.. _feature_flann_matcher:

Feature Matching with FLANN
****************************

Goal
=====

In this tutorial you will learn how to:

.. container:: enumeratevisibleitemswithsquare

   * Use the :flann_based_matcher:`FlannBasedMatcher<>` interface in order to perform a quick and efficient matching by using the :flann:`FLANN<>` ( *Fast Approximate Nearest Neighbor Search Library* )


Theory
======

Code
====

This tutorial code's is shown lines below.

.. code-block:: cpp

    /**
     * @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
      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()
    { std::cout << " Usage: ./SURF_FlannMatcher <img1> <img2>" << std::endl; }


Explanation
============

Result
======

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

   .. image:: images/Featur_FlannMatcher_Result.jpg
      :align: center
      :height: 250pt

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

   .. image:: images/Feature_FlannMatcher_Keypoints_Result.jpg
      :align: center
      :height: 250pt
