From 84514586f43a879ec3fd33a52bb557263bddc0f5 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?C=C3=A9dric=20Traizet?= <cedric.traizet@c-s.fr>
Date: Mon, 29 Jul 2019 17:59:22 +0200
Subject: [PATCH] ENH: make VectorClassifier template
---
.../app/otbVectorClassifier.cxx | 408 +---------------
.../include/otbVectorPrediction.h | 444 ++++++++++++++++++
2 files changed, 446 insertions(+), 406 deletions(-)
create mode 100644 Modules/Applications/AppClassification/include/otbVectorPrediction.h
diff --git a/Modules/Applications/AppClassification/app/otbVectorClassifier.cxx b/Modules/Applications/AppClassification/app/otbVectorClassifier.cxx
index 1f0401aeec..d6f5e25e42 100644
--- a/Modules/Applications/AppClassification/app/otbVectorClassifier.cxx
+++ b/Modules/Applications/AppClassification/app/otbVectorClassifier.cxx
@@ -18,418 +18,14 @@
* limitations under the License.
*/
-#include "otbWrapperApplication.h"
-#include "otbWrapperApplicationFactory.h"
-
-#include "otbOGRDataSourceWrapper.h"
-#include "otbOGRFeatureWrapper.h"
-
-#include "itkVariableLengthVector.h"
-#include "otbStatisticsXMLFileReader.h"
-
-#include "itkListSample.h"
-#include "otbShiftScaleSampleListFilter.h"
-
-#include "otbMachineLearningModelFactory.h"
-
-#include "otbMachineLearningModel.h"
-
-#include <time.h>
+#include "otbVectorPrediction.h"
namespace otb
{
namespace Wrapper
{
-/** Utility function to negate std::isalnum */
-bool IsNotAlphaNum(char c)
- {
- return !std::isalnum(c);
- }
-
-class VectorClassifier : public Application
-{
-public:
- /** Standard class typedefs. */
- typedef VectorClassifier Self;
- typedef Application Superclass;
- typedef itk::SmartPointer<Self> Pointer;
- typedef itk::SmartPointer<const Self> ConstPointer;
-
- /** Standard macro */
- itkNewMacro(Self);
-
- itkTypeMacro(Self, Application)
-
- /** Filters typedef */
- typedef float ValueType;
- typedef unsigned int 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 MachineLearningModelType::Pointer ModelPointerType;
- typedef MachineLearningModelType::ConfidenceListSampleType ConfidenceListSampleType;
-
- /** Statistics Filters typedef */
- typedef itk::VariableLengthVector<ValueType> MeasurementType;
- typedef otb::StatisticsXMLFileReader<MeasurementType> StatisticsReader;
-
- typedef itk::VariableLengthVector<ValueType> InputSampleType;
- typedef itk::Statistics::ListSample<InputSampleType> ListSampleType;
- typedef otb::Statistics::ShiftScaleSampleListFilter<ListSampleType, ListSampleType> ShiftScaleFilterType;
-
- ~VectorClassifier() override
- {
- MachineLearningModelFactoryType::CleanFactories();
- }
-
-private:
- void DoInit() override
- {
- 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"
- "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.");
-
- SetDocLimitations("Shapefiles are supported, but the SQLite format is only supported in update mode.");
- SetDocSeeAlso("TrainVectorClassifier");
- AddDocTag(Tags::Learning);
-
- AddParameter(ParameterType_InputVectorData, "in", "Name of the input vector data");
- 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.");
- MandatoryOff("instat");
-
- AddParameter(ParameterType_InputFilename, "model", "Model file");
- SetParameterDescription("model", "Model file produced by TrainVectorClassifier application.");
-
- AddParameter(ParameterType_String,"cfield","Field class");
- 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");
-
- AddParameter(ParameterType_OutputFilename, "out", "Output vector data file containing class labels");
- 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
- SetDocExampleParameterValue("in", "vectorData.shp");
- SetDocExampleParameterValue("instat", "meanVar.xml");
- SetDocExampleParameterValue("model", "svmModel.svm");
- SetDocExampleParameterValue("out", "vectorDataLabeledVector.shp");
