MRG: Merge branch 'clean_deprecated' into develop

Conflicts:
	Modules/Applications/AppClassification/app/otbTrainVectorClassifier.cxx
	Modules/Applications/AppClassification/test/CMakeLists.txt
	Modules/Learning/Supervised/otb-module.cmake

Examples/DisparityMap/FineRegistrationImageFilterExample.cxx

@@ -45,7 +45,7 @@
 #include "otbImageFileReader.h"
 #include "itkUnaryFunctorImageFilter.h"
 #include "itkRecursiveGaussianImageFilter.h"
-#include "otbWarpImageFilter.h"
+#include "itkWarpImageFilter.h"
 #include "itkMeanReciprocalSquareDifferenceImageToImageMetric.h"
 
 // Software Guide : BeginCodeSnippet
@@ -255,7 +255,7 @@ int main(int argc, char** argv)
   dfWriter->SetFileName(argv[4]);
   dfWriter->Update();
 
-  typedef otb::WarpImageFilter<InputImageType, InputImageType,
+  typedef itk::WarpImageFilter<InputImageType, InputImageType,
       DisplacementFieldType> WarperType;
   WarperType::Pointer warper = WarperType::New();
 

Examples/DisparityMap/NCCRegistrationFilterExample.cxx

@@ -42,7 +42,7 @@
 // Software Guide : BeginCodeSnippet
 #include "otbNCCRegistrationFilter.h"
 #include "itkRecursiveGaussianImageFilter.h"
-#include "otbWarpImageFilter.h"
+#include "itkWarpImageFilter.h"
 // Software Guide : EndCodeSnippet
 
 #include "otbImageOfVectorsToMonoChannelExtractROI.h"
@@ -211,7 +211,7 @@ int main(int argc, char** argv)
   dfWriter->SetFileName(argv[4]);
   dfWriter->Update();
 
-  typedef otb::WarpImageFilter<MovingImageType, MovingImageType,
+  typedef itk::WarpImageFilter<MovingImageType, MovingImageType,
       DisplacementFieldType> WarperType;
   WarperType::Pointer warper = WarperType::New();
 

Examples/DisparityMap/SimpleDisparityMapEstimationExample.cxx

@@ -47,7 +47,7 @@
 #include "itkWindowedSincInterpolateImageFunction.h"
 #include "itkGradientDescentOptimizer.h"
 #include "otbBSplinesInterpolateDisplacementFieldGenerator.h"
-#include "otbWarpImageFilter.h"
+#include "itkWarpImageFilter.h"
 // Software Guide : EndCodeSnippet
 
 #include "otbImageFileReader.h"
@@ -414,7 +414,7 @@ int main(int argc, char* argv[])
 
   // Software Guide :  BeginCodeSnippet
 
-  typedef otb::WarpImageFilter<ImageType, ImageType,
+  typedef itk::WarpImageFilter<ImageType, ImageType,
       DisplacementFieldType> ImageWarperType;
   // Software Guide : EndCodeSnippet
 

Examples/FeatureExtraction/AsymmetricFusionOfLineDetectorExample.cxx

@@ -35,7 +35,7 @@
 // Software Guide : EndLatex
 
 // Software Guide : BeginCodeSnippet
-#include "otbAssymmetricFusionOfLineDetectorImageFilter.h"
+#include "otbAsymmetricFusionOfLineDetectorImageFilter.h"
 // Software Guide : EndCodeSnippet
 
 #include "otbImage.h"
@@ -85,7 +85,7 @@ int main(int argc, char * argv[])
   //  Software Guide : EndLatex
 
   // Software Guide : BeginCodeSnippet
-  typedef otb::AssymmetricFusionOfLineDetectorImageFilter<InternalImageType,
+  typedef otb::AsymmetricFusionOfLineDetectorImageFilter<InternalImageType,
       InternalImageType>
   FilterType;
   // Software Guide : EndCodeSnippet

