DAT1::DAT1(VFS* vfs, const std::string& file) : VFSSource(vfs), m_datpath(file), m_data(vfs->open(file)) {
FL_LOG(_log, LMsg("MFFalloutDAT1")
<< "loading: " << file
<< " filesize: " << m_data->getDataLength());
m_data->setIndex(0);
const uint32_t dircount = m_data->read32Big();
m_data->moveIndex(4*3);
FL_LOG(_log, LMsg("MFFalloutDAT1")
<< "number of directories " << dircount);
// Sanity check. Each dir entry needs min. 16 bytes.
if( dircount*16 > m_data->getDataLength() ) {
throw InvalidFormat("directory count larger than filesize.");
}
std::list<std::string> dir_names;
for (uint32_t i = 0; i < dircount; ++i) {
std::string name = readString();
if (name == ".") {
name = "";
}
dir_names.push_back(name);
}
for(std::list<std::string>::iterator i= dir_names.begin(); i!= dir_names.end(); ++i)
loadFileList(*i);
}
mainWindow::mainWindow(QWidget *parent) :
QMainWindow(parent),
ui(new Ui::mainWindow)
{
ui->setupUi(this);
sc = new QScrollArea(this);
m_label = new QLabel("", this);
m_image = new QImage();
m_animated = new QMovie(m_label);
m_current = -1;
QStringList args = QApplication::arguments();
if(args.count() > 1)
{
loadFileList(args);
}
sc->setWidget(m_label);
sc->setHorizontalScrollBarPolicy(Qt::ScrollBarAsNeeded);
sc->setVerticalScrollBarPolicy(Qt::ScrollBarAsNeeded);
this->setCentralWidget(sc);
this->resize(800, 600);
m_oldSize = this->size();
setupConnexions();
}
bool SRTMProvider::fillTile(int index, SRTMTile **tile){
bool tileFilled = false;
QString fileName; // Name incl. Pfad (wird aus _cachedir und Index erstellt)
loadFileList(); // per loadFilelist(): fileListe vorhanden? falls nicht: erstellen!
if (fileList.contains(index)){
fileName = _cachedir+fileList[index];
QFile zipFile(fileName);
int fileIndex = index; //Bugfixversuch: fileIndex
if(!zipFile.open(QIODevice::ReadOnly) && !downloadZipFile(fileIndex, fileName, zipFile)){ //Bugfixversuch: fileIndex
return tileFilled;
}
tileFilled = (*tile)->fillTile(fileName);
}
return tileFilled;
}
void VFH::loadTrainingData()
{
if (!boost::filesystem::exists ("training_data.h5") || !boost::filesystem::exists ("training_data.list"))
{
pcl::console::print_error ("Could not find training data models files %s and %s!\n",
training_data_h5_file_name.c_str (), training_data_list_file_name.c_str ());
return;
}
else
{
loadFileList (models, training_data_list_file_name);
flann::load_from_file (data, training_data_h5_file_name, "training_data");
pcl::console::print_highlight ("Training data found. Loaded %d VFH models from %s/%s.\n",
(int)data.rows, training_data_h5_file_name.c_str (), training_data_list_file_name.c_str ());
}
if (!boost::filesystem::exists (kdtree_idx_file_name))
{
pcl::console::print_error ("Could not find kd-tree index in file %s!", kdtree_idx_file_name.c_str ());
return;
}
else
{
flann::Index<flann::ChiSquareDistance<float> > index (data, flann::SavedIndexParams (kdtree_idx_file_name));
index.buildIndex ();
}
}
void soundKonverter::setupActions()
{
KStandardAction::quit( this, SLOT(close()), actionCollection() );
KStandardAction::preferences( this, SLOT(showConfigDialog()), actionCollection() );
KAction *logviewer = actionCollection()->addAction("logviewer");
logviewer->setText(i18n("View logs..."));
logviewer->setIcon(KIcon("view-list-text"));
connect( logviewer, SIGNAL(triggered()), this, SLOT(showLogViewer()) );
KAction *replaygainscanner = actionCollection()->addAction("replaygainscanner");
replaygainscanner->setText(i18n("Replay Gain tool..."));
replaygainscanner->setIcon(KIcon("soundkonverter-replaygain"));
connect( replaygainscanner, SIGNAL(triggered()), this, SLOT(showReplayGainScanner()) );
KAction *aboutplugins = actionCollection()->addAction("aboutplugins");
aboutplugins->setText(i18n("About plugins..."));
