opencv2.4.9 的函数怎么调用
opencv2.4.9 调用sift特征，注意头文件，其他头文件可能造成sift检测器创建失败！！！！！！！！！！！
LIB:
opencv_core249d.lib
opencv_highgui249d.lib
opencv_imgproc249d.lib
opencv_nonfree249d.lib
opencv_features2d249d.lib
opencv_photo249d.lib
opencv_calib3d249.lib
opencv_calib3d249d.lib
opencv_contrib249.lib
opencv_contrib249d.lib
opencv_core249.lib
opencv_features2d249.lib
opencv_flann249.lib
opencv_flann249d.lib
opencv_gpu249.lib
opencv_highgui249.lib
opencv_imgproc249.lib
opencv_legacy249.lib
opencv_ml249.lib
opencv_nonfree249.lib
opencv_objdetect249.lib
opencv_ocl249.lib
opencv_photo249.lib
opencv_stitching249.lib
opencv_superres249.lib
opencv_ts249.lib
opencv_video249.lib
opencv_videostab249.lib
opencv_gpu249d.lib
opencv_legacy249d.lib
opencv_ml249d.lib
opencv_objdetect249d.lib
opencv_ocl249d.lib
opencv_stitching249d.lib
opencv_ts249d.lib
opencv_video249d.lib
opencv_videostab249d.lib
// opencv_empty_proj.cpp : 定义控制台应用程序的入口点。
//
#include "stdafx.h"
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/features2d/features2d.hpp>
#include <opencv2/nonfree/features2d.hpp>
#include <opencv2/nonfree/nonfree.hpp>
#include <iostream>
using namespace std;
using namespace cv;
int _tmain(int argc, _TCHAR* argv[])
{
initModule_nonfree();//初始化模块，使用SIFT或SURF时用到
Ptr<FeatureDetector> detector = FeatureDetector::create( "SIFT" );//创建SIFT特征检测器
Ptr<DescriptorExtractor> descriptor_extractor = DescriptorExtractor::create( "SIFT" );//创建特征向量生成器
Ptr<DescriptorMatcher> descriptor_matcher = DescriptorMatcher::create( "BruteForce" );//创建特征匹配器
if( detector.empty() || descriptor_extractor.empty() )
cout<<"fail to create detector!";
//读入图像
Mat img1 = imread("image1.jpg");
Mat img2 = imread("image2.jpg");
//特征点检测
double t = getTickCount();//当前滴答数
vector<KeyPoint> keypoints1,keypoints2;
detector->detect( img1, keypoints1 );//检测img1中的SIFT特征点，存储到keypoints1中
detector->detect( img2, keypoints2 );
cout<<"图像1特征点个数:"<<keypoints1.size()<<endl;
cout<<"图像2特征点个数:"<<keypoints2.size()<<endl;
//根据特征点计算特征描述子矩阵，即特征向量矩阵
Mat descriptors1,descriptors2;
descriptor_extractor->compute( img1, keypoints1, descriptors1 );
descriptor_extractor->compute( img2, keypoints2, descriptors2 );
t = ((double)getTickCount() - t)/getTickFrequency();
cout<<"SIFT算法用时："<<t<<"秒"<<endl;
cout<<"图像1特征描述矩阵大小："<<descriptors1.size()
<<"，特征向量个数："<<descriptors1.rows<<"，维数："<<descriptors1.cols<<endl;
cout<<"图像2特征描述矩阵大小："<<descriptors2.size()
<<"，特征向量个数："<<descriptors2.rows<<"，维数："<<descriptors2.cols<<endl;
opencv_highgui249d.lib
opencv_imgproc249d.lib
opencv_nonfree249d.lib
opencv_features2d249d.lib
opencv_photo249d.lib
opencv_calib3d249.lib
opencv_calib3d249d.lib
opencv_contrib249.lib
opencv_contrib249d.lib
opencv_core249.lib
opencv_features2d249.lib
opencv_flann249.lib
opencv_flann249d.lib
opencv_gpu249.lib
opencv_highgui249.lib
opencv_imgproc249.lib
opencv_legacy249.lib
opencv_ml249.lib
opencv_nonfree249.lib
opencv_objdetect249.lib
opencv_ocl249.lib
opencv_photo249.lib
opencv_stitching249.lib
opencv_superres249.lib
opencv_ts249.lib
opencv_video249.lib
opencv_videostab249.lib
opencv_gpu249d.lib
opencv_legacy249d.lib
opencv_ml249d.lib
opencv_objdetect249d.lib
opencv_ocl249d.lib
opencv_stitching249d.lib
opencv_ts249d.lib
opencv_video249d.lib
opencv_videostab249d.lib
// opencv_empty_proj.cpp : 定义控制台应用程序的入口点。
//
#include "stdafx.h"
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/features2d/features2d.hpp>
#include <opencv2/nonfree/features2d.hpp>
#include <opencv2/nonfree/nonfree.hpp>
#include <iostream>
using namespace std;
using namespace cv;
int _tmain(int argc, _TCHAR* argv[])
{
initModule_nonfree();//初始化模块，使用SIFT或SURF时用到
Ptr<FeatureDetector> detector = FeatureDetector::create( "SIFT" );//创建SIFT特征检测器
Ptr<DescriptorExtractor> descriptor_extractor = DescriptorExtractor::create( "SIFT" );//创建特征向量生成器
Ptr<DescriptorMatcher> descriptor_matcher = DescriptorMatcher::create( "BruteForce" );//创建特征匹配器
if( detector.empty() || descriptor_extractor.empty() )
cout<<"fail to create detector!";
//读入图像
Mat img1 = imread("image1.jpg");
Mat img2 = imread("image2.jpg");
//特征点检测
double t = getTickCount();//当前滴答数
vector<KeyPoint> keypoints1,keypoints2;
detector->detect( img1, keypoints1 );//检测img1中的SIFT特征点，存储到keypoints1中
detector->detect( img2, keypoints2 );
cout<<"图像1特征点个数:"<<keypoints1.size()<<endl;
cout<<"图像2特征点个数:"<<keypoints2.size()<<endl;
//根据特征点计算特征描述子矩阵，即特征向量矩阵
Mat descriptors1,descriptors2;
descriptor_extractor->compute( img1, keypoints1, descriptors1 );
descriptor_extractor->compute( img2, keypoints2, descriptors2 );
t = ((double)getTickCount() - t)/getTickFrequency();
cout<<"SIFT算法用时："<<t<<"秒"<<endl;
cout<<"图像1特征描述矩阵大小："<<descriptors1.size()
<<"，特征向量个数："<<descriptors1.rows<<"，维数："<<descriptors1.cols<<endl;
cout<<"图像2特征描述矩阵大小："<<descriptors2.size()
<<"，特征向量个数："<<descriptors2.rows<<"，维数："<<descriptors2.cols<<endl;
