2012-09-03 21:03:31 +08:00
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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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// This file originates from the openFABMAP project:
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// [http://code.google.com/p/openfabmap/]
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//
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// For published work which uses all or part of OpenFABMAP, please cite:
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// [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6224843]
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//
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// Original Algorithm by Mark Cummins and Paul Newman:
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// [http://ijr.sagepub.com/content/27/6/647.short]
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// [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5613942]
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// [http://ijr.sagepub.com/content/30/9/1100.abstract]
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//
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// License Agreement
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//
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// Copyright (C) 2012 Arren Glover [aj.glover@qut.edu.au] and
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// Will Maddern [w.maddern@qut.edu.au], all rights reserved.
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//
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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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//
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//M*/
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2013-04-15 19:27:09 +08:00
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#include <iostream>
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2012-09-03 21:03:31 +08:00
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2013-04-15 19:27:09 +08:00
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#include "opencv2/contrib.hpp"
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#include "opencv2/highgui.hpp"
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#include "opencv2/nonfree.hpp"
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2012-09-03 21:03:31 +08:00
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using namespace cv;
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using namespace std;
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int main(int argc, char * argv[]) {
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2012-09-24 00:57:34 +08:00
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/*
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2012-10-17 15:12:04 +08:00
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2012-09-24 00:57:34 +08:00
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Note: the vocabulary and training data is specifically made for this openCV
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2012-10-17 15:12:04 +08:00
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example. It is not reccomended for use with other datasets as it is
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2012-09-24 00:57:34 +08:00
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intentionally small to reduce baggage in the openCV project.
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A new vocabulary can be generated using the supplied BOWMSCtrainer (or other
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clustering method such as K-means
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2012-10-17 15:12:04 +08:00
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New training data can be generated by extracting bag-of-words using the
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2012-09-24 00:57:34 +08:00
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openCV BOWImgDescriptorExtractor class.
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vocabulary, chow-liu tree, training data, and test data can all be saved and
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2012-10-17 15:12:04 +08:00
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loaded using openCV's FileStorage class and it is not necessary to generate
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2012-09-24 00:57:34 +08:00
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data each time as done in this example
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*/
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2012-09-03 21:03:31 +08:00
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cout << "This sample program demonstrates the FAB-MAP image matching "
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"algorithm" << endl << endl;
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string dataDir;
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if (argc == 1) {
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dataDir = "fabmap/";
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} else if (argc == 2) {
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dataDir = string(argv[1]);
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dataDir += "/";
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} else {
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//incorrect arguments
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cout << "Usage: fabmap_sample <sample data directory>" <<
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endl;
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return -1;
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}
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FileStorage fs;
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//load/generate vocab
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cout << "Loading Vocabulary: " <<
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dataDir + string("vocab_small.yml") << endl << endl;
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fs.open(dataDir + string("vocab_small.yml"), FileStorage::READ);
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Mat vocab;
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fs["Vocabulary"] >> vocab;
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if (vocab.empty()) {
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cerr << "Vocabulary not found" << endl;
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return -1;
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}
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fs.release();
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//load/generate training data
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cout << "Loading Training Data: " <<
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dataDir + string("train_data_small.yml") << endl << endl;
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fs.open(dataDir + string("train_data_small.yml"), FileStorage::READ);
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Mat trainData;
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fs["BOWImageDescs"] >> trainData;
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if (trainData.empty()) {
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cerr << "Training Data not found" << endl;
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return -1;
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}
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fs.release();
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//create Chow-liu tree
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cout << "Making Chow-Liu Tree from training data" << endl <<
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endl;
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of2::ChowLiuTree treeBuilder;
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treeBuilder.add(trainData);
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Mat tree = treeBuilder.make();
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//generate test data
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cout << "Extracting Test Data from images" << endl <<
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endl;
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Ptr<FeatureDetector> detector =
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new DynamicAdaptedFeatureDetector(
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AdjusterAdapter::create("STAR"), 130, 150, 5);
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Ptr<DescriptorExtractor> extractor =
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new SurfDescriptorExtractor(1000, 4, 2, false, true);
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Ptr<DescriptorMatcher> matcher =
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DescriptorMatcher::create("FlannBased");
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BOWImgDescriptorExtractor bide(extractor, matcher);
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bide.setVocabulary(vocab);
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vector<string> imageNames;
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imageNames.push_back(string("stlucia_test_small0000.jpeg"));
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imageNames.push_back(string("stlucia_test_small0001.jpeg"));
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imageNames.push_back(string("stlucia_test_small0002.jpeg"));
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imageNames.push_back(string("stlucia_test_small0003.jpeg"));
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imageNames.push_back(string("stlucia_test_small0004.jpeg"));
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imageNames.push_back(string("stlucia_test_small0005.jpeg"));
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imageNames.push_back(string("stlucia_test_small0006.jpeg"));
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imageNames.push_back(string("stlucia_test_small0007.jpeg"));
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imageNames.push_back(string("stlucia_test_small0008.jpeg"));
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imageNames.push_back(string("stlucia_test_small0009.jpeg"));
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Mat testData;
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Mat frame;
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Mat bow;
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vector<KeyPoint> kpts;
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for(size_t i = 0; i < imageNames.size(); i++) {
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cout << dataDir + imageNames[i] << endl;
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frame = imread(dataDir + imageNames[i]);
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if(frame.empty()) {
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cerr << "Test images not found" << endl;
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return -1;
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}
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detector->detect(frame, kpts);
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bide.compute(frame, kpts, bow);
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testData.push_back(bow);
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drawKeypoints(frame, kpts, frame);
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imshow(imageNames[i], frame);
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waitKey(10);
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}
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//run fabmap
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cout << "Running FAB-MAP algorithm" << endl <<
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endl;
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Ptr<of2::FabMap> fabmap;
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fabmap = new of2::FabMap2(tree, 0.39, 0, of2::FabMap::SAMPLED |
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of2::FabMap::CHOW_LIU);
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fabmap->addTraining(trainData);
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vector<of2::IMatch> matches;
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fabmap->compare(testData, matches, true);
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//display output
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Mat result_small = Mat::zeros(10, 10, CV_8UC1);
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vector<of2::IMatch>::iterator l;
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for(l = matches.begin(); l != matches.end(); l++) {
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if(l->imgIdx < 0) {
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result_small.at<char>(l->queryIdx, l->queryIdx) =
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(char)(l->match*255);
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} else {
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result_small.at<char>(l->queryIdx, l->imgIdx) =
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(char)(l->match*255);
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}
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}
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Mat result_large(100, 100, CV_8UC1);
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2013-04-11 21:38:33 +08:00
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resize(result_small, result_large, Size(500, 500), 0, 0, INTER_NEAREST);
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2012-09-03 21:03:31 +08:00
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2012-09-24 00:57:34 +08:00
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cout << endl << "Press any key to exit" << endl;
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2012-09-03 21:03:31 +08:00
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imshow("Confusion Matrix", result_large);
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waitKey();
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return 0;
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}
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