///////////////////////////////////////////////////////////////
// Validate the Command line arguments.
bool CmdLineArgs::ValidateAndStoreArgs(int argc,char* argv[])
{
try
{
if ( argc < 4 ) { return false; }
std::string recurseOption = argv[1];
if ( recurseOption == "-r" )
{ recurse = true; }
else if ( recurseOption == "-nr")
{ recurse = false;}
else
{ return false; }
pathSpec = argv[2];
filePattern.clear();
for(int i = 3; i < argc;i++)
{ filePattern.push_back(argv[i]); }
fetchFiles();
return true;
}
catch (std::exception ex)
{
std::cout<<std::endl<<"Error Processing Arguments : "<<ex.what();
return false;
}
}
wb_pkg::wb_pkg( char *nodelist, bool distribute, bool config_only)
{
if ( nodelist) {
char node_str[32][20];
int num;
cdh_ToLower( nodelist, nodelist);
num = dcli_parse( nodelist, " ,", "", (char *)node_str,
sizeof(node_str)/sizeof(node_str[0]),
sizeof(node_str[0]), 0);
m_allnodes = false;
for ( int i = 0; i < num; i++) {
pkg_node n( node_str[i]);
m_nodelist.push_back( n);
}
}
else
m_allnodes = true;
readConfig();
if ( config_only)
return;
fetchFiles( distribute);
}
int main(int argc, char** argv)
{
if (argc != 3)
{
help();
return 0;
}
// ------------ TRAINING PHASE
{
// ----- LOAD IMAGES FROM PATH
QString path(argv[1]);
QStringList filesToOpen = fetchFiles(path);
if (filesToOpen.empty())
{
help();
return 1;
}
// Create feature detector and descriptor extractor:
cv::Ptr<cv::ORB> detector = cv::ORB::create(150);
cv::Mat trainingDescriptors(0, detector->descriptorSize(), detector->descriptorType());
std::cout << "Training descriptor size & type : " << detector->descriptorSize() << ", " << detector->descriptorType() << std::endl;
std::vector<cv::KeyPoint> keypoints;
// Loop on files :
std::cout << "Extract features from training images" << std::endl;
foreach (QString file, filesToOpen)
{
SD_TRACE1("Open file '%1'", file);
QString f = path + "/" + file;
cv::Mat inImage = cv::imread(f.toStdString(), cv::IMREAD_GRAYSCALE);
cv::Mat descriptors;
detector->detectAndCompute(inImage, cv::Mat(), keypoints, descriptors);
trainingDescriptors.push_back(descriptors);
cv::Mat imageWithKeyPoints;
cv::drawKeypoints(inImage, keypoints, imageWithKeyPoints);
// ImageCommon::displayMat(imageWithKeyPoints, true, "Input image with keypoints");
keypoints.clear();
}
std::cout << "Total number of descriptors : " << trainingDescriptors.rows << std::endl;
trainingDescriptors.convertTo(trainingDescriptors, CV_32F);
ImageCommon::printMat(trainingDescriptors, "trainingDescriptors");
cv::BOWKMeansTrainer bowTrainer(15);
bowTrainer.add(trainingDescriptors);
cv::Mat vocabulary = bowTrainer.cluster();
//ImageCommon::displayMat(vocabulary, true, "Vocabulary");
// Train
}
