Added base tutorial for using FlannBasedMatcher with SURF detector + descriptor in rst
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@ -368,7 +368,10 @@ extlinks = {'cvt_color': ('http://opencv.willowgarage.com/documentation/cpp/imgp
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'surf_descriptor_extractor' : ( 'http://opencv.willowgarage.com/documentation/cpp/features2d_common_interfaces_of_descriptor_extractors.html#surfdescriptorextractor%s', None ),
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'draw_matches' : ( 'http://opencv.willowgarage.com/documentation/cpp/features2d_drawing_function_of_keypoints_and_matches.html#cv-drawmatches%s', None ),
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'find_homography' : ('http://opencv.willowgarage.com/documentation/cpp/calib3d_camera_calibration_and_3d_reconstruction.html?#findHomography%s', None),
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'perspective_transform' : ('http://opencv.willowgarage.com/documentation/cpp/core_operations_on_arrays.html?#perspectiveTransform%s', None )
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'perspective_transform' : ('http://opencv.willowgarage.com/documentation/cpp/core_operations_on_arrays.html?#perspectiveTransform%s', None ),
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'flann_based_matcher' : ('http://opencv.willowgarage.com/documentation/cpp/features2d_common_interfaces_of_descriptor_matchers.html?#FlannBasedMatcher%s', None),
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'brute_force_matcher' : ('http://opencv.willowgarage.com/documentation/cpp/features2d_common_interfaces_of_descriptor_matchers.html?#BruteForceMatcher%s', None ),
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'flann' : ('http://opencv.willowgarage.com/documentation/cpp/flann_fast_approximate_nearest_neighbor_search.html?%s', None )
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}
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@ -13,6 +13,7 @@ In this tutorial you will learn how to:
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* Use the :descriptor_extractor:`DescriptorExtractor<>` interface in order to find the feature vector correspondent to the keypoints. Specifically:
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* Use :surf_descriptor_extractor:`SurfDescriptorExtractor<>` and its function :descriptor_extractor:`compute<>` to perform the required calculations.
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* Use a :brute_force_matcher:`BruteForceMatcher<>` to match the features vector
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* Use the function :draw_matches:`drawMatches<>` to draw the detected matches.
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@ -0,0 +1,132 @@
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.. _feature_flann_matcher:
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Feature Matching with FLANN
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****************************
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Goal
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=====
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In this tutorial you will learn how to:
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.. container:: enumeratevisibleitemswithsquare
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* 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* )
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Theory
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======
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Code
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====
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This tutorial code's is shown lines below. You can also download it from `here <https://code.ros.org/svn/opencv/trunk/opencv/samples/cpp/tutorial_code/features2D/SURF_FlannMatcher.cpp>`_
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.. code-block:: cpp
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#include <stdio.h>
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#include <iostream>
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#include "opencv2/core/core.hpp"
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#include "opencv2/features2d/features2d.hpp"
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#include "opencv2/highgui/highgui.hpp"
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using namespace cv;
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void readme();
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/** @function main */
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int main( int argc, char** argv )
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{
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if( argc != 3 )
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{ readme(); return -1; }
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Mat img_1 = imread( argv[1], CV_LOAD_IMAGE_GRAYSCALE );
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Mat img_2 = imread( argv[2], CV_LOAD_IMAGE_GRAYSCALE );
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if( !img_1.data || !img_2.data )
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{ std::cout<< " --(!) Error reading images " << std::endl; return -1; }
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//-- Step 1: Detect the keypoints using SURF Detector
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int minHessian = 400;
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SurfFeatureDetector detector( minHessian );
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std::vector<KeyPoint> keypoints_1, keypoints_2;
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detector.detect( img_1, keypoints_1 );
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detector.detect( img_2, keypoints_2 );
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//-- Step 2: Calculate descriptors (feature vectors)
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SurfDescriptorExtractor extractor;
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Mat descriptors_1, descriptors_2;
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extractor.compute( img_1, keypoints_1, descriptors_1 );
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extractor.compute( img_2, keypoints_2, descriptors_2 );
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//-- Step 3: Matching descriptor vectors using FLANN matcher
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FlannBasedMatcher matcher;
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std::vector< DMatch > matches;
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matcher.match( descriptors_1, descriptors_2, matches );
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double max_dist = 0; double min_dist = 100;
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//-- Quick calculation of max and min distances between keypoints
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for( int i = 0; i < descriptors_1.rows; i++ )
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{ double dist = matches[i].distance;
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if( dist < min_dist ) min_dist = dist;
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if( dist > max_dist ) max_dist = dist;
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}
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printf("-- Max dist : %f \n", max_dist );
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printf("-- Min dist : %f \n", min_dist );
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//-- Draw only "good" matches (i.e. whose distance is less than 2*min_dist )
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//-- PS.- radiusMatch can also be used here.
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std::vector< DMatch > good_matches;
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for( int i = 0; i < descriptors_1.rows; i++ )
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{ if( matches[i].distance < 2*min_dist )
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{ good_matches.push_back( matches[i]); }
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}
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//-- Draw only "good" matches
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Mat img_matches;
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drawMatches( img_1, keypoints_1, img_2, keypoints_2,
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good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
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vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
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//-- Show detected matches
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imshow( "Good Matches", img_matches );
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for( int i = 0; i < good_matches.size(); i++ )
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{ printf( "-- Good Match [%d] Keypoint 1: %d -- Keypoint 2: %d \n", i, good_matches[i].queryIdx, good_matches[i].trainIdx ); }
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waitKey(0);
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return 0;
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}
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/** @function readme */
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void readme()
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{ std::cout << " Usage: ./SURF_FlannMatcher <img1> <img2>" << std::endl; }
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Explanation
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============
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Result
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======
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#. Here is the result of the feature detection applied to the first image:
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.. image:: images/Featur_FlannMatcher_Result.jpg
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:align: center
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:height: 250pt
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#. Additionally, we get as console output the keypoints filtered:
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.. image:: images/Feature_FlannMatcher_Keypoints_Result.jpg
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:align: center
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:height: 250pt
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@ -94,4 +94,4 @@ int main( int argc, char** argv )
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* @function readme
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*/
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void readme()
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{ std::cout << " Usage: ./SURF_descriptor <img1> <img2>" << std::endl; }
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{ std::cout << " Usage: ./SURF_FlannMatcher <img1> <img2>" << std::endl; }
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@ -119,4 +119,4 @@ int main( int argc, char** argv )
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* @function readme
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*/
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void readme()
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{ std::cout << " Usage: ./SURF_descriptor <img1> <img2>" << std::endl; }
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{ std::cout << " Usage: ./SURF_Homography <img1> <img2>" << std::endl; }
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