// OpenCV_sift.cpp : 定义控制台应用程序的入口点。
//#include "stdafx.h"
#include <iostream>#include <vector>
#include "opencv2/core/core.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/legacy/legacy.hpp"
#include "opencv2/calib3d/calib3d.hpp"using namespace cv;
using namespace std;#pragma comment(lib,"opencv_core2410d.lib")
#pragma comment(lib,"opencv_highgui2410d.lib")
#pragma comment(lib,"opencv_objdetect2410d.lib")
#pragma comment(lib,"opencv_imgproc2410d.lib")
#pragma comment(lib,"opencv_features2d2410d.lib")
#pragma comment(lib,"opencv_legacy2410d.lib")
#pragma comment(lib,"opencv_calib3d2410d.lib")int main()
{Mat img_1 = imread("1.jpg");Mat img_2 = imread("2.jpg");if (!img_1.data || !img_2.data){cout << "error reading images " << endl;return -1;}ORB orb;vector<KeyPoint> keyPoints_1, keyPoints_2;Mat descriptors_1, descriptors_2;orb(img_1, Mat(), keyPoints_1, descriptors_1);orb(img_2, Mat(), keyPoints_2, descriptors_2);BruteForceMatcher<HammingLUT> matcher;vector<DMatch> matches;matcher.match(descriptors_1, descriptors_2, matches);double max_dist = 0; double min_dist = 100;//-- Quick calculation of max and min distances between keypointsfor( int i = 0; i < descriptors_1.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 0.6*max_dist )//-- PS.- radiusMatch can also be used here.std::vector< DMatch > good_matches;for( int i = 0; i < descriptors_1.rows; i++ ){ if( matches[i].distance < 0.6*max_dist ){ good_matches.push_back( matches[i]); }}Mat img_matches;drawMatches(img_1, keyPoints_1, img_2, keyPoints_2,good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS);// localize the objectstd::vector<Point2f> obj;std::vector<Point2f> scene;for (size_t i = 0; i < good_matches.size(); ++i){// get the keypoints from the good matchesobj.push_back(keyPoints_1[ good_matches[i].queryIdx ].pt);scene.push_back(keyPoints_2[ good_matches[i].trainIdx ].pt);}Mat H = findHomography( obj, scene, CV_RANSAC );// get the corners from the image_1std::vector<Point2f> obj_corners(4);obj_corners[0] = cvPoint(0,0);obj_corners[1] = cvPoint( img_1.cols, 0);obj_corners[2] = cvPoint( img_1.cols, img_1.rows);obj_corners[3] = cvPoint( 0, img_1.rows);std::vector<Point2f> scene_corners(4);perspectiveTransform( obj_corners, scene_corners, H);// draw lines between the corners (the mapped object in the scene - image_2)line( img_matches, scene_corners[0] + Point2f( img_1.cols, 0), scene_corners[1] + Point2f( img_1.cols, 0),Scalar(0,255,0));line( img_matches, scene_corners[1] + Point2f( img_1.cols, 0), scene_corners[2] + Point2f( img_1.cols, 0),Scalar(0,255,0));line( img_matches, scene_corners[2] + Point2f( img_1.cols, 0), scene_corners[3] + Point2f( img_1.cols, 0),Scalar(0,255,0));line( img_matches, scene_corners[3] + Point2f( img_1.cols, 0), scene_corners[0] + Point2f( img_1.cols, 0),Scalar(0,255,0));imshow( "Match", img_matches);cvWaitKey();return 0;
}

/*
SIFT sift;
sift(img_1, Mat(), keyPoints_1, descriptors_1);
sift(img_2, Mat(), keyPoints_2, descriptors_2);
BruteForceMatcher<L2<float> >  matcher;
*//*
SURF surf;
surf(img_1, Mat(), keyPoints_1);
surf(img_2, Mat(), keyPoints_2);
SurfDescriptorExtractor extrator;
extrator.compute(img_1, keyPoints_1, descriptors_1);
extrator.compute(img_2, keyPoints_2, descriptors_2);
BruteForceMatcher<L2<float> >  matcher;
*/