diff --git a/Plugins/org.mitk.gui.qt.activelearning/src/internal/QmitkActiveLearning.cpp b/Plugins/org.mitk.gui.qt.activelearning/src/internal/QmitkActiveLearning.cpp index 1f164fa1d5..11c7b9c8b6 100644 --- a/Plugins/org.mitk.gui.qt.activelearning/src/internal/QmitkActiveLearning.cpp +++ b/Plugins/org.mitk.gui.qt.activelearning/src/internal/QmitkActiveLearning.cpp @@ -1,792 +1,855 @@ /*=================================================================== The Medical Imaging Interaction Toolkit (MITK) Copyright (c) German Cancer Research Center, Division of Medical and Biological Informatics. All rights reserved. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See LICENSE.txt or http://www.mitk.org for details. ===================================================================*/ // Blueberry #include #include // Qt #include #include #include #include // Qmitk #include "QmitkActiveLearning.h" #include // MITK #include #include #include +#include #include #include #include #include #include #include // ITK #include #include #include #include #include #include #include #include +typedef double FeaturePixelType; + // Returns true if list has at least one entry and all entries are valid mitk::Images, otherwise false static bool SelectionAllImages(const QList& nodes) { if (nodes.empty()) { return false; } for (const auto& node : nodes) { if(!(node.IsNotNull() && dynamic_cast(node->GetData()) != nullptr)) return false; } return true; } // QColor to mitk::Color static mitk::Color QColorToMitkColor(const QColor& qcolor) { mitk::Color color; color.SetRed((float)qcolor.red() / 255); color.SetGreen((float)qcolor.green() / 255); color.SetBlue((float)qcolor.blue() / 255); return color; } // For debugging static void PrintAllLabels(mitk::LabelSetImage* image) { for (auto it=image->GetActiveLabelSet()->IteratorBegin(); it!=image->GetActiveLabelSet()->IteratorConstEnd(); ++it) { MITK_INFO << "Key: " << it->first << " - Name: " << it->second->GetName() << " - Value: " << it->second->GetValue() << " - Color: " << it->second->GetColor(); } } // Make values of labels a consistent range static void FillLabelValues(mitk::LabelSetImage* image) { int value(0); for (auto it=image->GetActiveLabelSet()->IteratorBegin(); it!=image->GetActiveLabelSet()->IteratorConstEnd(); ++it) { it->second->SetValue(value); value++; } image->GetActiveLabelSet()->SetActiveLabel(0); } // Fill image with zeros static void FillWithZeros(mitk::Image* image) { unsigned int size = image->GetPixelType().GetSize(); for (unsigned int i=0; iGetDimension(); i++) { size *= image->GetDimension(i); } for (unsigned int t=0; tGetTimeSteps(); t++) { mitk::ImageWriteAccessor accessor(image, image->GetVolumeData(0)); memset(accessor.GetData(), 0, size); } } /* ================================================================== * FEATURES * =============================================================== */ template -static void GaussianSmoothing(const itk::Image* inputImage, float sigma, mitk::Image::Pointer outputImage) +static void GaussianSmoothing(const itk::Image* inputImage, double sigma, mitk::Image::Pointer outputImage) { typedef itk::Image ImageType; - auto filter = itk::DiscreteGaussianImageFilter::New(); + typedef itk::Image FeatureImageType; + auto filter = itk::DiscreteGaussianImageFilter::New(); filter->SetInput(inputImage); filter->SetVariance(sigma*sigma); filter->Update(); mitk::GrabItkImageMemory(filter->GetOutput(), outputImage); } template -static void GaussianGradientMagnitude(const itk::Image* inputImage, float sigma, mitk::Image::Pointer outputImage) +static void GaussianGradientMagnitude(const itk::Image* inputImage, double sigma, mitk::Image::Pointer outputImage) { typedef itk::Image ImageType; - auto filter = itk::GradientMagnitudeRecursiveGaussianImageFilter::New(); + typedef itk::Image FeatureImageType; + auto filter = itk::GradientMagnitudeRecursiveGaussianImageFilter::New(); filter->SetInput(inputImage); filter->SetSigma(sigma); filter->Update(); mitk::GrabItkImageMemory(filter->GetOutput(), outputImage); } template -static void LaplacianOfGaussian(const itk::Image* inputImage, float sigma, mitk::Image::Pointer outputImage) +static void LaplacianOfGaussian(const