Vous avez reçu un message "Your GitLab account has been locked ..." ? Pas d'inquiétude : lisez cet article https://docs.gricad-pages.univ-grenoble-alpes.fr/help/unlock/

MeanFilter.cpp 8.57 KB
Newer Older
1
2
3
4
/*****************************************************************************
 * $CAMITK_LICENCE_BEGIN$
 *
 * CamiTK - Computer Assisted Medical Intervention ToolKit
saubatn's avatar
saubatn committed
5
 * (c) 2001-2016 Univ. Grenoble Alpes, CNRS, TIMC-IMAG UMR 5525 (GMCAO) 
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
 *
 * Visit http://camitk.imag.fr for more information
 *
 * This file is part of CamiTK.
 *
 * CamiTK is free software: you can redistribute it and/or modify
 * it under the terms of the GNU Lesser General Public License version 3
 * only, as published by the Free Software Foundation.
 *
 * CamiTK is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU Lesser General Public License version 3 for more details.
 *
 * You should have received a copy of the GNU Lesser General Public License
 * version 3 along with CamiTK.  If not, see <http://www.gnu.org/licenses/>.
 *
 * $CAMITK_LICENCE_END$
 ****************************************************************************/
saubatn's avatar
saubatn committed
25
// CamiTK includes
26
#include "MeanFilter.h"
27
28
#include <Application.h>
#include <ItkProgressObserver.h>
saubatn's avatar
saubatn committed
29
30
31
#include <Property.h>

// Qt includes
32
33
34
#include <QMessageBox>
#include <QString>
#include <QTextStream>
saubatn's avatar
saubatn committed
35
36

// Itk includes
37
38
#include <itkImageToVTKImageFilter.h>
#include <itkVTKImageToImageFilter.h>
39
40
41
42
43
44
45
#include <itkMeanImageFilter.h>

using namespace camitk;


// --------------- constructor -------------------
MeanFilter::MeanFilter(ActionExtension * extension) : Action(extension) {
46
    // Setting name, description and input component
47
48
    setName("Mean Filter");
    setDescription("<br>\
saubatn's avatar
saubatn committed
49
50
51
52
53
54
55
56
57
58
59
60
The <b>mean</b> filter is commonly used for noise reduction. <br/><br/> \
This filter computes the value of each output pixel by finding the statistical mean of the neighborhood of the corresponding input pixel. The following figure illustrates the local effect of the <b>mean</b> filter in 2D case.<br/>\
______________ <br/> \
| 28 | 26 | 50 | <br/> \
|-----|-----|------| <br/> \
| 27 | 25 | 29 |  -> 30.22 -> 30<br/> \
|-----|-----|------| <br/> \
| 25 | 30 | 32 | <br/> \
-------------------- <br/> \
<i>Note that this algorithm is sensitive to the presence of outliers in the neighborhood. </i><br/>\
<br/> \
The <b>parameters</b> are the size of the neighborhood along X, Y and Z directions. <br/>The value on each direction is used as the semi-size of a rectangular box. For example in <i>2D</i> a size of 1 in X direction and 2 in Y direction results in a 3x5 neighborhood.<br/> \
61
62
63
");
    setComponent("ImageComponent");

64
    // Setting classification family and tags
65
66
67
68
69
70
    this->setFamily("ITK Filter");
    this->addTag("Mean");
    this->addTag("Smoothing");
    this->addTag("Neighborhood Filter");

    // Setting parameters default values
saubatn's avatar
saubatn committed
71
72
73
74
75
76
77
    Property* halfNeighborhoodSizeProp_X = new Property(tr("Half neighborhood size along X"), 1, tr("Half the size of the X direction of the neighborhood taken into account for the mean computation. \nFor instance, a value of 2 will create a windows of size 4 along the X direction."), "");
    halfNeighborhoodSizeProp_X->setAttribute("minimum", 1);
    halfNeighborhoodSizeProp_X->setAttribute("maximum", 100);
    halfNeighborhoodSizeProp_X->setAttribute("singleStep", 1);
    addParameter(halfNeighborhoodSizeProp_X);

    Property* halfNeighborhoodSizeProp_Y = new Property(tr("Half neighborhood size along Y"), 1, tr("Half the size of the Y direction of the neighborhood taken into account for the mean computation. \nFor instance, a value of 2 will create a windows of size 4 along the Y direction."), "");
saubatn's avatar
saubatn committed
78
79
80
    halfNeighborhoodSizeProp_Y->setAttribute("minimum", 1);
    halfNeighborhoodSizeProp_Y->setAttribute("maximum", 100);
    halfNeighborhoodSizeProp_Y->setAttribute("singleStep", 1);
saubatn's avatar
saubatn committed
81
82
83
    addParameter(halfNeighborhoodSizeProp_Y);

    Property* halfNeighborhoodSizeProp_Z = new Property(tr("Half neighborhood size along Z"), 1, tr("Half the size of the Z direction of the neighborhood taken into account for the mean computation. \nFor instance, a value of 2 will create a windows of size 4 along the Z direction."), "");
saubatn's avatar
saubatn committed
84
85
86
    halfNeighborhoodSizeProp_Z->setAttribute("minimum", 1);
    halfNeighborhoodSizeProp_Z->setAttribute("maximum", 100);
    halfNeighborhoodSizeProp_Z->setAttribute("singleStep", 1);
saubatn's avatar
saubatn committed
87
    addParameter(halfNeighborhoodSizeProp_Z);
88
89
90
91
92
93
94
95
}

