q3DMASC/q3DMASC.cpp

//##########################################################################
//#                                                                        #
//#                     CLOUDCOMPARE PLUGIN: q3DMASC                       #
//#                                                                        #
//#  This program is free software; you can redistribute it and/or modify  #
//#  it under the terms of the GNU General Public License as published by  #
//#  the Free Software Foundation; version 2 or later of the License.      #
//#                                                                        #
//#  This program is distributed in the hope that it will be useful,       #
//#  but WITHOUT ANY WARRANTY; without even the implied warranty of        #
//#  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the          #
//#  GNU General Public License for more details.                          #
//#                                                                        #
//#                 COPYRIGHT: Dimitri Lague / CNRS / UEB                  #
//#                                                                        #
//##########################################################################

#include "q3DMASC.h"

//local
#include "q3DMASCDisclaimerDialog.h"
#include "q3DMASCClassifier.h"
#include "q3DMASCTools.h"
#include "qClassify3DMASCDialog.h"
#include "qTrain3DMASCDialog.h"
#include "q3DMASCCommands.h"

//qCC_db
#include <ccPointCloud.h>
#include <ccProgressDialog.h>

//Qt
#include <QtGui>
#include <QtCore>
#include <QApplication>
#include <QFileDialog>
#include <QMessageBox>
#include <QSettings>

q3DMASCPlugin::q3DMASCPlugin(QObject* parent/*=0*/)
	: QObject(parent)
	, ccStdPluginInterface( ":/CC/plugin/q3DMASCPlugin/info.json" )
	, m_classifyAction(0)
	, m_trainAction(0)
{
}

void q3DMASCPlugin::onNewSelection(const ccHObject::Container& selectedEntities)
{
	if (m_classifyAction)
	{
		//classification: only one point cloud
		//m_classifyAction->setEnabled(selectedEntities.size() == 1 && selectedEntities[0]->isA(CC_TYPES::POINT_CLOUD));
		m_classifyAction->setEnabled(m_app->dbRootObject()->getChildrenNumber() != 0);
	}

	if (m_trainAction)
	{
		//m_trainAction->setEnabled(m_app && m_app->dbRootObject() && m_app->dbRootObject()->getChildrenNumber() != 0); //need some loaded entities to train the classifier!
		m_trainAction->setEnabled(true);
	}

	m_selectedEntities = selectedEntities;
}

QList<QAction*> q3DMASCPlugin::getActions()
{
	QList<QAction*> group;

	if (!m_trainAction)
	{
		m_trainAction = new QAction("Train classifier", this);
		m_trainAction->setToolTip("Train classifier");
		m_trainAction->setIcon(QIcon(QString::fromUtf8(":/CC/plugin/q3DMASCPlugin/3DMASC TRAIN.png")));
		connect(m_trainAction, SIGNAL(triggered()), this, SLOT(doTrainAction()));
	}
	group.push_back(m_trainAction);

	if (!m_classifyAction)
	{
		m_classifyAction = new QAction("Classify", this);
		m_classifyAction->setToolTip("Classify cloud");
		m_classifyAction->setIcon(QIcon(QString::fromUtf8(":/CC/plugin/q3DMASCPlugin/3DMASC CLASSIFY.png")));
		connect(m_classifyAction, SIGNAL(triggered()), this, SLOT(doClassifyAction()));
	}
	group.push_back(m_classifyAction);

	return group;
}

void q3DMASCPlugin::doClassifyAction()
{
	if (!m_app)
	{
		assert(false);
		return;
	}

	//disclaimer accepted?
	if (!ShowClassifyDisclaimer(m_app))
	{
		return;
	}

	QString inputFilename;
	{
		QSettings settings;
		settings.beginGroup("3DMASC");
		QString inputPath = settings.value("FilePath", QCoreApplication::applicationDirPath()).toString();
		inputFilename = QFileDialog::getOpenFileName(m_app->getMainWindow(), "Load 3DMASC classifier file", inputPath, "*.txt");
		if (inputFilename.isNull())
		{
			//process cancelled by the user
			return;
		}
		settings.setValue("FilePath", QFileInfo(inputFilename).absolutePath());
		settings.endGroup();
	}

