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看到OpenCV2.3.1里面ORB特征提取算法也在里面了，套用给的SURF特征例子程序改为ORB特征一直提示错误，类型不匹配神马的，由于没有找到示例程序，只能自己找答案。
（ORB特征论文：ORB： an efficient alternative to SIFT or SURF.点击下载论文）
经过查找发现：
描述符数据类型有是float的，比如说SIFT，SURF描述符，还有是uchar的，比如说有ORB，BRIEF
对于float 匹配方式有：
FlannBased
BruteForce<L2<float> >
BruteForce<SL2<float> >
BruteForce<L1<float> >
对于uchar有：
BruteForce<Hammin>
BruteForce<HammingLUT>
BruteForceMatcher< L2<float> > matcher;//改动的地方
BruteForceMatcher< L2<float> > matcher;//改动的地方
完整代码如下：
#include <iostream> #include "opencv2/core/core.hpp" #include "opencv2/features2d/features2d.hpp" #include "opencv2/highgui/highgui.hpp" #include <iostream> #include <vector> using namespace cv; using namespace std; int main() { Mat img_1 = imread("D:\\image\\img1.jpg"); Mat img_2 = imread("D:\\image\\img2.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 keypoints for( 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); imshow( "Match", img_matches); cvWaitKey(); return 0; } 另外: SURF SIFT
/*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;*/效果：
另外一个是寻找目标匹配
在右边的场景图里面寻找左边那幅图的starbucks标志
效果如下：
需要在之前的那个imshow之前加上如下代码即可完成一个简单的功能展示：
// localize the object std::vector<Point2f> obj; std::vector<Point2f> scene; for (size_t i = 0; i < good_matches.size(); ++i) { // get the keypoints from the good matches obj.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_1 std::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)); 给我老师的人工智能教程打call！http://blog.csdn.net/jiangjunshow
