def test_base64_written():
with InTemporaryDirectory():
with open(DATA_FILE5, "rb") as fobj:
contents = fobj.read()
# Confirm the bad tags are still in the file
assert_true(b"GIFTI_ENCODING_B64BIN" in contents)
assert_true(b"GIFTI_ENDIAN_LITTLE" in contents)
# The good ones are missing
assert_false(b"Base64Binary" in contents)
assert_false(b"LittleEndian" in contents)
# Round trip
img5 = load(DATA_FILE5)
save(img5, "fixed.gii")
with open("fixed.gii", "rb") as fobj:
contents = fobj.read()
# The bad codes have gone, replaced by the good ones
assert_false(b"GIFTI_ENCODING_B64BIN" in contents)
assert_false(b"GIFTI_ENDIAN_LITTLE" in contents)
assert_true(b"Base64Binary" in contents)
if sys.byteorder == "little":
assert_true(b"LittleEndian" in contents)
else:
assert_true(b"BigEndian" in contents)
img5_fixed = load("fixed.gii")
darrays = img5_fixed.darrays
assert_array_almost_equal(darrays[0].data, DATA_FILE5_darr1)
assert_array_almost_equal(darrays[1].data, DATA_FILE5_darr2)
def test_readwritedata():
img = load(DATA_FILE2)
with InTemporaryDirectory():
save(img, "test.gii")
img2 = load("test.gii")
assert_equal(img.numDA, img2.numDA)
assert_array_almost_equal(img.darrays[0].data, img2.darrays[0].data)
def test_base64_written():
with InTemporaryDirectory():
with open(DATA_FILE5, 'rb') as fobj:
contents = fobj.read()
# Confirm the bad tags are still in the file
assert_true(b'GIFTI_ENCODING_B64BIN' in contents)
assert_true(b'GIFTI_ENDIAN_LITTLE' in contents)
# The good ones are missing
assert_false(b'Base64Binary' in contents)
assert_false(b'LittleEndian' in contents)
# Round trip
img5 = load(DATA_FILE5)
save(img5, 'fixed.gii')
with open('fixed.gii', 'rb') as fobj:
contents = fobj.read()
# The bad codes have gone, replaced by the good ones
assert_false(b'GIFTI_ENCODING_B64BIN' in contents)
assert_false(b'GIFTI_ENDIAN_LITTLE' in contents)
assert_true(b'Base64Binary' in contents)
if sys.byteorder == 'little':
assert_true(b'LittleEndian' in contents)
else:
assert_true(b'BigEndian' in contents)
img5_fixed = load('fixed.gii')
darrays = img5_fixed.darrays
assert_array_almost_equal(darrays[0].data, DATA_FILE5_darr1)
assert_array_almost_equal(darrays[1].data, DATA_FILE5_darr2)
def test_load_dataarray3():
img3 = load(DATA_FILE3)
with InTemporaryDirectory():
save(img3, 'test.gii')
bimg = load('test.gii')
for img in (img3, bimg):
assert_array_almost_equal(img.darrays[0].data[30:50], DATA_FILE3_darr1)
def test_readwritedata():
img = load(DATA_FILE2)
with InTemporaryDirectory():
save(img, 'test.gii')
img2 = load('test.gii')
assert_equal(img.numDA, img2.numDA)
assert_array_almost_equal(img.darrays[0].data, img2.darrays[0].data)
def test_load_dataarray3():
img3 = load(DATA_FILE3)
with InTemporaryDirectory():
save(img3, "test.gii")
bimg = load("test.gii")
for img in (img3, bimg):
assert_array_almost_equal(img.darrays[0].data[30:50], DATA_FILE3_darr1)
def test_load_dataarray2():
img2 = load(DATA_FILE2)
# Round trip
with InTemporaryDirectory():
save(img2, 'test.gii')
bimg = load('test.gii')
for img in (img2, bimg):
assert_array_almost_equal(img.darrays[0].data[:10], DATA_FILE2_darr1)
def test_load_dataarray4():
img4 = load(DATA_FILE4)
# Round trip
with InTemporaryDirectory():
save(img4, 'test.gii')
bimg = load('test.gii')
for img in (img4, bimg):
assert_array_almost_equal(img.darrays[0].data[:10], DATA_FILE4_darr1)
def test_load_dataarray4():
img4 = load(DATA_FILE4)
# Round trip
with InTemporaryDirectory():
save(img4, "test.gii")
bimg = load("test.gii")
for img in (img4, bimg):
assert_array_almost_equal(img.darrays[0].data[:10], DATA_FILE4_darr1)
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)
# build output image name
image_bg_name = args.folder + '/' + args.mask.split('/')[-1][:-4] + '.bg'
image_bg_name += args.mask.split('/')[-1][-4:]
# check if output image exists
if not args.force:
if os.path.exists(image_bg_name):
logger.warning('The output image {} already exists. Breaking.'.format(image_bg_name))
exit(1)
# load mask
logger.info('Loading mask {}...'.format(args.mask))
try:
mask_image = load(args.mask)
mask_image_data = numpy.squeeze(mask_image.get_data()).astype(scipy.bool_)
except ImageFileError as e:
logger.critical('The mask image does not exist or its file type is unknown.')
