Feature Matching with FLANN

Goal

In this tutorial you will learn how to:

• Use the cv::FlannBasedMatcher interface in order to perform a quick and efficient matching by using the Clustering and Search in Multi-Dimensional Spaces module

Theory

Code

This tutorial code's is shown lines below.

/*
 * @file SURF_FlannMatcher
 * @brief SURF detector + descriptor + FLANN Matcher
 * @author A. Huaman
 */

#include <stdio.h>
#include <iostream>
#include <stdio.h>
#include <iostream>
#include "opencv2/core.hpp"
#include "opencv2/features2d.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/xfeatures2d.hpp"

using namespace std;
using namespace cv;
using namespace cv::xfeatures2d;

void readme();

/*
 * @function main
 * @brief Main function
 */
int main( int argc, char** argv )
{
 if( argc != 3 )
 { readme(); return -1; }

 Mat img_1 = imread( argv[1], IMREAD_GRAYSCALE );
 Mat img_2 = imread( argv[2], IMREAD_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, compute the descriptors
 int minHessian = 400;

 Ptr<SURF> detector = SURF::create();
 detector->setHessianThreshold(minHessian);

 std::vector<KeyPoint> keypoints_1, keypoints_2;
 Mat descriptors_1, descriptors_2;

 detector->detectAndCompute( img_1, Mat(), keypoints_1, descriptors_1 );
 detector->detectAndCompute( img_2, Mat(), keypoints_2, descriptors_2 );

 //-- Step 2: 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,
 //-- or a small arbitary value ( 0.02 ) in the event that min_dist is very
 //-- small)
 //-- 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 <= max(2*min_dist, 0.02) )
 { 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 < (int)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; }

Explanation

Result

1. Here is the result of the feature detection applied to the first image:

   

2. Additionally, we get as console output the keypoints filtered: