From 50bca421f3d749907c5eeacaff24984f335a06fe Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?C=C3=A9dric=20Traizet?= <cedric.traizet@c-s.fr>
Date: Thu, 1 Aug 2019 14:19:18 +0200
Subject: [PATCH] STY: apply clang format
---
.../app/otbVectorClassifier.cxx | 102 ++++-----
.../app/otbVectorRegression.cxx | 60 ++---
.../include/otbVectorPrediction.h | 43 ++--
.../include/otbVectorPrediction.hxx | 212 +++++++++---------
4 files changed, 204 insertions(+), 213 deletions(-)
diff --git a/Modules/Applications/AppClassification/app/otbVectorClassifier.cxx b/Modules/Applications/AppClassification/app/otbVectorClassifier.cxx
index 92f4e90f54..4308f19ac6 100644
--- a/Modules/Applications/AppClassification/app/otbVectorClassifier.cxx
+++ b/Modules/Applications/AppClassification/app/otbVectorClassifier.cxx
@@ -27,67 +27,73 @@ namespace Wrapper
using VectorClassifier = VectorPrediction<false>;
-template<>
-void
-VectorClassifier
-::DoInitSpecialization()
+template <>
+void VectorClassifier::DoInitSpecialization()
{
SetName("VectorClassifier");
SetDescription("Performs a classification of the input vector data according to a model file.");
SetDocAuthors("OTB-Team");
- SetDocLongDescription("This application performs a vector data classification "
- "based on a model file produced by the TrainVectorClassifier application."
- "Features of the vector data output will contain the class labels decided by the classifier "
- "(maximal class label = 65535). \n"
- "There are two modes: \n"
+ SetDocLongDescription(
+ "This application performs a vector data classification "
+ "based on a model file produced by the TrainVectorClassifier application."
+ "Features of the vector data output will contain the class labels decided by the classifier "
+ "(maximal class label = 65535). \n"
+ "There are two modes: \n"
"1) Update mode: add of the 'cfield' field containing the predicted class in the input file. \n"
"2) Write mode: copies the existing fields of the input file to the output file "
- " and add the 'cfield' field containing the predicted class. \n"
- "If you have declared the output file, the write mode applies. "
- "Otherwise, the input file update mode will be applied.");
+ " and add the 'cfield' field containing the predicted class. \n"
+ "If you have declared the output file, the write mode applies. "
+ "Otherwise, the input file update mode will be applied.");
SetDocLimitations("Shapefiles are supported, but the SQLite format is only supported in update mode.");
SetDocSeeAlso("TrainVectorClassifier");
AddDocTag(Tags::Learning);
-
+
AddParameter(ParameterType_InputFilename, "in", "Name of the input vector data");
- SetParameterDescription("in","The input vector data file to classify.");
+ SetParameterDescription("in", "The input vector data file to classify.");
AddParameter(ParameterType_InputFilename, "instat", "Statistics file");
- SetParameterDescription("instat", "A XML file containing mean and standard deviation to center"
- "and reduce samples before classification, produced by ComputeImagesStatistics application.");
+ SetParameterDescription("instat",
+ "A XML file containing mean and standard deviation to center"
+ "and reduce samples before classification, produced by ComputeImagesStatistics application.");
MandatoryOff("instat");
AddParameter(ParameterType_InputFilename, "model", "Model file");
SetParameterDescription("model", "Model file produced by TrainVectorClassifier application.");
-
- AddParameter(ParameterType_String,"cfield","Output field");
- SetParameterDescription("cfield","Field containing the predicted class."
- "Only geometries with this field available will be taken into account.\n"
- "The field is added either in the input file (if 'out' off) or in the output file.\n"
- "Caution, the 'cfield' must not exist in the input file if you are updating the file.");
- SetParameterString("cfield","predicted");
-
+
+ AddParameter(ParameterType_String, "cfield", "Output field");
+ SetParameterDescription("cfield",
+ "Field containing the predicted class."
