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import ij.IJ;
import ij.ImagePlus;
import ij.gui.GenericDialog;
import ij.plugin.filter.PlugInFilter;
import ij.process.ByteProcessor;
import ij.process.ImageProcessor;
import java.util.ArrayList;
import java.util.Iterator;
import java.util.List;
/**
* Harris Corner Detector
*
* @author Xavier Philippeau
*
*/
public class Harris_ implements PlugInFilter {
// corners list
List<int[]> corners = new ArrayList<int[]>();
// halfwindow size (integration, kernels)
private int halfwindow = 0;
// variance of gaussians
private double gaussiansigma = 0;
// corner filtering
private int minDistance = 0;
private int minMeasure = 0;
// Gradient kernel
GradientVector gradient = new GradientVector();
// About...
private void showAbout() {
IJ.showMessage("Harris...","Harris Corner Detector by Pseudocode");
}
public int setup(String arg, ImagePlus imp) {
// about...
if (arg.equals("about")) {
showAbout();
return DONE;
}
// else...
if (imp==null) return DONE;
// Configuration dialog.
GenericDialog gd = new GenericDialog("Parameters");
gd.addNumericField("Half-Window size",1,0);
gd.addNumericField("Gaussian sigma",1.4,1);
gd.addNumericField("Min Harris measure for corners",10,0);
gd.addNumericField("Min distance between corners",8,0);
int halfwindow = 0;
double gaussiansigma = 0;
int minMeasure = 0;
int minDistance = 0;
while(true) {
gd.showDialog();
if ( gd.wasCanceled() ) return DONE;
halfwindow = (int) gd.getNextNumber();
gaussiansigma = (double) gd.getNextNumber();
minMeasure = (int) gd.getNextNumber();
minDistance = (int) gd.getNextNumber();
if (halfwindow<=0) continue;
if (gaussiansigma<=0) continue;
if (minMeasure<0) continue;
if (minDistance<0) continue;
break;
}
gd.dispose();
this.halfwindow = halfwindow;
this.gaussiansigma = gaussiansigma;
this.minMeasure = minMeasure;
this.minDistance = minDistance;
return PlugInFilter.DOES_8G;
}
public void run(ImageProcessor ip) {
// ImageProcessor -> ByteProcessor conversion
ByteProcessor bp = new ByteProcessor(ip.getWidth(),ip.getHeight());
for (int y = 0; y < ip.getHeight(); y++) {
for (int x = 0; x < ip.getWidth(); x++) {
bp.set(x,y,ip.getPixel(x,y));
}
}
// canny filter
ByteProcessor newbp = filter( bp, this.minMeasure, this.minDistance );
// ByteProcessor -> ImageProcessor conversion
ImageProcessor out = new ByteProcessor(ip.getWidth(),ip.getHeight());
for (int y = 0; y < ip.getHeight(); y++) {
for (int x = 0; x < ip.getWidth(); x++) {
out.set(x,y,newbp.get(x,y));
}
}
ImagePlus newImg = new ImagePlus("Canny Filter Result", out);
newImg.show();
}
// -------------------------------------------------------------------------
/**
* Gaussian window function
*
* @param x x coordinates
* @param y y coordinates
* @param sigma2 variance
* @return value of the function
*/
private double gaussian(double x, double y, double sigma2) {
double t = (x*x+y*y)/(2*sigma2);
double u = 1.0/(2*Math.PI*sigma2);
double e = u*Math.exp( -t );
return e;
}
/**
* compute harris measure for a pixel
*
* @param c Image map
* @param x x coord of the computation
* @param y y coord of the computation
* @return harris measure
*/
private double harrisMeasure(ByteProcessor c, int x, int y) {
double m00=0, m01=0, m10=0, m11=0;
// Harris estimator
// ----------------
//
// k = det(A) - lambda * trace(A)^2
//
// Where A is the second-moment matrix
//
// | Lx²(x+dx,y+dy) Lx.Ly(x+dx,y+dy) |
// A = Sum | | * Gaussian(dx,dy)
// dx,dy | Lx.Ly(x+dx,y+dy) Ly²(x+dx,y+dy) |
//
// and lambda = 0.06 (totaly empirical :-)
for(int dy=-halfwindow;dy<=halfwindow;dy++) {
for(int dx=-halfwindow;dx<=halfwindow;dx++) {
int xk = x + dx;
int yk = y + dy;
if (xk<0 || xk>=c.getWidth()) continue;
if (yk<0 || yk>=c.getHeight()) continue;
// gradient value