// --------------- destructor -------------------
MeanFilter::~MeanFilter() {
    // do not delete the widget has it might have been used in the ActionViewer (i.e. the ownership might have been taken by the stacked widget)
}

// --------------- apply -------------------
96
97
Action::ApplyStatus MeanFilter::apply() {
    foreach (Component *comp, getTargets()) {
98
        ImageComponent * input = dynamic_cast<ImageComponent *> ( comp );
99
        process(input);
100
    }
101
    return SUCCESS;
102
103
104
105
}

void MeanFilter::process(ImageComponent * comp) {
    // Get the parameters
saubatn's avatar
saubatn committed
106
107
108
    this->halfNeighborhoodSizeX = property("Half neighborhood size along X").toInt();
    this->halfNeighborhoodSizeY = property("Half neighborhood size along Y").toInt();
    this->halfNeighborhoodSizeZ = property("Half neighborhood size along Z").toInt();
109
110
    // ITK filter implementation using templates
    vtkSmartPointer<vtkImageData> inputImage = comp->getImageData();
111
    vtkSmartPointer<vtkImageData> outputImage = implementProcess (inputImage);
112
113
    QString newName;
    QTextStream(&newName) << comp->getName() << "_mean";
saubatn's avatar
saubatn committed
114
115
116
117
118
119
    
    ImageComponent* outputComp = new ImageComponent(outputImage, newName);
    
    // consider frame policy on new image created
    Action::applyTargetPosition(comp, outputComp);
    
120
    Application::refresh();
121
122
123

}

124
#include "MeanFilter.impl"
125
126

// ITK filter implementation
127
128
129
130
131
132
133
134
135
136
137
138
template <class InputPixelType, class OutputPixelType, const int dim>
vtkSmartPointer<vtkImageData> MeanFilter::itkProcess(vtkSmartPointer<vtkImageData> img) {
    vtkSmartPointer<vtkImageData> result = vtkSmartPointer<vtkImageData>::New();

    // --------------------- Filters declaration and creation ----------------------
    // Define ITK input and output image types with respect to the instanciation
    //    types of the tamplate.
    typedef itk::Image< InputPixelType,  dim > InputImageType;
    typedef itk::Image< OutputPixelType, dim > OutputImageType;

    // Convert the image from CamiTK in VTK format to ITK format to use ITK filters.
    typedef itk::VTKImageToImageFilter<InputImageType> vtkToItkFilterType;
139
140
    typename vtkToItkFilterType::Pointer vtkToItkFilter = vtkToItkFilterType::New();

141
142
143
    // Declare and create your own private ITK filter here...
    typedef itk::MeanImageFilter<InputImageType, OutputImageType> FilterType;
    typename FilterType::Pointer filter = FilterType::New();
144

145
146
147
148
    // In the same way, once the image is filtered, we need to convert it again to
    // VTK format to give it to CamiTK.
    typedef itk::ImageToVTKImageFilter<OutputImageType> itkToVtkFilterType;
    typename itkToVtkFilterType::Pointer itkToVtkFilter = itkToVtkFilterType::New();
149

150
151
152
    // To update CamiTK progress bar while filtering, add an ITK observer to the filters.
    ItkProgressObserver::Pointer observer = ItkProgressObserver::New();
    // ITK observers generally give values between 0 and 1, and CamiTK progress bar
153
    //    wants values between 0 and 100...
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
    observer->SetCoef(100.0);

    // --------------------- Plug filters and parameters ---------------------------
    // From VTK to ITK
    vtkToItkFilter->SetInput(img);
    vtkToItkFilter->AddObserver(itk::ProgressEvent(), observer);
    vtkToItkFilter->Update();
    observer->Reset();

    typename InputImageType::SizeType indexRadius;
    indexRadius[0] = halfNeighborhoodSizeX;
    indexRadius[1] = halfNeighborhoodSizeY;
    if (dim == 3)
        indexRadius[2] = halfNeighborhoodSizeZ;

    filter->SetInput(vtkToItkFilter->GetOutput());
    filter->SetRadius(indexRadius);
    filter->AddObserver(itk::ProgressEvent(), observer);
    filter->Update();
    observer->Reset();

    // From ITK to VTK
176
    // Change the following line to put your filter instead of vtkToItkFilter
177
178
    itkToVtkFilter->SetInput(filter->GetOutput());
    itkToVtkFilter->AddObserver(itk::ProgressEvent(), observer);
179

180
181
    // --------------------- Actually execute all filters parts --------------------
    itkToVtkFilter->Update();
182
183

    // --------------------- Create and return a copy (the filters will be deleted)--
184
    vtkSmartPointer<vtkImageData> resultImage = itkToVtkFilter->GetOutput();
185
    int extent[6];
186
187
188
189
    resultImage->GetExtent(extent);
    result->SetExtent(extent);
    result->DeepCopy(resultImage);
    result->Update();
190

191
    // Set CamiTK progress bar back to zero (the processing filter is over)
192
193
    observer->Reset();

194
195
    return result;
}
196