1 SurfFeatureDetector检测图像中的surf特征
2 计算特征向量,SurfDescriptorExtractor
3 再用强制匹配器匹配两幅图中的surf特征,使用BruteForceMatcher中的函数match。
例子如下:
#include <stdio.h> #include <iostream> #include "opencv2/core/core.hpp" #include "opencv2/features2d/features2d.hpp" #include "opencv2/highgui/highgui.hpp" #include "opencv2/nonfree/features2d.hpp" using namespace cv; void readme(); /** @function main */ int main( int argc, char** argv ) { Mat img_1 = imread( "1.jpg", CV_LOAD_IMAGE_GRAYSCALE ); Mat img_2 = imread( "3.jpg", CV_LOAD_IMAGE_GRAYSCALE ); if( !img_1.data || !img_2.data ) { return -1; } //-- 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; extractor.compute( img_1, keypoints_1, descriptors_1 ); extractor.compute( img_2, keypoints_2, descriptors_2 ); //-- Step 3: Matching descriptor vectors with a brute force matcher BFMatcher matcher(NORM_L2); 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; }
当然强行匹配效果是不理想,速度较慢。而FLANN特征匹配算法比较快。前两步与上述代码相同,第三步利用FlannBasedMatcher类进行特征匹配,并只保留好的特征匹配点,代码如下:
#include <stdio.h> #include <iostream> #include "opencv2/core/core.hpp" #include "opencv2/features2d/features2d.hpp" #include "opencv2/highgui/highgui.hpp" #include <opencv2/nonfree/features2d.hpp> #include <opencv2/nonfree/nonfree.hpp> using namespace cv; void readme(); /** @function main */ int main( int argc, char** argv ) { Mat img_1 = imread( "1.jpg", CV_LOAD_IMAGE_GRAYSCALE ); Mat img_2 = imread( "3.jpg", CV_LOAD_IMAGE_GRAYSCALE ); if( !img_1.data || !img_2.data ) { std::cout<< " --(!) Error reading images " << std::endl; return -1; } //-- 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; extractor.compute( img_1, keypoints_1, descriptors_1 ); extractor.compute( img_2, keypoints_2, descriptors_2 ); //-- Step 3: Matching descriptor vectors using FLANN matcher FlannBasedMatcher 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 < 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 2*min_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 <= 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 ); waitKey(0); return 0; } /** @function readme */ void readme() { std::cout << " Usage: ./SURF_FlannMatcher <img1> <img2>" << std::endl; }
#include <opencv2/nonfree/features2d.hpp> #include <opencv2/nonfree/nonfree.hpp>