C++ Pointer::GetCount方法代码示例

本文整理汇总了C++中typenamefiltertype::Pointer::GetCount方法的典型用法代码示例。如果您正苦于以下问题：C++ Pointer::GetCount方法的具体用法？C++ Pointer::GetCount怎么用？C++ Pointer::GetCount使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类typenamefiltertype::Pointer的用法示例。

在下文中一共展示了Pointer::GetCount方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C++代码示例。

示例1: CalculateVolumeStatistic

void
  CalculateVolumeStatistic(itk::Image<TPixel, VImageDimension>* itkImage, mitk::Image::Pointer mask, mitk::GIFVolumetricStatistics::FeatureListType & featureList, std::string prefix)
{
  typedef itk::Image<TPixel, VImageDimension> ImageType;
  typedef itk::Image<int, VImageDimension> MaskType;
  typedef itk::LabelStatisticsImageFilter<ImageType, MaskType> FilterType;

  typename MaskType::Pointer maskImage = MaskType::New();
  mitk::CastToItkImage(mask, maskImage);

  typename FilterType::Pointer labelStatisticsImageFilter = FilterType::New();
  labelStatisticsImageFilter->SetInput( itkImage );
  labelStatisticsImageFilter->SetLabelInput(maskImage);
  labelStatisticsImageFilter->Update();

  double volume = labelStatisticsImageFilter->GetCount(1);
  double voxelVolume = 1;
  for (int i = 0; i < (int)(VImageDimension); ++i)
  {
    volume *= itkImage->GetSpacing()[i];
    voxelVolume *= itkImage->GetSpacing()[i];
  }

  featureList.push_back(std::make_pair(prefix + "Voxel Volume", voxelVolume));
  featureList.push_back(std::make_pair(prefix + "Volume (voxel based)", volume));
}

开发者ID:Cdebus，项目名称:MITK，代码行数:26，代码来源:mitkGIFVolumetricStatistics.cpp

示例2: CalculateFirstOrderStatistics

void
  CalculateFirstOrderStatistics(itk::Image<TPixel, VImageDimension>* itkImage, mitk::Image::Pointer mask, mitk::GIFFirstOrderStatistics::FeatureListType & featureList)
{
  typedef itk::Image<TPixel, VImageDimension> ImageType;
  typedef itk::Image<int, VImageDimension> MaskType;
  typedef itk::LabelStatisticsImageFilter<ImageType, MaskType> FilterType;
  typedef typename FilterType::HistogramType HistogramType;
  typedef typename HistogramType::IndexType HIndexType;
  typedef itk::MinimumMaximumImageCalculator<ImageType> MinMaxComputerType;

  typename MaskType::Pointer maskImage = MaskType::New();
  mitk::CastToItkImage(mask, maskImage);

  typename MinMaxComputerType::Pointer minMaxComputer = MinMaxComputerType::New();
  minMaxComputer->SetImage(itkImage);
  minMaxComputer->Compute();
  double imageRange = minMaxComputer->GetMaximum() -  minMaxComputer->GetMinimum();

  typename FilterType::Pointer labelStatisticsImageFilter = FilterType::New();
  labelStatisticsImageFilter->SetInput( itkImage );
  labelStatisticsImageFilter->SetLabelInput(maskImage);
  labelStatisticsImageFilter->SetUseHistograms(true);
  labelStatisticsImageFilter->SetHistogramParameters(256, minMaxComputer->GetMinimum(),minMaxComputer->GetMaximum());
  labelStatisticsImageFilter->Update();

