算法实现（不调用函数）

#include <opencv2/opencv.hpp>

#include <iostream>

using namespace std;

using namespace cv;

//实现灰度直方图的绘制

void drawpicture(Mat &inpicture, Mat &outpicture)

{

MatND hist;

const int bins = 256;

int hist_size[] = { bins };

float range[] = { 0, 256 };

const float* ranges[] = { range };

int channels[] = { 0 };

calcHist(&inpicture, 1, channels, Mat(), hist, 1, hist_size, ranges, true, false);

double maxnum;

minMaxLoc(hist, 0, &maxnum, 0, 0);

int scale = 1;

int histheight = 256;

for (int i = 0; i < bins; i++)

{

float binvalue = hist.at<float>(i);

int height = cvRound(binvalue*histheight / maxnum);

// 绘制长方形

rectangle(outpicture, Point(i*scale, histheight), Point((i + 1)*scale, histheight - height), Scalar(255));

namedWindow("windown", CV_WINDOW_NORMAL);

imshow("windown", outpicture);

}

}

 

int main()

{

//以灰度方式读入图片

Mat img = imread("F:\\work_three_grade\\DigitalImage\\d.jpg", IMREAD_GRAYSCALE);

double max = 0.0; //方差

int otsu = 0; //阈值

//得到像素点总个数

int h = img.rows;

int w = img.cols;

int num = h * w;

//声明一个一维数组，用来记录各像素级的个数

int m[256];

for (int i = 0; i < 256; i++)

{

m[i] = 0;

}

//Mat M(256, CV_8U, m);

for (int i = 0; i < h; i++)

{

for (int j = 0; j < w; j++)

{

int temp = img.at<uchar>(i, j);

m[temp]++;

}

}

//小于等于t的和大于t的像素出现的概率和平均灰度

int t;//将阈值从0-255依次遍历，寻找结果最好的

double outcome[256];//存放阈值为i的类间方差

for (t = 0; t < 256; t++)

{

double w0=0.0;//小于等于t的像素出现的概率

double w1= 0.0;//大于t的像素出现的概率

double u0= 0.0;// 平均灰度

double u1= 0.0;// 平均灰度

double sum1 = 0.0;

double sum2 = 0.0;

for (int i = 0; i <= t; i++)

{

sum1 += m[i];

}

w0 = sum1 / num; //小于等于t的像素出现的概率,应该是double类型，后面再说

w1 = 1 - w0; //大于t的像素出现的概率,应该是double类型，后面再说

sum1 = 0;

for (int i = 0; i <= t; i++)

{

sum1 += i * m[i];

}

u0 = sum1 / num / w0;

for (int i = t + 1; i < 256; i++)

{

sum2 += i * m[i];

}

u1 = sum2 / num / w1;

//暂存结果

outcome[t] = w0 * w1*(u0 - u1)*(u0 - u1);

}

//求最大类间方差

for (int i = 0; i < 256; i++)

{

if (max < outcome[i])

{

max = outcome[i];

otsu = i;

}

}

//输出原图像

namedWindow("img",CV_WINDOW_NORMAL);

imshow("img", img);

//输出灰度直方图

Mat grayimg = Mat::zeros(256, 256, CV_8UC1);

std::string grayimgwin;

drawpicture(img, grayimg);

cout << "阈值： " << otsu << endl;

cout << "最大类间方差： " << max << endl;

waitKey();

return 0;

}