(Canny-算法)的Java实现-.doc

边缘检测算法的基本步骤
（1）滤波。边缘检测主要基于导数计算，但受噪声影响。但滤波器在降低噪声的同时也导致边缘强度的损失。 （2）增强。增强算法将邻域中灰度有显著变化的点突出显示。一般通过计算梯度幅值完成。
（3）检测。但在有些图象中梯度幅值较大的并不是边缘点。最简单的边缘检测是梯度幅值阈值判定。
（4）定位。精确确定边缘的位置。
Canny边缘检测算法
step1:用高斯滤波器平滑图象；
step2:用一阶偏导的有限差分来计算梯度的幅值和方向；
step3:对梯度幅值进行非极大值抑制；
step4:用双阈值算法检测和连接边缘。
效果图如下：

代码如下：
package tools;

import java.awt.*;
import java.awt.image.*;

public class EdgeDetector extends Component {

public EdgeDetector() {
threshold1 = 50;
threshold2 = 230;
setThreshold(128);
setWidGaussianKernel(15);
}

public void process() throws EdgeDetectorException {
if (threshold < 0 || threshold > 255)
throw new EdgeDetectorException("The value of the threshold is out of its valid range.");
if (widGaussianKernel < 3 || widGaussianKernel > 40)
throw new EdgeDetectorException("The value of the widGaussianKernel is out of its valid range.");
width = sourceImage.getWidth(this);
height = sourceImage.getHeight(this);
picsize = width * height;
data = new int[picsize];
magnitude = new int[picsize];
orientation = new int[picsize];
float f = 1.0F;
canny_core(f, widGaussianKernel);
thresholding_tracker(threshold1, threshold2);
for (int i = 0; i < picsize; i++)
if (data[i] > threshold)
data[i] = 0xff000000;
else
data[i] = -1;

edgeImage = pixels2image(data);
data = null;
magnitude = null;
orientation = null;
}

private void canny_core(float f, int i) {
boolean flag = false;
boolean flag1 = false;
derivative_mag = new int[picsize];
float af4[] = new float[i];
float af5[] = new float[i];
float af6[] = new float[i];
data = image2pixels(sourceImage);
int k4 = 0;
do {
if (k4 >= i)
break;
float f1 = gaussian(k4, f);
if (f1 <= 0.005F && k4 >= 2)
break;
float f2 = gaussian((float) k4 - 0.5F, f);
float f3 = gaussian((float) k4 + 0.5F, f);
float f4 = gaussian(k4, f * 0.5F);
af4[k4] = (f1 + f2 + f3) / 3F / (6.283185F * f * f);
af5[k4] = f3 - f2;
af6[k4] = 1.6F * f4 - f1;
k4++;
} while (true);
int j = k4;
float af[] = new float[picsize];
float af1[] = new float[picsize];
int j1 = width - (j - 1);
int l = width * (j - 1);
int i1 = width * (height - (j - 1));
for (int l4 = j - 1; l4 < j1; l4++) {
for (int l5 = l; l5 < i1; l5 += width) {
int k1 = l4 + l5;
float f8 = (float) data[k1] * af4[0];
float f10 = f8;
int l6 = 1;
int k7 = k1 - width;
for (int i8 = k1 + width; l6 < j; i8 += width) {
f8 += af4[l6] * (float) (data[k7] + data[i8]);
f10 += af4[l6] * (float) (data[k1 - l6] + data[k1 + l6]);
l6++;
k7 -= width;
}

af[k1] = f8;
af1[k1] = f10;
}

}

float af2[] = new float[picsize];
for (int i5 = j - 1; i5 < j1; i5++) {
for (int i6 = l; i6 < i1; i6 += width) {
float f9 = 0.0F;
int l1 = i5 + i6;
for (int i7 = 1; i7 < j; i7++)
f9 += af5[i7] * (af[l1 - i7] - af[l1 + i7]);

af2[l1] = f9;
}

}

af = null;
float af3[] = new float[picsize];
for (int j5 = k4; j5 < width - k4; j5++) {
for (int j6 = l; j6 < i1; j6 += width) {
float f11 = 0.0F;
int i2 = j5 + j6;
int j7 = 1;
for (int l7 = width; j7 < j; l7 += width) {
f11 += af5[j7] * (af1[i2 - l7] - af1[i2 + l7]);
j7++;
}

