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)
logger.info("Selected viscous type is {}".format(args.type))
# iterate over input images
for image in args.images:
# get and prepare image data
logger.info("Loading image {} using NiBabel...".format(image))
image_gradient = load(image)
# get and prepare image data
image_gradient_data = scipy.squeeze(image_gradient.get_data())
logger.debug(
"Intensity range of gradient image is ({}, {})".format(image_gradient_data.min(), image_gradient_data.max())
)
# build output file name and check for its existence, if not in sections mode
if "sections" != args.type:
# build output file name
image_viscous_name = (
args.folder
+ "/"
+ image.split("/")[-1][:-4]
+ "_viscous_{}_sec_{}_ds_{}".format(args.type, args.sections, args.dsize)
)
image_viscous_name += image.split("/")[-1][-4:]
# check if output file exists
if not args.force:
if os.path.exists(image_viscous_name):
logger.warning("The output file {} already exists. Skipping this image.".format(image_viscous_name))
continue
# execute plain closing i.e. a closing operation over the whole image, if in plain mode
if "plain" == args.type:
# prepare the disc structure (a ball with a diameter of (args.dsize * 2 + 1))
disc = iterate_structure(generate_binary_structure(3, 1), args.dsize).astype(scipy.int_)
# apply closing
logger.info("Applying the morphology over whole image at once...")
image_viscous_data = grey_closing(image_gradient_data, footprint=disc)
# save resulting gradient image
logger.info("Saving resulting gradient image as {}...".format(image_viscous_name))
image_viscous = image_like(image_viscous_data, image_gradient)
save(image_viscous, image_viscous_name)
# skip other morphologies
continue
# create gradient images flattened histogram
bins = hist_flatened(image_gradient_data, args.sections)
logger.debug("{} bins created".format(len(bins) - 1))
# check if the number of bins is consistent
if args.sections != len(bins) - 1:
raise Exception(
"Inconsistency between the number of requested and created bins ({} to {})".format(
args.sections, len(bins) - 1
)
)
# prepare result file
image_viscous_data = image_gradient_data
# transform the gradient images topography (Note: the content of one bin is: bins[slice - 1] <= content < bins[slice]
logger.info("Applying the viscous morphological operations {} times...".format(args.sections))
for slice in range(1, args.sections + 1):
# build output file name and check for its existence, if in sections mode
if "sections" == args.type:
# build output file name
image_viscous_name = (
args.folder
+ "/"
+ image.split("/")[-1][:-4]
+ "_viscous_{}_sec_{}_ds_{}_sl_{}".format(args.type, args.sections, args.dsize, slice)
)
image_viscous_name += image.split("/")[-1][-4:]
# check if output file exists
if not args.force:
if os.path.exists(image_viscous_name):
logger.warning(
"The output file {} already exists. Skipping this slice.".format(image_viscous_name)
)
continue
# prepare result file
image_viscous_data = image_gradient_data
# create masks to extract the affected voxels (i.e. the current slice of the topographic image representation)
mask_greater = image_gradient_data >= bins[slice] # all voxels with are over the current slice
mask_lower = image_gradient_data < bins[slice - 1] # all voxels which are under the current slice
mask_equal = scipy.invert(mask_greater | mask_lower) # all voxels in the current slice
if "mercury" == args.type:
dsize = int((args.dsize / float(args.sections)) * (slice))
disc = iterate_structure(generate_binary_structure(3, 1), dsize).astype(scipy.int_)
mask_equal_or_greater = mask_equal | mask_greater
image_threshold_data = image_gradient_data * mask_equal_or_greater
elif "oil" == args.type:
dsize = int((args.dsize / float(args.sections)) * (args.sections - slice + 1))
disc = iterate_structure(generate_binary_structure(3, 1), dsize).astype(scipy.int_)
image_threshold_data = image_gradient_data.copy()
mask_equal_or_lower = mask_equal | mask_lower
# set all voxels over the current slice to the max of all voxels in the current slice
image_threshold_data[mask_greater] = image_threshold_data[mask_equal_or_lower].max()
elif "sections" == args.type:
dsize = args.dsize
disc = iterate_structure(generate_binary_structure(3, 1), args.dsize).astype(scipy.int_)
