【OpenCV】笔记（6）——直方图

直方图（histogram）:当图像被定义为一种数据类型，并且能够访问该图像的灰度值（像素），从而得到不同灰度的概率密度函数，这种图像就称作直方图

对直方图进行改变，从而改变图像的对比度，这种处理叫做直方图的均衡化（hisotgram equalization）

openCV中使用calcHist来计算图像直方图

voidcalcHist( const Mat*images,int nimages ,

                         const int * channels, InputArray mask,

                         OutputArray hist , int dims,const int* histSize,

                         const float ** ranges, bool uniform= true, bool accumulate= false );

参数定义解释

constMat*images

@param images Source arrays. They all should have the same depth, CV_8U or CV_32F , and the same

size. Each of them can have an arbitrary number of channels.

集合中第一个图像的地址，用来处理一批图像

intnimages

@param nimages Number of source images.原图像的数量

constint * channels

@param channels List of the dims channels used to compute the histogram. The first array channels

are numerated from 0 to images[0].channels()-1 , the second array channels are counted from

images[0].channels() to images[0].channels() + images[1].channels()-1, and so on.

直方图的通道列表，从0~2

InputArraymask

@param mask Optional mask. If the matrix is not empty, it must be an 8-bit array of the same size

as images[i] . The non-zero mask elements mark the array elements counted in the histogram.

可选项mask，用来指示图像中像素的个数

OutputArrayhist

@param hist Output histogram, which is a dense or sparse dims -dimensional array.

输出直方图

intdims

@param dims Histogram dimensionality that must be positive and not greater than CV_MAX_DIMS

(equal to 32 in the current OpenCV version).

指示直方图的维数

constint*histSize

@param histSize Array of histogram sizes in each dimension.每一个维度上直方图的大小的数组

constfloat ** ranges

@param ranges Array of the dims arrays of the histogram bin boundaries in each dimension. When the

histogram is uniform ( uniform =true), then for each dimension i it is enough to specify the lower

(inclusive) boundary \f$L_0\f$ of the 0-th histogram bin and the upper (exclusive) boundary

\f$U_{\texttt{histSize}[i]-1}\f$ for the last histogram bin histSize[i]-1 . That is, in case of a

uniform histogram each of ranges[i] is an array of 2 elements. When the histogram is not uniform (

uniform=false ), then each of ranges[i] contains histSize[i]+1 elements:

\f$L_0, U_0=L_1, U_1=L_2, ..., U_{\texttt{histSize[i]}-2}=L_{\texttt{histSize[i]}-1}, U_{\texttt{histSize[i]}-1}\f$

. The array elements, that are not between \f$L_0\f$ and \f$U_{\texttt{histSize[i]}-1}\f$ , are not

counted in the histogram.

每一维度上直方图bin边界维度数组的数组

booluniform = true

@param uniform Flag indicating whether the histogram is uniform or not (see above).默认为真，表示直方图是均匀分布的

boolaccumulate = false

@param 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.

默认为假，表示直方图默认是不累加的

直方图均衡化

voidequalizeHist( InputArray src,OutputArray dst );

第一个参数是输入图像，第二个输出直方图均衡化后的图像

若要计算多幅图像的直方图，来比较或者计算出这些图像的联合直方图，可以使用

doublecompareHist( InputArray H1,InputArray H2, int method);

method用来计算两个直方图的匹配情况，opencv中提供了6种方法

HistCompMethods{

   /** Correlation

   /** Chi-Square

   /** Intersection

   /** Bhattacharyya distance

   /** Alternative Chi-Square

   /** Kullback-Leibler divergence

};

示例1：使用equalizeHist(InputArray src, OutputArray dst)对一幅彩色图形进行均衡化并且显示每个通道的直方图

其也可以用来计算同一幅彩色图像不同通道的直方图

#include<iostream>#include <stdio.h>#include "opencv2/highgui/highgui.hpp"#include "opencv2/imgproc/imgproc.hpp"#define HIST_SIZE 255#define HIST_W 512#define HIST_H 400using namespace std;using namespace cv;//计算RGB图像的颜色通道直方图bool histogramcalculation(const Mat &Image,Mat &histImage);int main(){Mat src, imageq;Mat histImage;//读取原始图像src = imread("dog.png");if (!src.data){printf("Error! Can not load image!\n");exit(1);}//将图像分为三个部分B,G,Rvector<Mat> bgr_planes;split(src, bgr_planes);//显示结果imshow("源图像", src);//计算原始图像的每个通道的直方图histogramcalculation(src, histImage);//显示每个颜色通道的直方图imshow("彩色图像的直方图", histImage);//均衡化图像//直方图均衡化应用于每个通道equalizeHist(bgr_planes[0], bgr_planes[0]);equalizeHist(bgr_planes[1], bgr_planes[1]);equalizeHist(bgr_planes[2], bgr_planes[2]);//将这些均衡化的图像合并得到其均衡化图像merge(bgr_planes, imageq);//显示均衡化图像imshow("均衡化图像", imageq);//计算每个均衡化图像通道的直方图histogramcalculation(imageq, histImage);//显示均衡化图像的直方图imshow("均衡化图像的直方图", histImage);waitKey();    return 0;}bool histogramcalculation(const Mat &Image, Mat &histoImage){bool bRtr = false;do{int histSize = HIST_SIZE;//对（B,G,R）设置范围float range[] = { 0,256 };const float* histRange = { range };bool uniform = true;bool accumulate = false;Mat b_hist, g_hist, r_hist;vector<Mat> bgr_planes;split(Image, bgr_planes);//计算各个直方图calcHist(&bgr_planes[0], 1, 0, Mat(), b_hist, 1, &histSize, &histRange, uniform, accumulate);calcHist(&bgr_planes[1], 1, 0, Mat(), g_hist, 1, &histSize, &histRange, uniform, accumulate);calcHist(&bgr_planes[2], 1, 0, Mat(), r_hist, 1, &histSize, &histRange, uniform, accumulate);//为B,G,R绘制直方图int hist_w = HIST_W;int hist_h = HIST_H;int bin_w = cvRound((double)hist_w / histSize);Mat histImage(hist_h, hist_w, CV_8UC3, Scalar(0, 0, 0));//将结果归一化为[ 0, histImage.rows ]normalize(b_hist, b_hist, 0, histImage.rows, NORM_MINMAX, -1, Mat());normalize(g_hist, g_hist, 0, histImage.rows, NORM_MINMAX, -1, Mat());normalize(r_hist, r_hist, 0, histImage.rows, NORM_MINMAX, -1, Mat());//对每个通道进行绘制for (int i = 1; i < histSize; i++){line(histImage, Point(bin_w*(i - 1), hist_h - cvRound(b_hist.at<float>(i - 1))), Point(bin_w*(i), hist_h - cvRound(b_hist.at<float>(i))), Scalar(255, 0, 0), 8, 0);//得到每一个举行条的左上方和右下方的点的坐标，从而绘制直方图line(histImage, Point(bin_w*(i - 1), hist_h - cvRound(g_hist.at<float>(i - 1))), Point(bin_w*(i), hist_h - cvRound(g_hist.at<float>(i))), Scalar(255, 0, 0), 8, 0);line(histImage, Point(bin_w*(i - 1), hist_h - cvRound(r_hist.at<float>(i - 1))), Point(bin_w*(i), hist_h - cvRound(r_hist.at<float>(i))), Scalar(255, 0, 0), 8, 0);}histoImage = histImage;bRtr = true;} while (false);return bRtr;}