/*========================================================================= * * Copyright NumFOCUS * * 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 * * https://www.apache.org/licenses/LICENSE-2.0.txt * * 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. * *=========================================================================*/ #ifndef itkMahalanobisDistanceThresholdImageFunction_hxx #define itkMahalanobisDistanceThresholdImageFunction_hxx namespace itk { template MahalanobisDistanceThresholdImageFunction::MahalanobisDistanceThresholdImageFunction() : m_Threshold(0.0) , m_MahalanobisDistanceMembershipFunction(MahalanobisDistanceFunctionType::New()) {} template void MahalanobisDistanceThresholdImageFunction::SetMean(const MeanVectorType & mean) { // Cache the mean m_Mean = mean; // Set the mean on the membership function typename MahalanobisDistanceFunctionType::MeanVectorType m; NumericTraits::SetLength(m, mean.size()); for (unsigned int i = 0; i < mean.size(); ++i) { m[i] = mean[i]; } m_MahalanobisDistanceMembershipFunction->SetMean(m); } template void MahalanobisDistanceThresholdImageFunction::SetCovariance( const CovarianceMatrixType & covariance) { // Cache the covariance m_Covariance = covariance; // Set the covariance on the membership function typename MahalanobisDistanceFunctionType::CovarianceMatrixType c; c = covariance; m_MahalanobisDistanceMembershipFunction->SetCovariance(c); } template bool MahalanobisDistanceThresholdImageFunction::Evaluate(const PointType & point) const { IndexType index; this->ConvertPointToNearestIndex(point, index); return (this->EvaluateAtIndex(index)); } template bool MahalanobisDistanceThresholdImageFunction::EvaluateAtContinuousIndex( const ContinuousIndexType & index) const { IndexType nindex; this->ConvertContinuousIndexToNearestIndex(index, nindex); return this->EvaluateAtIndex(nindex); } template bool MahalanobisDistanceThresholdImageFunction::EvaluateAtIndex(const IndexType & index) const { double mahalanobisDistance = this->EvaluateDistanceAtIndex(index); return (mahalanobisDistance <= m_Threshold); } template double MahalanobisDistanceThresholdImageFunction::EvaluateDistance(const PointType & point) const { IndexType index; this->ConvertPointToNearestIndex(point, index); const double mahalanobisDistance = this->EvaluateDistanceAtIndex(index); return mahalanobisDistance; } template double MahalanobisDistanceThresholdImageFunction::EvaluateDistanceAtIndex( const IndexType & index) const { double mahalanobisDistanceSquared = m_MahalanobisDistanceMembershipFunction->Evaluate(this->GetInputImage()->GetPixel(index)); double mahalanobisDistance; // Deal with cases that are barely negative. // In theory they should never appear, but // they may happen and would produce NaNs // in the std::sqrt if (mahalanobisDistanceSquared < 0.0) { mahalanobisDistance = 0.0; } else { mahalanobisDistance = std::sqrt(mahalanobisDistanceSquared); } return mahalanobisDistance; } template void MahalanobisDistanceThresholdImageFunction::PrintSelf(std::ostream & os, Indent indent) const { Superclass::PrintSelf(os, indent); os << indent << "Threshold: " << m_Threshold << std::endl; os << indent << "Mean: " << m_Mean << std::endl; os << indent << "Covariance: " << m_Covariance << std::endl; os << indent << "MahalanobisDistanceMembershipFunction: " << m_MahalanobisDistanceMembershipFunction << std::endl; } } // end namespace itk #endif