def main():
args = getArguments(getParser())
# prepare logger
logger = Logger.getInstance()
if args.debug: logger.setLevel(logging.DEBUG)
elif args.verbose: logger.setLevel(logging.INFO)
# load input image
data_input, header_input = load(args.input)
# transform to uin8
data_input = data_input.astype(scipy.uint8)
# reduce to 3D, if larger dimensionality
if data_input.ndim > 3:
for _ in range(data_input.ndim - 3): data_input = data_input[...,0]
# iter over slices (2D) until first with content is detected
for plane in data_input:
if scipy.any(plane):
# set pixel spacing
spacing = list(header.get_pixel_spacing(header_input))
spacing = spacing[1:3]
__update_header_from_array_nibabel(header_input, plane)
header.set_pixel_spacing(header_input, spacing)
# save image
save(plane, args.output, header_input, args.force)
break
logger.info("Successfully terminated.")
def main():
args = getArguments(getParser())
# prepare logger
logger = Logger.getInstance()
if args.debug: logger.setLevel(logging.DEBUG)
elif args.verbose: logger.setLevel(logging.INFO)
# load input image
data_input, header_input = load(args.input)
# transform to uin8
data_input = data_input.astype(scipy.uint8)
# reduce to 3D, if larger dimensionality
if data_input.ndim > 3:
for _ in range(data_input.ndim - 3): data_input = data_input[...,0]
# iter over slices (2D) until first with content is detected
for plane in data_input:
if scipy.any(plane):
# set pixel spacing
spacing = list(header.get_pixel_spacing(header_input))
spacing = spacing[1:3]
__update_header_from_array_nibabel(header_input, plane)
header.set_pixel_spacing(header_input, spacing)
# save image
save(plane, args.output, header_input, args.force)
break
logger.info("Successfully terminated.")
def main():
# parse cmd arguments
parser = getParser()
parser.parse_args()
args = getArguments(parser)
# prepare logger
logger = Logger.getInstance()
if args.debug: logger.setLevel(logging.DEBUG)
elif args.verbose: logger.setLevel(logging.INFO)
# load first input image as example
example_data, example_header = load(args.inputs[0])
# test if the supplied position is valid
if args.position > example_data.ndim or args.position < 0:
raise ArgumentError('The supplied position for the new dimension is invalid. It has to be between 0 and {}.'.format(example_data.ndim))
# prepare empty output volume
output_data = scipy.zeros([len(args.inputs)] + list(example_data.shape), dtype=example_data.dtype)
# add first image to output volume
output_data[0] = example_data
# load input images and add to output volume
for idx, image in enumerate(args.inputs[1:]):
image_data, _ = load(image)
if not args.ignore and image_data.dtype != example_data.dtype:
raise ArgumentError('The dtype {} of image {} differs from the one of the first image {}, which is {}.'.format(image_data.dtype, image, args.inputs[0], example_data.dtype))
if image_data.shape != example_data.shape:
raise ArgumentError('The shape {} of image {} differs from the one of the first image {}, which is {}.'.format(image_data.shape, image, args.inputs[0], example_data.shape))
output_data[idx + 1] = image_data
# move new dimension to the end or to target position
for dim in range(output_data.ndim - 1):
if dim >= args.position: break
output_data = scipy.swapaxes(output_data, dim, dim + 1)
# set pixel spacing
spacing = list(header.get_pixel_spacing(example_header))
spacing = tuple(spacing[:args.position] + [args.spacing] + spacing[args.position:])
# !TODO: Find a way to enable this also for PyDicom and ITK images
if __is_header_nibabel(example_header):
__update_header_from_array_nibabel(example_header, output_data)
header.set_pixel_spacing(example_header, spacing)
else:
raise ArgumentError("Sorry. Setting the voxel spacing of the new dimension only works with NIfTI images. See the description of this program for more details.")
# save created volume
save(output_data, args.output, example_header, args.force)
logger.info("Successfully terminated.")
def main():
# parse cmd arguments
parser = getParser()
parser.parse_args()
args = getArguments(parser)
# prepare logger
logger = Logger.getInstance()
if args.debug: logger.setLevel(logging.DEBUG)
elif args.verbose: logger.setLevel(logging.INFO)
# load input image
data_input, header_input = load(args.input)
# check if the supplied dimension is valid
if args.dimension >= data_input.ndim or args.dimension < 0:
raise ArgumentError(
'The supplied cut-dimension {} exceeds the image dimensionality of 0 to {}.'
