今天在网上看到一个比较健壮的图像特征匹配算法，遂拿出来与大家分享
1.首先看看类定义：
class RobustMatcher { private: // pointer to the feature point detector object cv::Ptr<cv::FeatureDetector> detector; // pointer to the feature descriptor extractor object cv::Ptr<cv::DescriptorExtractor> extractor; // pointer to the matcher object cv::Ptr<cv::DescriptorMatcher > matcher; float ratio; // max ratio between 1st and 2nd NN bool refineF; // if true will refine the F matrix double distance; // min distance to epipolar double confidence; // confidence level (probability) public: RobustMatcher() : ratio(0.65f), refineF(true), confidence(0.99), distance(3.0) { // ORB is the default feature detector= new cv::OrbFeatureDetector(); extractor= new cv::OrbDescriptorExtractor(); matcher= new cv::BruteForceMatcher<cv::HammingLUT>; } // Set the feature detector void setFeatureDetector(cv::Ptr<cv::FeatureDetector>& detect) { detector= detect; } // Set the descriptor extractor void setDescriptorExtractor(cv::Ptr<cv::DescriptorExtractor>& desc) { extractor= desc; } // Set the matcher void setDescriptorMatcher(cv::Ptr<cv::DescriptorMatcher>& match) { matcher= match; } // Set confidence level void setConfidenceLevel(double conf) { confidence= conf; } //Set MinDistanceToEpipolar void setMinDistanceToEpipolar(double dist) { distance= dist; } //Set ratio void setRatio(float rat) { ratio= rat; } cv::Mat match(cv::Mat& image1, cv::Mat& image2, // input images // output matches and keypoints std::vector<cv::DMatch>& matches, std::vector<cv::KeyPoint>& keypoints1, std::vector<cv::KeyPoint>& keypoints2); cv::Mat ransacTest( const std::vector<cv::DMatch>& matches, const std::vector<cv::KeyPoint>& keypoints1, const std::vector<cv::KeyPoint>& keypoints2, std::vector<cv::DMatch>& outMatches); void symmetryTest( const std::vector<std::vector<cv::DMatch> >& matches1, const std::vector<std::vector<cv::DMatch> >& matches2, std::vector<cv::DMatch>& symMatches); int ratioTest(std::vector<std::vector<cv::DMatch>>& matches); };
2.cpp中的内容为：
int RobustMatcher::ratioTest(std::vector<std::vector<cv::DMatch> > &matches) { int removed=0; // for all matches for (std::vector<std::vector<cv::DMatch> >::iterator matchIterator= matches.begin(); matchIterator!= matches.end(); ++matchIterator) { // if 2 NN has been identified if (matchIterator->size() > 1) { // check distance ratio if ((*matchIterator)[0].distance/ (*matchIterator)[1].distance > ratio) { matchIterator->clear(); // remove match removed++; } } else { // does not have 2 neighbours matchIterator->clear(); // remove match removed++; } } return removed;//返回被删除的点数量 } // Insert symmetrical matches in symMatches vector void RobustMatcher::symmetryTest( const std::vector<std::vector<cv::DMatch> >& matches1, const std::vector<std::vector<cv::DMatch> >& matches2, std::vector<cv::DMatch>& symMatches) { // for all matches image 1 -> image 2 for (std::vector<std::vector<cv::DMatch> >:: const_iterator matchIterator1= matches1.begin(); matchIterator1!= matches1.end(); ++matchIterator1) { // ignore deleted matches if (matchIterator1->size() < 2) continue; // for all matches image 2 -> image 1 for (std::vector<std::vector<cv::DMatch> >:: const_iterator matchIterator2= matches2.begin(); matchIterator2!