aboutplugins->setIcon(KIcon("preferences-plugin"));
connect( aboutplugins, SIGNAL(triggered()), this, SLOT(showAboutPlugins()) );
KAction *add_files = actionCollection()->addAction("add_files");
add_files->setText(i18n("Add files..."));
add_files->setIcon(KIcon("audio-x-generic"));
connect( add_files, SIGNAL(triggered()), m_view, SLOT(showFileDialog()) );
KAction *add_folder = actionCollection()->addAction("add_folder");
add_folder->setText(i18n("Add folder..."));
add_folder->setIcon(KIcon("folder"));
connect( add_folder, SIGNAL(triggered()), m_view, SLOT(showDirDialog()) );
KAction *add_audiocd = actionCollection()->addAction("add_audiocd");
add_audiocd->setText(i18n("Add CD tracks..."));
add_audiocd->setIcon(KIcon("media-optical-audio"));
connect( add_audiocd, SIGNAL(triggered()), m_view, SLOT(showCdDialog()) );
KAction *add_url = actionCollection()->addAction("add_url");
add_url->setText(i18n("Add url..."));
add_url->setIcon(KIcon("network-workgroup"));
connect( add_url, SIGNAL(triggered()), m_view, SLOT(showUrlDialog()) );
KAction *add_playlist = actionCollection()->addAction("add_playlist");
add_playlist->setText(i18n("Add playlist..."));
add_playlist->setIcon(KIcon("view-media-playlist"));
connect( add_playlist, SIGNAL(triggered()), m_view, SLOT(showPlaylistDialog()) );
KAction *load = actionCollection()->addAction("load");
load->setText(i18n("Load file list"));
load->setIcon(KIcon("document-open"));
connect( load, SIGNAL(triggered()), m_view, SLOT(loadFileList()) );
KAction *save = actionCollection()->addAction("save");
save->setText(i18n("Save file list"));
save->setIcon(KIcon("document-save"));
connect( save, SIGNAL(triggered()), m_view, SLOT(saveFileList()) );
actionCollection()->addAction("start", m_view->start());
actionCollection()->addAction("stop_menu", m_view->stopMenu());
}
PlotAlignmentValidation::PlotAlignmentValidation(const char *inputFile,std::string legendName, int lineColor, int lineStyle)
{
setOutputDir("$TMPDIR");
setTreeBaseDir();
sourcelist = NULL;
loadFileList( inputFile, legendName, lineColor, lineStyle);
moreThanOneSource=false;
}
//------------------------------------------------------------------------------
PlotAlignmentValidation::PlotAlignmentValidation(const char *inputFile,std::string legendName, int lineColor, int lineStyle)
{
setOutputDir("$TMPDIR");
setTreeBaseDir();
sourcelist = NULL;
loadFileList( inputFile, legendName, lineColor, lineStyle);
moreThanOneSource=false;
useFit_ = false;
// Force ROOT to use scientific notation even with smaller datasets
TGaxis::SetMaxDigits(4);
// (This sets a static variable: correct in .eps-images but must be set
// again manually when viewing the .root-files)
}
CoaDebugSettingsModel::CoaDebugSettingsModel ( QObject *parent )
:CoaAbstractItemModel ( parent )
{
// BEGIN;
m_columns = 2;
loadFileList();
connect (CoaDebugThread::getInstance(), SIGNAL(gotNewFileForDebugging(const QString, const int)),
this, SLOT (setNewFile(const QString, const int)),
Qt::QueuedConnection
);
}
fcitx::FileListModel::FileListModel(QObject *parent)
: QAbstractListModel(parent) {
loadFileList();
}
FileListModel::FileListModel(QObject* parent): QAbstractListModel(parent)
,m_langType(LPLT_Simplified)
{
loadFileList();
}
int main (int argc, char** argv)
{
PointCloudT::Ptr cloud (new PointCloudT);
PointCloudT::Ptr new_cloud (new PointCloudT);
bool new_cloud_available_flag = false;
//pcl::Grabber* grab = new pcl::OpenNIGrabber ();
PointCloudT::Ptr ddd;
boost::function<void (const PointCloudT::ConstPtr&)> f =
boost::bind(&grabberCallback, _1, cloud, &new_cloud_available_flag);
grab->registerCallback (f);
viewer->registerKeyboardCallback(keyboardEventOccurred);
grab->start ();
bool first_time = true;
