1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401
| 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.List;
/**
* Canny Filter (edge detector)
*
* @author Xavier Philippeau
*
*/
public class Canny_ implements PlugInFilter {
private int[][] gaussianKernel = null;
private int gaussianKernelFactor = 0;
private int lowThreshold=0;
private int highThreshold=0;
// About...
private void showAbout() {
IJ.showMessage("Canny...","Canny Filter 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("Gaussian kernel size",7,0);
gd.addNumericField("Gaussian sigma2",3.0,1);
gd.addNumericField("Hysteresis low value",30,0);
gd.addNumericField("Hysteresis high value",100,0);
int gaussianwindow = 0;
double gaussiansigma2 = 0;
int lowvalue = 0;
int highvalue = 0;
while(true) {
gd.showDialog();
if ( gd.wasCanceled() ) return DONE;
gaussianwindow = (int) gd.getNextNumber();
gaussiansigma2 = (double) gd.getNextNumber();
lowvalue = (int) gd.getNextNumber();
highvalue = (int) gd.getNextNumber();
if (gaussianwindow<=0) continue;
if (gaussiansigma2<=0) continue;
if (lowvalue<=0) continue;
if (highvalue<lowvalue) continue;
break;
}
gd.dispose();
initGaussianKernel(gaussianwindow,gaussiansigma2);
this.lowThreshold=lowvalue;
this.highThreshold=highvalue;
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.lowThreshold, this.highThreshold );
// 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();
}
// ---------------------------------------------------------------------------------
/**
* Compute the gaussian kernel G(x,y) = Exp[ - (x^2+y^2)/(2*sigma^2) ]
*
* @param window size of the kernel
* @param sigma std-dev of the gaussian
*/
private void initGaussianKernel(int window, double sigma2) {
int aperture = window/2;
this.gaussianKernel = new int[2*aperture+1][2*aperture+1];
// factor to have only integers in the kernel
int intFactor = 1000;
this.gaussianKernelFactor = 0;
for(int dy=-aperture;dy<=aperture;dy++) {
for(int dx=-aperture;dx<=aperture;dx++) {
double e = Math.exp( - (dx*dx+dy*dy) / (2*sigma2) );
int k = (int) Math.rint(intFactor * e);
this.gaussianKernel[dx+aperture][dy+aperture]=k;
this.gaussianKernelFactor += k;
}
}
}
/**
* Histogram stretching
*
* @param c Image map
*/
public static void histogramStretch(ByteProcessor bp) {
int width = bp.getWidth();
int height = bp.getHeight();
// search min/max value in the image
int min=255,max=0;
for (int y=0; y<height; y++) {
for (int x=0; x<width; x++) {
int v = bp.get(x, y);
if (v<min) min=v;
if (v>max) max=v;
}
}
// compute translation table
int color[] = new int[256];
for(int i=0;i<256;i++) {
double t = (double)(i-min)/(double)(max-min);
if (t<0) t=0;
if (t>1) t=1;
int v = (int)(255*t);
color[i] = v;
}
// replace value in the image
for (int y=0; y<height; y++) {
for (int x=0; x<width; x++) {
int v = bp.get(x, y);
bp.set(x, y, color[v]);
}
}
}
/**
* Perform convolution (Image x Kernel) at a single point
*
* @param c Image map
* @param x x coord of the computation
* @param y y coord of the computation
* @param kernel the kernel matrix
* @param factor the kernel factor
* @return convolution result
*/
private int convolve(ByteProcessor bp, int x, int y, int[][] kernel, int factor) {
int width = bp.getWidth();
int height = bp.getHeight();
// assume a square kernel
int aperture = kernel[0].length/2;
int v = 0;
for(int dy=-aperture;dy<=aperture;dy++) {
for(int dx=-aperture;dx<=aperture;dx++) {
int xk = x + dx;
int yk = y + dy;
if (xk<0) xk=0;
if (xk>=width) xk=width-1;
if (yk<0) yk=0;
if (yk>=height) yk=height-1;
int vk = bp.getPixel(xk,yk);
v += kernel[aperture+dy][aperture+dx] * vk;
}
}
v/=factor;
return v;
}
/**
* Compute the local gradient
*
* @param c Image map
* @param x x coord of the computation (int)
* @param y y coord of the computation (int)
* @return norme and direction (-pi...pi) of the gradient
*/
private double[] gradient(ByteProcessor bp, int x, int y) {
int width = bp.getWidth();
int height = bp.getHeight();
int px = x - 1; // previous x
int nx = x + 1; // next x
int py = y - 1; // previous y
int ny = y + 1; // next y
// limit to image dimension
if (px < 0) px = 0;
if (nx >= width) nx = width - 1;
if (py < 0) py = 0;
if (ny >= height) ny = height - 1;
// Intesity of the 8 neighbors
int Ipp = bp.getPixel(px,py);
int Icp = bp.getPixel( x,py);
int Inp = bp.getPixel(nx,py);
int Ipc = bp.getPixel(px, y);
int Inc = bp.getPixel(nx, y);
int Ipn = bp.getPixel(px,ny);
int Icn = bp.getPixel( x,ny);