	QList<QString> cloudLabels;
	QString corePointsLabel;
	bool filenamesSpecified = false;
	if (!masc::Tools::LoadClassifierCloudLabels(inputFilename, cloudLabels, corePointsLabel, filenamesSpecified))
	{
		m_app->dispToConsole("Failed to read classifier file (see Console)", ccMainAppInterface::ERR_CONSOLE_MESSAGE);
		return;
	}
	if (cloudLabels.empty())
	{
		m_app->dispToConsole("Invalid classifier file (no cloud label defined)", ccMainAppInterface::ERR_CONSOLE_MESSAGE);
		return;
	}
	else if (cloudLabels.size() > 4 + (cloudLabels.contains("TEST") ? 1 : 0))
	{
		m_app->dispToConsole("This classifier uses more than 4 clouds (the GUI version cannot handle it)", ccMainAppInterface::ERR_CONSOLE_MESSAGE);
		return;
	}

	//now show a dialog where the user will be able to set the cloud roles
	Classify3DMASCDialog classifDlg(m_app);
	classifDlg.setCloudRoles(cloudLabels, corePointsLabel);
	classifDlg.label_trainOrClassify->setText(corePointsLabel + " will be classified");
	classifDlg.classifierFileLineEdit->setText(inputFilename);
	classifDlg.testCloudComboBox->hide();
	classifDlg.testLabel->hide();
	if (!classifDlg.exec())
	{
		//process cancelled by the user
		return;
	}
	static bool s_keepAttributes = classifDlg.keepAttributesCheckBox->isChecked();

	masc::Tools::NamedClouds clouds;
	QString mainCloudLabel = corePointsLabel;
	classifDlg.getClouds(clouds);

	masc::Feature::Set features;
	masc::Classifier classifier;
	if (!masc::Tools::LoadClassifier(inputFilename, clouds, features, classifier, m_app->getMainWindow()))
	{
		return;
	}
	if (!classifier.isValid())
	{
		m_app->dispToConsole("No classifier or invalid classifier", ccMainAppInterface::ERR_CONSOLE_MESSAGE);
		return;
	}

	if (clouds.contains("TEST"))
	{
		//remove the test cloud (if any)
		clouds.remove("TEST");
	}

	//the 'main cloud' is the cloud that should be classified
	masc::CorePoints corePoints;
	corePoints.origin = corePoints.cloud = clouds[mainCloudLabel];
	corePoints.role = mainCloudLabel;

	//prepare the main cloud
	ccProgressDialog progressDlg(true, m_app->getMainWindow());
	progressDlg.show();
	progressDlg.setAutoClose(false); //we don't want the progress dialog to 'pop' for each feature
	QString error;
	SFCollector generatedScalarFields;
    if (!masc::Tools::PrepareFeatures(corePoints, features, error, &progressDlg, &generatedScalarFields))
	{
		m_app->dispToConsole(error, ccMainAppInterface::ERR_CONSOLE_MESSAGE);
		generatedScalarFields.releaseSFs(false);
		return;
	}
	progressDlg.close();
	QCoreApplication::processEvents();

	//apply classifier
	{
		QString errorMessage;
		masc::Feature::Source::Set featureSources;
		masc::Feature::ExtractSources(features, featureSources);
		if (!classifier.classify(featureSources, corePoints.cloud, errorMessage, m_app->getMainWindow()))
		{
			m_app->dispToConsole(errorMessage, ccMainAppInterface::ERR_CONSOLE_MESSAGE);
			generatedScalarFields.releaseSFs(false);
			return;
		}

		generatedScalarFields.releaseSFs(s_keepAttributes);
	}
}

struct FeatureSelection
{
	FeatureSelection(masc::Feature::Shared f = masc::Feature::Shared(nullptr)) : feature(f) {}
	masc::Feature::Shared feature;
	bool selected = true;
	bool prepared = false;
	float importance = std::numeric_limits<float>::quiet_NaN();
};

void q3DMASCPlugin::saveTrainParameters(const masc::TrainParameters& params)
{
	QSettings settings("OSUR", "q3DMASC");
	settings.setValue("TrainParameters/maxDepth", params.rt.maxDepth);
	settings.setValue("TrainParameters/minSampleCount", params.rt.minSampleCount);
	settings.setValue("TrainParameters/activeVarCount", params.rt.activeVarCount);
	settings.setValue("TrainParameters/maxTreeCount", params.rt.maxTreeCount);
}

