height - 1; ++y) {
if ((this.maze[x][y] & Constants.WALL_ABOVE) != 0)
outstring += this.drawLine( x * cell_width, y * cell_width, (x + 1) * cell_width, y * cell_width);
if ((this.maze[x][y] & Constants.WALL_BELOW) != 0)
outstring += this.drawLine( x * cell_width, (y + 1) * cell_width, (x + 1) * cell_width, (y + 1) * cell_width);
if ((this.maze[x][y] & Constants.WALL_LEFT) != 0)
outstring += this.drawLine( x * cell_width, y * cell_width, x * cell_width, (y + 1) * cell_width);
if ((this.maze[x][y] & Constants.WALL_RIGHT) != 0)
outstring += this.drawLine((x + 1) * cell_width, y * cell_width, (x + 1) * cell_width, (y + 1) * cell_width);
}
}
return outstring
}
/* Draw a line, either in the SVG file or on the screen. */
this.drawLine = function (x1, y1, x2, y2) {
return " <line x1=\"" + x1 + "\" y1=\"" + y1 + "\" x2=\"" + x2 + "\" y2=\"" + y2 + "\" />\n";
}
}
/* Initialization method that will be called when the program is
* run from the command-line. Maze will be written as SVG file. */
function main(args) {
var m = new Maze();
m.createMaze();
m.printSVG();
}
/* execute the program */
main()
Next step is to turn it into a six axis 3D maze and convert to Lua, which is my actual target platform. :)
PS - I'm trying to vote on answers, but it's telling me I need to login or register, wtf?