opencv 直方图 CV：：calcHist使用

本文转自：http://www.xuebuyuan.com/1014703.html

特别提醒读者：注意实例中数据成员很多都定义成数据，这是由于calcHist函数形参要求的。

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 image1,        //histogram from 1 image onlychannels, //the channel usedcv::Mat(),//no mask is uesdhist,     //the resulting histogram1,        //it is a 1D histogramhistSize, //number of binsranges    //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 firstcv::MatND hist = getHistogram(image);//get min and max bin valuesdouble maxVal = 0;double minVal = 0;cv::minMaxLoc(hist,&minVal,&maxVal,0,0);//Image on which to display histogramcv::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 levelsint hbins = 60,sbins = 64;int histSize[] = {hbins,sbins};//hue varies from 0 to 179float hranges[] = {0,180};//saturation varies from 0 to 255float sranges[] = {0,255};const float *ranges[] = {hranges,sranges};//two channels 0th,1thint channels[] = {0,1};MatND hist;//compute h-s histogramcalcHist(&hsv,1,channels,Mat(),hist,2,histSize,ranges);//get the max valuedouble maxVal = .0;minMaxLoc(hist,0,&maxVal,0,0);int scale = 8;//show the histogram on the imageMat 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>
using namespace cv;void main()
{//Mat source = imread("red.jpg");Mat source = imread("baboon.jpg");//Mat source(300,300,CV_8UC3,Scalar(1,1,244));//imwrite("red.jpg",source);namedWindow("Source");imshow("Source",source);int channels_r[1],channels_g[1],channels_b[1],histSize[1];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);//RcalcHist(&source,1,channels_r,Mat(),hist_r,1,histSize,ranges);minMaxLoc(hist_r,0,&max_val_r,0,0);//GcalcHist(&source,1,channels_g,Mat(),hist_g,1,histSize,ranges);minMaxLoc(hist_r,0,&max_val_g,0,0);//BcalcHist(&source,1,channels_b,Mat(),hist_b,1,histSize,ranges);minMaxLoc(hist_r,0,&max_val_b,0,0);for (int i =0;i<histSize[0];i++){float binVal_r = hist_r.at<float>(i);float binVal_g = hist_g.at<float>(i);float binVal_b = hist_b.at<float>(i);int intensity_r = static_cast<int>(0.9*histSize[0]*binVal_r/max_val_r);int intensity_g = static_cast<int>(0.9*histSize[0]*binVal_g/max_val_g);int intensity_b = static_cast<int>(0.9*histSize[0]*binVal_b/max_val_b);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);waitKey(0);
}

源图像：图上图

RGB-Histogram：

<pre class="cpp" name="code"><pre class="cpp" name="code"><pre class="cpp" name="code"><pre class="cpp" name="code"><pre class="cpp" name="code"><pre>

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