OpenCV手工实现灰度及RGB直方图

手工实现灰度及RGB直方图 ！库

1. 灰度图像直方图

算法

1. 图片灰度化；

2. 遍历Mat，统计各灰度级的像素个数；

3. 根据opencv画点线函数，绘制坐标轴及像素分布图

源码（编译环境：VS2017+OpenCV） 补充：三通道直方图（即RGB彩色图象直方图在后面）

 #include <iostream>
 #include <string>
 #include <algorithm>
 #include <opencv2/opencv.hpp>
 #include <opencv2/imgproc/imgproc.hpp>
 #include <opencv2/core/core.hpp>
 #include <opencv2/highgui/highgui.hpp>
 using namespace cv;
 using namespace std;
 //直方图绘制函数，参数vector<int> nums 是灰度图片256级灰度的像素个数
 void drawHist(vector<int> nums)
 {
     Mat hist = Mat::zeros(, , CV_8UC3);
     auto Max = max_element(nums.begin(), nums.end());//max迭代器类型,最大数目
     putText(hist, "Histogram", Point(, ), FONT_HERSHEY_DUPLEX, , Scalar(, , ));
     //*********绘制坐标系************//
     Point o = Point(, );
     Point x = Point(, );
     Point y = Point(, );
     //x轴
     line(hist, o, x, Scalar(, , ), , , );
     //y轴
     line(hist, o, y, Scalar(, , ), , , );

     //********绘制灰度曲线***********//
     Point pts[];
     //生成坐标点
     for (int i = ; i < ; i++)
     {
         pts[i].x = i *  + ;
         pts[i].y =  - int(nums[i]*(300.0/(*Max)));//归一化到[0, 300]
         //显示横坐标
         if ((i + ) %  == )
         {
             string num = format("%d", i + );
             putText(hist, num, Point(pts[i].x, ), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(, , ));
         }
     }
     //绘制线
     for (int i = ; i < ; i++)
     {
         line(hist, pts[i - ], pts[i], Scalar(, , ), );
     }
     //显示图像
     imshow("直方图" ,hist);
 }
 //计算直方图，统计各灰度级像素个数
 void calHist(const string img)
 {
     Mat src, grey;
     //读取图象
     src = imread(img);
     if (!src.data)
     {
         cout << "Image： " + img + " 读取失败" << endl;
         return;
     }
     //先转为灰度图
     cvtColor(src, grey, COLOR_BGR2GRAY);
     imshow("灰度图", grey);
     //计算各灰度级像素个数
     vector<int> nums();
     for (int i = ; i < grey.rows; i++)
     {
         uchar* p = grey.ptr<uchar>(i);
         for (int j = ; j < grey.cols; j++)
         {
             nums[p[j]]++;
         }
     }
     drawHist(nums);
 }

 int main()
 {
     string img = "D:\\trashBox\\testIMG\\lena.bmp";
     calHist(img);

     waitKey();
     return ;
 }

效果图

直方图hist

2. RGB彩色图象直方图

源码

#include <iostream>
#include <stdlib.h>
#include <string>
#include <algorithm>
#include <opencv2/opencv.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/video.hpp>
using namespace cv;
using namespace std;

//单通道图片直方图绘制
void drawHist(vector<int> nums)
{
    Mat hist = Mat::zeros(, , CV_8UC3);
    auto Max = max_element(nums.begin(), nums.end());//max迭代器类型,最大数目
    putText(hist, "Histogram", Point(, ), FONT_HERSHEY_DUPLEX, , Scalar(, , ));
    //*********绘制坐标系************//
    Point o = Point(, );
    Point x = Point(, );
    Point y = Point(, );
    //x轴
    line(hist, o, x, Scalar(, , ), , , );
    //y轴
    line(hist, o, y, Scalar(, , ), , , );