image_threshold_data = image_gradient_data.copy()
# set all voxels under the current slice to zero
image_threshold_data[mask_lower] = 0
# set all voxels over the current slice to the max of all voxels in the current slice
image_threshold_data[mask_greater] = image_threshold_data[mask_equal].max()
logger.debug(
"{} of {} voxels belong to this level.".format(
len(mask_equal.nonzero()[0]), scipy.prod(image_threshold_data.shape)
)
)
# apply the closing with the appropriate disc size
logger.debug(
"Applying a disk of {} to all values >= {} and < {}...".format(dsize, bins[slice - 1], bins[slice])
)
image_closed_data = grey_closing(image_threshold_data, footprint=disc)
# add result of this slice to the general results
image_viscous_data = scipy.maximum(image_viscous_data, image_closed_data)
# save created output file, if in sections mode
if "sections" == args.type:
# save resulting gradient image
logger.info("Saving resulting gradient image as {}...".format(image_viscous_name))
image_viscous = image_like(image_viscous_data, image_gradient)
save(image_viscous, image_viscous_name)
# save created output file, if not in sections mode
if "sections" != args.type:
# save resulting gradient image
logger.info("Saving resulting gradient image as {}...".format(image_viscous_name))
image_viscous = image_like(image_viscous_data, image_gradient)
save(image_viscous, image_viscous_name)
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)
logger.info('Selected viscous type is {}'.format(args.type))
# iterate over input images
for image in args.images:
# get and prepare image data
logger.info('Loading image {} using NiBabel...'.format(image))
image_gradient = load(image)
# get and prepare image data
image_gradient_data = scipy.squeeze(image_gradient.get_data())
logger.debug('Intensity range of gradient image is ({}, {})'.format(
image_gradient_data.min(), image_gradient_data.max()))
# build output file name and check for its existence, if not in sections mode
if 'sections' != args.type:
# build output file name
image_viscous_name = args.folder + '/' + image.split(
'/')[-1][:-4] + '_viscous_{}_sec_{}_ds_{}'.format(
args.type, args.sections, args.dsize)
image_viscous_name += image.split('/')[-1][-4:]
# check if output file exists
if not args.force:
if os.path.exists(image_viscous_name):
logger.warning(
'The output file {} already exists. Skipping this image.'
.format(image_viscous_name))
continue
# execute plain closing i.e. a closing operation over the whole image, if in plain mode
if 'plain' == args.type:
# prepare the disc structure (a ball with a diameter of (args.dsize * 2 + 1))
disc = iterate_structure(generate_binary_structure(3, 1),
args.dsize).astype(scipy.int_)
# apply closing
logger.info('Applying the morphology over whole image at once...')
image_viscous_data = grey_closing(image_gradient_data,
footprint=disc)
# save resulting gradient image
logger.info('Saving resulting gradient image as {}...'.format(
image_viscous_name))
image_viscous = image_like(image_viscous_data, image_gradient)
save(image_viscous, image_viscous_name)
# skip other morphologies
continue
# create gradient images flattened histogram
bins = hist_flatened(image_gradient_data, args.sections)
logger.debug('{} bins created'.format(len(bins) - 1))
# check if the number of bins is consistent
if args.sections != len(bins) - 1:
raise Exception(
'Inconsistency between the number of requested and created bins ({} to {})'
.format(args.sections,
len(bins) - 1))
# prepare result file
image_viscous_data = image_gradient_data
# transform the gradient images topography (Note: the content of one bin is: bins[slice - 1] <= content < bins[slice]
logger.info(
'Applying the viscous morphological operations {} times...'.format(
args.sections))
for slice in range(1, args.sections + 1):
# build output file name and check for its existence, if in sections mode
if 'sections' == args.type:
# build output file name
image_viscous_name = args.folder + '/' + image.split(
'/')[-1][:-4] + '_viscous_{}_sec_{}_ds_{}_sl_{}'.format(
args.type, args.sections, args.dsize, slice)
image_viscous_name += image.split('/')[-1][-4:]
# check if output file exists
if not args.force:
if os.path.exists(image_viscous_name):
logger.warning(
'The output file {} already exists. Skipping this slice.'
.format(image_viscous_name))
continue
# prepare result file
image_viscous_data = image_gradient_data
# create masks to extract the affected voxels (i.e. the current slice of the topographic image representation)
mask_greater = (image_gradient_data >= bins[slice]
) # all voxels with are over the current slice
mask_lower = (image_gradient_data < bins[slice - 1]
) # all voxels which are under the current slice
mask_equal = scipy.invert(
mask_greater | mask_lower) # all voxels in the current slice
if 'mercury' == args.type:
dsize = int((args.dsize / float(args.sections)) * (slice))
disc = iterate_structure(generate_binary_structure(3, 1),
dsize).astype(scipy.int_)
mask_equal_or_greater = mask_equal | mask_greater
image_threshold_data = image_gradient_data * mask_equal_or_greater
elif 'oil' == args.type:
dsize = int((args.dsize / float(args.sections)) *
(args.sections - slice + 1))
disc = iterate_structure(generate_binary_structure(3, 1),
dsize).astype(scipy.int_)
image_threshold_data = image_gradient_data.copy()
mask_equal_or_lower = mask_equal | mask_lower
# set all voxels over the current slice to the max of all voxels in the current slice
image_threshold_data[mask_greater] = image_threshold_data[
mask_equal_or_lower].max()
elif 'sections' == args.type:
dsize = args.dsize
disc = iterate_structure(generate_binary_structure(3, 1),
args.dsize).astype(scipy.int_)
image_threshold_data = image_gradient_data.copy()
# set all voxels under the current slice to zero
image_threshold_data[mask_lower] = 0
# set all voxels over the current slice to the max of all voxels in the current slice
image_threshold_data[mask_greater] = image_threshold_data[
mask_equal].max()
logger.debug('{} of {} voxels belong to this level.'.format(
len(mask_equal.nonzero()[0]),
scipy.prod(image_threshold_data.shape)))
# apply the closing with the appropriate disc size
logger.debug(
'Applying a disk of {} to all values >= {} and < {}...'.format(
dsize, bins[slice - 1], bins[slice]))
image_closed_data = grey_closing(image_threshold_data,
footprint=disc)
# add result of this slice to the general results
image_viscous_data = scipy.maximum(image_viscous_data,
image_closed_data)
# save created output file, if in sections mode
if 'sections' == args.type:
# save resulting gradient image
logger.info('Saving resulting gradient image as {}...'.format(
image_viscous_name))
image_viscous = image_like(image_viscous_data, image_gradient)
save(image_viscous, image_viscous_name)
# save created output file, if not in sections mode
if 'sections' != args.type:
# save resulting gradient image
logger.info('Saving resulting gradient image as {}...'.format(
image_viscous_name))
image_viscous = image_like(image_viscous_data, image_gradient)
save(image_viscous, image_viscous_name)
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)
logger.info(
'Executing weighted viscous morphology with {} ({} bins).'.format(
','.join(map(str, args.func)), len(args.func)))
# iterate over input images
for image in args.images:
# build output file name
image_viscous_name = args.folder + '/' + image.split(
'/')[-1][:-4] + '_wviscous_' + '_'.join(map(str, args.func))
image_viscous_name += image.split('/')[-1][-4:]
# check if output file exists
if not args.force:
if os.path.exists(image_viscous_name):
logger.warning(
'The output file {} already exists. Skipping this image.'.
format(image_viscous_name))
continue
# get and prepare image data
logger.info('Loading image {} using NiBabel...'.format(image))
image_gradient = load(image)
# get and prepare image data
image_gradient_data = scipy.squeeze(image_gradient.get_data())
# prepare result image and extract required attributes of input image
if args.debug:
logger.debug(
'Intensity range of gradient image is ({}, {})'.format(
image_gradient_data.min(), image_gradient_data.max()))
# create gradient images flattened histogram
bins = hist_flatened(image_gradient_data, len(args.func))
logger.debug('{} bins created'.format(len(bins) - 1))
# check if the number of bins is consistent
if len(args.func) != len(bins) - 1:
raise Exception(
'Inconsistency between the number of requested and created bins ({} to {})'
.format(args.sections,
len(bins) - 1))
# prepare result file
image_viscous_data = image_gradient_data
# transform the gradient images topography
logger.info(
'Applying the viscous morphological operations on {} sections...'.
