mirror of
https://github.com/dgirardeau/q3DMASC.git
synced 2026-08-29 16:40:49 +08:00
Cleaner way to handle test subsets
This commit is contained in:
Daniel Girardeau-Montaut
+188
-134
@@ -42,20 +42,82 @@ bool Classifier::isValid() const
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return (m_rtrees && m_rtrees->isTrained());
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}
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bool Classifier::train(const TrainParameters& params, const Feature::Set& features, QString& errorMessage, QWidget* parentWidget/*=nullptr*/)
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static QSharedPointer<IScalarFieldWrapper> GetSource(const Feature::Shared& f, ccPointCloud* cloud)
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{
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QSharedPointer<IScalarFieldWrapper> source(nullptr);
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if (!f)
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{
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assert(false);
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ccLog::Warning(QObject::tr("Internal error: invalid feature (nullptr)"));
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}
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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->sourceName));
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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::Warning(QObject::tr("Internal error: unknwon scalar field '%1'").arg(f->sourceName));
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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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return source;
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}
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bool Classifier::evaluate(const Feature::Set& features, CCLib::ReferenceCloud* testSubset, AccuracyMetrics& metrics, QString& errorMessage, QWidget* parentWidget/*=nullptr*/)
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{
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metrics.sampleCount = metrics.goodGuess = 0;
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metrics.ratio = 0.0f;
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if (!m_rtrees || !m_rtrees->isTrained())
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{
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errorMessage = QObject::tr("Classifier hasn't been trained yet");
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return false;
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}
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if (features.empty())
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{
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errorMessage = QObject::tr("Training method called without any feature?!");
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return false;
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}
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if (!features.front() || !features.front()->cloud)
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if (!testSubset)
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{
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errorMessage = QObject::tr("Invalid feature (no associated point cloud");
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errorMessage = QObject::tr("No test subset provided");
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return false;
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}
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ccPointCloud* cloud = dynamic_cast<ccPointCloud*>(testSubset->getAssociatedCloud());
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if (!cloud)
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{
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errorMessage = QObject::tr("Invalid test subset (associated point cloud is not a ccPointCloud)");
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return false;
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}
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ccPointCloud* cloud = features.front()->cloud;
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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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@@ -72,61 +134,123 @@ bool Classifier::train(const TrainParameters& params, const Feature::Set& featur
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return false;
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}
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if (params.testDataRatio < 0 || params.testDataRatio > 0.99f)
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int testSampleCount = static_cast<int>(testSubset->size());
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int attributesPerSample = static_cast<int>(features.size());
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ccLog::Print(QObject::tr("[3DMASC] Testing data: %1 samples with %2 feature(s)").arg(testSampleCount).arg(attributesPerSample));
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//allocate the data matrix
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cv::Mat test_data;
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try
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{
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errorMessage = QObject::tr("Invalid parameter (test data ratio)");
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test_data.create(testSampleCount, attributesPerSample, CV_32FC1);
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}
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catch (const cv::Exception& cvex)
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{
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errorMessage = cvex.msg.c_str();
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return false;
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}
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//std::vector<Feature::Shared> features;
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//features.push_back(Feature::Shared(new PointFeature(cloud, PointFeature::Z, Feature::DimZ, "Z")));
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//features.push_back(Feature::Shared(new PointFeature(cloud, PointFeature::Intensity, 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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ccLog::Print(QString("[3DMASC] Training data: %1 samples with %2 feature(s) / %3 test samples").arg(sampleCount).arg(attributesPerSample).arg(testSampleCount));
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//randomly choose the sample indexes
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std::vector<bool> isSample;
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if (testSampleCount > 0)
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//fill the data matrix
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for (int fIndex = 0; fIndex < attributesPerSample; ++fIndex)
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{
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try
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const Feature::Shared &f = features[fIndex];
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if (!f)
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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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errorMessage = QObject::tr("Not enough memory");
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assert(false);
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return false;
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}
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int randomCount = 0;
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int randIndex = 0;
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while (randomCount < testSampleCount)
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QSharedPointer<IScalarFieldWrapper> source = GetSource(f, cloud);
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if (!source || !source->isValid())
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{
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randIndex = ((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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assert(false);
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errorMessage = QObject::tr("Internal error: invalid source '%1'").arg(f->sourceName);
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return false;
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}
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for (unsigned i = 0; i < testSubset->size(); ++i)
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{
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unsigned pointIndex = testSubset->getPointGlobalIndex(i);
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double value = source->pointValue(pointIndex);
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test_data.at<float>(i, fIndex) = static_cast<float>(value);
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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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//estimate the efficiency of the classiier
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{
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metrics.sampleCount = testSubset->size();
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metrics.goodGuess = 0;
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for (unsigned i = 0; i < testSubset->size(); ++i)
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{
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unsigned pointIndex = testSubset->getPointGlobalIndex(i);
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ScalarType pointClass = classifSF->getValue(pointIndex);
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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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// errorMessage = QObject::tr("Classification values out of range (0-255)");
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// return false;
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//}
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float predictedClass = m_rtrees->predict(test_data.row(i));
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if (static_cast<int>(predictedClass) == iClass)
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{
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++metrics.goodGuess;
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}
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}
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metrics.ratio = static_cast<float>(metrics.goodGuess) / metrics.sampleCount;
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}
