ENH: Select strategy depending on provided Vector and do some refac.

Modules/Applications/AppClassification/app/otbTrainImagesClassifier.cxx

@@ -5,15 +5,130 @@ namespace otb
 namespace Wrapper
 {
 
-class TrainImagesClassifier : public TrainImagesBase<true>
+class TrainImagesClassifier : public TrainImagesBase
 {
 public:
   typedef TrainImagesClassifier         Self;
-  typedef TrainImagesBase<true>         Superclass;
+  typedef TrainImagesBase               Superclass;
   typedef itk::SmartPointer<Self>       Pointer;
   typedef itk::SmartPointer<const Self> ConstPointer;
   itkNewMacro( Self )
   itkTypeMacro( Self, Superclass )
+
+  void DoInit() ITK_OVERRIDE
+  {
+    SetName( "TrainImagesClassifier" );
+    SetDescription( "Train a classifier from multiple pairs of images and training vector data." );
+
+    // Documentation
+    SetDocName( "Train a classifier from multiple images" );
+    SetDocLongDescription(
+            "This application performs a classifier training from multiple pairs of input images and training vector data. "
+                    "Samples are composed of pixel values in each band optionally centered and reduced using an XML statistics file produced by "
+                    "the ComputeImagesStatistics application.\n The training vector data must contain polygons with a positive integer field "
+                    "representing the class label. The name of this field can be set using the \"Class label field\" parameter. Training and validation "
+                    "sample lists are built such that each class is equally represented in both lists. One parameter allows controlling the ratio "
+                    "between the number of samples in training and validation sets. Two parameters allow managing the size of the training and "
+                    "validation sets per class and per image.\n Several classifier parameters can be set depending on the chosen classifier. In the "
+                    "validation process, the confusion matrix is organized the following way: rows = reference labels, columns = produced labels. "
+                    "In the header of the optional confusion matrix output file, the validation (reference) and predicted (produced) class labels"
+                    " are ordered according to the rows/columns of the confusion matrix.\n This application is based on LibSVM and OpenCV Machine Learning "
+                    "(2.3.1 and later)." );
+    SetDocLimitations( "None" );
+    SetDocAuthors( "OTB-Team" );
+    SetDocSeeAlso( "OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html " );
+
+    AddDocTag( Tags::Learning );
+
+    // Perform initialization
+    ClearApplications();
+    InitIO();
+    InitSampling();
+    InitClassification( true );
+
+
+    // Doc example parameter settings
+    SetDocExampleParameterValue("io.il", "QB_1_ortho.tif");
+    SetDocExampleParameterValue("io.vd", "VectorData_QB1.shp");
+    SetDocExampleParameterValue("io.imstat", "EstimateImageStatisticsQB1.xml");
+    SetDocExampleParameterValue("sample.mv", "100");
+    SetDocExampleParameterValue("sample.mt", "100");
+    SetDocExampleParameterValue("sample.vtr", "0.5");
+    SetDocExampleParameterValue("sample.vfn", "Class");
