#convert 3D images to stacks of 2D slice images
#create a transform for each 2D slice
#create a initialisation using 2D slice stacks
#compute H for each LR image
psfList = []
for i in range(len(inputImages)):
LRSpacing = np.float32(np.array(inputImages[i].header['pixdim'][1:4]))
HRpsf = compute_psf(LRSpacing, HRSpacing, args.psf)
psfList.append(HRpsf)
#Compute H
HList = []
for i in range(len(inputImages)):
HList.append(
compute_H(inputImages[i], initHRImage, inputTransforms[i],
psfList[i], maskImages[i]))
#Intensity correction To do
#N4 on initHR
#local correction
#New init HR
if args.bias == True:
initHRImage_N4 = apply_N4_on_image(initHRImage, shrink_factor=1)
xN4 = convert_image_to_vector(initHRImage_N4)
hrN4Data = np.zeros(initHRImage.get_data().shape)
for i in range(len(inputImages)):
simu = convert_vector_to_image(HList[i].dot(xN4), inputImages[i])
im = gaussian_biais_correction(inputImages[i], simu, 5)
warped = apply_affine_itk_transform_on_image(
else:
#no transform provided : use identity as transform and zero as center
m = np.identity(4)
c = np.array([0, 0, 0, 1])
inputTransform = (m,c)
print('Creating mask image using the following padding value:'+str(args.padding))
data = np.zeros(HRimage.get_data().shape)
data[HRimage.get_data() > args.padding] = 1
maskHRImage = nibabel.Nifti1Image(data, HRimage.affine)
print('Percentage of HR masked values : %.2f '%( np.size(np.nonzero((data))) / (1.0*np.size(data.shape)) * 100.0 / np.size(data) ) )
data = np.zeros(LRimage.get_data().shape)
data[LRimage.get_data() > args.padding] = 1
maskLRImage = nibabel.Nifti1Image(data, LRimage.affine)
print('Percentage of LR masked values : %.2f '%( np.size(np.nonzero((data))) / (1.0*np.size(data.shape)) * 100.0 / np.size(data) ) )
HRSpacing = np.float32(np.array(HRimage.header['pixdim'][1:4]))
LRSpacing = np.float32(np.array(LRimage.header['pixdim'][1:4]))
psf = compute_psf(LRSpacing, HRSpacing, args.psf)
H = compute_H(LRimage, HRimage, inputTransform, psf, maskLRImage)
x = convert_image_to_vector(HRimage)
maskX = convert_image_to_vector(maskHRImage)
#Let mask the HR image
x = x*maskX
nibabel.save(convert_vector_to_image(H.dot(x),LRimage),args.output)
m = np.identity(4)
c = np.array([0, 0, 0, 1])
inputTransform = (m, c)
print('Creating mask image using the following padding value:' +
str(args.padding))
data = np.zeros(HRimage.get_data().shape)
data[HRimage.get_data() > args.padding] = 1
maskHRImage = nibabel.Nifti1Image(data, HRimage.affine)
print('Percentage of HR masked values : %.2f ' %
(np.size(np.nonzero(
(data))) / (1.0 * np.size(data.shape)) * 100.0 / np.size(data)))
data = np.zeros(LRimage.get_data().shape)
data[LRimage.get_data() > args.padding] = 1
maskLRImage = nibabel.Nifti1Image(data, LRimage.affine)
print('Percentage of LR masked values : %.2f ' %
(np.size(np.nonzero(
(data))) / (1.0 * np.size(data.shape)) * 100.0 / np.size(data)))
HRSpacing = np.float32(np.array(HRimage.header['pixdim'][1:4]))
LRSpacing = np.float32(np.array(LRimage.header['pixdim'][1:4]))
psf = compute_psf(LRSpacing, HRSpacing, args.psf)
H = compute_H(LRimage, HRimage, inputTransform, psf, maskLRImage)
x = convert_image_to_vector(HRimage)
maskX = convert_image_to_vector(maskHRImage)
#Let mask the HR image
x = x * maskX
nibabel.save(convert_vector_to_image(H.dot(x), LRimage), args.output)
# convert 3D images to stacks of 2D slice images
# create a transform for each 2D slice
# create a initialisation using 2D slice stacks
# compute H for each LR image
psfList = []
for i in range(len(inputImages)):
LRSpacing = np.float32(np.array(inputImages[i].header["pixdim"][1:4]))
HRpsf = compute_psf(LRSpacing, HRSpacing, args.psf)
psfList.append(HRpsf)
# Compute H
HList = []
for i in range(len(inputImages)):
HList.append(compute_H(inputImages[i], initHRImage, inputTransforms[i], psfList[i], maskImages[i]))
# Intensity correction To do
# N4 on initHR
# local correction
# New init HR
if args.bias == True:
initHRImage_N4 = apply_N4_on_image(initHRImage, shrink_factor=1)
xN4 = convert_image_to_vector(initHRImage_N4)
hrN4Data = np.zeros(initHRImage.get_data().shape)
for i in range(len(inputImages)):
simu = convert_vector_to_image(HList[i].dot(xN4), inputImages[i])
im = gaussian_biais_correction(inputImages[i], simu, 5)
warped = apply_affine_itk_transform_on_image(