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return true;
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}
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bool Classifier::train(const RandomTreesParams& params, const Feature::Set& features, QString& errorMessage, CCLib::ReferenceCloud* trainSubset/*=nullptr*/, QWidget* parentWidget/*=nullptr*/)
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{
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if (features.empty())
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{
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errorMessage = QObject::tr("Training method called without any feature?!");
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return false;
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}
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if (!features.front() || !features.front()->cloud)
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{
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errorMessage = QObject::tr("Invalid feature (no associated point cloud)");
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return false;
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}
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ccPointCloud* cloud = features.front()->cloud;
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if (trainSubset && trainSubset->getAssociatedCloud() != cloud)
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{
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errorMessage = QObject::tr("Invalid train subset (associated point cloud is different)");
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return false;
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}
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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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errorMessage = QObject::tr("Missing 'Classification' field on input cloud");
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return false;
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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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errorMessage = QObject::tr("Invalid 'Classification' field on input cloud");
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return false;
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}
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int sampleCount = static_cast<int>(trainSubset ? trainSubset->size() : cloud->size());
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int attributesPerSample = static_cast<int>(features.size());
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ccLog::Print(QString("[3DMASC] Training data: %1 samples with %2 feature(s)").arg(sampleCount).arg(attributesPerSample));
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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_32FC1);
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test_data.create(testSampleCount, attributesPerSample, CV_32FC1);
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test_labels.create(testSampleCount, 1, CV_32FC1);
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}
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catch (const cv::Exception& cvex)
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{
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@@ -136,75 +260,32 @@ bool Classifier::train(const TrainParameters& params, const Feature::Set& featur
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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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for (int i = 0; i < sampleCount; ++i)
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{
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ScalarType pointClass = classifSF->getValue(i);
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int pointIndex = (trainSubset ? static_cast<int>(trainSubset->getPointGlobalIndex(i)) : i);
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ScalarType pointClass = classifSF->getValue(pointIndex);
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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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errorMessage = QObject::tr("Classification values out of range (0-255)");
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return false;
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}
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//if (iClass < 0 || iClass > 255)
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//{
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// errorMessage = QObject::tr("Classification values out of range (0-255)");
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// return false;
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//}
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if (isSample[i])
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{
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train_labels.at<float>(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<float>(testSampleIndex++) = static_cast<unsigned char>(iClass);
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}
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train_labels.at<float>(i) = static_cast<unsigned char>(iClass);
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}
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assert(sampleIndex + 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::Shared &f = features[fIndex];
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switch (f->source)
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if (f->cloud != cloud)
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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->sourceName));
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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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errorMessage = QObject::tr("Internal error: unknwon scalar field '%1'").arg(f->sourceName);
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return false;
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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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errorMessage = QObject::tr("Invalid feature (%1): associated cloud is different than the others").arg(f->toString());
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return false;
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}
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QSharedPointer<IScalarFieldWrapper> source = GetSource(f, cloud);
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if (!source || !source->isValid())
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{
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assert(false);
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@@ -212,23 +293,12 @@ bool Classifier::train(const TrainParameters& params, const Feature::Set& featur
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return false;
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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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for (int i = 0; i < sampleCount; ++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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assert(sampleIndex < sampleCount);
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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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assert(testSampleIndex< testSampleCount);
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test_data.at<float>(testSampleIndex++, fIndex) = static_cast<float>(value);
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}
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int pointIndex = (trainSubset ? static_cast<int>(trainSubset->getPointGlobalIndex(i)) : i);
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double value = source->pointValue(pointIndex);
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training_data.at<float>(i, fIndex) = static_cast<float>(value);
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}
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assert(sampleIndex + testSampleIndex == totalSampleCount);
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}
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QProgressDialog pDlg(parentWidget);
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@@ -238,11 +308,11 @@ bool Classifier::train(const TrainParameters& params, const Feature::Set& featur
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QCoreApplication::processEvents();
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m_rtrees = cv::ml::RTrees::create();
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m_rtrees->setMaxDepth(params.rt.maxDepth);
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m_rtrees->setMinSampleCount(params.rt.minSampleCount);
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m_rtrees->setCalculateVarImportance(params.rt.calcVarImportance);
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m_rtrees->setActiveVarCount(params.rt.activeVarCount);
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cv::TermCriteria terminationCriteria(cv::TermCriteria::MAX_ITER, params.rt.maxTreeCount, std::numeric_limits<double>::epsilon());
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m_rtrees->setMaxDepth(params.maxDepth);
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m_rtrees->setMinSampleCount(params.minSampleCount);
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m_rtrees->setCalculateVarImportance(params.calcVarImportance);
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m_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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m_rtrees->setTermCriteria(terminationCriteria);
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//rtrees->setRegressionAccuracy(0);
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@@ -280,22 +350,6 @@ bool Classifier::train(const TrainParameters& params, const Feature::Set& featur
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return false;
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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 (m_rtrees->predict(test_data.row(j)) == test_labels.at<float>(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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ccLog::Print(QString("Correct = %1 / %2 --> Accuracy = %3").arg(goodGuessCount).arg(testSampleCount).arg(acc));
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}
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//QString outputFilename = QCoreApplication::applicationDirPath() + "/classifier.yaml";
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return true;
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}
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