ENH: Add Python Example GlobalRegistrationOfTwoImages

src/Registration/Common/GlobalRegistrationOfTwoImages/CMakeLists.txt

@@ -24,3 +24,9 @@ enable_testing()
 
 add_test(NAME GlobalRegistrationOfTwoImagesTest
   COMMAND ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/GlobalRegistrationOfTwoImages)
+
+if( ITK_WRAP_PYTHON )
+  add_test( NAME GlobalRegistrationOfTwoImagesTestPython
+    COMMAND ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/Code.py
+    )
+endif()

src/Registration/Common/GlobalRegistrationOfTwoImages/Code.py

@@ -0,0 +1,277 @@
+import itk
+
+Dimension = 2
+PixelType = itk.UC
+Double = itk.D
+Float = itk.F
+ImageType = itk.Image[PixelType, Dimension]
+
+def main():
+#   The transform that will map the fixed image into the moving image.
+    TransformType = itk.TranslationTransform[Double, Dimension]
+
+#   An optimizer is required to explore the parameter space of the transform
+#   in search of optimal values of the metric.
+    OptimizerType = itk.RegularStepGradientDescentOptimizer
+
+#   The metric will compare how well the two images match each other. Metric
+#   types are usually parameterized by the image types as it can be seen in
+#   the following type declaration.
+    MetricType = itk.MeanSquaresImageToImageMetric[ImageType, ImageType]
+
+#   Finally, the type of the interpolator is declared. The interpolator will
+#   evaluate the intensities of the moving image at non-grid positions.
+    InterpolatorType = itk.LinearInterpolateImageFunction[ImageType, Double]
+
+#   The registration method type is instantiated using the types of the
+#   fixed and moving images. This class is responsible for interconnecting
+#   all the components that we have described so far.
+    RegistrationType = itk.ImageRegistrationMethod[ImageType, ImageType]
+
+#   Create components
+    metric = MetricType.New()
+    transform = TransformType.New()
+    optimizer = OptimizerType.New()
+    interpolator = InterpolatorType.New()
+    registration = RegistrationType.New()
+
+#   Each component is now connected to the instance of the registration method.
+    registration.SetMetric(metric)
+    registration.SetOptimizer(optimizer)
+    registration.SetTransform(transform)
+    registration.SetInterpolator(interpolator)
+
+#   Get the two images
+    fixedImage = ImageType.New()
+    movingImage = ImageType.New()
+
+    CreateSphereImage(fixedImage)
+    CreateEllipseImage(movingImage)
+
+#   Write the two synthetic inputs
+    itk.imwrite(fixedImage, "fixed.png")
+    itk.imwrite(movingImage, "moving.png")
+
+#   Set the registration inputs
+    registration.SetFixedImage(fixedImage)
+    registration.SetMovingImage(movingImage)
+
+    registration.SetFixedImageRegion(fixedImage.GetLargestPossibleRegion())
+
+#   Initialize the transform
+    initialParameters = itk.OptimizerParameters[itk.D](transform.GetNumberOfParameters())
+
+    initialParameters[0] = 0.0 # Initial offset along X
+    initialParameters[1] = 0.0 # Initial offset along Y
+
+    registration.SetInitialTransformParameters(initialParameters)
+
+    optimizer.SetMaximumStepLength(4.00)
+    optimizer.SetMinimumStepLength(0.01)
+
+#   Set a stopping criterion
+    optimizer.SetNumberOfIterations(200)
+
+#   Connect an observer
+#     surface = dict()
+#     def print_iteration():
+#         surface[tuple(optimizer.GetCurrentPosition())] = optimizer.GetValue()
+#         print(surface)
+
+#     optimizer.AddObserver(itk.IterationEvent(), print_iteration)
+
+
+    try:
+        registration.Update()
+    except:
+        print("ExceptionObject caught !")
+        return 
+
+#     The result of the registration process is an array of parameters that
+#     defines the spatial transformation in an unique way. This final result is
+#     obtained using the \code{GetLastTransformParameters()} method.
+
+    finalParameters = registration.GetLastTransformParameters()
+
+#     In the case of the \doxygen{TranslationTransform}, there is a
+#     straightforward interpretation of the parameters.  Each element of the
+#     array corresponds to a translation along one spatial dimension.
+
+    TranslationAlongX = finalParameters[0]
+    TranslationAlongY = finalParameters[1]
+
+#     The optimizer can be queried for the actual number of iterations
+#     performed to reach convergence.  The \code{GetCurrentIteration()}
+#     method returns this value. A large number of iterations may be an
+#     indication that the maximum step length has been set too small, which
+#     is undesirable since it results in long computational times.
+
+    numberOfIterations = optimizer.GetCurrentIteration()
+
+#     The value of the image metric corresponding to the last set of parameters
+#     can be obtained with the \code{GetValue()} method of the optimizer.
+
+    bestValue = optimizer.GetValue()
+
+#     Print out results
+    print("Result = ")
+    print(" Translation X = {}".format(TranslationAlongX))
+    print(" Translation Y = {}".format(TranslationAlongY))
+    print(" Iterations    = {}".format(numberOfIterations))
+    print(" Metric value  = {}".format(bestValue))
+
+#     It is common, as the last step of a registration task, to use the
+#     resulting transform to map the moving image into the fixed image space.