af3[i2] = f11;
}

}

af1 = null;
j1 = width - j;
l = width * j;
i1 = width * (height - j);
for (int k5 = j; k5 < j1; k5++) {
for (int k6 = l; k6 < i1; k6 += width) {
int j2 = k5 + k6;
int k2 = j2 - width;
int l2 = j2 + width;
int i3 = j2 - 1;
int j3 = j2 + 1;
int k3 = k2 - 1;
int l3 = k2 + 1;
int i4 = l2 - 1;
int j4 = l2 + 1;
float f6 = af2[j2];
float f7 = af3[j2];
float f12 = hypotenuse(f6, f7);
int k = (int) ((double) f12 * 20D);
derivative_mag[j2] = k >= 256 ? 255 : k;
float f13 = hypotenuse(af2[k2], af3[k2]);
float f14 = hypotenuse(af2[l2], af3[l2]);
float f15 = hypotenuse(af2[i3], af3[i3]);
float f16 = hypotenuse(af2[j3], af3[j3]);
float f18 = hypotenuse(af2[l3], af3[l3]);
float f20 = hypotenuse(af2[j4], af3[j4]);
float f19 = hypotenuse(af2[i4], af3[i4]);
float f17 = hypotenuse(af2[k3], af3[k3]);
float f5;
if (f6 * f7 <= (float) 0
? Math.abs(f6) >= Math.abs(f7)
? (f5 = Math.abs(f6 * f12))
>= Math.abs(f7 * f18 - (f6 + f7) * f16)
&& f5
> Math.abs(f7 * f19 - (f6 + f7) * f15) : (
f5 = Math.abs(f7 * f12))
>= Math.abs(f6 * f18 - (f7 + f6) * f13)
&& f5
> Math.abs(f6 * f19 - (f7 + f6) * f14) : Math.abs(f6)
>= Math.abs(f7)
? (f5 = Math.abs(f6 * f12))
>= Math.abs(f7 * f20 + (f6 - f7) * f16)
&& f5
> Math.abs(f7 * f17 + (f6 - f7) * f15) : (
f5 = Math.abs(f7 * f12))
>= Math.abs(f6 * f20 + (f7 - f6) * f14)
&& f5 > Math.abs(f6 * f17 + (f7 - f6) * f13)) {
magnitude[j2] = derivative_mag[j2];
orientation[j2] = (int) (Math.atan2(f7, f6) * (double) 40F);
}
}

}

derivative_mag = null;
af2 = null;
af3 = null;
}

private float hypotenuse(float f, float f1) {
if (f == 0.0F && f1 == 0.0F)
return 0.0F;
else
return (float) Math.sqrt(f * f + f1 * f1);
}

private float gaussian(float f, float f1) {
return (float) Math.exp((-f * f) / ((float) 2 * f1 * f1));
}

private void thresholding_tracker(int i, int j) {
for (int k = 0; k < picsize; k++)
data[k] = 0;

for (int l = 0; l < width; l++) {
for (int i1 = 0; i1 < height; i1++)
if (magnitude[l + width * i1] >= i)
follow(l, i1, j);

}

}

private boolean follow(int i, int j, int k) {
int j1 = i + 1;
int k1 = i - 1;
int l1 = j + 1;
int i2 = j - 1;
int j2 = i + j * width;
if (l1 >= height)
l1 = height - 1;
if (i2 < 0)
i2 = 0;
if (j1 >= width)
j1 = width - 1;
if (k1 < 0)
k1 = 0;
if (data[j2] == 0) {
data[j2] = magnitude[j2];
boolean flag = false;
int l = k1;
do {
if (l > j1)
break;
int i1 = i2;
do {
if (i1 > l1)
break;
int k2 = l + i1 * width;
if ((i1 != j || l != i)
&& magnitude[k2] >= k
&& follow(l, i1, k)) {
flag = true;
break;
}
i1++;
} while (true);
if (!flag)
break;
l++;
}
while (true);
return true;
} else {
return false;
}
}

private Image pixels2image(int ai[]) {
MemoryImageSource memoryimagesource =
new MemoryImageSource(
width,
height,
ColorModel.getRGBdefault(),
ai,
0,
width);
return Toolkit.getDefaultToolkit().createImage(memoryimagesource);
}

private int[] image2pixels(Image image) {
int ai[] = new int[picsize];
PixelGrabber pixelgrabber =
new PixelGrabber(image, 0, 0, width, height, ai, 0, width);
try {
pixelgrabber.grabPixels();
} catch (InterruptedException interruptedexception) {
interruptedexception.printStackTrace();
}
boolean flag = false;
int k1 = 0;
do {
if (k1 >= 16)
break;
int i = (ai[k1] & 0xff0000) >> 16;
int k = (ai[k1] & 0xff00) >> 8;
int i1 = ai[k1] & 0xff;
if (i != k || k != i1) {
flag = true;
break;
}
k1++;
} while (true);
if (flag) {
for (int l1 = 0; l1 < picsize; l1++) {
int j = (ai[l1] & 0xff0000) >> 16;
int l = (ai[l1] & 0xff00) >> 8;
int j1 = ai[l1] & 0xff;
ai[l1] =
(int) (0.29799999999999999D * (double) j
+ 0.58599999999999997D * (double) l
+ 0.113D * (double) j1);
}

} else {
for (int i2 = 0; i2 < picsize; i2++)
ai[i2] = ai[i2] & 0xff;

}
return ai;
}

public void setSourceImage(Image image) {
sourceImage = image;
}

public Image getEdgeImage() {
return edgeImage;
}

public void setThreshold(int i) {
threshold = i;
}

public void setWidGaussianKernel(int i) {
widGaussianKernel = i;
}

final float ORIENT_SCALE = 40F;
private int height;
private int width;
private int picsize;
private int data[];
private int derivative_mag[];
private int magnitude[];
private int orientation[];
private Image sourceImage;
private Image edgeImage;
private int threshold1;
private int threshold2;
private int threshold;
private int widGaussianKernel;
}

//second file

package tools;

public class EdgeDetectorException extends Exception
{
public EdgeDetectorException()
{
//do something?
}
public EdgeDetectorException(String s)
{
super(s);
}
}

//使用示例
EdgeDetector edgeDetector=new EdgeDetector();
edgeDetector.setSourceImage(sourceImage);
edgeDetector.setThreshold(128);
edgeDetector.setWidGaussianKernel(5);
try {
edgeDetector.process();
}
catch(EdgeDetectorException e) {
System.out.println(e.getMessage());
}
Image edgeImage=edgeDetector.getEdgeImage();
yourPanel.show(edgeImage);

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