+    SetDocExampleParameterValue("classifier", "libsvm");
+    SetDocExampleParameterValue("classifier.libsvm.k", "linear");
+    SetDocExampleParameterValue("classifier.libsvm.c", "1");
+    SetDocExampleParameterValue("classifier.libsvm.opt", "false");
+    SetDocExampleParameterValue("io.out", "svmModelQB1.txt");
+    SetDocExampleParameterValue("io.confmatout", "svmConfusionMatrixQB1.csv");
+  }
+
+  void DoUpdateParameters() ITK_OVERRIDE
+  {
+    if( HasValue( "io.vd" ) )
+      {
+      std::vector<std::string> vectorFileList = GetParameterStringList( "io.vd" );
+      GetInternalApplication( "polystat" )->SetParameterString( "vec", vectorFileList[0], false );
+      UpdateInternalParameters( "polystat" );
+      }
+  }
+
+  void DoExecute() ITK_OVERRIDE
+  {
+    TrainFileNamesHandler fileNames;
+    FloatVectorImageListType *imageList = GetParameterImageList( "io.il" );
+    std::vector<std::string> vectorFileList = GetParameterStringList( "io.vd" );
+    unsigned long nbInputs = imageList->Size();
+
+    if( nbInputs > vectorFileList.size() )
+      {
+      otbAppLogFATAL( "Missing input vector data files to match number of images (" << nbInputs << ")." );
+      }
+
+    // check if validation vectors are given
+    std::vector<std::string> validationVectorFileList;
+    bool dedicatedValidation = false;
+    if( IsParameterEnabled( "io.valid" ) && HasValue( "io.valid" ) )
+      {
+      validationVectorFileList = GetParameterStringList( "io.valid" );
+      if( nbInputs > validationVectorFileList.size() )
+        {
+        otbAppLogFATAL( "Missing validation vector data files to match number of images (" << nbInputs << ")." );
+        }
+
+      dedicatedValidation = true;
+      }
+
+    fileNames.CreateTemporaryFileNames( GetParameterString( "io.out" ), nbInputs, dedicatedValidation );
+
+    // Compute final maximum sampling rates for both training and validation samples
+    SamplingRates rates = ComputeFinalMaximumSamplingRates( dedicatedValidation );
+
+    // Select and Extract samples for training with computed statistics and rates
+    ComputePolygonStatistics(imageList, vectorFileList, fileNames.polyStatTrainOutputs);
+    ComputeSamplingRate(fileNames.polyStatTrainOutputs, fileNames.rateTrainOut, rates.fmt);
+    SelectAndExtractTrainSamples(fileNames, imageList, vectorFileList, SamplingStrategy::CLASS);
+
+    // Select and Extract samples for validation with computed statistics and rates
+    // Validation samples could be empty if sample.vrt == 0 and if no dedicated validation are provided
+    if( dedicatedValidation ) {
+      ComputePolygonStatistics(imageList, validationVectorFileList, fileNames.polyStatValidOutputs);
+      ComputeSamplingRate(fileNames.polyStatValidOutputs, fileNames.rateValidOut, rates.fmv);
+      }
+    SelectAndExtractValidationSamples(fileNames, imageList, validationVectorFileList, dedicatedValidation);
+
+
+    // Then train the model with extracted samples
+    TrainModel( imageList, fileNames.sampleTrainOutputs, fileNames.sampleValidOutputs);
+
+    // cleanup
+    if( IsParameterEnabled( "cleanup" ) )
+      {
+      otbAppLogINFO( <<"Final clean-up ..." );
+      fileNames.clear();
+      }
+  }
+
 };
 