void q3DMASCPlugin::loadTrainParameters(masc::TrainParameters& params)
{
	QSettings settings("OSUR", "q3DMASC");
	params.rt.maxDepth = settings.value("TrainParameters/maxDepth", 25).toInt();
	params.rt.minSampleCount = settings.value("TrainParameters/minSampleCount", 10).toInt();
	params.rt.activeVarCount = settings.value("TrainParameters/activeVarCount", 0).toInt();
	params.rt.maxTreeCount = settings.value("TrainParameters/maxTreeCount", 100).toInt();
}

void q3DMASCPlugin::doTrainAction()
{
	//disclaimer accepted?
	if (!ShowTrainDisclaimer(m_app))
		return;

	QString inputFilename;
	{
		QSettings settings;
		settings.beginGroup("3DMASC");
		QString inputPath = settings.value("FilePath", QCoreApplication::applicationDirPath()).toString();
		inputFilename = QFileDialog::getOpenFileName(m_app->getMainWindow(), "Load 3DMASC training file", inputPath, "*.txt");
		if (inputFilename.isNull())
		{
			//process cancelled by the user
			return;
		}
		settings.setValue("FilePath", QFileInfo(inputFilename).absolutePath());
		settings.endGroup();
	}

	//load the cloud labels (PC1, PC2, CTX, etc.)
	QList<QString> cloudLabels;
	QString corePointsLabel;
	bool filenamesSpecified = false;
	if (!masc::Tools::LoadClassifierCloudLabels(inputFilename, cloudLabels, corePointsLabel, filenamesSpecified))
	{
		m_app->dispToConsole("Failed to read classifier file (see Console)", ccMainAppInterface::ERR_CONSOLE_MESSAGE);
		return;
	}
	if (cloudLabels.empty())
	{
		m_app->dispToConsole("Invalid classifier file (no cloud label defined)", ccMainAppInterface::ERR_CONSOLE_MESSAGE);
		return;
	}

	masc::Tools::NamedClouds loadedClouds;
	masc::CorePoints corePoints;

	//if no filename is specified in the training file, we are bound to ask the user to specify them
	bool useCloudsFromDB = (!filenamesSpecified || QMessageBox::question(m_app->getMainWindow(), "Use clouds in DB", "Use clouds in db (yes) or clouds specified in the file(no)?", QMessageBox::Yes, QMessageBox::No) == QMessageBox::Yes);
	QString mainCloudLabel = corePointsLabel;
	if (useCloudsFromDB)
	{
		if (cloudLabels.size() > 4 + (cloudLabels.contains("TEST") ? 1 : 0))
		{
			m_app->dispToConsole("This classifier uses more than 4 different clouds (the GUI version cannot handle it)", ccMainAppInterface::WRN_CONSOLE_MESSAGE);
			return;
		}

		//now show a dialog where the user will be able to set the cloud roles
		Classify3DMASCDialog classifDlg(m_app, true);
		classifDlg.setWindowTitle("3DMASC Train");
		classifDlg.setCloudRoles(cloudLabels, corePointsLabel);
		classifDlg.label_trainOrClassify->setText("The classifier will be trained on " + corePointsLabel);
		classifDlg.classifierFileLineEdit->setText(inputFilename);
		classifDlg.keepAttributesCheckBox->hide(); // this parameter is set in the trainDlg dialog
		if (!classifDlg.exec())
		{
			//process cancelled by the user
			return;
		}

		classifDlg.getClouds(loadedClouds);
		m_app->dispToConsole("Training cloud: " + mainCloudLabel, ccMainAppInterface::STD_CONSOLE_MESSAGE);
		corePoints.origin = loadedClouds[mainCloudLabel];
		corePoints.role = mainCloudLabel;
	}

	static masc::TrainParameters s_params;
	loadTrainParameters(s_params); // load the saved parameters or the default values
	masc::Feature::Set features;
	std::vector<double> scales;
	if (!masc::Tools:: LoadTrainingFile(inputFilename, features, scales, loadedClouds, s_params, &corePoints, m_app->getMainWindow()))
	{
		m_app->dispToConsole("Failed to load the training file", ccMainAppInterface::ERR_CONSOLE_MESSAGE);
		return;
	}

