mirror of
https://github.com/dgirardeau/q3DMASC.git
synced 2026-08-29 16:40:49 +08:00
350 lines
10 KiB
C++
350 lines
10 KiB
C++
//##########################################################################
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//# #
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//# CLOUDCOMPARE PLUGIN: q3DMASC #
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//# #
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//# This program is free software; you can redistribute it and/or modify #
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//# it under the terms of the GNU General Public License as published by #
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//# the Free Software Foundation; version 2 or later of the License. #
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//# #
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//# This program is distributed in the hope that it will be useful, #
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//# but WITHOUT ANY WARRANTY; without even the implied warranty of #
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//# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #
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//# GNU General Public License for more details. #
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//# #
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//# COPYRIGHT: Dimitri Lague / CNRS / UEB #
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//# #
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//##########################################################################
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#include "q3DMASC.h"
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//local
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#include "q3DMASCDisclaimerDialog.h"
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#include "Features.h"
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//qCC_db
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#include <ccPointCloud.h>
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//qCC_io
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#include <LASFields.h>
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//Qt
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#include <QtGui>
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#include <QtCore>
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#include <QApplication>
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#include <QMessageBox>
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#include <QStringList>
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//OpenCV
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#include <opencv2/ml.hpp>
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q3DMASCPlugin::q3DMASCPlugin(QObject* parent/*=0*/)
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: QObject(parent)
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, ccStdPluginInterface( ":/CC/plugin/q3DMASCPlugin/info.json" )
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, m_classifyAction(0)
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, m_trainAction(0)
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{
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}
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void q3DMASCPlugin::onNewSelection(const ccHObject::Container& selectedEntities)
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{
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if (m_classifyAction)
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{
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//classification: only one point cloud
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m_classifyAction->setEnabled(selectedEntities.size() == 1 && selectedEntities[0]->isA(CC_TYPES::POINT_CLOUD));
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}
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if (m_trainAction)
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{
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m_trainAction->setEnabled(m_app && m_app->dbRootObject() && m_app->dbRootObject()->getChildrenNumber() != 0); //need some loaded entities to train the classifier!
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}
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m_selectedEntities = selectedEntities;
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}
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QList<QAction*> q3DMASCPlugin::getActions()
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{
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QList<QAction*> group;
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if (!m_trainAction)
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{
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m_trainAction = new QAction("Train classifier", this);
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m_trainAction->setToolTip("Train classifier");
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m_trainAction->setIcon(QIcon(QString::fromUtf8(":/CC/plugin/q3DMASCPlugin/iconCreate.png")));
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connect(m_trainAction, SIGNAL(triggered()), this, SLOT(doTrainAction()));
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}
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group.push_back(m_trainAction);
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if (!m_classifyAction)
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{
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m_classifyAction = new QAction("Classify", this);
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m_classifyAction->setToolTip("Classify cloud");
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m_classifyAction->setIcon(QIcon(QString::fromUtf8(":/CC/plugin/q3DMASCPlugin/iconClassify.png")));
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connect(m_classifyAction, SIGNAL(triggered()), this, SLOT(doClassifyAction()));
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}
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group.push_back(m_classifyAction);
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return group;
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}
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void q3DMASCPlugin::doClassifyAction()
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{
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if (!m_app)
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{
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assert(false);
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return;
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}
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//disclaimer accepted?
