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/*=========================================================================
*
* 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.
*
*=========================================================================*/
// First include the header file to be tested:
#include "itkConnectedImageNeighborhoodShape.h"
#include "itkConstShapedNeighborhoodIterator.h"
#include "itkImage.h"
#include "itkImageNeighborhoodOffsets.h"
#include "itkLexicographicCompare.h"
#include "itkOffset.h"
#include "itkSize.h"
#include <algorithm> // For is_sorted and lexicographical_compare.
#include <climits> // For INT_MAX and SIZE_MAX.
#include <vector>
#include <gtest/gtest.h>
namespace
{
template <unsigned int VImageDimension,
size_t VMaximumCityblockDistance,
bool VIncludeCenterPixel,
size_t VExpectedNumberOfOffsets>
void
Assert_GetNumberOfOffsets_returns_expected_number()
{
using ShapeType = itk::ConnectedImageNeighborhoodShape<VImageDimension>;
// Test GetNumberOfOffsets() on a 'constexpr shape', at compile-time:
constexpr ShapeType constexprShape(VMaximumCityblockDistance, VIncludeCenterPixel);
static_assert(constexprShape.GetNumberOfOffsets() == VExpectedNumberOfOffsets,
"Checked ConnectedImageNeighborhoodShape::GetNumberOfOffsets().");
// Test GetNumberOfOffsets() on a non-const shape, at run-time:
ShapeType nonConstShape = constexprShape;
ASSERT_EQ(nonConstShape.GetNumberOfOffsets(), VExpectedNumberOfOffsets);
}
template <unsigned int VImageDimension,
size_t VMaximumCityblockDistance,
size_t VExpectedNumberOfOffsetsExcludingCenterPixel>
void
Assert_GetNumberOfOffsets_returns_expected_number()
{
// Test GetNumberOfOffsets() for both VIncludeCenterPixel = false and
// VIncludeCenterPixel = true:
Assert_GetNumberOfOffsets_returns_expected_number<VImageDimension,
VMaximumCityblockDistance,
false,
VExpectedNumberOfOffsetsExcludingCenterPixel>();
Assert_GetNumberOfOffsets_returns_expected_number<VImageDimension,
VMaximumCityblockDistance,
true,
VExpectedNumberOfOffsetsExcludingCenterPixel + 1>();
}
// Asserts that GenerateImageNeighborhoodOffsets(shape) returns the expected
// result for a shape with the specified ImageDimension and MaximumCityblockDistance,
template <unsigned int VImageDimension, size_t VMaximumCityblockDistance>
void
Assert_GenerateImageNeighborhoodOffsets_returns_expected_offsets_excluding_center_pixel(
const std::vector<itk::Offset<VImageDimension>> & expectedOffsets)
{
using ShapeType = itk::ConnectedImageNeighborhoodShape<VImageDimension>;
const bool includeCenterPixel = false;
const ShapeType shape{ VMaximumCityblockDistance, includeCenterPixel };
ASSERT_EQ(GenerateImageNeighborhoodOffsets(shape), expectedOffsets);
}
template <unsigned int VImageDimension>
void
Assert_The_middle_offset_is_all_zero_when_center_pixel_is_included()
{
using ShapeType = itk::ConnectedImageNeighborhoodShape<VImageDimension>;
using OffsetType = itk::Offset<VImageDimension>;
const bool includeCenterPixel = true;
const OffsetType allZeroOffset{ {} };
for (unsigned int maximumCityblockDistance = 0; maximumCityblockDistance < VImageDimension;
++maximumCityblockDistance)
{
const ShapeType shape{ maximumCityblockDistance, includeCenterPixel };
const size_t numberOfOffsets = shape.GetNumberOfOffsets();
const std::vector<OffsetType> offsets = GenerateImageNeighborhoodOffsets(shape);
ASSERT_FALSE(offsets.empty());
ASSERT_TRUE((offsets.size() % 2) == 1);
const OffsetType middleOffset = offsets[(numberOfOffsets - 1) / 2];
ASSERT_EQ(middleOffset, allZeroOffset);
}
}
template <unsigned int VImageDimension>
void
Assert_Offsets_are_unique_and_colexicographically_ordered()
{
using ShapeType = itk::ConnectedImageNeighborhoodShape<VImageDimension>;
using OffsetType = itk::Offset<VImageDimension>;
for (unsigned int maximumCityblockDistance = 0; maximumCityblockDistance < VImageDimension;
++maximumCityblockDistance)
{
for (bool includeCenterPixel : { false, true })
{
const ShapeType shape{ maximumCityblockDistance, includeCenterPixel };
const std::vector<OffsetType> offsets = GenerateImageNeighborhoodOffsets(shape);
const auto beginOfOffsets = offsets.begin();
const auto endOfOffsets = offsets.end();
ASSERT_TRUE(std::is_sorted(beginOfOffsets, endOfOffsets, itk::Functor::CoLexicographicCompare{}));
// adjacent_find allows checking that each offset is unique, as we can
// assume at this point that the offsets are sorted.
