opencv学习之路（19）、直方图

一、概述

 二、一维灰度直方图

#include "opencv2/opencv.hpp"
#include<iostream>
using namespace cv;
using namespace std;

void main()
{
    Mat img = imread("E://0.jpg", 0);  //以灰度方式打开，灰度图和彩色图直方图结果不同
    imshow("src", img);

    Mat dstHist;  //定义存储直方图变量
    int dims = 1;  //需要统计的特征数目(只统计灰度值)
    float hranges[] = {0, 256};  //范围[0,256),注意是最大值加1
    const float* ranges[] = {hranges};
    int bins = 256;//组距：简单理解为直方图分为几个柱子
    int channels = 0;
    //计算直方图
    calcHist(&img, 1, &channels, Mat(), dstHist, dims, &bins, ranges);

    int scale = 1;
    Mat dstImg(bins * scale, bins*1, CV_8UC3, Scalar(0));  //定义直方图输出图像,长×宽
    double minValue = 0;
    double maxValue = 0;
    minMaxLoc(dstHist, &minValue, &maxValue);//统计直方图dstHist的最小最大值,并返回该值位置
    int hpt = saturate_cast<int>(0.9*bins);  //设置最大值并防止溢出
    int j=0;
    //绘制直方图
    for(int i=0; i<256; i++)
    {
        float binValue = dstHist.at<float>(i);
        //if(i>250)
            cout<<"i="<<i<<"---Value="<<binValue<<endl;
        int realValue = saturate_cast<int>(binValue*hpt/maxValue);  //归一化数据
            //cout<<"i="<<i<<"---Value="<<realValue<<endl;
        line(dstImg, Point(i*scale, bins-1), Point(i*scale, bins-realValue), Scalar(0, 255, 0), 1, 8);//左上角和右下角坐标，坐标系如下图所示
        //rectangle(dstImg, Point(i*scale, bins-1), Point(i*scale+1, bins-realValue), Scalar(0, 255, 0), 1);
        //rectangle(dstImg, Point(j*scale, bins-1), Point(j*scale+1, bins-realValue), Scalar(rand()%255, rand()%255, rand()%255), -1);
        //rectangle(dstImg, Point(j*scale, bins-1), Point(j*scale+1, bins-realValue), Scalar(rand()%255, rand()%255, rand()%255), -1);
        //j=j+3;
    }
    imshow("Histogram", dstImg);
    waitKey(0);
    destroyAllWindows();
}

第35行代码的坐标为Point(i*scale, bins-1), Point(i*scale, bins-realValue)的原因。

如果觉得直方图太密集了，可以调整宽度

 三、不均匀灰度直方图

#include "opencv2/opencv.hpp"
#include<iostream>
using namespace cv;
using namespace std;

void main()
{
    Mat img = imread("E://0.jpg",0); 
    imshow("src", img);

    Mat dstHist;  
    int channels = 0; 
    int histSize[1]={5};
    float hranges[6]={0,50,100,150,200,256};
    const float* ranges[1]={hranges};
    int size=256;
    //计算直方图
    calcHist(&img,1,&channels,Mat(),dstHist,1,histSize,ranges,false);

    int scale=1;
    Mat dstImg(size*scale,size,CV_8UC3,Scalar(0));
    double minValue=0;
    double maxValue=0;
    minMaxLoc(dstHist,&minValue,&maxValue,0,0);
    int hpt=saturate_cast<int>(0.9*size);//设置最大值并防止溢出
    int j=0;
    for (int i = 0; i < 5; i++)
    {
        float binValue=dstHist.at<float>(i);
        cout<<"i="<<i<<"---Value="<<binValue<<endl;
        int realValue=saturate_cast<int>(binValue*hpt/maxValue);
        rectangle(dstImg,Point(j*scale,size-1),Point((j+20)*scale-1,size-realValue),Scalar(0, 255, 0), -1);
        j+=35;
    }

    imshow("Histogram", dstImg);
    waitKey(0);
    destroyAllWindows();