.format(args.dimension, data_input.ndim - 1))
# prepare output file string
name_output = args.output.replace('{}', '{:03d}')
# compute the new the voxel spacing
spacing = list(header.get_pixel_spacing(header_input))
del spacing[args.dimension]
# iterate over the cut dimension
slices = data_input.ndim * [slice(None)]
for idx in range(data_input.shape[args.dimension]):
# cut the current slice from the original image
slices[args.dimension] = slice(idx, idx + 1)
data_output = scipy.squeeze(data_input[slices])
# update the header and set the voxel spacing
__update_header_from_array_nibabel(header_input, data_output)
header.set_pixel_spacing(header_input, spacing)
# save current slice
save(data_output, name_output.format(idx), header_input, args.force)
logger.info("Successfully terminated.")
def main():
# parse cmd arguments
parser = getParser()
parser.parse_args()
args = getArguments(parser)
# prepare logger
logger = Logger.getInstance()
if args.debug: logger.setLevel(logging.DEBUG)
elif args.verbose: logger.setLevel(logging.INFO)
# load input image
data_input, header_input = load(args.input)
# check if the supplied dimension is valid
if args.dimension >= data_input.ndim or args.dimension < 0:
raise ArgumentError('The supplied cut-dimension {} exceeds the image dimensionality of 0 to {}.'.format(args.dimension, data_input.ndim - 1))
# prepare output file string
name_output = args.output.replace('{}', '{:03d}')
# compute the new the voxel spacing
spacing = list(header.get_pixel_spacing(header_input))
del spacing[args.dimension]
# iterate over the cut dimension
slices = data_input.ndim * [slice(None)]
for idx in range(data_input.shape[args.dimension]):
# cut the current slice from the original image
slices[args.dimension] = slice(idx, idx + 1)
data_output = scipy.squeeze(data_input[slices])
# update the header and set the voxel spacing
__update_header_from_array_nibabel(header_input, data_output)
header.set_pixel_spacing(header_input, spacing)
# save current slice
save(data_output, name_output.format(idx), header_input, args.force)
logger.info("Successfully terminated.")
def main():
# parse cmd arguments
parser = getParser()
parser.parse_args()
args = getArguments(parser)
# prepare logger
logger = Logger.getInstance()
if args.debug: logger.setLevel(logging.DEBUG)
elif args.verbose: logger.setLevel(logging.INFO)
# load first input image as example
example_data, example_header = load(args.inputs[0])
# test if the supplied position is valid
if args.position > example_data.ndim or args.position < 0:
raise ArgumentError(
'The supplied position for the new dimension is invalid. It has to be between 0 and {}.'
.format(example_data.ndim))
# prepare empty output volume
output_data = scipy.zeros([len(args.inputs)] + list(example_data.shape),
dtype=example_data.dtype)
# add first image to output volume
output_data[0] = example_data
# load input images and add to output volume
for idx, image in enumerate(args.inputs[1:]):
image_data, _ = load(image)
if not args.ignore and image_data.dtype != example_data.dtype:
raise ArgumentError(
'The dtype {} of image {} differs from the one of the first image {}, which is {}.'
.format(image_data.dtype, image, args.inputs[0],
example_data.dtype))
if image_data.shape != example_data.shape:
raise ArgumentError(
'The shape {} of image {} differs from the one of the first image {}, which is {}.'
.format(image_data.shape, image, args.inputs[0],
example_data.shape))
output_data[idx + 1] = image_data
# move new dimension to the end or to target position
for dim in range(output_data.ndim - 1):
if dim >= args.position: break
output_data = scipy.swapaxes(output_data, dim, dim + 1)
# set pixel spacing
spacing = list(header.get_pixel_spacing(example_header))
spacing = tuple(spacing[:args.position] + [args.spacing] +
spacing[args.position:])
# !TODO: Find a way to enable this also for PyDicom and ITK images
if __is_header_nibabel(example_header):
__update_header_from_array_nibabel(example_header, output_data)
header.set_pixel_spacing(example_header, spacing)
else:
raise ArgumentError(
"Sorry. Setting the voxel spacing of the new dimension only works with NIfTI images. See the description of this program for more details."
)
# save created volume
save(output_data, args.output, example_header, args.force)
logger.info("Successfully terminated.")
