方法简介：

g=ω0ω1(μ0-μ1)^2
对于图像I(x,y),前景(即目标)和背景的分割阈值记作T,属于前景的像素点数占整幅图像的比例记为ω0,其平均灰度μ0;
背景像素点数占整幅图像的比例为ω1,其平均灰度为μ1,类间方差记为g。采用遍历的方法得到使类间方差最大的阈值T

int Process::image_binary(unsigned char* buffer, int w, int h)
{int height = h;int width = w;int size = h*w;int histogram[256] = { 0 };int tmp_val = 0;for (int i = 0; i<height; i++)  //计算直方图{for (int j = 0; j<width; j++){histogram[(int)buffer[i*width + j]]++;}}//计算阈值int gSum0 = 0, gSum1 = 0, N0 = 0, N1 = 0;float w0 = 0.0, w1 = 0.0, u0 = 0.0, u1 = 0.0, g = 0.0, tempg = 0.0;int thr = 0;for (int i = 0; i<256; i++){gSum0 = 0;gSum1 = 0;N0 += histogram[i];w0 = (float)N0 / size;N1 = size - N0;w1 = 1 - w0;for (int j = 0; j <= i; j++){gSum0 += j*histogram[j];}u0 = (float)gSum0 / N0;for (int k = i + 1; k<256; k++){gSum1 += k*histogram[k];}u1 = (float)gSum1 / N1;g = w0*w1*(u0 - u1)*(u0 - u1);if (tempg<g){tempg = g;thr = i;}}for (int i = 0; i<height; i++)  //二值化处理{for (int j = 0; j<width; j++){tmp_val = buffer[i*width + j];if (tmp_val <= thr)buffer[i*width + j] = 0;elsebuffer[i*width + j] = 255;}}return 0;
}

处理效果对比：