added openfabmap code, contributed by Arren Glover. fixed several warnings in…

added openfabmap code, contributed by Arren Glover. fixed several warnings in the new versions of retina filters

modules/contrib/include/opencv2/contrib/contrib.hpp

@@ -967,6 +967,8 @@ namespace cv
 
 #include "opencv2/contrib/retina.hpp"
 
+#include "opencv2/contrib/openfabmap.hpp"
+
 #endif
 
 #endif

modules/contrib/src/bowmsctrainer.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.
+//
+//
+// 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 "precomp.hpp"
+#include "opencv2/contrib/openfabmap.hpp"
+
+namespace cv {
+
+namespace of2 {
+
+BOWMSCTrainer::BOWMSCTrainer(double _clusterSize) :
+    clusterSize(_clusterSize) {
+}
+
+BOWMSCTrainer::~BOWMSCTrainer() {
+}
+
+Mat BOWMSCTrainer::cluster() const {
+    CV_Assert(!descriptors.empty());
+    int descCount = 0;
+    for(size_t i = 0; i < descriptors.size(); i++)
+    descCount += descriptors[i].rows;
+
+    Mat mergedDescriptors(descCount, descriptors[0].cols,
+        descriptors[0].type());
+    for(size_t i = 0, start = 0; i < descriptors.size(); i++)
+    {
+        Mat submut = mergedDescriptors.rowRange((int)start,
+            (int)(start + descriptors[i].rows));
+        descriptors[i].copyTo(submut);
+        start += descriptors[i].rows;
+    }
+    return cluster(mergedDescriptors);
+}
+
+Mat BOWMSCTrainer::cluster(const Mat& descriptors) const {
+
+    CV_Assert(!descriptors.empty());
+
+    // TODO: sort the descriptors before clustering.
+
+
+    Mat icovar = Mat::eye(descriptors.cols,descriptors.cols,descriptors.type());
+
+    vector<Mat> initialCentres;
+    initialCentres.push_back(descriptors.row(0));
+    for (int i = 1; i < descriptors.rows; i++) {
+        double minDist = DBL_MAX;
+        for (size_t j = 0; j < initialCentres.size(); j++) {
+            minDist = std::min(minDist,
+                cv::Mahalanobis(descriptors.row(i),initialCentres[j],
+                icovar));
+        }
+        if (minDist > clusterSize)
+            initialCentres.push_back(descriptors.row(i));
+    }
+
+    std::vector<std::list<cv::Mat> > clusters;
+    clusters.resize(initialCentres.size());
+    for (int i = 0; i < descriptors.rows; i++) {
+        int index = 0; double dist = 0, minDist = DBL_MAX;
+        for (size_t j = 0; j < initialCentres.size(); j++) {
+            dist = cv::Mahalanobis(descriptors.row(i),initialCentres[j],icovar);
+            if (dist < minDist) {
+                minDist = dist;
+                index = (int)j;
+            }
+        }
+        clusters[index].push_back(descriptors.row(i));
+    }
+
+    // TODO: throw away small clusters.
+
+    Mat vocabulary;
+    Mat centre = Mat::zeros(1,descriptors.cols,descriptors.type());
+    for (size_t i = 0; i < clusters.size(); i++) {
+        centre.setTo(0);
+        for (std::list<cv::Mat>::iterator Ci = clusters[i].begin(); Ci != clusters[i].end(); Ci++) {
+            centre += *Ci;
+        }
+        centre /= (double)clusters[i].size();
+        vocabulary.push_back(centre);
+    }
+
+    return vocabulary;
+}
+
+}
+
+}
+