FILE* objects;
objects = fopen ("objects.txt","a");
while(!new_cloud_available_flag)
boost::this_thread::sleep(boost::posix_time::milliseconds(1));
new_cloud_available_flag=false;
////////////////////
// invert correction
////////////////////
Eigen::Matrix4f transMat = Eigen::Matrix4f::Identity();
transMat (1,1) = -1;
////////////////////
// pass filter
////////////////////
PointCloudT::Ptr passed_cloud;
pcl::PassThrough<PointT> pass;
passed_cloud = boost::shared_ptr<PointCloudT>(new PointCloudT);
////////////////////
// voxel grid
////////////////////
PointCloudT::Ptr voxelized_cloud;
voxelized_cloud = boost::shared_ptr<PointCloudT>(new PointCloudT);
pcl::VoxelGrid<PointT> vg;
vg.setLeafSize (0.001, 0.001, 0.001);
////////////////////
// sac segmentation
////////////////////
PointCloudT::Ptr cloud_f;
PointCloudT::Ptr cloud_plane;
PointCloudT::Ptr cloud_filtered;
cloud_f = boost::shared_ptr<PointCloudT>(new PointCloudT);
cloud_plane = boost::shared_ptr<PointCloudT> (new PointCloudT);
cloud_filtered = boost::shared_ptr<PointCloudT> (new PointCloudT);
pcl::SACSegmentation<PointT> seg;
pcl::PointIndices::Ptr inliers (new pcl::PointIndices);
pcl::ModelCoefficients::Ptr coefficients (new pcl::ModelCoefficients);
seg.setOptimizeCoefficients (true);
seg.setModelType (pcl::SACMODEL_PLANE);
seg.setMethodType (pcl::SAC_RANSAC);
seg.setMaxIterations (100);
seg.setDistanceThreshold (0.02);
////////////////////
// euclidean clustering
////////////////////
std::vector<pcl::PointIndices> cluster_indices;
std::vector<PointCloudT::Ptr> extracted_clusters;
pcl::search::KdTree<PointT>::Ptr eucl_tree (new pcl::search::KdTree<PointT>);
pcl::EuclideanClusterExtraction<PointT> ec;
ec.setClusterTolerance (0.04);
ec.setMinClusterSize (100);
ec.setMaxClusterSize (25000);
ec.setSearchMethod (eucl_tree);
PointCloudT::Ptr cloud_cluster;
////////////////////
// vfh estimate
////////////////////
pcl::NormalEstimation<PointT, pcl::Normal> ne;
pcl::search::KdTree<PointT>::Ptr vfh_tree1 (new pcl::search::KdTree<PointT> ());
pcl::VFHEstimation<PointT, pcl::Normal, pcl::VFHSignature308> vfh;
pcl::search::KdTree<PointT>::Ptr vfh_tree2 (new pcl::search::KdTree<PointT> ());
std::vector<pcl::PointCloud<pcl::VFHSignature308>::Ptr> computed_vfhs;
ne.setSearchMethod (vfh_tree1);
ne.setRadiusSearch (0.05);
vfh.setSearchMethod (vfh_tree2);
vfh.setRadiusSearch (0.05);
pcl::PointCloud<pcl::Normal>::Ptr normals;
pcl::PointCloud<pcl::VFHSignature308>::Ptr vfhs;
////////////////////
// nearest neighbour
////////////////////
int k = 6;
std::string kdtree_idx_file_name = "kdtree.idx";
std::string training_data_h5_file_name = "training_data.h5";
std::string training_data_list_file_name = "training_data.list";
// std::vector<vfh_model> models;
// flann::Matrix<float> data;
// loadFileList (models, training_data_list_file_name);
// flann::load_from_file (data,
// training_data_h5_file_name,
// "training_data");
//
// flann::Index<flann::ChiSquareDistance<float> > index (data,
// flann::SavedIndexParams
// ("kdtree.idx"));
////////////////////
// process nearest neighbour
////////////////////
std::vector<hypothesis> final_hypothesis;
final_hypothesis.clear();
double last = pcl::getTime();
while (! viewer->wasStopped())
{
if (new_cloud_available_flag)
{
new_cloud_available_flag = false;
double now = pcl::getTime();
////////////////////
// pass filter
////////////////////
//passed_cloud = boost::shared_ptr<PointCloudT>(new PointCloudT);
////////////////////
// voxel grid
////////////////////
//voxelized_cloud = boost::shared_ptr<PointCloudT>(new PointCloudT);
////////////////////
// sac segmentation
////////////////////
//cloud_f = boost::shared_ptr<PointCloudT>(new PointCloudT);
//cloud_plane = boost::shared_ptr<PointCloudT> (new PointCloudT);