}

 四、RGB三色直方图

#include "opencv2/opencv.hpp"
using namespace cv;

void main()
{
    Mat src=imread("E://0.jpg");
    int bins = 256;
    int hist_size[] = {bins};
    float range[] = { 0, 256 };
    const float* ranges[] = { range};
    Mat hist_r,hist_g,hist_b;

    int channels_r[] = {2};
    calcHist( &src, 1, channels_r, Mat(), // do not use mask
        hist_r, 1, hist_size, ranges,
        true, // the histogram is uniform
        false );

    int channels_g[] = {1};
    calcHist( &src, 1, channels_g, Mat(),hist_g, 1, hist_size, ranges,true,false);
    int channels_b[] = {0};
    calcHist( &src, 1, channels_b, Mat(),hist_b, 1, hist_size, ranges,true,false);

    double max_val_r,max_val_g,max_val_b;
    minMaxLoc(hist_r, 0, &max_val_r, 0, 0);
    minMaxLoc(hist_g, 0, &max_val_g, 0, 0);
    minMaxLoc(hist_b, 0, &max_val_b, 0, 0);
    int scale = 1;
    int hist_height=256;
    Mat hist_img = Mat::zeros(hist_height, bins*3+5, CV_8UC3);
    for(int i=0;i<bins;i++)
    {
        float bin_val_r = hist_r.at<float>(i); 
        float bin_val_g = hist_g.at<float>(i);
        float bin_val_b = hist_b.at<float>(i);
        int intensity_r = cvRound(bin_val_r*hist_height/max_val_r);  //要绘制的高度
        int intensity_g = cvRound(bin_val_g*hist_height/max_val_g);  //要绘制的高度
        int intensity_b = cvRound(bin_val_b*hist_height/max_val_b);  //要绘制的高度
        rectangle(hist_img,Point(i*scale,hist_height-1),
            Point((i+1)*scale - 1, hist_height - intensity_r),
            CV_RGB(255,0,0));

        rectangle(hist_img,Point((i+bins)*scale,hist_height-1),
            Point((i+bins+1)*scale - 1, hist_height - intensity_g),
            CV_RGB(0,255,0));

        rectangle(hist_img,Point((i+bins*2)*scale,hist_height-1),
            Point((i+bins*2+1)*scale - 1, hist_height - intensity_b),
            CV_RGB(0,0,255));

    }
    imshow( "src", src );
    imshow( "RGB Histogram", hist_img );

    waitKey(0);
    destroyAllWindows();

}

五、二维直方图

#include "opencv2/opencv.hpp"
using namespace cv;

void main()
{
    Mat src=imread("E://0.jpg");
    Mat hsv;
    int Bbins = 256, Gbins = 256;
    int histSize[] = {Bbins, Gbins};
    float hranges[] = { 0, 256 };
    float sranges[] = { 0, 256 };
    const float* ranges[] = { hranges, sranges };
    Mat hist;

    int channels[] = {0, 1};
    calcHist( &src, 1, channels, Mat(), hist, 2, histSize, ranges);
    double maxVal=0;
    minMaxLoc(hist, 0, &maxVal, 0, 0);
    int scale = 2;
    Mat histImg = Mat::zeros(Bbins*(scale), Bbins*(scale), CV_8UC3);
    for( int h = 0; h < Bbins; h++ )
        for( int s = 0; s < Bbins; s++ )
        {
            float binVal = hist.at<float>(h, s);
            //cout<<"h="<<h<<"s="<<s<<"binValue="<<binVal<<endl;
            //int intensity = cvRound(binVal*255/maxVal);
            int intensity = int(binVal*255/maxVal);
            rectangle( histImg, Point(h*scale, s*scale),
                Point((h+1)*scale - 1, (s+1)*scale - 1),
                Scalar::all(intensity),CV_FILLED );
        }
    namedWindow( "Source", 1 );
    imshow( "Source", src );
    namedWindow( "B-G Histogram", 1 );
    imshow( "B-G Histogram", histImg );

    waitKey(0);
    destroyAllWindows();

}