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I am trying to generate a height map using Perlin Noise, but am having trouble with generating truly unique maps. That is, each one is a minor variation of all the others. Two examples are below:
And here is my code (most was just copied and pasted from Ken Perlin's implementation, though adapted for 2D):
public class HeightMap {
private ArrayList<Point> map = new ArrayList<>();
private double elevationMax, elevationMin;
private final int[] P = new int[512], PERMUTATION = { 151,160,137,91,90,15,
131,13,201,95,96,53,194,233,7,225,140,36,103,30,69,142,8,99,37,240,21,10,23,
190, 6,148,247,120,234,75,0,26,197,62,94,252,219,203,117,35,11,32,57,177,33,
88,237,149,56,87,174,20,125,136,171,168, 68,175,74,165,71,134,139,48,27,166,
77,146,158,231,83,111,229,122,60,211,133,230,220,105,92,41,55,46,245,40,244,
102,143,54, 65,25,63,161, 1,216,80,73,209,76,132,187,208, 89,18,169,200,196,
135,130,116,188,159,86,164,100,109,198,173,186, 3,64,52,217,226,250,124,123,
5,202,38,147,118,126,255,82,85,212,207,206,59,227,47,16,58,17,182,189,28,42,
223,183,170,213,119,248,152, 2,44,154,163, 70,221,153,101,155,167, 43,172,9,
129,22,39,253, 19,98,108,110,79,113,224,232,178,185, 112,104,218,246,97,228,
251,34,242,193,238,210,144,12,191,179,162,241, 81,51,145,235,249,14,239,107,
49,192,214, 31,181,199,106,157,184, 84,204,176,115,121,50,45,127, 4,150,254,
138,236,205,93,222,114,67,29,24,72,243,141,128,195,78,66,215,61,156,180
};
public HeightMap() {
this.map = null;
this.elevationMax = 0.0;
this.elevationMin = 0.0;
}
public HeightMap(HeightMap map) {
this.map = map.getPoints();
this.elevationMax = map.getElevationMax();
this.elevationMin = map.getElevationMin();
}
/**
* Generates a Height Map that is, along an imaginary z-axis, centered around the median elevation, given the following parameters:
* #param mapWidth the width [x] of the map
* #param mapHeight the height [y] of the map
* #param tileWidth the width [x] of each tile, or Point
* #param tileHeight the height [y] of each tile, or Point
* #param elevationMax the maximum elevation [z] of the map
* #param elevationMin the minimum elevation [z] of the map
*/
public HeightMap(int mapWidth, int mapHeight, int tileWidth, int tileHeight, double elevationMax, double elevationMin) {
this.elevationMax = elevationMax;
this.elevationMin = elevationMin;
for (int i=0; i < 256 ; i++) {
P[256+i] = P[i] = PERMUTATION[i];
}
int numTilesX = mapWidth / tileWidth;
int numTilesY = mapHeight / tileHeight;
Random r = new Random();
for (int t = 0; t < numTilesX * numTilesY; t++) {
double x = t % numTilesX;
double y = (t - x) / numTilesX;
r = new Random();
x += r.nextDouble();
y += r.nextDouble();
this.map.add(new Point(x, y, lerp(noise(x, y, 13), (elevationMin + elevationMax) / 2, elevationMax), tileWidth, tileHeight));
}
}
/**
* Ken Perlin's Improved Noise Java Implementation (https://mrl.cs.nyu.edu/~perlin/noise/)
* Adapted for 2D
* #param x the x-coordinate on the map
* #param y the y-coordinate on the map
* #param stretch the factor by which adjacent points are smoothed
* #return a value between -1.0 and 1.0 to represent the height of the terrain at (x, y)
*/
private double noise(double x, double y, double stretch) {
x /= stretch;
y /= stretch;
int X = (int)Math.floor(x) & 255, Y = (int)Math.floor(y) & 255;
x -= Math.floor(x);
y -= Math.floor(y);
double u = fade(x),
v = fade(y);
int AA = P[P[X ] + Y ],
AB = P[P[X ] + Y + 1],
BA = P[P[X + 1] + Y ],
BB = P[P[X + 1] + Y + 1];
return lerp(v, lerp(u, grad(P[AA], x, y), grad(P[BA], x - 1, y)), lerp(u, grad(P[AB], x, y - 1), grad(P[BB], x - 1, y - 1)));
}
private double fade(double t) {
return t * t * t * (t * (t * 6 - 15) + 10);
}
private double lerp(double t, double a, double b) {
return a + t * (b - a);
}
//Riven's Optimization (http://riven8192.blogspot.com/2010/08/calculate-perlinnoise-twice-as-fast.html)
private double grad(int hash, double x, double y) {
switch(hash & 0xF)
{
case 0x0:
case 0x8:
return x + y;
case 0x1:
case 0x9:
return -x + y;
case 0x2:
case 0xA:
return x - y;
case 0x3:
case 0xB:
return -x - y;
case 0x4:
case 0xC:
return y + x;
case 0x5:
case 0xD:
return -y + x;
case 0x6:
case 0xE:
return y - x;
case 0x7:
case 0xF:
return -y - x;
default: return 0; // never happens
}
}
}
Is this problem inherent in Perlin Noise because the 'height' is calculated from nearly the same (x, y) coordinate each time? Is there a way to implement the noise function so that it doesn't depend on the (x, y) coordinate of each point but still looks like terrain? Any help is greatly appreciated.
