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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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After Width: | Height: | Size: 79 KiB |
After Width: | Height: | Size: 77 KiB |
After Width: | Height: | Size: 79 KiB |
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