103 lines
2.7 KiB
ReStructuredText
103 lines
2.7 KiB
ReStructuredText
.. _feature_description:
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Feature Description
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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 :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:`BFMatcher<>` to match the features vector
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* Use the function :draw_matches:`drawMatches<>` to draw the detected matches.
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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://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/features2D/SURF_descriptor.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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#include "opencv2/nonfree/features2d.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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{ 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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{ 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 with a brute force matcher
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BFMatcher matcher(NORM_L2);
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std::vector< DMatch > matches;
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matcher.match( descriptors_1, descriptors_2, matches );
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//-- Draw matches
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Mat img_matches;
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drawMatches( img_1, keypoints_1, img_2, keypoints_2, matches, img_matches );
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//-- Show detected matches
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imshow("Matches", img_matches );
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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_descriptor <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 after applying the BruteForce matcher between the two original images:
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.. image:: images/Feature_Description_BruteForce_Result.jpg
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:align: center
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:height: 200pt
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