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Forked from Cresson Remi / otbtf
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otbTrainClassifierFromDeepFeatures.cxx 4.38 KiB
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/*=========================================================================
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  Copyright (c) Remi Cresson (IRSTEA). All rights reserved.
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     This software is distributed WITHOUT ANY WARRANTY; without even
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     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
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     PURPOSE.  See the above copyright notices for more information.
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=========================================================================*/
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#include "itkFixedArray.h"
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#include "itkObjectFactory.h"
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// Elevation handler
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#include "otbWrapperElevationParametersHandler.h"
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#include "otbWrapperApplicationFactory.h"
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#include "otbWrapperCompositeApplication.h"
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// Application engine
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#include "otbStandardFilterWatcher.h"
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#include "itkFixedArray.h"
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// TF (used to get the environment variable for the number of inputs)
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#include "otbTensorflowCommon.h"
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namespace otb
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{
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namespace Wrapper
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{
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class TrainClassifierFromDeepFeatures : public CompositeApplication
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{
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public:
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  /** Standard class typedefs. */
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  typedef TrainClassifierFromDeepFeatures              Self;
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  typedef Application                         Superclass;
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  typedef itk::SmartPointer<Self>             Pointer;
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  typedef itk::SmartPointer<const Self>       ConstPointer;
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  /** Standard macro */
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  itkNewMacro(Self);
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  itkTypeMacro(TrainClassifierFromDeepFeatures, otb::Wrapper::CompositeApplication);
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private:
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  //
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  // Add an input source, which includes:
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  // -an input image list
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  // -an input patchsize (dimensions of samples)
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  //
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  void AddAnInputImage(int inputNumber = 0)
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  {
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    inputNumber++;
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    // Create keys and descriptions
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    std::stringstream ss_key_group, ss_desc_group;
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    ss_key_group << "source" << inputNumber;
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    ss_desc_group << "Parameters for source " << inputNumber;
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    // Populate group
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    ShareParameter(ss_key_group.str(), "tfmodel." + ss_key_group.str(), ss_desc_group.str());
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  }
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  void DoInit()
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  {
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  SetName("TrainClassifierFromDeepFeatures");
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  SetDescription("Train a classifier from deep net based features of an image and training vector data.");
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// Documentation SetDocName("TrainClassifierFromDeepFeatures"); SetDocLongDescription("See TrainImagesClassifier application"); SetDocLimitations("None"); SetDocAuthors("Remi Cresson"); SetDocSeeAlso(" "); ClearApplications(); // Add applications AddApplication("TrainImagesClassifier", "train" , "Train images classifier" ); AddApplication("TensorflowModelServe" , "tfmodel", "Serve the TF model" ); // Model shared parameters AddAnInputImage(); for (int i = 1; i < tf::GetNumberOfSources() ; i++) { AddAnInputImage(i); } ShareParameter("model", "tfmodel.model", "Deep net model parameters", "Deep net model parameters"); ShareParameter("output", "tfmodel.output", "Deep net outputs parameters", "Deep net outputs parameters"); ShareParameter("optim", "tfmodel.optim", "This group of parameters allows optimization of processing time", "This group of parameters allows optimization of processing time"); // Train shared parameters ShareParameter("vd" , "train.io.vd" , "Input vector data list" , "Input vector data list" ); ShareParameter("valid" , "train.io.valid" , "Validation vector data list" , "Validation vector data list" ); ShareParameter("out" , "train.io.out" , "Output model" , "Output model" ); ShareParameter("confmatout" , "train.io.confmatout" , "Output model confusion matrix" , "Output model confusion matrix" ); // Shared parameter groups ShareParameter("sample" , "train.sample" , "Training and validation samples parameters" , "Training and validation samples parameters" ); ShareParameter("elev" , "train.elev" , "Elevation management" , "Elevation management" ); ShareParameter("classifier" , "train.classifier" , "Classifier" , "Classifier" ); ShareParameter("rand" , "train.rand" , "User defined rand seed" , "User defined rand seed" ); } void DoUpdateParameters() { UpdateInternalParameters("train"); } void DoExecute() { ExecuteInternal("tfmodel"); GetInternalApplication("train")->AddImageToParameterInputImageList("io.il", GetInternalApplication("tfmodel")->GetParameterOutputImage("out")); UpdateInternalParameters("train"); ExecuteInternal("train"); } // DOExecute() void AfterExecuteAndWriteOutputs() { // Nothing to do } }; } } OTB_APPLICATION_EXPORT( otb::Wrapper::TrainClassifierFromDeepFeatures )