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if (!ShowClassifyDisclaimer(m_app))
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{
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return;
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}
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if (m_selectedEntities.empty() || !m_selectedEntities.front()->isA(CC_TYPES::POINT_CLOUD))
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{
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m_app->dispToConsole("Select one and only one point cloud!", ccMainAppInterface::ERR_CONSOLE_MESSAGE);
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return;
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}
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ccPointCloud* cloud = static_cast<ccPointCloud*>(m_selectedEntities.front());
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//look for the classification field
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int classifSFIdx = cloud->getScalarFieldIndexByName(LAS_FIELD_NAMES[LAS_CLASSIFICATION]); //LAS_FIELD_NAMES[LAS_CLASSIFICATION] = "Classification"
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if (!classifSFIdx)
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{
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m_app->dispToConsole("Missing 'Classification' field", ccMainAppInterface::ERR_CONSOLE_MESSAGE);
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return;
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}
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CCLib::ScalarField* classifSF = cloud->getScalarField(classifSFIdx);
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if (!classifSF || classifSF->size() < cloud->size())
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{
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assert(false);
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return;
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}
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RTParams params;
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if (params.testDataRatio < 0 || params.testDataRatio > 0.99f)
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{
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m_app->dispToConsole("Invalid test data ratio", ccMainAppInterface::ERR_CONSOLE_MESSAGE);
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return;
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}
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std::vector<Feature> features;
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features.push_back(Feature(Feature::DimZ, "Z"));
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features.push_back(Feature(Feature::ScalarField, "Intensity"));
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features.push_back(Feature(Feature::ScalarField, "Intensity"));
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int totalSampleCount = static_cast<int>(cloud->size());
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int testSampleCount = static_cast<int>(floor(totalSampleCount * params.testDataRatio));
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int sampleCount = totalSampleCount - testSampleCount;
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int attributesPerSample = static_cast<int>(features.size());
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m_app->dispToConsole(QString("[3DMASC] Training data: %1 samples with %2 feature(s) / %3 test samples").arg(sampleCount).arg(attributesPerSample).arg(testSampleCount), ccMainAppInterface::STD_CONSOLE_MESSAGE);
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//choose the sample indexes
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std::vector<bool> isSample;
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{
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try
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{
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isSample.resize(totalSampleCount, true);
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}
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catch (const std::bad_alloc&)
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{
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m_app->dispToConsole("Not enough memory", ccMainAppInterface::STD_CONSOLE_MESSAGE);
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return;
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}
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int randomCount = 0;
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while (randomCount < testSampleCount)
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{
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int randIndex = (std::rand() % totalSampleCount);
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if (isSample[randIndex])
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{
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isSample[randIndex] = false;
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++randomCount;
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}
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}
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}
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//NUMBER_OF_TRAINING_SAMPLES = number of points
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//ATTRIBUTES_PER_SAMPLE = number of scalar fields
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cv::Mat training_data, train_labels;
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cv::Mat test_data, test_labels;
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try
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{
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training_data.create(sampleCount, attributesPerSample, CV_32FC1);
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train_labels.create(sampleCount, 1, CV_8U);
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test_data.create(testSampleCount, attributesPerSample, CV_32FC1);
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test_labels.create(testSampleCount, 1, CV_8U);
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}
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catch (const cv::Exception& cvex)
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{
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ccLog::Error(cvex.msg.c_str());
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return;
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}
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//fill the classification labels vector
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{
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unsigned sampleIndex = 0;
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unsigned testSampleIndex = 0;
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for (unsigned i = 0; i < cloud->size(); ++i)
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{
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ScalarType pointClass = classifSF->getValue(i);
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int iClass = static_cast<int>(pointClass);
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if (iClass < 0 || iClass > 255)
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{
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m_app->dispToConsole("Classification values out of range (0-255)", ccMainAppInterface::ERR_CONSOLE_MESSAGE);
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return;
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}
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if (isSample[i])
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{
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train_labels.at<unsigned char>(sampleIndex++) = static_cast<unsigned char>(iClass);
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}
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else
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{
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test_labels.at<unsigned char>(testSampleIndex++) = static_cast<unsigned char>(iClass);
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}
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}
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assert(testSampleIndex + testSampleIndex == totalSampleCount);
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}
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//fill the training data matrix
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for (int fIndex = 0; fIndex < attributesPerSample; ++fIndex)
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{
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QScopedPointer<IScalarFieldWrapper> source(nullptr);
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const Feature& f = features[fIndex];
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switch (f.source)
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{
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case Feature::ScalarField:
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{
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int sfIdx = cloud->getScalarFieldIndexByName(qPrintable(f.name));
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if (sfIdx >= 0)
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{
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source.reset(new ScalarFieldWrapper(cloud->getScalarField(sfIdx)));
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}
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else
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{
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ccLog::Error(QString("Internal error: unknwon scalar field '%1'").arg(f.name));
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return;
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}
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}
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break;
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case Feature::DimX:
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source.reset(new DimScalarFieldWrapper(cloud, DimScalarFieldWrapper::DimX));
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break;
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case Feature::DimY:
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source.reset(new DimScalarFieldWrapper(cloud, DimScalarFieldWrapper::DimY));
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break;
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case Feature::DimZ:
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source.reset(new DimScalarFieldWrapper(cloud, DimScalarFieldWrapper::DimZ));
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break;
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case Feature::Red:
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source.reset(new ColorScalarFieldWrapper(cloud, ColorScalarFieldWrapper::Red));
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break;
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case Feature::Green:
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source.reset(new ColorScalarFieldWrapper(cloud, ColorScalarFieldWrapper::Green));
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break;
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case Feature::Blue:
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source.reset(new ColorScalarFieldWrapper(cloud, ColorScalarFieldWrapper::Blue));
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break;
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}
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if (!source || !source->isValid())
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{
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assert(false);
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ccLog::Error(QString("Internal error: invalid source '%1'").arg(f.name));
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}
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unsigned sampleIndex = 0;
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unsigned testSampleIndex = 0;
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for (unsigned i = 0; i < cloud->size(); ++i)
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{
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double value = source->pointValue(i);
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if (isSample[i])
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{
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training_data.at<float>(sampleIndex++, fIndex) = static_cast<float>(value);
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}
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else
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{
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test_data.at<float>(testSampleIndex++, fIndex) = static_cast<float>(value);
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}
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}
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assert(testSampleIndex + testSampleIndex == totalSampleCount);
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}
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cv::Ptr<cv::ml::RTrees> rtrees;
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rtrees = cv::ml::RTrees::create();
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rtrees->setMaxDepth(params.maxDepth);
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rtrees->setMinSampleCount(params.minSampleCount);
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rtrees->setCalculateVarImportance(params.calcVarImportance);
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rtrees->setActiveVarCount(params.activeVarCount);
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cv::TermCriteria terminationCriteria(cv::TermCriteria::MAX_ITER, params.maxTreeCount, std::numeric_limits<double>::epsilon());
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rtrees->setTermCriteria(terminationCriteria);
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//rtrees->setRegressionAccuracy(0);
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//rtrees->setUseSurrogates(false);
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//rtrees->setMaxCategories(params.maxCategories); //not important?
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//rtrees->setPriors(cv::Mat());
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rtrees->train(training_data, cv::ml::ROW_SAMPLE, train_labels);
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if (!rtrees->isTrained())
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{
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//an error occurred?
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return;
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}
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//estimate the efficiency of the classiier
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{
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int goodGuessCount = 0;
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for (int j = 0; j < testSampleCount; ++j)
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{
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if (rtrees->predict(test_data.row(j)) == test_labels.at<int>(j))
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{
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++goodGuessCount;
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}
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}
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float acc = static_cast<float>(goodGuessCount) / testSampleCount;
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m_app->dispToConsole(QString("Correct = %1 / %2 --> Accuracy = %3").arg(goodGuessCount).arg(testSampleCount).arg(acc), ccMainAppInterface::STD_CONSOLE_MESSAGE);
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}
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}
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//OpenCV
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void q3DMASCPlugin::doTrainAction()
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{
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//disclaimer accepted?
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if (!ShowTrainDisclaimer(m_app))
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return;
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//if (m_selectedEntities.size() != 2
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// || !m_selectedEntities[0]->isA(CC_TYPES::POINT_CLOUD)
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// || !m_selectedEntities[1]->isA(CC_TYPES::POINT_CLOUD))
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//{
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// m_app->dispToConsole("Select two point clouds!",ccMainAppInterface::ERR_CONSOLE_MESSAGE);
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// return;
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//}
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//
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//ccPointCloud* cloud1 = static_cast<ccPointCloud*>(m_selectedEntities[0]);
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//ccPointCloud* cloud2 = static_cast<ccPointCloud*>(m_selectedEntities[1]);
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}
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void q3DMASCPlugin::registerCommands(ccCommandLineInterface* cmd)
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{
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if (!cmd)
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{
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assert(false);
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return;
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}
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//cmd->registerCommand(ccCommandLineInterface::Command::Shared(new CommandCanupoClassif));
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}
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