利用特征点（Brief,ORB,SIFT）进行图像匹配，模板匹配
头文件
在VS2010+OpenCV2.3.1
#include "StdAfx.h"
#include "opencv2/core/core.hpp"
#include "opencv2/calib3d/calib3d.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/highgui/highgui.hpp"
//#include
#include
#include
#include
#include
using namespace cv;
using namespace std;
void getMatchLR(string imgpath);
void getMatchDT(string imgpath);
//void getSURFFeature(string imgpath);
void getORBFeature(string imgpath);
void MatchTemplate(string imgname);
void MatchTemplateORB(string imgname);
void MatchTemplateSIFT(string imgname);
void testMatch();
static string imgdir = "E:\\input_resize\\";
static string Matchsavedir = "E:\\macth_result\\";
static string TemplateDir = "E:\\template_resize\\";
//static string ORBsavedir = "ORB_result\\";
IplImage mat_src_lpl;
IplImage OutImage_lpl;
IplImage TemplateIpl;
IplImage* dst_left;
IplImage* dst_right;
IplImage *src;
IplImage *TemplateIplPtr;
Mat mat_src;
Mat OutImage;
Mat TemplateMat;
在UBUNTU12.04UTL+opencv2.4.
#include "opencv2/core/core.hpp"
#include "opencv2/calib3d/calib3d.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/highgui/highgui.hpp"
#include
#include
#include
#include
#include 主函数
int main()
{
string imgname = "07_in.jpg";
//testMatch();
MatchTemplateSIFT(imgname);
//getMatchLR(imgname);
return 0;
}SIFT
利用SIFT特征进行模板匹配，template1为一个物体的小图，然后在一张含有多个同样物体的大图上进行匹配。
在大图上滑窗处理，得到每一个滑窗的特征，进行匹配，计算距离均值，作为一个灰度值，最后生成一个大图。
特征子，描述符，匹配方法分别为：
new SiftFeatureDetector
new SiftDescriptorExtractor;
BruteForceMatcher详细代码
void MatchTemplateSIFT(string imgname)
{
vector LeftKey;
vector RightKey;
Mat LeftDescriptor;
Mat RightDescriptor;
vector Matches;
//vector meanDisances;
IplImage* dst;
ofstream fout("E:\\picin\\rBRIEF_Test\\templateCompareResult.txt");
int xstep = 2;
int ystep = 2;
string TemplateName = "template1.jpg";
string TemplateImgPath = TemplateDir + TemplateName;
string imgpath = imgdir + imgname;
string resultPath = Matchsavedir+imgname;
TemplateMat = imread(TemplateImgPath,CV_LOAD_IMAGE_GRAYSCALE);
if(!TemplateMat.data){
cout<<"no template exist";
return ;
}
int TemplateWidth = TemplateMat.cols;
int TemplateHeight = TemplateMat.rows;
std::cout<<"TemplateWidth "<detect(TemplateMat, LeftKey);
DescriptorExtractor *pExtractor = new SiftDescriptorExtractor;
pExtractor->compute(TemplateMat, LeftKey, LeftDescriptor);
DescriptorMatcher *pMatcher = new BruteForceMatcher>;
mat_src = imread(imgpath, CV_LOAD_IMAGE_GRAYSCALE );
if(!mat_src.data) {
cout<<"no src img";
return ;
} mat_src_lpl = IplImage(mat_src);
src = &mat_src_lpl;
long ImgWidth = src->width;
long ImgHeight = src->height;
std::cout<<"ImgWidth "<nChannels);
cvCopy(src,dst,0);
cvResetImageROI(src);
Mat DstImage = Mat(dst, false); // Do not copy
pDetector->detect(DstImage, RightKey);
pExtractor->compute(DstImage, RightKey, RightDescriptor);
pMatcher->match(LeftDescriptor, RightDescriptor, Matches);
//double sum = 0;
double sum = 0;
//int i = 0;
for(vector::iterator dite = Matches.begin();dite distance;
}
int matchSize = Matches.size()*10;
if(matchSize>1){
fout< SURF
SURF特征同SIFT特征。
ORB
描述子和描述符，匹配代码如下 ORB orb;
orb(TemplateMat,Mat(),LeftKey,LeftDescriptor);
DescriptorMatcher *pMatcher = new BruteForceMatcher; 详细代码如下
void MatchTemplateORB(string imgname)
{
vector LeftKey;
vector RightKey;
Mat LeftDescriptor;
Mat RightDescriptor;
vector Matches;
//vector meanDisances;
IplImage* dst;
ofstream fout("E:\\picin\\rBRIEF_Test\\templateCompareResult.txt");
int xstep = 2;
int ystep = 2;
string TemplateName = "template2.jpg";
string TemplateImgPath = TemplateDir + TemplateName;
string imgpath = imgdir + imgname;
string resultPath = Matchsavedir+imgname;
TemplateMat = imread(TemplateImgPath,CV_LOAD_IMAGE_GRAYSCALE);
if(!TemplateMat.data){
cout<<"no template exist";
return ;
}
int TemplateWidth = TemplateMat.cols;
int TemplateHeight = TemplateMat.rows;
std::cout<<"TemplateWidth "<;
mat_src = imread(imgpath, CV_LOAD_IMAGE_GRAYSCALE );
if(!mat_src.data) {
cout<<"no src img";
return ;
}
mat_src_lpl = IplImage(mat_src);
