【opencv】大津法二值化

大津法代码

int otsuThreshold(IplImage* img)
{

    int T = 0;//阈值
    int height = img->height;
    int width  = img->width;
    int step      = img->widthStep;
    int channels  = img->nChannels;
    uchar* data  = (uchar*)img->imageData;
    double gSum0;//第一类灰度总值
    double gSum1;//第二类灰度总值
    double N0 = 0;//前景像素数
    double N1 = 0;//背景像素数
    double u0 = 0;//前景像素平均灰度
    double u1 = 0;//背景像素平均灰度
    double w0 = 0;//前景像素点数占整幅图像的比例为ω0
    double w1 = 0;//背景像素点数占整幅图像的比例为ω1
    double u = 0;//总平均灰度
    double tempg = -1;//临时类间方差
    double g = -1;//类间方差
    double Histogram[256]={0};// = new double[256];//灰度直方图
    double N = width*height;//总像素数
    for(int i=0;i<height;i++)
    {//计算直方图
        for(int j=0;j<width;j++)
        {
            double temp =data[i*step + j * 3] * 0.114 + data[i*step + j * 3+1] * 0.587 + data[i*step + j * 3+2] * 0.299;
            temp = temp<0? 0:temp;
            temp = temp>255? 255:temp;
            Histogram[(int)temp]++;
        } 
    }
    //计算阈值
    for (int i = 0;i<256;i++)
    {
        gSum0 = 0;
        gSum1 = 0;
        N0 += Histogram[i];         
        N1 = N-N0;
        if(0==N1)break;//当出现前景无像素点时，跳出循环
        w0 = N0/N;
        w1 = 1-w0;
        for (int j = 0;j<=i;j++)
        {
            gSum0 += j*Histogram[j];
        }
        u0 = gSum0/N0;
        for(int k = i+1;k<256;k++)
        {
            gSum1 += k*Histogram[k];
        }
        u1 = gSum1/N1;
        //u = w0*u0 + w1*u1;
        g = w0*w1*(u0-u1)*(u0-u1);
        if (tempg<g)
        {
            tempg = g;
            T = i;
        }
    }
    return T; 
}