ImageRegistrationMethodBSpline1/ImageRegistrationMethodBSpline1.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.
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# =========================================================================
 
""" A SimpleITK example demonstrating image registration using the
    BSplineTransform and the ComplexCorrelation metric. """
 
import sys
import os
import SimpleITK as sitk
 
 
def command_iteration(method):
    """ Callback invoked when the optimization has an iteration """
    print(f"{method.GetOptimizerIteration():3} " + f"= {method.GetMetricValue():10.5f}")
 
 
def main(args):
    """ A SimpleITK example demonstrating image registration using the
        BSplineTransform and the ComplexCorrelation metric. """
    if len(args) < 4:
        print(
            "Usage:",
            sys.argv[0],
            "<fixedImageFilter> <movingImageFile>",
            "<outputTransformFile>",
        )
        sys.exit(1)
 
    fixed = sitk.ReadImage(args[1], sitk.sitkFloat32)
 
    moving = sitk.ReadImage(args[2], sitk.sitkFloat32)
 
    transformDomainMeshSize = [8] * moving.GetDimension()
    tx = sitk.BSplineTransformInitializer(fixed, transformDomainMeshSize)
 
    print("Initial Parameters:")
    print(tx.GetParameters())
 
    R = sitk.ImageRegistrationMethod()
    R.SetMetricAsCorrelation()
 
    R.SetOptimizerAsLBFGSB(
        gradientConvergenceTolerance=1e-5,
        numberOfIterations=100,
        maximumNumberOfCorrections=5,
        maximumNumberOfFunctionEvaluations=1000,
        costFunctionConvergenceFactor=1e7,
    )
    R.SetInitialTransform(tx, True)
    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, args[3])
 
    resampler = sitk.ResampleImageFilter()
    resampler.SetReferenceImage(fixed)
    resampler.SetInterpolator(sitk.sitkLinear)
    resampler.SetDefaultPixelValue(100)
    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)
 
    return_images = {"fixed": fixed, "moving": moving, "composition": cimg}
    return return_images
 
 
if __name__ == "__main__":
    return_dict = main(sys.argv)
    if "SITK_NOSHOW" not in os.environ:
        sitk.Show(
            return_dict["composition"], "ImageRegistrationMethodBSpline1 Composition"
        )