diff --git a/Modules/Applications/AppClassification/include/otbVectorPrediction.h b/Modules/Applications/AppClassification/include/otbVectorPrediction.h
index d895324665f20035a8361e3b443b8a0acc43a598..65fe591977d3ac690f860a6c3d74dc4fe9ec7b1f 100644
--- a/Modules/Applications/AppClassification/include/otbVectorPrediction.h
+++ b/Modules/Applications/AppClassification/include/otbVectorPrediction.h
@@ -98,7 +98,37 @@ private:
/** Method returning whether the confidence map should be computed, depending on the regression mode and input parameters */
bool shouldComputeConfidenceMap() const;
+ /** Method returning the input list sample from the input layer */
+ typename ListSampleType::Pointer ReadInputListSample(otb::ogr::Layer const& layer);
+
+ /** Normalize a list sample using the statistic file given */
+ typename ListSampleType::Pointer NormalizeListSample(ListSampleType::Pointer input);
+
+ /** Create the output DataSource, in update mode the input layer is buffered and the input
+ * data source is re opened in update mode. */
+ otb::ogr::DataSource::Pointer CreateOutputDataSource(otb::ogr::DataSource::Pointer source,
+ otb::ogr::Layer & layer,
+ bool updateMode);
+
+ /** Add a prediction field in the output layer if it does not exist.
+ * If computeConfidenceMap evaluates to true a confidence field will be
+ * added. */
+ void AddPredictionField(otb::ogr::Layer & outLayer,
+ otb::ogr::Layer const& layer,
+ bool computeConfidenceMap);
+
+ /** Fill the output layer with the predicted values and optionnaly the confidence */
+ void FillOutputLayer(otb::ogr::Layer & outLayer,
+ otb::ogr::Layer const& layer,
+ typename LabelListSampleType::Pointer target,
+ typename ConfidenceListSampleType::Pointer quality,
+ bool updateMode,
+ bool computeConfidenceMap);
+
ModelPointerType m_Model;
+
+ /** Name used for the confidence field */
+ std::string confFieldName = "confidence";
};
}
}
diff --git a/Modules/Applications/AppClassification/include/otbVectorPrediction.hxx b/Modules/Applications/AppClassification/include/otbVectorPrediction.hxx
index 20d8d357aa814130afe66117d4dadd58d7a7137d..54b8d889d1eb1747c1edc547a2d404b1f1157705 100644
--- a/Modules/Applications/AppClassification/include/otbVectorPrediction.hxx
+++ b/Modules/Applications/AppClassification/include/otbVectorPrediction.hxx
@@ -73,13 +73,10 @@ void VectorPrediction<RegressionMode>::DoUpdateParameters()
}
template <bool RegressionMode>
-void VectorPrediction<RegressionMode>::DoExecute()
+typename VectorPrediction<RegressionMode>::ListSampleType::Pointer
+VectorPrediction<RegressionMode>
+::ReadInputListSample(otb::ogr::Layer const& layer)
{
- auto shapefileName = GetParameterString("in");
-
- auto source = otb::ogr::DataSource::New(shapefileName, otb::ogr::DataSource::Modes::Read);
- auto layer = source->GetLayer(0);
-
typename ListSampleType::Pointer input = ListSampleType::New();
const int nbFeatures = GetSelectedItems("feat").size();
@@ -110,7 +107,16 @@ void VectorPrediction<RegressionMode>::DoExecute()
}
input->PushBack(mv);
}
+ return input;
+}
+template <bool RegressionMode>
+typename VectorPrediction<RegressionMode>::ListSampleType::Pointer
+VectorPrediction<RegressionMode>
+::NormalizeListSample(ListSampleType::Pointer input)
+{
+ const int nbFeatures = GetSelectedItems("feat").size();
+
// Statistics for shift/scale
MeasurementType meanMeasurementVector;
MeasurementType stddevMeasurementVector;
@@ -139,39 +145,31 @@ void VectorPrediction<RegressionMode>::DoExecute()
otbAppLogINFO("standard deviation used: " << stddevMeasurementVector);
otbAppLogINFO("Loading model");
- 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);
-
- m_Model->Load(GetParameterString("model"));
- otbAppLogINFO("Model loaded");
-
- ListSampleType::Pointer listSample = trainingShiftScaleFilter->GetOutput();
-
- typename ConfidenceListSampleType::Pointer quality;
+
+ return trainingShiftScaleFilter->GetOutput();
+}
- bool computeConfidenceMap = shouldComputeConfidenceMap();
- typename LabelListSampleType::Pointer target;
- if (computeConfidenceMap)
+template <bool RegressionMode>
+otb::ogr::DataSource::Pointer
+VectorPrediction<RegressionMode>
