opencv/samples/cpp/fabmap_sample.cpp

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
// This file originates from the openFABMAP project:
// [http://code.google.com/p/openfabmap/]
//
// For published work which uses all or part of OpenFABMAP, please cite:
// [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6224843]
//
// Original Algorithm by Mark Cummins and Paul Newman:
// [http://ijr.sagepub.com/content/27/6/647.short]
// [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5613942]
// [http://ijr.sagepub.com/content/30/9/1100.abstract]
//
// License Agreement
//
// Copyright (C) 2012 Arren Glover [aj.glover@qut.edu.au] and
// Will Maddern [w.maddern@qut.edu.au], all rights reserved.
//
//
// 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.
//
//M*/
#include <iostream>
#include "opencv2/contrib.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/nonfree.hpp"
using namespace cv;
using namespace std;
int main(int argc, char * argv[]) {
/*
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Note: the vocabulary and training data is specifically made for this openCV
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example. It is not reccomended for use with other datasets as it is
intentionally small to reduce baggage in the openCV project.
A new vocabulary can be generated using the supplied BOWMSCtrainer (or other
clustering method such as K-means
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New training data can be generated by extracting bag-of-words using the
openCV BOWImgDescriptorExtractor class.
vocabulary, chow-liu tree, training data, and test data can all be saved and
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loaded using openCV's FileStorage class and it is not necessary to generate
data each time as done in this example
*/
cout << "This sample program demonstrates the FAB-MAP image matching "
"algorithm" << endl << endl;
string dataDir;
if (argc == 1) {
dataDir = "fabmap/";
} else if (argc == 2) {
dataDir = string(argv[1]);
dataDir += "/";
} else {
//incorrect arguments
cout << "Usage: fabmap_sample <sample data directory>" <<
endl;
return -1;
}
FileStorage fs;
//load/generate vocab
cout << "Loading Vocabulary: " <<
dataDir + string("vocab_small.yml") << endl << endl;
fs.open(dataDir + string("vocab_small.yml"), FileStorage::READ);
Mat vocab;
fs["Vocabulary"] >> vocab;
if (vocab.empty()) {
cerr << "Vocabulary not found" << endl;
return -1;
}
fs.release();
//load/generate training data
cout << "Loading Training Data: " <<
dataDir + string("train_data_small.yml") << endl << endl;
fs.open(dataDir + string("train_data_small.yml"), FileStorage::READ);
Mat trainData;
fs["BOWImageDescs"] >> trainData;
if (trainData.empty()) {
cerr << "Training Data not found" << endl;
return -1;
}
fs.release();
//create Chow-liu tree
cout << "Making Chow-Liu Tree from training data" << endl <<
endl;
of2::ChowLiuTree treeBuilder;
treeBuilder.add(trainData);
Mat tree = treeBuilder.make();
//generate test data
cout << "Extracting Test Data from images" << endl <<
endl;
Ptr<FeatureDetector> detector =
new DynamicAdaptedFeatureDetector(
AdjusterAdapter::create("STAR"), 130, 150, 5);
Ptr<DescriptorExtractor> extractor =
new SurfDescriptorExtractor(1000, 4, 2, false, true);
Ptr<DescriptorMatcher> matcher =
DescriptorMatcher::create("FlannBased");
BOWImgDescriptorExtractor bide(extractor, matcher);
bide.setVocabulary(vocab);
vector<string> imageNames;
imageNames.push_back(string("stlucia_test_small0000.jpeg"));
imageNames.push_back(string("stlucia_test_small0001.jpeg"));
imageNames.push_back(string("stlucia_test_small0002.jpeg"));
imageNames.push_back(string("stlucia_test_small0003.jpeg"));
imageNames.push_back(string("stlucia_test_small0004.jpeg"));
imageNames.push_back(string("stlucia_test_small0005.jpeg"));
imageNames.push_back(string("stlucia_test_small0006.jpeg"));
imageNames.push_back(string("stlucia_test_small0007.jpeg"));
imageNames.push_back(string("stlucia_test_small0008.jpeg"));
imageNames.push_back(string("stlucia_test_small0009.jpeg"));
Mat testData;
Mat frame;
Mat bow;
vector<KeyPoint> kpts;
for(size_t i = 0; i < imageNames.size(); i++) {
cout << dataDir + imageNames[i] << endl;
frame = imread(dataDir + imageNames[i]);
if(frame.empty()) {
cerr << "Test images not found" << endl;
return -1;
}
detector->detect(frame, kpts);
bide.compute(frame, kpts, bow);
testData.push_back(bow);
drawKeypoints(frame, kpts, frame);
imshow(imageNames[i], frame);
waitKey(10);
}
//run fabmap
cout << "Running FAB-MAP algorithm" << endl <<
endl;
Ptr<of2::FabMap> fabmap;
fabmap = new of2::FabMap2(tree, 0.39, 0, of2::FabMap::SAMPLED |
of2::FabMap::CHOW_LIU);
fabmap->addTraining(trainData);
vector<of2::IMatch> matches;
fabmap->compare(testData, matches, true);
//display output
Mat result_small = Mat::zeros(10, 10, CV_8UC1);
vector<of2::IMatch>::iterator l;
for(l = matches.begin(); l != matches.end(); l++) {
if(l->imgIdx < 0) {
result_small.at<char>(l->queryIdx, l->queryIdx) =
(char)(l->match*255);
} else {
result_small.at<char>(l->queryIdx, l->imgIdx) =
(char)(l->match*255);
}
}
Mat result_large(100, 100, CV_8UC1);
resize(result_small, result_large, Size(500, 500), 0, 0, INTER_NEAREST);
cout << endl << "Press any key to exit" << endl;
imshow("Confusion Matrix", result_large);
waitKey();
return 0;
}