android图像边缘提取（Rober算子）

效果展示：





下面帖主要的图片计算类：
图片灰度化：public class BitmapUtil {
public static Bitmap bitmap2Gray(Bitmap bmSrc) {
// 得到图片的长和宽
int width = bmSrc.getWidth();
int height = bmSrc.getHeight();
// 创建目标灰度图像
Bitmap bmpGray = null;
bmpGray = Bitmap.createBitmap(width, height, Bitmap.Config.RGB_565);
// 创建画布
Canvas c = new Canvas(bmpGray);
Paint paint = new Paint();
ColorMatrix cm = new ColorMatrix();
cm.setSaturation(0);
ColorMatrixColorFilter f = new ColorMatrixColorFilter(cm);
paint.setColorFilter(f);
c.drawBitmap(bmSrc, 0, 0, paint);
return bmpGray;
}

}Rober算子边缘提取计算类：public class RobertsEdgeDetect {
int width;//图像宽
int height;//图像高
int[] grayData;//图像灰度值
int size; //图像大小
int gradientThreshold = -1;//判断时用到的阈值
//BufferedImage outBinary;//输出的边缘图像
public RobertsEdgeDetect(int threshold) {
gradientThreshold = threshold;
}

public void readImage(Bitmap bitmap) throws IOException {
width = bitmap.getWidth();
height = bitmap.getHeight();
size = width * height;

//获取图像像素值
int imageData[] = new int[width * height];
int count = 0;
for (int i = 0;i<height;i++){
for (int j = 0;j<width;j++){
imageData[count] = bitmap.getPixel(j,i);
count ++;
}
}
grayData = new int[width * height];// 开辟内存空间
for (int i = 0; i < imageData.length; i++) {
grayData[i] = (imageData[i] & 0xff0000) >> 16;// 由于读的是灰度图，故只考虑一个分量（三分量值相同）
}
}

public Bitmap createEdgeImage() {
int[] colors = new int[width * height];
float[] gradient = gradientM();// 计算图像各像素点的梯度值
float maxGradient = gradient[0];
for (int i = 1; i < gradient.length; ++i)
if (gradient[i] > maxGradient)
maxGradient = gradient[i];// 获取梯度最大值

float scaleFactor = 255.0f / maxGradient;// 比例因子用于调整梯度大小

int[][] cc = new int[width][height];
if (gradientThreshold >= 0) {
for (int y = 1; y < height - 1; ++y)
for (int x = 1; x < width - 1; ++x)
if (Math.round(scaleFactor * gradient[y * width + x]) >= gradientThreshold){
cc[x][y] = Color.BLUE;
}else {
cc[x][y] = Color.parseColor("#00000000");
}
}// 对梯度大小进行阈值处理
int count = 0;
for (int i = 0;i<height;i++)
for (int j = 0;j<width;j++)
{
colors[count] = cc[j][i];
count++;
}
Bitmap bitmap = Bitmap.createBitmap(colors,width,height, Bitmap.Config.ARGB_4444);
return bitmap;
}
//得到点(x,y)处的灰度值
public int getGrayPoint(int x, int y) {
return grayData[y * width + x];
}
//算子计算 图像每个像素点 的 梯度大小
protected float[] gradientM() {
float[] mag = new float[size];
@SuppressWarnings("unused")
int gx, gy;
for (int y = 1; y < height - 1; ++y)
for (int x = 1; x < width - 1; ++x) {
gx = GradientX(x, y);
//用公式 g=|gx|+|gy|计算图像每个像素点的梯度大小.原因是避免平方和开方耗费大量时间
mag[y * width + x] = (float) (Math.abs(gx));
}
return mag;
}
//算子 计算 点(x,y)处的x方向梯度大小
protected final int GradientX(int x, int y) {
return getGrayPoint(x , y ) - getGrayPoint(x + 1, y+1)
+ getGrayPoint(x + 1, y ) - getGrayPoint(x , y +1);
}// 计算像素点(x,y)X方向上的梯度值
// 算子 计算 点(x,y)处的y方向梯度大小

}
参考博客：http://blog.csdn.net/qq_20925635/article/details/53115883