raise ArgumentError('The mask image does not exist or its file type is unknown.', e)
# array of indices to access desired slices
sls = [(slice(1), slice(None), slice(None)),
(slice(-1, None), slice(None), slice(None)),
(slice(None), slice(1), slice(None)),
(slice(None), slice(-1, None), slice(None)),
(slice(None), slice(None), slice(1)),
(slice(None), slice(None), slice(-1, None))]
# security check
logger.info('Determine if the slices are not intersection with the reference liver mask...')
for sl in sls:
if not 0 == len(mask_image_data[sl].nonzero()[0]):
logger.critical('Reference mask reaches till the image border.')
raise ArgumentError('Reference mask reaches till the image border.')
# create and save background marker image
logger.info('Creating background marker image...')
image_bg_data = scipy.zeros(mask_image_data.shape, dtype=scipy.bool_)
for sl in sls:
image_bg_data[sl] = True
logger.info('Saving background marker image...')
mask_image.get_header().set_data_dtype(scipy.int8)
save(image_like(image_bg_data, mask_image), image_bg_name)
logger.info('Successfully terminated.')
def test_load_dataarray1():
img1 = load(DATA_FILE1)
# Round trip
with InTemporaryDirectory():
save(img1, "test.gii")
bimg = load("test.gii")
for img in (img1, bimg):
assert_array_almost_equal(img.darrays[0].data, DATA_FILE1_darr1)
assert_array_almost_equal(img.darrays[1].data, DATA_FILE1_darr2)
me = img.darrays[0].meta.metadata
assert_true("AnatomicalStructurePrimary" in me)
assert_true("AnatomicalStructureSecondary" in me)
assert_equal(me["AnatomicalStructurePrimary"], "CortexLeft")
assert_array_almost_equal(img.darrays[0].coordsys.xform, np.eye(4, 4))
assert_equal(xform_codes.niistring[img.darrays[0].coordsys.dataspace], "NIFTI_XFORM_TALAIRACH")
assert_equal(xform_codes.niistring[img.darrays[0].coordsys.xformspace], "NIFTI_XFORM_TALAIRACH")
def main(args=None):
"""Main program function."""
parser = _get_parser()
opts = parser.parse_args(args)
from_img = load(opts.infile)
if not opts.force and Path(opts.outfile).exists():
raise FileExistsError(f"Output file exists: {opts.outfile}")
out_img = conform(from_img=from_img,
out_shape=opts.out_shape,
voxel_size=opts.voxel_size,
order=3,
cval=0.0,
orientation=opts.orientation)
save(out_img, opts.outfile)
def test_volume(tmpdir, capsys):
mask_data = np.zeros((20, 20, 20), dtype='u1')
mask_data[5:15, 5:15, 5:15] = 1
img = Nifti1Image(mask_data, np.eye(4))
infile = tmpdir / "input.nii"
save(img, infile)
args = (f"{infile} --Volume")
main(args.split())
vol_mm3 = capsys.readouterr()
args = (f"{infile} --Volume --units vox")
main(args.split())
vol_vox = capsys.readouterr()
assert float(vol_mm3[0]) == 1000.0
assert int(vol_vox[0]) == 1000
def test_load_labeltable():
img6 = load(DATA_FILE6)
# Round trip
with InTemporaryDirectory():
save(img6, "test.gii")
bimg = load("test.gii")
for img in (img6, bimg):
assert_array_almost_equal(img.darrays[0].data[:3], DATA_FILE6_darr1)
assert_equal(len(img.labeltable.labels), 36)
labeldict = img.labeltable.get_labels_as_dict()
assert_true(660700 in labeldict)
assert_equal(labeldict[660700], "entorhinal")
assert_equal(img.labeltable.labels[1].key, 2647065)
assert_equal(img.labeltable.labels[1].red, 0.0980392)
assert_equal(img.labeltable.labels[1].green, 0.392157)
assert_equal(img.labeltable.labels[1].blue, 0.156863)
assert_equal(img.labeltable.labels[1].alpha, 1)
def test_load_labeltable():
img6 = load(DATA_FILE6)
# Round trip
with InTemporaryDirectory():
save(img6, 'test.gii')
bimg = load('test.gii')
for img in (img6, bimg):
assert_array_almost_equal(img.darrays[0].data[:3], DATA_FILE6_darr1)
assert_equal(len(img.labeltable.labels), 36)
labeldict = img.labeltable.get_labels_as_dict()
assert_true(660700 in labeldict)
assert_equal(labeldict[660700], 'entorhinal')
assert_equal(img.labeltable.labels[1].key, 2647065)
assert_equal(img.labeltable.labels[1].red, 0.0980392)
assert_equal(img.labeltable.labels[1].green, 0.392157)
assert_equal(img.labeltable.labels[1].blue, 0.156863)
assert_equal(img.labeltable.labels[1].alpha, 1)
def test_parse_dataarrays():
fn = 'bad_daa.gii'
img = gi.GiftiImage()
with InTemporaryDirectory():
save(img, fn)
with open(fn, 'r') as fp:
txt = fp.read()
# Make a bad gifti.
txt = txt.replace('NumberOfDataArrays="0"', 'NumberOfDataArrays ="1"')
with open(fn, 'w') as fp:
fp.write(txt)
with clear_and_catch_warnings() as w:
warnings.filterwarnings('once', category=UserWarning)
load(fn)
assert_equal(len(w), 1)
assert_equal(img.numDA, 0)
def test_parse_dataarrays():
fn = "bad_daa.gii"
img = gi.GiftiImage()
with InTemporaryDirectory():
save(img, fn)
with open(fn, "r") as fp:
txt = fp.read()
# Make a bad gifti.
txt = txt.replace('NumberOfDataArrays="0"', 'NumberOfDataArrays ="1"')
with open(fn, "w") as fp:
fp.write(txt)
with clear_and_catch_warnings() as w:
warnings.filterwarnings("once", category=UserWarning)
load(fn)
assert_equal(len(w), 1)
assert_equal(img.numDA, 0)
def test_load_dataarray1():
img1 = load(DATA_FILE1)
# Round trip
with InTemporaryDirectory():
save(img1, 'test.gii')
bimg = load('test.gii')
for img in (img1, bimg):
assert_array_almost_equal(img.darrays[0].data, DATA_FILE1_darr1)
assert_array_almost_equal(img.darrays[1].data, DATA_FILE1_darr2)
me = img.darrays[0].meta.metadata
assert_true('AnatomicalStructurePrimary' in me)
assert_true('AnatomicalStructureSecondary' in me)
assert_equal(me['AnatomicalStructurePrimary'], 'CortexLeft')
assert_array_almost_equal(img.darrays[0].coordsys.xform, np.eye(4, 4))
assert_equal(xform_codes.niistring[img.darrays[0].coordsys.dataspace],
'NIFTI_XFORM_TALAIRACH')
assert_equal(xform_codes.niistring[img.darrays[0].coordsys.xformspace],
'NIFTI_XFORM_TALAIRACH')
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)
# check if output file exists
if not args.force:
if os.path.exists(args.image):
logger.warning('The output file {} already exists. Exiting.'.format(args.image))
sys.exit(0)
logger.info('Unpickle testbench and loading label image...')