//cloud_filtered = boost::shared_ptr<PointCloudT> (new PointCloudT);
////////////////////
// euclidean clustering
////////////////////
extracted_clusters.clear();
cluster_indices.clear();
////////////////////
// vfh estimate
////////////////////
computed_vfhs.clear();
////////////////////
// nearest neighbour
////////////////////
cloud_mutex.lock();
//displayCloud(viewer,cloud);
boost::thread displayCloud_(displayCloud,viewer,cloud);
if(now-last > 13 || first_time)
{
first_time = false;
last=now;
////////////////////
// invert correction
////////////////////
pcl::transformPointCloud(*cloud,*new_cloud,transMat);
////////////////////
// pass filter
////////////////////
pass.setInputCloud (new_cloud);
pass.setFilterFieldName ("x");
pass.setFilterLimits (-0.5, 0.5);
//pass.setFilterLimitsNegative (true);
pass.filter (*passed_cloud);
////////////////////
// voxel grid
////////////////////
vg.setInputCloud (passed_cloud);
vg.filter (*voxelized_cloud);
////////////////////
// sac segmentation
////////////////////
*cloud_filtered = *voxelized_cloud;
int i=0, nr_points = (int) voxelized_cloud->points.size ();
while (cloud_filtered->points.size () > 0.3 * nr_points)
{
// Segment the largest planar component from the remaining cloud
seg.setInputCloud (cloud_filtered);
seg.segment (*inliers, *coefficients);
if (inliers->indices.size () == 0)
{
std::cout << "Couldnt estimate a planar model for the dataset.\n";
break;
}
// Extract the planar inliers from the input cloud
pcl::ExtractIndices<PointT> extract;
extract.setInputCloud (cloud_filtered);
extract.setIndices (inliers);
extract.setNegative (false);
// Get the points associated with the planar surface
extract.filter (*cloud_plane);
// Remove the planar inliers, extract the rest
extract.setNegative (true);
extract.filter (*cloud_f);
*cloud_filtered = *cloud_f;
}
////////////////////
// euclidean clustering
////////////////////
// Creating the KdTree object for the search method of the extraction
eucl_tree->setInputCloud (cloud_filtered);
ec.setInputCloud (cloud_filtered);
ec.extract (cluster_indices);
for (std::vector<pcl::PointIndices>::const_iterator it = cluster_indices.begin (); it != cluster_indices.end (); ++it)
{
//PointCloudT::Ptr cloud_cluster (new PointCloudT);
cloud_cluster = boost::shared_ptr<PointCloudT>(new PointCloudT);
for (std::vector<int>::const_iterator pit = it->indices.begin (); pit != it->indices.end (); pit++)
cloud_cluster->points.push_back (cloud_filtered->points [*pit]);
cloud_cluster->width = cloud_cluster->points.size ();
cloud_cluster->height = 1;
cloud_cluster->is_dense = true;
extracted_clusters.push_back(cloud_cluster);
}
cloud_cluster.reset();
////////////////////
// vfh estimate
////////////////////
for (int z=0; z<extracted_clusters.size(); ++z)
{
vfhs = boost::shared_ptr<pcl::PointCloud<pcl::VFHSignature308> > (new pcl::PointCloud<pcl::VFHSignature308>);
normals = boost::shared_ptr<pcl::PointCloud<pcl::Normal> > (new pcl::PointCloud<pcl::Normal>);
ne.setInputCloud (extracted_clusters.at(z));
ne.compute (*normals);
vfh.setInputCloud (extracted_clusters.at(z));
vfh.setInputNormals (normals);
vfh.compute (*vfhs);
computed_vfhs.push_back(vfhs);
}
vfhs.reset();
normals.reset();
////////////////////
// nearest neighbour
////////////////////
std::vector<vfh_model> models;
flann::Matrix<int> k_indices;
flann::Matrix<float> k_distances;
flann::Matrix<float> data;
loadFileList (models, training_data_list_file_name);
flann::load_from_file (data,
training_data_h5_file_name,
"training_data");
flann::Index<flann::ChiSquareDistance<float> > index
(data,
flann::SavedIndexParams
("kdtree.idx"));
for(int z=0; z<computed_vfhs.size(); ++z)
{
vfh_model histogram;
