ENH: add new VectorClassifier application

Modules/Applications/AppClassification/app/CMakeLists.txt

@@ -130,3 +130,8 @@ otb_create_application(
   NAME           MultiImageSamplingRate
   SOURCES        otbMultiImageSamplingRate.cxx
   LINK_LIBRARIES ${${otb-module}_LIBRARIES})
+
+otb_create_application(
+  NAME           VectorClassifier
+  SOURCES        otbVectorClassifier.cxx
+  LINK_LIBRARIES ${${otb-module}_LIBRARIES})

Modules/Applications/AppClassification/app/otbVectorClassifier.cxx

+/*
+ * Copyright (C) 2005-2017 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.
+ */
+
+#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
+{
+
+/** 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 double                                        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() ITK_OVERRIDE
+    {
+    MachineLearningModelFactoryType::CleanFactories();
+    }
+
+private:
+  void DoInit() ITK_OVERRIDE
+  {
+    SetName("VectorClassifier");
+    SetDescription("Performs a classification of the input vector data according to a model file.");
+
+    SetDocName("Vector Classification");
+    SetDocAuthors("OTB-Team");
+    SetDocLongDescription("This application performs a vector data classification based on a model file produced by the TrainVectorClassifier application.");
+    SetDocLimitations("Only shapefiles are supported for now. SQLite format is not supported.");
+    SetDocSeeAlso("TrainVectorClassifier");
+
+    AddParameter(ParameterType_InputVectorData, "in", "Name of the input vector data");
+    SetParameterDescription("in","The input vector data 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", "A model file (produced by TrainVectorClassifier application,"
+      "maximal class label = 65535).");
+
+    AddParameter(ParameterType_String,"cfield","Field containing the predicted class");
+    SetParameterDescription("cfield","Field containing the predicted class."
+      "Only geometries with this field available will be taken into account.");
+    SetParameterString("cfield","predicted", false);
+
+    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."); //
+
+    AddParameter(ParameterType_Empty, "confmap",  "Confidence map");
+    SetParameterDescription( "confmap", "Confidence map of the produced classification. The confidence index depends on the model : \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"
+      "  - OpenCV\n"
+      "    * Boost : sum of votes\n"
+      "    * DecisionTree : (not supported)\n"
+      "    * GradientBoostedTree : (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");
+    MandatoryOff("confmap");
+
+    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", "svmModel.svm");
+    SetDocExampleParameterValue("feat", "perimeter  area  width");
+    SetDocExampleParameterValue("cfield", "predicted");
+  }
+
+  void DoUpdateParameters() ITK_OVERRIDE
+  {
+    if ( HasValue("in") )
+    {
+      std::string shapefile = GetParameterString("in");
+
+      otb::ogr::DataSource::Pointer ogrDS;
+      otb::ogr::Layer layer(ITK_NULLPTR, false);
+
+      OGRSpatialReference oSRS("");
+      std::vector<std::string> options;
+
+      ogrDS = otb::ogr::DataSource::New(shapefile, otb::ogr::DataSource::Modes::Read);
+      std::string layername = itksys::SystemTools::GetFilenameName(shapefile);
+      layername = layername.substr(0,layername.size()-4);
+      layer = ogrDS->GetLayer(0);
+
+      otb::ogr::Feature feature = layer.ogr().GetNextFeature();
+
+      ClearChoices("feat");
+
+      for(int iField=0; iField<feature.ogr().GetFieldCount(); iField++)
+      {
+        std::string key, item = feature.ogr().GetFieldDefnRef(iField)->GetNameRef();
+        key = item;
+        std::string::iterator end = std::remove_if(key.begin(),key.end(),IsNotAlphaNum);
+        std::transform(key.begin(), end, key.begin(), tolower);
+
+        OGRFieldType fieldType = feature.ogr().GetFieldDefnRef(iField)->GetType();
+        if(fieldType == OFTInteger ||  ogr::version_proxy::IsOFTInteger64(fieldType) || fieldType == OFTReal)
+          {
+          std::string tmpKey="feat."+key.substr(0, end - key.begin());
+          AddChoice(tmpKey,item);
+          }
+      }
+    }
+  }
+
+  void DoExecute() ITK_OVERRIDE
+  {
+    clock_t tic = clock();
+
+    std::string shapefile = GetParameterString("in").c_str();
+
+    otb::ogr::DataSource::Pointer source = otb::ogr::DataSource::New(shapefile, otb::ogr::DataSource::Modes::Read);
+    otb::ogr::Layer layer = source->GetLayer(0);
+    bool goesOn = true;
+    otb::ogr::Feature feature = layer.ogr().GetNextFeature();
+
+    ListSampleType::Pointer input = ListSampleType::New();
+    LabelListSampleType::Pointer target = LabelListSampleType::New();
+
+    const int nbFeatures = GetSelectedItems("feat").size();
