Python/FFTConvolution.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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# =========================================================================
 
""" Example script showing Fast Fourier Transform convolution in SimpleITK """
 
import sys
import os
import math
import SimpleITK as sitk
 
if len(sys.argv) < 4:
    print("Usage: FFTConvolution <input> <kernel> <output>")
    sys.exit(1)
 
inputFileName = sys.argv[1]
kernelFileName = sys.argv[2]
outputFileName = sys.argv[3]
 
# -- Input Image --
# read the input image
img = sitk.ReadImage(inputFileName)
 
# save the type of pixel the input is, so that we can cast the result
# back out to the same type
pixelID = img.GetPixelID()
 
# pad the image
img = sitk.MirrorPad(img, [128] * 2, [128] * 2)
size = img.GetSize()
 
# perform the FFT
fftimg = sitk.ForwardFFT(sitk.Cast(img, sitk.sitkFloat32))
 
# -- Kernel Image --
# Read the kernel image file
kernel = sitk.ReadImage(kernelFileName)
 
# flip kernel about all axis
kernel = sitk.Flip(kernel, [True] * 2)
 
# normalize the kernel to sum to ~1
stats = sitk.StatisticsImageFilter()
stats.Execute(kernel)
kernel = sitk.Cast(kernel / stats.GetSum(), sitk.sitkFloat32)
 
upadding = [0] * 2
upadding[0] = int(math.floor((size[0] - kernel.GetSize()[0]) / 2.0))
upadding[1] = int(math.floor((size[1] - kernel.GetSize()[1]) / 2.0))
 
lpadding = [0] * 2
lpadding[0] = int(math.ceil((size[0] - kernel.GetSize()[0]) / 2.0))
lpadding[1] = int(math.ceil((size[1] - kernel.GetSize()[1]) / 2.0))
 
# pad the kernel to prevent edge artifacts
kernel = sitk.ConstantPad(kernel, upadding, lpadding, 0.0)
 
# perform FFT on kernel
fftkernel = sitk.ForwardFFT(sitk.FFTShift(kernel))
 
# meta-data must match for multiplication
fftkernel.SetSpacing(fftimg.GetSpacing())
fftkernel.SetOrigin(fftimg.GetOrigin())
fftkernel.SetDirection(fftimg.GetDirection())
 
# -- Convolution --
# Finally perform the convolution in Fourier space by multiplication
img = sitk.InverseFFT(fftimg * fftkernel)
 
# remove the padding
img = sitk.Crop(img, [128] * 2, [128] * 2)
 
# -- Writing --
# write the output image the same type as the input
sitk.WriteImage(sitk.Cast(img, pixelID), outputFileName)
 
if "SITK_NOSHOW" not in os.environ:
    sitk.Show(sitk.ReadImage(inputFileName), "original")
    sitk.Show(sitk.ReadImage(kernelFileName), "kernel")
    sitk.Show(sitk.Cast(img, pixelID), "FFT_Convolution")