【C++】如何利用直方图实现快速中值滤波

1、参考博客：https://www.cnblogs.com/riddick/p/7989871.html

2、但是博客代码的i和j的下标表示有些问题

3、修改code-1之后，可以成功运行的代码

#include <opencv2opencv.hpp>
#include <iostream>
#include <string>

using namespace cv;
using namespace std;

//计算亮度中值和灰度<=中值的像素点个数
int calMediaValue(const int histogram[], int thresh)
{
    int sum = 0;
    for (int i = 0; i < 256; ++i)
    {
        sum += histogram[i];
        if (sum >= thresh)
        {
            return i;
        }
    }
    return 255;
}

void fastMedianBlur(const Mat &srcImg, Mat &dstImg, int diameter)
{
    int radius = (diameter - 1) / 2;
    int imgW = srcImg.cols;   // 1920    宽
    int imgH = srcImg.rows;   // 1200    高
    int channels = srcImg.channels();  // 1
    dstImg = Mat::zeros(imgH, imgW, CV_8UC1);
    int windowSize = diameter*diameter;  // 25

    //直方图
    int Hist[256] = { 0 };
    int histogramSize = 256;//灰度等级
    int thresholdValue = windowSize / 2 + 1;   // 13

    uchar *pSrcData = srcImg.data;
    uchar *pDstData = dstImg.data;

    int right = imgW - radius;   // 1918
    int bot = imgH - radius;     // 1198
    for (int j = radius; j < bot; j++)   // 行  
    {
        
        for (int i = radius; i < right; i++)   //列
        {
            //每一行第一个待滤波像素建立直方图
            if (i == radius)
            {
                memset(Hist, 0, histogramSize*sizeof(int));
                for (int y = j - radius; y <= min(j + radius, imgH); y++)
                {
                    for (int x = i - radius; x <= min(i + radius,imgW); x++)
                    {
                        uchar value = pSrcData[y*imgW + x];
                        Hist[value]++;
                    }
                }
            }
            else//更新直方图
            {
                int left = i - radius - 1;
                int right = i + radius;
                for (int y = j - radius; y <= min(j + radius, imgH); y++)
                {
                    //减去左边一列
                    int leftIdx = y*imgW + left;
                    uchar leftValue = pSrcData[leftIdx];
                    Hist[leftValue]--;

                    //加上右边一列
                    int rightIdx = y*imgW + right;
                    uchar rightValue = pSrcData[rightIdx];
                    Hist[rightValue]++;
                }
            }

            //直方图求中值
            uchar filterValue = calMediaValue(Hist, thresholdValue);
            pDstData[j*imgW + i] = filterValue;
        }
    }

    //边界直接赋原始值，不做滤波处理
    pSrcData = srcImg.data;
    pDstData = dstImg.data;
    //上下边界
    for (int i = 0; i < imgW; i++)
    {
        for (int j = 0; j < radius; j++)
        {
            int idxTop = j*imgW + i;
            pDstData[idxTop] = pSrcData[idxTop];
            int idxBot = (imgH - j - 1)*imgW + i;
            pDstData[idxBot] = pSrcData[idxBot];
        }
    }
    //左右边界
    for (int j = radius; j < imgH - radius - 1; j++)
    {
        for (int i = 0; i < radius; i++)
        {
            int idxLeft = j*imgW + i;
            pDstData[idxLeft] = pSrcData[idxLeft];
            int idxRight = j*imgW + imgW - i - 1;
            pDstData[idxRight] = pSrcData[idxRight];
        }
    }
}


int main()
{
    string imgPath = "D:\Images\漂亮的图片\";
    Mat srcImg = imread(imgPath + "2.jpg", 0);
    Mat dstImg;

    double t0 = cv::getTickCount();
    fastMedianBlur(srcImg, dstImg, 5);
    double t1 = cv::getTickCount();

    cout << "time=" << (t1 - t0) / cv::getTickFrequency() << "  seconds" << endl;

    imwrite("srcImg.jpg", srcImg);
    imwrite("myFilter.jpg", dstImg);

    return 0;
}

结果示例，中值滤波后的图片