自定义直方图

2020-12-20

在实际的应用场景中，我们可以根据需求调整计算的直方图区间。针对灰度直方图，我们可以将区间[0,255]分成不同的区间范围，然后分别统计各个区间的灰度分布，进而实现自定义的直方图：

代码示例如下：

#include <iostream>
#include <opencv2/opencv.hpp>

int main(int argc,char* argv[])
{

    cv::Mat srcImage = cv::imread("E:/CodeBlocks/data/lena.jpg");
    if (!srcImage.data)
    {
        std::cerr << "fail to load image" << std::endl;
        return -1;
    }
    //转换为灰度图像
    cv::Mat grayImage;
    cv::cvtColor(srcImage,grayImage,cv::COLOR_BGR2GRAY);
    //设置计算直方图的参数
    const int channles[1] = {0};
    const int histSize[1] = {256};
    float range[] = {0,40,80,120,160,200,220,255};
    const float *ranges[1] = {range};
    cv::MatND hist;
    cv::calcHist(&grayImage,1,channles,cv::Mat(),hist,1,histSize,ranges,true,false);
    //求直方图中得最大值
    double maxVal = 0.0;
    cv::minMaxLoc(hist,0,&maxVal,0,0);
    //设置直方图绘图参数
    int Hist_size = hist.rows;
    cv::Mat histImage(Hist_size,Hist_size,CV_8U,cv::Scalar(255));
    for(int i=0; i< Hist_size;i++)
    {
        float binVal = hist.at<float>(i);
        //归一化
        int intensity = static_cast<int>(binVal * Hist_size / maxVal);
        //绘制直方图信息
        cv::line(histImage,cv::Point(i,Hist_size),cv::Point(i,Hist_size-intensity),cv::Scalar::all(0),2,8,0);
    }
    cv::imshow("srcImage",srcImage);
    cv::imshow("gray",grayImage);
    cv::imshow("hist",histImage);
    cv::waitKey(0);
    cv::destroyAllWindows();
    return 0;
}