/*========================================================================= Copyright (c) Remi Cresson (IRSTEA). All rights reserved. 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"); 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"); } // DOExecute() void AfterExecuteAndWriteOutputs() { // Nothing to do } }; } // namespace Wrapper } // namespace otb OTB_APPLICATION_EXPORT( otb::Wrapper::ImageClassifierFromDeepFeatures )