ASSERT_EQ(std::adjacent_find(beginOfOffsets, endOfOffsets), endOfOffsets);
}
}
}
} // namespace
TEST(ConnectedImageNeighborhoodShape, GetNumberOfOffsetsReturnsExpectedValue)
{
// 0-dimensional:
Assert_GetNumberOfOffsets_returns_expected_number<0, 0, 0>();
Assert_GetNumberOfOffsets_returns_expected_number<0, SIZE_MAX, 0>();
// 1-dimensional:
Assert_GetNumberOfOffsets_returns_expected_number<1, 0, 0>();
Assert_GetNumberOfOffsets_returns_expected_number<1, 1, 2>();
Assert_GetNumberOfOffsets_returns_expected_number<1, SIZE_MAX, 2>();
// 2-dimensional (0-connected, 4-connected, 8-connected):
Assert_GetNumberOfOffsets_returns_expected_number<2, 0, 0>();
Assert_GetNumberOfOffsets_returns_expected_number<2, 1, 4>();
Assert_GetNumberOfOffsets_returns_expected_number<2, 2, 8>();
Assert_GetNumberOfOffsets_returns_expected_number<2, SIZE_MAX, 8>();
// 3-dimensional (0-connected, 6-connected, 18-connected, 26-connected):
Assert_GetNumberOfOffsets_returns_expected_number<3, 0, 0>();
Assert_GetNumberOfOffsets_returns_expected_number<3, 1, 6>();
Assert_GetNumberOfOffsets_returns_expected_number<3, 2, 18>();
Assert_GetNumberOfOffsets_returns_expected_number<3, 3, 26>();
Assert_GetNumberOfOffsets_returns_expected_number<3, SIZE_MAX, 26>();
// INT_MAX-dimensional:
Assert_GetNumberOfOffsets_returns_expected_number<INT_MAX, 0, 0>();
Assert_GetNumberOfOffsets_returns_expected_number<INT_MAX, 1, 2U * INT_MAX>();
}
TEST(ConnectedImageNeighborhoodShape, GenerateImageNeighborhoodOffsetsReturnsExpectedOffsets)
{
Assert_GenerateImageNeighborhoodOffsets_returns_expected_offsets_excluding_center_pixel<1, 1>(
{ { { -1 } }, { { 1 } } });
Assert_GenerateImageNeighborhoodOffsets_returns_expected_offsets_excluding_center_pixel<2, 1>(
{ { { 0, -1 } }, { { -1, 0 } }, { { 1, 0 } }, { { 0, 1 } } });
Assert_GenerateImageNeighborhoodOffsets_returns_expected_offsets_excluding_center_pixel<2, 2>({ { { -1, -1 } },
{ { 0, -1 } },
{ { 1, -1 } },
{ { -1, 0 } },
{ { 1, 0 } },
{ { -1, 1 } },
{ { 0, 1 } },
{ { 1, 1 } } });
Assert_GenerateImageNeighborhoodOffsets_returns_expected_offsets_excluding_center_pixel<3, 1>(
{ { { 0, 0, -1 } }, { { 0, -1, 0 } }, { { -1, 0, 0 } }, { { 1, 0, 0 } }, { { 0, 1, 0 } }, { { 0, 0, 1 } } });
Assert_GenerateImageNeighborhoodOffsets_returns_expected_offsets_excluding_center_pixel<3, 2>({ { { 0, -1, -1 } },
{ { -1, 0, -1 } },
{ { 0, 0, -1 } },
{ { 1, 0, -1 } },
{ { 0, 1, -1 } },
{ { -1, -1, 0 } },
{ { 0, -1, 0 } },
{ { 1, -1, 0 } },
{ { -1, 0, 0 } },
{ { 1, 0, 0 } },
{ { -1, 1, 0 } },
{ { 0, 1, 0 } },
{ { 1, 1, 0 } },
{ { 0, -1, 1 } },
{ { -1, 0, 1 } },
{ { 0, 0, 1 } },
{ { 1, 0, 1 } },
{ { 0, 1, 1 } } });
Assert_GenerateImageNeighborhoodOffsets_returns_expected_offsets_excluding_center_pixel<3, 3>(
{ { { -1, -1, -1 } }, { { 0, -1, -1 } }, { { 1, -1, -1 } }, { { -1, 0, -1 } }, { { 0, 0, -1 } }, { { 1, 0, -1 } },
{ { -1, 1, -1 } }, { { 0, 1, -1 } }, { { 1, 1, -1 } }, { { -1, -1, 0 } }, { { 0, -1, 0 } }, { { 1, -1, 0 } },
