opencv2 直方图之calchist函数使用（转）

OpenCV提供了calcHist函数来计算图像直方图。

其中C++的函数原型如下：void calcHist(const Mat* arrays, int narrays, const int* channels, InputArray mask, OutputArray
hist, int dims, const int* histSize, const float** ranges, bool uniform=true, bool accumulate=
false )；

void calcHist(const Mat* arrays, int narrays, const int* channels, InputArray mask, SparseMat&
hist, int dims, const int* histSize, const float** ranges, bool uniform=true, bool accumulate=
false )；

参数解释：

arrays。输入的图像的指针，可以是多幅图像，所有的图像必须有同样的深度（CV_8U or CV_32F）。同时一副图像可以有多个channes。

narrays。输入的图像的个数。

channels。用来计算直方图的channes的数组。比如输入是2副图像，第一副图像有0，1，2共三个channel，第二幅图像只有0一个channel，

那么输入就一共有4个channes，如果int channels[3] = {3, 2, 0}，那么就表示是使用第二副图像的第一个通道和第一副图像的第2和第0个通道来计

算直方图。

mask。掩码。如果mask不为空，那么它必须是一个8位（CV_8U）的数组，并且它的大小的和arrays[i]的大小相同，值为1的点将用来计算

直方图。

hist。计算出来的直方图

dims。计算出来的直方图的维数。

histSize。在每一维上直方图的个数。简单把直方图看作一个一个的竖条的话，就是每一维上竖条的个数。

ranges。用来进行统计的范围。比如

float rang1[] = {0, 20};

float rang2[] = {30, 40};

const float *rangs[] = {rang1, rang2};那么就是对0，20和30，40范围的值进行统计。

uniform。每一个竖条的宽度是否相等。

accumulate。Accumulation flag. If it is set, the histogram is not cleared in the beginning
when it is allocated. This feature enables you to compute a single histogram from several
sets of arrays, or to update the histogram in time.  是否累加。如果为true，在下次计算的时候不会首先清空hist。这个地方我是这样理解的，不知道有没有错，

请高手指点。

 Histogram1D::Histogram1D(){
     histSize[] = ;
     hranges[] = 0.0;
     hranges[] = 255.0;
     ranges[] = hranges;
     channels[] = ;
 }  

 cv::MatND Histogram1D::getHistogram(const cv::Mat &image){
     cv::MatND hist;
     cv::calcHist(&image,   //source image
              ,        //histogram from 1 image only
              channels, //the channel used
              cv::Mat(),//no mask is uesd
              hist,     //the resulting histogram
              ,        //it is a 1D histogram
              histSize, //number of bins
              ranges    //pixel value range
                 );//直方图函数
     return hist;
 }  

下面是计算1维图像的直方图：

cv::Mat Histogram1D::getHistogramImage(const cv::Mat &image){
    //compute histogram first
    cv::MatND hist = getHistogram(image);
    //get min and max bin values
    double maxVal = ;
    double minVal = ;
    cv::minMaxLoc(hist,&minVal,&maxVal,,);
    //Image on which to display histogram
    cv::Mat histImg(histSize[],histSize[],CV_8U,cv::Scalar());
    //set highest point at 90% of nbins
    int hpt = static_cast<int>(0.9*histSize[]);
    //Draw a vertical line for each bin
    for (int h =;h<histSize[];h++)
{
        float binVal = hist.at<float>(h);
        int intensity = static_cast<int>(binVal*hpt/maxVal);
        cv::line(histImg,cv::Point(h,histSize[]),cv::Point(h,histSize[]-intensity),cv::Scalar::all());
    }
    return histImg;
}  

计算H-S直方图分布：

/*********************************************
             内容：计算H-S 直方图分布
             时间：2013 5.27
         作者：恋上蛋炒面
*********************************************/
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
using namespace cv;  

