if args.init is not None:
print('Initialization image: ', args.init)
else:
print('No initialization image provided.')
###---- Load input data -----------------------------------------------------
inputImages = []
for i in args.input:
inputImages.append(nibabel.load(i))
print('Loading Transforms')
print('Warning: Transforms depend on the HR image.')
inputTransforms = []
if args.transform is not None:
for t in args.transform:
inputTransforms.append(read_itk_transform(t))
else:
#no transform provided : use identity as transform and zero as center
m = np.identity(4)
c = np.array([0, 0, 0, 1])
for i in args.input:
inputTransforms.append((m, c))
maskImages = []
if args.mask is not None:
for i in args.mask:
maskImages.append(nibabel.load(i))
else:
print('Creating mask images using the following padding value:',
str(args.padding))
parser = argparse.ArgumentParser()
parser.add_argument('-r', '--ref', help='Reference Image filename (i.e. ground truth) (required)', type=str, required = True)
parser.add_argument('-i', '--input', help='Low-resolution image filename (required), created using btkImageResampling', type=str, required = True)
parser.add_argument('-t', '--transform', help='Transform for each input image (optional)')
parser.add_argument('-o', '--output', help='Estimated high-resolution image filename (required)', type=str, required = True)
parser.add_argument('-p', '--psf', help='3D PSF type (boxcar (default), gauss)', type=str, default='boxcar')
parser.add_argument('--padding', help='Padding value used when no mask is provided (default is 0)', type=float, default=0)
args = parser.parse_args()
HRimage = nibabel.load(args.ref)
LRimage = nibabel.load(args.input)
inputTransform = None
if args.transform is not None :
inputTransform = read_itk_transform(args.transform)
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) ) )
help='3D PSF type (boxcar (default), gauss)',
type=str,
default='boxcar')
parser.add_argument(
'--padding',
help='Padding value used when no mask is provided (default is 0)',
type=float,
default=0)
args = parser.parse_args()
HRimage = nibabel.load(args.ref)
LRimage = nibabel.load(args.input)
inputTransform = None
if args.transform is not None:
inputTransform = read_itk_transform(args.transform)
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)
if args.init is not None:
print("Initialization image: ", args.init)
else:
print("No initialization image provided.")
###---- Load input data -----------------------------------------------------
inputImages = []
for i in args.input:
inputImages.append(nibabel.load(i))
print("Loading Transforms")
print("Warning: Transforms depend on the HR image.")
inputTransforms = []
if args.transform is not None:
for t in args.transform:
inputTransforms.append(read_itk_transform(t))
else:
# no transform provided : use identity as transform and zero as center
m = np.identity(4)
c = np.array([0, 0, 0, 1])
for i in args.input:
inputTransforms.append((m, c))
maskImages = []
if args.mask is not None:
for i in args.mask:
maskImages.append(nibabel.load(i))
else:
print("Creating mask images using the following padding value:", str(args.padding))
for i in range(len(inputImages)):