 }

Modules/Applications/AppClassification/app/otbTrainImagesClustering.cxx

@@ -5,15 +5,188 @@ namespace otb
 namespace Wrapper
 {
 
-class TrainImagesClustering : public TrainImagesBase<false>
+class TrainImagesClustering : public TrainImagesBase
 {
 public:
   typedef TrainImagesClustering         Self;
-  typedef TrainImagesBase<false>        Superclass;
+  typedef TrainImagesBase               Superclass;
   typedef itk::SmartPointer<Self>       Pointer;
   typedef itk::SmartPointer<const Self> ConstPointer;
   itkNewMacro( Self )
   itkTypeMacro( Self, Superclass )
+
+  void DoInit() ITK_OVERRIDE
+  {
+    SetName( "TrainImagesClustering" );
+    SetDescription( "Train a classifier from multiple pairs of images and optional input training vector data." );
+
+    // Documentation
+    SetDocName( "Train a classifier from multiple images" );
+    SetDocLongDescription( "TODO" );
+    SetDocLimitations( "None" );
+    SetDocAuthors( "OTB-Team" );
+    SetDocSeeAlso( "OpenCV documentation for machine learning http://docs.opencv.org/modules/ml/doc/ml.html " );
+
+    AddDocTag( Tags::Learning );
+
+    ClearApplications();
+    InitIO();
+    InitSampling();
+    InitClassification( false );
+
+    // Doc example parameter settings
+    SetDocExampleParameterValue( "io.il", "QB_1_ortho.tif" );
+    SetDocExampleParameterValue( "io.vd", "VectorData_QB1.shp" );
+    SetDocExampleParameterValue( "io.imstat", "EstimateImageStatisticsQB1.xml" );
+    SetDocExampleParameterValue( "sample.mv", "100" );
+    SetDocExampleParameterValue( "sample.mt", "100" );
+    SetDocExampleParameterValue( "sample.vtr", "0.5" );
+    SetDocExampleParameterValue( "sample.vfn", "Class" );
+    SetDocExampleParameterValue( "classifier", "sharkkm" );
+    SetDocExampleParameterValue( "classifier.sharkkm.k", "2" );
+    SetDocExampleParameterValue( "io.out", "sharkKMModelQB1.txt" );
+  }
+
+  void DoUpdateParameters() ITK_OVERRIDE
+  {
+    if( HasValue( "io.vd" ) )
+      {
+      UpdatePolygonClassStatisticsParameters();
+      }
+  }
+
+  void DoExecute() ITK_OVERRIDE
+  {
+    TrainFileNamesHandler fileNames;
+    FloatVectorImageListType *imageList = GetParameterImageList( "io.il" );
+    bool HasInputVector = IsParameterEnabled( "io.vd" ) && HasValue( "io.vd" );
+    std::vector<std::string> vectorFileList = GetVectorFileList( GetParameterString( "io.out" ), fileNames );
+
+
+    unsigned long nbInputs = imageList->Size();
+
+    if( nbInputs > vectorFileList.size() )
+      {
+      otbAppLogFATAL( "Missing input vector data files to match number of images (" << nbInputs << ")." );
+      }
+
+    // check if validation vectors are given
+    std::vector<std::string> validationVectorFileList;
+    bool dedicatedValidation = false;
+    if( IsParameterEnabled( "io.valid" ) && HasValue( "io.valid" ) )
+      {
+      validationVectorFileList = GetParameterStringList( "io.valid" );
+      if( nbInputs > validationVectorFileList.size() )
+        {
+        otbAppLogFATAL( "Missing validation vector data files to match number of images (" << nbInputs << ")." );
+        }
+
+      dedicatedValidation = true;
+      }
+
+    fileNames.CreateTemporaryFileNames( GetParameterString( "io.out" ), nbInputs, dedicatedValidation );
+
+    // Compute final maximum sampling rates for both training and validation samples
+    SamplingRates rates = ComputeFinalMaximumSamplingRates( dedicatedValidation );
+
+    if( HasInputVector )
+    {
+      // Select and Extract samples for training with computed statistics and rates
+      ComputePolygonStatistics( imageList, vectorFileList, fileNames.polyStatTrainOutputs );
+      ComputeSamplingRate( fileNames.polyStatTrainOutputs, fileNames.rateTrainOut, rates.fmt );
+      SelectAndExtractTrainSamples( fileNames, imageList, vectorFileList, SamplingStrategy::CLASS );
+    }
+    else
+    {
+      SelectAndExtractTrainSamples( fileNames, imageList, vectorFileList, SamplingStrategy::GEOMETRIC );
+    }
+
+    // Select and Extract samples for validation with computed statistics and rates