label, label_img, bounding_boxes, model_fg_ids, model_bg_ids, truth_fg, truth_bg = __load(args.testbench, args.label)
logger.info('Composing image image...')
image = scipy.zeros(label.shape, dtype=scipy.int8)
# set foreground ids
for rid in truth_fg:
image[bounding_boxes[rid - 1]][label[bounding_boxes[rid - 1]] == rid] = 1
# set background ids
for rid in truth_bg:
image[bounding_boxes[rid - 1]][label[bounding_boxes[rid - 1]] == rid] = 2
# set foreground model ids
for rid in model_fg_ids:
image[bounding_boxes[rid - 1]][label[bounding_boxes[rid - 1]] == rid] = 3
# set background model ids
for rid in model_bg_ids:
image[bounding_boxes[rid - 1]][label[bounding_boxes[rid - 1]] == rid] = 4
logger.info('Saving image as {} with data-type int8...'.format(args.image))
image_img = image_like(image, label_img)
image_img.get_header().set_data_dtype(scipy.int8)
save(image_img, args.image)
logger.info('Successfully terminated.')
def main():
# prepare logger
logger = Logger.getInstance()
logger.setLevel(logging.DEBUG)
# input image locations
#i = '/home/omaier/Experiments/Regionsegmentation/Evaluation_Viscous/00originalvolumes/o09.nii' # original image
#i = '/home/omaier/Temp/test.nii' # original image
#i = '/home/omaier/Temp/o09_smoothed_i4.0_c0.1_t0.0625.nii'
i = '/home/omaier/Experiments/GraphCut/BoundaryTerm/Stawiaski/01gradient/o09_gradient.nii'
# output image locations
r = '/home/omaier/Temp/result_gradient.nii' # result mask
# load images
i_i = load(i)
# extract and prepare image data
i_d = scipy.squeeze(i_i.get_data())
# crop input images to achieve faster execution
crop = [slice(50, -200),
slice(50, -150),
slice(50, -100)]
#i_d = i_d[crop]
i_d = scipy.copy(i_d)
# !TODO: Test if input image is of size 0
logger.debug('input image shape={},ndims={},dtype={}'.format(i_d.shape, i_d.ndim, i_d.dtype))
result = watershed8(i_d, logger)
logger.info('Saving resulting region map...')
result_i = image_like(result, i_i)
result_i.get_header().set_data_dtype(scipy.int32)
save(result_i, r)
logger.info('Done!')
def main():
# prepare logger
logger = Logger.getInstance()
logger.setLevel(logging.DEBUG)
# input image locations
#i = '/home/omaier/Experiments/Regionsegmentation/Evaluation_Viscous/00originalvolumes/o09.nii' # original image
#i = '/home/omaier/Temp/test.nii' # original image
#i = '/home/omaier/Temp/o09_smoothed_i4.0_c0.1_t0.0625.nii'
i = '/home/omaier/Experiments/GraphCut/BoundaryTerm/Stawiaski/01gradient/o09_gradient.nii'
# output image locations
r = '/home/omaier/Temp/result_gradient.nii' # result mask
# load images
i_i = load(i)
# extract and prepare image data
i_d = scipy.squeeze(i_i.get_data())
# crop input images to achieve faster execution
crop = [slice(50, -200), slice(50, -150), slice(50, -100)]
#i_d = i_d[crop]
i_d = scipy.copy(i_d)
# !TODO: Test if input image is of size 0
logger.debug('input image shape={},ndims={},dtype={}'.format(
i_d.shape, i_d.ndim, i_d.dtype))
result = watershed8(i_d, logger)
logger.info('Saving resulting region map...')
result_i = image_like(result, i_i)
result_i.get_header().set_data_dtype(scipy.int32)
save(result_i, r)
logger.info('Done!')
