图像二值化分割，最重要的就是计算阈值

阈值计算的方法有很多，基本分为两类，全局阈值和自适应阈值

全局分割方法

1.均值法

2.OTSU阈值

原理图

直方图分割，计算各个部分的方差 按比重求方差的最小值

3.三角阈值

原理图
API调用

#include<opencv2/opencv.hpp>
#include<iostream>
using namespace cv;
using namespace std;
int main() {
	Mat src = imread("D:/ps/jisoo.png");
	if (src.empty())
	{
		cout << "could not find the image";
		return -1;
	}
	namedWindow("input", WINDOW_FREERATIO);
	imshow("input", src);

	//阈值计算算法很多，基本分为两类, 全局阈值与自适应阈值
	Mat gray, binary;
	cvtColor(src, gray, COLOR_BGR2GRAY);
	imshow("gray", gray);
	Scalar m = mean(gray);//就均值
	cout << "mean :" << m[0];
	threshold(gray, binary, m[0], 255, THRESH_BINARY);
	imshow("binary1", binary);
	//OTSU阈值分割 一般优先选这个
	double t1=threshold(gray, binary, m[0], 255, THRESH_BINARY|THRESH_OTSU);
	imshow("binary2", binary);
	//三角法
	double t2 = threshold(gray, binary, m[0], 255, THRESH_BINARY|THRESH_TRIANGLE);
	imshow("binary3", binary);
	cout << t1 << endl;
	cout << t2 << endl;
	waitKey(0);
	destroyAllWindows();
	return 0;
}