diff --git a/Modules/Applications/AppClassification/app/otbImageRegression.cxx b/Modules/Applications/AppClassification/app/otbImageRegression.cxx
index 56e07622ad80ede4a6ba83c7da6f413eaf000338..f1892417da6d1e853d3cb6412f890d8c7ec7ade6 100644
--- a/Modules/Applications/AppClassification/app/otbImageRegression.cxx
+++ b/Modules/Applications/AppClassification/app/otbImageRegression.cxx
@@ -38,37 +38,41 @@ namespace Functor
/**
* simple affine function : y = ax+b
*/
-template<class TInput, class TOutput>
+template <class TInput, class TOutput>
class AffineFunctor
{
public:
typedef double InternalType;
-
+
// constructor
- AffineFunctor() : m_A(1.0),m_B(0.0) {}
-
+ AffineFunctor() : m_A(1.0), m_B(0.0)
+ {
+ }
+
// destructor
- virtual ~AffineFunctor() {}
-
+ virtual ~AffineFunctor()
+ {
+ }
+
void SetA(InternalType a)
- {
+ {
m_A = a;
- }
-
+ }
+
void SetB(InternalType b)
- {
+ {
m_B = b;
- }
-
- inline TOutput operator()(const TInput & x) const
- {
- return static_cast<TOutput>( static_cast<InternalType>(x)*m_A + m_B);
- }
+ }
+
+ inline TOutput operator()(const TInput& x) const
+ {
+ return static_cast<TOutput>(static_cast<InternalType>(x) * m_A + m_B);
+ }
+
private:
InternalType m_A;
InternalType m_B;
};
-
}
namespace Wrapper
@@ -89,28 +93,24 @@ public:
itkTypeMacro(ImageRegression, otb::Application);
/** Filters typedef */
- typedef UInt8ImageType MaskImageType;
- typedef itk::VariableLengthVector<FloatVectorImageType::InternalPixelType> MeasurementType;
- typedef otb::StatisticsXMLFileReader<MeasurementType> StatisticsReader;
- typedef otb::ShiftScaleVectorImageFilter<FloatVectorImageType, FloatVectorImageType> RescalerType;
- typedef itk::UnaryFunctorImageFilter<
- FloatImageType,
- FloatImageType,
- otb::Functor::AffineFunctor<float,float> > OutputRescalerType;
- typedef otb::ImageClassificationFilter<FloatVectorImageType, FloatImageType, MaskImageType> ClassificationFilterType;
- typedef ClassificationFilterType::Pointer ClassificationFilterPointerType;
- typedef ClassificationFilterType::ModelType ModelType;
- typedef ModelType::Pointer ModelPointerType;
- typedef ClassificationFilterType::ValueType ValueType;
- typedef ClassificationFilterType::LabelType LabelType;
- typedef otb::MachineLearningModelFactory<ValueType, LabelType> MachineLearningModelFactoryType;
+ typedef UInt8ImageType MaskImageType;
+ typedef itk::VariableLengthVector<FloatVectorImageType::InternalPixelType> MeasurementType;
+ typedef otb::StatisticsXMLFileReader<MeasurementType> StatisticsReader;
+ typedef otb::ShiftScaleVectorImageFilter<FloatVectorImageType, FloatVectorImageType> RescalerType;
+ typedef itk::UnaryFunctorImageFilter<FloatImageType, FloatImageType, otb::Functor::AffineFunctor<float, float>> OutputRescalerType;
+ typedef otb::ImageClassificationFilter<FloatVectorImageType, FloatImageType, MaskImageType> ClassificationFilterType;
+ typedef ClassificationFilterType::Pointer ClassificationFilterPointerType;
+ typedef ClassificationFilterType::ModelType ModelType;
+ typedef ModelType::Pointer ModelPointerType;
+ typedef ClassificationFilterType::ValueType ValueType;
+ typedef ClassificationFilterType::LabelType LabelType;
+ typedef otb::MachineLearningModelFactory<ValueType, LabelType> MachineLearningModelFactoryType;
protected:
-
~ImageRegression() override
- {
+ {
MachineLearningModelFactoryType::CleanFactories();
- }
+ }
private:
void DoInit() override
@@ -119,58 +119,63 @@ private:
SetDescription("Performs a prediction of the input image according to a regression model file.");
// Documentation
- SetDocLongDescription("This application predict output values from an input "
- "image, based on a regression model file produced either by "
- "TrainVectorRegression or TrainImagesRegression. "
- "Pixels of the output image will contain the predicted values from "
- "the regression model (single band). The input pixels "
- "can be optionally centered and reduced according "
- "to the statistics file produced by the "
- "ComputeImagesStatistics application. An optional "
- "input mask can be provided, in which case only "
- "input image pixels whose corresponding mask value "
- "is greater than zero will be processed. The remaining "
- "of pixels will be given the value zero in the output "
- "image.");
-
- SetDocLimitations("The input image must contain the feature bands used for "