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)
# build output image name
output_hdr_name = args.output + '.hdr'
output_img_name = args.output + '.img'
output_msk_name = args.output + '.msk'
# check if output image exists
if not args.force:
if os.path.exists(output_hdr_name):
logger.warning(
'The output header {} already exists. Breaking.'.format(
output_hdr_name))
exit(1)
elif os.path.exists(output_img_name):
logger.warning(
'The output image {} already exists. Breaking.'.format(
output_img_name))
exit(1)
elif os.path.exists(output_msk_name):
logger.warning(
'The output infor file {} already exists. Breaking.'.format(
output_msk_name))
exit(1)
# decide on most suitable bit format
if len(args.masks) / 2 <= 8:
bit_format = scipy.uint8
elif len(args.masks) / 2 <= 16:
bit_format = scipy.uint16
elif len(args.masks) / 2 <= 32:
bit_format = scipy.uint32
elif len(args.masks) / 2 <= 64:
bit_format = scipy.uint64
else:
raise ArgumentError(
'It is not possible to combine more than 64 single masks.')
logger.info(
'Creating a Radiance® segmentation image in {} bit format...'.format(
bit_format))
# loading first mask image as reference and template for saving
logger.info('Loading mask {} ({} segmentation) using NiBabel...'.format(
args.masks[0], args.masks[1]))
image_mask = load(args.masks[0])
image_mask_data = scipy.squeeze(image_mask.get_data())
# prepare result image
image_radiance_data = scipy.zeros(image_mask_data.shape, dtype=bit_format)
logger.debug('Result image is of dimensions {} and type {}.'.format(
image_radiance_data.shape, image_radiance_data.dtype))
# preparing .msk file
f = open(output_msk_name, 'w')
# adding first mask to result image
image_radiance_data[image_mask_data > 0] = 1
# adding first mask segmentation identifier to the .msk file
f.write('{}\t1\t{}\t{}\t{}\n'.format(args.masks[1],
*__COLOURS[0 % len(__COLOURS)]))
for i in range(2, len(args.masks), 2):
# loading mask image
logger.info(
'Loading mask {} ({} segmentation) using NiBabel...'.format(
args.masks[i], args.masks[i + 1]))
image_mask_data = scipy.squeeze(load(args.masks[i]).get_data())
# check if the shape of the images is consistent
if image_mask_data.shape != image_radiance_data.shape:
raise ArgumentError(
'Mask {} is with {} of a different shape as the first mask image (which has {}).'
.format(args.masks[i], image_mask_data.shape,
image_radiance_data.shape))
# adding mask to result image
image_radiance_data[image_mask_data > 0] += pow(2, i / 2)
# adding mask segmentation identifier to the .msk file
f.write('{}\t{}\t{}\t{}\t{}\n'.format(
args.masks[i + 1], pow(2, i / 2),
*__COLOURS[(i / 2) % len(__COLOURS)]))
logger.info(
'Saving Radiance® segmentation image as {}/.img/.msk...'.format(
output_hdr_name))
image_mask.get_header().set_data_dtype(bit_format)
save(image_like(image_radiance_data, image_mask), output_hdr_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)
# build output image name
image_fg_name = args.folder + '/' + args.mask.split('/')[-1][:-4] + '.fg'
image_fg_name += args.mask.split('/')[-1][-4:]
image_bg_name = args.folder + '/' + args.mask.split('/')[-1][:-4] + '.bg'
image_bg_name += args.mask.split('/')[-1][-4:]
# check if output image exists
if not args.force:
if os.path.exists(image_fg_name):
logger.warning(
'The output image {} already exists. Breaking.'.format(
image_fg_name))
exit(1)
elif os.path.exists(image_bg_name):
logger.warning(
'The output image {} already exists. Breaking.'.format(
image_bg_name))
exit(1)
# load mask
logger.info('Loading mask {}...'.format(args.mask))
try:
mask_image = load(args.mask)
mask_image_data = numpy.squeeze(mask_image.get_data()).astype(
scipy.bool_)
except ImageFileError as e:
logger.critical(
'The mask image does not exist or its file type is unknown.')