	if (!corePoints.origin)
	{
		m_app->dispToConsole("Core points not defined", ccMainAppInterface::ERR_CONSOLE_MESSAGE);
		return;
	}

	if (mainCloudLabel.isEmpty())
	{
		mainCloudLabel = corePoints.role;
	}

	if (!masc::Tools::GetClassificationSF(corePoints.origin))
	{
		m_app->dispToConsole("Missing 'Classification' field on core points cloud", ccMainAppInterface::ERR_CONSOLE_MESSAGE);
		return;
	}

	ccHObject* group = new ccHObject("3DMASC");
	if (!useCloudsFromDB)
	{
		//add the loaded clouds to the main DB (so that we don't need to handle them anymore)
		for (masc::Tools::NamedClouds::const_iterator it = loadedClouds.begin(); it != loadedClouds.end(); ++it)
		{
			group->addChild(it.value());
		}
	}

	for (masc::Tools::NamedClouds::iterator it = loadedClouds.begin(); it != loadedClouds.end(); ++it)
	{
		if (it.value())
			m_app->dispToConsole(it.key() + " = " + it.value()->getName(), ccMainAppInterface::STD_CONSOLE_MESSAGE);
		else
			ccLog::Warning(it.key() + " is not associated to a point cloud");
	}

	//test role
	ccPointCloud* testCloud = nullptr;
	bool needTestSuite = false;
	masc::Feature::Set featuresTest;
	std::vector<double> scalesTest;
	if (loadedClouds.contains("TEST"))
	{
		testCloud = loadedClouds["TEST"];
		loadedClouds.remove("TEST");

		if (testCloud != corePoints.origin && testCloud != corePoints.cloud)
		{
			//we need a duplicated test suite!!!
			needTestSuite = true;
			//replace the main cloud by the test cloud
			masc::Tools::NamedClouds loadedCloudsTest;
			loadedCloudsTest = loadedClouds;
			loadedCloudsTest[mainCloudLabel] = testCloud;

			//simply reload the classification file to create duplicated features
			masc::TrainParameters tempParams;
			if (!masc::Tools::LoadTrainingFile(inputFilename, featuresTest, scalesTest, loadedCloudsTest, tempParams))
			{
				m_app->dispToConsole("Failed to load the training file (for test)", ccMainAppInterface::ERR_CONSOLE_MESSAGE);
				return;
			}
		}
	}

	//show the training dialog for the first time
	Train3DMASCDialog trainDlg(m_app->getMainWindow());
	trainDlg.setWindowModality(Qt::WindowModal); // to be able to move the confusion matrix window
	trainDlg.maxDepthSpinBox->setValue(s_params.rt.maxDepth);
	trainDlg.maxTreeCountSpinBox->setValue(s_params.rt.maxTreeCount);
	trainDlg.activeVarCountSpinBox->setValue(s_params.rt.activeVarCount);
	trainDlg.minSampleCountSpinBox->setValue(s_params.rt.minSampleCount);
	trainDlg.testDataRatioSpinBox->setValue(static_cast<int>(s_params.testDataRatio * 100));
	trainDlg.testDataRatioSpinBox->setEnabled(testCloud == nullptr);
	trainDlg.setInputFilePath(inputFilename);

	//display the loaded features and let the user select the ones to use
	trainDlg.setResultText("Select features and press 'Run'");
	std::vector<FeatureSelection> originalFeatures;
	originalFeatures.reserve(features.size());
	for (const masc::Feature::Shared& f : features)
	{
		originalFeatures.push_back(FeatureSelection(f));
		trainDlg.addFeature(f->toString(), originalFeatures.back().importance, originalFeatures.back().selected);
	}
	for(double scale : scales)
		trainDlg.addScale(scale, true);
	trainDlg.connectScaleSelectionToFeatureSelection();

	std::vector<FeatureSelection> originalFeaturesTest;
	if (testCloud && needTestSuite)
	{
		originalFeaturesTest.reserve(featuresTest.size());
		for (const masc::Feature::Shared& f : featuresTest)
		{
			originalFeaturesTest.push_back(FeatureSelection(f));
		}
	}

	static bool s_keepAttributes = trainDlg.keepAttributesCheckBox->isChecked();
	if (!trainDlg.exec())
	{
		delete group;
		return;
	}
	assert(!trainDlg.shouldSaveClassifier()); //the save button should be disabled at this point

