| #include <iostream> |
| #include <opencv2/core/core.hpp> |
| #include <opencv2/features2d/features2d.hpp> |
| #include <opencv2/highgui/highgui.hpp> |
| |
| using namespace std; |
| using namespace cv; |
| |
| int main ( int argc, char** argv ) |
| { |
| if ( argc != 3 ) |
| { |
| cout<<"usage: feature_extraction img1 img2"<<endl; |
| return 1; |
| } |
| //-- 讀取圖像 |
| Mat img_1 = imread ( argv[1], CV_LOAD_IMAGE_COLOR ); |
| Mat img_2 = imread ( argv[2], CV_LOAD_IMAGE_COLOR ); |
| |
| //-- 初始化 |
| std::vector<KeyPoint> keypoints_1, keypoints_2; |
| Mat descriptors_1, descriptors_2; |
| Ptr<FeatureDetector> detector = ORB::create(); |
| Ptr<DescriptorExtractor> descriptor = ORB::create(); |
| // Ptr<FeatureDetector> detector = FeatureDetector::create(detector_name); |
| // Ptr<DescriptorExtractor> descriptor = DescriptorExtractor::create(descriptor_name); |
| Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create ( "BruteForce-Hamming" ); |
| |
| //-- 第一步:檢測 Oriented FAST 角點(diǎn)位置 |
| detector->detect ( img_1,keypoints_1 ); |
| detector->detect ( img_2,keypoints_2 ); |
| |
| //-- 第二步:根據(jù)角點(diǎn)位置計(jì)算 BRIEF 描述子 |
| descriptor->compute ( img_1, keypoints_1, descriptors_1 ); |
| descriptor->compute ( img_2, keypoints_2, descriptors_2 ); |
| |
| Mat outimg1; |
| drawKeypoints( img_1, keypoints_1, outimg1, Scalar::all(-1), DrawMatchesFlags::DEFAULT ); |
| imshow("ORB特征點(diǎn)",outimg1); |
| |
| //-- 第三步:對兩幅圖像中的BRIEF描述子進(jìn)行匹配，使用 Hamming 距離 |
| vector<DMatch> matches; |
| //BFMatcher matcher ( NORM_HAMMING ); |
| matcher->match ( descriptors_1, descriptors_2, matches ); |
| |
| //-- 第四步:匹配點(diǎn)對篩選 |
| double min_dist=10000, max_dist=0; |
| |
| //找出所有匹配之間的最小距離和最大距離, 即是最相似的和最不相似的兩組點(diǎn)之間的距離 |
| for ( int i = 0; i < descriptors_1.rows; i++ ) |
| { |
| double dist = matches.distance; |
| if ( dist < min_dist ) min_dist = dist; |
| if ( dist > max_dist ) max_dist = dist; |
| } |
| |
| // 僅供娛樂的寫法 |
| min_dist = min_element( matches.begin(), matches.end(), [](const DMatch& m1, const DMatch& m2) {return m1.distance<m2.distance;} )->distance; |
| max_dist = max_element( matches.begin(), matches.end(), [](const DMatch& m1, const DMatch& m2) {return m1.distance<m2.distance;} )->distance; |
| |
| printf ( "-- Max dist : %f \n", max_dist ); |
| printf ( "-- Min dist : %f \n", min_dist ); |
| |
| //當(dāng)描述子之間的距離大于兩倍的最小距離時,即認(rèn)為匹配有誤.但有時候最小距離會非常小,設(shè)置一個經(jīng)驗(yàn)值30作為下限. |
| std::vector< DMatch > good_matches; |
| for ( int i = 0; i < descriptors_1.rows; i++ ) |
| { |
| if ( matches.distance <= max ( 2*min_dist, 30.0 ) ) |
| { |
| good_matches.push_back ( matches ); |
| } |
| } |
| |
| //-- 第五步:繪制匹配結(jié)果 |
| Mat img_match; |
| Mat img_goodmatch; |
| drawMatches ( img_1, keypoints_1, img_2, keypoints_2, matches, img_match ); |
| drawMatches ( img_1, keypoints_1, img_2, keypoints_2, good_matches, img_goodmatch ); |
| imshow ( "所有匹配點(diǎn)對", img_match ); |
| imshow ( "優(yōu)化后匹配點(diǎn)對", img_goodmatch ); |
| waitKey(0); |
| |
| return 0; |
| } |