    //********绘制灰度曲线***********//
    Point pts[];
    //生成坐标点
    for (int i = ; i < ; i++)
    {
        pts[i].x = i *  + ;
        pts[i].y =  - int(nums[i]*(300.0/(*Max)));//归一化到[0, 300]
        //显示横坐标
        if ((i + ) %  == )
        {
            string num = format("%d", i + );
            putText(hist, num, Point(pts[i].x, ), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(, , ));
        }
    }
    //绘制线
    for (int i = ; i < ; i++)
    {
        line(hist, pts[i - ], pts[i], Scalar(, , ), );
    }
    //显示图像
    imshow("直方图" ,hist);
}

//三通道图片直方图绘制
void drawHist(vector<int> &r, vector<int> &g, vector<int> &b)
{
    Mat hist = Mat::zeros(, , CV_8UC3);
    putText(hist, "Histogram", Point(, ), FONT_HERSHEY_DUPLEX, , Scalar(, , ));
    //*********绘制坐标系************//
    Point o = Point(, );
    Point x = Point(, );
    Point y = Point(, );
    //x轴
    line(hist, o, x, Scalar(, , ), , , );
    //y轴
    line(hist, o, y, Scalar(, , ), , , );

    //********绘制灰度曲线***********//
    auto Max_r = max_element(r.begin(), r.end());
    auto Max_g = max_element(g.begin(), g.end());
    auto Max_b = max_element(b.begin(), b.end());
    Point pts[][];
    //生成坐标点
    for (int i = ; i < ; i++)
    {
        pts[][i].x = i *  + ;
        pts[][i].y =  - int(r[i] * (300.0 / (*Max_r)));//归一化到[0, 300]
        pts[][i].x = i *  + ;
        pts[][i].y =  - int(g[i] * (300.0 / (*Max_g)));//归一化到[0, 300]
        pts[][i].x = i *  + ;
        pts[][i].y =  - int(b[i] * (300.0 / (*Max_b)));//归一化到[0, 300]
        //显示横坐标
        if ((i + ) %  == )
        {
            string num = format("%d", i + );
            putText(hist, num, Point(pts[][i].x, ), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(, , ));
        }
    }
    //绘制线
    for (int i = ; i < ; i++)
    {
        line(hist, pts[][i - ], pts[][i], Scalar(, , ), );
        line(hist, pts[][i - ], pts[][i], Scalar(, , ), );
        line(hist, pts[][i - ], pts[][i], Scalar(, , ), );
    }
    //显示图像
    imshow("直方图", hist);
}

//灰度直方图计算
void calHist(const string img)
{
    Mat src, grey;
    //读取图象
    src = imread(img);
    if (!src.data)
    {
        cout << "Image： " + img + " 读取失败" << endl;
        return;
    }
    //先转为灰度图
    cvtColor(src, grey, COLOR_BGR2GRAY);
    imshow("灰度图", grey);
    //计算各灰度级像素个数
    vector<int> nums();
    for (int i = ; i < grey.rows; i++)
    {
        uchar* p = grey.ptr<uchar>(i);
        for (int j = ; j < grey.cols; j++)
        {
            nums[p[j]]++;
        }
    }
    drawHist(nums);
}

//多通道直方图计算
void calHist(const string img, int pattern)
{
    Mat src, grey;
    //读取图象
    src = imread(img);
    if (!src.data)
    {
        cout << "Image： " + img + " 读取失败" << endl;
        return;
    }
    imshow("原图像",src);
    //计算各灰度级像素个数
    vector<int> r(, );
    vector<int> g(, );
    vector<int> b(, );
    for (int i = ; i < src.rows; i++)
    {
        uchar* p = src.ptr<uchar>(i);
        for (int j = ; j < src.cols; j++)
        {
            r[p[j *  + ]]++;
            g[p[j *  + ]]++;
            b[p[j *  + ]]++;
        }
    }
    drawHist(r, g, b);
}

int main()
{
    string img = "D:\\trashBox\\testIMG\\tiger.jpg";
    calHist(img);//计算灰度直方图
    calHist(img, );//计算三色直方图

    waitKey();
    return ;
}

算法

1. 遍历Mat，统计RGB三通道各灰度级的像素个数；

2. 根据opencv画点线函数，绘制坐标轴及像素分布图

效果

三色（三通道）直方图