= matches2.end(); ++matchIterator2) { // ignore deleted matches if (matchIterator2->size() < 2) continue; // Match symmetry test if ((*matchIterator1)[0].queryIdx == (*matchIterator2)[0].trainIdx && (*matchIterator2)[0].queryIdx == (*matchIterator1)[0].trainIdx) { // add symmetrical match symMatches.push_back( cv::DMatch((*matchIterator1)[0].queryIdx, (*matchIterator1)[0].trainIdx, (*matchIterator1)[0].distance)); break; // next match in image 1 -> image 2 } } } } // Identify good matches using RANSAC // Return fundemental matrix cv::Mat RobustMatcher::ransacTest( const std::vector<cv::DMatch>& matches, const std::vector<cv::KeyPoint>& keypoints1, const std::vector<cv::KeyPoint>& keypoints2, std::vector<cv::DMatch>& outMatches) { // Convert keypoints into Point2f std::vector<cv::Point2f> points1, points2; cv::Mat fundemental; for (std::vector<cv::DMatch>:: const_iterator it= matches.begin(); it!= matches.end(); ++it) { // Get the position of left keypoints float x= keypoints1[it->queryIdx].pt.x; float y= keypoints1[it->queryIdx].pt.y; points1.push_back(cv::Point2f(x,y)); // Get the position of right keypoints x= keypoints2[it->trainIdx].pt.x; y= keypoints2[it->trainIdx].pt.y; points2.push_back(cv::Point2f(x,y)); } // Compute F matrix using RANSAC std::vector<uchar> inliers(points1.size(),0); if (points1.size()>0&&points2.size()>0) { cv::Mat fundemental= cv::findFundamentalMat( cv::Mat(points1),cv::Mat(points2), // matching points inliers, // match status (inlier or outlier) CV_FM_RANSAC, // RANSAC method distance, // distance to epipolar line confidence); // confidence probability // extract the surviving (inliers) matches std::vector<uchar>::const_iterator itIn= inliers.begin(); std::vector<cv::DMatch>::const_iterator itM= matches.begin(); // for all matches for ( ;itIn!= inliers.end(); ++itIn, ++itM) { if (*itIn) { // it is a valid match outMatches.push_back(*itM); } } if (refineF) { // The F matrix will be recomputed with // all accepted matches // Convert keypoints into Point2f // for final F computation points1.clear(); points2.clear(); for (std::vector<cv::DMatch>:: const_iterator it= outMatches.begin(); it!= outMatches.end(); ++it) { // Get the position of left keypoints float x= keypoints1[it->queryIdx].pt.x; float y= keypoints1[it->queryIdx].pt.y; points1.push_back(cv::Point2f(x,y)); // Get the position of right keypoints x= keypoints2[it->trainIdx].pt.x; y= keypoints2[it->trainIdx].pt.y; points2.push_back(cv::Point2f(x,y)); } // Compute 8-point F from all accepted matches if (points1.size()>0&&points2.size()>0) { fundemental= cv::findFundamentalMat( cv::Mat(points1),cv::Mat(points2), // matches CV_FM_8POINT); // 8-point method } } } return fundemental; } // Match feature points using symmetry test and RANSAC // returns fundemental matrix cv::Mat RobustMatcher:: match(cv::Mat& image1, cv::Mat& image2, // input images // output matches and keypoints std::vector<cv::DMatch>& matches, std::vector<cv::KeyPoint>& keypoints1, std::vector<cv::KeyPoint>& keypoints2) { // 1a. Detection of the ORB features detector->detect(image1,keypoints1); detector->detect(image2,keypoints2); // 1b. Extraction of the ORB descriptors cv::Mat descriptors1, descriptors2; extractor->compute(image1,keypoints1,descriptors1); extractor->compute(image2,keypoints2,descriptors2); // 2. Match the two image descriptors // Construction of the matcher //cv::BruteForceMatcher<cv::L2<float>> matcher; // from image 1 to image 2 // based on k nearest neighbours (with k=2) std::vector<std::vector<cv::DMatch> > matches1; matcher->knnMatch(descriptors1,descriptors2, matches1, // vector of matches (up to 2 per entry) 2); // return 2 nearest neighbours // from image 2 to image 1 // based on k nearest neighbours (with k=2) std::vector<std::vector<cv::DMatch> > matches2; matcher->knnMatch(descriptors2,descriptors1, matches2, // vector of matches (up to 2 per entry) 