+ "Only geometries with this field available will be taken into account.\n"
+ "The field is added either in the input file (if 'out' off) or in the output file.\n"
+ "Caution, the 'cfield' must not exist in the input file if you are updating the file.");
+ SetParameterString("cfield", "predicted");
+
AddParameter(ParameterType_ListView, "feat", "Field names to be calculated");
- SetParameterDescription("feat","List of field names in the input vector data used as features for training. "
- "Put the same field names as the TrainVectorClassifier application.");
-
- AddParameter(ParameterType_Bool, "confmap", "Confidence map");
- SetParameterDescription( "confmap", "Confidence map of the produced classification. The confidence index depends on the model: \n\n"
- "* LibSVM: difference between the two highest probabilities (needs a model with probability estimates, so that classes probabilities can be computed for each sample)\n"
- "* Boost: sum of votes\n"
- "* DecisionTree: (not supported)\n"
- "* KNearestNeighbors: number of neighbors with the same label\n"
- "* NeuralNetwork: difference between the two highest responses\n"
- "* NormalBayes: (not supported)\n"
- "* RandomForest: Confidence (proportion of votes for the majority class). Margin (normalized difference of the votes of the 2 majority classes) is not available for now.\n"
- "* SVM: distance to margin (only works for 2-class models)\n");
+ SetParameterDescription("feat",
+ "List of field names in the input vector data used as features for training. "
+ "Put the same field names as the TrainVectorClassifier application.");
+
+ AddParameter(ParameterType_Bool, "confmap", "Confidence map");
+ SetParameterDescription("confmap",
+ "Confidence map of the produced classification. The confidence index depends on the model: \n\n"
+ "* LibSVM: difference between the two highest probabilities (needs a model with probability estimates, so that classes probabilities "
+ "can be computed for each sample)\n"
+ "* Boost: sum of votes\n"
+ "* DecisionTree: (not supported)\n"
+ "* KNearestNeighbors: number of neighbors with the same label\n"
+ "* NeuralNetwork: difference between the two highest responses\n"
+ "* NormalBayes: (not supported)\n"
+ "* RandomForest: Confidence (proportion of votes for the majority class). Margin (normalized difference of the votes of the 2 "
+ "majority classes) is not available for now.\n"
+ "* SVM: distance to margin (only works for 2-class models)\n");
AddParameter(ParameterType_OutputFilename, "out", "Output vector data file");
MandatoryOff("out");
- SetParameterDescription("out","Output vector data file storing sample values (OGR format)."
- "If not given, the input vector data file is updated.");
+ SetParameterDescription("out",
+ "Output vector data file storing sample values (OGR format)."
+ "If not given, the input vector data file is updated.");
// Doc example parameter settings
SetDocExampleParameterValue("in", "vectorData.shp");
@@ -96,26 +102,22 @@ VectorClassifier
SetDocExampleParameterValue("out", "vectorDataLabeledVector.shp");
SetDocExampleParameterValue("feat", "perimeter area width");
SetDocExampleParameterValue("cfield", "predicted");
-
+
SetOfficialDocLink();
}
-template<>
-bool
-VectorClassifier
-::shouldComputeConfidenceMap() const
+template <>
+bool VectorClassifier::shouldComputeConfidenceMap() const
{
- bool computeConfidenceMap(GetParameterInt("confmap") && m_Model->HasConfidenceIndex() );
+ bool computeConfidenceMap(GetParameterInt("confmap") && m_Model->HasConfidenceIndex());
if (!m_Model->HasConfidenceIndex() && GetParameterInt("confmap"))
- {
+ {
otbAppLogWARNING("Confidence map requested but the classifier doesn't support it!");
- }
-
+ }
+
return computeConfidenceMap;
}
-
-
}
}
diff --git a/Modules/Applications/AppClassification/app/otbVectorRegression.cxx b/Modules/Applications/AppClassification/app/otbVectorRegression.cxx
index 9b655fe01e..d1b8a02f66 100644
--- a/Modules/Applications/AppClassification/app/otbVectorRegression.cxx
+++ b/Modules/Applications/AppClassification/app/otbVectorRegression.cxx
@@ -27,56 +27,59 @@ namespace Wrapper
using VectorRegression = VectorPrediction<true>;
-template<>
-void
-VectorRegression
-::DoInitSpecialization()
+template <>
+void VectorRegression::DoInitSpecialization()
{
SetName("VectorRegression");
SetDescription("Performs regression on the input vector data according to a model file.");
SetDocAuthors("OTB-Team");
- SetDocLongDescription("This application performs a vector data regression "
- "based on a model file produced by the TrainVectorRegression application."
- "Features of the vector data output will contain the values predicted by the classifier. \n"
- "There are two modes: \n"
+ SetDocLongDescription(
+ "This application performs a vector data regression "
+ "based on a model file produced by the TrainVectorRegression application."
