def main():
parser = getParser()
args = getArguments(parser)
# prepare logger
logger = Logger.getInstance()
if args.debug: logger.setLevel(logging.DEBUG)
elif args.verbose: logger.setLevel(logging.INFO)
# loading input images
img, hdr = load(args.input)
img = img.astype(numpy.bool)
# check spacing values
if not len(args.spacing) == img.ndim:
parser.error(
'The image has {} dimensions, but {} spacing parameters have been supplied.'
.format(img.ndim, len(args.spacing)))
# check if output image exists
if not args.force:
if os.path.exists(args.output):
parser.error('The output image {} already exists.'.format(
args.output))
logger.debug('target voxel spacing: {}'.format(args.spacing))
# determine number of required complete slices for up-sampling
vs = header.get_pixel_spacing(hdr)
rcss = [
int(y // x - 1) for x, y in zip(args.spacing, vs)
] # TODO: For option b, remove the - 1; better: no option b, since I am rounding later anyway
# remove negatives and round up to next even number
rcss = [x if x > 0 else 0 for x in rcss]
rcss = [x if 0 == x % 2 else x + 1 for x in rcss]
logger.debug('intermediate slices to add per dimension: {}'.format(rcss))
# for each dimension requiring up-sampling, from the highest down, perform shape based slice interpolation
logger.info('Adding required slices using shape based interpolation.')
for dim, rcs in enumerate(rcss):
if rcs > 0:
logger.debug(
'adding {} intermediate slices to dimension {}'.format(
rcs, dim))
img = shape_based_slice_interpolation(img, dim, rcs)
logger.debug('resulting new image shape: {}'.format(img.shape))
# compute and set new voxel spacing
nvs = [x / (y + 1.) for x, y in zip(vs, rcss)]
header.set_pixel_spacing(hdr, nvs)
logger.debug('intermediate voxel spacing: {}'.format(nvs))
# interpolate with nearest neighbour
logger.info('Re-sampling the image with a b-spline order of {}.'.format(
args.order))
img, hdr = resample(img, hdr, args.spacing, args.order, mode='nearest')
# saving the resulting image
save(img, args.output, hdr, args.force)
def main():
parser = getParser()
args = getArguments(parser)
# prepare logger
logger = Logger.getInstance()
if args.debug: logger.setLevel(logging.DEBUG)
elif args.verbose: logger.setLevel(logging.INFO)
# loading input images
img, hdr = load(args.input)
img = img.astype(numpy.bool)
# check spacing values
if not len(args.spacing) == img.ndim:
parser.error('The image has {} dimensions, but {} spacing parameters have been supplied.'.format(img.ndim, len(args.spacing)))
# check if output image exists
if not args.force:
if os.path.exists(args.output):
parser.error('The output image {} already exists.'.format(args.output))
logger.debug('target voxel spacing: {}'.format(args.spacing))
# determine number of required complete slices for up-sampling
vs = header.get_pixel_spacing(hdr)
rcss = [int(y // x - 1) for x, y in zip(args.spacing, vs)] # TODO: For option b, remove the - 1; better: no option b, since I am rounding later anyway
# remove negatives and round up to next even number
rcss = [x if x > 0 else 0 for x in rcss]
rcss = [x if 0 == x % 2 else x + 1 for x in rcss]
logger.debug('intermediate slices to add per dimension: {}'.format(rcss))
# for each dimension requiring up-sampling, from the highest down, perform shape based slice interpolation
logger.info('Adding required slices using shape based interpolation.')
for dim, rcs in enumerate(rcss):
if rcs > 0:
logger.debug('adding {} intermediate slices to dimension {}'.format(rcs, dim))
img = shape_based_slice_interpolation(img, dim, rcs)
logger.debug('resulting new image shape: {}'.format(img.shape))
# compute and set new voxel spacing
nvs = [x / (y + 1.) for x, y in zip(vs, rcss)]
header.set_pixel_spacing(hdr, nvs)
logger.debug('intermediate voxel spacing: {}'.format(nvs))
# interpolate with nearest neighbour
logger.info('Re-sampling the image with a b-spline order of {}.'.format(args.order))
img, hdr = resample(img, hdr, args.spacing, args.order, mode='nearest')
# saving the resulting image
save(img, args.output, hdr, args.force)
def sresample(src, dest, spacing, order = 3):
r"""
Secure-re-sample an image located at ``src`` to ``spacing`` and save it under
``dest``.
Parameters
----------
src : string
Source image file.
dest : string
Destination image file.
spacing : sequence of numbers
The target voxel spacing.
order : integer
The b-spline-order as used by `scipy.ndimage.zoom`.
"""
img, hdr = load(src)
img, hdr = filter.resample(img, hdr, spacing, order)
save(img, dest, hdr)