+    input->SetMeasurementVectorSize(nbFeatures);
+
+    if(feature.addr())
+      while(goesOn)
+      {
+        MeasurementType mv;
+        mv.SetSize(nbFeatures);
+
+        for(int idx=0; idx < nbFeatures; ++idx)
+        {
+          mv[idx] = feature.ogr().GetFieldAsDouble(GetSelectedItems("feat")[idx]);
+        }
+        input->PushBack(mv);
+        feature = layer.ogr().GetNextFeature();
+        goesOn = feature.addr() != ITK_NULLPTR;
+      }
+
+    // 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;
+    listSample = trainingShiftScaleFilter->GetOutput();
+
+    typename ConfidenceListSampleType::Pointer quality;
+
+    bool computeConfidenceMap(IsParameterEnabled("confmap") && m_Model->HasConfidenceIndex() 
+                              && !m_Model->GetRegressionMode());
+
+    if (!m_Model->HasConfidenceIndex() && IsParameterEnabled("confmap"))
+      {
+      otbAppLogWARNING("Confidence map requested but the classifier doesn't support it!");
+      }
+
+    if (computeConfidenceMap)
+      {
+      quality = ConfidenceListSampleType::New();
+      target = m_Model->PredictBatch(listSample, quality);
+      }
+      else
+      {
+      target = m_Model->PredictBatch(listSample);
+      }
+
+    LabelListSampleType::Pointer labelListSample = target;
+
+    ogr::DataSource::Pointer output;
+    otb::ogr::Layer outLayer(ITK_NULLPTR, false);
+
+    if (IsParameterEnabled("out") && HasValue("out"))
+      {
+      // Copy input layer
+      output = ogr::DataSource::New(GetParameterString("out"), ogr::DataSource::Modes::Overwrite);
+      outLayer = output->CopyLayer(layer, GetParameterString("out").c_str());
+      }
+    else
+      {
+      // Update mode
+      otbAppLogINFO("Update input vector data.");
+      output = otb::ogr::DataSource::New(shapefile, otb::ogr::DataSource::Modes::Update_LayerUpdate);
+      outLayer = output->GetLayer(0);
+      }
+
+    OGRErr errStart = outLayer.ogr().StartTransaction();
+    if (errStart != OGRERR_NONE)
+      {
+      itkExceptionMacro(<< "Unable to start transaction for OGR layer " << outLayer.ogr().GetName() << ".");
+      }
+
+    // First get list of current fields
+    OGRFeatureDefn &layerDefn = outLayer.GetLayerDefn();
+    std::map<std::string, OGRFieldType> currentFields;
+    for (int k=0 ; k<layerDefn.GetFieldCount() ; k++)
+      {
+      OGRFieldDefn fieldDefn(layerDefn.GetFieldDefn(k));
+      std::string currentName(fieldDefn.GetNameRef());
+      currentFields[currentName] = fieldDefn.GetType();
+      }
+
+    // Add the field of prediction in the output layer if field not exist
+    OGRFieldDefn predictedField(GetParameterString("cfield").c_str(), OFTInteger);
+    ogr::FieldDefn predictedFieldDef(predictedField);
+    //test if field is already present
+    if (currentFields.count(predictedFieldDef.GetName()))
+    {
+      // test the field type
+      if (currentFields[predictedFieldDef.GetName()] != predictedFieldDef.GetType())
+        itkExceptionMacro("Field name "<< predictedFieldDef.GetName() << " already exists with a different type!");
+    }
+    else
+    {
+      outLayer.CreateField(predictedFieldDef);
+    }
+
+    // Add confidence field in the output layer
+    if (computeConfidenceMap)
+     {
+      OGRFieldDefn confidenceField("confidence", OFTReal);
+      confidenceField.SetWidth(confidenceField.GetWidth());
+      confidenceField.SetPrecision(confidenceField.GetPrecision());
+      ogr::FieldDefn confFieldDefn(confidenceField);
+      //test if field is already present
+      if (currentFields.count(confFieldDefn.GetName()))
+      {
+        // test the field type
+        if (currentFields[confFieldDefn.GetName()] != confFieldDefn.GetType())
+          itkExceptionMacro("Field name "<< confFieldDefn.GetName() << " already exists with a different type!");
+      }
+      else
+      {
+        outLayer.CreateField(confFieldDefn);
+      }
+     }
+
+    bool goesOn2 = true;
+    unsigned int count=0;
+    outLayer.ogr().ResetReading();
+    otb::ogr::Feature feature2 = outLayer.ogr().GetNextFeature();
+    std::string classfieldname = GetParameterString("cfield");
+
+    if(feature2.addr())
+      while(goesOn2)
+       {
+        feature2.ogr().SetField(classfieldname.c_str(),(int)labelListSample->GetMeasurementVector(count)[0]);
+        if (computeConfidenceMap) feature2.ogr().SetField("confidence",(int)quality->GetMeasurementVector(count)[0]);
+        outLayer.SetFeature(feature2);
+        feature2 = outLayer.ogr().GetNextFeature();
+        goesOn2 = feature2.addr() != ITK_NULLPTR;
+        count++;
+       }
+
+    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;
+};
+
+}
+}
+
+OTB_APPLICATION_EXPORT(otb::Wrapper::VectorClassifier)