void trainSURFMatcher(
const KeyframeVector& keyframes,
cv::FlannBasedMatcher& matcher)
{
std::vector<cv::Mat> descriptors_vector;
for (unsigned int kf_idx = 0; kf_idx < keyframes.size(); ++kf_idx)
{
const RGBDKeyframe& keyframe = keyframes[kf_idx];
descriptors_vector.push_back(keyframe.descriptors);
}
matcher.add(descriptors_vector);
matcher.train();
}
void trainSURFMatcher_Iterative(
const KeyframeVector& keyframes,u_int min, u_int max,
cv::FlannBasedMatcher& matcher)
{
std::vector<cv::Mat> descriptors_vector;
for (unsigned int kf_idx = min; kf_idx < max; ++kf_idx)
{
const RGBDKeyframe& keyframe = keyframes[kf_idx];
descriptors_vector.push_back(keyframe.descriptors);
}
matcher.add(descriptors_vector);
matcher.train();
}
/*****************************************************************************
// MAIN
*/
int main(int argc, const char * argv[])
{
//*************************************************************************
// 1. Read the input files
// This code reads the arguments from the input variable argv which is supposed to contain the
// path of the input and reference database.
std::string teachdb_folder, querydb_folder;
if (argc > 2)
{
std::string command = argv[2];
std::string type = argv[1];
if(type.compare("-SIFT")== 0)
{
_ftype = SIFT;
std::cout << "NFT with SIFT feature detector and extractor." << std::endl;
}
else if(type.compare("-SURF")== 0)
{
_ftype = SURF;
std::cout << "NFT with SURF feature detector and extractor." << std::endl;
}
else if(type.compare("-ORB")== 0)
{
_ftype = ORB;
std::cout << "NFT with ORB feature detector and extractor." << std::endl;
}
if(command.compare("-file") == 0)
{
if(argc > 4)
{
teachdb_folder = argv[3];
querydb_folder = argv[4];
run_video = false;
}
else
{
std::cout << "No folder with query or reference images has been specified" << std::endl;
std::cout << "Call: ./HCI571X_Feature_Matching -file folder_reference folder_query" << std::endl;
system("pause");
exit(0);
}
}
else if(command.compare("-video") == 0)
{
run_video = true;
if(argc > 4)
{
teachdb_folder = argv[3];
device_id = atoi(argv[4]);
}
}
}
else
{
std::cout << "No command has been specified. use -file or -video" << std::endl;
system("pause");
exit(0);
}
// Read the filenames inside the teach database directory.
std::vector<std::string> ref_filename;
readDirFiles(teachdb_folder, &ref_filename);
// Read the filenames inside the query database directory.
std::vector<std::string> query_filename;
readDirFiles(querydb_folder, &query_filename);
//*************************************************************************
// 2. Create a detector and a descriptor extractor
// In this case, the SIFT detector and extractor are used
// Corner detector
if(_ftype == SIFT)_detector = new cv::SiftFeatureDetector(_num_feature, _octaves, _contrast_threshold, _edge_threshold, _sigma);
else if(_ftype == SURF)_detector = new cv::SurfFeatureDetector( _hessianThreshold, _surf_Octaves, _surf_OctaveLayers, _surf_extended, _surf_upright );
else if(_ftype == ORB)_detector = new cv::OrbFeatureDetector(1000);
// Corner extractor
if(_ftype == SIFT) _extractor = new cv::SiftDescriptorExtractor(_num_feature, _octaves, _contrast_threshold, _edge_threshold, _sigma);
else if(_ftype == SURF) _extractor = new cv::SurfDescriptorExtractor( _hessianThreshold, _surf_Octaves, _surf_OctaveLayers, _surf_extended, _surf_upright );
else if(_ftype == ORB)_extractor = new cv::OrbDescriptorExtractor(1000);
// Check whether files are in the database list.
if(ref_filename.size() == 0)
{
std::cout << "STOP: no files in the reference database!!! Specify a folder or a set of files." << std::cout;
system("pause");
return -1;
}
//*************************************************************************
// 3. Init the database
// The code reads all the images in ref_filename, detect keypoints, extract descriptors and
// stores them in the datbase variables.
init_database(std::string(teachdb_folder), ref_filename);
//*************************************************************************
// 4. The data of the database _descriptorsRefDB is added to the featue matcher
// and the mathcer is trained
_matcher.add(_descriptorsRefDB);
_matcher.train();
// Read the number of reference images in the database
_num_ref_images = _matcher.getTrainDescriptors().size();
//*************************************************************************
// 5. Here we run the matching.
// for images from files
if(!run_video)
{
if(_mtype == KNN)run_matching( querydb_folder, query_filename);
else if(_mtype == BRUTEFORCE) run_bf_matching(querydb_folder, query_filename);
else
{
std::cout << "No matching type specified. Specify a matching type" << std::endl;
system("pause");
}
}
else
// and image from a video camera
{
if(_mtype == KNN)run_matching( device_id);
else if(_mtype == BRUTEFORCE) run_bf_matching(device_id);
else
{
std::cout << "No matching type specified. Specify a matching type" << std::endl;
system("pause");
}
}
//*************************************************************************
// 6. Cleanup: release the keypoint detector and feature descriptor extractor
_extractor.release();
_detector.release();
return 1;
}