raise ArgumentError(
'The mask image does not exist or its file type is unknown.', e)
# erode mask stepwise
logger.info('Step-wise reducing mask to find center...')
mask_remains = mask_image_data.copy()
while (True):
mask_remains_next = ndimage.binary_erosion(mask_remains, iterations=2)
if 0 == len(mask_remains_next.nonzero()[0]):
break
mask_remains = mask_remains_next
# extract one of the remaining voxels
voxels = mask_remains.nonzero()
marker = (voxels[0][0], voxels[1][0], voxels[2][0])
logger.debug('Extracted foreground seed is {}.'.format(marker))
# check suitability of corners as background markers
logger.info(
'Checking if the corners are suitable background seed candidates...')
if True == mask_image_data[0,0,0] or \
True == mask_image_data[-1,0,0] or \
True == mask_image_data[0,-1,0] or \
True == mask_image_data[0,0,-1] or \
True == mask_image_data[-1,-1,0] or \
True == mask_image_data[-1,0,-1] or \
True == mask_image_data[0,-1,-1] or \
True == mask_image_data[-1,-1,-1]:
logger.critical(
'The corners of the image do not correspond to background voxels.')
raise ArgumentError(
'The corners of the image do not correspond to background voxels.')
# create and save foreground marker image
logger.info('Creating foreground marker image...')
image_fg_data = scipy.zeros(mask_image_data.shape, dtype=scipy.bool_)
image_fg_data[marker[0], marker[1], marker[2]] = True
logger.info('Saving foreground marker image...')
mask_image.get_header().set_data_dtype(scipy.int8)
save(image_like(image_fg_data, mask_image), image_fg_name)
# create and save background marker image
logger.info('Creating background marker image...')
image_bg_data = scipy.zeros(mask_image_data.shape, dtype=scipy.bool_)
image_bg_data[0, 0, 0] = True
image_bg_data[-1, 0, 0] = True
image_bg_data[0, -1, 0] = True
image_bg_data[0, 0, -1] = True
image_bg_data[-1, -1, 0] = True
image_bg_data[-1, 0, -1] = True
image_bg_data[0, -1, -1] = True
image_bg_data[-1, -1, -1] = True
logger.info('Saving background marker image...')
mask_image.get_header().set_data_dtype(scipy.int8)
save(image_like(image_bg_data, mask_image), image_bg_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)
# build output image name
image_fg_name = args.folder + '/' + args.mask.split('/')[-1][:-4] + '.fg'
image_fg_name += args.mask.split('/')[-1][-4:]
image_bg_name = args.folder + '/' + args.mask.split('/')[-1][:-4] + '.bg'
image_bg_name += args.mask.split('/')[-1][-4:]
# check if output image exists
if not args.force:
if os.path.exists(image_fg_name):
logger.warning('The output image {} already exists. Breaking.'.format(image_fg_name))
exit(1)
elif os.path.exists(image_bg_name):
logger.warning('The output image {} already exists. Breaking.'.format(image_bg_name))
exit(1)
# load mask
logger.info('Loading mask {}...'.format(args.mask))
try:
mask_image = load(args.mask)
mask_image_data = numpy.squeeze(mask_image.get_data()).astype(scipy.bool_)
except ImageFileError as e:
logger.critical('The mask image does not exist or its file type is unknown.')
raise ArgumentError('The mask image does not exist or its file type is unknown.', e)
# erode mask stepwise
logger.info('Step-wise reducing mask to find center...')
mask_remains = mask_image_data.copy()
while (True):
mask_remains_next = ndimage.binary_erosion(mask_remains, iterations=2)
if 0 == len(mask_remains_next.nonzero()[0]):
break
mask_remains = mask_remains_next
# extract one of the remaining voxels
voxels = mask_remains.nonzero()
marker = (voxels[0][0], voxels[1][0], voxels[2][0])
logger.debug('Extracted foreground seed is {}.'.format(marker))
# check suitability of corners as background markers
logger.info('Checking if the corners are suitable background seed candidates...')
if True == mask_image_data[0,0,0] or \
True == mask_image_data[-1,0,0] or \
True == mask_image_data[0,-1,0] or \
True == mask_image_data[0,0,-1] or \
True == mask_image_data[-1,-1,0] or \
True == mask_image_data[-1,0,-1] or \
True == mask_image_data[0,-1,-1] or \
True == mask_image_data[-1,-1,-1]:
logger.critical('The corners of the image do not correspond to background voxels.')
raise ArgumentError('The corners of the image do not correspond to background voxels.')