	//compute the core points (if necessary)
	ccProgressDialog progressDlg(true, m_app->getMainWindow());
	progressDlg.setAutoClose(false);
	if (!corePoints.prepare(&progressDlg))
	{
		m_app->dispToConsole("Failed to compute/prepare the core points!", ccMainAppInterface::ERR_CONSOLE_MESSAGE);
		delete group;
		return;
	}

	if (corePoints.cloud != corePoints.origin)
	{
		//auto-hide the other clouds
		for (ccPointCloud* pc : loadedClouds)
		{
			pc->setEnabled(false);
		}
		//set an explicit name for the core points
		QString corePointsName = corePoints.origin->getName();
		switch (corePoints.selectionMethod)
		{
		case masc::CorePoints::NONE:
			break;
		case masc::CorePoints::RANDOM:
			corePointsName += "_SS_Random@" + QString::number(corePoints.selectionParam);
			break;
		case masc::CorePoints::SPATIAL:
			corePointsName += "_SS_Spatial@" + QString::number(corePoints.selectionParam);
			break;
		default:
			assert(false);
		}
		corePoints.cloud->setName(QString("Core points (%1)").arg(corePointsName));
		group->addChild(corePoints.cloud);
	}
	
	if (group->getChildrenNumber() != 0)
	{
		m_app->addToDB(group);
		QCoreApplication::processEvents();
	}
	else
	{
		delete group;
		group = nullptr;
	}

	//train / test subsets
	QSharedPointer<CCCoreLib::ReferenceCloud> trainSubset, testSubset;
	float previousTestSubsetRatio = -1.0f;
	SFCollector generatedScalarFields, generatedScalarFieldsTest;

	//we will train + evaluate the classifier, then display the results
	//then let the user change parameters and (potentially) start again
	for (int iteration = 0; ; ++iteration)
	{
		//look for selected features
		features.clear();
		masc::Feature::Set toPrepare;
		for (size_t i = 0; i < originalFeatures.size(); ++i)
		{
			originalFeatures[i].selected = trainDlg.isFeatureSelected(originalFeatures[i].feature->toString());

			//if the feature is selected
			if (originalFeatures[i].selected)
			{
				if (!originalFeatures[i].prepared)
				{
					//we should prepare it first!
					toPrepare.push_back(originalFeatures[i].feature);
				}
				features.push_back(originalFeatures[i].feature);
			}
		}

		masc::Classifier classifier;
		if (features.empty())
		{
			m_app->dispToConsole("No feature selected!", ccMainAppInterface::ERR_CONSOLE_MESSAGE);
		}
		else
		{
			//prepare the features (should be done once)
			if (!toPrepare.empty())
			{
				progressDlg.show();
				QString error;
				if (!masc::Tools::PrepareFeatures(corePoints, toPrepare, error, &progressDlg, &generatedScalarFields))
				{
					m_app->dispToConsole(error, ccMainAppInterface::ERR_CONSOLE_MESSAGE);
					generatedScalarFields.releaseSFs(false);
					generatedScalarFieldsTest.releaseSFs(false);
					return;
				}
				progressDlg.close();
				QCoreApplication::processEvents();
				m_app->redrawAll();

				//flag the prepared features as 'prepared' ;)
				for (FeatureSelection& fs : originalFeatures)
				{
					if (fs.selected && !fs.prepared)
						fs.prepared = true;
				}
			}

			//retrieve parameters
			s_params.rt.maxDepth = trainDlg.maxDepthSpinBox->value();
			s_params.rt.maxTreeCount = trainDlg.maxTreeCountSpinBox->value();
			s_params.rt.activeVarCount = trainDlg.activeVarCountSpinBox->value();
			s_params.rt.minSampleCount = trainDlg.minSampleCountSpinBox->value();
			float testDataRatio = 0.0f;

			if (!testCloud)
			{
				//we need to generate test subsets
				testDataRatio = s_params.testDataRatio = trainDlg.testDataRatioSpinBox->value() / 100.0f;
				if (testDataRatio < 0.0f || testDataRatio > 0.99f)
				{
					assert(false);
					m_app->dispToConsole("Invalid test data ratio", ccMainAppInterface::ERR_CONSOLE_MESSAGE);
					trainSubset.clear();
					testSubset.clear();
				}
				else if (previousTestSubsetRatio != testDataRatio)
				{
					if (!trainSubset)
						trainSubset.reset(new CCCoreLib::ReferenceCloud(corePoints.cloud));
					trainSubset->clear();

					if (!testSubset)
						testSubset.reset(new CCCoreLib::ReferenceCloud(corePoints.cloud));
					testSubset->clear();

