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Train a true "classifier" so as to be able to call predict with the PREDICT_MAX_VOTE flag

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This commit is contained in:
Daniel Girardeau-Montaut
2019-03-29 13:40:29 +01:00
parent 0cdd0c8f1c
commit 568ed7e292
2 changed files with 49 additions and 16 deletions
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q3DMASCClassifier.cpp
+48 -15
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@@ -45,7 +45,7 @@ Classifier::Classifier()
bool Classifier::isValid() const
{
return (m_rtrees && m_rtrees->isTrained());
return (m_rtrees && m_rtrees->isClassifier() && m_rtrees->isTrained());
}
static IScalarFieldWrapper::Shared GetSource(const Feature::Source& fs, const ccPointCloud* cloud)
@@ -107,9 +107,9 @@ bool Classifier::classify( const Feature::Source::Set& featureSources,
return false;
}
if (!m_rtrees || !m_rtrees->isTrained())
if (!isValid())
{
errorMessage = QObject::tr("Classifier hasn't been trained yet");
errorMessage = QObject::tr("Invalid classifier");
return false;
}
@@ -216,10 +216,23 @@ bool Classifier::classify( const Feature::Source::Set& featureSources,
double value = wrappers[fIndex]->pointValue(i);
test_data.at<float>(0, fIndex) = static_cast<float>(value);
}
float predictedClass = m_rtrees->predict(test_data.row(0));
classificationSF->setValue(i, static_cast<int>(predictedClass));
#if 0
cv::Mat result;
try
{
m_rtrees->getVotes(test_data, result, cv::ml::DTrees::PREDICT_AUTO);
}
catch (const cv::Exception& cvex)
{
errorMessage = cvex.msg.c_str();
success = false;
break;
}
#else
float predictedClass = m_rtrees->predict(test_data.row(0), cv::noArray(), cv::ml::DTrees::PREDICT_MAX_VOTE);
classificationSF->setValue(i, static_cast<int>(predictedClass));
#endif
if (pDlg && !nProgress.oneStep())
{
//process cancelled by the user
@@ -376,8 +389,8 @@ bool Classifier::evaluate(const Feature::Source::Set& featureSources,
// return false;
//}
float predictedClass = m_rtrees->predict(test_data.row(i));
int iPredictedClass = static_cast<int>(predictedClass);
float fPredictedClass = m_rtrees->predict(test_data.row(i), cv::noArray(), cv::ml::DTrees::PREDICT_MAX_VOTE);
int iPredictedClass = static_cast<int>(fPredictedClass);
if (iPredictedClass == iClass)
{
++metrics.goodGuess;
@@ -508,22 +521,37 @@ bool Classifier::train( const ccPointCloud* cloud,
m_rtrees = cv::ml::RTrees::create();
m_rtrees->setMaxDepth(params.maxDepth);
m_rtrees->setMinSampleCount(params.minSampleCount);
m_rtrees->setRegressionAccuracy(0);
m_rtrees->setUseSurrogates(false);
m_rtrees->setPriors(cv::Mat());
//m_rtrees->setMaxCategories(params.maxCategories); //not important?
m_rtrees->setCalculateVarImportance(true);
m_rtrees->setActiveVarCount(params.activeVarCount);
cv::TermCriteria terminationCriteria(cv::TermCriteria::MAX_ITER, params.maxTreeCount, std::numeric_limits<double>::epsilon());
m_rtrees->setTermCriteria(terminationCriteria);
//rtrees->setRegressionAccuracy(0);
//rtrees->setUseSurrogates(false);
//rtrees->setMaxCategories(params.maxCategories); //not important?
//rtrees->setPriors(cv::Mat());
QFuture<bool> future = QtConcurrent::run([&]()
{
// Code in this block will run in another thread
try
{
m_rtrees->train(training_data, cv::ml::ROW_SAMPLE, train_labels);
cv::Mat sampleIndexes = cv::Mat::zeros(1, training_data.rows, CV_8U);
cv::Mat trainSamples = sampleIndexes.colRange(0, sampleCount);
trainSamples.setTo(cv::Scalar::all(1));
cv::Mat varTypes(training_data.cols + 1, 1, CV_8U);
varTypes.setTo(cv::Scalar::all(cv::ml::VAR_ORDERED));
varTypes.at<uchar>(training_data.cols) = cv::ml::VAR_CATEGORICAL;
cv::Ptr<cv::ml::TrainData> trainData = cv::ml::TrainData::create(training_data, cv::ml::ROW_SAMPLE, train_labels, cv::noArray(), sampleIndexes, cv::noArray(), varTypes);
bool success = m_rtrees->train(trainData);
if (!success || !m_rtrees->isClassifier())
{
errorMessage = "Training failed";
return false;
}
//m_rtrees->train(training_data, cv::ml::ROW_SAMPLE, train_labels);
}
catch (const cv::Exception& cvex)
{
@@ -634,7 +662,12 @@ bool Classifier::fromFile(QString filename, QWidget* parentWidget/*=nullptr*/)
QCoreApplication::processEvents();
}
if (!m_rtrees->isTrained())
if (m_rtrees->empty() || !m_rtrees->isClassifier())
{
ccLog::Error(QObject::tr("Loaded classifier is invalid"));
return false;
}
else if (!m_rtrees->isTrained())
{
ccLog::Warning(QObject::tr("Loaded classifier doesn't seem to be trained"));
}