With some help from a friend of mine, I resolved the problem. Because I was using the same PERMUTATION array each generation cycle, the noise calculation was using the same base values each time. To fix this, I made a method permute() that filled PERMUTATION with the numbers 0 to 255 in a random, non-repeating order. I changed the instantiation of PERMUTATION to just be a new int[].
private final int[] P = new int[512], PERMUTATION = new int[256];
...
public void permute() {
for (int i = 0; i < PERMUTATION.length; i++) {
PERMUTATION[i] = i;
}
Random r = new Random();
int rIndex, rIndexVal;
for (int i = 0; i < PERMUTATION.length; i++) {
rIndex = r.nextInt(PERMUTATION.length);
rIndexVal = PERMUTATION[rIndex];
PERMUTATION[rIndex] = PERMUTATION[i];
PERMUTATION[i] = rIndexVal;
}
}
Hi I want to drawing image inside in Pane , when a bounds are heigh I want to cut image. This image consists of tile. One tile size is 256. Now my full image is bigger tham Pane. I don't know how I can cut image.
Hi I want to draw one large image, which consists of tiles. One tile has dimensions of 256x256. This image is in Pane. At this time, image dimensions are larger than the dimension Pane. I do not know how to do to draw only in Pane. Thanks for help
public class TestURLImage8 {
public static ArrayList<BusStop2> list = new ArrayList<>();
public static ArrayList<PositionTilesAndURLPaths> positionTilesAndURLPathsList = new ArrayList<>();
public static HashMap<String, Image> imgCache = new HashMap<>();
public static double lat;
public static double lon;
public static double deltaY;
public static double deltaX;
public static double positionX;
public static double positionY;
public static int[] imageCount = getCountImage();
public static int [] countImage = countImage();
public static int []x = new int [countImage[0]];
public static int []y = new int [countImage[1]];
private File file = new File("C:/Users/022/workspace22/EkranLCD/res/images/kropka.png");
private Image bus = new Image(file.toURI().toString());
static ArrayList<UtlToImageConverter> threadList = new ArrayList<>();
public TestURLImage8(Pane pane) {
}
/**
* Method use to get count of image what we need
* #return
*/
private static int[] getCountImage(){
int xImageCount = (int) Math.ceil(Main4.width/256);
int yImageCount = (int) Math.ceil(Main4.height/256);
return new int[] {xImageCount, yImageCount};
}
/**
* Method use to get count of tiles
* #return
*/
public static int[] countImage(){
int xImageCount = imageCount[0];
int yImageCount = imageCount[1];
if(xImageCount-1 %2 != 0){
xImageCount = xImageCount + 2;
}
if(yImageCount-1 %2 != 0){
yImageCount = yImageCount + 2;
}
return new int[] {xImageCount, yImageCount};
}
/**
* Method use to get tiles
* #param lat
* #param lon
* #return
*/
private static ArrayList<BusStop2> getTiles(double lat, double lon ){
int [] numberTile = getTileNumber(lat, lon, Config.mapZoom);
int a1 = 1;
int a2 = 1;
int a3 = 1;
int a4 = 1;
x[0] = numberTile[0];
y[0] = numberTile[1];
for (int i = 1; i<x.length; i++){
if(i%2==0){
x[i] = numberTile[0]+(a1);
a1++;
}
else{
x[i] = numberTile[0]-(a2);
a2++;
}
}
for (int i = 1; i<y.length; i++){
if(i%2==0){
y[i] = numberTile[1]+(a3);
a3++;
}
else{
y[i] = numberTile[1]-(a4);
a4++;
}
}
for(int i = 0 ; i<x.length ; i++){
for (int j = 0 ;j<y.length ; j++ ){
