Commit 252e4f2d authored by Arnaud Jaen's avatar Arnaud Jaen

ENH: Add an ImageClassifier application that classifies an image using a model...

ENH: Add an ImageClassifier application that classifies an image using a model obtained with TrainMachineLearningImagesClassifier application.
parent 667f493a
......@@ -38,4 +38,8 @@ IF(OTB_USE_OPENCV)
OTB_CREATE_APPLICATION(NAME TrainMachineLearningImagesClassifier
SOURCES otbTrainMachineLearningImagesClassifier.cxx
LINK_LIBRARIES OTBIO;OTBCommon;OTBBasicFilters;OTBFeatureExtraction;OTBLearning;OTBMachineLearning)
OTB_CREATE_APPLICATION(NAME ImageClassifier
SOURCES otbImageClassifier.cxx
LINK_LIBRARIES OTBIO;OTBCommon;OTBBasicFilters;OTBFeatureExtraction;OTBLearning;OTBMachineLearning)
ENDIF()
/*=========================================================================
Program: ORFEO Toolbox
Language: C++
Date: $Date$
Version: $Revision$
Copyright (c) Centre National d'Etudes Spatiales. All rights reserved.
See OTBCopyright.txt for details.
This software is distributed WITHOUT ANY WARRANTY; without even
the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
PURPOSE. See the above copyright notices for more information.
=========================================================================*/
#include "otbWrapperApplication.h"
#include "otbWrapperApplicationFactory.h"
#include "itkVariableLengthVector.h"
#include "otbChangeLabelImageFilter.h"
#include "otbStandardWriterWatcher.h"
#include "otbStatisticsXMLFileReader.h"
#include "otbShiftScaleVectorImageFilter.h"
#include "otbImageClassificationFilter.h"
#include "otbMultiToMonoChannelExtractROI.h"
#include "otbImageToVectorImageCastFilter.h"
#include "otbMachineLearningModelFactory.h"
namespace otb
{
namespace Wrapper
{
class ImageClassifier : public Application
{
public:
/** Standard class typedefs. */
typedef ImageClassifier Self;
typedef Application Superclass;
typedef itk::SmartPointer<Self> Pointer;
typedef itk::SmartPointer<const Self> ConstPointer;
/** Standard macro */
itkNewMacro(Self);
itkTypeMacro(ImageClassifier, otb::Application);
/** Filters typedef */
typedef itk::VariableLengthVector<FloatVectorImageType::InternalPixelType> MeasurementType;
typedef otb::StatisticsXMLFileReader<MeasurementType> StatisticsReader;
typedef otb::ShiftScaleVectorImageFilter<FloatVectorImageType, FloatVectorImageType> RescalerType;
typedef otb::ImageClassificationFilter<FloatVectorImageType, UInt8ImageType> 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;
private:
void DoInit()
{
SetName("ImageClassifier");
SetDescription("Performs a classification of the input image according to a model file.");
// Documentation
SetDocName("Image Classification");
SetDocLongDescription("This application performs an image classification based on a model file (*.txt extension) produced by the TrainImagesClassifier application. Pixels of the output image will contain the class label decided by the classifier. The input pixels can be optionnaly 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 0 will be classified. The remaining of pixels will be given the label 0 in the output image.");
SetDocLimitations("The input image must have the same type, order and number of bands than the images used to produce the statistics file and the SVM model file. If a statistics file was used during training by the TrainSVMImagesClassifier, it is mandatory to use the same statistics file for classification. If an input mask is used, its size must match the input image size.");
SetDocAuthors("OTB-Team");
SetDocSeeAlso("TrainImagesClassifier, ValidateImagesClassifier, ComputeImagesStatistics");
AddDocTag(Tags::Learning);
AddParameter(ParameterType_InputImage, "in", "Input Image");
SetParameterDescription( "in", "The input image to classify.");
AddParameter(ParameterType_InputImage, "mask", "Input Mask");
SetParameterDescription( "mask", "The mask allows to restrict classification of the input image to the area where mask pixel values are greater than 0.");