2); // return 2 nearest neighbours // 3. Remove matches for which NN ratio is // > than threshold // clean image 1 -> image 2 matches int removed= ratioTest(matches1); // clean image 2 -> image 1 matches removed= ratioTest(matches2); // 4. Remove non-symmetrical matches std::vector<cv::DMatch> symMatches; symmetryTest(matches1,matches2,symMatches); //=========================================测试代码 cv::Mat img_matches; cv::drawMatches( image1, keypoints1, image2, keypoints2, symMatches, img_matches, cv::Scalar::all(-1), cv::Scalar::all(-1), std::vector<char>(), cv::DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS ); /*imshow("Test",img_matches);*/ //cvWaitKey(0); //=========================================测试代码 // 5. Validate matches using RANSAC cv::Mat fundemental= ransacTest(symMatches, keypoints1, keypoints2, matches); //=========================================测试代码 std::vector<cv::Point2f> obj; std::vector<cv::Point2f> scene; for( int i = 0; i < symMatches.size(); i++ ) { //-- Get the keypoints from the good matches obj.push_back( keypoints1[ symMatches[i].queryIdx ].pt ); scene.push_back( keypoints2[ symMatches[i].trainIdx ].pt ); } cv::Mat H = cv::findHomography( obj, scene, CV_RANSAC ,2); //-- Get the corners from the image_1 ( the object to be "detected" ) std::vector<cv::Point2f> obj_corners(4); obj_corners[0]=cvPoint(0,0); obj_corners[1]=cvPoint(image1.cols, 0 ); obj_corners[2]=cvPoint(image1.cols,image1.rows); obj_corners[3]=cvPoint(0,image1.rows); std::vector<cv::Point2f> scene_corners(4); cv::perspectiveTransform( obj_corners, scene_corners, H); for( int i = 0; i < 4; i++ ) { scene_corners[i].x+=image1.cols; } line( img_matches, scene_corners[0], scene_corners[1], cv::Scalar(0, 255, 0), 2 ); line( img_matches, scene_corners[1], scene_corners[2], cv::Scalar( 0, 255, 0), 2 ); line( img_matches, scene_corners[2], scene_corners[3], cv::Scalar( 0, 255, 0), 2 ); line( img_matches, scene_corners[3], scene_corners[0], cv::Scalar( 0, 255, 0), 2 ); imshow("Test",img_matches); cvWaitKey(0); //=========================================测试代码 // return the found fundemental matrix return fundemental; }
剔除低质量匹配点
ratioTest用来剔除距离比例相差过大的配对点，配对点之间的距离相差越大，能匹配上的概率也就越小。这里使用一个参数ratio来控制剔除距离相差在一定范围之外的特征点。
symmetryTest用来判断两个图像间的特征点匹配是否是一一映射，对于不是的点则剔除掉
3.具体调用的例子：
void main() { // set parameters int numKeyPoints = 1500; //Instantiate robust matcher RobustMatcher rmatcher; //instantiate detector, extractor, matcher cv::Ptr<cv::FeatureDetector> detector = new cv::OrbFeatureDetector(numKeyPoints); cv::Ptr<cv::DescriptorExtractor> extractor = new cv::OrbDescriptorExtractor; cv::Ptr<cv::DescriptorMatcher> matcher = new cv::BruteForceMatcher<cv::HammingLUT>; rmatcher.setFeatureDetector(detector); rmatcher.setDescriptorExtractor(extractor); rmatcher.setDescriptorMatcher(matcher); //Load input image detect keypoints cv::Mat img1; std::vector<cv::KeyPoint> img1_keypoints; cv::Mat img1_descriptors; cv::Mat img2; std::vector<cv::KeyPoint> img2_keypoints; cv::Mat img2_descriptors; std::vector<cv::DMatch> matches; img1 = cv::imread("C:\\temp\\PyramidPattern.jpg", CV_LOAD_IMAGE_GRAYSCALE); /*img2 = cv::imread("C:\\temp\\PyramidPatternTest.bmp", CV_LOAD_IMAGE_GRAYSCALE);*/ //img2 = cv::imread("C:\\temp\\test1.jpg", CV_LOAD_IMAGE_GRAYSCALE); img2 = cv::imread("C:\\temp\\test2.jpg", CV_LOAD_IMAGE_GRAYSCALE); rmatcher.match(img1, img2, matches, img1_keypoints, img2_keypoints); }
最后，对于OpenCV中的所有特征匹配算法都可以用这个办法来做，比如SIFT, SURF等等。只需要简单的替换第一步中的extractor和detector就可以了。
匹配效果还不错，上个图
但是缺点也是存在的，如果完整匹配下来，大约一帧耗时100+ms,不适合实时性要求较高的应用