+::CreateOutputDataSource(otb::ogr::DataSource::Pointer source, otb::ogr::Layer & layer, bool updateMode)
+{
+ ogr::DataSource::Pointer output;
+ ogr::DataSource::Pointer buffer = ogr::DataSource::New();
+ if (updateMode)
{
- quality = ConfidenceListSampleType::New();
- target = m_Model->PredictBatch(listSample, quality);
+ // Update mode
+ otbAppLogINFO("Update input vector data.");
+ // fill temporary buffer for the transfer
+ otb::ogr::Layer inputLayer = layer;
+ 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(GetParameterString("in"), otb::ogr::DataSource::Modes::Update_LayerUpdate);
}
else
- {
- target = m_Model->PredictBatch(listSample);
- }
-
- ogr::DataSource::Pointer output;
- 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);
@@ -184,32 +182,18 @@ void VectorPrediction<RegressionMode>::DoExecute()
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"));
- // close input data source
- source->Clear();
- // Re-open input data source in update mode
- output = otb::ogr::DataSource::New(shapefileName, otb::ogr::DataSource::Modes::Update_LayerUpdate);
- }
- otb::ogr::Layer outLayer = output->GetLayer(0);
+ return output;
+}
- OGRErr errStart = outLayer.ogr().StartTransaction();
- if (errStart != OGRERR_NONE)
- {
- itkExceptionMacro(<< "Unable to start transaction for OGR layer " << outLayer.ogr().GetName() << ".");
- }
+template <bool RegressionMode>
+void
+VectorPrediction<RegressionMode>
+::AddPredictionField(otb::ogr::Layer & outLayer, otb::ogr::Layer const& layer, bool computeConfidenceMap)
+{
OGRFeatureDefn& layerDefn = layer.GetLayerDefn();
- // Add the field of prediction in the output layer if field not exist
-
const OGRFieldType labelType = RegressionMode ? OFTReal : OFTInteger;
int idx = layerDefn.GetFieldIndex(GetParameterString("cfield").c_str());
@@ -226,7 +210,6 @@ void VectorPrediction<RegressionMode>::DoExecute()
}
// Add confidence field in the output layer
- std::string confFieldName("confidence");
if (computeConfidenceMap)
{
idx = layerDefn.GetFieldIndex(confFieldName.c_str());
@@ -244,8 +227,14 @@ void VectorPrediction<RegressionMode>::DoExecute()
outLayer.CreateField(confFieldDefn);
}
}
+}
- // Fill output layer
+template <bool RegressionMode>
+void
+VectorPrediction<RegressionMode>
+::FillOutputLayer(otb::ogr::Layer & outLayer, otb::ogr::Layer const& layer, typename LabelListSampleType::Pointer target,
+ typename ConfidenceListSampleType::Pointer quality, bool updateMode, bool computeConfidenceMap)
+{
unsigned int count = 0;
std::string classfieldname = GetParameterString("cfield");
for (auto const& feature : layer)
@@ -281,6 +270,62 @@ void VectorPrediction<RegressionMode>::DoExecute()
}
count++;
}
+}
+
+template <bool RegressionMode>
+void VectorPrediction<RegressionMode>::DoExecute()
+{
+ 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);
+
+ m_Model->Load(GetParameterString("model"));
+ otbAppLogINFO("Model loaded");
+
+ auto shapefileName = GetParameterString("in");
+
+ auto source = otb::ogr::DataSource::New(shapefileName, otb::ogr::DataSource::Modes::Read);
+ auto layer = source->GetLayer(0);
+
+ auto input = ReadInputListSample(layer);
+
+ ListSampleType::Pointer listSample = NormalizeListSample(input);
+
+ typename LabelListSampleType::Pointer target;
+
+ // The quality listSample containing confidence values is defined here, but is only used when
+ // computeConfidenceMap evaluates to true. This listSample is also used in FillOutputLayer(...)
+ const bool computeConfidenceMap = shouldComputeConfidenceMap();
+ typename ConfidenceListSampleType::Pointer quality;
+
+ if (computeConfidenceMap)
+ {
+ quality = ConfidenceListSampleType::New();
+ target = m_Model->PredictBatch(listSample, quality);
+ }
+ else
+ {
+ target = m_Model->PredictBatch(listSample);
+ }
+
+ const bool updateMode = !(IsParameterEnabled("out") && HasValue("out"));
+
+ auto output = CreateOutputDataSource(source, layer, updateMode);
+ 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() << ".");
+ }
+
+ AddPredictionField(outLayer, layer, computeConfidenceMap);
+ FillOutputLayer(outLayer, layer, target, quality, updateMode, computeConfidenceMap);
if (outLayer.ogr().TestCapability("Transactions"))
{