int Inn = bp.getPixel(nx,ny);
// Local gradient
double r2 = 2*Math.sqrt(2);
double gradx = (Inc-Ipc)/2.0 + (Inn-Ipp)/r2 + (Inp-Ipn)/r2; // horizontal + 2 diagonals
double grady = (Icn-Icp)/2.0 + (Inn-Ipp)/r2 + (Ipn-Inp)/r2; // vertical + 2 diagonals
// compute polar coordinates
double norme = Math.sqrt(gradx*gradx+grady*grady);
double angle = Math.atan2(grady, gradx);
// multiply norme by 8/3, to have the same values as Sobel Kernel 3x3
return new double[] { norme*8.0/3.0, angle };
}
/**
* Compute the local gradient (subpixel with bilinear interpolation)
*
* @param c Image map
* @param x x coord of the computation (double)
* @param y y coord of the computation (double)
* @return norme and direction (-pi...pi) of the gradient
*/
private double[] gradientInterpolated(ByteProcessor bp, double x, double y) {
double wx = x - (int)x;
double wy = y - (int)y;
double[] Gpp = gradient(bp,(int)x,(int)y);
double[] Gnp = gradient(bp,(int)x+1,(int)y);
double[] Gpn = gradient(bp,(int)x,(int)y+1);
double[] Gnn = gradient(bp,(int)x+1,(int)y+1);
double norme = (1-wx)*(1-wy)*Gpp[0] + wx*(1-wy)*Gnp[0] + (1-wx)*wy*Gpn[0] + wx*wy*Gnn[0];
double angle = (1-wx)*(1-wy)*Gpp[1] + wx*(1-wy)*Gnp[1] + (1-wx)*wy*Gpn[1] + wx*wy*Gnn[1];
return new double[] { norme, angle };
}
/**
* compute if a position is a local maxima
*
* @param c Image map
* @param x x coord of the computation
* @param y y coord of the computation
* @return true if position is a local maxima
*/
private boolean isLocalMaxima(ByteProcessor bp, int x, int y) {
// gradient at current position
double[] grad = gradient(bp,x,y);
// gradient direction
double gx = Math.cos(grad[1]);
double gy = Math.sin(grad[1]);
// gradient value at next position in the gradient direction
double nx = x + gx;
double ny = y + gy;
double[] gradn = gradientInterpolated(bp,nx,ny);
// gradient value at previous position in the gradient direction
double px = x - gx;
double py = y - gy;
double[] gradp = gradientInterpolated(bp,px,py);
// is the current gradient value a local maxima ?
// synthetic image
if (grad[0]==gradn[0] && grad[0]!=gradp[0]) return (x>nx || y>ny);
if (grad[0]!=gradn[0] && grad[0]==gradp[0]) return (x>px || y>py);
// real world image
double EPSILON=1E-7;
if ((grad[0]-gradn[0])<EPSILON) return false;
if ((grad[0]-gradp[0])<EPSILON) return false;
return true;
}
/**
* Perfom Canny filtering
*
* @param c Image map
* @param lowThreshold low value of the hysteresis
* @param highThreshold high value of the hysteresis
* @return filtered image map
*/
public ByteProcessor filter(ByteProcessor c, int lowThreshold, int highThreshold) {
int width = c.getWidth();
int height = c.getHeight();
// Gaussian filter
ByteProcessor bpg = new ByteProcessor(width,height);
for (int y=0; y<height; y++) {
for (int x=0; x<width; x++) {
int v = convolve(c,x,y,this.gaussianKernel,this.gaussianKernelFactor);
bpg.set(x,y,v);
}
}
// histogram stretching (compensate the loss of energy caused by gaussian filtering)
histogramStretch(bpg);
ByteProcessor out = new ByteProcessor(width,height);
List<int[]> highpixels = new ArrayList<int[]>();
// gradient thresholding
for (int y=0; y<height; y++) {
for (int x=0; x<width; x++) {
// gradient intesity
double g = gradient(bpg,x,y)[0];
// low-threshold -> not an edge
if (g<lowThreshold) continue;
// not a local maxima -> not an edge
if (!isLocalMaxima(bpg,x,y)) continue;
// high-threshold -> is an edge
if (g>highThreshold) {
out.set(x,y,255);
highpixels.add(new int[]{x,y});
continue;
}
// between thresholds -> "unknown state" (depends on neighbors)
out.set(x,y,128);
}
}
// edge continuation
int[] dx8 = new int[] {-1, 0, 1, 1, 1, 0,-1,-1};
int[] dy8 = new int[] {-1,-1,-1, 0, 1, 1, 1, 0};
List<int[]> newhighpixels = new ArrayList<int[]>();
while(!highpixels.isEmpty()) {
newhighpixels.clear();
for(int[] pixel : highpixels) {
int x=pixel[0], y=pixel[1];
// move low-state pixel in the 3x3 neighborhood to high-state
for(int k=0;k<8;k++) {
int xk=x+dx8[k], yk=y+dy8[k];
if (xk<0 || xk>=width) continue;
if (yk<0 || yk>=height) continue;
if (out.get(xk, yk)==128) {
out.set(xk, yk, 255);
newhighpixels.add(new int[]{xk, yk});
}
}
}
// swap highpixels lists
List<int[]> swap = highpixels; highpixels = newhighpixels; newhighpixels = swap;
}
// remove remaining low-state pixels
for (int y=0; y<height; y++)
for (int x=0; x<width; x++)
if (out.get(x, y)!=255) out.set(x,y,0);
return out;
}
} |
Partager