平臺：Windows 10 20H2
Visual Studio 2015
OpenCV 4.5.3

本文所用原始碼修改自C++ opencv 圖片二值化最佳閾值確定（大津法,OTSU演算法)——Sharon Liu

概念

Otsu演算法，也叫最大類間方差法，是1979年由日本學者大津提 出的（所以也叫大津法），是一種自適應閾值確定的方法，是一種全域的二值化演算法，
它是根據影像的灰度特性，將影像分為前景和背景兩個部分， 當取最佳閾值時，兩部分之間的差別應該是最大的，在Otsu演算法中所采用的衡量差別的標準就是較為常見的最大類間方差，前景和背景之間的類間方差如果越大，就說明構成影像的兩個部分之間的差別越大，
當部分目標被錯分為背景或部分背景被錯分為目標，都會導致兩部分差別變小，
當所取閾值的分割使類間方差最大時，就意味著錯分概率最小，

C++原始碼

OtsuThreshold

/******************************************************************************************
Function:       OtsuThreshold
Description:	圖片二值化最佳閾值確定（大津法,OTSU演算法)
Input:          src:原圖片
Return:         閾值
******************************************************************************************/
int OtsuThreshold(Mat src)
{
	int threshold;

	try
	{
		int height = src.rows;
		int width = src.cols;

		//histogram  
		float histogram[256] = { 0 };
		for (int i = 0; i < height; i++) 
		{
			unsigned char* p = (unsigned char*)src.data + src.step*i;
			for (int j = 0; j < width; j++) 
			{
				histogram[*p++]++;
			}
		}
		//normalize histogram  
		int size = height*width;
		for (int i = 0; i < 256; i++) 
		{
			histogram[i] = histogram[i] / size;
		}

		//average pixel value  
		float avgValue = 0;
		for (int i = 0; i < 256; i++) 
		{
			avgValue += i*histogram[i];
		}

		float maxVariance = 0;
		float w = 0, u = 0;
		for (int i = 0; i < 256; i++) 
		{
			w += histogram[i];
			u += i*histogram[i];

			float t = avgValue*w - u;
			float variance = t*t / (w*(1 - w));
			if (variance > maxVariance) 
			{
				maxVariance = variance;
				threshold = i;
			}
		}
	}
	catch (cv::Exception e)
	{
	}

	return threshold;
}
//————————————————
//著作權宣告：本文為CSDN博主「Sharon Liu」的原創文章，遵循CC 4.0 BY - SA著作權協議，轉載請附上原文出處鏈接及本宣告，
//原文鏈接：https ://blog.csdn.net/sylsjane/article/details/80872744

主函式

圖片路徑根據實際情況調整，注意反斜杠是轉義字符的開頭，故“\”應替換為“\\”

int main(int argc, char * argv[])
{
	Mat Image = imread("D:\\Work\\OpenCV\\Workplace\\Test_1\\1.jpg", 0);

	int thresholdValue = OtsuThreshold(Image);
	cout << "類間方差為： " << thresholdValue << endl;

	Mat imageOutput;
	threshold(Image, imageOutput, thresholdValue, 255, CV_THRESH_BINARY);

	Mat imageOtsu;
	threshold(Image, imageOtsu, 0, 255, CV_THRESH_OTSU); //Opencv Otsu演算法

	imshow("原圖", Image);
	imshow("Output Image", imageOutput);
	imshow("Opencv Otsu", imageOtsu);

	waitKey(0);

	return 0;
}

效果

原圖

效果

OpenCv自帶的Otsu演算法結果，與上圖一致

完整原始碼

#include <opencv2\opencv.hpp>
#include <iostream>
#include <opencv2\imgproc\types_c.h>

using namespace cv;
using namespace std;


/******************************************************************************************
Function:       OtsuThreshold
Description:	圖片二值化最佳閾值確定（大津法,OTSU演算法)
Input:          src:原圖片
Return:         閾值
******************************************************************************************/
int OtsuThreshold(Mat src)
{
	int threshold;

	try
	{
		int height = src.rows;
		int width = src.cols;

		//histogram  
		float histogram[256] = { 0 };
		for (int i = 0; i < height; i++) 
		{
			unsigned char* p = (unsigned char*)src.data + src.step*i;
			for (int j = 0; j < width; j++) 
			{
				histogram[*p++]++;
			}
		}
		//normalize histogram  
		int size = height*width;
		for (int i = 0; i < 256; i++) 
		{
			histogram[i] = histogram[i] / size;
		}

		//average pixel value  
		float avgValue = 0;
		for (int i = 0; i < 256; i++) 
		{
			avgValue += i*histogram[i];
		}

		float maxVariance = 0;
		float w = 0, u = 0;
		for (int i = 0; i < 256; i++) 
		{
			w += histogram[i];
			u += i*histogram[i];

			float t = avgValue*w - u;
			float variance = t*t / (w*(1 - w));
			if (variance > maxVariance) 
			{
				maxVariance = variance;
				threshold = i;
			}
		}
	}
	catch (cv::Exception e)
	{
	}

	return threshold;
}
//————————————————
//著作權宣告：本文為CSDN博主「Sharon Liu」的原創文章，遵循CC 4.0 BY - SA著作權協議，轉載請附上原文出處鏈接及本宣告，
//原文鏈接：https ://blog.csdn.net/sylsjane/article/details/80872744

int main(int argc, char * argv[])
{
	Mat Image = imread("D:\\Work\\OpenCV\\Workplace\\Test_1\\1.jpg", 0);

	int thresholdValue = OtsuThreshold(Image);
	cout << "類間方差為： " << thresholdValue << endl;

	Mat imageOutput;
	threshold(Image, imageOutput, thresholdValue, 255, CV_THRESH_BINARY);

	Mat imageOtsu;
	threshold(Image, imageOtsu, 0, 255, CV_THRESH_OTSU); //Opencv Otsu演算法

	imshow("原圖", Image);
	imshow("Output Image", imageOutput);
	imshow("Opencv Otsu", imageOtsu);

	waitKey(0);

	return 0;
}