list.add(new BusStop2(x[i], y[j], x[0] - x[i], y[0]-y[j]));
}
}
return list;
}
/**
*
* #param list
* #return
*/
private static ArrayList<PositionTilesAndURLPaths> getImgPositionAndURLsPath(ArrayList<BusStop2> list){
for(BusStop2 bus : list){
positionTilesAndURLPathsList.add(new PositionTilesAndURLPaths(256*bus.getX(), 256*bus.getY(),
Config.mapPath + "/" + bus.getA() + "/" + bus.getB() + ".png"));
}
return positionTilesAndURLPathsList;
}
public static int [] getTileNumber(final double lat, final double lon, final int zoom) {
int xtile = (int)Math.floor( (lon + 180) / 360 * (1<<zoom) ) ;
int ytile = (int)Math.floor( (1 - Math.log(Math.tan(Math.toRadians(lat)) + 1 / Math.cos(Math.toRadians(lat))) / Math.PI) / 2 * (1<<zoom) ) ;
if (xtile < 0)
xtile=0;
if (xtile >= (1<<zoom))
xtile=((1<<zoom)-1);
if (ytile < 0)
ytile=0;
if (ytile >= (1<<zoom))
ytile=((1<<zoom)-1);
return new int[] {xtile, ytile};
}
static double tile2lon(int x, int z) {
return x / Math.pow(2.0, z) * 360.0 - 180;
}
static double tile2lat(int y, int z) {
double n = Math.PI - (2.0 * Math.PI * y) / Math.pow(2.0, z);
return Math.toDegrees(Math.atan(Math.sinh(n)));
}
public void start(Pane pane ,double lat, double lon) throws Exception {
int [] tiles= getTileNumber(lat, lon, Config.mapZoom);
Canvas canvas = new Canvas(Config.xSize, Config.ySize);
GraphicsContext gc = canvas.getGraphicsContext2D();
int [] aa =getTileNumber(lat,lon, Config.mapZoom);
getTiles(lat,lon);
getImgPositionAndURLsPath(list);
ExecutorService executor = Executors.newFixedThreadPool(10);
ArrayList<UtlToImageConverter2> threadList = new ArrayList<>();
for(PositionTilesAndURLPaths url : positionTilesAndURLPathsList){
threadList.add(new UtlToImageConverter2(url.getPath()));
}
try {
executor.invokeAll(threadList);
} catch (InterruptedException e1) {
e1.printStackTrace();
}
System.out.println(imgCache.size());
System.out.println( aa[0] + " " + aa[1] );
deltaX = tile2lon(tiles[0] + 1 , Config.mapZoom) - tile2lon(tiles[0], Config.mapZoom);
deltaY = tile2lat(tiles[1], Config.mapZoom) - tile2lat(tiles[1] + 1 , Config.mapZoom);
positionX = (lon - tile2lon(tiles[0], Config.mapZoom)) * Config.imgSize/deltaX;
positionY = (tile2lat(tiles[1], Config.mapZoom) - lat) * Config.imgSize/deltaY;
gc.drawImage(bus,847.0-100 ,621.0-100);
gc.strokeText("aalala", 847.0-10 ,621.0-10);
for(PositionTilesAndURLPaths pos : getImgPositionAndURLsPath(list)){
gc.drawImage(imgCache.get(pos.getPath()),Config.xSize/2-pos.getX()-Config.imgSize/2 ,(Config.ySize/2)- pos.getY()-Config.imgSize/2, Config.imgSize, Config.imgSize);
System.out.println(pos.getX() + " " + pos.getY());
}
gc.drawImage(bus,Config.xSize/2-Config.imgSize/2-Config.markWidth/2+positionX, Config.ySize/2+positionY-Config.imgSize/2-Config.markHeight/2, Config.markWidth, Config.markHeight);
pane.getChildren().add(canvas);
}
#SuppressWarnings("unused")
public static void clear(){
for(PositionTilesAndURLPaths url : positionTilesAndURLPathsList){
url = null;
}
positionTilesAndURLPathsList.clear();
threadList.clear();
for(UtlToImageConverter utl : threadList){
utl = null;
}
for(BusStop2 bus :list){
bus = null;
}
list.clear();
}
}
There are multiple options to display a part of a image in JavaFX:
When using a Canvas node, use the correct drawImage, i.e. the one that does not scale the image to the target rectangle. e.g.