					//randomly select the training points
					if (!masc::Tools::RandomSubset(corePoints.cloud, testDataRatio, testSubset.data(), trainSubset.data()))
					{
						m_app->dispToConsole("Not enough memory to generate the test subsets", ccMainAppInterface::ERR_CONSOLE_MESSAGE);
						generatedScalarFields.releaseSFs(false);
						generatedScalarFieldsTest.releaseSFs(false);
						return;
					}
					previousTestSubsetRatio = testDataRatio;
				}
			}

			//extract the sources (after having prepared the features!)
			masc::Feature::Source::Set featureSources;
			masc::Feature::ExtractSources(features, featureSources);

			//train the classifier
			{
				QString errorMessage;
				if (!classifier.train(	corePoints.cloud,
										s_params.rt,
										featureSources,
										errorMessage,
										trainSubset.data(),
										m_app,
										m_app->getMainWindow()
									))
				{
					m_app->dispToConsole(errorMessage, ccMainAppInterface::ERR_CONSOLE_MESSAGE);
					generatedScalarFields.releaseSFs(false);
					generatedScalarFieldsTest.releaseSFs(false);
					return;
				}
				trainDlg.setFirstRunDone();
//				trainDlg.shouldSaveClassifier(); // useless?
			}

			//test the trained classifier
			{
				if (testCloud)
				{
					//look for selected features
					if (needTestSuite)
					{
						featuresTest.clear();
						masc::Feature::Set toPrepareTest;
						for (size_t i = 0; i < originalFeaturesTest.size(); ++i)
						{
							originalFeaturesTest[i].selected = trainDlg.isFeatureSelected(originalFeatures[i].feature->toString());
							//if the feature is selected
							if (originalFeaturesTest[i].selected)
							{
								if (!originalFeaturesTest[i].prepared)
								{
									//we should prepare it first!
									toPrepareTest.push_back(originalFeaturesTest[i].feature);
								}
								featuresTest.push_back(originalFeaturesTest[i].feature);
							}
						}

						//prepare the features and the test cloud
						if (!toPrepareTest.empty())
						{
							progressDlg.show();
							QString error;
							masc::CorePoints corePointsTest;
							corePointsTest.cloud = corePointsTest.origin = testCloud;
							corePointsTest.role = mainCloudLabel;
							if (!masc::Tools::PrepareFeatures(corePointsTest, toPrepareTest, error, &progressDlg, &generatedScalarFieldsTest))
							{
								m_app->dispToConsole(error, ccMainAppInterface::ERR_CONSOLE_MESSAGE);
								generatedScalarFields.releaseSFs(false);
								generatedScalarFieldsTest.releaseSFs(false);
								return;
							}
							progressDlg.close();
							QCoreApplication::processEvents();
							m_app->redrawAll();

							//flag the prepared features as 'prepared' ;)
							for (FeatureSelection& fs : originalFeaturesTest)
							{
								if (fs.selected && !fs.prepared)
									fs.prepared = true;
							}
						}
					}
				}

				masc::Classifier::AccuracyMetrics metrics;
				QString errorMessage;
				if (!classifier.evaluate(	featureSources,
											testCloud ? testCloud : corePoints.cloud,
											metrics,
											errorMessage,
											trainDlg,
											testCloud ? nullptr : testSubset.data(),
											testCloud ? "Classification_prediction" : "", // outputSFName, empty is the test cloud is not a separate cloud
											m_app->getMainWindow()))
				{
					m_app->dispToConsole(errorMessage, ccMainAppInterface::ERR_CONSOLE_MESSAGE);
					generatedScalarFields.releaseSFs(false);
					generatedScalarFieldsTest.releaseSFs(false);
					return;
				}

