saberbud/q3DMASC
1
0
Fork 0
mirror of https://github.com/dgirardeau/q3DMASC.git synced 2026-08-31 01:20:53 +08:00
Code Issues Packages Projects Releases Wiki Activity

Prevent the user from cancelling the training.

Browse Source
This commit is contained in:
Paul Leroy
2023-05-31 22:00:10 +02:00
parent 4f6cd5fe6d
commit 9bbf288b8f
7 changed files with 109 additions and 72 deletions
Download Patch File Download Diff File Expand all files Collapse all files
q3DMASCClassifier.cpp
+71 -37
View File
@@ -37,6 +37,7 @@
#include <QCoreApplication>
#include <QProgressDialog>
#include <QtConcurrent>
#include <QMessageBox>
#include "qTrain3DMASCDialog.h"
#include "confusionmatrix.h"
@@ -123,28 +124,30 @@ bool Classifier::classify( const Feature::Source::Set& featureSources,
return false;
}
//look for the classification field
CCCoreLib::ScalarField* classificationSF = Tools::GetClassificationSF(cloud);
// add a ccConfidence value if needed
int cvConfidenceIdx = cloud->getScalarFieldIndexByName("Classification_confidence");
if (cvConfidenceIdx > 0) // if the scalar field exists, delete it
if (cvConfidenceIdx >= 0) // if the scalar field exists, delete it
cloud->deleteScalarField(cvConfidenceIdx);
cvConfidenceIdx = cloud->addScalarField("Classification_confidence");
CCCoreLib::ScalarField* cvConfidenceSF = cloud->getScalarField(cvConfidenceIdx);
//look for the classification field
CCCoreLib::ScalarField* classificationSF = Tools::GetClassificationSF(cloud);
ccScalarField* classifSFBackup = nullptr;
if (classificationSF)
if (classificationSF) //save classification field (if any)
{
//save previous classification field (if any)
int sfIdx = cloud->getScalarFieldIndexByName("Classification_prev");
if (sfIdx > 0)
ccLog::Warning("Classification SF found: copy it in Classification_backup, a confusion matrix will be generated");
// delete Classification_backup field (if any)
int sfIdx = cloud->getScalarFieldIndexByName("Classification_backup");
if (sfIdx >= 0)
cloud->deleteScalarField(sfIdx);
// backup the classification field
try
{
classifSFBackup = new ccScalarField(*static_cast<ccScalarField*>(classificationSF));
classifSFBackup->setName("Classification_prev");
classifSFBackup = new ccScalarField(*static_cast<ccScalarField*>(classificationSF)); // copy constructor
classifSFBackup->setName("Classification_backup");
cloud->addScalarField(classifSFBackup);
}
catch (const std::bad_alloc)
@@ -237,7 +240,7 @@ bool Classifier::classify( const Feature::Source::Set& featureSources,
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++)
for (int col = 0; col < result.cols; col++) // look for the index of the predicted class
if (predictedClass == result.at<int>(0, col))
{
classIndex = col;
@@ -245,8 +248,8 @@ bool Classifier::classify( const Feature::Source::Set& featureSources,
}
if (classIndex != -1)
{
float nbVotes = result.at<int>(1, classIndex);
cvConfidenceSF->setValue(i, static_cast<ScalarType>(nbVotes / numberOfTrees));
float nbVotes = result.at<int>(1, classIndex); // get the number of votes
cvConfidenceSF->setValue(i, static_cast<ScalarType>(nbVotes / numberOfTrees)); // compute the confidence
}
else
cvConfidenceSF->setValue(i, CCCoreLib::NAN_VALUE);
@@ -326,28 +329,31 @@ bool Classifier::evaluate(const Feature::Source::Set& featureSources,
return false;
}
CCCoreLib::ScalarField* outputSF = nullptr;
CCCoreLib::ScalarField* outSF = nullptr;