+    // Validation samples could be empty if sample.vrt == 0 and if no dedicated validation are provided
+    if( dedicatedValidation ) {
+      ComputePolygonStatistics(imageList, validationVectorFileList, fileNames.polyStatValidOutputs);
+      ComputeSamplingRate(fileNames.polyStatValidOutputs, fileNames.rateValidOut, rates.fmv);
+      }
+    SelectAndExtractValidationSamples(fileNames, imageList, validationVectorFileList, dedicatedValidation);
+
+
+    // Then train the model with extracted samples
+    TrainModel( imageList, fileNames.sampleTrainOutputs, fileNames.sampleValidOutputs);
+
+    // cleanup
+    if( IsParameterEnabled( "cleanup" ) )
+      {
+      otbAppLogINFO( <<"Final clean-up ..." );
+      fileNames.clear();
+      }
+  }
+
+private :
+
+  void UpdatePolygonClassStatisticsParameters()
+  {
+    std::vector<std::string> vectorFileList = GetParameterStringList( "io.vd" );
+    GetInternalApplication( "polystat" )->SetParameterString( "vec", vectorFileList[0], false );
+    UpdateInternalParameters( "polystat" );
+  }
+
+
+  /**
+   * Retrieve input vector data if provided otherwise generate a default vector shape file for each image.
+   * \param output vector file path
+   * \param fileNames
+   * \return list of input vector data file names
+   */
+  std::vector<std::string> GetVectorFileList(std::string output, TrainFileNamesHandler &fileNames)
+  {
+    std::vector<std::string> vectorFileList;
+    bool HasInputVector = IsParameterEnabled( "io.vd" ) && HasValue( "io.vd" );
+
+    // Retrieve provided input vector data if available.
+    if( !HasInputVector )
+      {
+      FloatVectorImageListType *imageList = GetParameterImageList( "io.il" );
+      unsigned int nbInputs = static_cast<unsigned int>(imageList->Size());
+
+      for( unsigned int i = 0; i < nbInputs; ++i )
+        {
+        std::string name = output + "_vector_" + std::to_string( i ) + ".shp";
+        GenerateVectorDataFile( imageList->GetNthElement( i ), name );
+        fileNames.tmpVectorFileList.push_back( name );
+        }
+      vectorFileList = fileNames.tmpVectorFileList;
+      SetParameterStringList( "io.vd", vectorFileList, false );
+      UpdatePolygonClassStatisticsParameters();
+      GetInternalApplication( "polystat" )->SetParameterString( "field", "fid" );
+      }
+    else
+      {
+      vectorFileList = GetParameterStringList( "io.vd" );
+      }
+
+    return vectorFileList;
+  }
+
+
+
+  void GenerateVectorDataFile(const FloatVectorImageListType::ObjectPointerType &floatVectorImage, std::string name)
+  {
+    typedef otb::ImageToEnvelopeVectorDataFilter<FloatVectorImageType, VectorDataType> ImageToEnvelopeFilterType;
+    typedef ImageToEnvelopeFilterType::OutputVectorDataType OutputVectorData;
+    typedef otb::VectorDataFileWriter<OutputVectorData> VectorDataWriter;
+
+    ImageToEnvelopeFilterType::Pointer imageToEnvelopeVectorData = ImageToEnvelopeFilterType::New();
+    imageToEnvelopeVectorData->SetInput( floatVectorImage );
+    imageToEnvelopeVectorData->SetOutputProjectionRef( floatVectorImage->GetProjectionRef().c_str() );
+    OutputVectorData::Pointer vectorData = imageToEnvelopeVectorData->GetOutput();
+
+    // write temporary generated vector file to disk.
+    VectorDataWriter::Pointer vectorDataFileWriter = VectorDataWriter::New();
+    vectorDataFileWriter->SetInput( vectorData );
+    vectorDataFileWriter->SetFileName( name.c_str() );
+    vectorDataFileWriter->Write();
+  }
+
 };
 
 }

Modules/Applications/AppClassification/app/otbTrainVectorClustering.cxx

@@ -57,9 +57,8 @@ private:
 
     // Doc example parameter settings
     SetDocExampleParameterValue( "io.vd", "vectorData.shp" );
-    SetDocExampleParameterValue( "io.stats", "meanVar.xml" );
-    SetDocExampleParameterValue( "io.out", "svmModel.svm" );
-    SetDocExampleParameterValue( "feat", "perimeter  area  width" );
+    SetDocExampleParameterValue( "io.out", "kmeansModel.txt" );
+    SetDocExampleParameterValue( "feat", "perimeter width area" );
 
   }