def main():
args = parse_args()
mask = dwi.mask.read_mask(args.input)
selected_slices = list(mask.selected_slices())
mbb = mask.bounding_box()
mbb_shape = tuple([b - a for a, b in mbb])
d = dict(infile=args.input, shape=mask.shape(), size=np.prod(mask.shape()),
nsel=mask.n_selected(), mbb=mbb, mbb_shape=mbb_shape,
mbb_size=np.prod(mbb_shape), mbb_all=mask.mbb_equals_selection(),
nsl=len(selected_slices), sl=selected_slices,
msl=mask.max_slices())
s = (
'mask: {infile}\n'
'mask shape: {shape}\n'
'mask size: {size}\n'
'selected voxels: {nsel}\n'
'minimum bounding box coordinates: {mbb}\n'
'minimum bounding box shape: {mbb_shape}\n'
'minimum bounding box size: {mbb_size} (equals selection: {mbb_all})\n'
'selected slices: {nsl}: {sl}\n'
'maximum slices: {msl}'
)
print(s.format(**d))
if args.subregion:
write_subregion(mask, args.pad, args.input, args.subregion)
def main():
args = parse_args()
mask = dwi.mask.read_mask(args.input)
selected_slices = list(mask.selected_slices())
mbb = mask.bounding_box()
mbb_shape = tuple([b - a for a, b in mbb])
d = dict(infile=args.input, shape=mask.shape(), size=np.prod(mask.shape()),
nsel=mask.n_selected(), mbb=mbb, mbb_shape=mbb_shape,
mbb_size=np.prod(mbb_shape), mbb_all=mask.mbb_equals_selection(),
nsl=len(selected_slices), sl=selected_slices,
msl=mask.max_slices())
s = (
'mask: {infile}\n'
'mask shape: {shape}\n'
'mask size: {size}\n'
'selected voxels: {nsel}\n'
'minimum bounding box coordinates: {mbb}\n'
'minimum bounding box shape: {mbb_shape}\n'
'minimum bounding box size: {mbb_size} (equals selection: {mbb_all})\n'
'selected slices: {nsl}: {sl}\n'
'maximum slices: {msl}'
)
print(s.format(**d))
if args.subregion:
write_subregion(mask, args.pad, args.input, args.subregion)
def main():
"""Main."""
args = parse_args()
print(args.image, args.mask)
mask = dwi.mask.read_mask(args.mask)
slices = get_slices(args.image)
if mask.shape()[0] != len(slices):
raise Exception('Number of slices mismatch.')
for mask_slice, paths, i in zip(mask.array, slices, range(len(slices))):
for p in paths:
if args.verbose:
d = dict(i=i, n_selected=np.sum(mask_slice is not False), p=p)
print('Slice {i:d} ({n_selected:d}): {p:s}'.format(**d))
mask_out_slice(mask_slice, p)
def main():
"""Main."""
args = parse_args()
print(args.image, args.mask)
mask = dwi.mask.read_mask(args.mask)
slices = get_slices(args.image)
if mask.shape()[0] != len(slices):
raise Exception('Number of slices mismatch.')
for mask_slice, paths, i in zip(mask.array, slices, range(len(slices))):
for p in paths:
if args.verbose:
d = dict(i=i, n_selected=np.sum(mask_slice is not False), p=p)
print('Slice {i:d} ({n_selected:d}): {p:s}'.format(**d))
mask_out_slice(mask_slice, p)
def main():
args = parse_args()
loglevel = logging.INFO if args.verbose else logging.WARNING
logging.basicConfig(level=loglevel, stream=logging.sys.stdout)
logging.info('Reading image: %s', args.input)
img, attrs = dwi.files.read_pmap(args.input)
if args.mode == 'T2':
assert attrs['echotimes'][0] == 0 # TODO: Could be another?
img = img[..., 0]
assert img.ndim == 3
if args.mask is None:
mask = np.zeros_like(img, dtype=np.bool)
mbb = dwi.util.bbox(img, pad=0)
logging.info('MBB mask: %s', mbb)
mask[mbb] = True
mask = dwi.mask.Mask3D(mask)
else:
logging.info('Using mask: %s', args.mask)
mask = dwi.mask.read_mask(args.mask)
if isinstance(mask, dwi.mask.Mask):
mask = mask.convert_to_3d(img.shape[0])
if img.shape != mask.shape():
raise Exception('Image shape {} does not match mask shape {}'.format(
img.shape, mask.shape()))
if mask.n_selected() == 0:
raise ValueError('Empty mask.')
if args.slices == 'maxfirst':
slice_indices = [mask.max_slices()[0]]
elif args.slices == 'max':
slice_indices = mask.max_slices()
elif args.slices == 'all':
slice_indices = mask.selected_slices()
else:
raise Exception('Invalid slice set specification', args.slices)
# Zero other slices in mask.
for i in range(len(mask.array)):
if i not in slice_indices:
mask.array[i, :, :] = 0
# Use only selected slices to save memory.
if not args.voxel == 'all':
img = img[slice_indices].copy()
mask.array = mask.array[slice_indices].copy()
# Get portion mask.
if args.winspec in ('all', 'mbb'):
pmask = mask.array # Some methods don't use window.
