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，就是颜色的描述子RGBHistogram。
下面给出计算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，只能读取
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
运行结果：
第二种：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，只能读取
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
运行结果：（我只提取前两副距离最近的图片）