histogram.second.resize(308);
for (size_t i = 0; i < 308; ++i)
{
histogram.second[i] = computed_vfhs.at(z)->points[0].histogram[i];
}
index.buildIndex ();
nearestKSearch (index, histogram, k, k_indices, k_distances);
hypothesis x;
x.distance = k_distances[0][0];
size_t index = models.at(k_indices[0][0]).first.find("_v",5);
x.object_name = models.at(k_indices[0][0]).first.substr(5,index-5);
ddd = boost::shared_ptr<PointCloudT>(new PointCloudT);
pcl::transformPointCloud(*extracted_clusters.at(z),*ddd,transMat);
x.cluster = ddd;
ddd.reset();
std::string filename ="";
filename += "inputcloud_" + boost::lexical_cast<std::string>(j+1);
filename += "_" + boost::lexical_cast<std::string>(z) + ".pcd";
x.cluster_name = filename.c_str();
final_hypothesis.push_back(x);
x.cluster.reset();
//delete x;
// std::string filename ="";
// filename += "inputcloud_" + boost::lexical_cast<std::string>(j+1);
// filename += "_" + boost::lexical_cast<std::string>(z) + ".pcd";
// const char* filen = filename.c_str();
// fprintf(objects,"%s",filen);
// fprintf(objects,"::");
// fprintf(objects,models.at (k_indices[0][0]).first.c_str());
// fprintf(objects,"::");
// fprintf(objects,"%f",k_distances[0][0]);
// fprintf(objects,"\n");
}
delete[] k_indices.ptr ();
delete[] k_distances.ptr ();
delete[] data.ptr ();
std::cout << final_hypothesis.size() << std::endl;
viewer->removeAllShapes();
for(int z=0; z<final_hypothesis.size();++z)
{
if(final_hypothesis.at(z).distance < 100)
{
fprintf(objects,"%s",final_hypothesis.at(z).cluster_name.c_str());
fprintf(objects,"::");
fprintf(objects,"%s",final_hypothesis.at(z).object_name.c_str());
fprintf(objects,"::");
fprintf(objects,"%f",final_hypothesis.at(z).distance);
fprintf(objects,"\n");
std::stringstream ddd;
ddd << final_hypothesis.at(z).object_name;
ddd << "\n" << "(";
ddd << final_hypothesis.at(z).distance;
ddd << ")";
viewer->addText3D(ddd.str().c_str(),
final_hypothesis.at(z).cluster->points[0],
0.02,1,1,1,
boost::lexical_cast<std::string>(z));
drawBoundingBox(final_hypothesis.at(z).cluster,viewer,z);
}
}
//boost::thread allBoxes_(allBoxes,viewer,final_hypothesis);
//allBoxes_.join();
viewer->spinOnce();
final_hypothesis.clear();
j++;
}
// for(int z=0; z<extracted_clusters.size(); ++z)
// {
// //viewer->addPointCloud<PointT>(extracted_clusters.at(z),
// // boost::lexical_cast<std::string>(z));
//
// std::string filename ="";
// filename += "inputcloud_" + boost::lexical_cast<std::string>(j);
// filename += "_" + boost::lexical_cast<std::string>(z) + ".pcd";
// pcl::io::savePCDFile(filename,*extracted_clusters.at(z),false);
// }
// for(int z=0; z<computed_vfhs.size(); ++z)
// {
// //viewer->addPointCloud<PointT>(extracted_clusters.at(z),
// // boost::lexical_cast<std::string>(z));
//
// std::string filename ="";
// filename += "inputcloud_" + boost::lexical_cast<std::string>(j);
// filename += "_" + boost::lexical_cast<std::string>(z) + "_vfhs.pcd";
// pcl::io::savePCDFileASCII<pcl::VFHSignature308> (filename, *computed_vfhs.at(z));
// }
//viewer->removeAllShapes();
// viewer->removeAllPointClouds();
// viewer->setCameraPosition(0, 0, 0, 0, 0, 1, 0, -1, 0);
// pcl::visualization::PointCloudColorHandlerRGBField<PointT> rgb(cloud);
// viewer->addPointCloud<PointT>(cloud,rgb,"input_cloud");
// viewer->spinOnce();
//std::cout << final_hypothesis.at(0).cluster->points[0];
//boost::this_thread::sleep(boost::posix_time::milliseconds(10));
displayCloud_.join();
cloud_mutex.unlock();
}
}
grab->stop();
return 0;
}
bool Resource::reset() {
unloadAllPakFiles();
Common::FSNode dir(ConfMan.get("path"));
if (!dir.exists() || !dir.isDirectory())
error("invalid game path '%s'", dir.getPath().c_str());
if (_vm->game() == GI_KYRA1) {
// We only need kyra.dat for the demo.
