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128 lines
5.0 KiB
128 lines
5.0 KiB
// demo.cpp |
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// |
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// Here is an example on how to use the descriptor presented in the following paper: |
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// A. Alahi, R. Ortiz, and P. Vandergheynst. FREAK: Fast Retina Keypoint. In IEEE Conference on Computer Vision and Pattern Recognition, 2012. |
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// CVPR 2012 Open Source Award winner |
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// |
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// Copyright (C) 2011-2012 Signal processing laboratory 2, EPFL, |
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// Kirell Benzi (kirell.benzi@epfl.ch), |
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// Raphael Ortiz (raphael.ortiz@a3.epfl.ch), |
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// Alexandre Alahi (alexandre.alahi@epfl.ch) |
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// and Pierre Vandergheynst (pierre.vandergheynst@epfl.ch) |
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// |
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// Redistribution and use in source and binary forms, with or without modification, |
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// are permitted provided that the following conditions are met: |
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// |
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// * Redistribution's of source code must retain the above copyright notice, |
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// this list of conditions and the following disclaimer. |
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// |
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// * Redistribution's in binary form must reproduce the above copyright notice, |
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// this list of conditions and the following disclaimer in the documentation |
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// and/or other materials provided with the distribution. |
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// |
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// * The name of the copyright holders may not be used to endorse or promote products |
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// derived from this software without specific prior written permission. |
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// |
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// This software is provided by the copyright holders and contributors "as is" and |
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// any express or implied warranties, including, but not limited to, the implied |
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// warranties of merchantability and fitness for a particular purpose are disclaimed. |
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// In no event shall the Intel Corporation or contributors be liable for any direct, |
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// indirect, incidental, special, exemplary, or consequential damages |
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// (including, but not limited to, procurement of substitute goods or services; |
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// loss of use, data, or profits; or business interruption) however caused |
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// and on any theory of liability, whether in contract, strict liability, |
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// or tort (including negligence or otherwise) arising in any way out of |
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// the use of this software, even if advised of the possibility of such damage. |
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#include <iostream> |
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#include <string> |
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#include <vector> |
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#include <opencv2/core.hpp> |
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#include "opencv2/core/utility.hpp" |
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#include <opencv2/highgui.hpp> |
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#include <opencv2/features2d.hpp> |
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#include <opencv2/nonfree.hpp> |
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using namespace cv; |
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static void help( char** argv ) |
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{ |
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std::cout << "\nUsage: " << argv[0] << " [path/to/image1] [path/to/image2] \n" |
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<< "This is an example on how to use the keypoint descriptor presented in the following paper: \n" |
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<< "A. Alahi, R. Ortiz, and P. Vandergheynst. FREAK: Fast Retina Keypoint. \n" |
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<< "In IEEE Conference on Computer Vision and Pattern Recognition, 2012. CVPR 2012 Open Source Award winner \n" |
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<< std::endl; |
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} |
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int main( int argc, char** argv ) { |
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// check http://docs.opencv.org/doc/tutorials/features2d/table_of_content_features2d/table_of_content_features2d.html |
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// for OpenCV general detection/matching framework details |
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if( argc != 3 ) { |
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help(argv); |
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return -1; |
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} |
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// Load images |
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Mat imgA = imread(argv[1], IMREAD_GRAYSCALE ); |
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if( !imgA.data ) { |
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std::cout<< " --(!) Error reading image " << argv[1] << std::endl; |
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return -1; |
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} |
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Mat imgB = imread(argv[2], IMREAD_GRAYSCALE ); |
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if( !imgB.data ) { |
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std::cout << " --(!) Error reading image " << argv[2] << std::endl; |
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return -1; |
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} |
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std::vector<KeyPoint> keypointsA, keypointsB; |
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Mat descriptorsA, descriptorsB; |
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std::vector<DMatch> matches; |
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// DETECTION |
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// Any openCV detector such as |
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SurfFeatureDetector detector(2000,4); |
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// DESCRIPTOR |
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// Our proposed FREAK descriptor |
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// (roation invariance, scale invariance, pattern radius corresponding to SMALLEST_KP_SIZE, |
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// number of octaves, optional vector containing the selected pairs) |
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// FREAK extractor(true, true, 22, 4, std::vector<int>()); |
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FREAK extractor; |
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// MATCHER |
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// The standard Hamming distance can be used such as |
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// BFMatcher matcher(NORM_HAMMING); |
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// or the proposed cascade of hamming distance using SSSE3 |
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BFMatcher matcher(NORM_HAMMING); |
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// detect |
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double t = (double)getTickCount(); |
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detector.detect( imgA, keypointsA ); |
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detector.detect( imgB, keypointsB ); |
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t = ((double)getTickCount() - t)/getTickFrequency(); |
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std::cout << "detection time [s]: " << t/1.0 << std::endl; |
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// extract |
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t = (double)getTickCount(); |
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extractor.compute( imgA, keypointsA, descriptorsA ); |
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extractor.compute( imgB, keypointsB, descriptorsB ); |
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t = ((double)getTickCount() - t)/getTickFrequency(); |
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std::cout << "extraction time [s]: " << t << std::endl; |
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// match |
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t = (double)getTickCount(); |
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matcher.match(descriptorsA, descriptorsB, matches); |
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t = ((double)getTickCount() - t)/getTickFrequency(); |
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std::cout << "matching time [s]: " << t << std::endl; |
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// Draw matches |
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Mat imgMatch; |
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drawMatches(imgA, keypointsA, imgB, keypointsB, matches, imgMatch); |
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namedWindow("matches", WINDOW_KEEPRATIO); |
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imshow("matches", imgMatch); |
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waitKey(0); |
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}
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