opencv/samples/cpp/fabmap_sample.cpp

/*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.
//
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// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
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//   * Redistribution's of source code must retain the above copyright notice,
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//     derived from this software without specific prior written permission.
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// This software is provided by the copyright holders and contributors "as is" and
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#include "opencv2/opencv_modules.hpp"
#include <iostream>

#ifndef HAVE_OPENCV_NONFREE

int main(int, char**)
{
    std::cout << "The sample requires nonfree module that is not available in your OpenCV distribution." << std::endl;
    return -1;
}

#else

# include "opencv2/opencv.hpp"
# include "opencv2/nonfree/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 = 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, CV_INTER_NN);

    cout << endl << "Press any key to exit" << endl;
    imshow("Confusion Matrix", result_large);
    waitKey();

    return 0;
}

#endif