# create and save foreground marker image
logger.info('Creating foreground marker image...')
image_fg_data = scipy.zeros(mask_image_data.shape, dtype=scipy.bool_)
image_fg_data[marker[0], marker[1], marker[2]] = True
logger.info('Saving foreground marker image...')
mask_image.get_header().set_data_dtype(scipy.int8)
save(image_like(image_fg_data, mask_image), image_fg_name)
# create and save background marker image
logger.info('Creating background marker image...')
image_bg_data = scipy.zeros(mask_image_data.shape, dtype=scipy.bool_)
image_bg_data[0,0,0] = True
image_bg_data[-1,0,0] = True
image_bg_data[0,-1,0] = True
image_bg_data[0,0,-1] = True
image_bg_data[-1,-1,0] = True
image_bg_data[-1,0,-1] = True
image_bg_data[0,-1,-1] = True
image_bg_data[-1,-1,-1] = True
logger.info('Saving background marker image...')
mask_image.get_header().set_data_dtype(scipy.int8)
save(image_like(image_bg_data, mask_image), image_bg_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)
# iterate over input images
for image in args.images:
# build output image name
image_real_name = image.split('/')[-1][:-4] + '_real' + image.split('/')[-1][-4:]
image_imag_name = image.split('/')[-1][:-4] + '_imag' + image.split('/')[-1][-4:]
# check if output images exists
if not args.force:
if os.path.exists(image_real_name):
logger.warning('The output image {} already exists. Skipping this step.'.format(image_real_name))
continue
elif os.path.exists(image_imag_name):
logger.warning('The output image {} already exists. Skipping this step.'.format(image_imag_name))
continue
# load image using nibabel
logger.info('Loading image {} using NiBabel...'.format(image))
image_original = load(image)
# get and prepare image data
image_original_data = scipy.squeeze(image_original.get_data())
# apply the discrete fast Fourier transformation
logger.info('Executing the discrete fast Fourier transformation...')
image_fft_data = scipy.fftpack.fftn(image_original_data)
# transform to logarithmic scale
logger.info('To logarithmic space...')
image_real_data = image_fft_data.real
print image_real_data.min(), image_real_data.max()
image_real_data = image_real_data + abs(image_real_data.min())
constant = 65535./(math.log(1 + image_real_data.max())) # scale by 0.0001, log and then scale to fir uint16
myfunc = lambda x: constant * math.log(1 + x * 0.0001)
new_func = numpy.vectorize(myfunc)
logger.info('Apply...')