itk::Image* inputImage, double sigma, mitk::Image::Pointer outputImage) { typedef itk::Image ImageType; - auto filter = itk::LaplacianRecursiveGaussianImageFilter::New(); + typedef itk::Image FeatureImageType; + auto filter = itk::LaplacianRecursiveGaussianImageFilter::New(); filter->SetInput(inputImage); filter->SetSigma(sigma); filter->Update(); mitk::GrabItkImageMemory(filter->GetOutput(), outputImage); } -template -static void StructureTensorEigenvalues(const itk::Image* inputImage, float sigma, std::vector outputImages) -{ - typedef itk::Image ImageType; - auto filter = itk::StructureTensorEigenvalueImageFilter::New(); - filter->SetInput(inputImage); - filter->SetInnerScale(sigma); - filter->SetOuterScale(sigma); - filter->Update(); - for (unsigned int i=0; iGetNumberOfOutputs(); i++) - { - mitk::GrabItkImageMemory(filter->GetOutput(i), outputImages[i]); - } -} - -template -static void HessianEigenvalues(const itk::Image* inputImage, float sigma, std::vector outputImages) -{ - typedef itk::Image ImageType; - typedef itk::Image, imageDimension> TensorImageType; - auto filter = itk::HessianRecursiveGaussianImageFilter::New(); - - ImageType::Pointer o1, o2, o3; - o1->Allocate(); - o2->Allocate(); - o3->Allocate(); - - filter->SetInput(inputImage); - filter->SetSigma(sigma); - filter->Update(); - TensorImageType::Pointer tensorImage = filter->GetOutput(); - - itk::ImageRegionIterator tensorIt(tensorImage, tensorImage->GetLargestPossibleRegion()); - itk::ImageRegionIterator o1It(o1, o1->GetLargestPossibleRegion()); - itk::ImageRegionIterator o2It(o2, o2->GetLargestPossibleRegion()); - itk::ImageRegionIterator o3It(o3, o3->GetLargestPossibleRegion()); - - while (!tensorIt.IsAtEnd()) - { - itk::SymmetricSecondRankTensor::EigenValue - - for (unsigned int i=0; iGetNumberOfOutputs(); i++) - { - mitk::GrabItkImageMemory(filter->GetOutput(i), outputImages[i]); - } -} +//template +//static void StructureTensorEigenvalues(const itk::Image* inputImage, float sigma, std::vector outputImages) +//{ +// typedef itk::Image ImageType; +// auto filter = itk::StructureTensorEigenvalueImageFilter::New(); +// filter->SetInput(inputImage); +// filter->SetInnerScale(sigma); +// filter->SetOuterScale(sigma); +// filter->Update(); +// for (unsigned int i=0; iGetNumberOfOutputs(); i++) +// { +// mitk::GrabItkImageMemory(filter->GetOutput(i), outputImages[i]); +// } +//} + +//template +//static void HessianEigenvalues(const itk::Image* inputImage, float sigma, std::vector outputImages) +//{ +// typedef itk::Image ImageType; +// typedef itk::Image, imageDimension> TensorImageType; +// auto filter = itk::HessianRecursiveGaussianImageFilter::New(); + +// ImageType::Pointer o1, o2, o3; +// o1->Allocate(); +// o2->Allocate(); +// o3->Allocate(); + +// filter->SetInput(inputImage); +// filter->SetSigma(sigma); +// filter->Update(); +// TensorImageType::Pointer tensorImage = filter->GetOutput(); + +// itk::ImageRegionIterator tensorIt(tensorImage, tensorImage->GetLargestPossibleRegion()); +// itk::ImageRegionIterator o1It(o1, o1->GetLargestPossibleRegion()); +// itk::ImageRegionIterator o2It(o2, o2->GetLargestPossibleRegion()); +// itk::ImageRegionIterator o3It(o3, o3->GetLargestPossibleRegion()); + +// while (!tensorIt.IsAtEnd()) +// { +// itk::SymmetricSecondRankTensor::EigenValue + +// for (unsigned int i=0; iGetNumberOfOutputs(); i++) +// { +// mitk::GrabItkImageMemory(filter->GetOutput(i), outputImages[i]); +// } +//} /* ================================================================== * PUBLIC SLOTS * =============================================================== */ void ActiveLearning::Initialize() { // Get selected nodes and check again if these are all images m_Nodes = this->GetDataManagerSelection(); if (!SelectionAllImages(m_Nodes)) return; m_Controls.m_InitializePushButton->setDisabled(true); // Set names to the label (again) QString nameList = QString::fromStdString(m_Nodes[0]->GetName()); if (m_Nodes.length() >= 2) { for (int i=1; i"); nameList += QString::fromStdString(m_Nodes[i]->GetName()); } } m_Controls.m_InitializeLabel->setText(nameList); // ======================================= // ANNOTATION IMAGE // ======================================= m_AnnotationImage = mitk::LabelSetImage::New(); try { auto referenceImage = dynamic_cast(m_Nodes[0]->GetData()); m_AnnotationImage->Initialize(referenceImage); } catch (mitk::Exception& e) { MITK_ERROR << "Exception caught: " << e.GetDescription(); QMessageBox::information(m_Parent, "Error", "Could not initialize annotation image"); return; } FillWithZeros(m_AnnotationImage); m_AnnotationNode = mitk::DataNode::New(); m_AnnotationNode->SetData(m_AnnotationImage); m_AnnotationNode->SetName("Labels"); - m_AnnotationNode->SetColor(1., 1., 1.); + m_AnnotationNode->SetColor(0., 0., 0.); // m_AnnotationNode->SetBoolProperty("helper object", true); m_AnnotationImage->GetExteriorLabel()->SetProperty("name.parent", mitk::StringProperty::New(m_Nodes[0]->GetName().c_str())); m_AnnotationImage->GetExteriorLabel()->SetProperty("name.image", mitk::StringProperty::New("Labels")); this->GetDataStorage()->Add(m_AnnotationNode, m_Nodes[0]); // ======================================= // PREDICTION IMAGE // ======================================= m_PredictionImage = mitk::Image::New(); try { mitk::Image::Pointer referenceImage = dynamic_cast(m_Nodes[0]->GetData()); m_PredictionImage->Initialize(referenceImage); } catch (mitk::Exception& e) { MITK_ERROR << "Exception caught: " << e.GetDescription(); QMessageBox::information(m_Parent, "Error", "Could not initialize prediction image"); return; } FillWithZeros(m_PredictionImage); m_PredictionNode = mitk::DataNode::New(); m_PredictionNode->SetData(m_PredictionImage); m_PredictionNode->SetName("Predictions"); - m_PredictionNode->SetColor(1., 1., 1.); + m_PredictionNode->SetColor(0., 0., 0.); // m_PredictionNode->SetBoolProperty("helper object", true); // m_PredictionImage->GetExteriorLabel()->SetProperty("name.parent", mitk::StringProperty::New(nodes[0]->GetName().c_str())); // m_PredictionImage->GetExteriorLabel()->SetProperty("name.image", mitk::StringProperty::New("Predictions")); this->GetDataStorage()->Add(m_PredictionNode, m_Nodes[0]); // ======================================= // SEGMENTATION IMAGE // ======================================= m_SegmentationImage = mitk::LabelSetImage::New(); try { auto referenceImage = dynamic_cast(m_Nodes[0]->GetData()); m_SegmentationImage->Initialize(referenceImage); } catch (mitk::Exception& e) { MITK_ERROR << "Exception caught: " << e.GetDescription(); QMessageBox::information(m_Parent, "Error", "Could not initialize segmentation image"); return; } FillWithZeros(m_SegmentationImage); m_SegmentationNode = mitk::DataNode::New(); m_SegmentationNode->SetData(m_SegmentationImage); m_SegmentationNode->SetName("Segmentation"); - m_SegmentationNode->SetColor(1., 1., 1.); + m_SegmentationNode->SetColor(0., 0., 0.); // m_PredictionNode->SetBoolProperty("helper object", true); m_SegmentationImage->GetExteriorLabel()->SetProperty("name.parent", mitk::StringProperty::New(m_Nodes[0]->GetName().c_str())); m_SegmentationImage->GetExteriorLabel()->SetProperty("name.image", mitk::StringProperty::New("Segmentation")); this->GetDataStorage()->Add(m_SegmentationNode, m_Nodes[0]); + // Convert input images to FeaturePixelType + for (auto node : m_Nodes) + { + mitk::Image::Pointer image = dynamic_cast(node->GetData()); + auto itkImage = itk::Image::New(); + mitk::CastToItkImage(image, itkImage); + image = mitk::GrabItkImageMemory(itkImage); + node->SetData(image); + } + // Calculate features for (const auto node : m_Nodes) { mitk::Image::Pointer currentImage = dynamic_cast(node->GetData()); QFuture>> future; future = QtConcurrent::run(this, &ActiveLearning::CalculateFeatures, currentImage); auto futureWatcher = new QFutureWatcher>>(); futureWatcher->setFuture(future); connect(futureWatcher, SIGNAL(finished()), this, SLOT(OnInitializationFinished())); m_FeatureCalculationWatchers.push_back(futureWatcher); } // Interactor auto activeLearningLib = us::ModuleRegistry::GetModule("MitkCLActiveLearning"); m_Interactor = mitk::ActiveLearningInteractor::New(); m_Interactor->LoadStateMachine("Paint.xml", activeLearningLib); m_Interactor->SetEventConfig("PaintConfig.xml", activeLearningLib); m_Interactor->SetDataNode(m_AnnotationNode); + // Classifier +// m_Classifier = mitk::VigraRandomForestClassifier::New(); +// m_Classifier->SetTreeCount(50); +// m_Classifier->SetMaximumTreeDepth(10); + // Automatically add first label OnAddLabelPushButtonClicked(); m_Active = true; } /* ================================================================== * PUBLIC * =============================================================== */ ActiveLearning::ActiveLearning() : m_Parent(nullptr), m_AnnotationImage(nullptr), m_AnnotationNode(nullptr), m_PredictionImage(nullptr), m_PredictionNode(nullptr), m_SegmentationImage(nullptr), m_SegmentationNode(nullptr), m_Active(false) { } ActiveLearning::~ActiveLearning() { } void ActiveLearning::CreateQtPartControl( QWidget *parent ) { m_Controls.setupUi(parent); m_Parent = parent; // Label model m_LabelListModel = new QStandardItemModel(0, 3, this); m_Controls.m_LabelTableView->setModel(m_LabelListModel); m_Controls.m_LabelTableView->horizontalHeader()->setDefaultSectionSize(20); m_Controls.m_LabelTableView->verticalHeader()->setDefaultSectionSize(20); NotEditableDelegate* itemDelegate = new NotEditableDelegate(parent); m_Controls.m_LabelTableView->setItemDelegateForColumn(1, itemDelegate); connect(m_Controls.m_LabelTableView, SIGNAL(doubleClicked(QModelIndex)), this, SLOT(OnColorIconDoubleClicked(QModelIndex))); connect(m_Controls.m_LabelTableView->selectionModel(), SIGNAL(selectionChanged(QItemSelection, QItemSelection)), this, SLOT(OnLabelListSelectionChanged(QItemSelection, QItemSelection))); connect(m_LabelListModel, SIGNAL(dataChanged(QModelIndex, QModelIndex)), this, SLOT(OnLabelNameChanged(QModelIndex, QModelIndex))); // Buttons connect(m_Controls.m_InitializePushButton, SIGNAL(clicked()), this, SLOT(Initialize())); connect(m_Controls.m_AddLabelPushButton, SIGNAL(clicked()), this, SLOT(OnAddLabelPushButtonClicked())); connect(m_Controls.m_RemoveLabelPushButton, SIGNAL(clicked()), this, SLOT(OnRemoveLabelPushButtonClicked())); connect(m_Controls.m_PaintToolButton, SIGNAL(clicked()), this, SLOT(OnPaintToolButtonClicked())); connect(m_Controls.m_EraseToolButton, SIGNAL(clicked()), this, SLOT(OnEraseToolButtonClicked())); // Set start configuration m_Controls.m_LabelControlsFrame->setVisible(false); SetInitializeReady(false); } void ActiveLearning::ResetLabels() { // Remove all labels but the first for (auto it=m_AnnotationImage->GetActiveLabelSet()->IteratorBegin(); it!=m_AnnotationImage->GetActiveLabelSet()->IteratorConstEnd(); it++) { if (it->first != 0) { m_AnnotationImage->GetActiveLabelSet()->RemoveLabel(it->first); } } // Fill with labels from list for (int i=0; irowCount(); i++) { QString name = m_LabelListModel->item(i, 2)->text(); m_LabelListModel->item(i, 1)->setText(QString::number(i + 1)); QColor color = m_LabelListModel->item(i)->background().color(); m_AnnotationImage->GetActiveLabelSet()->AddLabel(name.toStdString(), QColorToMitkColor(color)); } } std::vector > ActiveLearning::CalculateFeatures(const mitk::Image::Pointer inputImage) { std::vector> result; // TODO: Get features from preference page std::vector sigmas = {0.7, 1.6}; for (auto sigma : sigmas) { std::stringstream ss; auto gaussImage = mitk::Image::New(); AccessByItk_n(inputImage, GaussianSmoothing, (sigma, gaussImage)); ss << "GaussianSmoothing (" << std::fixed << std::setprecision(2) << sigma << ")"; result.push_back(std::pair(gaussImage, ss.str())); ss.str(""); auto gradMagImage = mitk::Image::New(); AccessByItk_n(inputImage, GaussianGradientMagnitude, (sigma, gradMagImage)); ss << "GaussianGradientMagnitude (" << std::fixed << std::setprecision(2) << sigma << ")"; result.push_back(std::pair(gradMagImage, ss.str())); ss.str(""); auto logImage = mitk::Image::New(); AccessByItk_n(inputImage, LaplacianOfGaussian, (sigma, logImage)); ss << "LaplacianOfGaussian (" << std::fixed << std::setprecision(2) << sigma << ")"; result.push_back(std::pair(logImage, ss.str())); ss.str(""); // auto structImage1 = mitk::Image::New(); // auto structImage2 = mitk::Image::New(); // auto structImage3 = mitk::Image::New(); // std::vector structImages = {structImage1, structImage2, structImage3}; // AccessByItk_n(inputImage, StructureTensorEigenvalues, (sigma, structImages)); // ss << "StructureTensorEigenvalue1 (" << std::fixed << std::setprecision(2) << sigma << ")"; // result.push_back(std::pair(structImage1, ss.str())); // ss.str(""); // ss << "StructureTensorEigenvalue2 (" << std::fixed << std::setprecision(2) << sigma << ")"; // result.push_back(std::pair(structImage2, ss.str())); // ss.str(""); // if (inputImage->GetDimension() == 3) // { // ss << "StructureTensorEigenvalue3 (" << std::fixed << std::setprecision(2) << sigma << ")"; // result.push_back(std::pair(structImage3, ss.str())); // ss.str(""); // } - auto hessianImage1 = mitk::Image::New(); - auto hessianImage2 = mitk::Image::New(); - auto hessianImage3 = mitk::Image::New(); - std::vector hessianImages = {hessianImage1, hessianImage2, hessianImage3}; - AccessByItk_n(inputImage, HessianEigenvalues, (sigma, hessianImages)); - ss << "HessianEigenvalue1 (" << std::fixed << std::setprecision(2) << sigma << ")"; - result.push_back(std::pair(hessianImage1, ss.str())); - ss.str(""); - ss << "HessianEigenvalue2 (" << std::fixed << std::setprecision(2) << sigma << ")"; - result.push_back(std::pair(hessianImage2, ss.str())); - ss.str(""); - if (inputImage->GetDimension() == 3) +// auto hessianImage1 = mitk::Image::New(); +// auto hessianImage2 = mitk::Image::New(); +// auto hessianImage3 = mitk::Image::New(); +// std::vector hessianImages = {hessianImage1, hessianImage2, hessianImage3}; +// AccessByItk_n(inputImage, HessianEigenvalues, (sigma, hessianImages)); +// ss << "HessianEigenvalue1 (" << std::fixed << std::setprecision(2) << sigma << ")"; +// result.push_back(std::pair(hessianImage1, ss.str())); +// ss.str(""); +// ss << "HessianEigenvalue2 (" << std::fixed << std::setprecision(2) << sigma << ")"; +// result.push_back(std::pair(hessianImage2, ss.str())); +// ss.str(""); +// if (inputImage->GetDimension() == 3) +// { +// ss << "HessianEigenvalue3 (" << std::fixed << std::setprecision(2) << sigma << ")"; +// result.push_back(std::pair(hessianImage3, ss.str())); +// ss.str(""); +// } + } + + return result; +} + +std::pair ActiveLearning::GetTrainingData(const mitk::LabelSetImage::Pointer annotationImage, const std::vector featureImageVector) +{ + // Get indices and labels + std::vector> indices; + std::vector labels; + itk::Image::Pointer annotationImageItk; + mitk::CastToItkImage(annotationImage, annotationImageItk); + + itk::ImageRegionIteratorWithIndex> it(annotationImageItk, annotationImageItk->GetLargestPossibleRegion()); + while (!it.IsAtEnd()) + { + if (it.Get() != 0) { - ss << "HessianEigenvalue3 (" << std::fixed << std::setprecision(2) << sigma << ")"; - result.push_back(std::pair(hessianImage3, ss.str())); - ss.str(""); + indices.push_back(it.GetIndex()); + labels.push_back(it.Get()); } } - return result; + Eigen::MatrixXd trainingData(indices.size(), featureImageVector.size()); + Eigen::VectorXi trainingLabels = Eigen::VectorXi::Map(labels.data(), labels.size()); + + int i = 0; + int j = 0; + for (mitk::Image::Pointer feature : featureImageVector) + { + mitk::ImagePixelReadAccessor access(feature, feature->GetVolumeData()); + for (auto index : indices) + { + trainingData(i, j) = access.GetPixelByIndexSafe(index); + i++; + } + j++; + } + + return std::pair(&trainingLabels, &trainingData); } const std::string ActiveLearning::VIEW_ID = "org.mitk.views.activelearning"; /* ================================================================== * PROTECTED SLOTS * =============================================================== */ void ActiveLearning::OnAddLabelPushButtonClicked() { QString labelName = QString("Label ") + QString::number(m_AnnotationImage->GetActiveLabelSet()->ReverseIteratorBegin()->first + 1); QColor labelColor = Qt::GlobalColor(m_LabelListModel->rowCount() % 12 + 7); // We only want Qt default colors 7 to 18 // Create icon QStandardItem* colorSquare = new QStandardItem; colorSquare->setBackground(labelColor); colorSquare->setEditable(false); QPixmap colorPixmap(20, 20); colorPixmap.fill(labelColor); colorSquare->setIcon(QIcon(colorPixmap)); // Key is the highest existing key + 1 int value = (int)m_AnnotationImage->GetActiveLabelSet()->ReverseIteratorBegin()->first + 1; QStandardItem* valueItem = new QStandardItem(); valueItem->setText(QString::number(value)); // Create label item QStandardItem* label = new QStandardItem(labelName); // Add to image m_AnnotationImage->GetActiveLabelSet()->AddLabel(labelName.toStdString(), QColorToMitkColor(labelColor)); PrintAllLabels(m_AnnotationImage); // Make list and insert QList list; list.append(colorSquare); list.append(valueItem); list.append(label); m_LabelListModel->appendRow(list); // If this is the first label, we activate the paint button // We also have to set the data node color for this one, because for 1 values that color seems to define the rendered color if (m_LabelListModel->rowCount() == 1) { OnPaintToolButtonClicked(); m_AnnotationNode->SetColor(QColorToMitkColor(labelColor)); } // Select newly added label m_Controls.m_LabelTableView->selectRow(m_LabelListModel->rowCount() - 1); } void ActiveLearning::OnRemoveLabelPushButtonClicked() { QItemSelectionModel* selection = m_Controls.m_LabelTableView->selectionModel(); if (selection->hasSelection()) { unsigned int removeIndex = selection->selectedRows().first().row(); QString removeMessage = QString("Remove label '") + m_LabelListModel->item(removeIndex, 2)->text() + QString("'?"); QMessageBox::StandardButton removeReply; removeReply = QMessageBox::question(m_Parent, "Remove Label", removeMessage, QMessageBox::Yes | QMessageBox::No); if (removeReply == QMessageBox::Yes) { // if there are no annotations, reset labels if (m_Interactor->IsUsed()) { std::vector labels; labels.push_back(m_LabelListModel->item(removeIndex, 1)->text().toInt()); m_AnnotationImage->RemoveLabels(labels); m_LabelListModel->removeRow(removeIndex); } else { m_LabelListModel->removeRow(removeIndex); ResetLabels(); } PrintAllLabels(m_AnnotationImage); } } } void ActiveLearning::OnPaintToolButtonClicked() { m_Controls.m_PaintToolButton->setChecked(true); QItemSelectionModel* selection = m_Controls.m_LabelTableView->selectionModel(); int row(0); if (selection->hasSelection()) { row = selection->selectedRows().first().row(); } else { m_Controls.m_LabelTableView->selectRow(0); } m_Interactor->SetPaintingPixelValue(m_LabelListModel->item(row, 1)->text().toInt()); m_AnnotationImage->GetActiveLabelSet()->SetActiveLabel(m_LabelListModel->item(row, 1)->text().toInt()); } void ActiveLearning::OnEraseToolButtonClicked() { m_Controls.m_EraseToolButton->setChecked(true); m_Interactor->SetPaintingPixelValue(0); m_AnnotationImage->GetActiveLabelSet()->SetActiveLabel(0); } void ActiveLearning::OnActivateGuidancePushButtonToggled(bool toggled) { } void ActiveLearning::OnSaveSegmentationPushButtonClicked() { } void ActiveLearning::OnSavePredictionsPushButtonClicked() { } void ActiveLearning::OnExportSegmentationPushButtonClicked() { } void ActiveLearning::OnExportPredictionsPushButtonClicked() { } void ActiveLearning::OnColorIconDoubleClicked(const QModelIndex& index) { // Check if click is really from color icon if (index.column() != 0) { return; } else { // Color change dialog QColor setColor = QColorDialog::getColor(m_LabelListModel->itemFromIndex(index)->background().color(), m_Parent, "Select Label Color"); if (setColor.isValid()) { m_LabelListModel->itemFromIndex(index)->setBackground(setColor); QPixmap colorPixmap(20, 20); colorPixmap.fill(setColor); m_LabelListModel->itemFromIndex(index)->setIcon(QIcon(colorPixmap)); // Set color on label m_AnnotationImage->GetActiveLabelSet()->GetLabel(m_LabelListModel->item(index.row(), 1)->text().toInt())->SetColor(QColorToMitkColor(setColor)); m_AnnotationImage->GetActiveLabelSet()->UpdateLookupTable(m_LabelListModel->item(index.row(), 1)->text().toInt()); PrintAllLabels(m_AnnotationImage); // If this is the label with value 1 we have to change the data node color if (m_LabelListModel->item(index.row(), 1)->text().toInt() == 1) { m_AnnotationNode->SetColor(QColorToMitkColor(setColor)); } } } } void ActiveLearning::OnLabelListSelectionChanged(const QItemSelection& selected, const QItemSelection& /*deselected*/) { if (selected.empty()) return; // This assumes that only one item can be selected (single selection table view) try { int labelValue = m_LabelListModel->item(selected.indexes()[0].row(), 1)->text().toInt(); m_Interactor->SetPaintingPixelValue(labelValue); m_AnnotationImage->GetActiveLabelSet()->SetActiveLabel(labelValue); } catch (...) { m_Interactor->SetPaintingPixelValue(0); m_AnnotationImage->GetActiveLabelSet()->SetActiveLabel(0); } } void ActiveLearning::OnLabelNameChanged(const QModelIndex& topLeft, const QModelIndex& /*bottomRight*/) { auto item = m_LabelListModel->itemFromIndex(topLeft); if (item->column() != 2) return; m_AnnotationImage->GetActiveLabelSet()->GetLabel(m_LabelListModel->item(item->row(), 1)->text().toInt())->SetName(item->text().toStdString()); } void ActiveLearning::OnInitializationFinished() { // Check if all futures are finished for (auto watcher : m_FeatureCalculationWatchers) { if (watcher->isFinished() == false) {return;} } // Empty feature vector m_FeatureImageVector.clear(); // Insert features into feature vector and data storage for (unsigned int i=0; iresult(); for (unsigned int j=0; jSetData(result[j].first); node->SetName(result[j].second); node->SetBoolProperty("helper object", true); node->SetVisibility(false); this->GetDataStorage()->Add(node, m_Nodes[i]); } } // Show controls m_Controls.m_LabelControlsFrame->setVisible(true); m_Controls.m_InitializePushButton->setHidden(true); MITK_INFO << "Features: " << m_FeatureImageVector.size(); // Delete watchers for (auto watcher : m_FeatureCalculationWatchers) { delete watcher; } m_FeatureCalculationWatchers.clear(); } +void ActiveLearning::OnUpdatePredictionsPushButtonClicked() +{ + +} + /* ================================================================== * PROTECTED * =============================================================== */ void ActiveLearning::OnSelectionChanged(berry::IWorkbenchPart::Pointer /*source*/, const QList& nodes) { if (!SelectionAllImages(nodes)) { SetInitializeReady(false); return; } if (nodes.length() >= 2) { // First selection is the reference (could be any other) mitk::Image::Pointer referenceImage = dynamic_cast(nodes[0]->GetData()); mitk::BaseGeometry* referenceGeometry = referenceImage->GetTimeGeometry()->GetGeometryForTimeStep(0); // Adjust for multiple timesteps for (int i=1; i(nodes[i]->GetData()); mitk::BaseGeometry* currentGeometry = currentImage->GetTimeGeometry()->GetGeometryForTimeStep(0); // Adjust for multiple timesteps if (!mitk::Equal(*currentGeometry, *referenceGeometry, mitk::eps, true)) { SetInitializeReady(false); return; } } } // All nodes have the same geometry, allow init SetInitializeReady(true); } void ActiveLearning::SetFocus() { } /* ================================================================== * PRIVATE * =============================================================== */ void ActiveLearning::SetInitializeReady(bool ready) { if (ready) { // get selection, check again just to be sure auto nodes = this->GetDataManagerSelection(); if (!SelectionAllImages(nodes)) return; m_Controls.m_InitializePushButton->setEnabled(true); if (!m_Active) { QString nameList = QString::fromStdString(nodes[0]->GetName()); if (nodes.length() >= 2) { for (int i=1; i"); nameList += QString::fromStdString(nodes[i]->GetName()); } } m_Controls.m_InitializeLabel->setText(nameList); } } else { m_Controls.m_InitializePushButton->setDisabled(true); if (!m_Active) { m_Controls.m_InitializeLabel->setText("Selected images must have matching geometries"); } } } diff --git a/Plugins/org.mitk.gui.qt.activelearning/src/internal/QmitkActiveLearning.h b/Plugins/org.mitk.gui.qt.activelearning/src/internal/QmitkActiveLearning.h index 7db3301237..e0ce932d75 100644 --- a/Plugins/org.mitk.gui.qt.activelearning/src/internal/QmitkActiveLearning.h +++ b/Plugins/org.mitk.gui.qt.activelearning/src/internal/QmitkActiveLearning.h @@ -1,137 +1,148 @@ /*=================================================================== The Medical Imaging Interaction Toolkit (MITK) Copyright (c) German Cancer Research Center, Division of Medical and Biological Informatics. All rights reserved. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See LICENSE.txt or http://www.mitk.org for details. ===================================================================*/ #ifndef ActiveLearning_h #define ActiveLearning_h #include #include #include "ui_QmitkActiveLearningControls.h" // Qt #include #include #include #include // MITK #include #include +#include + +#include /** \brief ActiveLearning \warning This class is not yet documented. Use "git blame" and ask the author to provide basic documentation. \sa QmitkAbstractView \ingroup ${plugin_target}_internal */ // Just a helper class class NotEditableDelegate : public QItemDelegate { Q_OBJECT public: explicit NotEditableDelegate(QObject* parent = nullptr) : QItemDelegate(parent) {} protected: QWidget* createEditor(QWidget*, const QStyleOptionViewItem&, const QModelIndex&) const {return Q_NULLPTR;} }; class ActiveLearning : public QmitkAbstractView { Q_OBJECT public slots: void Initialize(); public: ActiveLearning(); ~ActiveLearning(); void CreateQtPartControl(QWidget *parent) override; - void ResetLabels(); - std::vector> CalculateFeatures(const mitk::Image::Pointer inputImage); + std::pair GetTrainingData(const mitk::LabelSetImage::Pointer annotationImage, const std::vector featureImageVector); + static const std::string VIEW_ID; - QStandardItemModel* m_LabelListModel; protected slots: void OnAddLabelPushButtonClicked(); void OnRemoveLabelPushButtonClicked(); void OnPaintToolButtonClicked(); void OnEraseToolButtonClicked(); void OnActivateGuidancePushButtonToggled(bool toggled); void OnSaveSegmentationPushButtonClicked(); void OnSavePredictionsPushButtonClicked(); void OnExportSegmentationPushButtonClicked(); void OnExportPredictionsPushButtonClicked(); void OnColorIconDoubleClicked(const QModelIndex& index); void OnLabelListSelectionChanged(const QItemSelection& selected, const QItemSelection& /*deselected*/); void OnLabelNameChanged(const QModelIndex& topLeft, const QModelIndex& /*bottomRight*/); + void OnUpdatePredictionsPushButtonClicked(); + void OnInitializationFinished(); protected: void OnSelectionChanged(berry::IWorkbenchPart::Pointer /*source*/, const QList& nodes) override; void SetFocus() override; + void ResetLabels(); + + void SetInitializeReady(bool ready); + Ui::ActiveLearningControls m_Controls; QWidget* m_Parent; mitk::LabelSetImage::Pointer m_AnnotationImage; mitk::DataNode::Pointer m_AnnotationNode; mitk::Image::Pointer m_PredictionImage; mitk::DataNode::Pointer m_PredictionNode; mitk::LabelSetImage::Pointer m_SegmentationImage; mitk::DataNode::Pointer m_SegmentationNode; std::vector m_FeatureImageVector; - std::vector>>*> m_FeatureCalculationWatchers; -private: - - void SetInitializeReady(bool ready); + QStandardItemModel* m_LabelListModel; - bool m_Active; mitk::ActiveLearningInteractor::Pointer m_Interactor; + QList m_Nodes; +// mitk::VigraRandomForestClassifier::Pointer m_Classifier; + +private: + + bool m_Active; + }; #endif // ActiveLearning_h diff --git a/Plugins/org.mitk.gui.qt.activelearning/src/internal/QmitkActiveLearningControls.ui b/Plugins/org.mitk.gui.qt.activelearning/src/internal/QmitkActiveLearningControls.ui index b89037e343..2d6d40291f 100644 --- a/Plugins/org.mitk.gui.qt.activelearning/src/internal/QmitkActiveLearningControls.ui +++ b/Plugins/org.mitk.gui.qt.activelearning/src/internal/QmitkActiveLearningControls.ui @@ -1,456 +1,469 @@ ActiveLearningControls 0 0 352 581 0 0 QmitkTemplate Initialize with selection false false false false <font color="red">Selection must have matching dimensions and transforms</font> true QFrame::NoFrame QFrame::Raised 0 0 0 0 Qt::Vertical QSizePolicy::Fixed 20 10 QFrame::Sunken 1 Qt::Horizontal Qt::Vertical QSizePolicy::Fixed 20 10 QFrame::NoFrame QFrame::Raised 0 0 0 0 Add Label Remove Label Qt::Horizontal 40 20 QFrame::Sunken false QAbstractItemView::SingleSelection QAbstractItemView::SelectRows false true false QFrame::NoFrame QFrame::Raised 0 0 0 0 0 0 0 40 - + :/org.mitk.gui.qt.activelearning/resources/paint_icon_200.png:/org.mitk.gui.qt.activelearning/resources/paint_icon_200.png 25 25 true true 0 0 0 40 ... - + :/org.mitk.gui.qt.activelearning/resources/erase_icon_100.png:/org.mitk.gui.qt.activelearning/resources/erase_icon_100.png 25 25 true true + + + + + 0 + 40 + + + + Update Predictions + + + 0 40 Activate Guidance true Qt::Vertical QSizePolicy::Fixed 20 10 Qt::Horizontal Qt::Vertical QSizePolicy::Fixed 20 10 Save to Data Manager QFrame::NoFrame QFrame::Raised 0 0 0 0 Segmentation Predictions Export QFrame::NoFrame QFrame::Raised 0 0 0 0 Segmentation Predictions Qt::Vertical 20 40 - +