Examples/Learning/CMakeLists.txt

@@ -33,23 +33,8 @@ add_executable(SOMExample SOMExample.cxx)
 target_link_libraries(SOMExample ${OTB_LIBRARIES})
 
 if(OTBLibSVM_LOADED)
-add_executable(SVMImageClassificationExample SVMImageClassificationExample.cxx)
-target_link_libraries(SVMImageClassificationExample ${OTB_LIBRARIES})
-
 add_executable(SVMImageEstimatorClassificationMultiExample SVMImageEstimatorClassificationMultiExample.cxx)
 target_link_libraries(SVMImageEstimatorClassificationMultiExample ${OTB_LIBRARIES})
-
-add_executable(SVMImageModelEstimatorExample SVMImageModelEstimatorExample.cxx)
-target_link_libraries(SVMImageModelEstimatorExample ${OTB_LIBRARIES})
-
-add_executable(SVMPointSetClassificationExample SVMPointSetClassificationExample.cxx)
-target_link_libraries(SVMPointSetClassificationExample ${OTB_LIBRARIES})
-
-add_executable(SVMPointSetExample SVMPointSetExample.cxx)
-target_link_libraries(SVMPointSetExample ${OTB_LIBRARIES})
-
-add_executable(SVMPointSetModelEstimatorExample SVMPointSetModelEstimatorExample.cxx)
-target_link_libraries(SVMPointSetModelEstimatorExample ${OTB_LIBRARIES})
 endif()
 
 if(OTBOpenCV_LOADED)

