ImageRegistrationMethodExhaustive/ImageRegistrationMethodExhaustive.py

#!/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 SimpleITK as sitk
import sys
import os
from math import pi
 
 
def command_iteration(method):
    if method.GetOptimizerIteration() == 0:
        print("Scales: ", method.GetOptimizerScales())
    print(
        f"{method.GetOptimizerIteration():3} "
        + f"= {method.GetMetricValue():7.5f} "
        + f": {method.GetOptimizerPosition()}"
    )
 
 
if len(sys.argv) < 4:
    print(
        "Usage:",
        sys.argv[0],
        "<fixedImageFilter> <movingImageFile>",
        "<outputTransformFile>",
    )
    sys.exit(1)
 
fixed = sitk.ReadImage(sys.argv[1], sitk.sitkFloat32)
 
moving = sitk.ReadImage(sys.argv[2], sitk.sitkFloat32)
 
R = sitk.ImageRegistrationMethod()
 
R.SetMetricAsMattesMutualInformation(numberOfHistogramBins=50)
 
sample_per_axis = 12
if fixed.GetDimension() == 2:
    tx = sitk.Euler2DTransform()
    # Set the number of samples (radius) in each dimension, with a
    # default step size of 1.0
    R.SetOptimizerAsExhaustive([sample_per_axis // 2, 0, 0])
    # Utilize the scale to set the step size for each dimension
    R.SetOptimizerScales([2.0 * pi / sample_per_axis, 1.0, 1.0])
elif fixed.GetDimension() == 3:
    tx = sitk.Euler3DTransform()
    R.SetOptimizerAsExhaustive(
        [
            sample_per_axis // 2,
            sample_per_axis // 2,
            sample_per_axis // 4,
            0,
            0,
            0,
        ]
    )
    R.SetOptimizerScales(
        [
            2.0 * pi / sample_per_axis,
            2.0 * pi / sample_per_axis,
            2.0 * pi / sample_per_axis,
            1.0,
            1.0,
            1.0,
        ]
    )
 
# Initialize the transform with a translation and the center of
# rotation from the moments of intensity.
tx = sitk.CenteredTransformInitializer(fixed, moving, tx)
 
R.SetInitialTransform(tx)
 
R.SetInterpolator(sitk.sitkLinear)
 
R.AddCommand(sitk.sitkIterationEvent, lambda: command_iteration(R))
 
outTx = R.Execute(fixed, moving)
 
print("-------")
print(outTx)
print(f"Optimizer stop condition: {R.GetOptimizerStopConditionDescription()}")
print(f" Iteration: {R.GetOptimizerIteration()}")
print(f" Metric value: {R.GetMetricValue()}")
 
sitk.WriteTransform(outTx, sys.argv[3])
 
if "SITK_NOSHOW" not in os.environ:
    resampler = sitk.ResampleImageFilter()
    resampler.SetReferenceImage(fixed)
    resampler.SetInterpolator(sitk.sitkLinear)
    resampler.SetDefaultPixelValue(1)
    resampler.SetTransform(outTx)
 
    out = resampler.Execute(moving)
 
    simg1 = sitk.Cast(sitk.RescaleIntensity(fixed), sitk.sitkUInt8)
    simg2 = sitk.Cast(sitk.RescaleIntensity(out), sitk.sitkUInt8)
    cimg = sitk.Compose(simg1, simg2, simg1 // 2.0 + simg2 // 2.0)
    sitk.Show(cimg, "ImageRegistrationExhaustive Composition")