通过灰度直方图进行简单的阈值分割

用到calcHist函数，返回hist，用minMaxloc来找到hist中数目最大的灰度级，表明其背景大概在这个区域，并以此作为阈值，进行分割。

其代码如下：

// opencv_6.cpp : 定义控制台应用程序的入口点。
//

#include "stdafx.h"
#include<opencv245.h>

using namespace std;
using namespace cv;

class Histogram1D
{
private:
	int histSize[1];
	float hranges[2];
	const float* ranges[1];
	int channels[1];

public:
	Histogram1D()
	{
		histSize[0] = 256;
		hranges[0] = 0.0;
		hranges[1] = 255.0;
		ranges[0] = hranges;
		channels[0] = 0;

	}

	Mat getHistogram(const Mat& image)
	{
		Mat hist;
		calcHist(&image,
			     1,
				 channels,
				 Mat(),
				 hist,
				 1,
				 histSize,
				 ranges);
		return hist;
	}

	double lookMaxpixel(Mat & hist)
	{
		double MaxVal = 0.0;
		/*double MinVal = 0.0;*/
		minMaxLoc(hist, NULL, &MaxVal);
		return MaxVal;
	}
	
};


int _tmain(int argc, _TCHAR* argv[])
{
	Histogram1D h;
	Mat image = imread("C:\\Users\\sony\\Desktop\\cloud.jpg", 0);
	Mat dst1 , dst2;
	double thresholds = 0.0;
	dst1 = h.getHistogram(image);
	normalize( dst1, dst1, 1.0);
	thresholds = h.lookMaxpixel(dst1);

	threshold(image, dst2, thresholds*255, 255, THRESH_BINARY);

	imshow("dst",dst2);
	waitKey(0);
	


	return 0;
}

效果如下：