  // --------------- Range --------------------
  double range = labelStatisticsImageFilter->GetMaximum(1) - labelStatisticsImageFilter->GetMinimum(1);
  // --------------- Uniformity, Entropy --------------------
  double count = labelStatisticsImageFilter->GetCount(1);
  double std_dev = labelStatisticsImageFilter->GetSigma(1);
  double mean = labelStatisticsImageFilter->GetMean(1);
  auto histogram = labelStatisticsImageFilter->GetHistogram(1);
  HIndexType index;
  index.SetSize(1);
  double binWidth = histogram->GetBinMax(0, 0) - histogram->GetBinMin(0, 0);

  double uniformity = 0;
  double entropy = 0;
  double squared_sum = 0;
  double kurtosis = 0;
  double mean_absolut_deviation = 0;
  double skewness = 0;

  for (int i = 0; i < (int)(histogram->GetSize(0)); ++i)
  {
    index[0] = i;
    double prob = histogram->GetFrequency(index);
    double voxelValue = histogram->GetBinMin(0, i) +binWidth * 0.5;

    squared_sum += prob * voxelValue*voxelValue;
    kurtosis += prob* (voxelValue - mean) * (voxelValue - mean) * (voxelValue - mean) * (voxelValue - mean);
    skewness += prob* (voxelValue - mean) * (voxelValue - mean) * (voxelValue - mean);
    mean_absolut_deviation = prob* std::abs(voxelValue - mean);

    prob /= count;
    uniformity += prob*prob;
    if (prob > 0)
      entropy += prob * std::log(prob);
  }
  double rms = std::sqrt(squared_sum / count);
  kurtosis = kurtosis / count / (std_dev * std_dev);
  skewness = skewness / count / (std_dev * std_dev * std_dev);
  mean_absolut_deviation = mean_absolut_deviation / count;
  double coveredGrayValueRange = range / imageRange;

  featureList.push_back(std::make_pair("FirstOrder Range",range));
  featureList.push_back(std::make_pair("FirstOrder Uniformity",uniformity));
  featureList.push_back(std::make_pair("FirstOrder Entropy",entropy));
  featureList.push_back(std::make_pair("FirstOrder Energy",squared_sum));
  featureList.push_back(std::make_pair("FirstOrder RMS",rms));
  featureList.push_back(std::make_pair("FirstOrder Kurtosis",kurtosis));
  featureList.push_back(std::make_pair("FirstOrder Skewness",skewness));
  featureList.push_back(std::make_pair("FirstOrder Mean absolute deviation",mean_absolut_deviation));
  featureList.push_back(std::make_pair("FirstOrder Covered Image Intensity Range",coveredGrayValueRange));

  featureList.push_back(std::make_pair("FirstOrder Minimum",labelStatisticsImageFilter->GetMinimum(1)));
  featureList.push_back(std::make_pair("FirstOrder Maximum ",labelStatisticsImageFilter->GetMaximum(1)));
  featureList.push_back(std::make_pair("FirstOrder Mean",labelStatisticsImageFilter->GetMean(1)));
  featureList.push_back(std::make_pair("FirstOrder Variance",labelStatisticsImageFilter->GetVariance(1)));
  featureList.push_back(std::make_pair("FirstOrder Sum",labelStatisticsImageFilter->GetSum(1)));
  featureList.push_back(std::make_pair("FirstOrder Median",labelStatisticsImageFilter->GetMedian(1)));
  featureList.push_back(std::make_pair("FirstOrder Standard deviation",labelStatisticsImageFilter->GetSigma(1)));
  featureList.push_back(std::make_pair("FirstOrder No. of Voxel",labelStatisticsImageFilter->GetCount(1)));
}

开发者ID:AndreasFetzer，项目名称:MITK，代码行数:84，代码来源:mitkGIFFirstOrderStatistics.cpp

示例3: if

void
CalculateFirstOrderStatistics(itk::Image<TPixel, VImageDimension>* itkImage, mitk::Image::Pointer mask, mitk::GIFFirstOrderStatistics::FeatureListType & featureList, mitk::GIFFirstOrderStatistics::ParameterStruct params)
{
  typedef itk::Image<TPixel, VImageDimension> ImageType;
  typedef itk::Image<unsigned short, VImageDimension> MaskType;
  typedef itk::LabelStatisticsImageFilter<ImageType, MaskType> FilterType;
  typedef typename FilterType::HistogramType HistogramType;
  typedef typename HistogramType::IndexType HIndexType;
  typedef itk::MinimumMaximumImageCalculator<ImageType> MinMaxComputerType;

  typename MaskType::Pointer maskImage = MaskType::New();
  mitk::CastToItkImage(mask, maskImage);

