Add MutualInformationAffine Python example file

src/Core/Transform/MutualInformationAffine/CMakeLists.txt

@@ -27,3 +27,10 @@ add_test(NAME MutualInformationAffineTest
   moving.png
   output.png)
 
+  if(ITK_WRAP_PYTHON)
+  add_test(NAME MutualInformationAffineTestPython
+  COMMAND ${PYTHON_EXECUTABLE} Code.py
+  ${CMAKE_CURRENT_BINARY_DIR}/fixed.png
+  ${CMAKE_CURRENT_BINARY_DIR}/moving.png
+  output.png)
+  endif()

src/Core/Transform/MutualInformationAffine/Code.py

@@ -0,0 +1,101 @@
+#!/usr/bin/env python
+
+# =========================================================================
+#
+#   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
+#
+#          http://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.
+#
+# =========================================================================*/
+
+import sys
+import numpy as np
+
+import itk
+
+if len(sys.argv) < 4:
+    raise Exception(f"Usage: {sys.argv[0]} fixed_image moving_image output_image")
+
+# Import images
+fixed_image = itk.imread(sys.argv[1], itk.UC)
+moving_image = itk.imread(sys.argv[2], itk.UC)
+
+# Preprocess images
+fixed_normalized_image = itk.normalize_image_filter(fixed_image)
+fixed_smoothed_image = itk.discrete_gaussian_image_filter(
+    fixed_normalized_image, variance=2.0
+)
+
+moving_normalized_image = itk.normalize_image_filter(moving_image)
+moving_smoothed_image = itk.discrete_gaussian_image_filter(
+    moving_normalized_image, variance=2.0
+)
+
+# Run gradient ascent
+transform = itk.AffineTransform[itk.D, fixed_image.GetImageDimension()].New()
+interpolator = itk.LinearInterpolateImageFunction[type(fixed_image), itk.D].New()
+
+metric = itk.MutualInformationImageToImageMetric[
+    type(fixed_image), type(moving_image)
+].New()
+
+number_of_pixels = fixed_image.GetBufferedRegion().GetNumberOfPixels()
+number_of_samples = int(number_of_pixels * 0.01)
+metric.SetNumberOfSpatialSamples(number_of_samples)
+
+metric.SetFixedImageStandardDeviation(25.0)
+metric.SetMovingImageStandardDeviation(25.0)
+
+metric.ReinitializeSeed(121212)
+
+optimizer = itk.GradientDescentOptimizer.New()
+
+optimizer.SetLearningRate(1.0)
+optimizer.SetNumberOfIterations(200)
+optimizer.MaximizeOff()
+
+# Set scales so that the optimizer can take
+# large steps along translation parameters,
+# moderate steps along rotational parameters, and
+# small steps along scale parameters
+optimizer.SetScales([100, 100, 100, 100, 0.01, 0.01])
+
+result_object = itk.image_registration_method(
+    fixed_image=fixed_image,
+    moving_image=moving_image,
+    transform=transform,
+    interpolator=interpolator,
+    metric=metric,
+    optimizer=optimizer,
+    fixed_image_region=fixed_image.GetBufferedRegion(),
+    initial_transform_parameters=transform.GetParameters(),
+)
+
+# Print final results
+print("Number of samples: " + str(number_of_samples))
+print(f"Its: {optimizer.GetCurrentIteration()}")
+print(f"Final Value: {optimizer.GetValue()}")
+print(f"Final Position: {list(transform.GetParameters())}")
+
+# Resample and write out image
+resampled = itk.resample_image_filter(
+    input=moving_image,
+    transform=result_object,
+    size=fixed_image.GetLargestPossibleRegion().GetSize(),
+    output_origin=fixed_image.GetOrigin(),
+    output_spacing=fixed_image.GetSpacing(),
+    output_direction=fixed_image.GetDirection(),
+    default_pixel_value=100,
+)
+
+itk.imwrite(resampled, sys.argv[3])

src/Core/Transform/MutualInformationAffine/Documentation.rst

@@ -61,6 +61,11 @@ C++
 .. literalinclude:: Code.cxx
    :lines: 18-
 
+Python
+...
+
+.. literalinclude:: Code.py
+   :lines: 21-
 
 Classes demonstrated
 --------------------