RGBHistogram：

分别计算把彩色图像的三个通道R、G、B的一维直方图，然后把这三个通道的颜色直方图结合起来，就是颜色的描写叙述子RGBHistogram。

以下给出计算RGBHistogram的代码：

<span style="font-family:Microsoft YaHei;font-size:18px;">#include "opencv2/highgui/highgui.hpp"

#include "opencv2/imgproc/imgproc.hpp"

#include <iostream>

#include <stdio.h>

using namespace std;

using namespace cv;

const int HISTSIZE = 8;

int main( int, char** argv )

{

  Mat src, dst;

  /// Load image

  src = imread( argv[1], 1 );

  if( !src.data || (src.channels() !=3))

    { return -1; }

  Mat rgbFeature = bgrHistogram(src);

  return 0;

}

Mat bgrHistogram(const Mat& src)

{

	//分离B、G、R通道

	vector<Mat> bgr_planes;

	split(src,bgr_planes);

  float range[] = { 0, 256 } ;

  const float* histRange = { range };

  bool uniform = true; bool accumulate = false;

  Mat hist1d,normHist1d,hist;

  for(int i = 0 ;i < 3;i++)

  {

	  calcHist( &bgr_planes[i], 1, 0, Mat(), hist1d, 1, &HISTSIZE, &histRange, uniform, accumulate );

	  normalize(hist1d,hist1d,1.0,0.0,CV_L1);

	  hist.push_back(hist1d);

  }

  return hist;

}

</span>

第二步：颜色描写叙述子已经计算出，选取什么样的距离。

对于距离我们先选取两种：

第一种：欧几里得距离

#include<iostream>

#include <fstream>

#include <stdio.h>

using namespace std;

#include "opencv2/highgui/highgui.hpp"

#include "opencv2/imgproc/imgproc.hpp"

using namespace cv;

const int HISTSIZE = 16;

Mat bgrHistogram(const Mat& src);

double  euclideanDistance(const Mat & src1,const Mat &src2);

int main( int, char** argv )

{

  //定义文件流，仅仅能读取

	ifstream inPutFile(argv[1],ios::in);

	if(! inPutFile)

	{

		cerr << "File Open Erro !"<<endl;

		return -1;

	}

	//读取文件流中的每一行。并赋值给fileName,形成查询数据库

	string fileName ;

	Mat image,histogram,sourceHisrogram;

	vector<Mat> histograms;

	map<int,string>index;//图像的索引

	index.clear();

	int  number = 0;

	histograms.clear();

	while(getline(inPutFile,fileName))

	{

		index.insert(pair<int,string>(number,fileName));

		number++;

		image = imread(fileName,1);

		histogram = bgrHistogram(image);

		histograms.push_back(histogram);

	}

	//待搜索的图像

	number = 0;

	Mat imageSource = imread(argv[2],1);

	sourceHisrogram = bgrHistogram(imageSource);

	vector<Mat>::iterator iter;

	map<double,int>distance;

	for(iter = histograms.begin();iter != histograms.end();iter++)

	{

		distance.insert(pair<double,int>(euclideanDistance(sourceHisrogram,*iter),number));

		number++;

	}

	//显示距离最小的前五名的检索图像

	number = 0;

	map<double,int>::iterator mapiter;

	for(mapiter = distance.begin();mapiter != distance.end() && number <2;mapiter++,number++)

	{

		string simage = index.find((*mapiter).second) ->second;

		image = imread(simage,1);

		namedWindow(simage,1);

		imshow(simage,image);

	}

	waitKey(0);

}

Mat bgrHistogram(const Mat& src)

{

	//分离B、G、R通道

	vector<Mat> bgr_planes;

	split(src,bgr_planes);

  float range[] = { 0, 256 } ;

  const float* histRange = { range };

  bool uniform = true; bool accumulate = false;

  Mat hist1d,normHist1d,hist;

  for(int i = 0 ;i < 3;i++)

  {

	  calcHist( &bgr_planes[i], 1, 0, Mat(), hist1d, 1, &HISTSIZE, &histRange, uniform, accumulate );

	  normalize(hist1d,hist1d,1.0,0.0,CV_L1);

	  hist.push_back(hist1d);

  }

  return hist;

}

double  euclideanDistance(const Mat & src1,const Mat &src2)

{

	Mat pow2;

	pow(src1-src2,2.0,pow2);

	return 	sqrt(sum(pow2)[0]);

}

搜索数据库

执行结果：

另外一种：卡方距离

#include<iostream>

#include <fstream>

#include <stdio.h>

using namespace std;

#include "opencv2/highgui/highgui.hpp"

#include "opencv2/imgproc/imgproc.hpp"

using namespace cv;

const int HISTSIZE = 16;

Mat bgrHistogram(const Mat& src);

int main( int, char** argv )

{

  //定义文件流，仅仅能读取

	ifstream inPutFile(argv[1],ios::in);

	if(! inPutFile)

	{

		cerr << "File Open Erro !"<<endl;

		return -1;

	}

	//读取文件流中的每一行。并赋值给fileName,形成查询数据库

	string fileName ;

	Mat image,histogram,sourceHisrogram;

	vector<Mat> histograms;

	map<int,string>index;//图像的索引

	index.clear();

	int  number = 0;

	histograms.clear();

	while(getline(inPutFile,fileName))

	{

		index.insert(pair<int,string>(number,fileName));

		number++;

		image = imread(fileName,1);

		histogram = bgrHistogram(image);

		histograms.push_back(histogram);

	}

	//待搜索的图像

	number = 0;

	Mat imageSource = imread(argv[2],1);

	sourceHisrogram = bgrHistogram(imageSource);

	vector<Mat>::iterator iter;

	map<double,int>distance;

	for(iter = histograms.begin();iter != histograms.end();iter++)

	{

		distance.insert(pair<double,int>(compareHist(sourceHisrogram,*iter,CV_COMP_CHISQR),number));

		number++;

	}

	//显示距离最小的前五名的检索图像

	number = 0;

	map<double,int>::iterator mapiter;

	for(mapiter = distance.begin();mapiter != distance.end() && number <2;mapiter++,number++)

	{

		string simage = index.find((*mapiter).second) ->second;

		image = imread(simage,1);

		namedWindow(simage,1);

		imshow(simage,image);

	}

	waitKey(0);

}

Mat bgrHistogram(const Mat& src)

{

	//分离B、G、R通道

	vector<Mat> bgr_planes;

	split(src,bgr_planes);

  float range[] = { 0, 256 } ;

  const float* histRange = { range };

  bool uniform = true; bool accumulate = false;

  Mat hist1d,normHist1d,hist;

  for(int i = 0 ;i < 3;i++)

  {

	  calcHist( &bgr_planes[i], 1, 0, Mat(), hist1d, 1, &HISTSIZE, &histRange, uniform, accumulate );

	  normalize(hist1d,hist1d,1.0,0.0,CV_L1);

	  hist.push_back(hist1d);

  }

  return hist;

}

搜索图片数据库

执行结果：（我仅仅提取前两副距离近期的图片）