  double voxelVolume = 1;
  for (unsigned int i = 0; i < std::min<unsigned int>(3, VImageDimension); ++i)
    voxelVolume *= itkImage->GetSpacing()[i];
  double voxelSpace = 1;
  for (unsigned int i = 0; i < VImageDimension; ++i)
    voxelSpace *= itkImage->GetSpacing()[i];

  typename MinMaxComputerType::Pointer minMaxComputer = MinMaxComputerType::New();
  minMaxComputer->SetImage(itkImage);
  minMaxComputer->Compute();
  double imageRange = minMaxComputer->GetMaximum() -  minMaxComputer->GetMinimum();

  typename FilterType::Pointer labelStatisticsImageFilter = FilterType::New();
  labelStatisticsImageFilter->SetInput( itkImage );
  labelStatisticsImageFilter->SetLabelInput(maskImage);
  labelStatisticsImageFilter->SetUseHistograms(true);

  double min = params.MinimumIntensity;
  double max = params.MaximumIntensity;

  labelStatisticsImageFilter->SetHistogramParameters(params.Bins, min,max);
  labelStatisticsImageFilter->Update();

  // --------------- Range --------------------
  double range = labelStatisticsImageFilter->GetMaximum(1) - labelStatisticsImageFilter->GetMinimum(1);
  // --------------- Uniformity, Entropy --------------------
  double count = labelStatisticsImageFilter->GetCount(1);
  //double std_dev = labelStatisticsImageFilter->GetSigma(1);
  double mean = labelStatisticsImageFilter->GetMean(1);
  double median = labelStatisticsImageFilter->GetMedian(1);
  auto histogram = labelStatisticsImageFilter->GetHistogram(1);
  bool histogramIsCalculated = histogram;

  HIndexType index;
  index.SetSize(1);

  double uniformity = 0;
  double entropy = 0;
  double squared_sum = 0;
  double kurtosis = 0;
  double mean_absolut_deviation = 0;
  double median_absolut_deviation = 0;
  double skewness = 0;
  double sum_prob = 0;
  double binWidth = 0;
  double p05th = 0, p10th = 0, p15th = 0, p20th = 0, p25th = 0, p30th = 0, p35th = 0, p40th = 0, p45th = 0, p50th = 0;
  double p55th = 0, p60th = 0, p65th = 0, p70th = 0, p75th = 0, p80th = 0, p85th = 0, p90th = 0, p95th = 0;

  double voxelValue = 0;

  if (histogramIsCalculated)
  {
    binWidth = histogram->GetBinMax(0, 0) - histogram->GetBinMin(0, 0);
    p05th = histogram->Quantile(0, 0.05);
    p10th = histogram->Quantile(0, 0.10);
    p15th = histogram->Quantile(0, 0.15);
    p20th = histogram->Quantile(0, 0.20);
    p25th = histogram->Quantile(0, 0.25);
    p30th = histogram->Quantile(0, 0.30);
    p35th = histogram->Quantile(0, 0.35);
    p40th = histogram->Quantile(0, 0.40);
    p45th = histogram->Quantile(0, 0.45);
    p50th = histogram->Quantile(0, 0.50);
    p55th = histogram->Quantile(0, 0.55);
    p60th = histogram->Quantile(0, 0.60);
    p65th = histogram->Quantile(0, 0.65);
    p70th = histogram->Quantile(0, 0.70);
    p75th = histogram->Quantile(0, 0.75);
    p80th = histogram->Quantile(0, 0.80);
    p85th = histogram->Quantile(0, 0.85);
    p90th = histogram->Quantile(0, 0.90);
    p95th = histogram->Quantile(0, 0.95);
  }
  double Log2=log(2);
  double mode_bin;
  double mode_value = 0;
  double variance = 0;
  if (histogramIsCalculated)
  {
    for (int i = 0; i < (int)(histogram->GetSize(0)); ++i)
    {
      index[0] = i;
      double prob = histogram->GetFrequency(index);


      if (prob < 0.00000001)
        continue;
//.........这里部分代码省略.........

开发者ID:Cdebus，项目名称:MITK，代码行数:101，代码来源:mitkGIFFirstOrderStatistics.cpp

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