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confidence handling

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This commit is contained in:
Paul Leroy
2022-12-08 09:19:52 +01:00
parent 9ddb986351
commit bb5b7a6ddd
2 changed files with 29 additions and 19 deletions
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q3DMASCClassifier.cpp
+28 -18
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@@ -122,6 +122,11 @@ bool Classifier::classify( const Feature::Source::Set& featureSources,
//look for the classification field
CCCoreLib::ScalarField* classificationSF = Tools::GetClassificationSF(cloud);
// add a ccConfidence value if needed
int cvConfidenceIdx = cloud->getScalarFieldIndexByName("cvConfidence");
if (cvConfidenceIdx < 0) // if the scalar field does not exists, create it
cvConfidenceIdx = cloud->addScalarField("cvConfidence");
CCCoreLib::ScalarField* cvConfidenceSF = cloud->getScalarField(cvConfidenceIdx);
if (classificationSF)
{
@@ -193,6 +198,8 @@ bool Classifier::classify( const Feature::Source::Set& featureSources,
CCCoreLib::NormalizedProgress nProgress(pDlg.data(), cloud->size());
bool success = true;
cv::TermCriteria termCriteria = m_rtrees->getTermCriteria();
int numberOfTrees = termCriteria.maxCount;
#ifndef _DEBUG
#if defined(_OPENMP)
#pragma omp parallel for
@@ -219,22 +226,23 @@ bool Classifier::classify( const Feature::Source::Set& featureSources,
test_data.at<float>(0, fIndex) = static_cast<float>(value);
}
#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
// compute the confidence
cv::Mat result;
m_rtrees->getVotes(test_data, result, cv::ml::DTrees::PREDICT_MAX_VOTE);
int classIndex = -1;
for (int col = 0; col < result.cols; col++)
if (predictedClass == result.at<float>(0, col))
{
classIndex = col;
break;
}
if (classIndex != -1)
cvConfidenceSF->setValue(i, static_cast<ScalarType>(result.at<float>(1, classIndex) / numberOfTrees));
else
cvConfidenceSF->setValue(i, CCCoreLib::NAN_VALUE);
if (pDlg && !nProgress.oneStep())
{
//process cancelled by the user
@@ -542,14 +550,16 @@ bool Classifier::train( const ccPointCloud* cloud,
{
ccLog::Warning("[QFuture] cv::getNumThreads " + QString::number(cv::getNumThreads()));
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 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);
cv::Ptr<cv::ml::TrainData> trainData = cv::ml::TrainData::create(training_data, cv::ml::ROW_SAMPLE, train_labels, /* samples layout responses */
cv::noArray(), sampleIndexes, /* varIdx sampleIdx */
cv::noArray(), varTypes); // sampleWeights varType
bool success = m_rtrees->train(trainData);
if (!success || !m_rtrees->isClassifier())
@@ -557,7 +567,6 @@ bool Classifier::train( const ccPointCloud* cloud,
errorMessage = "Training failed";
return false;
}
//m_rtrees->train(training_data, cv::ml::ROW_SAMPLE, train_labels);
}
catch (const cv::Exception& cvex)
{
@@ -575,6 +584,7 @@ bool Classifier::train( const ccPointCloud* cloud,
errorMessage = QObject::tr("Unknown error");
return false;
}
return true;
});