modules/contrib/src/chowliutree.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.
+//
+//
+// 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 "precomp.hpp"
+#include "opencv2/contrib/openfabmap.hpp"
+
+namespace cv {
+
+namespace of2 {
+
+ChowLiuTree::ChowLiuTree() {
+}
+
+ChowLiuTree::~ChowLiuTree() {
+}
+
+void ChowLiuTree::add(const Mat& imgDescriptor) {
+    CV_Assert(!imgDescriptor.empty());
+    if (!imgDescriptors.empty()) {
+        CV_Assert(imgDescriptors[0].cols == imgDescriptor.cols);
+        CV_Assert(imgDescriptors[0].type() == imgDescriptor.type());
+    }
+
+    imgDescriptors.push_back(imgDescriptor);
+
+}
+
+void ChowLiuTree::add(const vector<Mat>& _imgDescriptors) {
+    for (size_t i = 0; i < _imgDescriptors.size(); i++) {
+        add(_imgDescriptors[i]);
+    }
+}
+
+const std::vector<cv::Mat>& ChowLiuTree::getImgDescriptors() const {
+    return imgDescriptors;
+}
+
+Mat ChowLiuTree::make(double infoThreshold) {
+    CV_Assert(!imgDescriptors.empty());
+
+    unsigned int descCount = 0;
+    for (size_t i = 0; i < imgDescriptors.size(); i++)
+        descCount += imgDescriptors[i].rows;
+
+    mergedImgDescriptors = cv::Mat(descCount, imgDescriptors[0].cols,
+        imgDescriptors[0].type());
+    for (size_t i = 0, start = 0; i < imgDescriptors.size(); i++)
+    {
+        Mat submut = mergedImgDescriptors.rowRange((int)start,
+            (int)(start + imgDescriptors[i].rows));
+        imgDescriptors[i].copyTo(submut);
+        start += imgDescriptors[i].rows;
+    }
+
+    std::list<info> edges;
+    createBaseEdges(edges, infoThreshold);
+
+    // TODO: if it cv_asserts here they really won't know why.
+
+    CV_Assert(reduceEdgesToMinSpan(edges));
+
+    return buildTree(edges.front().word1, edges);
+}
+
+double ChowLiuTree::P(int a, bool za) {
+
+    if(za) {
+        return (0.98 * cv::countNonZero(mergedImgDescriptors.col(a)) /
+            mergedImgDescriptors.rows) + 0.01;
+    } else {
+        return 1 - ((0.98 * cv::countNonZero(mergedImgDescriptors.col(a)) /
+            mergedImgDescriptors.rows) + 0.01);
+    }
+
+}
+double ChowLiuTree::JP(int a, bool za, int b, bool zb) {
+
+    double count = 0;
+    for(int i = 0; i < mergedImgDescriptors.rows; i++) {
+        if((mergedImgDescriptors.at<float>(i,a) > 0) == za &&
+            (mergedImgDescriptors.at<float>(i,b) > 0) == zb) {
+                count++;
+        }
+    }
+    return count / mergedImgDescriptors.rows;
+
+}
+double ChowLiuTree::CP(int a, bool za, int b, bool zb){
+
+    int count = 0, total = 0;
+    for(int i = 0; i < mergedImgDescriptors.rows; i++) {
+        if((mergedImgDescriptors.at<float>(i,b) > 0) == zb) {
+            total++;
+            if((mergedImgDescriptors.at<float>(i,a) > 0) == za) {
+                count++;
+            }
+        }
+    }
+    if(total) {
+        return (double)(0.98 * count)/total + 0.01;
+    } else {
+        return (za) ? 0.01 : 0.99;
+    }
+}
+
+cv::Mat ChowLiuTree::buildTree(int root_word, std::list<info> &edges) {
+
+    int q = root_word;
+    cv::Mat cltree(4, (int)edges.size()+1, CV_64F);
+
+    cltree.at<double>(0, q) = q;
+    cltree.at<double>(1, q) = P(q, true);
+    cltree.at<double>(2, q) = P(q, true);
+    cltree.at<double>(3, q) = P(q, true);
+    //setting P(zq|zpq) to P(zq) gives the root node of the chow-liu
+    //independence from a parent node.
+
+    //find all children and do the same
+    vector<int> nextqs = extractChildren(edges, q);
+
+    int pq = q;
+    vector<int>::iterator nextq;
+    for(nextq = nextqs.begin(); nextq != nextqs.end(); nextq++) {
+        recAddToTree(cltree, *nextq, pq, edges);
+    }
+
+    return cltree;
+
+
+}
+
+void ChowLiuTree::recAddToTree(cv::Mat &cltree, int q, int pq,
+                               std::list<info>& remaining_edges) {
+
+    cltree.at<double>(0, q) = pq;
+    cltree.at<double>(1, q) = P(q, true);
+    cltree.at<double>(2, q) = CP(q, true, pq, true);
+    cltree.at<double>(3, q) = CP(q, true, pq, false);
+
+    //find all children and do the same
+    vector<int> nextqs = extractChildren(remaining_edges, q);
+
+    pq = q;
+    vector<int>::iterator nextq;