if (_vm->gameFlags().isDemo && !_vm->gameFlags().isTalkie)
return true;
if (!_vm->gameFlags().isDemo && _vm->gameFlags().isTalkie) {
// List of files in the talkie version, which can never be unload.
static const char * const list[] = {
"ADL.PAK", "CHAPTER1.VRM", "COL.PAK", "FINALE.PAK", "INTRO1.PAK", "INTRO2.PAK",
"INTRO3.PAK", "INTRO4.PAK", "MISC.PAK", "SND.PAK", "STARTUP.PAK", "XMI.PAK",
"CAVE.APK", "DRAGON1.APK", "DRAGON2.APK", "LAGOON.APK", 0
};
loadProtectedFiles(list);
} else {
Common::ArchiveMemberList files;
_files.listMatchingMembers(files, "*.PAK");
_files.listMatchingMembers(files, "*.APK");
for (Common::ArchiveMemberList::const_iterator i = files.begin(); i != files.end(); ++i) {
Common::String name = (*i)->getName();
name.toUppercase();
// No PAK file
if (name == "TWMUSIC.PAK")
continue;
// We need to only load the script archive for the language the user specified
if (name == ((_vm->gameFlags().lang == Common::EN_ANY) ? "JMC.PAK" : "EMC.PAK"))
continue;
Common::Archive *archive = loadArchive(name, *i);
if (archive)
_files.add(name, archive, 0, false);
else
error("Couldn't load PAK file '%s'", name.c_str());
}
}
} else if (_vm->game() == GI_KYRA2) {
if (_vm->gameFlags().useInstallerPackage)
_files.add("installer", loadInstallerArchive("WESTWOOD", "%03d", 6), 2, false);
// mouse pointer, fonts, etc. required for initialization
if (_vm->gameFlags().isDemo && !_vm->gameFlags().isTalkie) {
loadPakFile("GENERAL.PAK");
} else {
loadPakFile("INTROGEN.PAK");
loadPakFile("OTHER.PAK");
}
} else if (_vm->game() == GI_KYRA3) {
if (_vm->gameFlags().useInstallerPackage) {
if (!loadPakFile("WESTWOOD.001"))
error("Couldn't load file: 'WESTWOOD.001'");
}
if (!loadFileList("FILEDATA.FDT"))
error("Couldn't load file: 'FILEDATA.FDT'");
} else if (_vm->game() == GI_LOL) {
if (_vm->gameFlags().useInstallerPackage)
_files.add("installer", loadInstallerArchive("WESTWOOD", "%d", 0), 2, false);
if (!_vm->gameFlags().isTalkie && !_vm->gameFlags().isDemo) {
static const char * const list[] = {
"GENERAL.PAK", 0
};
loadProtectedFiles(list);
}
} else {
error("Unknown game id: %d", _vm->game());
return false; // for compilers that don't support NORETURN
}
return true;
}
void Finder::findFiles()
{
if(!QDir(m_targetFolder).mkdir("Pliki_PDF")) {
emit finished(false, "Folder \"Pliki_PDF\" już istnieje.");
return;
}
bool isFileListLoaded = loadFileList();
if(!isFileListLoaded)
return;
if(isFileListLoaded && m_fileList.size() == 0) {
removeCopiedFiles();
emit finished(false, "Nie znaleziono pasujących pozycji w harmonogramie.");
return;
}
emit signalProgress( 100, "Określenie liczby plików do przeszukania ...");
QDir dir(m_searchedFolder, QString("*.pdf"), QDir::NoSort, QDir::Files | QDir::NoSymLinks);
QDirIterator counterIt(dir, QDirIterator::Subdirectories);
filesCounter = 0;
while (counterIt.hasNext()) {
bool abort = m_abort;
if (abort) {
removeCopiedFiles();
emit finished(false);
return;
}
filesCounter++;
counterIt.next();
}
if(filesCounter == 0) {
emit finished(false, "Nie znaleziono plików PDF w wybranej lokalizacji.");
return;
}
QStringList indexList;
QStringList copiedFilesList;
QString renamedFile;
int count = 0;
QDirIterator finalIt(dir, QDirIterator::Subdirectories);
while (finalIt.hasNext()) {
bool abort = m_abort;
if (abort) {
removeCopiedFiles();
emit finished(false);
return;
}
if(m_fileList.contains(QFileInfo(finalIt.filePath()).fileName(), Qt::CaseInsensitive)) {
indexList = getFileListIdx(QFileInfo(finalIt.filePath()).fileName());