+ "Features of the vector data output will contain the values predicted by the classifier. \n"
+ "There are two modes: \n"
"1) Update mode: add of the 'cfield' field containing the predicted value in the input file. \n"
"2) Write mode: copies the existing fields of the input file to the output file "
- " and add the 'cfield' field containing the predicted value. \n"
- "If you have declared the output file, the write mode applies. "
- "Otherwise, the input file update mode will be applied.");
+ " and add the 'cfield' field containing the predicted value. \n"
+ "If you have declared the output file, the write mode applies. "
+ "Otherwise, the input file update mode will be applied.");
SetDocLimitations("Shapefiles are supported, but the SQLite format is only supported in update mode.");
SetDocSeeAlso("TrainVectorRegression");
AddDocTag(Tags::Learning);
AddParameter(ParameterType_InputFilename, "in", "Name of the input vector data");
- SetParameterDescription("in","The input vector data file to classify.");
+ SetParameterDescription("in", "The input vector data file to classify.");
AddParameter(ParameterType_InputFilename, "instat", "Statistics file");
- SetParameterDescription("instat", "A XML file containing mean and standard deviation to center"
- "and reduce samples before classification, produced by ComputeImagesStatistics application.");
+ SetParameterDescription("instat",
+ "A XML file containing mean and standard deviation to center"
+ "and reduce samples before classification, produced by ComputeImagesStatistics application.");
MandatoryOff("instat");
AddParameter(ParameterType_InputFilename, "model", "Model file");
SetParameterDescription("model", "Model file produced by TrainVectorRegression application.");
- AddParameter(ParameterType_String,"cfield","Output field");
- SetParameterDescription("cfield","Field containing the predicted value."
- "Only geometries with this field available will be taken into account.\n"
- "The field is added either in the input file (if 'out' off) or in the output file.\n"
- "Caution, the 'cfield' must not exist in the input file if you are updating the file.");
- SetParameterString("cfield","predicted");
-
+ AddParameter(ParameterType_String, "cfield", "Output field");
+ SetParameterDescription("cfield",
+ "Field containing the predicted value."
+ "Only geometries with this field available will be taken into account.\n"
+ "The field is added either in the input file (if 'out' off) or in the output file.\n"
+ "Caution, the 'cfield' must not exist in the input file if you are updating the file.");
+ SetParameterString("cfield", "predicted");
+
AddParameter(ParameterType_ListView, "feat", "Field names to be calculated");
- SetParameterDescription("feat","List of field names in the input vector data used as features for training. "
- "Put the same field names as the TrainVectorRegression application.");
+ SetParameterDescription("feat",
+ "List of field names in the input vector data used as features for training. "
+ "Put the same field names as the TrainVectorRegression application.");
AddParameter(ParameterType_OutputFilename, "out", "Output vector data file");
MandatoryOff("out");
- SetParameterDescription("out","Output vector data file storing sample values (OGR format)."
- "If not given, the input vector data file is updated.");
+ SetParameterDescription("out",
+ "Output vector data file storing sample values (OGR format)."
+ "If not given, the input vector data file is updated.");
MandatoryOff("out");
// Doc example parameter settings
@@ -91,14 +94,11 @@ VectorRegression
}
// Confidence map computation is not support for regression.
