3.6.1最大熵阈值分割寻找阈值实现图像二值化

#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <iostream>

using namespace cv;
using namespace std;

//按给定的threshold，计算hist的背景熵和目标熵的总和
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 << "	" << 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，根据该阈值进行二值化
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:\hand.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;
}

 运行效果：