format(len(args.func)))
for sl in range(1, len(args.func) + 1):
# create sphere to use in this step
if 0 >= args.func[sl - 1]:
continue # sphere of sizes 0 or below lead to no changes and are not executed
sphere = iterate_structure(generate_binary_structure(3, 1),
args.func[sl - 1]).astype(scipy.int_)
# create masks to extract the affected voxels (i.e. the current slice of the topographic image representation)
mask_greater = (image_gradient_data >= bins[sl]
) # all voxels with are over the current slice
mask_lower = (image_gradient_data < bins[sl - 1]
) # all voxels which are under the current slice
mask_equal = scipy.invert(
mask_greater | mask_lower) # all voxels in the current slice
# extract slice
image_threshold_data = image_gradient_data.copy()
image_threshold_data[
mask_lower] = 0 # set all voxels under the current slice to zero
image_threshold_data[mask_greater] = image_threshold_data[
mask_equal].max(
) # set all voxels over the current slice to the max of all voxels in the current slice
logger.debug('{} of {} voxels belong to this level.'.format(
len(mask_equal.nonzero()[0]),
scipy.prod(image_threshold_data.shape)))
# apply the closing with the appropriate sphere
logger.debug(
'Applying a disk of {} to all values >= {} and < {} (sec {})...'
.format(args.func[sl - 1], bins[sl - 1], bins[sl], sl))
image_closed_data = grey_closing(image_threshold_data,
footprint=sphere)
# add result of this slice to the general results
image_viscous_data = scipy.maximum(image_viscous_data,
image_closed_data)
# save resulting gradient image
logger.info('Saving resulting gradient image as {}...'.format(
image_viscous_name))
image_viscous = image_like(image_viscous_data, image_gradient)
save(image_viscous, image_viscous_name)
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)
logger.info('Executing weighted viscous morphology with {} ({} bins).'.format(','.join(map(str, args.func)), len(args.func)))
# iterate over input images
for image in args.images:
# build output file name
image_viscous_name = args.folder + '/' + image.split('/')[-1][:-4] + '_wviscous_' + '_'.join(map(str, args.func))
image_viscous_name += image.split('/')[-1][-4:]
# check if output file exists
if not args.force:
if os.path.exists(image_viscous_name):
logger.warning('The output file {} already exists. Skipping this image.'.format(image_viscous_name))
continue
# get and prepare image data
logger.info('Loading image {} using NiBabel...'.format(image))
image_gradient = load(image)
# get and prepare image data
image_gradient_data = scipy.squeeze(image_gradient.get_data())
# prepare result image and extract required attributes of input image
if args.debug:
logger.debug('Intensity range of gradient image is ({}, {})'.format(image_gradient_data.min(), image_gradient_data.max()))
# create gradient images flattened histogram
bins = hist_flatened(image_gradient_data, len(args.func))
logger.debug('{} bins created'.format(len(bins) -1))
# check if the number of bins is consistent
if len(args.func) != len(bins) - 1:
raise Exception('Inconsistency between the number of requested and created bins ({} to {})'.format(args.sections, len(bins) - 1))
# prepare result file
image_viscous_data = image_gradient_data
# transform the gradient images topography
logger.info('Applying the viscous morphological operations on {} sections...'.format(len(args.func)))
for sl in range(1, len(args.func) + 1):
# create sphere to use in this step
if 0 >= args.func[sl - 1]: continue # sphere of sizes 0 or below lead to no changes and are not executed
sphere = iterate_structure(generate_binary_structure(3, 1), args.func[sl - 1]).astype(scipy.int_)
# create masks to extract the affected voxels (i.e. the current slice of the topographic image representation)
mask_greater = (image_gradient_data >= bins[sl]) # all voxels with are over the current slice
mask_lower = (image_gradient_data < bins[sl - 1]) # all voxels which are under the current slice
mask_equal = scipy.invert(mask_greater | mask_lower) # all voxels in the current slice
# extract slice
image_threshold_data = image_gradient_data.copy()
image_threshold_data[mask_lower] = 0 # set all voxels under the current slice to zero
image_threshold_data[mask_greater] = image_threshold_data[mask_equal].max() # set all voxels over the current slice to the max of all voxels in the current slice
logger.debug('{} of {} voxels belong to this level.'.format(len(mask_equal.nonzero()[0]), scipy.prod(image_threshold_data.shape)))
# apply the closing with the appropriate sphere
logger.debug('Applying a disk of {} to all values >= {} and < {} (sec {})...'.format(args.func[sl - 1], bins[sl - 1], bins[sl], sl))
image_closed_data = grey_closing(image_threshold_data, footprint=sphere)
# add result of this slice to the general results
image_viscous_data = scipy.maximum(image_viscous_data, image_closed_data)
# save resulting gradient image
logger.info('Saving resulting gradient image as {}...'.format(image_viscous_name))
image_viscous = image_like(image_viscous_data, image_gradient)
save(image_viscous, image_viscous_name)
logger.info('Successfully terminated.')