MandatoryOff("mask");
AddParameter(ParameterType_InputFilename, "model", "Model file");
SetParameterDescription("model", "A model file (*.txt extension, produced by TrainImagesClassifier application).");
AddParameter(ParameterType_InputFilename, "imstat", "Statistics file");
SetParameterDescription("imstat", "A XML file containing mean and standard deviation to center and reduce samples before classification (produced by ComputeImagesStatistics application).");
MandatoryOff("imstat");
AddParameter(ParameterType_OutputImage, "out", "Output Image");
SetParameterDescription( "out", "Output image containing class labels");
SetParameterOutputImagePixelType( "out", ImagePixelType_uint8);
AddRAMParameter();
// Doc example parameter settings
SetDocExampleParameterValue("in", "QB_1_ortho.tif");
SetDocExampleParameterValue("imstat", "EstimateImageStatisticsQB1.xml");
SetDocExampleParameterValue("model", "clsvmModelQB1.svm");
SetDocExampleParameterValue("out", "clLabeledImageQB1.tif");
}
void DoUpdateParameters()
{
// Nothing to do here : all parameters are independent
}
void DoExecute()
{
// Load input image
FloatVectorImageType::Pointer inImage = GetParameterImage("in");
inImage->UpdateOutputInformation();
// Load svm model
otbAppLogINFO("Loading model");
m_Model = MachineLearningModelFactoryType::CreateMachineLearningModel(GetParameterString("model"),
MachineLearningModelFactoryType::ReadMode);
m_Model->Load(GetParameterString("model"));
otbAppLogINFO("Model loaded");
// Normalize input image (optional)
StatisticsReader::Pointer statisticsReader = StatisticsReader::New();
MeasurementType meanMeasurementVector;
MeasurementType stddevMeasurementVector;
m_Rescaler = RescalerType::New();
// Classify
m_ClassificationFilter = ClassificationFilterType::New();
m_ClassificationFilter->SetModel(m_Model);
// Normalize input image if asked
if(IsParameterEnabled("imstat") )
{
otbAppLogINFO("Input image normalization activated.");
// 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 );
// Rescale vector image
m_Rescaler->SetScale(stddevMeasurementVector);
m_Rescaler->SetShift(meanMeasurementVector);
m_Rescaler->SetInput(inImage);
m_ClassificationFilter->SetInput(m_Rescaler->GetOutput());
}
else
{
otbAppLogINFO("Input image normalization deactivated.");
m_ClassificationFilter->SetInput(inImage);
}
if(IsParameterEnabled("mask"))
{
otbAppLogINFO("Using input mask");
// Load mask image and cast into LabeledImageType
UInt8ImageType::Pointer inMask = GetParameterUInt8Image("mask");
m_ClassificationFilter->SetInputMask(inMask);
}
SetParameterOutputImage<UInt8ImageType>("out", m_ClassificationFilter->GetOutput());
}
ClassificationFilterType::Pointer m_ClassificationFilter;
ModelPointerType m_Model;
RescalerType::Pointer m_Rescaler;
};
}
}
OTB_APPLICATION_EXPORT(otb::Wrapper::ImageClassifier)
......@@ -636,7 +636,7 @@ private:
confMatCalc->SetReferenceLabels(validationLabeledListSample);
confMatCalc->SetProducedLabels(predictedList);
confMatCalc->Update();
confMatCalc->Compute();
otbAppLogINFO("SVM training performances");
LogConfusionMatrix(confMatCalc);
......
......@@ -48,7 +48,7 @@ public:
typedef enum { ReadMode, WriteMode } FileModeType;
/** Create the appropriate MachineLearningModel depending on the particulars of the file. */
static MachineLearningModelTypePointer CreateMachineLearningModel(const char* path, FileModeType mode);
static MachineLearningModelTypePointer CreateMachineLearningModel(const std::string& path, FileModeType mode);
/** Register Built-in factories */
static void RegisterBuiltInFactories();
......
......@@ -31,7 +31,7 @@ namespace otb
template <class TInputValue, class TOutputValue>
typename MachineLearningModel<TInputValue,TOutputValue>::Pointer
MachineLearningModelFactory<TInputValue,TOutputValue>
::CreateMachineLearningModel(const char* path, FileModeType mode)
::CreateMachineLearningModel(const std::string& path, FileModeType mode)
{
RegisterBuiltInFactories();
......
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment