Feature Matching with FLANN — OpenCV 2.4.13.4 documentation

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# Feature Matching with FLANN

## Goal

In this tutorial you will learn how to:

- Use the FlannBasedMatcher interface in order to perform a quick and efficient matching by using the FLANN ( Fast Approximate Nearest Neighbor Search Library )

## Theory

## Code

This tutorial code’s is shown lines below. You can also download it from here

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

    #include "opencv2/opencv_modules.hpp"
    #include <stdio.h>

    #ifndef HAVE_OPENCV_NONFREE

    int main(int, char**)
    {
        printf("The sample requires nonfree module that is not available in your OpenCV distribution.\n");
        return -1;
    }

    #else

    # 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
     * @brief Main function
     */
    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 )
      { printf(" --(!) Error reading images \n"); 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,
      //-- 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()
    { printf(" Usage: ./SURF_FlannMatcher <img1> <img2>\n"); }

    #endif

## 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:

## Help and Feedback

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- Ask a question on the Q&A forum.
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### Navigation

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