opencv2 直方图之calchist函数使用

直方图在图形处理中很常用，直方图可以统计图像的像素特征分布，用于修改图像显示，修改图像内容，通过比较不同图片的直方图可以识别和跟踪特殊纹理的物体和图像，下面先学习怎么计算图像的直方图。

opencv2提供calchist函数可以方便的计算直方图。

calchist函数头文件 #include <opencv2/imgproc/imgproc.hpp>

calchist函数定义：

//! computes the joint dense histogram for a set of images.
CV_EXPORTS void calcHist( 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 );

//! computes the joint sparse histogram for a set of images.
CV_EXPORTS void calcHist( const Mat* images, int nimages,
const int* channels, InputArray mask,
SparseMat& hist, int dims,
const int* histSize, const float** ranges,
bool uniform=true, bool accumulate=false );

CV_EXPORTS_W void calcHist( InputArrayOfArrays images,
const vector<int>& channels,
InputArray mask, OutputArray hist,
const vector<int>& histSize,
const vector<float>& ranges,
bool accumulate=false );

举例说明函数应用：

Histogram1D::Histogram1D(){
histSize[0] = 256;
hranges[0] = 0.0;
hranges[1] = 255.0;
ranges[0] = hranges;
channels[0] = 0;
}

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

函数参数介绍：

const Mat* images //源图像组

int nimages （Number of source arrays） //源图像组图像个数

const int* channels （List of the dims channels used to compute the histogram.） //图像信道

InputArray mask （ Optional mask. If the matrix is not empty, it must be an 8-bit array of the same size as arrays[i]. The non-zero mask elements mark the array elements counted in the histogram.）

//可选的掩码，如果不为空，则必须是8-bit数组，而且大小和原图像相同，非零位置为要计算的直方 图区域

OutputArray hist （Output histogram, which is a dense or sparse dims -dimensional array.）

//输出直方图数组，稠密或者稀疏，dims维的数组

int dims （ Histogram dimensionality that must be positive and not greater than CV_MAX_DIMS）

//处理直方图的维数正数，最大32维，CV_MAX_DIMS是32.

const int* histSize （ Array of histogram sizes in each dimension.）

//每一维的直方图的尺寸大小

const float** 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
of the 0-th histogram bin and the upper(exclusive) boundary 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:. The array elements, that are not between and,are not counted in the histogram.）

//直方图每一维的数据大小范围

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 = 0;
double minVal = 0;
cv::minMaxLoc(hist,&minVal,&maxVal,0,0);
//Image on which to display histogram
cv::Mat histImg(histSize[0],histSize[0],CV_8U,cv::Scalar(255));
//set highest point at 90% of nbins
int hpt = static_cast<int>(0.9*histSize[0]);
//Draw a vertical line for each bin
for (int h =0;h<histSize[0];h++)
{
float binVal = hist.at<float>(h);
int intensity = static_cast<int>(binVal*hpt/maxVal);
cv::line(histImg,cv::Point(h,histSize[0]),cv::Point(h,histSize[0]-intensity),cv::Scalar::all(0));
}
return histImg;
}

源图像：



histogram：



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 = 60,sbins = 64;
int histSize[] = {hbins,sbins};
//hue varies from 0 to 179
float hranges[] = {0,180};
//saturation varies from 0 to 255
float sranges[] = {0,255};
const float *ranges[] = {hranges,sranges};
//two channels 0th,1th
int channels[] = {0,1};
MatND hist;
//compute h-s histogram
calcHist(&hsv,1,channels,Mat(),hist,2,histSize,ranges);
//get the max value
double maxVal = .0;
minMaxLoc(hist,0,&maxVal,0,0);
int scale = 8;
//show the histogram on the image
Mat histImg = Mat::zeros(sbins*scale,hbins*scale,CV_8UC3);
for (int h = 0;h < hbins;h++)
{
for (int s = 0;s<sbins;s++)
{
float binVal = hist.at<float>(h,s);
int intensity = cvRound(binVal*0.9*255/maxVal);
rectangle(histImg,Point(h*scale,s*scale),Point((h+1)*scale-1,(s+1)*scale-1),Scalar::all(intensity),CV_FILLED);
}
}

namedWindow("H-S Histogram");
imshow("H-S Histogram",histImg);
imwrite("hshistogram.jpg",histImg);
waitKey(0);
}

源图像：



h-s histogram:



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[1],channels_g[1],channels_b[1],histSize[1],range;
float hranges[2];
const float *ranges[1];
histSize[0] = 256;
hranges[0] = 0.0;
hranges[1] = 255.0;
ranges[0] = hranges;
channels_b[0] = 0;
channels_g[0] = 1;
channels_r[0] = 2;
MatND hist_r,hist_g,hist_b;

double max_val_r,max_val_g,max_val_b;
Mat histImage(histSize[0],3*histSize[0],CV_8UC3); //定义一个显示直方图的图片，长256*3 高256
//R
calcHist(&source,1,channels_r,Mat(),hist_r,1,histSize,ranges);//分别计算R，G，B的直方图分布
minMaxLoc(hist_r,0,&max_val_r,0,0);//计算直方图中统计最大值
//G
calcHist(&source,1,channels_g,Mat(),hist_g,1,histSize,ranges);
minMaxLoc(hist_g,0,&max_val_g,0,0);
//B
calcHist(&source,1,channels_b,Mat(),hist_b,1,histSize,ranges);
minMaxLoc(hist_b,0,&max_val_b,0,0);

//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 =0;i<histSize[0];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[0]*binVal_r/max_val_r);
outfile1<<i<<" "<<binVal_r<<" "<<intensity_r<<endl;
int intensity_g = static_cast<int>(0.9*histSize[0]*binVal_g/max_val_g);
outfile2<<i<<" "<<binVal_g<<" "<<intensity_g<<endl;
int intensity_b = static_cast<int>(0.9*histSize[0]*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(0,0,255));
line(histImage,Point(i+histSize[0],histImage.rows),Point(i+histSize[0],histImage.rows-intensity_g),Scalar(0,255,0));
line(histImage,Point(i+histSize[0]*2,histImage.rows),Point(i+histSize[0]*2,histImage.rows-intensity_b),Scalar(255,0,0));
}
namedWindow("RGB Histogram");
imshow("RGB Histogram",histImage);
imwrite("RGB_Histogram.jpg",histImage);
waitKey(0);
}

源图像：如上图

程序运行结果：