+#     This is easily done with the \doxygen{ResampleImageFilter}. Please
+#     refer to Section~\ref{sec:ResampleImageFilter} for details on the use
+#     of this filter.  First, a ResampleImageFilter type is instantiated
+#     using the image types. It is convenient to use the fixed image type as
+#     the output type since it is likely that the transformed moving image
+#     will be compared with the fixed image.
+    ResampleFilterType = itk.ResampleImageFilter[ImageType, ImageType]
+
+#     A resampling filter is created and the moving image is connected as  its input.
+
+    resampler = ResampleFilterType.New()
+    resampler.SetInput(movingImage)
+
+#     The Transform that is produced as output of the Registration method is
+#     also passed as input to the resampling filter. Note the use of the
+#     methods \code{GetOutput()} and \code{Get()}. This combination is needed
+#     here because the registration method acts as a filter whose output is a
+#     transform decorated in the form of a \doxygen{DataObject}. For details in
+#     this construction you may want to read the documentation of the
+#     \doxygen{DataObjectDecorator}.
+
+    resampler.SetTransform(registration.GetOutput().Get())
+
+#     As described in Section \ref{sec:ResampleImageFilter}, the
+#     ResampleImageFilter requires additional parameters to be specified, in
+#     particular, the spacing, origin and size of the output image. The default
+#     pixel value is also set to a distinct gray level in order to highlight
+#     the regions that are mapped outside of the moving image.
+
+    resampler.SetSize(itk.size(fixedImage))
+    resampler.SetOutputOrigin(fixedImage.GetOrigin())
+    resampler.SetOutputSpacing(fixedImage.GetSpacing())
+    resampler.SetOutputDirection(fixedImage.GetDirection())
+    resampler.SetDefaultPixelValue(100)
+
+#     The output of the filter is passed to a writer that will store the
+#     image in a file. An \doxygen{CastImageFilter} is used to convert the
+#     pixel type of the resampled image to the final type used by the
+#     writer. The cast and writer filters are instantiated below.
+    CastFilterType = itk.CastImageFilter[ImageType, ImageType]
+
+    caster = CastFilterType.New()
+    caster.SetInput(resampler.GetOutput())
+    itk.imwrite(caster.GetOutput(), "outputPython.png")
+
+
+#     The fixed image and the transformed moving image can easily be compared
+#     using the \doxygen{SubtractImageFilter}. This pixel-wise filter computes
+#     the difference between homologous pixels of its two input images.
+
+
+    DifferenceFilterType = itk.SubtractImageFilter[
+      ImageType,
+      ImageType,
+      ImageType]
+
+    difference = DifferenceFilterType.New()
+    difference.SetInput1( fixedImage )
+    difference.SetInput2( resampler.GetOutput() )
+
+    return 
+
+
+def CreateEllipseImage(image):
+    EllipseType = itk.EllipseSpatialObject[Dimension]
+
+    SpatialObjectToImageFilterType = itk.SpatialObjectToImageFilter[itk.SpatialObject[2], ImageType]
+
+    imageFilter = SpatialObjectToImageFilterType.New()
+
+    size = itk.Size[Dimension]()
+    size[0] = 100
+    size[1] = 100
+
+    imageFilter.SetSize(size)
+
+    spacing = [1, 1]
+    imageFilter.SetSpacing(spacing)
+
+    ellipse = EllipseType.New()
+    radiusArray = itk.Array[Float]()
+    radiusArray.SetSize(Dimension)
+    radiusArray[0] = 10
+    radiusArray[1] = 20
+    ellipse.SetRadiusInObjectSpace(radiusArray)
+
+    TransformType = itk.AffineTransform[Double, Dimension]
+    transform = TransformType.New()
+    transform.SetIdentity()
+
+    translation = itk.Vector[Float, Dimension]()
+    translation[0] = 65
+    translation[1] = 45
+    transform.Translate(translation, False)
+
+    ellipse.SetObjectToParentTransform(transform)
+
+    imageFilter.SetInput(ellipse)
+
+    ellipse.SetDefaultInsideValue(255)
+    ellipse.SetDefaultOutsideValue(0)
+    imageFilter.SetUseObjectValue(True)
+    imageFilter.SetOutsideValue(0)
+
+    imageFilter.Update()
+    image.Graft(imageFilter.GetOutput())
+
+
+def CreateSphereImage(image):
+    EllipseType = itk.EllipseSpatialObject[Dimension]
+
+    SpatialObjectToImageFilterType = itk.SpatialObjectToImageFilter[itk.SpatialObject[2], itk.Image[itk.UC,2]]
+
+    imageFilter = SpatialObjectToImageFilterType.New()
+
+    size = itk.Size[Dimension]()
+    size[0] = 100
+    size[1] = 100
+    imageFilter.SetSize(size)
+
+    spacing = [1, 1]
+    imageFilter.SetSpacing(spacing)
+
+    ellipse = EllipseType.New()
+    radiusArray = itk.Array[Float]()
+    radiusArray.SetSize(Dimension)
+    radiusArray[0] = 10
+    radiusArray[1] = 10
+    ellipse.SetRadiusInObjectSpace(radiusArray)
+
+    TransformType = itk.AffineTransform[Double, Dimension]
+    transform = TransformType.New()
+    transform.SetIdentity()
+
+    translation =  itk.Vector[PixelType, Dimension]()
+    translation[0] = 50
+    translation[1] = 50
+    transform.Translate(translation, False)
+
+    ellipse.SetObjectToParentTransform(transform)
+
+    imageFilter.SetInput(ellipse)
+
+    ellipse.SetDefaultInsideValue(255)
+    ellipse.SetDefaultOutsideValue(0)
+    imageFilter.SetUseObjectValue(True)
+    imageFilter.SetOutsideValue(0)
+
+    imageFilter.Update()
+    image.Graft(imageFilter.GetOutput())
+
+if __name__ == "__main__":
+    main()