opencv/samples/cpp/fabmap_sample.cpp
2013-09-05 19:02:59 +04:00

217 lines
7.4 KiB
C++

/*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[]) {
/*
Note: the vocabulary and training data is specifically made for this openCV
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
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
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.reset(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;
}