Feature Description

Goal

In this tutorial you will learn how to:

Use the @ref cv::DescriptorExtractor interface in order to find the feature vector correspondent to the keypoints. Specifically:
- Use @ref cv::SurfDescriptorExtractor and its function @ref cv::compute to perform the required calculations.
- Use a @ref cv::BFMatcher to match the features vector
- Use the function @ref cv::drawMatches to draw the detected matches.

Theory

Code

This tutorial code's is shown lines below. @code{.cpp} #include <stdio.h> #include #include "opencv2/core.hpp" #include "opencv2/features2d.hpp" #include "opencv2/highgui.hpp" #include "opencv2/xfeatures2d.hpp"

using namespace cv; using namespace cv::xfeatures2d;

void readme();

/* @function main / int main( int argc, char* argv ) { if( argc != 3 ) { 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 ) { return -1; }

//-- Step 1: Detect the keypoints using SURF Detector, compute the descriptors int minHessian = 400;

Ptr detector = SURF::create(); detector->setMinHessian(minHessian);

std::vector keypoints_1, keypoints_2; Mat descriptors_1, descriptors_2;

detector->detectAndCompute( img_1, keypoints_1, descriptors_1 ); detector->detectAndCompute( img_2, keypoints_2, descriptors_2 );

//-- Step 2: Matching descriptor vectors with a brute force matcher BFMatcher matcher(NORM_L2); std::vector< DMatch > matches; matcher.match( descriptors_1, descriptors_2, matches );

//-- Draw matches Mat img_matches; drawMatches( img_1, keypoints_1, img_2, keypoints_2, matches, img_matches );

//-- Show detected matches imshow("Matches", img_matches );

waitKey(0);

return 0; }

/* @function readme */ void readme() { std::cout << " Usage: ./SURF_descriptor " << std::endl; } @endcode Explanation

Result

Here is the result after applying the BruteForce matcher between the two original images:

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