.. _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. You can also download it from `here <http://code.opencv.org/projects/opencv/repository/revisions/master/raw/samples/cpp/tutorial_code/features2D/SURF_FlannMatcher.cpp>`_

.. code-block:: cpp

   #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; }

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
