opencv实现最大熵求解

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#include<opencv2/imgproc/imgproc.hpp>
#include<opencv2/highgui/highgui.hpp>
#include<opencv2/core/core.hpp>
#include<iostream>
using namespace std;
using namespace cv;
//计算给定阈值下的熵
float calCurrentEntropy(Mat& hist, int threshold)
{
	float backgroundSum = 0, targetSum = 0;
	const float* pDataHist = (float*)hist.ptr<float>(0);
	for (int i = 0; i < 256; i++)
	{
		if (i < threshold)
		{
			backgroundSum += pDataHist[i];
		}
		else
			targetSum += pDataHist[i];
	}
	float backgroundEntropy = 0, targetEntropy = 0;
	for (int i = 0; i < 256; i++)
	{
		if (i < threshold)
		{
			if (pDataHist[i] == 0)
				continue;
			float ratio1 = pDataHist[i] / backgroundSum;
			backgroundEntropy += -ratio1*logf(ratio1);
		}
		else
		{
			if (pDataHist[i] == 0)
				continue;
			float ratio2 = pDataHist[i] / targetSum;
			targetEntropy += -ratio2*logf(ratio2);
		}
	}
	return (targetEntropy+backgroundEntropy);
}
//计算最大熵
Mat maxEntropySegMentation(Mat& src)
{
	const int channels[1] = {0};
	const int histSize[1] = {256};
	float pranges[2] = {0,256};
	const float* ranges[1] = {pranges};
	MatND hist;
	calcHist(&src,1,channels,Mat(),hist,1,histSize,ranges);
	float maxEntropy = 0;
	int max_index = 0;
	Mat result;
	for (int i = 0; i < 256; i++)
	{
		//计算当前的熵
		float cur_entropy = calCurrentEntropy(hist,i);
		if (cur_entropy > maxEntropy)
		{
			maxEntropy = cur_entropy;
			max_index = i;
		}
	}
	//二值分割
	threshold(src,result,max_index,255,CV_THRESH_BINARY);
	return result;
}
int main()
{
	Mat src = imread("E:\\研究生\\学习材料\\学习书籍\\OpenCV图像处理编程实例-源码-20160801\\《OpenCV图像处理编程实例-源码-20160801\\images\\flower3.jpg");
	if (!src.data)
		return -1;
	Mat gray;
	cvtColor(src,gray,CV_BGR2GRAY);
	Mat result = maxEntropySegMentation(gray);
	imshow("gray",gray);
	imshow("result",result);
	waitKey(0);

}

结果如下：