otbImageClassifierFromDeepFeatures.cxx 4.12 KiB
/*=========================================================================
     Copyright (c) 2018-2019 IRSTEA
     Copyright (c) 2020-2021 INRAE
     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 "itkFixedArray.h"
#include "itkObjectFactory.h"
// Elevation handler
#include "otbWrapperElevationParametersHandler.h"
#include "otbWrapperApplicationFactory.h"
#include "otbWrapperCompositeApplication.h"
// Application engine
#include "otbStandardFilterWatcher.h"
#include "itkFixedArray.h"
// TF (used to get the environment variable for the number of inputs)
#include "otbTensorflowCommon.h"
namespace otb
namespace Wrapper
class ImageClassifierFromDeepFeatures : public CompositeApplication
public:
  /** Standard class typedefs. */
  typedef ImageClassifierFromDeepFeatures              Self;
  typedef Application                         Superclass;
  typedef itk::SmartPointer<Self>             Pointer;
  typedef itk::SmartPointer<const Self>       ConstPointer;
  /** Standard macro */
  itkNewMacro(Self);
  itkTypeMacro(ImageClassifierFromDeepFeatures, otb::Wrapper::CompositeApplication);
private:
  // Add an input source, which includes:
  // -an input image list
  // -an input patchsize (dimensions of samples)
  void AddAnInputImage(int inputNumber = 0)
    inputNumber++;
    // Create keys and descriptions
    std::stringstream ss_key_group, ss_desc_group;
    ss_key_group << "source" << inputNumber;
    ss_desc_group << "Parameters for source " << inputNumber;
    // Populate group
    ShareParameter(ss_key_group.str(), "tfmodel." + ss_key_group.str(), ss_desc_group.str());
  void DoInit()
    SetName("ImageClassifierFromDeepFeatures");
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SetDescription("Classify image using features from a deep net and an OTB machine learning classification model"); // Documentation SetDocLongDescription("See ImageClassifier application"); SetDocLimitations("None"); SetDocAuthors("Remi Cresson"); SetDocSeeAlso(" "); AddDocTag(Tags::Learning); ClearApplications(); // Add applications AddApplication("ImageClassifier", "classif", "Images classifier" ); AddApplication("TensorflowModelServe", "tfmodel", "Serve the TF model" ); // Model shared parameters AddAnInputImage(); for (int i = 1; i < tf::GetNumberOfSources() ; i++) { AddAnInputImage(i); } ShareParameter("deepmodel", "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"); // Classify shared parameters ShareParameter("model" , "classif.model" , "Model file" , "Model file" ); ShareParameter("imstat" , "classif.imstat" , "Statistics file" , "Statistics file" ); ShareParameter("nodatalabel", "classif.nodatalabel", "Label mask value" , "Label mask value" ); ShareParameter("out" , "classif.out" , "Output image" , "Output image" ); ShareParameter("confmap" , "classif.confmap" , "Confidence map image", "Confidence map image"); ShareParameter("ram" , "classif.ram" , "Ram" , "Ram" ); } void DoUpdateParameters() { UpdateInternalParameters("classif"); } void DoExecute() { ExecuteInternal("tfmodel"); GetInternalApplication("classif")->SetParameterInputImage("in", GetInternalApplication("tfmodel")->GetParameterOutputImage("out")); UpdateInternalParameters("classif"); ExecuteInternal("classif"); } }; } // namespace Wrapper } // namespace otb OTB_APPLICATION_EXPORT( otb::Wrapper::ImageClassifierFromDeepFeatures )