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Commit 2d159b08 authored by Charles Peyrega's avatar Charles Peyrega
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DOC: Add a generic image classification example in the SG

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Examples/Classification/CMakeLists.txt
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− 2
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......@@ -25,8 +25,8 @@ TARGET_LINK_LIBRARIES(ScalarImageMarkovRandomField1 OTBIO OTBCommon )
ADD_EXECUTABLE(SOMImageClassificationExample SOMImageClassificationExample.cxx )
TARGET_LINK_LIBRARIES(SOMImageClassificationExample OTBIO OTBLearning)
ADD_EXECUTABLE(SVMImageClassificationExample2 SVMImageClassifierExample.cxx )
TARGET_LINK_LIBRARIES(SVMImageClassificationExample2 OTBIO OTBLearning)
ADD_EXECUTABLE(SupervisedImageClassificationExample SupervisedImageClassificationExample.cxx )
TARGET_LINK_LIBRARIES(SupervisedImageClassificationExample OTBIO OTBLearning)
ADD_EXECUTABLE(ClassificationMapRegularizationExample ClassificationMapRegularizationExample.cxx )
TARGET_LINK_LIBRARIES(ClassificationMapRegularizationExample OTBIO OTBCommon)
......
Examples/Classification/SVMImageClassifierExample.cxx → Examples/Classification/SupervisedImageClassificationExample.cxx
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− 24
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......@@ -18,20 +18,25 @@
// Software Guide : BeginLatex
//
// In previous examples, we have used the
// \doxygen{otb}{SVMClassifier}, which uses the ITK classification
// framework. This good for compatibility with the ITK framework, but
// introduces the limitations of not being able to use streaming and
// being able to know at compilation time the number of bands of the
// image to be classified. In OTB we have avoided this limitation by
// developing the \doxygen{otb}{SVMImageClassificationFilter}. In
// this example we will illustrate its use. We start by including the
// appropriate header file.
// In OTB, a generic streamed filter called \doxygen{otb}{ImageClassificationFilter}
// is available to classify any input multi-channel image according to an input
// classification model file. This filter is generic because it works with any
// classification model type (SVM, KNN, Artificial Neural Network,...) generated
// within the OTB generic Machine Learning framework based on OpenCV (\cite{opencv_library}).
// The input model file is smartly parsed according to its content in order to
// identify which learning method was used to generate it. Once the classification
// method and model are known, the input image can be classified. More details are
// given in subsections \ref{ssec:LearningFromSamples} and \ref{ssec:LearningFromImages}
// to generate a classification model either from samples or from images.
// In this example we will illustrate its use. We start by including the
// appropriate header files.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
#include "otbSVMImageClassificationFilter.h"
#include "otbMacro.h"
#include "otbMachineLearningModelFactory.h"
#include "otbImageClassificationFilter.h"
// Software Guide : EndCodeSnippet
#include "otbVectorImage.h"
......@@ -53,34 +58,46 @@ int main(int argc, char * argv[])
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
const unsigned int Dimension = 2;
const unsigned int Dimension = 2;
typedef double PixelType;
typedef unsigned short LabeledPixelType;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Our classifier will be generic enough to be able to process images
// with any number of bands. We read the images as
// \doxygen{otb}{VectorImage}s. The labeled image will be a scalar image.
// Our classifier is generic enough to be able to process images
// with any number of bands. We read the input image as a
// \doxygen{otb}{VectorImage}. The labeled image will be a scalar image.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef otb::VectorImage<PixelType, Dimension> ImageType;
typedef otb::Image<LabeledPixelType, Dimension> LabeledImageType;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// We can now define the type for the classifier filter, which is
// templated over its input and output image types.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef otb::SVMImageClassificationFilter
<ImageType, LabeledImageType> ClassificationFilterType;
typedef otb::ImageClassificationFilter<ImageType, LabeledImageType>
ClassificationFilterType;
typedef ClassificationFilterType::ModelType ModelType;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// Moreover, it is necessary to define a \doxygen{otb}{MachineLearningModelFactory}
// which is templated over its input and output pixel types. This factory is used
// to parse the input model file and to define which classification method to use.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef otb::MachineLearningModelFactory<PixelType, LabeledPixelType>
MachineLearningModelFactoryType;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// And finally, we define the reader and the writer. Since the images
......@@ -88,29 +105,40 @@ int main(int argc, char * argv[])
// will trigger the streaming ability of the classifier.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef otb::ImageFileReader<ImageType> ReaderType;
typedef otb::ImageFileReader<ImageType> ReaderType;
typedef otb::ImageFileWriter<LabeledImageType> WriterType;
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// We instantiate the classifier and the reader objects and we set
// the existing SVM model obtained in a previous training step.
// the existing model obtained in a previous training step.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
ClassificationFilterType::Pointer filter = ClassificationFilterType::New();
ReaderType::Pointer reader = ReaderType::New();
reader->SetFileName(infname);
// Software Guide : EndCodeSnippet
ModelType::Pointer model = ModelType::New();
model->LoadModel(modelfname);
// Software Guide : BeginLatex
//
// The input model file is parsed according to its content and the generated model
// is then loaded within the \doxygen{otb}{ImageClassificationFilter}.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
ModelType::Pointer model;
model = MachineLearningModelFactoryType::CreateMachineLearningModel(
modelfname,
MachineLearningModelFactoryType::ReadMode);
model->Load(modelfname);
filter->SetModel(model);
// Software Guide : EndCodeSnippet
// Software Guide : BeginLatex
//
// We plug the pipeline and
......
