OpenCV-绘制图像的像素直方图-Histogram

随手翻了翻之前推荐的那本OpenCV2的教程，突然发现了之前看图像处理中的像素直方图的实现教程，原本就打算找时间自己实现的，没想到opencv是有这个功能的

而且这个功能可以辅助很多的图像处理和识别的算法过程，是值得看一看的

OpenCV对于像素直方图的统计有这么一个函数：

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 )

下面贴出官方的参数解释，我觉得很好明白的

Parameters

arrays – 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.

narrays – Number of source arrays.

channels – List of the dims channels used to compute the histogram. The first ar-

ray channels are numerated from 0 to arrays[0].channels()-1 , the second ar-

ray channels are counted from arrays[0].channels() to arrays[0].channels() +

arrays[1].channels()-1, and so on.

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.

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

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

(equal to 32 in the current OpenCV version).

histSize – Array of histogram sizes in each dimension.

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 L0 of the 0-th histogram bin and the upper (exclusive)

boundary UhistSize[i]−1 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:

L0 , U0 = L1 , U1 = L2 , ..., UhistSize[i]−2 = LhistSize[i]−1 , UhistSize[i]−1 . The array

elements, that are not between L0 and UhistSize[i]−1 , are not counted in the histogram.

uniform – Flag indicatinfg whether the histogram is uniform or not (see above).

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.

代码：

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

class Histogram
{
private:
int histSize[1];//number of bins
float hranges[2];//min and max pixel value
const float* ranges[1];
int channels[1];//only 1 channel used here
public:
Histogram()
{
//Prepare arguments for 1D histogram
histSize[0] = 256;
hranges[0] = 0.0;
hranges[1] = 255.0;
ranges[0] = hranges;
channels[0] = 0;//by default , we look at channel 0
}
cv::MatND getHistogram(const cv::Mat &image)
{
cv::MatND hist;

//Compute histogram
cv::calcHist(&image,
1,
channels,
cv::Mat(),
hist,
1,
histSize,
ranges
);
return hist;
}
//Compute the 1D histogram and returns an image of it
cv::Mat 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);

//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);

//This function draw a line between 2 points
cv::line(histImg,cv::Point(h,histSize[0]),
cv::Point(h,histSize[0] - intensity),
cv::Scalar::all(0));
}
return histImg;
}
};

int main()
{
//Read input image
cv::Mat image = cv::imread("1.png",0);//open in b&w
//the histogram object
Histogram h;
//Compute the histogram
/*cv::MatND histo = h.getHistogram(image);
for(int i = 0;i<256;i++)
{
cout<<"Value "<<i<<"="<<histo.at<float>(i) <<endl;
}*/
cv::namedWindow("Histogram");
cv::imshow("Histogram",h.getHistogramImage(image));
cv::waitKey(0);
return 0;
}

需要注意的是imread这个函数后面的那个参数，不清楚的可以查阅官方的手册～

截图：



上面的代码只是读取的灰度图像反馈的1D的直方图，那么如果我是3通道的RGB/BGR图像呢？只需要做相应的修改然后函数便会返回3*256的一个Mat值

private:
int histSize[3];//number of bins
float hranges[2];//min and max pixel value
const float* ranges[3];
int channels[3];//only 1 channel used here
public:
Histogram()
{
//Prepare arguments for 1D histogram
histSize[0] = 256;histSize[1] = 256;histSize[2] = 256;
hranges[0] = 0.0;
hranges[1] = 255.0;
ranges[0] = hranges;ranges[1] =hranges;
ranges[2] =hranges;
channels[0] = 0;channels[1] = 1;channels[2] = 2;
}
cv::MatND getHistogram(const cv::Mat &image)
{
cv::MatND hist;

//Compute histogram
cv::calcHist(&image,
1,
channels,
cv::Mat(),
hist,
3,
histSize,
ranges
);
return hist;
}

但是在3通道的直方图计算过程中，有可能会觉得计算量过大了，同样可以使用稀疏矩阵（sparse matrix），calcHist同样支持

cv::SparseMat getSparseHistogram(const cv::Mat &image)
{
cv::SparseMat hist(3,histSize,CV_32F);

//Compute histogram
cv::calcHist(&image,
1,
channels,
cv::Mat(),
hist,
3,
histSize,
ranges
);
return hist;
}

接下来一段时间，就要回归水鱼的比赛了，OpenCV就要放一放

打算晚上写一下北大提供的PoseToPose函数的分析指导，由于自己的学识有限，很难分析全面，真的希望有人可以来指正我的错误～

张巍骞
2012-4-10