for(int i = 0; i < indexList.size(); ++i) {
renamedFile = renameFile(indexList.at(i).toInt(), QFileInfo(finalIt.filePath()).fileName());
if(!QFile(m_targetFolder + "/Pliki_PDF/" + renamedFile).exists()) {
QFile::copy(QFileInfo(finalIt.filePath()).filePath(), m_targetFolder + "/Pliki_PDF/" + renamedFile);
copiedFilesList.append(renamedFile);
emit itemFound(renamedFile, true);
}
}
}
finalIt.next();
count++;
emit signalProgress( int((double(count)/double(filesCounter)*100)),
"Przeszukiwanie plików: " + QString::number(count) + "/" +
QString::number(filesCounter));
}
QStringList missedFiless = checkMissingFiles(copiedFilesList);
QString information = generateCSV(missedFiless,copiedFilesList);
emit finished(true,information);
}
int
main (int argc, char** argv)
{
int k = 6;
double thresh = DBL_MAX; // No threshold, disabled by default
if (argc < 2)
{
pcl::console::print_error
("Need at least three parameters! Syntax is: %s <query_vfh_model.pcd> [options] {kdtree.idx} {training_data.h5} {training_data.list}\n", argv[0]);
pcl::console::print_info (" where [options] are: -k = number of nearest neighbors to search for in the tree (default: ");
pcl::console::print_value ("%d", k); pcl::console::print_info (")\n");
pcl::console::print_info (" -thresh = maximum distance threshold for a model to be considered VALID (default: ");
pcl::console::print_value ("%f", thresh); pcl::console::print_info (")\n\n");
return (-1);
}
// this won't be needed for flann > 1.6.10
flann::ObjectFactory<flann::IndexParams, flann_algorithm_t>::instance().register_<flann::LinearIndexParams>(FLANN_INDEX_LINEAR);
std::string extension (".pcd");
transform (extension.begin (), extension.end (), extension.begin (), (int(*)(int))tolower);
// Load the test histogram
std::vector<int> pcd_indices = pcl::console::parse_file_extension_argument (argc, argv, ".pcd");
vfh_model histogram;
if (!loadHist (argv[pcd_indices.at (0)], histogram))
{
pcl::console::print_error ("Cannot load test file %s\n", argv[pcd_indices.at (0)]);
return (-1);
}
pcl::console::parse_argument (argc, argv, "-thresh", thresh);
// Search for the k closest matches
pcl::console::parse_argument (argc, argv, "-k", k);
pcl::console::print_highlight ("Using "); pcl::console::print_value ("%d", k); pcl::console::print_info (" nearest neighbors.\n");
std::string kdtree_idx_file_name = "kdtree.idx";
std::string training_data_h5_file_name = "training_data.h5";
std::string training_data_list_file_name = "training_data.list";
std::vector<vfh_model> models;
flann::Matrix<int> k_indices;
flann::Matrix<float> k_distances;
flann::Matrix<float> data;
// Check if the data has already been saved to disk
if (!boost::filesystem::exists ("training_data.h5") || !boost::filesystem::exists ("training_data.list"))
{
pcl::console::print_error ("Could not find training data models files %s and %s!\n",
training_data_h5_file_name.c_str (), training_data_list_file_name.c_str ());
return (-1);
}
else
{
loadFileList (models, training_data_list_file_name);
flann::load_from_file (data, training_data_h5_file_name, "training_data");
pcl::console::print_highlight ("Training data found. Loaded %d VFH models from %s/%s.\n",
(int)data.rows, training_data_h5_file_name.c_str (), training_data_list_file_name.c_str ());
}
// Check if the tree index has already been saved to disk
if (!boost::filesystem::exists (kdtree_idx_file_name))
{
pcl::console::print_error ("Could not find kd-tree index in file %s!", kdtree_idx_file_name.c_str ());