CCCoreLib::ScalarField* cvConfidenceSF = nullptr;
ccLog::Warning("[evaluate] TEST cloud " + testCloud->getName());
if (!outputSFName.isEmpty())
{
int outSFIndex = testCloud->getScalarFieldIndexByName(qPrintable(outputSFName));
if (outSFIndex < 0)
{
ccScalarField* _outputSF = new ccScalarField(qPrintable(outputSFName));
if (!_outputSF->resizeSafe(testCloud->size()))
{
errorMessage = QObject::tr("Not enough memory to create output scalar field");
_outputSF->release();
return false;
}
testCloud->addScalarField(_outputSF);
outputSF = _outputSF;
}
int outIdx = testCloud->getScalarFieldIndexByName(qPrintable(outputSFName));
if (outIdx >= 0)
testCloud->deleteScalarField(outIdx);
else
{
outputSF = testCloud->getScalarField(outSFIndex);
}
outputSF->fill(CCCoreLib::NAN_VALUE);
outputSF->computeMinAndMax();
ccLog::Warning("add " + outputSFName + " to the TEST cloud");
outIdx = testCloud->addScalarField(qPrintable(outputSFName));
outSF = testCloud->getScalarField(outIdx);
}
if (outSF) // add a Classification_confidence value to the test cloud if needed
{
int cvConfidenceIdx = testCloud->getScalarFieldIndexByName("Classification_confidence");
if (cvConfidenceIdx >= 0) // if the scalar field exists, delete it
testCloud->deleteScalarField(cvConfidenceIdx);
else
ccLog::Warning("add Classification_confidence to the TEST cloud");
cvConfidenceIdx = testCloud->addScalarField("Classification_confidence");
cvConfidenceSF = testCloud->getScalarField(cvConfidenceIdx);
}
unsigned testSampleCount = (testSubset ? testSubset->size() : testCloud->size());
@@ -397,6 +403,7 @@ bool Classifier::evaluate(const Feature::Source::Set& featureSources,
}
}
int numberOfTrees = m_rtrees->getRoots().size();
//estimate the efficiency of the classifier
std::vector<ScalarType> actualClass(testSampleCount);
@@ -424,9 +431,29 @@ bool Classifier::evaluate(const Feature::Source::Set& featureSources,
{
++metrics.goodGuess;
}
if (outputSF)
if (outSF)
{
outputSF->setValue(pointIndex, static_cast<ScalarType>(iPredictedClass));
outSF->setValue(pointIndex, static_cast<ScalarType>(iPredictedClass));
if (cvConfidenceSF)
{
// compute the confidence
cv::Mat result;
m_rtrees->getVotes(test_data.row(i), result, cv::ml::DTrees::PREDICT_MAX_VOTE);
int classIndex = -1;
for (int col = 0; col < result.cols; col++) // look for the index of the predicted class
if (iPredictedClass == result.at<int>(0, col))
{
classIndex = col;
break;
}
if (classIndex != -1)
{
float nbVotes = result.at<int>(1, classIndex); // get the number of votes
cvConfidenceSF->setValue(i, static_cast<ScalarType>(nbVotes / numberOfTrees)); // compute the confidence
}
else
cvConfidenceSF->setValue(i, CCCoreLib::NAN_VALUE);
}
}
if (pDlg && !nProgress.oneStep())
@@ -436,8 +463,10 @@ bool Classifier::evaluate(const Feature::Source::Set& featureSources,
}
}
if (outputSF)
outputSF->computeMinAndMax();
if (outSF)
outSF->computeMinAndMax();
if (cvConfidenceSF)
cvConfidenceSF->computeMinAndMax();
metrics.ratio = static_cast<float>(metrics.goodGuess) / metrics.sampleCount;
}
@@ -617,8 +646,13 @@ bool Classifier::train( const ccPointCloud* cloud,
{
if (pDlg->wasCanceled())
{
future.cancel();
break;
// future.cancel();
QMessageBox msgBox;
msgBox.setText("The training is still in progress, not possible to cancel.");
msgBox.exec();
// break;
pDlg->reset();
pDlg->show();
}
pDlg->setValue(pDlg->value() + 1);
}