- SetDocExampleParameterValue("feat", "perimeter area width");
- SetDocExampleParameterValue("cfield", "predicted");
-
- SetOfficialDocLink();
- }
-
- void DoUpdateParameters() override
- {
- if ( HasValue("in") )
- {
- std::string shapefile = GetParameterString("in");
-
- otb::ogr::DataSource::Pointer ogrDS;
-
- OGRSpatialReference oSRS("");
- std::vector<std::string> options;
-
- 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++)
- {
- std::string item = layerDefn.GetFieldDefn(iField)->GetNameRef();
- std::string key(item);
- 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);
- }
- }
- }
- }
-
- void DoExecute() override
- {
- 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);
-
- 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 itEnd = layer.cend();
- for( ; it!=itEnd ; ++it)
- {
- MeasurementType mv;
- mv.SetSize(nbFeatures);
- 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];
- switch ((*it)[fieldName].GetType())
- {
- case OFTInteger:
- mv[idx] = static_cast<ValueType>((*it)[fieldName].GetValue<int>());
- break;
- case OFTInteger64:
- mv[idx] = static_cast<ValueType>((*it)[fieldName].GetValue<int>());
- break;
- case OFTReal:
- mv[idx] = static_cast<ValueType>((*it)[fieldName].GetValue<double>());
- break;
- default:
- itkExceptionMacro(<< "incorrect field type: " << (*it)[fieldName].GetType() << ".");
- }
-
-
- }
- input->PushBack(mv);
- }
-
- // Statistics for shift/scale
- MeasurementType meanMeasurementVector;
- MeasurementType stddevMeasurementVector;
- if (HasValue("instat") && IsParameterEnabled("instat"))
- {
- StatisticsReader::Pointer statisticsReader = StatisticsReader::New();
- std::string XMLfile = GetParameterString("instat");
- statisticsReader->SetFileName(XMLfile);
- meanMeasurementVector = statisticsReader->GetStatisticVectorByName("mean");
- stddevMeasurementVector = statisticsReader->GetStatisticVectorByName("stddev");
- }
- else
- {
- meanMeasurementVector.SetSize(nbFeatures);
- meanMeasurementVector.Fill(0.);
- stddevMeasurementVector.SetSize(nbFeatures);
- stddevMeasurementVector.Fill(1.);
- }
-
- ShiftScaleFilterType::Pointer trainingShiftScaleFilter = ShiftScaleFilterType::New();
- trainingShiftScaleFilter->SetInput(input);
- trainingShiftScaleFilter->SetShifts(meanMeasurementVector);
- trainingShiftScaleFilter->SetScales(stddevMeasurementVector);
- trainingShiftScaleFilter->Update();
- otbAppLogINFO("mean used: " << meanMeasurementVector);
- 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->Load(GetParameterString("model"));
- otbAppLogINFO("Model loaded");
-
- ListSampleType::Pointer listSample = trainingShiftScaleFilter->GetOutput();
-
- ConfidenceListSampleType::Pointer quality;
-
- bool computeConfidenceMap(GetParameterInt("confmap") && m_Model->HasConfidenceIndex()
- && !m_Model->GetRegressionMode());
-
- if (!m_Model->HasConfidenceIndex() && GetParameterInt("confmap"))
- {
- otbAppLogWARNING("Confidence map requested but the classifier doesn't support it!");
- }
-
- LabelListSampleType::Pointer target;
- if (computeConfidenceMap)
- {
- quality = ConfidenceListSampleType::New();
- 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;
- 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());
- // Copy existing fields
- 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"));
- // 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() << ".");
- }
-
- // Add the field of prediction in the output layer if field not exist
- OGRFeatureDefn &layerDefn = layer.GetLayerDefn();
- int idx = layerDefn.GetFieldIndex(GetParameterString("cfield").c_str());
- if (idx >= 0)
- {
- if (layerDefn.GetFieldDefn(idx)->GetType() != OFTInteger)
- itkExceptionMacro("Field name "<< GetParameterString("cfield") << " already exists with a different type!");
- }