Examples/Learning/SVMImageClassificationExample.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 <fstream>
-
-#include "otbImageFileReader.h"
-#include "otbImageFileWriter.h"
-#include "itkUnaryFunctorImageFilter.h"
-#include "itkRescaleIntensityImageFilter.h"
-#include "otbImage.h"
-
-//  Software Guide : BeginCommandLineArgs
-//    INPUTS: {ROI_QB_MUL_1.png}
-//    OUTPUTS: {ROI_QB_MUL_1_SVN_CLASS.png}
-//    ${OTB_DATA_ROOT}/Examples/svm_image_model.svm
-//  Software Guide : EndCommandLineArgs
-
-//  Software Guide : BeginLatex
-// This example illustrates the use of the
-// \doxygen{otb}{SVMClassifier} class for performing SVM
-// classification on images.
-// In this example, we will use an SVM model estimated in the example
-// of section \ref{sec:LearningWithImages}
-// to separate between water and non-water pixels by using the RGB
-// values only. The images used for this example are shown in
-// figure~\ref{fig:SVMROIS}.
-// The first thing to do is include the header file for the
-// class. Since the \doxygen{otb}{SVMClassifier} takes
-// \doxygen{itk}{ListSample}s as input, the class
-// \doxygen{itk}{PointSetToListAdaptor} is needed.
-//
-//
-//  Software Guide : EndLatex
-
-#include "itkImageToListSampleAdaptor.h"
-
-// Software Guide : BeginCodeSnippet
-#include "otbSVMClassifier.h"
-// Software Guide : EndCodeSnippet
-
-int main(int argc, char* argv[])
-{
-
-  if (argc != 4)
-    {
-    std::cout << "Usage : " << argv[0] << " inputImage outputImage modelFile "
-              << std::endl;
-    return EXIT_FAILURE;
-    }
-
-  const char * imageFilename  = argv[1];
-  const char * modelFilename  = argv[3];
-  const char * outputFilename = argv[2];
-
-// Software Guide : BeginLatex
-//
-// In the framework of supervised learning and classification, we will
-// always use feature vectors for the characterization of the
-// classes. On the other hand, the class labels are scalar
-// values. Here, we start by defining the type of the features as the
-// \code{PixelType}, which will be used to define the feature
-// \code{VectorType}. We also declare the type for the labels.
-//
-// Software Guide : EndLatex
-
-// Software Guide : BeginCodeSnippet
-  typedef double                 PixelType;
-  typedef int                    LabelPixelType;
-// Software Guide : EndCodeSnippet
-  const unsigned int Dimension = 2;
-
-// Software Guide : BeginLatex
-//
-// We can now proceed to define the image type used for storing the
-// features. We also define the reader.
-//
-// Software Guide : EndLatex
-
-// Software Guide : BeginCodeSnippet
-  typedef otb::Image<itk::FixedArray<PixelType, 3>,
-      Dimension>          InputImageType;
-
-  typedef otb::ImageFileReader<InputImageType> ReaderType;
-// Software Guide : EndCodeSnippet
-
-// Software Guide : BeginLatex
-//
-// We can now read the image by calling the \code{Update} method of the reader.
-//
-// Software Guide : EndLatex
-
-// Software Guide : BeginCodeSnippet
-  ReaderType::Pointer reader = ReaderType::New();
-
-  reader->SetFileName(imageFilename);
-
-  reader->Update();
-// Software Guide : EndCodeSnippet
-
-// Software Guide : BeginLatex
-//
-// The image has now to be transformed to a sample which
-// is compatible with the classification framework. We will use a
-// \doxygen{itk}{Statistics::ImageToListSampleAdaptor} for this
-// task. This class is templated over the image type used for
-// storing the measures.
-//
-// Software Guide : EndLatex
-
-// Software Guide : BeginCodeSnippet
-  typedef itk::Statistics::ImageToListSampleAdaptor<InputImageType> SampleType;
-  SampleType::Pointer sample = SampleType::New();
-// Software Guide : EndCodeSnippet
-
-// Software Guide : BeginLatex
-//
-// After instantiation, we can set the image as an imput of our
-// sample adaptor.
-//
-// Software Guide : EndLatex
-
-// Software Guide : BeginCodeSnippet
-  sample->SetImage(reader->GetOutput());
-// Software Guide : EndCodeSnippet
-
-// Software Guide : BeginLatex
-//
-// Now, we need to declare the SVM model which is to be used by the
-// classifier. The SVM model is templated over the type of value used
-// for the measures and the type of pixel used for the labels.
-//
-// Software Guide : EndLatex
-
-// Software Guide : BeginCodeSnippet
-  typedef otb::SVMModel<PixelType, LabelPixelType> ModelType;
-
-  ModelType::Pointer model = ModelType::New();
-// Software Guide : EndCodeSnippet
-
-// Software Guide : BeginLatex
-//
-// After instantiation, we can load a model saved to a file (see
-// section \ref{sec:LearningWithImages} for an example of model
-// estimation and storage to a file.
-//
-// Software Guide : EndLatex
-
-// Software Guide : BeginCodeSnippet
-  model->LoadModel(modelFilename);
-// Software Guide : EndCodeSnippet
-
-// Software Guide : BeginLatex
-//
-// We have now all the elements to create a classifier. The classifier
-// is templated over the sample type (the type of the data to be
-// classified) and the label type (the type of the output of the classifier).
-//
-// Software Guide : EndLatex
