OpenCV学习笔记（九）——2维特征Feature2D
基于特征点的图像匹配是图像处理中经常会遇到的问题，手动选取特征点太麻烦了。比较经典常用的特征点自动提取的办法有Harris特征、SIFT特征、SURF特征。先介绍利用SURF特征的特征描述办法，其操作封装在类SurfFeatureDetector中，利用类内的detect函数可以检测出SURF特征的关键点，保存在vector容器中。第二部利用SurfDescriptorExtractor类进行特征向量的相关计算。将之前的vector变量变成向量矩阵形式保存在Mat中。最后强行匹配两幅图像的特征向量，利用了类BruteForceMatcher中的函数match。代码如下：view plain/** * @file SURF_descriptor * @brief SURF detector + descritpor + BruteForce Matcher + drawing matches with OpenCV functions * @author A. Huaman */ #include <stdio.h> #include <iostream> #include "opencv2/core/core.hpp" #include "opencv2/features2d/features2d.hpp" #include "opencv2/highgui/highgui.hpp" using namespace cv; void readme(); /** * @function main * @brief Main function */ int main( int argc, char** argv ) { if( argc != 3 ) { return -1; } Mat img_1 = imread( argv[1], CV_LOAD_IMAGE_GRAYSCALE ); Mat img_2 = imread( argv[2], CV_LOAD_IMAGE_GRAYSCALE ); if( !img_1.data || !img_2.data ) { return -1; } //--
世界足球先生 Step 1: Detect the keypoints using SURF Detector int minHessian = 400; SurfFeatureDetector detector( minHessian ); std::vector<KeyPoint> keypoints_1, keypoints_2; detector.detect( img_1, keypoints_1 ); detector.detect( img_2, keypoints_2 ); //-- Step 2: Calculate descriptors (feature vectors) SurfDescriptorExtractor extractor; Mat descriptors_1, descriptors_2; pute( img_1, keypoints_1, descriptors_1 ); pute( img_2, keypoints_2, descriptors_2 ); //-- Step 3: Matching descriptor vectors with a brute force matcher BruteForceMatcher< L2<float> > matcher; std::vector< DMatch > matches; matcher.match( descriptors_1, descriptors_2, matches ); //-- Draw matches Mat img_matches; drawMatches( img_1, keypoints_1, img_2, keypoints_2, matches, img_matches ); //-- Show detected matches imshow("Matches", img_matches ); waitKey(0); return 0; } /** * @function readme */ void readme() { std::cout << " Usage: ./SURF_descriptor <img1> <img2>" << std::endl; } 文征明简介
当然，进行强匹配的效果不够理想，这里再介绍一种FLANN特征匹配算法。前两步与上述代码相同，第三步利用FlannBadMatcher类进行特征匹配，并只保留好的特征匹配点，
代码如下：view plain//-- Step 3: Matching descriptor vectors using FLANN matcher FlannBadMatcher matcher; std::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 < ws; 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. who distance is less than 2*min_dist ) //-- PS.- radiusMatch can also be ud here. std::vector< DMatch > good_matches; for( int i = 0; i < ws; i++ ) { if( matches[i].distance < 2*min_dist ) { good_matches.push_back( matches[i]); } } //-- Draw only "good" matches 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 ); //-- Show detected matches imshow( "Good Matches", img_matches );
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熔化焊在FLANN特征匹配的基础上，还可以进一步利用Homography映射找出已知物体。具体来说就是利用findHomography函数利用匹配的关键点找出相应的变换，再利用perspectiveTransform函数映射点群。具体代码如下：view plain//-- Localize the object from img_1 in img_2 std::vector<Point2f> obj; std::vector<Point2f> scene; for( int 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 ( the object to be "detected" ) Point2f obj_corners[4] = { cvPoint(0,0), cvPoint( ls, 0 ), cvPoint( ls, ws ), cvPoint( 0, ws ) }; Point scene_corners[4]; //-- Map the corners in the scene ( image_2) for( int i = 0; i < 4; i++ ) { double x = obj_corners[i].x; double y = obj_corners[i].y; double Z = 1./( H.at<double>(2,0)*x + H.at<double>(2,1)*y + H.at<double>(2,2) ); double X = ( H.at<double>(0,0)*x + H.at<double>(0,1)*y + H.at<double>(0,2) )*Z; double Y = ( H.at<double>(1,0)*x + H.at<double>(1,1)*y + H.at<double>(1,2) )*Z; scene_corners[i] = cvPoint( cvRound(X) + ls, cvRound(
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学习规律Y) ); } //-- Draw lines between the corners (the mapped object in the scene - image_2 ) line( img_matches, scene_corners[0], scene_corners[1], Scalar(0, 255, 0), 2 ); line( img_matches, scene_corners[1], scene_corners[2], Scalar( 0, 255, 0), 2 ); line( img_matches, scene_corners[2], scene_corners[3], Scalar( 0, 255, 0), 2 ); line( img_matches, scene_corners[3], scene_corners[0], Scalar( 0, 255, 0), 2 ); //-- Show detected matches imshow( "Good Matches & Object detection", img_matches );
