File: itkGaussianDerivativeOperator.h

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 (275 lines) | stat: -rw-r--r-- 8,494 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
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
/*=========================================================================
 *
 *  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 itkGaussianDerivativeOperator_h
#define itkGaussianDerivativeOperator_h

#include "itkGaussianOperator.h"
#include "itkDerivativeOperator.h"

#include <algorithm>

namespace itk
{

/**
 * \class GaussianDerivativeOperatorEnums
 * \brief GaussianDerivativeOperator class enum classes.
 * \ingroup ITKCommon
 */
class GaussianDerivativeOperatorEnums
{
public:
  /**
   * \class InterpolationMode
   * \ingroup ITKCommon
   * Interpolation modes
   */
  enum class InterpolationMode : uint8_t
  {
    NearestNeighbourInterpolation,
    LinearInterpolation
  };
};
// Define how to print enumeration
extern ITKCommon_EXPORT std::ostream &
                        operator<<(std::ostream & out, GaussianDerivativeOperatorEnums::InterpolationMode value);

/**
 * \class GaussianDerivativeOperator
 * \brief A NeighborhoodOperator whose coefficients are a one dimensional,
 * discrete derivative Gaussian kernel.
 *
 * GaussianDerivativeOperator can be used to calculate Gaussian derivatives
 * by taking its inner product with to a Neighborhood
 * (NeighborhoodIterator) that is swept across an image region.
 * It is a directional operator.  N successive applications
 * oriented along each dimensional direction will calculate separable,
 * efficient, N-D Gaussian derivatives of an image region.
 *
 * GaussianDerivativeOperator takes three parameters:
 *
 * (1) The floating-point variance of the desired Gaussian function.
 *
 * (2) The order of the derivative to be calculated (zero order means
 *     it performs only smoothing as a standard itk::GaussianOperator)
 *
 * (3) The "maximum error" allowed in the discrete Gaussian
 * function.  "Maximum error" is defined as the difference between the area
 * under the discrete Gaussian curve and the area under the continuous
 * Gaussian. Maximum error affects the Gaussian operator size. Care should
 * be taken not to make this value too small relative to the variance
 * lest the operator size become unreasonably large.
 *
 * References:
 * The Gaussian kernel contained in this operator was described
 * by Tony Lindeberg  (Discrete Scale-Space Theory and the Scale-Space
 * Primal Sketch. Dissertation. Royal Institute of Technology, Stockholm,
 * Sweden. May 1991.).
 *
 * \author Ivan Macia, Vicomtech, Spain, https://www.vicomtech.org/en
 *
 * This implementation is derived from the Insight Journal paper:
 * https://www.insight-journal.org/browse/publication/179
 *
 * \note GaussianDerivativeOperator does not have any user-declared "special member function",
 * following the C++ Rule of Zero: the compiler will generate them if necessary.
 *
 * \sa GaussianOperator
 * \sa NeighborhoodOperator
 * \sa NeighborhoodIterator
 * \sa Neighborhood
 *
 * \ingroup Operators
 * \ingroup ITKCommon
 *
 * \sphinx
 * \sphinxexample{Core/Common/CreateGaussianDerivativeKernel,Create Gaussian Derivative Kernel}
 * \endsphinx
 */
template <typename TPixel, unsigned int VDimension = 2, typename TAllocator = NeighborhoodAllocator<TPixel>>
class ITK_TEMPLATE_EXPORT GaussianDerivativeOperator : public NeighborhoodOperator<TPixel, VDimension, TAllocator>
{
public:
  /** Standard class type aliases. */
  using Self = GaussianDerivativeOperator;
  using Superclass = NeighborhoodOperator<TPixel, VDimension, TAllocator>;

  /** \see LightObject::GetNameOfClass() */
  itkOverrideGetNameOfClassMacro(GaussianDerivativeOperator);

  using InterpolationModeEnum = GaussianDerivativeOperatorEnums::InterpolationMode;

  /** Neighborhood operator types. */
  using GaussianOperatorType = GaussianOperator<TPixel, VDimension, TAllocator>;
  using DerivativeOperatorType = DerivativeOperator<TPixel, VDimension, TAllocator>;