elif args.winspec.isdigit():
winsize = int(args.winspec)
assert winsize > 0
winshape = (1, winsize, winsize)
pmask = portion_mask(mask.array, winshape, portion=args.portion)
else:
raise ValueError('Invalid window spec: {}'.format(args.winspec))
logging.info('Image: %s, slice: %s, voxels: %s, window: %s', img.shape,
slice_indices, np.count_nonzero(mask.array), args.winspec)
logging.info('Calculating %s texture features for %s...', args.method,
args.mode)
dwi.rcParams.texture_avg = args.voxel
if dwi.rcParams.texture_avg != 'all':
if args.mode.startswith('T2w') and args.method.startswith('gabor'):
# These result arrays can get quite huge (if float64).
dwi.rcParams.texture_path = args.output
if args.method in ('glcm', 'glcm_mbb'):
img = dwi.util.quantize(dwi.util.normalize(img, args.mode))
tmap, names = dwi.texture.get_texture(img, args.method, args.winspec,
pmask)
attrs['parameters'] = names
# Number of windows, or resulting texture map volume in general.
attrs['tmap_voxels'] = np.count_nonzero(pmask)
logging.info('Writing shape %s, type %s to %s', tmap.shape, tmap.dtype,
args.output)
if dwi.rcParams.texture_path:
attrs['shape'] = tmap.shape
attrs['dtype'] = str(tmap.dtype)
dwi.hdf5.write_attrs(tmap, attrs) # Attributes may need conversion.
else:
dwi.files.write_pmap(args.output, tmap, attrs)
def main():
args = parse_args()
loglevel = logging.INFO if args.verbose else logging.WARNING
logging.basicConfig(level=loglevel, stream=logging.sys.stdout)
logging.info('Reading image: %s', args.input)
img, attrs = dwi.files.read_pmap(args.input)
if args.mode == 'T2':
assert attrs['echotimes'][0] == 0 # TODO: Could be another?
img = img[..., 0]
assert img.ndim == 3
if args.mask is None:
mask = np.zeros_like(img, dtype=np.bool)
mbb = dwi.util.bbox(img, pad=0)
logging.info('MBB mask: %s', mbb)
mask[mbb] = True
mask = dwi.mask.Mask3D(mask)
else:
logging.info('Using mask: %s', args.mask)
mask = dwi.mask.read_mask(args.mask)
if isinstance(mask, dwi.mask.Mask):
mask = mask.convert_to_3d(img.shape[0])
if img.shape != mask.shape():
raise Exception('Image shape {} does not match mask shape {}'.format(
img.shape, mask.shape()))
if mask.n_selected() == 0:
raise ValueError('Empty mask.')
if args.slices == 'maxfirst':
slice_indices = [mask.max_slices()[0]]
elif args.slices == 'max':
slice_indices = mask.max_slices()
elif args.slices == 'all':
slice_indices = mask.selected_slices()
else:
raise Exception('Invalid slice set specification', args.slices)
# Zero other slices in mask.
for i in range(len(mask.array)):
if i not in slice_indices:
mask.array[i, :, :] = 0
# Use only selected slices to save memory.
if not args.voxel == 'all':
img = img[slice_indices].copy()
mask.array = mask.array[slice_indices].copy()
# Get portion mask.
if args.winspec in ('all', 'mbb'):
pmask = mask.array # Some methods don't use window.
elif args.winspec.isdigit():
winsize = int(args.winspec)
assert winsize > 0
winshape = (1, winsize, winsize)
pmask = portion_mask(mask.array, winshape, portion=args.portion)
else:
raise ValueError('Invalid window spec: {}'.format(args.winspec))
logging.info('Image: %s, slice: %s, voxels: %s, window: %s', img.shape,
slice_indices, np.count_nonzero(mask.array), args.winspec)
logging.info('Calculating %s texture features for %s...', args.method,
args.mode)
dwi.rcParams.texture_avg = args.voxel
if dwi.rcParams.texture_avg != 'all':
if args.mode.startswith('T2w') and args.method.startswith('gabor'):
# These result arrays can get quite huge (if float64).
dwi.rcParams.texture_path = args.output
if args.method in ('glcm', 'glcm_mbb'):
img = dwi.util.quantize(dwi.util.normalize(img, args.mode))
tmap, names = dwi.texture.get_texture(img, args.method, args.winspec,
pmask)
attrs['parameters'] = names
# Number of windows, or resulting texture map volume in general.
attrs['tmap_voxels'] = np.count_nonzero(pmask)
logging.info('Writing shape %s, type %s to %s', tmap.shape, tmap.dtype,
args.output)
if dwi.rcParams.texture_path:
attrs['shape'] = tmap.shape
attrs['dtype'] = str(tmap.dtype)
dwi.hdf5.write_attrs(tmap, attrs) # Attributes may need conversion.
else:
dwi.files.write_pmap(args.output, tmap, attrs)