void main()
{
    Mat source = imread("baboon.jpg");
    namedWindow("Source");
    imshow("Source",source);
    Mat hsv;
    cvtColor(source,hsv,CV_BGR2HSV);
    //Quantize the hue to 60 levels
    //and the saturation to 64 levels
    int hbins = ,sbins = ;
    int histSize[] = {hbins,sbins};
   int histSize[] = {hbins,sbins};
    //hue varies from 0 to 179
    float hranges[] = {,};
    //saturation varies from 0 to 255
    float sranges[] = {,};
    const float *ranges[] = {hranges,sranges};
    //two channels 0th,1th
    int channels[] = {,};
    MatND hist;
    //compute h-s histogram
    calcHist(&hsv,,channels,Mat(),hist,,histSize,ranges);
    //get the max value
    double maxVal = .;
    minMaxLoc(hist,,&maxVal,,);
    int scale = ;
    //show the histogram on the image
    Mat histImg = Mat::zeros(sbins*scale,hbins*scale,CV_8UC3);
    for (int h = ;h < hbins;h++)
{
        for (int s = ;s<sbins;s++)
        {
            float binVal = hist.at<float>(h,s);
            int intensity = cvRound(binVal*0.9*/maxVal);
            rectangle(histImg,Point(h*scale,s*scale),Point((h+)*scale-,(s+)*scale-),Scalar::all(intensity),CV_FILLED);
        }
    }  

    namedWindow("H-S Histogram");
    imshow("H-S Histogram",histImg);
    imwrite("hshistogram.jpg",histImg);
    waitKey();
}  

RGB直方图：

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

 #include <fstream>  

 using namespace cv;
 using namespace std;  

 void main()
 {
     //Mat source = imread("red.jpg");
     Mat source = imread("baboon.jpg"); //读取图片
     //Mat source(300,300,CV_8UC3,Scalar(1,1,244));
     //imwrite("red.jpg",source);
     namedWindow("Source");//窗口显示图片
     imshow("Source",source);
     //初始化calcHist函数的参数
     int channels_r[],channels_g[],channels_b[],histSize[],range;
     float hranges[];
     const float *ranges[];
     histSize[] = ;
     hranges[] = 0.0;
     hranges[] = 255.0;
     ranges[] = hranges;
     channels_b[] = ;
     channels_g[] = ;
     channels_r[] = ;
     MatND hist_r,hist_g,hist_b;  

     double max_val_r,max_val_g,max_val_b;
     Mat histImage(histSize[],*histSize[],CV_8UC3); //定义一个显示直方图的图片，长256*3 高256
     //R
     calcHist(&source,,channels_r,Mat(),hist_r,,histSize,ranges);//分别计算R，G，B的直方图分布
     minMaxLoc(hist_r,,&max_val_r,,);//计算直方图中统计最大值
     //G
     calcHist(&source,,channels_g,Mat(),hist_g,,histSize,ranges);
     minMaxLoc(hist_g,,&max_val_g,,);
     //B
     calcHist(&source,,channels_b,Mat(),hist_b,,histSize,ranges);
     minMaxLoc(hist_b,,&max_val_b,,);  

     //将r，g，b的最大统计值，以及像素点从0-255的统计值写入txt中
     ofstream outfile1("d:\\r.txt");
     ofstream outfile2("d:\\g.txt");
     ofstream outfile3("d:\\b.txt");  

     //在txt中写入最大统计值
     outfile1<<"max_val_r = "<<max_val_r<<endl;
     outfile2<<"max_val_g = "<<max_val_g<<endl;
     outfile3<<"max_val_b = "<<max_val_b<<endl;  

     for (int i =;i<histSize[];i++)
     {
         //R，G，B= i的统计值
         float binVal_r = hist_r.at<float>(i);
         float binVal_g = hist_g.at<float>(i);
         float binVal_b = hist_b.at<float>(i);
         //统一R，G，B统计值的大小，以高度的90%封顶
         int intensity_r = static_cast<int>(0.9*histSize[]*binVal_r/max_val_r);
         outfile1<<i<<" "<<binVal_r<<" "<<intensity_r<<endl;
         int intensity_g = static_cast<int>(0.9*histSize[]*binVal_g/max_val_g);
         outfile2<<i<<" "<<binVal_g<<" "<<intensity_g<<endl;
         int intensity_b = static_cast<int>(0.9*histSize[]*binVal_b/max_val_b);
         outfile3<<i<<" "<<binVal_b<<" "<<intensity_b<<endl;
         //画出R，G，B的直方图直线
         line(histImage,Point(i,histImage.rows),Point(i,histImage.rows-intensity_r),Scalar(,,));
         line(histImage,Point(i+histSize[],histImage.rows),Point(i+histSize[],histImage.rows-intensity_g),Scalar(,,));
         line(histImage,Point(i+histSize[]*,histImage.rows),Point(i+histSize[]*,histImage.rows-intensity_b),Scalar(,,));
     }
     namedWindow("RGB Histogram");
     imshow("RGB Histogram",histImage);
  imwrite("RGB_Histogram.jpg",histImage);
     waitKey();
 }