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图像分割-最大熵阈值分割

程序员文章站 2024-02-11 12:41:40
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转自:https://blog.csdn.net/spw_1201/article/details/53510711

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#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <iostream>
using namespace cv;
using namespace std;
float caculateCurrentEntropy(Mat hist, int threshold)
{
	float BackgroundSum = 0, targetSum = 0;
	const float* pDataHist = (float*)hist.ptr<float>(0);
	for (int i = 0; i < 256; i++)
	{
		//累计背景值
		if (i < threshold)
		{
			BackgroundSum += pDataHist[i];
		}
		//累计目标值
		else
		{
			targetSum += pDataHist[i];
		}
	}
	cout << BackgroundSum << "\t" << targetSum << endl;
	float BackgroundEntropy = 0, targetEntropy = 0;
	for (int i = 0; i < 256; i++)          
	{
		//计算背景熵
		if (i < threshold)
		{
			if (pDataHist[i] == 0)
				continue;
			float ratio1 = pDataHist[i] / BackgroundSum;
			//计算当前能量熵
			BackgroundEntropy += -ratio1*logf(ratio1);
		}
		else  //计算目标熵
		{
			if (pDataHist[i] == 0)
				continue;
			float ratio2 = pDataHist[i] / targetSum;
			targetEntropy += -ratio2*logf(ratio2);
		}
	}
	return (targetEntropy + BackgroundEntropy);
}
//寻找最大熵阈值并分割
Mat maxEntropySegMentation(Mat inputImage)
{
	const int channels[1] = { 0 };
	const int histSize[1] = { 256 };
	float pranges[2] = { 0,256 };
	const float* ranges[1] = { pranges };
	MatND hist;
	calcHist(&inputImage, 1, channels, Mat(), hist, 1, histSize, ranges);
	float maxentropy = 0;
	int max_index = 0;
	Mat result;
	for (int i = 0; i < 256; i++)
	{
		float cur_entropy = caculateCurrentEntropy(hist, i);
		if (cur_entropy > maxentropy)
		{
			maxentropy = cur_entropy;
			max_index = i;
		}
	}
	threshold(inputImage, result, max_index, 255, CV_THRESH_BINARY);
	return result;
}
int main()
{
	Mat srcImage = imread("D:\\1.jpg");
	if (!srcImage.data)
		return -1;
	Mat grayImage;
	cvtColor(srcImage, grayImage, CV_BGR2GRAY);
	Mat result = maxEntropySegMentation(grayImage);
	imshow("grayImage", grayImage);
	imshow("result", result);
	waitKey(0);
        return 0;
}
图像分割-最大熵阈值分割
图像分割-最大熵阈值分割