- "the model training. "
- "If a statistics file was used during training by the "
- "TrainRegression, it is mandatory to use the same "
- "statistics file for prediction. If an input mask is "
- "used, its size must match the input image size.");
+ SetDocLongDescription(
+ "This application predict output values from an input "
+ "image, based on a regression model file produced either by "
+ "TrainVectorRegression or TrainImagesRegression. "
+ "Pixels of the output image will contain the predicted values from "
+ "the regression model (single band). The input pixels "
+ "can be optionally centered and reduced according "
+ "to the statistics file produced by the "
+ "ComputeImagesStatistics application. An optional "
+ "input mask can be provided, in which case only "
+ "input image pixels whose corresponding mask value "
+ "is greater than zero will be processed. The remaining "
+ "of pixels will be given the value zero in the output "
+ "image.");
+
+ SetDocLimitations(
+ "The input image must contain the feature bands used for "
+ "the model training. "
+ "If a statistics file was used during training by the "
+ "TrainRegression, it is mandatory to use the same "
+ "statistics file for prediction. If an input mask is "
+ "used, its size must match the input image size.");
SetDocAuthors("OTB-Team");
SetDocSeeAlso("TrainImagesRegression, TrainVectorRegression, VectorRegression, ComputeImagesStatistics");
AddDocTag(Tags::Learning);
AddParameter(ParameterType_InputImage, "in", "Input Image");
- SetParameterDescription( "in", "The input image to predict.");
+ SetParameterDescription("in", "The input image to predict.");
AddParameter(ParameterType_InputImage, "mask", "Input Mask");
- SetParameterDescription( "mask", "The mask restrict the "
- "classification of the input image to the area where mask pixel values "
- "are greater than zero.");
+ SetParameterDescription("mask",
+ "The mask restrict the "
+ "classification of the input image to the area where mask pixel values "
+ "are greater than zero.");
MandatoryOff("mask");
AddParameter(ParameterType_InputFilename, "model", "Model file");
- SetParameterDescription("model", "A regression model file (produced either by "
- "TrainVectorRegression application or the TrainImagesRegression application).");
+ SetParameterDescription("model",
+ "A regression model file (produced either by "
+ "TrainVectorRegression application or the TrainImagesRegression application).");
AddParameter(ParameterType_InputFilename, "imstat", "Statistics file");
- SetParameterDescription("imstat", "An XML file containing mean and standard"
- " deviation to center and reduce samples before prediction "
- "(produced by the ComputeImagesStatistics application). If this file contains "
- "one more band than the sample size, the last stat of the last band will be"
- "applied to expand the output predicted value.");
+ SetParameterDescription("imstat",
+ "An XML file containing mean and standard"
+ " deviation to center and reduce samples before prediction "
+ "(produced by the ComputeImagesStatistics application). If this file contains "
+ "one more band than the sample size, the last stat of the last band will be"
+ "applied to expand the output predicted value.");
MandatoryOff("imstat");
AddParameter(ParameterType_OutputImage, "out", "Output Image");
- SetParameterDescription( "out", "Output image containing predicted values");
+ SetParameterDescription("out", "Output image containing predicted values");
AddRAMParameter();
- // Doc example parameter settings
+ // Doc example parameter settings
SetDocExampleParameterValue("in", "QB_1_ortho.tif");
SetDocExampleParameterValue("imstat", "EstimateImageStatisticsQB1.xml");
SetDocExampleParameterValue("model", "clsvmModelQB1.svm");
@@ -187,97 +192,88 @@ private:
void DoExecute() override
{
// Load input image
- FloatVectorImageType::Pointer inImage = GetParameterImage("in");
+ auto inImage = GetParameterImage("in");
inImage->UpdateOutputInformation();
unsigned int nbFeatures = inImage->GetNumberOfComponentsPerPixel();
// Load svm model
otbAppLogINFO("Loading model");
- m_Model = MachineLearningModelFactoryType::CreateMachineLearningModel(GetParameterString("model"),
- MachineLearningModelFactoryType::ReadMode);
+ auto model = MachineLearningModelFactoryType::CreateMachineLearningModel(GetParameterString("model"), MachineLearningModelFactoryType::ReadMode);
