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 Detectorint 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 matcherFlannBasedMatcher 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 keypointsfor( 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" matchesMat 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 matchesimshow( "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

這里是第一張圖特征點檢測結果:

此外我們通過控制臺輸出FLANN匹配關鍵點結果:

from:?http://www.opencv.org.cn/opencvdoc/2.3.2/html/doc/tutorials/features2d/feature_flann_matcher/feature_flann_matcher.html 《新程序員》：云原生和全面數字化實踐50位技術專家共同創作，文字、視頻、音頻交互閱讀

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