return (-1);
}
else
{
flann::Index<flann::ChiSquareDistance<float> > index (data, flann::SavedIndexParams ("kdtree.idx"));
index.buildIndex ();
nearestKSearch (index, histogram, k, k_indices, k_distances);
}
// Output the results on screen
pcl::console::print_highlight ("The closest %d neighbors for %s are:\n", k, argv[pcd_indices[0]]);
for (int i = 0; i < k; ++i)
pcl::console::print_info (" %d - %s (%d) with a distance of: %f\n",
i, models.at (k_indices[0][i]).first.c_str (), k_indices[0][i], k_distances[0][i]);
// Load the results
pcl::visualization::PCLVisualizer p (argc, argv, "VFH Cluster Classifier");
int y_s = (int)floor (sqrt ((double)k));
int x_s = y_s + (int)ceil ((k / (double)y_s) - y_s);
double x_step = (double)(1 / (double)x_s);
double y_step = (double)(1 / (double)y_s);
pcl::console::print_highlight ("Preparing to load ");
pcl::console::print_value ("%d", k);
pcl::console::print_info (" files (");
pcl::console::print_value ("%d", x_s);
pcl::console::print_info ("x");
pcl::console::print_value ("%d", y_s);
pcl::console::print_info (" / ");
pcl::console::print_value ("%f", x_step);
pcl::console::print_info ("x");
pcl::console::print_value ("%f", y_step);
pcl::console::print_info (")\n");
int viewport = 0, l = 0, m = 0;
for (int i = 0; i < k; ++i)
{
std::string cloud_name = models.at (k_indices[0][i]).first;
boost::replace_last (cloud_name, "_vfh", "");
p.createViewPort (l * x_step, m * y_step, (l + 1) * x_step, (m + 1) * y_step, viewport);
l++;
if (l >= x_s)
{
l = 0;
m++;
}
sensor_msgs::PointCloud2 cloud;
pcl::console::print_highlight (stderr, "Loading "); pcl::console::print_value (stderr, "%s ", cloud_name.c_str ());
if (pcl::io::loadPCDFile (cloud_name, cloud) == -1)
break;
// Convert from blob to PointCloud
pcl::PointCloud<pcl::PointXYZ> cloud_xyz;
pcl::fromROSMsg (cloud, cloud_xyz);
if (cloud_xyz.points.size () == 0)
break;
pcl::console::print_info ("[done, ");
pcl::console::print_value ("%d", (int)cloud_xyz.points.size ());
pcl::console::print_info (" points]\n");
pcl::console::print_info ("Available dimensions: ");
pcl::console::print_value ("%s\n", pcl::getFieldsList (cloud).c_str ());
// Demean the cloud
Eigen::Vector4f centroid;
pcl::compute3DCentroid (cloud_xyz, centroid);
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_xyz_demean (new pcl::PointCloud<pcl::PointXYZ>);
pcl::demeanPointCloud<pcl::PointXYZ> (cloud_xyz, centroid, *cloud_xyz_demean);
// Add to renderer*
p.addPointCloud (cloud_xyz_demean, cloud_name, viewport);
// Check if the model found is within our inlier tolerance
std::stringstream ss;
ss << k_distances[0][i];
if (k_distances[0][i] > thresh)
{
p.addText (ss.str (), 20, 30, 1, 0, 0, ss.str (), viewport); // display the text with red
// Create a red line
pcl::PointXYZ min_p, max_p;
pcl::getMinMax3D (*cloud_xyz_demean, min_p, max_p);
std::stringstream line_name;
line_name << "line_" << i;
p.addLine (min_p, max_p, 1, 0, 0, line_name.str (), viewport);
p.setShapeRenderingProperties (pcl::visualization::PCL_VISUALIZER_LINE_WIDTH, 5, line_name.str (), viewport);
}
else
p.addText (ss.str (), 20, 30, 0, 1, 0, ss.str (), viewport);
// Increase the font size for the score*
p.setShapeRenderingProperties (pcl::visualization::PCL_VISUALIZER_FONT_SIZE, 18, ss.str (), viewport);
// Add the cluster name
p.addText (cloud_name, 20, 10, cloud_name, viewport);
}
// Add coordianate systems to all viewports
p.addCoordinateSystem (0.1, 0);
p.spin ();
return (0);
}
