// -----------------------------------------------------------------------------
//
// -----------------------------------------------------------------------------
void SineParamsSegmentFeatures::execute()
{
setErrorCondition(0);
dataCheck();
if(getErrorCondition() < 0) { return; }
DataContainer::Pointer m = getDataContainerArray()->getDataContainer(getDataContainerName());
QVector<size_t> tDims(1, 1);
m->getAttributeMatrix(getCellFeatureAttributeMatrixName())->resizeAttributeArrays(tDims);
// This runs a subfilter
int64_t totalPoints = m_FeatureIdsPtr.lock()->getNumberOfTuples();
// Tell the user we are starting the filter
notifyStatusMessage(getMessagePrefix(), getHumanLabel(), "Starting");
//Convert user defined tolerance to radians.
//angleTolerance = m_AngleTolerance * SIMPLib::Constants::k_Pi / 180.0f;
for(int64_t i = 0; i < totalPoints; i++)
{
m_FeatureIds[i] = 0;
}
// Generate the random voxel indices that will be used for the seed points to start a new grain growth/agglomeration
const size_t rangeMin = 0;
const size_t rangeMax = totalPoints - 1;
initializeVoxelSeedGenerator(rangeMin, rangeMax);
SegmentFeatures::execute();
size_t totalFeatures = m->getAttributeMatrix(getCellFeatureAttributeMatrixName())->getNumTuples();
if (totalFeatures < 2)
{
setErrorCondition(-87000);
notifyErrorMessage(getHumanLabel(), "The number of Features was 0 or 1 which means no features were detected. Is a threshold value set to high?", getErrorCondition());
return;
}
// By default we randomize grains
if (true == m_RandomizeFeatureIds)
{
randomizeFeatureIds(totalPoints, totalFeatures);
}
// If there is an error set this to something negative and also set a message
notifyStatusMessage(getHumanLabel(), "Completed");
}
// -----------------------------------------------------------------------------
//
// -----------------------------------------------------------------------------
void VectorSegmentFeatures::execute()
{
setErrorCondition(0);
dataCheck();
if(getErrorCondition() < 0) { return; }
DataContainer::Pointer m = getDataContainerArray()->getDataContainer(getDataContainerName());
QVector<size_t> tDims(1, 1);
m->getAttributeMatrix(getCellFeatureAttributeMatrixName())->resizeAttributeArrays(tDims);
updateFeatureInstancePointers();
int64_t totalPoints = static_cast<int64_t>(m_FeatureIdsPtr.lock()->getNumberOfTuples());
m_BeenPickedPtr = BoolArrayType::CreateArray(totalPoints, "BeenPicked INTERNAL ARRAY ONLY");
m_BeenPickedPtr->initializeWithValue(0);
m_BeenPicked = m_BeenPickedPtr->getPointer(0);
// Convert user defined tolerance to radians.
angleTolerance = m_AngleTolerance * SIMPLib::Constants::k_Pi / 180.0f;
// Generate the random voxel indices that will be used for the seed points to start a new grain growth/agglomeration
const int64_t rangeMin = 0;
const int64_t rangeMax = totalPoints - 1;
initializeVoxelSeedGenerator(rangeMin, rangeMax);
SegmentFeatures::execute();
int32_t totalFeatures = static_cast<int32_t>(m->getAttributeMatrix(getCellFeatureAttributeMatrixName())->getNumTuples());
if (totalFeatures < 2)
{
setErrorCondition(-87000);
notifyErrorMessage(getHumanLabel(), "The number of Features was 0 or 1 which means no Features were detected. A threshold value may be set too high", getErrorCondition());
return;
}
// By default we randomize grains
if (true == m_RandomizeFeatureIds)
{
randomizeFeatureIds(totalPoints, totalFeatures);
}
// If there is an error set this to something negative and also set a message
notifyStatusMessage(getHumanLabel(), "Completed");
}
// -----------------------------------------------------------------------------
//
// -----------------------------------------------------------------------------
void EBSDSegmentFeatures::execute()
{
setErrorCondition(0);
dataCheck();
if(getErrorCondition() < 0) { return; }
DataContainer::Pointer m = getDataContainerArray()->getDataContainer(getDataContainerName());
int64_t totalPoints = static_cast<int64_t>(m_FeatureIdsPtr.lock()->getNumberOfTuples());
QVector<size_t> tDims(1, 1);
m->getAttributeMatrix(getCellFeatureAttributeMatrixName())->resizeAttributeArrays(tDims);
updateFeatureInstancePointers();
// Convert user defined tolerance to radians.
misoTolerance = m_MisorientationTolerance * DREAM3D::Constants::k_Pi / 180.0f;
// Generate the random voxel indices that will be used for the seed points to start a new grain growth/agglomeration
const int64_t rangeMin = 0;
const int64_t rangeMax = totalPoints - 1;
initializeVoxelSeedGenerator(rangeMin, rangeMax);
SegmentFeatures::execute();
int64_t totalFeatures = static_cast<int64_t>(m_ActivePtr.lock()->getNumberOfTuples());
if (totalFeatures < 2)
{
setErrorCondition(-87000);
notifyErrorMessage(getHumanLabel(), "The number of Features was 0 or 1 which means no Features were detected. A threshold value may be set too high", getErrorCondition());
return;
}
// By default we randomize grains
if (true == getRandomizeFeatureIds())
{
totalPoints = static_cast<int64_t>(m->getGeometryAs<ImageGeom>()->getNumberOfElements());
randomizeFeatureIds(totalPoints, totalFeatures);
}
// If there is an error set this to something negative and also set a message
notifyStatusMessage(getHumanLabel(), "Complete");
}
// -----------------------------------------------------------------------------
//
// -----------------------------------------------------------------------------
void GroupMicroTextureRegions::execute()
{
setErrorCondition(0);
dataCheck();
if(getErrorCondition() < 0) { return; }
// Convert user defined tolerance to radians.
caxisTolerance = m_CAxisTolerance * SIMPLib::Constants::k_Pi / 180.0f;
avgCaxes[0] = 0.0f;
avgCaxes[1] = 0.0f;
avgCaxes[2] = 0.0f;
GroupFeatures::execute();
size_t totalFeatures = m_ActivePtr.lock()->getNumberOfTuples();
if (totalFeatures < 2)
{
setErrorCondition(-87000);
notifyErrorMessage(getHumanLabel(), "The number of grouped Features was 0 or 1 which means no grouped Features were detected. A grouping value may be set too high", getErrorCondition());
return;
}
int64_t totalPoints = static_cast<int64_t>(m_FeatureIdsPtr.lock()->getNumberOfTuples());
for (int64_t k = 0; k < totalPoints; k++)
{
int32_t featurename = m_FeatureIds[k];
m_CellParentIds[k] = m_FeatureParentIds[featurename];
}
// By default we randomize grains
if (true == m_RandomizeParentIds)
{
randomizeFeatureIds(totalPoints, totalFeatures);
}
// If there is an error set this to something negative and also set a message
notifyStatusMessage(getHumanLabel(), "Complete");
}