  /** Set/Get the flag for calculating scale-space normalized
   * derivatives.
   *
   * Normalized derivatives are obtained multiplying by the scale
   * parameter $t^1/order$. This use useful for scale-space selection
   * algorithms such as blob detection. The scaling results in the
   * value of the derivatives being independent of the size of an
   * object. */
  void
  SetNormalizeAcrossScale(bool flag)
  {
    m_NormalizeAcrossScale = flag;
  }
  bool
  GetNormalizeAcrossScale() const
  {
    return m_NormalizeAcrossScale;
  }
  itkBooleanMacro(NormalizeAcrossScale);

  /** Set/Get the variance of the Gaussian kernel.
   *
   */
  void
  SetVariance(const double variance)
  {
    m_Variance = variance;
  }
  double
  GetVariance() const
  {
    return m_Variance;
  }

  /** Set/Get the spacing for the direction of this kernel. */
  void
  SetSpacing(const double spacing)
  {
    m_Spacing = spacing;
  }
  double
  GetSpacing() const
  {
    return m_Spacing;
  }

  /** Set/Get the desired maximum error of the gaussian approximation.  Maximum
   * error is the difference between the area under the discrete Gaussian curve
   * and the area under the continuous Gaussian. Maximum error affects the
   * Gaussian operator size. The value is clamped between 0.00001 and 0.99999. */
  void
  SetMaximumError(const double maxerror)
  {
    constexpr double Min = 0.00001;
    const double     Max = 1.0 - Min;

    m_MaximumError = std::clamp(maxerror, Min, Max);
  }
  double
  GetMaximumError()
  {
    return m_MaximumError;
  }

  /** Sets/Get a limit for growth of the kernel.  Small maximum error values with
   *  large variances will yield very large kernel sizes.  This value can be
   *  used to truncate a kernel in such instances.  A warning will be given on
   *  truncation of the kernel. */
  void
  SetMaximumKernelWidth(unsigned int n)
  {
    m_MaximumKernelWidth = n;
  }
  itkGetConstMacro(MaximumKernelWidth, unsigned int);

  /** Sets/Get the order of the derivative. */
  void
  SetOrder(const unsigned int order)
  {
    m_Order = order;
  }
  unsigned int
  GetOrder() const
  {
    return m_Order;
  }

  void
  PrintSelf(std::ostream & os, Indent indent) const override;

protected:
  /** Type alias support for coefficient vector type.*/
  using typename Superclass::CoefficientVector;

  /** Returns the value of the modified Bessel function I0(x) at a point x >= 0.
   */
  static double
  ModifiedBesselI0(double);

  /** Returns the value of the modified Bessel function I1(x) at a point x,
   * x real.  */
  static double
  ModifiedBesselI1(double);

  /** Returns the value of the modified Bessel function Ik(x) at a point x>=0,
   * where k>=2. */
  static double
  ModifiedBesselI(int, double);

  /** Calculates operator coefficients. */
  CoefficientVector
  GenerateCoefficients() override;

  /** Arranges coefficients spatially in the memory buffer. */
  void
  Fill(const CoefficientVector & coeff) override
  {
    this->FillCenteredDirectional(coeff);
  }

private:
  /* Methods for generations of the coefficients for a Gaussian
   * operator of 0-order respecting the remaining parameters. */
  CoefficientVector
  GenerateGaussianCoefficients() const;

  /** Normalize derivatives across scale space */
  bool m_NormalizeAcrossScale{ true };

  /** Desired variance of the discrete Gaussian function. */
  double m_Variance{ 1.0 };

  /** Difference between the areas under the curves of the continuous and
   * discrete Gaussian functions. */
  double m_MaximumError{ 0.005 };

  /** Maximum kernel size allowed.  This value is used to truncate a kernel
   *  that has grown too large.  A warning is given when the specified maximum
   *  error causes the kernel to exceed this size. */
  unsigned int m_MaximumKernelWidth{ 30 };

  /** Order of the derivative. */
  unsigned int m_Order{ 1 };

  /** Spacing in the direction of this kernel. */
  double m_Spacing{ 1.0 };
};

} // namespace itk

#ifndef ITK_MANUAL_INSTANTIATION
#  include "itkGaussianDerivativeOperator.hxx"
#endif

#endif