src = &mat_src_lpl;
long ImgWidth = src->width;
long ImgHeight = src->height;
std::cout<<"ImgWidth "<"<<" ";
cvSetImageROI(src,cvRect(x,y,TemplateWidth,TemplateHeight));
dst = cvCreateImage(cvSize(TemplateWidth,TemplateHeight),
IPL_DEPTH_8U,
src->nChannels);
cvCopy(src,dst,0);
cvResetImageROI(src);
Mat DstImage = Mat(dst, false); // Do not copy
orb(DstImage,Mat(),RightKey,RightDescriptor);
//std::cout<match(LeftDescriptor, RightDescriptor, Matches);
//double sum = 0;
double sum = 0;
//int i = 0;
for(vector::iterator dite = Matches.begin();dite distance;
}
int matchSize = Matches.size()*10;
//std::cout<1){
//int meanDis = sum/matchSize;
fout<(x,y) = meanDis;
}else{
fout< 对利用特征点对图片左右部分进行匹配
void getMatchLR(string imgname)
{
string imgpath = imgdir + imgname;
mat_src = imread(imgpath, CV_LOAD_IMAGE_GRAYSCALE );
if(!mat_src.data) {
cout<<"no img";
return ;
}
mat_src_lpl = IplImage(mat_src);
src = &mat_src_lpl;
//
cvSetImageROI(src,cvRect(0,0,0.5*src->width,src->height));
dst_left = cvCreateImage(cvSize(0.5*src->width,src->height),
IPL_DEPTH_8U,
src->nChannels);
cvCopy(src,dst_left,0);
cvResetImageROI(src);
cvSetImageROI(src,cvRect(0.5*src->width,0,0.5*src->width,src->height));
dst_right = cvCreateImage(cvSize(0.5*src->width,src->height),
IPL_DEPTH_8U,
src->nChannels);
cvCopy(src,dst_right,0);
cvResetImageROI(src);
// Convert IplImage to cv::Mat
Mat matLeftImage = Mat(dst_left, false); // Do not copy
Mat matRightImage = Mat(dst_right, false);
// Key point and its descriptor
vector LeftKey;
vector RightKey;
Mat LeftDescriptor;
Mat RightDescriptor;
vector Matches;
/*
// Detect key points from image
FeatureDetector *pDetector = new FastFeatureDetector; //
pDetector->detect(matLeftImage, LeftKey);
pDetector->detect(matRightImage, RightKey);
delete pDetector;
// Extract descriptors
DescriptorExtractor *pExtractor = new BriefDescriptorExtractor; //
pExtractor->compute(matLeftImage, LeftKey, LeftDescriptor);
pExtractor->compute(matRightImage, RightKey, RightDescriptor);
delete pExtractor;
*/
ORB orb;
orb(matLeftImage,Mat(),LeftKey,LeftDescriptor);
orb(matRightImage,Mat(),RightKey,RightDescriptor);
// Matching features
//DescriptorMatcher *pMatcher = new FlannBasedMatcher; //
DescriptorMatcher *pMatcher = new BruteForceMatcher; //
pMatcher->match(LeftDescriptor, RightDescriptor, Matches);
delete pMatcher;
double max_dist = 0; double min_dist = 200;
//-- Quick calculation of max and min distances between keypoints
for( int i = 0; i < LeftDescriptor.rows; i++ )
{ double dist = Matches[i].distance;
if( dist < min_dist ) min_dist = dist;
if( dist > max_dist ) max_dist = dist;
}
//printf("-- Max dist : %f \n", max_dist );
//printf("-- Min dist : %f \n", min_dist );
//-- Draw only "good" matches (i.e. whose distance is less than 2*min_dist )
//-- PS.- radiusMatch can also be used here.
std::vector< DMatch > good_matches;
for( int i = 0; i < LeftDescriptor.rows; i++ )
{ if( Matches[i].distance < 0.5*max_dist )
{ good_matches.push_back( Matches[i]); }
}
// Show result
//drawMatches(matLeftImage, LeftKey, matRightImage, RightKey, Matches, OutImage);
drawMatches( matLeftImage, LeftKey, matRightImage, RightKey,
good_matches, OutImage, Scalar::all(-1), Scalar::all(-1),
vector(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
OutImage_lpl = IplImage(OutImage);
cvNamedWindow( "Match features", 1);
cvShowImage("Match features", &(OutImage_lpl));
cvWaitKey( 0 );
cvDestroyWindow( "Match features" );
string savepath = Matchsavedir + imgname;
//-- Show detected (drawn) keypoints
imwrite(savepath, OutImage );//
}
以上代码中被注释的部分
// Detect key points from image
FeatureDetector *pDetector = new FastFeatureDetector; //
pDetector->detect(matLeftImage, LeftKey);
pDetector->detect(matRightImage, RightKey);
delete pDetector;
// Extract descriptors
DescriptorExtractor *pExtractor = new BriefDescriptorExtractor; //
pExtractor->compute(matLeftImage, LeftKey, LeftDescriptor);
pExtractor->compute(matRightImage, RightKey, RightDescriptor);
是利用的BRIEF特征，进行左右匹配