-
-// Software Guide : BeginCodeSnippet
-  typedef otb::SVMClassifier<SampleType, LabelPixelType> ClassifierType;
-
-  ClassifierType::Pointer classifier = ClassifierType::New();
-// Software Guide : EndCodeSnippet
-
-// Software Guide : BeginLatex
-//
-// We set the classifier parameters : number of classes, SVM model,
-// the sample data. And we trigger the classification process by
-// calling the \code{Update} method.
-//
-// Software Guide : EndLatex
-
-// Software Guide : BeginCodeSnippet
-  int numberOfClasses = model->GetNumberOfClasses();
-  classifier->SetNumberOfClasses(numberOfClasses);
-  classifier->SetModel(model);
-  classifier->SetInput(sample.GetPointer());
-  classifier->Update();
-// Software Guide : EndCodeSnippet
-
-// Software Guide : BeginLatex
-//
-// After the classification step, we usually want to get the
-// results. The classifier gives an output under the form of a sample
-// list. This list supports the classical STL iterators. Therefore, we
-// will create an output image and fill it up with the results of the
-// classification. The pixel type of the output image is the same as
-// the one used for the labels.
-//
-// Software Guide : EndLatex
-
-// Software Guide : BeginCodeSnippet
-  typedef ClassifierType::ClassLabelType         OutputPixelType;
-  typedef otb::Image<OutputPixelType, Dimension> OutputImageType;
-
-  OutputImageType::Pointer outputImage = OutputImageType::New();
-// Software Guide : EndCodeSnippet
-
-// Software Guide : BeginLatex
-//
-// We allocate the memory for the output image using the information
-// from the input image.
-//
-// Software Guide : EndLatex
-
-// Software Guide : BeginCodeSnippet
-  typedef itk::Index<Dimension>       myIndexType;
-  typedef itk::Size<Dimension>        mySizeType;
-  typedef itk::ImageRegion<Dimension> myRegionType;
-
-  mySizeType size;
-  size[0] = reader->GetOutput()->GetRequestedRegion().GetSize()[0];
-  size[1] = reader->GetOutput()->GetRequestedRegion().GetSize()[1];
-
-  myIndexType start;
-  start[0] = 0;
-  start[1] = 0;
-
-  myRegionType region;
-  region.SetIndex(start);
-  region.SetSize(size);
-
-  outputImage->SetRegions(region);
-  outputImage->Allocate();
-// Software Guide : EndCodeSnippet
-
-// Software Guide : BeginLatex
-//
-// We can now declare the iterators on the list that we get at the
-// output of the classifier as well as the iterator to fill the output image.
-//
-// Software Guide : EndLatex
-
-// Software Guide : BeginCodeSnippet
-  ClassifierType::OutputType* membershipSample =
-    classifier->GetOutput();
-  ClassifierType::OutputType::ConstIterator m_iter =
-    membershipSample->Begin();
-  ClassifierType::OutputType::ConstIterator m_last =
-    membershipSample->End();
-
-  typedef itk::ImageRegionIterator<OutputImageType> OutputIteratorType;
-  OutputIteratorType outIt(outputImage,
-                           outputImage->GetBufferedRegion());
-
-  outIt.GoToBegin();
-// Software Guide : EndCodeSnippet
-
-// Software Guide : BeginLatex
-//
-// We will iterate through the list, get the labels and assign pixel
-// values to the output image.
-//
-// Software Guide : EndLatex
-
-// Software Guide : BeginCodeSnippet
-  while (m_iter != m_last && !outIt.IsAtEnd())
-    {
-    outIt.Set(m_iter.GetClassLabel());
-    ++m_iter;
-    ++outIt;
-    }
-// Software Guide : EndCodeSnippet
-
-// Software Guide : BeginLatex
-//
-// Only for visualization purposes, we choose to rescale the image of
-// classes before saving it to a file. We will use the
-// \doxygen{itk}{RescaleIntensityImageFilter} for this purpose.
-//
-// Software Guide : EndLatex
-
-// Software Guide : BeginCodeSnippet
-  typedef otb::Image<unsigned char, Dimension> FileImageType;
-
-  typedef itk::RescaleIntensityImageFilter<OutputImageType,
-      FileImageType> RescalerType;
-
-  RescalerType::Pointer rescaler = RescalerType::New();
-
-  rescaler->SetOutputMinimum(itk::NumericTraits<unsigned char>::min());
-  rescaler->SetOutputMaximum(itk::NumericTraits<unsigned char>::max());
-
-  rescaler->SetInput(outputImage);
-// Software Guide : EndCodeSnippet
-
-// Software Guide : BeginLatex
-//
-// We can now create an image file writer and save the image.
-//
-// Software Guide : EndLatex
-
-// Software Guide : BeginCodeSnippet
-  typedef otb::ImageFileWriter<FileImageType> WriterType;
-
-  WriterType::Pointer writer = WriterType::New();
-
-  writer->SetFileName(outputFilename);
-  writer->SetInput(rescaler->GetOutput());
-
-  writer->Update();
-// Software Guide : EndCodeSnippet
-
-//  Software Guide : BeginLatex
-// Figure \ref{fig:SVMCLASS} shows the result of the SVM classification.
-// \begin{figure}
-// \center
-// \includegraphics[width=0.45\textwidth]{ROI_QB_MUL_1.eps}
-// \includegraphics[width=0.45\textwidth]{ROI_QB_MUL_1_SVN_CLASS.eps}
-// \itkcaption[SVM Image Classification]{Result of the SVM
-// classification . Left: RGB image. Right: image of classes.}
-// \label{fig:SVMCLASS}
-// \end{figure}
-//  Software Guide : EndLatex
-
-  return EXIT_SUCCESS;
-}