{ { -1, 0, 0 } }, { { 1, 0, 0 } }, { { -1, 1, 0 } }, { { 0, 1, 0 } }, { { 1, 1, 0 } }, { { -1, -1, 1 } },
{ { 0, -1, 1 } }, { { 1, -1, 1 } }, { { -1, 0, 1 } }, { { 0, 0, 1 } }, { { 1, 0, 1 } }, { { -1, 1, 1 } },
{ { 0, 1, 1 } }, { { 1, 1, 1 } } });
}
TEST(ConnectedImageNeighborhoodShape, TheMiddleOffsetIsAllZeroWhenCenterPixelIsIncluded)
{
Assert_The_middle_offset_is_all_zero_when_center_pixel_is_included<1>();
Assert_The_middle_offset_is_all_zero_when_center_pixel_is_included<2>();
Assert_The_middle_offset_is_all_zero_when_center_pixel_is_included<3>();
}
TEST(ConnectedImageNeighborhoodShape, OffsetsAreUniqueAndColexicographicallyOrdered)
{
Assert_Offsets_are_unique_and_colexicographically_ordered<1>();
Assert_Offsets_are_unique_and_colexicographically_ordered<2>();
Assert_Offsets_are_unique_and_colexicographically_ordered<3>();
}
// Tests that the shape class supports a typical use case of itk::ConstShapedNeighborhoodIterator,
// allowing to set the active offsets directly by GenerateImageNeighborhoodOffsets(shape).
TEST(ConnectedImageNeighborhoodShape, SupportsConstShapedNeighborhoodIterator)
{
using ImageType = itk::Image<int>;
constexpr auto ImageDimension = ImageType::ImageDimension;
using SizeType = itk::Size<ImageDimension>;
using OffsetType = itk::Offset<ImageDimension>;
// Create a "dummy" image.
const auto image = ImageType::New();
SizeType imageSize;
imageSize.Fill(1);
image->SetRegions(imageSize);
image->AllocateInitialized();
// Create a radius, (just) large enough for all offsets activated below here.
SizeType radius;
radius.Fill(1);
itk::ConstShapedNeighborhoodIterator<ImageType> shapedNeighborhoodIterator{ radius,
image,
image->GetRequestedRegion() };
// Obvious initial expectation.
EXPECT_TRUE(shapedNeighborhoodIterator.GetActiveIndexList().empty());
// Activate offsets one by one (the "old-fashioned" way):
OffsetType offset = { {} };
for (auto & offsetValue : offset)
{
offsetValue = -1;
shapedNeighborhoodIterator.ActivateOffset(offset);
offsetValue = 1;
shapedNeighborhoodIterator.ActivateOffset(offset);
offsetValue = 0;
}
const auto activeIndexList = shapedNeighborhoodIterator.GetActiveIndexList();
// Obvious expectation after the previous ActivateOffset(offset) calls.
EXPECT_FALSE(activeIndexList.empty());
shapedNeighborhoodIterator.ClearActiveList();
// Obvious expectation after having called ClearActiveList().
EXPECT_TRUE(shapedNeighborhoodIterator.GetActiveIndexList().empty());
// Define a shape that should generate the same offsets as in the
// previous ActivateOffset(offset) calls.
constexpr size_t cityBlockDistance = 1;
constexpr bool includeCenterPixel = false;
shapedNeighborhoodIterator.ActivateOffsets(
itk::GenerateConnectedImageNeighborhoodShapeOffsets<ImageDimension, cityBlockDistance, includeCenterPixel>());
ASSERT_EQ(shapedNeighborhoodIterator.GetActiveIndexList(), activeIndexList);
}
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