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129 lines
5.0 KiB
C++
129 lines
5.0 KiB
C++
// 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://opencv.itseez.com/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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