OpenCV使用FLANN進行特征點匹配

使用FLANN進行特征點匹配

目標

在本教程中我們將涉及以下內容:

• 使用 FlannBasedMatcher 接口以及函數 FLANN 實現快速高效匹配( 快速最近鄰逼近搜索函數庫(Fast Approximate Nearest Neighbor Search Library) )

理論

代碼

這個教程的源代碼如下所示。你還可以從 以下鏈接下載得到源代碼

#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 */ int main( int argc, char** argv ) { if( argc != 3 ) { readme(); 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 ) { 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 ); for( int i = 0; i < good_matches.size(); i++ ) { printf( "-- Good Match [%d] Keypoint 1: %d -- Keypoint 2: %d \n", i, good_matches[i].queryIdx, good_matches[i].trainIdx ); } waitKey(0); return 0; } /** @function readme */ void readme() { std::cout << " Usage: ./SURF_FlannMatcher <img1> <img2>" << std::endl; } 

解釋

結果

1. 這里是第一張圖特征點檢測結果:

   

2. 此外我們通過控制台輸出FLANN匹配關鍵點結果:

   

翻譯者

Shuai Zheng, <[e-mail]>, http://www.cbsr.ia.ac.cn/users/szheng/

from: http://www.opencv.org.cn/opencvdoc/2.3.2/html/doc/tutorials/features2d/feature_flann_matcher/feature_flann_matcher.html#feature-flann-matcher