-template<>
-bool
-VectorRegression
-::shouldComputeConfidenceMap() const
+template <>
+bool VectorRegression::shouldComputeConfidenceMap() const
{
return false;
}
-
}
}
diff --git a/Modules/Applications/AppClassification/include/otbVectorPrediction.h b/Modules/Applications/AppClassification/include/otbVectorPrediction.h
index 637ee4ba91..12cea01487 100644
--- a/Modules/Applications/AppClassification/include/otbVectorPrediction.h
+++ b/Modules/Applications/AppClassification/include/otbVectorPrediction.h
@@ -59,52 +59,53 @@ public:
itkTypeMacro(Self, Application)
- /** Filters typedef */
- typedef float ValueType;
- // Label type is float for regression and unsigned int for classification
- typedef typename std::conditional<RegressionMode, float, unsigned int>::type LabelType;
-
- typedef itk::FixedArray<LabelType,1> LabelSampleType;
- typedef itk::Statistics::ListSample<LabelSampleType> LabelListSampleType;
-
- typedef otb::MachineLearningModel<ValueType,LabelType> MachineLearningModelType;
- typedef otb::MachineLearningModelFactory<ValueType, LabelType> MachineLearningModelFactoryType;
- typedef typename MachineLearningModelType::Pointer ModelPointerType;
- typedef typename MachineLearningModelType::ConfidenceListSampleType ConfidenceListSampleType;
+ /** Filters typedef */
+ typedef float ValueType;
+ // Label type is float for regression and unsigned int for classification
+ typedef typename std::conditional<RegressionMode, float, unsigned int>::type LabelType;
+
+ typedef itk::FixedArray<LabelType, 1> LabelSampleType;
+ typedef itk::Statistics::ListSample<LabelSampleType> LabelListSampleType;
+
+ typedef otb::MachineLearningModel<ValueType, LabelType> MachineLearningModelType;
+ typedef otb::MachineLearningModelFactory<ValueType, LabelType> MachineLearningModelFactoryType;
+ typedef typename MachineLearningModelType::Pointer ModelPointerType;
+ typedef typename MachineLearningModelType::ConfidenceListSampleType ConfidenceListSampleType;
/** Statistics Filters typedef */
- typedef itk::VariableLengthVector<ValueType> MeasurementType;
- typedef otb::StatisticsXMLFileReader<MeasurementType> StatisticsReader;
+ typedef itk::VariableLengthVector<ValueType> MeasurementType;
+ typedef otb::StatisticsXMLFileReader<MeasurementType> StatisticsReader;
- typedef itk::VariableLengthVector<ValueType> InputSampleType;
- typedef itk::Statistics::ListSample<InputSampleType> ListSampleType;
+ typedef itk::VariableLengthVector<ValueType> InputSampleType;
+ typedef itk::Statistics::ListSample<InputSampleType> ListSampleType;
typedef otb::Statistics::ShiftScaleSampleListFilter<ListSampleType, ListSampleType> ShiftScaleFilterType;
~VectorPrediction() override
- {
+ {
MachineLearningModelFactoryType::CleanFactories();
- }
+ }
private:
/** Utility function to negate std::isalnum */
static bool IsNotAlphaNum(char c)
{
- return !std::isalnum(c);
+ return !std::isalnum(c);
}
-
+
void DoInit() override;
+ /** Method defining the parameters used in the application and their documentation, specialized for RegressionMode=1 and RegrssionMode=0 */
void DoInitSpecialization();
void DoUpdateParameters() override;
void DoExecute() override;
+ /** Method returning whether the confidence map should be computed, depending on the regression mode and input parameters */
bool shouldComputeConfidenceMap() const;
ModelPointerType m_Model;
};
-
}
}
diff --git a/Modules/Applications/AppClassification/include/otbVectorPrediction.hxx b/Modules/Applications/AppClassification/include/otbVectorPrediction.hxx
index 73435e8fda..1310fdfb29 100644
--- a/Modules/Applications/AppClassification/include/otbVectorPrediction.hxx
+++ b/Modules/Applications/AppClassification/include/otbVectorPrediction.hxx
@@ -29,12 +29,10 @@ namespace Wrapper
{
template <bool RegressionMode>
-void
-VectorPrediction <RegressionMode>
-::DoInit()