				QString resultText = QString("Correct guess = %1 / %2 --> accuracy = %3").arg(metrics.goodGuess).arg(metrics.sampleCount).arg(metrics.ratio);
				m_app->dispToConsole(resultText, ccMainAppInterface::STD_CONSOLE_MESSAGE);
				trainDlg.setResultText(resultText);

				cv::Mat importanceMat = classifier.getVarImportance();
				//m_app->dispToConsole(QString("Var importance size = %1 x %2").arg(importanceMat.rows).arg(importanceMat.cols));
				assert(static_cast<int>(features.size()) == importanceMat.rows);
				int selectedFeatureIndex = 0;
				for (size_t i = 0; i < originalFeatures.size(); ++i)
				{
					if (originalFeatures[i].selected)
					{
						//m_app->dispToConsole(QString("Feature #%1 importance = %2").arg(i + 1).arg(importanceMat.at<float>(i, 0)));
						assert(selectedFeatureIndex < importanceMat.rows);
						originalFeatures[i].importance = importanceMat.at<float>(selectedFeatureIndex, 0);
						++selectedFeatureIndex;
					}
					else
					{
						originalFeatures[i].importance = std::numeric_limits<float>::quiet_NaN();
					}
					trainDlg.setFeatureImportance(originalFeatures[i].feature->toString(), originalFeatures[i].importance);
				}

				trainDlg.sortByFeatureImportance();

				// if the checkbox "Save traces" is checked
				if (trainDlg.getSaveTrace())
				{
					//save the classifier in the trace directory with a generic name depending on the run
					QString tracePath = trainDlg.getTracePath();
					if (!tracePath.isEmpty())
					{
						QString outputFilePath = tracePath + "/run_" + QString::number(trainDlg.getRun()) + ".txt";
						if (masc::Tools::SaveClassifier(outputFilePath, features, mainCloudLabel, classifier, m_app->getMainWindow()))
						{
							m_app->dispToConsole("Classifier succesfully saved to " + outputFilePath, ccMainAppInterface::STD_CONSOLE_MESSAGE);
							trainDlg.setClassifierSaved();
						}
						else
						{
							m_app->dispToConsole("Failed to save classifier file");
						}
						QString exportFilePath = tracePath + "/run_" + QString::number(trainDlg.getRun()) + ".csv";

					}
				}
			}
		}

		//now wait for the user input
		while (true) // ew!
		{
			if (!trainDlg.exec())
			{
				saveTrainParameters(s_params);
				//the dialog can be closed
				if (trainDlg.keepAttributesCheckBox->isChecked())
					s_keepAttributes = true;
				else
					s_keepAttributes = false;
				generatedScalarFields.releaseSFs(s_keepAttributes);
				generatedScalarFieldsTest.releaseSFs(s_keepAttributes);
				return;
			}

			//if the save button has been clicked
			if (trainDlg.shouldSaveClassifier())
			{
				//ask for the output filename
				QString outputFilename;
				{
					QSettings settings;
					settings.beginGroup("3DMASC");
					QString outputPath = settings.value("FilePath", QCoreApplication::applicationDirPath()).toString();
					outputFilename = QFileDialog::getSaveFileName(m_app->getMainWindow(), "Save 3DMASC classifier", outputPath, "*.txt");
					if (outputFilename.isNull())
					{
						//process cancelled by the user
						continue;
					}
					settings.setValue("FilePath", QFileInfo(outputFilename).absolutePath());
					settings.endGroup();
				}

				//save the classifier
				if (masc::Tools::SaveClassifier(outputFilename, features, mainCloudLabel, classifier, m_app->getMainWindow()))
				{
					m_app->dispToConsole("Classifier succesfully saved to " + outputFilename, ccMainAppInterface::STD_CONSOLE_MESSAGE);
					trainDlg.setClassifierSaved();
				}
				else
				{
					m_app->dispToConsole("Failed to save classifier file");
				}
			}
			else //we will run the classifier another time
			{
				//stop the local loop
				break;
			}
		}
	}
}

void q3DMASCPlugin::registerCommands(ccCommandLineInterface* cmd)
{
	if (!cmd)
	{
		assert(false);
		return;
	}
	
	cmd->registerCommand(ccCommandLineInterface::Command::Shared(new Command3DMASCClassif));
}