Examples/Learning/SVMImageEstimatorClassificationMultiExample.cxx

@@ -52,15 +52,16 @@
 #include <iostream>
 
 //  Software Guide : BeginCodeSnippet
-#include "otbSVMImageModelEstimator.h"
-#include "itkImageToListSampleAdaptor.h"
-#include "otbSVMClassifier.h"
+#include "otbLibSVMMachineLearningModel.h"
+#include "itkImageToListSampleFilter.h"
+#include "otbImageClassificationFilter.h"
 //  Software Guide : EndCodeSnippet
 
 #include "otbImageFileWriter.h"
 
 #include "itkUnaryFunctorImageFilter.h"
 #include "itkScalarToRGBPixelFunctor.h"
+#include "itkBinaryThresholdImageFilter.h"
 
 #include "otbImageFileReader.h"
 
@@ -92,13 +93,13 @@ int main(int itkNotUsed(argc), char *argv[])
 
 //  Software Guide : BeginLatex
 //
-//  The \doxygen{otb}{SVMImageModelEstimator} class is templated over
-//  the input (features) and the training (labels) images.
+//  The \doxygen{otb}{LibSVMMachineLearningModel} class is templated over
+//  the input (features) and the training (labels) values.
 //
 // Software Guide : EndLatex
 //  Software Guide : BeginCodeSnippet
-  typedef otb::SVMImageModelEstimator<InputImageType,
-      TrainingImageType>   EstimatorType;
+  typedef otb::LibSVMMachineLearningModel<InputPixelType,
+      InputPixelType>   ModelType;
 
 //  Software Guide : EndCodeSnippet
 
@@ -128,107 +129,67 @@ int main(int itkNotUsed(argc), char *argv[])
   inputReader->SetFileName(inputImageFileName);
   trainingReader->SetFileName(trainingImageFileName);
 
-  inputReader->Update();
-  trainingReader->Update();
+  //~ inputReader->Update();
+  //~ trainingReader->Update();
 
 //  Software Guide : EndCodeSnippet
 
 //  Software Guide : BeginLatex
 //
-//  We can now instantiate the model estimator and set its parameters.
+//  The input data is contained in images. Only label values greater than 0
+//  shall be used, so we create two iterators to fill the input and target
+//  ListSamples.
 //
 // Software Guide : EndLatex
-//  Software Guide : BeginCodeSnippet
-  EstimatorType::Pointer svmEstimator = EstimatorType::New();
-
-  svmEstimator->SetInputImage(inputReader->GetOutput());
-  svmEstimator->SetTrainingImage(trainingReader->GetOutput());
-
-//  Software Guide : EndCodeSnippet
 
-//  Software Guide : BeginLatex
-//
-//  The model estimation procedure is triggered by calling the
-//  estimator's \code{Update} method.
-//
-// Software Guide : EndLatex
 //  Software Guide : BeginCodeSnippet
-  svmEstimator->Update();
-
-//  Software Guide : EndCodeSnippet
-
-// Software Guide : BeginLatex
-//
-//  We can now proceed to the image classification. We start by
-//  declaring the type of the image to be classified. ITK's
-//  classification framework needs the type of the pixel to be of
-//  fixed type, so we declare the following types.
-//
-// Software Guide : EndLatex
 
-// Software Guide : BeginCodeSnippet
-  typedef otb::Image<itk::FixedArray<InputPixelType, 3>,
-      Dimension>          ClassifyImageType;
+  typedef itk::BinaryThresholdImageFilter<TrainingImageType,TrainingImageType> ThresholdFilterType;
+  ThresholdFilterType::Pointer thresholder = ThresholdFilterType::New();
+  thresholder->SetInput(trainingReader->GetOutput());
+  thresholder->SetLowerThreshold(1);
+  thresholder->SetOutsideValue(0);
+  thresholder->SetInsideValue(1);
 
-  typedef otb::ImageFileReader<ClassifyImageType> ClassifyReaderType;
-// Software Guide : EndCodeSnippet
+  typedef itk::Statistics::ImageToListSampleFilter<InputImageType,TrainingImageType> ImageToListSample;
+  typedef itk::Statistics::ImageToListSampleFilter<TrainingImageType,TrainingImageType> ImageToTargetListSample;
 
-// Software Guide : BeginLatex
-//
-// We can now read the image by calling the \code{Update} method of the reader.
-//
-// Software Guide : EndLatex
-
-// Software Guide : BeginCodeSnippet
-  ClassifyReaderType::Pointer cReader = ClassifyReaderType::New();
+  ImageToListSample::Pointer imToList = ImageToListSample::New();
+  imToList->SetInput(inputReader->GetOutput());
+  imToList->SetMaskImage(thresholder->GetOutput());
+  imToList->SetMaskValue(1);
+  imToList->Update();
 
-  cReader->SetFileName(inputImageFileName);
+  ImageToTargetListSample::Pointer imToTargetList = ImageToTargetListSample::New();
+  imToTargetList->SetInput(trainingReader->GetOutput());
+  imToTargetList->SetMaskImage(thresholder->GetOutput());
+  imToTargetList->SetMaskValue(1);
+  imToTargetList->Update();
 
-  cReader->Update();
-// Software Guide : EndCodeSnippet
-// Software Guide : BeginLatex
-//
-// The image has now to be transformed to a sample which
-// is compatible with the classification framework. We will use a
-// \doxygen{itk}{Statistics::ImageToListSampleAdaptor} for this
-// task. This class is templated over the image type used for
-// storing the measures.