void OpenFABMap::compare(cv::Mat &frame, std::vector< cv::of2::IMatch > &matches, cv::Mat &bow, std::vector<cv::KeyPoint> &kpts, std::vector< std::vector < int > > &pointIDXOfCLusters, cv::Mat *completeDescriptors)
{
/// Get the BoW of the frame
//use a FLANN matcher to generate bag-of-words representations
cv::Ptr<cv::DescriptorMatcher>
matcher = cv::DescriptorMatcher::create("FlannBased");
cv::BOWImgDescriptorExtractor
bide(descriptor_extractor_, matcher);
bide.setVocabulary(vocabulary_);
feature_detector_->detect(frame, kpts);
bide.compute(frame, kpts, bow, &pointIDXOfCLusters, completeDescriptors);
/// TODO: find a way to use the returned descriptors from the bide extractor
/// why? because for now it returns a 0x0 matrix, so find out why :(
cv::SurfDescriptorExtractor
extractor;
extractor.compute(frame, kpts, *completeDescriptors);
// std::cout << "#of keypoints: " << kpts.size() << " - Descriptor size: " << completeDescriptors->cols << "x" << completeDescriptors->rows << std::endl;
/// Compare with the map
open_fab_map_->compare(bow, matches);
}
/*
generate FabMap bag-of-words data : an image descriptor for each frame
*/
int generateBOWImageDescs(std::string dataPath,
std::string bowImageDescPath,
std::string vocabPath,
cv::Ptr<cv::FeatureDetector> &detector,
cv::Ptr<cv::DescriptorExtractor> &extractor,
int minWords)
{
cv::FileStorage fs;
//ensure not overwriting training data
std::ifstream checker;
checker.open(bowImageDescPath.c_str());
if(checker.is_open()) {
std::cerr << bowImageDescPath << ": FabMap Training/Testing Data "
"already present" << std::endl;
checker.close();
return -1;
}
//load vocabulary
std::cout << "Loading Vocabulary" << std::endl;
fs.open(vocabPath, cv::FileStorage::READ);
cv::Mat vocab;
fs["Vocabulary"] >> vocab;
if (vocab.empty()) {
std::cerr << vocabPath << ": Vocabulary not found" << std::endl;
return -1;
}
fs.release();
//use a FLANN matcher to generate bag-of-words representations
cv::Ptr<cv::DescriptorMatcher> matcher =
cv::DescriptorMatcher::create("FlannBased");
cv::BOWImgDescriptorExtractor bide(extractor, matcher);
bide.setVocabulary(vocab);
//load movie
cv::VideoCapture movie;
movie.open(dataPath);
if(!movie.isOpened()) {
std::cerr << dataPath << ": movie not found" << std::endl;
return -1;
}
//extract image descriptors
cv::Mat fabmapTrainData;
std::cout << "Extracting Bag-of-words Image Descriptors" << std::endl;
std::cout.setf(std::ios_base::fixed);
std::cout.precision(0);
std::ofstream maskw;
if(minWords) {
maskw.open(std::string(bowImageDescPath + "mask.txt").c_str());
}
cv::Mat frame, bow;
std::vector<cv::KeyPoint> kpts;
while(movie.read(frame)) {
detector->detect(frame, kpts);
bide.compute(frame, kpts, bow);
if(minWords) {
//writing a mask file
if(cv::countNonZero(bow) < minWords) {
//frame masked
maskw << "0" << std::endl;
} else {
//frame accepted
maskw << "1" << std::endl;
fabmapTrainData.push_back(bow);
}
} else {
fabmapTrainData.push_back(bow);
}
std::cout << 100.0 * (movie.get(CV_CAP_PROP_POS_FRAMES) /
movie.get(CV_CAP_PROP_FRAME_COUNT)) << "% \r";
fflush(stdout);
}
std::cout << "Done " << std::endl;
movie.release();
//save training data
fs.open(bowImageDescPath, cv::FileStorage::WRITE);
fs << "BOWImageDescs" << fabmapTrainData;
fs.release();
return 0;
}