Rectangle2D rectInSource = ...
Rectangle2D targetRect = ...
gc.drawImage(image,
rectInSource.getMinX(),
rectInSource.getMinY(),
rectInSource.getWidth(),
rectInSource.getHeight(),
targetRect.getMinX(),
targetRect.getMinY(),
targetRect.getWidth(),
targetRect.getHeight());
Alternatively you could also use a ImageView for displaying part of a Image by setting the viewport property accordingly:
ImageView imageView = new ImageView(image);
imageView.setViewport(rectInSource);
// ----------- Only required, if rescaling is desired -----------
imageView.setFitWidth(targetRect.getWidth());
imageView.setFitHeight(targetRect.getHeight());
// --------------------------------------------------------------
imageView.relocate(targetRect.getMinX(), targetRect.getMinY());
pane.getChildren().add(imageView);
rectInSource denotes the part of the image that should be displayed, targetRect the position where it should be drawn.
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.
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.
public static final int SQUARE = 0,
LINE = 1,
S_FIGURE = 2,
Z_FIGURE = 3,
RIGHT_ANGLE_FIGURE = 4,
LEFT_ANGLE_FIGURE = 5,
TRIANGLE = 6;
Here above is my Tetris midlet/J2ME code for the block. But i don't know how to explain these parameters. can anyone give me advice? Let me know if you need the full code to work.
this is my full code:
public class Block {
public static final int SQUARE = 0,
LINE = 1,
S_FIGURE = 2,
Z_FIGURE = 3,
RIGHT_ANGLE_FIGURE = 4,
LEFT_ANGLE_FIGURE = 5,
TRIANGLE = 6;
public int kind;
public int color;
/**
* The horizontal figure position on the board. This value has no
* meaning when the figure is not attached to a square board.
*/
public int xPos = 0;
/**
* The vertical figure position on the board. This value has no
* meaning when the figure is not attached to a square board.
*/
public int yPos = 0;
/**
* The figure orientation (or rotation). This value is normally
* between 0 and 3, but must also be less than the maxOrientation
* value.
*
* #see #maxOrientation
*/
private int orientation = 0;
public int maxOrientation;
public int[] shapeX, shapeY;
private GameCanvas board = null;
public Block(int k) {
shapeX = new int[4];
shapeY = new int[4];
initialize(k);
this.kind = k;
}
private void initialize(int kind) {
switch (kind) {
case SQUARE :
maxOrientation = 1;
color = 0x0000ff; // blau
shapeX[0] = -1;
shapeY[0] = 0;
shapeX[1] = 0;
shapeY[1] = 0;
shapeX[2] = -1;
shapeY[2] = 1;
shapeX[3] = 0;
shapeY[3] = 1;
break;
case LINE :
maxOrientation = 2;
color = 0xff0000; // rot
shapeX[0] = -2;
shapeY[0] = 0;
shapeX[1] = -1;
shapeY[1] = 0;
shapeX[2] = 0;
shapeY[2] = 0;
shapeX[3] = 1;
shapeY[3] = 0;
break;
case S_FIGURE :
maxOrientation = 2;
color = 0xff00ff; // lila
shapeX[0] = 0;
shapeY[0] = 0;
shapeX[1] = 1;
shapeY[1] = 0;
shapeX[2] = -1;
shapeY[2] = 1;
shapeX[3] = 0;
shapeY[3] = 1;
break;
case Z_FIGURE :
maxOrientation = 2;
color = 0x008000; // grün
shapeX[0] = -1;
shapeY[0] = 0;
shapeX[1] = 0;
shapeY[1] = 0;
shapeX[2] = 0;
shapeY[2] = 1;
shapeX[3] = 1;
shapeY[3] = 1;
break;
case RIGHT_ANGLE_FIGURE :
maxOrientation = 4;
color = 0x800080; // violett
shapeX[0] = -1;
shapeY[0] = 0;
shapeX[1] = 0;
shapeY[1] = 0;
shapeX[2] = 1;
shapeY[2] = 0;
shapeX[3] = 1;
shapeY[3] = 1;
break;
case LEFT_ANGLE_FIGURE :
maxOrientation = 4;
color = 0x000000; // schwarz
shapeX[0] = -1;
shapeY[0] = 0;
shapeX[1] = 0;
shapeY[1] = 0;
shapeX[2] = 1;
shapeY[2] = 0;
shapeX[3] = -1;
shapeY[3] = 1;
break;
case TRIANGLE :
maxOrientation = 4; // gelb
color = 0xCECE00;
shapeX[0] = -1;
shapeY[0] = 0;
shapeX[1] = 0;
shapeY[1] = 0;
shapeX[2] = 1;
shapeY[2] = 0;
shapeX[3] = 0;
shapeY[3] = 1;
break;
}
}
/**
* Checks if this figure is attached to a square board.