+void VectorPrediction<RegressionMode>::DoInit()
{
DoInitSpecialization();
-
+
// Assert that the all needed parameters have ben definied in DoInitSpecialization
assert(GetParameterByKey("in") != nullptr);
assert(GetParameterByKey("instat") != nullptr);
@@ -45,67 +43,63 @@ VectorPrediction <RegressionMode>
}
template <bool RegressionMode>
-void
-VectorPrediction <RegressionMode>
-::DoUpdateParameters()
+void VectorPrediction<RegressionMode>::DoUpdateParameters()
{
- if ( HasValue("in") )
+ if (HasValue("in"))
{
std::string shapefile = GetParameterString("in");
otb::ogr::DataSource::Pointer ogrDS;
- ogrDS = otb::ogr::DataSource::New(shapefile, otb::ogr::DataSource::Modes::Read);
- otb::ogr::Layer layer = ogrDS->GetLayer(0);
- OGRFeatureDefn &layerDefn = layer.GetLayerDefn();
+ ogrDS = otb::ogr::DataSource::New(shapefile, otb::ogr::DataSource::Modes::Read);
+ otb::ogr::Layer layer = ogrDS->GetLayer(0);
+ OGRFeatureDefn& layerDefn = layer.GetLayerDefn();
ClearChoices("feat");
- for(int iField=0; iField< layerDefn.GetFieldCount(); iField++)
+ for (int iField = 0; iField < layerDefn.GetFieldCount(); iField++)
{
std::string item = layerDefn.GetFieldDefn(iField)->GetNameRef();
std::string key(item);
- key.erase( std::remove_if(key.begin(),key.end(),IsNotAlphaNum), key.end());
+ key.erase(std::remove_if(key.begin(), key.end(), IsNotAlphaNum), key.end());
std::transform(key.begin(), key.end(), key.begin(), tolower);
OGRFieldType fieldType = layerDefn.GetFieldDefn(iField)->GetType();
- if(fieldType == OFTInteger || fieldType == OFTInteger64 || fieldType == OFTReal)
- {
- std::string tmpKey="feat."+key;
- AddChoice(tmpKey,item);
- }
+ if (fieldType == OFTInteger || fieldType == OFTInteger64 || fieldType == OFTReal)
+ {
+ std::string tmpKey = "feat." + key;
+ AddChoice(tmpKey, item);
+ }
}
}
}
template <bool RegressionMode>
-void
-VectorPrediction <RegressionMode>
-::DoExecute()
+void VectorPrediction<RegressionMode>::DoExecute()
{
clock_t tic = clock();
std::string shapefile = GetParameterString("in");
otb::ogr::DataSource::Pointer source = otb::ogr::DataSource::New(shapefile, otb::ogr::DataSource::Modes::Read);
- otb::ogr::Layer layer = source->GetLayer(0);
+ otb::ogr::Layer layer = source->GetLayer(0);
typename ListSampleType::Pointer input = ListSampleType::New();
const int nbFeatures = GetSelectedItems("feat").size();
input->SetMeasurementVectorSize(nbFeatures);
- otb::ogr::Layer::const_iterator it = layer.cbegin();
+ otb::ogr::Layer::const_iterator it = layer.cbegin();
otb::ogr::Layer::const_iterator itEnd = layer.cend();
- for( ; it!=itEnd ; ++it)
- {
+ for (; it != itEnd; ++it)
+ {
MeasurementType mv;
mv.SetSize(nbFeatures);
- for(int idx=0; idx < nbFeatures; ++idx)
- {
+ for (int idx = 0; idx < nbFeatures; ++idx)
+ {
// Beware that itemIndex differs from ogr layer field index
unsigned int itemIndex = GetSelectedItems("feat")[idx];
- std::string fieldName = GetChoiceNames( "feat" )[itemIndex];
+ std::string fieldName = GetChoiceNames("feat")[itemIndex];
switch ((*it)[fieldName].GetType())
{
case OFTInteger:
@@ -120,30 +114,28 @@ VectorPrediction <RegressionMode>
default:
itkExceptionMacro(<< "incorrect field type: " << (*it)[fieldName].GetType() << ".");
}
-
-
- }
- input->PushBack(mv);
}
+ input->PushBack(mv);
+ }
// Statistics for shift/scale
MeasurementType meanMeasurementVector;
MeasurementType stddevMeasurementVector;
if (HasValue("instat") && IsParameterEnabled("instat"))
- {
+ {
typename StatisticsReader::Pointer statisticsReader = StatisticsReader::New();
- std::string XMLfile = GetParameterString("instat");
+ std::string XMLfile = GetParameterString("instat");
statisticsReader->SetFileName(XMLfile);
- meanMeasurementVector = statisticsReader->GetStatisticVectorByName("mean");
+ meanMeasurementVector = statisticsReader->GetStatisticVectorByName("mean");
