File: itkBinaryStatisticsKeepNObjectsImageFilter.hxx

package info (click to toggle)
insighttoolkit5 5.4.3-5
  • links: PTS, VCS
  • area: main
  • in suites: forky, sid, trixie
  • size: 704,384 kB
  • sloc: cpp: 783,592; ansic: 628,724; xml: 44,704; fortran: 34,250; python: 22,874; sh: 4,078; pascal: 2,636; lisp: 2,158; makefile: 464; yacc: 328; asm: 205; perl: 203; lex: 146; tcl: 132; javascript: 98; csh: 81
file content (131 lines) | stat: -rw-r--r-- 5,072 bytes parent folder | download
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
/*=========================================================================
 *
 *  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 itkBinaryStatisticsKeepNObjectsImageFilter_hxx
#define itkBinaryStatisticsKeepNObjectsImageFilter_hxx

#include "itkProgressAccumulator.h"

namespace itk
{
template <typename TInputImage, typename TFeatureImage>
BinaryStatisticsKeepNObjectsImageFilter<TInputImage, TFeatureImage>::BinaryStatisticsKeepNObjectsImageFilter()
  : m_BackgroundValue(NumericTraits<OutputImagePixelType>::NonpositiveMin())
  , m_ForegroundValue(NumericTraits<OutputImagePixelType>::max())
  , m_Attribute(LabelObjectType::MEAN)
{
  this->SetNumberOfRequiredInputs(2);
}

template <typename TInputImage, typename TFeatureImage>
void
BinaryStatisticsKeepNObjectsImageFilter<TInputImage, TFeatureImage>::GenerateInputRequestedRegion()
{
  // call the superclass' implementation of this method
  Superclass::GenerateInputRequestedRegion();

  // We need all the input.
  InputImagePointer input = const_cast<InputImageType *>(this->GetInput());
  if (input)
  {
    input->SetRequestedRegion(input->GetLargestPossibleRegion());
  }
}

template <typename TInputImage, typename TFeatureImage>
void
BinaryStatisticsKeepNObjectsImageFilter<TInputImage, TFeatureImage>::EnlargeOutputRequestedRegion(DataObject *)
{
  this->GetOutput()->SetRequestedRegion(this->GetOutput()->GetLargestPossibleRegion());
}

template <typename TInputImage, typename TFeatureImage>
void
BinaryStatisticsKeepNObjectsImageFilter<TInputImage, TFeatureImage>::GenerateData()
{
  // Create a process accumulator for tracking the progress of this minipipeline
  auto progress = ProgressAccumulator::New();

  progress->SetMiniPipelineFilter(this);

  // Allocate the output
  this->AllocateOutputs();

  auto labelizer = LabelizerType::New();
  labelizer->SetInput(this->GetInput());
  labelizer->SetInputForegroundValue(m_ForegroundValue);
  labelizer->SetOutputBackgroundValue(m_BackgroundValue);
  labelizer->SetFullyConnected(m_FullyConnected);
  labelizer->SetNumberOfWorkUnits(this->GetNumberOfWorkUnits());
  progress->RegisterInternalFilter(labelizer, .3f);

  auto valuator = LabelObjectValuatorType::New();
  valuator->SetInput(labelizer->GetOutput());
  valuator->SetFeatureImage(this->GetFeatureImage());
  valuator->SetNumberOfWorkUnits(this->GetNumberOfWorkUnits());
  valuator->SetComputeHistogram(false);
  if (m_Attribute != LabelObjectType::PERIMETER && m_Attribute != LabelObjectType::ROUNDNESS)
  {
    valuator->SetComputePerimeter(false);
  }
  if (m_Attribute == LabelObjectType::FERET_DIAMETER)
  {
    valuator->SetComputeFeretDiameter(true);
  }
  progress->RegisterInternalFilter(valuator, .3f);

  auto opening = KeepNObjectsType::New();
  opening->SetInput(valuator->GetOutput());
  opening->SetNumberOfObjects(m_NumberOfObjects);
  opening->SetReverseOrdering(m_ReverseOrdering);
  opening->SetAttribute(m_Attribute);
  opening->SetNumberOfWorkUnits(this->GetNumberOfWorkUnits());
  progress->RegisterInternalFilter(opening, .2f);

  auto binarizer = BinarizerType::New();
  binarizer->SetInput(opening->GetOutput());
  binarizer->SetForegroundValue(m_ForegroundValue);
  binarizer->SetBackgroundValue(m_BackgroundValue);
  binarizer->SetBackgroundImage(this->GetInput());
  binarizer->SetNumberOfWorkUnits(this->GetNumberOfWorkUnits());
  progress->RegisterInternalFilter(binarizer, .2f);

  binarizer->GraftOutput(this->GetOutput());
  binarizer->Update();
  this->GraftOutput(binarizer->GetOutput());
}

template <typename TInputImage, typename TFeatureImage>
void
BinaryStatisticsKeepNObjectsImageFilter<TInputImage, TFeatureImage>::PrintSelf(std::ostream & os, Indent indent) const
{
  Superclass::PrintSelf(os, indent);

  os << indent << "FullyConnected: " << (m_FullyConnected ? "On" : "Off") << std::endl;
  os << indent
     << "BackgroundValue: " << static_cast<typename NumericTraits<OutputImagePixelType>::PrintType>(m_BackgroundValue)
     << std::endl;
  os << indent
     << "ForegroundValue: " << static_cast<typename NumericTraits<OutputImagePixelType>::PrintType>(m_ForegroundValue)
     << std::endl;
  os << indent << "NumberOfObjects: " << m_NumberOfObjects << std::endl;
  os << indent << "ReverseOrdering: " << m_ReverseOrdering << std::endl;
  os << indent << "Attribute: " << LabelObjectType::GetNameFromAttribute(m_Attribute) << " (" << m_Attribute << ')'
     << std::endl;
}
} // end namespace itk
#endif