*
* #return true if the figure is already attached, or
* false otherwise
*/
public boolean isAttached() {
return board != null;
}
public boolean attach(GameCanvas board, boolean center) {
int newX;
int newY;
int i;
// Check for previous attachment
if (isAttached()) {
detach();
}
// Reset position (for correct controls)
xPos = 0;
yPos = 0;
// Calculate position
newX = board.getBoardWidth() / 2;
if (center) {
newY = board.getBoardHeight() / 2;
} else {
newY = 0;
for (i = 0; i < shapeX.length; i++) {
if (getRelativeY(i, orientation) < 0 && getRelativeY(i, orientation)<newY) {
newY = Math.abs(getRelativeY(i, orientation))+1;
}
}
}
// Check position
this.board = board;
if (!canMoveTo(newX, newY, orientation)) {
this.board = null;
return false;
}
// Draw figure
xPos = newX;
yPos = newY;
paint(color);
board.repaint();
return true;
}
/**
* Checks if the figure is fully visible on the square board. If
* the figure isn't attached to a board, false will be returned.
*
* #return true if the figure is fully visible, or
* false otherwise
*/
public boolean isAllVisible() {
if (!isAttached()) {
return false;
}
for (int i = 0; i < shapeX.length; i++) {
if (yPos + getRelativeY(i, orientation) < 0) {
return false;
}
}
return true;
}
/**
* Detaches this figure from its square board. The figure will not
* be removed from the board by this operation, resulting in the
* figure being left intact.
*/
public void detach() {
board = null;
}
/**
* Checks if the figure has landed. If this method returns true,
* the moveDown() or the moveAllWayDown() methods should have no
* effect. If no square board is attached, this method will return
* true.
*
* #return true if the figure has landed, or false otherwise
*/
public boolean hasLanded() {
return !isAttached() || !canMoveTo(xPos, yPos + 1, orientation);
}
/**
* Moves the figure one step to the left. If such a move is not
* possible with respect to the square board, nothing is done. The
* square board will be changed as the figure moves, clearing the
* previous cells. If no square board is attached, nothing is
* done.
*/
public void moveLeft() {
if (isAttached() && canMoveTo(xPos - 1, yPos, orientation)) {
paint(-1);
xPos--;
paint(color);
board.repaint();
}
}
/**
* Moves the figure one step to the right. If such a move is not
* possible with respect to the square board, nothing is done. The
* square board will be changed as the figure moves, clearing the
* previous cells. If no square board is attached, nothing is
* done.
*/
public void moveRight() {
if (isAttached() && canMoveTo(xPos + 1, yPos, orientation)) {
paint(-1);
xPos++;
paint(color);
board.repaint();
}
}
/**
* Moves the figure one step down. If such a move is not possible
* with respect to the square board, nothing is done. The square
* board will be changed as the figure moves, clearing the
* previous cells. If no square board is attached, nothing is
* done.
*/
public void moveDown() {
if (isAttached() && canMoveTo(xPos, yPos + 1, orientation)) {
paint(-1);
yPos++;
paint(color);
board.repaint();
}
}
/**
* Moves the figure all the way down. The limits of the move are
* either the square board bottom, or squares not being empty. If
* no move is possible with respect to the square board, nothing
* is done. The square board will be changed as the figure moves,
* clearing the previous cells. If no square board is attached,
* nothing is done.
*/
public void moveAllWayDown() {
int y = yPos;
// Check for board
if (!isAttached()) {
return;
}
// Find lowest position
while (canMoveTo(xPos, y + 1, orientation)) {
y++;
}
// Update
if (y != yPos) {
paint(-1);
yPos = y;
paint(color);
board.repaint();
}
}
/**
* Returns the current figure rotation (orientation).
*
* #return the current figure rotation
*/
public int getRotation() {
return orientation;
}
/**
* Sets the figure rotation (orientation). If the desired rotation
* is not possible with respect to the square board, nothing is
* done. The square board will be changed as the figure moves,
* clearing the previous cells. If no square board is attached,
* the rotation is performed directly.