stddevMeasurementVector = statisticsReader->GetStatisticVectorByName("stddev");
- }
+ }
else
- {
+ {
meanMeasurementVector.SetSize(nbFeatures);
meanMeasurementVector.Fill(0.);
stddevMeasurementVector.SetSize(nbFeatures);
stddevMeasurementVector.Fill(1.);
- }
+ }
typename ShiftScaleFilterType::Pointer trainingShiftScaleFilter = ShiftScaleFilterType::New();
trainingShiftScaleFilter->SetInput(input);
@@ -154,13 +146,12 @@ VectorPrediction <RegressionMode>
otbAppLogINFO("standard deviation used: " << stddevMeasurementVector);
otbAppLogINFO("Loading model");
- m_Model = MachineLearningModelFactoryType::CreateMachineLearningModel(GetParameterString("model"),
- MachineLearningModelFactoryType::ReadMode);
+ m_Model = MachineLearningModelFactoryType::CreateMachineLearningModel(GetParameterString("model"), MachineLearningModelFactoryType::ReadMode);
if (m_Model.IsNull())
- {
+ {
otbAppLogFATAL(<< "Error when loading model " << GetParameterString("model") << " : unsupported model type");
- }
+ }
m_Model->SetRegressionMode(RegressionMode);
@@ -175,154 +166,151 @@ VectorPrediction <RegressionMode>
typename LabelListSampleType::Pointer target;
if (computeConfidenceMap)
- {
+ {
quality = ConfidenceListSampleType::New();
- target = m_Model->PredictBatch(listSample, quality);
- }
- else
- {
+ target = m_Model->PredictBatch(listSample, quality);
+ }
+ else
+ {
target = m_Model->PredictBatch(listSample);
- }
+ }
ogr::DataSource::Pointer output;
- ogr::DataSource::Pointer buffer = ogr::DataSource::New();
- bool updateMode = false;
+ ogr::DataSource::Pointer buffer = ogr::DataSource::New();
+ bool updateMode = false;
if (IsParameterEnabled("out") && HasValue("out"))
- {
+ {
// Create new OGRDataSource
- output = ogr::DataSource::New(GetParameterString("out"), ogr::DataSource::Modes::Overwrite);
- otb::ogr::Layer newLayer = output->CreateLayer(
- GetParameterString("out"),
- const_cast<OGRSpatialReference*>(layer.GetSpatialRef()),
- layer.GetGeomType());
+ output = ogr::DataSource::New(GetParameterString("out"), ogr::DataSource::Modes::Overwrite);
+ otb::ogr::Layer newLayer = output->CreateLayer(GetParameterString("out"), const_cast<OGRSpatialReference*>(layer.GetSpatialRef()), layer.GetGeomType());
// Copy existing fields
- OGRFeatureDefn &inLayerDefn = layer.GetLayerDefn();
- for (int k=0 ; k<inLayerDefn.GetFieldCount() ; k++)
- {
+ OGRFeatureDefn& inLayerDefn = layer.GetLayerDefn();
+ for (int k = 0; k < inLayerDefn.GetFieldCount(); k++)
+ {
OGRFieldDefn fieldDefn(inLayerDefn.GetFieldDefn(k));
newLayer.CreateField(fieldDefn);
- }
}
+ }
else
- {
+ {
// Update mode
updateMode = true;
otbAppLogINFO("Update input vector data.");
// fill temporary buffer for the transfer
otb::ogr::Layer inputLayer = layer;
- layer = buffer->CopyLayer(inputLayer, std::string("Buffer"));
+ layer = buffer->CopyLayer(inputLayer, std::string("Buffer"));
// close input data source
source->Clear();
// Re-open input data source in update mode
output = otb::ogr::DataSource::New(shapefile, otb::ogr::DataSource::Modes::Update_LayerUpdate);
- }
+ }
otb::ogr::Layer outLayer = output->GetLayer(0);
OGRErr errStart = outLayer.ogr().StartTransaction();
if (errStart != OGRERR_NONE)
- {
+ {
itkExceptionMacro(<< "Unable to start transaction for OGR layer " << outLayer.ogr().GetName() << ".");
- }
+ }
- OGRFeatureDefn &layerDefn = layer.GetLayerDefn();
+ OGRFeatureDefn& layerDefn = layer.GetLayerDefn();
// Add the field of prediction in the output layer if field not exist
-
+
OGRFieldType labelType;
- if (RegressionMode==true)
+ if (RegressionMode == true)
labelType = OFTReal;
else
labelType = OFTInteger;
-
+
int idx = layerDefn.GetFieldIndex(GetParameterString("cfield").c_str());
if (idx >= 0)
{
if (layerDefn.GetFieldDefn(idx)->GetType() != labelType)
- itkExceptionMacro("Field name "<< GetParameterString("cfield") << " already exists with a different type!");