double[] g = gradient.getVector(c,xk,yk);
double gx = g[0];
double gy = g[1];
// gaussian window
double gw = gaussian(dx,dy,gaussiansigma);
// second-moment matrix elements
m00 += gx * gx * gw;
m01 += gx * gy * gw;
m10 = m01;
m11 += gy * gy * gw;
}
}
// Harris corner measure
double harris = m00*m11 - m01*m10 - 0.06*(m00+m11)*(m00+m11);
// scaled down
return harris/(256*256);
}
/**
* return the the measure at pixel (x,y) if the pixel is a spatial Maxima, else return -1
*
* @param c original image
* @param x x coordinates of pixel
* @param y y coordinates of pixel
* @return the measure if the pixel is a spatial Maxima, else -1
*/
private double spatialMaximaofHarrisMeasure(ByteProcessor c, int x, int y) {
int n=8;
int[] dx = new int[] {-1,0,1,1,1,0,-1,-1};
int[] dy = new int[] {-1,-1,-1,0,1,1,1,0};
double w = harrisMeasure(c,x,y);
for(int i=0;i<n;i++) {
double wk = harrisMeasure(c,x+dx[i],y+dy[i]);
if (wk>=w) return -1;
}
return w;
}
/**
* Perfom Harris Corner Detection
*
* @param c Image map
* @param tilesize size of a tile
* @param nmbmax max number of corner to keep
* @return filtered image map
*/
public ByteProcessor filter(ByteProcessor c, int minMeasure, int minDistance) {
int width = c.getWidth();
int height = c.getHeight();
// copy of the original image (at little darker)
ByteProcessor c2 = new ByteProcessor(width,height);
for (int y=0; y<height; y++)
for (int x=0; x<width; x++)
c2.set(x,y,(int)(c.get(x,y)*0.80));
// for each tile in the image
for (int y=0; y<height; y++) {
for (int x=0; x<width; x++) {
// harris measure
int h = (int)spatialMaximaofHarrisMeasure(c, x, y);
// add the corner to the list
if (h>=minMeasure) corners.add( new int[]{x,y,h} );
}
}
// remove corners to close to each other (keep the highest measure)
Iterator<int[]> iter = corners.iterator();
while(iter.hasNext()) {
int[] p = iter.next();
for(int[] n:corners) {
if (n==p) continue;
int dist = (int)Math.sqrt( (p[0]-n[0])*(p[0]-n[0])+(p[1]-n[1])*(p[1]-n[1]) );
if( dist>minDistance) continue;
if (n[2]<p[2]) continue;
iter.remove();
break;
}
}
// Display corners over the image (cross)
for (int[] p:corners) {
for (int dx=-2; dx<=2; dx++) {
if (p[0]+dx<0 || p[0]+dx>=width) continue;
c2.set(p[0]+dx,p[1],255);
}
for (int dy=-2; dy<=2; dy++) {
if (p[1]+dy<0 || p[1]+dy>=height) continue;
c2.set(p[0],p[1]+dy,255);
}
//System.out.println("corner found at: "+p[0]+","+p[1]+" ("+p[2]+")");
}
return c2;
}
public class GradientVector {
int halfwindow = 1; // 3x3 kernel
double sigma2 = 1.4;
double[][] kernelGx = new double[2*halfwindow+1][2*halfwindow+1];
double[][] kernelGy = new double[2*halfwindow+1][2*halfwindow+1];
// Constructor
public GradientVector() {
for(int y=-halfwindow;y<=halfwindow;y++) {
for(int x=-halfwindow;x<=halfwindow;x++) {
kernelGx[halfwindow+y][halfwindow+x] = Gx(x, y);
kernelGy[halfwindow+y][halfwindow+x] = Gy(x, y);
}
}
}
// Kernel functions (gaussian 1st order partial derivatives)
private double Gx(int x, int y) {
double t = (x*x+y*y)/(2*sigma2);
double d2t = -x / sigma2;
double e = d2t * Math.exp( -t );
return e;
}
private double Gy(int x, int y) {
double t = (x*x+y*y)/(2*sigma2);
double d2t = -y / sigma2;
double e = d2t * Math.exp( -t );
return e;
}
// return the Gradient Vector for pixel(x,y)
public double[] getVector(ByteProcessor c, int x, int y) {
double gx=0, gy=0;
for(int dy=-halfwindow;dy<=halfwindow;dy++) {
for(int dx=-halfwindow;dx<=halfwindow;dx++) {
int xk = x + dx;
int yk = y + dy;
double vk = c.getPixel(xk,yk); // <-- value of the pixel
gx += kernelGx[halfwindow-dy][halfwindow-dx] * vk;
gy += kernelGy[halfwindow-dy][halfwindow-dx] * vk;
}
}
double[] gradientVector = new double[] { gx, gy };
return gradientVector;
}
}
} |
[Image] Détecteur de Harris pour ImageJ
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