- else
- {
- OGRFieldDefn predictedField(GetParameterString("cfield").c_str(), OFTInteger);
- 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!");
- }
- 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)
- {
- ogr::Feature dstFeature(outLayer.GetLayerDefn());
- 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() << ".");
- }
- if (computeConfidenceMap)
- dstFeature[confFieldName].SetValue<double>(quality->GetMeasurementVector(count)[0]);
- if (updateMode)
- {
- outLayer.SetFeature(dstFeature);
- }
- else
- {
- outLayer.CreateFeature(dstFeature);
- }
- }
-
- 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.");
-
- }
-
- ModelPointerType m_Model;
-};
+typedef VectorPrediction<false, float, unsigned int> VectorClassifier;
}
}
diff --git a/Modules/Applications/AppClassification/include/otbVectorPrediction.h b/Modules/Applications/AppClassification/include/otbVectorPrediction.h
new file mode 100644
index 0000000000..a138572574
--- /dev/null
+++ b/Modules/Applications/AppClassification/include/otbVectorPrediction.h
@@ -0,0 +1,444 @@
+/*
+ * Copyright (C) 2005-2019 Centre National d'Etudes Spatiales (CNES)
+ *
+ * This file is part of Orfeo Toolbox
+ *
+ * https://www.orfeo-toolbox.org/
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#ifndef otbVectorPrediction_h
+#define otbVectorPrediction_h
+
+#include "otbWrapperApplication.h"
+#include "otbWrapperApplicationFactory.h"
+
+#include "otbOGRDataSourceWrapper.h"
+#include "otbOGRFeatureWrapper.h"
+
+#include "itkVariableLengthVector.h"
+#include "otbStatisticsXMLFileReader.h"
+
+#include "itkListSample.h"
+#include "otbShiftScaleSampleListFilter.h"
+
+#include "otbMachineLearningModelFactory.h"
+
+#include "otbMachineLearningModel.h"
+
+#include <time.h>
+
+namespace otb
+{
+namespace Wrapper
+{
+
+template <bool RegressionMode, class ValueType, class LabelType>
+class VectorPrediction : public Application
+{
+public:
+ /** Standard class typedefs. */
+ typedef VectorPrediction Self;
+ typedef Application Superclass;
+ typedef itk::SmartPointer<Self> Pointer;
+ typedef itk::SmartPointer<const Self> ConstPointer;
+
+ /** Standard macro */
+ itkNewMacro(Self);
+
+ itkTypeMacro(Self, Application)
+
+ /** Filters typedef */
+ //typedef float ValueType;
+ //typedef unsigned int 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> 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);
+ }
+
+ void DoInit() override
+ {
+ 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"
+ "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.");
+
+ SetDocLimitations("Shapefiles are supported, but the SQLite format is only supported in update mode.");
+ SetDocSeeAlso("TrainVectorClassifier");
+ AddDocTag(Tags::Learning);
+
+ AddParameter(ParameterType_InputVectorData, "in", "Name of the input vector data");
+ 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.");
+ MandatoryOff("instat");
+
+ AddParameter(ParameterType_InputFilename, "model", "Model file");
+ SetParameterDescription("model", "Model file produced by TrainVectorClassifier application.");
+
+ AddParameter(ParameterType_String,"cfield","Field class");
+ 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");
+
+ AddParameter(ParameterType_OutputFilename, "out", "Output vector data file containing class labels");
+ 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
+ SetDocExampleParameterValue("in", "vectorData.shp");
+ SetDocExampleParameterValue("instat", "meanVar.xml");
+ SetDocExampleParameterValue("model", "svmModel.svm");
+ SetDocExampleParameterValue("out", "vectorDataLabeledVector.shp");
+ SetDocExampleParameterValue("feat", "perimeter area width");
+ SetDocExampleParameterValue("cfield", "predicted");
+
+ SetOfficialDocLink();
+ }
+
+ void DoUpdateParameters() override
+ {
+ if ( HasValue("in") )