+ itkExceptionMacro("Field name " << GetParameterString("cfield") << " already exists with a different type!");
}
else
{
- OGRFieldDefn predictedField(GetParameterString("cfield").c_str(), labelType);
+ OGRFieldDefn predictedField(GetParameterString("cfield").c_str(), labelType);
ogr::FieldDefn predictedFieldDef(predictedField);
outLayer.CreateField(predictedFieldDef);
}
-
+
// Add confidence field in the output layer
std::string confFieldName("confidence");
if (computeConfidenceMap)
- {
+ {
idx = layerDefn.GetFieldIndex(confFieldName.c_str());
if (idx >= 0)
- {
+ {
if (layerDefn.GetFieldDefn(idx)->GetType() != OFTReal)
- itkExceptionMacro("Field name "<< confFieldName << " already exists with a different type!");
- }
+ itkExceptionMacro("Field name " << confFieldName << " already exists with a different type!");
+ }
else
- {
+ {
OGRFieldDefn confidenceField(confFieldName.c_str(), OFTReal);
confidenceField.SetWidth(confidenceField.GetWidth());
confidenceField.SetPrecision(confidenceField.GetPrecision());
ogr::FieldDefn confFieldDefn(confidenceField);
outLayer.CreateField(confFieldDefn);
- }
}
+ }
// Fill output layer
- unsigned int count=0;
- std::string classfieldname = GetParameterString("cfield");
- it = layer.cbegin();
- itEnd = layer.cend();
- for( ; it!=itEnd ; ++it, ++count)
- {
+ unsigned int count = 0;
+ std::string classfieldname = GetParameterString("cfield");
+ it = layer.cbegin();
+ itEnd = layer.cend();
+ for (; it != itEnd; ++it, ++count)
+ {
ogr::Feature dstFeature(outLayer.GetLayerDefn());
- dstFeature.SetFrom( *it , TRUE);
+ dstFeature.SetFrom(*it, TRUE);
dstFeature.SetFID(it->GetFID());
switch (dstFeature[classfieldname].GetType())
- {
- case OFTInteger:
- dstFeature[classfieldname].SetValue<int>(target->GetMeasurementVector(count)[0]);
- break;
- case OFTInteger64:
- dstFeature[classfieldname].SetValue<int>(target->GetMeasurementVector(count)[0]);
- break;
- case OFTReal:
- dstFeature[classfieldname].SetValue<double>(target->GetMeasurementVector(count)[0]);
- break;
- case OFTString:
- dstFeature[classfieldname].SetValue<std::string>(std::to_string(target->GetMeasurementVector(count)[0]));
- break;
- default:
- itkExceptionMacro(<< "incorrect field type: " << dstFeature[classfieldname].GetType() << ".");
- }
+ {
+ case OFTInteger:
+ dstFeature[classfieldname].SetValue<int>(target->GetMeasurementVector(count)[0]);
+ break;
+ case OFTInteger64:
+ dstFeature[classfieldname].SetValue<int>(target->GetMeasurementVector(count)[0]);
+ break;
+ case OFTReal:
+ dstFeature[classfieldname].SetValue<double>(target->GetMeasurementVector(count)[0]);
+ break;
+ case OFTString:
+ dstFeature[classfieldname].SetValue<std::string>(std::to_string(target->GetMeasurementVector(count)[0]));
+ break;
+ default:
+ itkExceptionMacro(<< "incorrect field type: " << dstFeature[classfieldname].GetType() << ".");
+ }
if (computeConfidenceMap)
dstFeature[confFieldName].SetValue<double>(quality->GetMeasurementVector(count)[0]);
if (updateMode)
- {
+ {
outLayer.SetFeature(dstFeature);
- }
+ }
else
- {
+ {
outLayer.CreateFeature(dstFeature);
- }
}
+ }
- if(outLayer.ogr().TestCapability("Transactions"))
- {
+ if (outLayer.ogr().TestCapability("Transactions"))
+ {
const OGRErr errCommitX = outLayer.ogr().CommitTransaction();
if (errCommitX != OGRERR_NONE)
- {
+ {
itkExceptionMacro(<< "Unable to commit transaction for OGR layer " << outLayer.ogr().GetName() << ".");
- }
}
+ }
output->SyncToDisk();
clock_t toc = clock();
- otbAppLogINFO( "Elapsed: "<< ((double)(toc - tic) / CLOCKS_PER_SEC)<<" seconds.");
+ otbAppLogINFO("Elapsed: " << ((double)(toc - tic) / CLOCKS_PER_SEC) << " seconds.");
}
-} //end namespace wrapper
-} //end namespace otb
+} // end namespace wrapper
+} // end namespace otb
#endif
--
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