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
|
/*=========================================================================
*
* 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.
*
*=========================================================================*/
#include "itkGTest.h"
#include "itkSLICImageFilter.h"
#include "itkVectorImage.h"
#include "itkCommand.h"
#include "itkTestDriverIncludeRequiredFactories.h"
#include "itkTestingHashImageFilter.h"
namespace
{
class SLICFixture : public ::testing::Test
{
public:
SLICFixture() = default;
~SLICFixture() override = default;
protected:
void
SetUp() override
{
RegisterRequiredFactories();
}
template <typename TImageType>
static std::string
MD5Hash(const TImageType * image)
{
using HashFilter = itk::Testing::HashImageFilter<TImageType>;
auto hasher = HashFilter::New();
hasher->SetInput(image);
hasher->Update();
return hasher->GetHash();
}
template <unsigned int D, typename TPixelType = unsigned short>
struct FixtureUtilities
{
static const unsigned int Dimension = D;
using PixelType = TPixelType;
using OutputPixelType = unsigned int;
using InputImageType = itk::Image<PixelType, Dimension>;
using OutputImageType = itk::Image<OutputPixelType, Dimension>;
using FilterType = itk::SLICImageFilter<InputImageType, OutputImageType>;
// Create a black image or empty
static typename InputImageType::Pointer
CreateImage(unsigned int size = 100)
{
auto image = InputImageType::New();
typename InputImageType::SizeType imageSize;
imageSize.Fill(size);
image->SetRegions(typename InputImageType::RegionType(imageSize));
image->Allocate();
image->FillBuffer(0);
return image;
}
};
};
} // namespace
TEST_F(SLICFixture, SetGet)
{
using namespace itk::GTest::TypedefsAndConstructors::Dimension3;
using Utils = FixtureUtilities<3>;
auto filter = Utils::FilterType::New();
typename Utils::FilterType::ConstPointer constfilter = (const Utils::FilterType *)(filter.GetPointer());
Utils::FilterType::SuperGridSizeType gridSize3(3);
EXPECT_NO_THROW(filter->SetSuperGridSize(gridSize3));
ITK_EXPECT_VECTOR_NEAR(gridSize3, filter->GetSuperGridSize(), 0);
EXPECT_NO_THROW(filter->SetSuperGridSize(4));
ITK_EXPECT_VECTOR_NEAR(Utils::FilterType::SuperGridSizeType(4), filter->GetSuperGridSize(), 0);
EXPECT_NO_THROW(filter->SetMaximumNumberOfIterations(6));
EXPECT_EQ(6, filter->GetMaximumNumberOfIterations());
EXPECT_NO_THROW(filter->SetSpatialProximityWeight(9.1));
EXPECT_EQ(9.1, filter->GetSpatialProximityWeight());
EXPECT_NO_THROW(filter->EnforceConnectivityOn());
EXPECT_TRUE(filter->GetEnforceConnectivity());
EXPECT_NO_THROW(filter->EnforceConnectivityOff());
EXPECT_FALSE(filter->GetEnforceConnectivity());
EXPECT_NO_THROW(filter->SetEnforceConnectivity(true));
EXPECT_TRUE(filter->GetEnforceConnectivity());
EXPECT_NO_THROW(filter->InitializationPerturbationOn());
EXPECT_TRUE(filter->GetInitializationPerturbation());
EXPECT_NO_THROW(filter->InitializationPerturbationOff());
EXPECT_FALSE(filter->GetInitializationPerturbation());
EXPECT_NO_THROW(filter->SetInitializationPerturbation(true));
EXPECT_TRUE(filter->GetInitializationPerturbation());
}
TEST_F(SLICFixture, Blank2DImage)
{
using namespace itk::GTest::TypedefsAndConstructors::Dimension2;
using Utils = FixtureUtilities<2>;
auto filter = Utils::FilterType::New();
auto image = Utils::CreateImage(100);
filter->SetInput(image);
filter->SetSuperGridSize(10);
filter->Update();
EXPECT_EQ("68707adc3df2f7d210b1db96847fc3c5", MD5Hash(filter->GetOutput()));
filter->SetSuperGridSize(1);
filter->Update();
EXPECT_EQ("10d461742d48d15b8df75387187de426", MD5Hash(filter->GetOutput()));
filter->SetSuperGridSize(200);
filter->Update();
EXPECT_EQ("4e0a293a5b638f0aba2c4fe2c3418d0e", MD5Hash(filter->GetOutput()));
}
TEST_F(SLICFixture, ClusterInitializationOverflow)
{
// Tests a case failure caused by numeric overflow during initialization of clusters.
using namespace itk::GTest::TypedefsAndConstructors::Dimension2;
using Utils = FixtureUtilities<2, unsigned char>;
auto filter = Utils::FilterType::New();
auto image = Utils::CreateImage(100);
image->FillBuffer(255);
for (unsigned int x = 2; x < 5; ++x)
{
for (unsigned int y = 2; y < 5; ++y)
{
image->SetPixel(itk::MakeIndex(x, y), 254);
}
}
filter->SetInput(image);
filter->SetMaximumNumberOfIterations(1);
filter->SetSuperGridSize(10);
filter->Update();
EXPECT_EQ("be2250b1d36e8a418f6487189db1ea64", MD5Hash(filter->GetOutput()));
EXPECT_FLOAT_EQ(0.023752308, filter->GetAverageResidual());
}
|