- if (m_Model.IsNull())
- {
+ if (model.IsNull())
+ {
otbAppLogFATAL(<< "Error when loading model " << GetParameterString("model") << " : unsupported model type");
- }
+ }
- m_Model->Load(GetParameterString("model"));
- m_Model->SetRegressionMode(true);
+ model->Load(GetParameterString("model"));
+ model->SetRegressionMode(true);
otbAppLogINFO("Model loaded");
// Classify
- m_ClassificationFilter = ClassificationFilterType::New();
- m_ClassificationFilter->SetModel(m_Model);
-
- FloatImageType::Pointer outputImage = m_ClassificationFilter->GetOutput();
+ auto classificationFilter = ClassificationFilterType::New();
+ classificationFilter->SetModel(model);
+
+ auto outputImage = classificationFilter->GetOutput();
+ RescalerType::Pointer rescaler;
+ OutputRescalerType::Pointer outRescaler;
// Normalize input image if asked
- if(IsParameterEnabled("imstat") )
- {
+ if (IsParameterEnabled("imstat"))
+ {
otbAppLogINFO("Input image normalization activated.");
// Normalize input image (optional)
- StatisticsReader::Pointer statisticsReader = StatisticsReader::New();
- MeasurementType meanMeasurementVector;
- MeasurementType stddevMeasurementVector;
- m_Rescaler = RescalerType::New();
+ auto statisticsReader = StatisticsReader::New();
+ MeasurementType meanMeasurementVector;
+ MeasurementType stddevMeasurementVector;
+ rescaler = RescalerType::New();
// Load input image statistics
statisticsReader->SetFileName(GetParameterString("imstat"));
meanMeasurementVector = statisticsReader->GetStatisticVectorByName("mean");
stddevMeasurementVector = statisticsReader->GetStatisticVectorByName("stddev");
- otbAppLogINFO( "mean used: " << meanMeasurementVector );
- otbAppLogINFO( "standard deviation used: " << stddevMeasurementVector );
+ otbAppLogINFO("mean used: " << meanMeasurementVector);
+ otbAppLogINFO("standard deviation used: " << stddevMeasurementVector);
if (meanMeasurementVector.Size() == nbFeatures + 1)
- {
- double outMean = meanMeasurementVector[nbFeatures];
+ {
+ double outMean = meanMeasurementVector[nbFeatures];
double outStdDev = stddevMeasurementVector[nbFeatures];
- meanMeasurementVector.SetSize(nbFeatures,false);
- stddevMeasurementVector.SetSize(nbFeatures,false);
- m_OutRescaler = OutputRescalerType::New();
- m_OutRescaler->SetInput(m_ClassificationFilter->GetOutput());
- m_OutRescaler->GetFunctor().SetA(outStdDev);
- m_OutRescaler->GetFunctor().SetB(outMean);
- outputImage = m_OutRescaler->GetOutput();
- }
+ meanMeasurementVector.SetSize(nbFeatures, false);
+ stddevMeasurementVector.SetSize(nbFeatures, false);
+ outRescaler = OutputRescalerType::New();
+ outRescaler->SetInput(classificationFilter->GetOutput());
+ outRescaler->GetFunctor().SetA(outStdDev);
+ outRescaler->GetFunctor().SetB(outMean);
+ outputImage = outRescaler->GetOutput();
+ }
else if (meanMeasurementVector.Size() != nbFeatures)
- {
- otbAppLogFATAL("Wrong number of components in statistics file : "<<meanMeasurementVector.Size());
- }
-
+ {
+ otbAppLogFATAL("Wrong number of components in statistics file : " << meanMeasurementVector.Size());
+ }
+
// Rescale vector image
- m_Rescaler->SetScale(stddevMeasurementVector);
- m_Rescaler->SetShift(meanMeasurementVector);
- m_Rescaler->SetInput(inImage);
+ rescaler->SetScale(stddevMeasurementVector);
+ rescaler->SetShift(meanMeasurementVector);
+ rescaler->SetInput(inImage);
- m_ClassificationFilter->SetInput(m_Rescaler->GetOutput());
- }
+ classificationFilter->SetInput(rescaler->GetOutput());
+ }
else
- {
+ {
otbAppLogINFO("Input image normalization deactivated.");
- m_ClassificationFilter->SetInput(inImage);
- }
-
+ classificationFilter->SetInput(inImage);
+ }
- if(IsParameterEnabled("mask"))
- {
+ if (IsParameterEnabled("mask"))
+ {
otbAppLogINFO("Using input mask");
// Load mask image and cast into LabeledImageType
- MaskImageType::Pointer inMask = GetParameterUInt8Image("mask");
-
- m_ClassificationFilter->SetInputMask(inMask);
- }
+ auto inMask = GetParameterUInt8Image("mask");
+ classificationFilter->SetInputMask(inMask);
+ }
SetParameterOutputImage<FloatImageType>("out", outputImage);
-
+ RegisterPipeline();
}
-
- ClassificationFilterType::Pointer m_ClassificationFilter;
- ModelPointerType m_Model;
- RescalerType::Pointer m_Rescaler;
- OutputRescalerType::Pointer m_OutRescaler;
-
};
-
-
}
}