[f3f8b8]: samples / cpp / freak_demo.cpp  Maximize  Restore  History

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

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