+ {
+ std::string shapefile = GetParameterString("in");
+
+ otb::ogr::DataSource::Pointer ogrDS;
+
+ OGRSpatialReference oSRS("");
+ std::vector<std::string> options;
+
+ 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++)
+ {
+ std::string item = layerDefn.GetFieldDefn(iField)->GetNameRef();
+ std::string key(item);
+ 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);
+ }
+ }
+ }
+ }
+
+ void DoExecute() override
+ {
+ 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);
+
+ 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 itEnd = layer.cend();
+ for( ; it!=itEnd ; ++it)
+ {
+ MeasurementType mv;
+ mv.SetSize(nbFeatures);
+ 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];
+ switch ((*it)[fieldName].GetType())
+ {
+ case OFTInteger:
+ mv[idx] = static_cast<ValueType>((*it)[fieldName].GetValue<int>());
+ break;
+ case OFTInteger64:
+ mv[idx] = static_cast<ValueType>((*it)[fieldName].GetValue<int>());
+ break;
+ case OFTReal:
+ mv[idx] = static_cast<ValueType>((*it)[fieldName].GetValue<double>());
+ break;
+ default:
+ itkExceptionMacro(<< "incorrect field type: " << (*it)[fieldName].GetType() << ".");
+ }
+
+
+ }
+ 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");
+ statisticsReader->SetFileName(XMLfile);
+ 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);
+ trainingShiftScaleFilter->SetShifts(meanMeasurementVector);
+ trainingShiftScaleFilter->SetScales(stddevMeasurementVector);
+ trainingShiftScaleFilter->Update();
+ otbAppLogINFO("mean used: " << meanMeasurementVector);
+ 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->Load(GetParameterString("model"));
+ otbAppLogINFO("Model loaded");
+
+ typename ListSampleType::Pointer listSample = trainingShiftScaleFilter->GetOutput();
+
+ typename ConfidenceListSampleType::Pointer quality;
+
+ bool computeConfidenceMap(GetParameterInt("confmap") && m_Model->HasConfidenceIndex()
+ && !m_Model->GetRegressionMode());
+
+ if (!m_Model->HasConfidenceIndex() && GetParameterInt("confmap"))
+ {
+ otbAppLogWARNING("Confidence map requested but the classifier doesn't support it!");
+ }
+
+ typename LabelListSampleType::Pointer target;
+ if (computeConfidenceMap)
+ {
+ quality = ConfidenceListSampleType::New();
+ 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;
+ 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());
+ // Copy existing fields
+ 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"));
+ // 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() << ".");
+ }
+
+ // Add the field of prediction in the output layer if field not exist
+ OGRFeatureDefn &layerDefn = layer.GetLayerDefn();
+ int idx = layerDefn.GetFieldIndex(GetParameterString("cfield").c_str());
+ if (idx >= 0)
+ {
+ if (layerDefn.GetFieldDefn(idx)->GetType() != OFTInteger)
+ itkExceptionMacro("Field name "<< GetParameterString("cfield") << " already exists with a different type!");
+ }
+ else
+ {
+ OGRFieldDefn predictedField(GetParameterString("cfield").c_str(), OFTInteger);
+ 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!");
+ }
+ 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)
+ {
+ ogr::Feature dstFeature(outLayer.GetLayerDefn());
+ 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() << ".");
+ }
+ if (computeConfidenceMap)
+ dstFeature[confFieldName].SetValue<double>(quality->GetMeasurementVector(count)[0]);
+ if (updateMode)
+ {
+ outLayer.SetFeature(dstFeature);
+ }
+ else
+ {
+ outLayer.CreateFeature(dstFeature);
+ }
+ }
+
+ 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.");
+
+ }
+
+ ModelPointerType m_Model;
+};
+
+typedef VectorPrediction<false, float, unsigned int> VectorClassifier;
+typedef VectorPrediction<true, float, float> VectorRegression;
+
+}
+}
+
+#endif
--
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