Develop Reference

Java hsb degrees or 255

I was wondering if when you call color.HSBtoRGB if the hue value would be entered as a range of 0-255, 0-1, 0-360? I am inquiring because I am trying to convert an edge angle to a color but it is only giving me blue or purple? can anyone explain what I am doing?
public void sobelGrey(){
this.greyScale();
double edgex;
double edgey;
Picture pi = new Picture(this.getWidth(), this.getHeight());
Picture tou = new Picture(this.getWidth(), this.getHeight());
Pixel[][] Y = pi.getPixels2D();
Pixel[][] X = tou.getPixels2D();
Pixel[][] h = this.getPixels2D();
for (int y = 1; y< X.length-1; y++){
for(int x= 1; x<X[1].length-1; x++){
edgex =
h[y-1][x-1].getRed() * -1 +
h[y][x-1].getRed() * -2+
h[y+1][x-1].getRed() * -1+
h[y-1][x+1].getRed() * 1 +
h[y][x+1].getRed() * 2+
h[y+1][x+1].getRed() * 1;
Y[y][x].setRed((int)Math.abs(edgex/2));
Y[y][x].setGreen((int)Math.abs(edgex/2));
Y[y][x].setBlue((int)Math.abs(edgex/2));
}
}
for (int y = 1; y< X.length-1; y++){
for(int x= 1; x<X[1].length-1; x++){
edgex =
h[y-1][x-1].getRed() * -1 +
h[y-1][x].getRed() * -2+
h[y-1][x+1].getRed() * -1+
h[y+1][x-1].getRed() * 1 +
h[y+1][x].getRed() * 2+
h[y+1][x+1].getRed() * 1;
X[y][x].setRed((int)Math.abs(edgex/2));
X[y][x].setGreen((int)Math.abs(edgex/2));
X[y][x].setBlue((int)Math.abs(edgex/2));
}
}
for (int y = 1; y< X.length-1; y++){
for(int x= 1; x<X[1].length-1; x++){
int x1 = (int) Math.sqrt(Math.pow(X[y][x].getRed(), 2) + Math.pow(X[y][x].getGreen(), 2) + Math.pow(X[y][x].getBlue(), 2));
int y1 = (int) Math.sqrt(Math.pow(Y[y][x].getRed(), 2) + Math.pow(Y[y][x].getGreen(), 2) + Math.pow(Y[y][x].getBlue(), 2));
int hr = (int) (200/(2*Math.PI)*(Math.tanh(y1/ (x1+.000000000000001))));
int rgb = Color.HSBtoRGB(hr/255, hr, (int) Math.sqrt(Math.pow(x1, 2) + Math.pow(y1, 2)));
Color fixed = new Color(rgb&0xFF*7/10, (rgb>>8)&0xFF*80/255/10, (rgb>>16)&0xFF*4/10);
if( !(Math.sqrt(Math.pow(x1, 2) + Math.pow(y1, 2))< 40))
h[y][x].setColor(fixed);
else
h[y][x].setColor(Color.black);
}
}
pi.explore();
tou.explore();
explore();
}
i am using a computer science AP image processing from Eimacs, and using the swan

You declared hr (and the other variables) to be an int. Then in Color.HSBtoRGB(hr/255, ... you divide an int by an int. For all values of hr below 255, the result will be 0.
Probably it is sufficient to divide by 255.0 to force a floating point division.

How can I make my shape "point" at my player

I am trying to make a game where enemies spawn from the top (like a vertical scrolling game) and one kind of enemy will basically follow the player's X coordinate while going down. The enemy class is called Follower and right now it does points to the player (see update function on Follower) but it's not as accurate as I need it to be. For example, if the player accelerates, the follower won't be able to see him all the time.
One way to look at it is that I want the position of the player to be a coordinate in a radiant system and make the vertices of my Follower accurately just rotate and create a straight line looking at it every frame
here is the Follower Class:
public class Follower {
Player target; //follow this
//position
private Vector2 position;
private float x;
private float y;
//speed
private Vector2 velocity;
private float speed;
private float radians;
private float faceTarget;
//dimensions
private float[] shapeX;
private float[] shapeY;
private int numPoints; //vertices for the shape
private boolean remove; //to remove from the game
public Follower(float x,float y, Player target){
this.target = target;
this.x = x;
this.y = y;
velocity = new Vector2(0, 0);
numPoints = 4;
speed = 200;
shapeX = new float[numPoints];
shapeY = new float[numPoints];
radians = 3.1415f / 2;
setShape();
}
public void setShape(){
//top vertice
shapeX[0] = x + MathUtils.cos(radians) * 30;
shapeY[0] = y + MathUtils.sin(radians) * 30;
//left vertice
shapeX[1] = x + MathUtils.cos(radians - 4 * 3.1415f / 10) * 30;
shapeY[1] = y + MathUtils.sin(radians - 4 * 3.1415f / 10) * 30;
//bottom vertice
shapeX[2] = x + MathUtils.cos(radians + 3.1415f) * 60;
shapeY[2] = y + MathUtils.sin(radians + 3.1415f) * 60;
//left vertice
shapeX[3] = x + MathUtils.cos(radians + 4 * 3.1415f / 10) * 30;
shapeY[3] = y + MathUtils.sin(radians + 4 * 3.1415f / 10) * 30;
}
public boolean shouldRemove() {
return remove;
}
public void update(float dt) {
float angle = (float) Math.atan2(target.getPosition().y - y, target.getPosition().x - x); //angle between the follower and target
velocity.set((float) Math.cos(angle) * speed , -speed); //setting direction to follow the target
radians += Math.cos(angle) * dt; //THIS HERE IS MAKING IT ROTATE
x += velocity.x * dt;
y += velocity.y * dt;
setShape();
if(y <= 0 - 60)
remove = true;
else
remove = false;
}
public void draw(ShapeRenderer sp){
sp.setColor(1, 1, 1 ,1);
sp.begin(ShapeRenderer.ShapeType.Line);
for(int i = 0, j = shapeX.length - 1;
i < shapeX.length;
j = i++) {
sp.line(shapeX[i], shapeY[i], shapeX[j], shapeY[j]);
}
sp.end();
}
}
I am not adding the GameScreen because I do not see the need of showing how they are rendered, either way, it'll stay the same.
Also, with the line of code, I am using the Follower points to the player with the bottom vertice as the "eyes"
Thanks for the answers!

How to increase continuance of edges detected by the canny filtrer

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.

Java heightmap using lwjgl

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.

canny edge detector in java

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.