*
* #param rotation the new figure orientation
*/
public void setRotation(int rotation) {
int newOrientation;
// Set new orientation
newOrientation = rotation % maxOrientation;
// Check new position
if (!isAttached()) {
orientation = newOrientation;
} else if (canMoveTo(xPos, yPos, newOrientation)) {
paint(-1);
orientation = newOrientation;
paint(color);
board.repaint();
}
}
/**
* Rotates the figure clockwise. If such a rotation is not
* possible with respect to the square board, nothing is done.
* The square board will be changed as the figure moves,
* clearing the previous cells. If no square board is attached,
* the rotation is performed directly.
*/
public void rotateClockwise() {
if (maxOrientation == 1) {
return;
} else {
setRotation((orientation + 1) % maxOrientation);
}
}
/**
* Rotates the figure counter-clockwise. If such a rotation
* is not possible with respect to the square board, nothing
* is done. The square board will be changed as the figure
* moves, clearing the previous cells. If no square board is
* attached, the rotation is performed directly.
*/
public void rotateCounterClockwise() {
if (maxOrientation == 1) {
return;
} else {
setRotation((orientation + 3) % 4);
}
}
/**
* Checks if a specified pair of (square) coordinates are inside
* the figure, or not.
*
* #param x the horizontal position
* #param y the vertical position
*
* #return true if the coordinates are inside the figure, or
* false otherwise
*/
private boolean isInside(int x, int y) {
for (int i = 0; i < shapeX.length; i++) {
if (x == xPos + getRelativeX(i, orientation)
&& y == yPos + getRelativeY(i, orientation)) {
return true;
}
}
return false;
}
/**
* Checks if the figure can move to a new position. The current
* figure position is taken into account when checking for
* collisions. If a collision is detected, this method will return
* false.
*
* #param newX the new horizontal position
* #param newY the new vertical position
* #param newOrientation the new orientation (rotation)
*
* #return true if the figure can be moved, or
* false otherwise
*/
private boolean canMoveTo(int newX, int newY, int newOrientation) {
int x;
int y;
for (int i = 0; i < 4; i++) {
x = newX + getRelativeX(i, newOrientation);
y = newY + getRelativeY(i, newOrientation);
if (!isInside(x, y) && !board.isSquareEmpty(x, y)) {
return false;
}
}
return true;
}
/**
* Returns the relative horizontal position of a specified square.
* The square will be rotated according to the specified
* orientation.
*
* #param square the square to rotate (0-3)
* #param orientation the orientation to use (0-3)
*
* #return the rotated relative horizontal position
*/
public int getRelativeX(int square, int orientation) {
switch (orientation % 4) {
case 0 :
return shapeX[square];
case 1 :
return -shapeY[square];
case 2 :
return -shapeX[square];
case 3 :
return shapeY[square];
default:
return 0; // Should never occur
}
}
/**
* Rotates the relative vertical position of a specified square.
* The square will be rotated according to the specified
* orientation.
*
* #param square the square to rotate (0-3)
* #param orientation the orientation to use (0-3)
*
* #return the rotated relative vertical position
*/
public int getRelativeY(int square, int orientation) {
switch (orientation % 4) {
case 0 :
return shapeY[square];
case 1 :
return shapeX[square];
case 2 :
return -shapeY[square];
case 3 :
return -shapeX[square];
default:
return 0; // Should never occur
}
}
/**
* Paints the figure on the board with the specified color.
*
* #param color the color to paint with, or -1 for clearing
*/
private void paint(int color) {
int x, y;
for (int i = 0; i < shapeX.length; i++) {
x = xPos + getRelativeX(i, orientation);
y = yPos + getRelativeY(i, orientation);
board.setSquareColor(x, y, color);
}
}
}
First of all, I think what you're looking for is an enum:
public enum Tetrimonos{
SQUARE , LINE, S_FIGURE, Z_FIGURE ,
RIGHT_ANGLE_FIGURE, LEFT_ANGLE_FIGURE , TRIANGLE
}
Using constants is a bad idea, relic from old languages.
Secondly, what do you mean 'explain these parameters'? Explain the tetris structure of each thing you named? In this case you should go to the relevant literature: a tetris piece is called tetrimono, and each one is called by a letter. Take a look here:
http://en.wikipedia.org/wiki/Tetris#Gameplay
You might like to link to this in your documentation as well.