Examples/Learning/TrainMachineLearningModelFromImagesExample.cxx
+ 31
− 27
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......@@ -16,25 +16,6 @@
=========================================================================*/
//Image
#include "otbVectorImage.h"
#include "otbVectorData.h"
#include "otbListSampleGenerator.h"
//Reader
#include "otbImageFileReader.h"
#include "otbVectorDataFileReader.h"
//Estimator
# include "otbSVMMachineLearningModel.h"
#include "otbMachineLearningModelFactory.h"
// Normalize the samples
#include "otbShiftScaleSampleListFilter.h"
// Extract a ROI of the vectordata
#include "otbVectorDataIntoImageProjectionFilter.h"
// Software Guide : BeginCommandLineArgs
// INPUTS: {QB_1_ortho.tif}, {VectorData_QB1.shp}
// OUTPUTS: {clLIBSVMModelQB1.libsvm}
......@@ -44,13 +25,35 @@
// This example illustrates the use of the
// \doxygen{otb}{MachineLearningModel} class. This class allows the
// estimation of a classification model (supervised learning) from images. In this example, we will train an SVM
// with 4 classes.
// with 4 classes. We start by including the appropriate header files.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// List sample generator
#include "otbListSampleGenerator.h"
// Extract a ROI of the vectordata
#include "otbVectorDataIntoImageProjectionFilter.h"
// SVM model Estimator
#include "otbSVMMachineLearningModel.h"
// Software Guide : EndCodeSnippet
// Image
#include "otbVectorImage.h"
#include "otbVectorData.h"
// Reader
#include "otbImageFileReader.h"
#include "otbVectorDataFileReader.h"
// Normalize the samples
//#include "otbShiftScaleSampleListFilter.h"
int main(int argc, char* argv[])
{
const char* inputImageFileName = argv[1];
const char* trainingShpFileName = argv[2];
const char* outputModelFileName = argv[3];
......@@ -64,8 +67,8 @@ int main(int argc, char* argv[])
typedef otb::VectorData<double, 2> VectorDataType;
typedef otb::ImageFileReader<InputImageType> InputReaderType;
typedef otb::VectorDataFileReader<VectorDataType> VectorDataReaderType;
typedef otb::ImageFileReader<InputImageType> InputReaderType;
typedef otb::VectorDataFileReader<VectorDataType> VectorDataReaderType;
// Software Guide : BeginLatex
//
......@@ -84,9 +87,9 @@ int main(int argc, char* argv[])
// Software Guide : BeginCodeSnippet
// VectorData projection filter
typedef otb::VectorDataIntoImageProjectionFilter<VectorDataType, InputImageType>
VectorDataReprojectionType;
VectorDataReprojectionType;
InputReaderType::Pointer inputReader = InputReaderType::New();
InputReaderType::Pointer inputReader = InputReaderType::New();
inputReader->SetFileName(inputImageFileName);
InputImageType::Pointer image = inputReader->GetOutput();
......@@ -149,8 +152,9 @@ int main(int argc, char* argv[])
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
typedef otb::SVMMachineLearningModel<InputImageType::InternalPixelType,
ListSampleGeneratorType::ClassLabelType> SVMType;
typedef otb::SVMMachineLearningModel
<InputImageType::InternalPixelType,
ListSampleGeneratorType::ClassLabelType> SVMType;
SVMType::Pointer SVMClassifier = SVMType::New();
......
Examples/Learning/TrainMachineLearningModelFromSamplesExample.cxx
+ 13
− 11
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......@@ -16,29 +16,31 @@
=========================================================================*/
//Random
#include "itkMersenneTwisterRandomVariateGenerator.h"
//List sample generator
#include "otbListSampleGenerator.h"
//Estimator
# include "otbSVMMachineLearningModel.h"
#include "otbMachineLearningModelFactory.h"
// Software Guide : BeginCommandLineArgs
// INPUTS: {1000}, {4}, {5}, {121212}
// OUTPUTS: {clSVMModelFromSamples.svm}
// Software Guide : EndCommandLineArgs
// Software Guide : BeginLatex
//
// This example illustrates the use of the \doxygen{otb}{SVMMachineLearningModel} class, which inherits from the
// \doxygen{otb}{MachineLearningModel} class. This class allows the
// estimation of a classification model (supervised learning) from samples. In this example, we will train an SVM model
// with 4 output classes, from 1000 randomly generated training samples, each of them having 7 components.
// We start by including the appropriate header files.
//
// Software Guide : EndLatex
// Software Guide : BeginCodeSnippet
// List sample generator
#include "otbListSampleGenerator.h"
// Random number generator
#include "itkMersenneTwisterRandomVariateGenerator.h"
// SVM model Estimator
#include "otbSVMMachineLearningModel.h"
// Software Guide : EndCodeSnippet
int main(int argc, char* argv[])
{
......
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