+    for(nextq = nextqs.begin(); nextq != nextqs.end(); nextq++) {
+        recAddToTree(cltree, *nextq, pq, remaining_edges);
+    }
+}
+
+vector<int> ChowLiuTree::extractChildren(std::list<info> &remaining_edges, int q) {
+
+    std::vector<int> children;
+    std::list<info>::iterator edge = remaining_edges.begin();
+
+    while(edge != remaining_edges.end()) {
+        if(edge->word1 == q) {
+            children.push_back(edge->word2);
+            edge = remaining_edges.erase(edge);
+            continue;
+        }
+        if(edge->word2 == q) {
+            children.push_back(edge->word1);
+            edge = remaining_edges.erase(edge);
+            continue;
+        }
+        edge++;
+    }
+
+    return children;
+}
+
+bool ChowLiuTree::sortInfoScores(const info& first, const info& second) {
+    return first.score > second.score;
+}
+
+double ChowLiuTree::calcMutInfo(int word1, int word2) {
+    double accumulation = 0;
+
+    double P00 = JP(word1, false, word2, false);
+    if(P00) accumulation += P00 * log(P00 / (P(word1, false)*P(word2, false)));
+
+    double P01 = JP(word1, false, word2, true);
+    if(P01) accumulation += P01 * log(P01 / (P(word1, false)*P(word2, true)));
+
+    double P10 = JP(word1, true, word2, false);
+    if(P10) accumulation += P10 * log(P10 / (P(word1, true)*P(word2, false)));
+
+    double P11 = JP(word1, true, word2, true);
+    if(P11) accumulation += P11 * log(P11 / (P(word1, true)*P(word2, true)));
+
+    return accumulation;
+}
+
+void ChowLiuTree::createBaseEdges(std::list<info>& edges, double infoThreshold) {
+
+    int nWords = imgDescriptors[0].cols;
+    info mutInfo;
+
+    for(int word1 = 0; word1 < nWords; word1++) {
+        for(int word2 = word1 + 1; word2 < nWords; word2++) {
+            mutInfo.word1 = (short)word1;
+            mutInfo.word2 = (short)word2;
+            mutInfo.score = (float)calcMutInfo(word1, word2);
+            if(mutInfo.score >= infoThreshold)
+            edges.push_back(mutInfo);
+        }
+    }
+    edges.sort(sortInfoScores);
+}
+
+bool ChowLiuTree::reduceEdgesToMinSpan(std::list<info>& edges) {
+
+    std::map<int, int> groups;
+    std::map<int, int>::iterator groupIt;
+    for(int i = 0; i < imgDescriptors[0].cols; i++) groups[i] = i;
+    int group1, group2;
+
+    std::list<info>::iterator edge = edges.begin();
+    while(edge != edges.end()) {
+        if(groups[edge->word1] != groups[edge->word2]) {
+            group1 = groups[edge->word1];
+            group2 = groups[edge->word2];
+            for(groupIt = groups.begin(); groupIt != groups.end(); groupIt++)
+            if(groupIt->second == group2) groupIt->second = group1;
+            edge++;
+        } else {
+            edge = edges.erase(edge);
+        }
+    }
+
+    if(edges.size() != (unsigned int)imgDescriptors[0].cols - 1) {
+        return false;
+    } else {
+        return true;
+    }
+
+}
+
+}
+
+}
+

modules/objdetect/src/linemod.cpp

@@ -856,8 +856,8 @@ bool DepthNormalPyramid::extractTemplate(Template& templ) const
   std::stable_sort(candidates.begin(), candidates.end());
 
   // Use heuristic based on object area for initial distance threshold
-  int area = static_cast<int>(no_mask ? normal.total() : countNonZero(local_mask));
-  float distance = sqrtf(static_cast<float>(area)) / sqrtf(static_cast<float>(num_features)) + 1.5f;
+  float area = no_mask ? (float)normal.total() : (float)countNonZero(local_mask);
+  float distance = sqrtf(area) / sqrtf((float)num_features) + 1.5f;
   selectScatteredFeatures(candidates, templ.features, num_features, distance);
 
   // Size determined externally, needs to match templates for other modalities

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.
+//
+//
+// 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 "opencv2/opencv.hpp"
+#include "opencv2/nonfree/nonfree.hpp"
+
+using namespace cv;
+using namespace std;
+
+int main(int argc, char * argv[]) {
+
+    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);
+
+    imshow("Confusion Matrix", result_large);
+    waitKey();
+
+    cout << endl << "Press any key to exit" << endl;
+
+    return 0;
+}