otbImageClassifier.cxx 11.6 KB
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/*
 * Copyright (C) 2005-2017 Centre National d'Etudes Spatiales (CNES)
 *
 * This file is part of Orfeo Toolbox
 *
 *     https://www.orfeo-toolbox.org/
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
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#include "otbWrapperApplication.h"
#include "otbWrapperApplicationFactory.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 */
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  //typedef UInt16ImageType                                                                    OutputImageType;
  typedef Int32ImageType                                                                       OutputImageType;
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  typedef UInt8ImageType                                                                       MaskImageType;
  typedef itk::VariableLengthVector<FloatVectorImageType::InternalPixelType>                   MeasurementType;
  typedef otb::StatisticsXMLFileReader<MeasurementType>                                        StatisticsReader;
  typedef otb::ShiftScaleVectorImageFilter<FloatVectorImageType, FloatVectorImageType>         RescalerType;
  typedef otb::ImageClassificationFilter<FloatVectorImageType, OutputImageType, 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;
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  typedef ClassificationFilterType::ConfidenceImageType                                        ConfidenceImageType;
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  typedef ClassificationFilterType::ProbaImageType                                             ProbaImageType;
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protected:

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  ~ImageClassifier() override
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    {
    MachineLearningModelFactoryType::CleanFactories();
    }

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private:
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  void DoInit() override
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  {
    SetName("ImageClassifier");
    SetDescription("Performs a classification of the input image according to a model file.");

    // Documentation
    SetDocName("Image Classification");
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    SetDocLongDescription("This application performs an image classification based on a model file produced by the TrainImagesClassifier application. Pixels of the output image will contain the class labels decided by the classifier (maximal class label = 65535). 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 0 will be classified. By default, the remaining of pixels will be given the label 0 in the output image.");
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    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 TrainImagesClassifier, 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.");
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    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");
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    SetParameterDescription( "mask", "The mask allows restricting classification of the input image to the area where mask pixel values are greater than 0.");
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    MandatoryOff("mask");

    AddParameter(ParameterType_InputFilename, "model", "Model file");
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    SetParameterDescription("model", "A model file (produced by TrainImagesClassifier application, maximal class label = 65535).");
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    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");

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    AddParameter(ParameterType_Int, "nodatalabel", "Label mask value");
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    SetParameterDescription("nodatalabel", "By default, "
      "hidden pixels will have the assigned label 0 in the output image. "
      "It's possible to define the label mask by another value, "
      "but be careful to not take a label from another class (max. 65535).");

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    SetDefaultParameterInt("nodatalabel", 0);
    MandatoryOff("nodatalabel");

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    AddParameter(ParameterType_OutputImage, "out",  "Output Image");
    SetParameterDescription( "out", "Output image containing class labels");
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    SetDefaultOutputPixelType( "out", ImagePixelType_uint8);
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    AddParameter(ParameterType_OutputImage, "confmap",  "Confidence map");
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    SetParameterDescription( "confmap", "Confidence map of the produced classification. The confidence index depends on the model : \n"
      "  - LibSVM : difference between the two highest probabilities (needs a model with probability estimates, so that classes probabilities can be computed for each sample)\n"
      "  - OpenCV\n"
      "    * Boost : sum of votes\n"
      "    * DecisionTree : (not supported)\n"
      "    * GradientBoostedTree : (not supported)\n"
      "    * KNearestNeighbors : number of neighbors with the same label\n"
      "    * NeuralNetwork : difference between the two highest responses\n"
      "    * NormalBayes : (not supported)\n"
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      "    * RandomForest : Confidence (proportion of votes for the majority class). Margin (normalized difference of the votes of the 2 majority classes) is not available for now.\n"
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      "    * SVM : distance to margin (only works for 2-class models)\n");
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    SetDefaultOutputPixelType( "confmap", ImagePixelType_double);
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    MandatoryOff("confmap");

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    AddParameter(ParameterType_OutputImage,"probamap", "Probability map");
    SetParameterDescription("probamap","");
    SetDefaultOutputPixelType("probamap",ImagePixelType_uint16);
    MandatoryOff("probamap");
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    AddRAMParameter();

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    AddParameter(ParameterType_Int, "classe", "number of output classes");
    SetDefaultParameterInt("classe", 20);
   
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   // Doc example parameter settings
    SetDocExampleParameterValue("in", "QB_1_ortho.tif");
    SetDocExampleParameterValue("imstat", "EstimateImageStatisticsQB1.xml");
    SetDocExampleParameterValue("model", "clsvmModelQB1.svm");
    SetDocExampleParameterValue("out", "clLabeledImageQB1.tif");
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    SetOfficialDocLink();
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  }

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  void DoUpdateParameters() override
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  {
    // Nothing to do here : all parameters are independent
  }

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  void DoExecute() override
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  {
    // Load input image
    FloatVectorImageType::Pointer inImage = GetParameterImage("in");
    inImage->UpdateOutputInformation();

    // Load svm model
    otbAppLogINFO("Loading model");
    m_Model = MachineLearningModelFactoryType::CreateMachineLearningModel(GetParameterString("model"),
                                                                          MachineLearningModelFactoryType::ReadMode);
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    if (m_Model.IsNull())
      {
      otbAppLogFATAL(<< "Error when loading model " << GetParameterString("model") << " : unsupported model type");
      }

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    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);
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    m_ClassificationFilter->SetDefaultLabel(GetParameterInt("nodatalabel"));

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    // 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
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      MaskImageType::Pointer inMask = GetParameterUInt8Image("mask");
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      m_ClassificationFilter->SetInputMask(inMask);
      }
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    SetParameterOutputImage<OutputImageType>("out", m_ClassificationFilter->GetOutput());
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    // output confidence map
    if (IsParameterEnabled("confmap") && HasValue("confmap"))
      {
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      m_ClassificationFilter->SetUseConfidenceMap(true);
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      if (m_Model->HasConfidenceIndex())
        {
        SetParameterOutputImage<ConfidenceImageType>("confmap",m_ClassificationFilter->GetOutputConfidence());
        }
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      else
        {
        otbAppLogWARNING("Confidence map requested but the classifier doesn't support it!");
        this->DisableParameter("confmap");
        }
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      }
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    if(IsParameterEnabled("probamap") && HasValue("probamap"))
      {
      m_ClassificationFilter->SetUseProbaMap(true);
      if(m_Model->HasProbaIndex())
	{
	  m_ClassificationFilter->SetNumberOfClasses(GetParameterInt("classe"));
	  SetParameterOutputImage<ProbaImageType>("probamap",m_ClassificationFilter->GetOutputProba());
	}
      else
	{
	  otbAppLogWARNING("Probability map requested but the classifier doesn't support it!");
	  this->DisableParameter("probamap");
	}
      }
    
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  }

  ClassificationFilterType::Pointer m_ClassificationFilter;
  ModelPointerType m_Model;
  RescalerType::Pointer m_Rescaler;
};


}
}

OTB_APPLICATION_EXPORT(otb::Wrapper::ImageClassifier)