image_real_data = new_func(image_real_data)
print image_real_data.min(), image_real_data.max()
image_imag_data = image_fft_data.imag
# save resulting images
logger.info('Saving resulting images real part as {} in the same format as input image, only with data-type float32...'.format(image_real_name))
image_real = image_like(image_real_data, image_original)
image_real.get_header().set_data_dtype(scipy.uint16)
save(image_real, image_real_name)
logger.info('Saving resulting images real part as {} in the same format as input image, only with data-type float32...'.format(image_imag_name))
image_imag = image_like(image_imag_data, image_original)
image_imag.get_header().set_data_dtype(scipy.float32)
save(image_imag, image_imag_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():
# prepare logger
logger = Logger.getInstance()
logger.setLevel(logging.DEBUG)
# input image locations
#i = '/home/omaier/Experiments/Regionsegmentation/Evaluation_Viscous/00originalvolumes/o09.nii' # original image
g = '/home/omaier/Experiments/Regionsegmentation/Evaluation_Viscous/01gradient/o09_gradient.nii' # gradient magnitude image
l = '/home/omaier/Experiments/GraphCut/RegionalTerm/images/label_full.nii' # watershed label image
fg = '/home/omaier/Experiments/GraphCut/RegionalTerm/images/fg_markers.nii'
bg = '/home/omaier/Experiments/GraphCut/RegionalTerm/images/bg_markers.nii'
# output image locations
r = '/home/omaier/Experiments/GraphCut/BoundaryTerm/graphcut_full.nii' # liver mask
# load images
#i_i = load(i)
g_i = load(g)
l_i = load(l)
fg_i = load(fg)
bg_i = load(bg)
# extract and prepare image data
#i_d = scipy.squeeze(i_i.get_data())
g_d = scipy.squeeze(g_i.get_data())
l_d = scipy.squeeze(l_i.get_data())
fg_d = scipy.squeeze(fg_i.get_data())
bg_d = scipy.squeeze(bg_i.get_data())
# crop input images to achieve faster execution
#crop = [slice(50, -100),
# slice(50, -100),
# slice(50, -100)]
#g_d = g_d[crop]
#l_d = l_d[crop]
#fg_d = fg_d[crop]
#bg_d = bg_d[crop]
# recompute the label ids to start from id
logger.info('Relabel input image...')
l_d = filter.relabel(l_d)
# generate graph
logger.info('Preparing graph...')
gr = graphcut.graph_from_labels(l_d, fg_d, bg_d, boundary_term = graphcut.boundary_stawiaski, boundary_term_args = g_d)
#inconsistent = gr.inconsistent()
#if inconsistent:
# logger.error('The created graph contains inconsistencies: {}'.format('\n'.join(inconsistent)))
# build graph cut graph from graph
logger.info('Generating BK_MFMC C++ graph...')
gcgraph = graphcut.GraphDouble(len(gr.get_nodes()), len(gr.get_nweights()))
gcgraph.add_node(len(gr.get_nodes()))
for node, weight in gr.get_tweights().iteritems():
gcgraph.add_tweights(int(node - 1), weight[0], weight[1])
for edge, weight in gr.get_nweights().iteritems():
gcgraph.add_edge(int(edge[0] - 1), int(edge[1] - 1), weight[0], weight[1])
# execute min-cut
logger.info('Executing min-cut...')
maxflow = gcgraph.maxflow()
logger.debug('Maxflow is {}'.format(maxflow))
# collect
logger.info('Applying results...')
l_d = filter.relabel_map(l_d, gcgraph.what_segment, lambda fun, rid: 0 if gcgraph.termtype.SINK == fun(int(rid) - 1) else 1)
logger.info('Saving images resulting mask...')
# save resulting mask
l_d = l_d.astype(scipy.bool_)
save(image_like(l_d, fg_i), r)
logger.info('Done!')
def main():
# prepare logger
logger = Logger.getInstance()
logger.setLevel(logging.DEBUG)
# input image locations
#i = '/home/omaier/Experiments/Regionsegmentation/Evaluation_Viscous/00originalvolumes/o09.nii' # original image
g = '/home/omaier/Experiments/Regionsegmentation/Evaluation_Viscous/01gradient/o09_gradient.nii' # gradient magnitude image
l = '/home/omaier/Experiments/GraphCut/RegionalTerm/images/label_full.nii' # watershed label image
fg = '/home/omaier/Experiments/GraphCut/RegionalTerm/images/fg_markers.nii'
bg = '/home/omaier/Experiments/GraphCut/RegionalTerm/images/bg_markers.nii'
# output image locations
r = '/home/omaier/Experiments/GraphCut/BoundaryTerm/graphcut_full.nii' # liver mask
# load images
#i_i = load(i)
g_i = load(g)
l_i = load(l)
fg_i = load(fg)
bg_i = load(bg)
# extract and prepare image data
#i_d = scipy.squeeze(i_i.get_data())
g_d = scipy.squeeze(g_i.get_data())
l_d = scipy.squeeze(l_i.get_data())
fg_d = scipy.squeeze(fg_i.get_data())
bg_d = scipy.squeeze(bg_i.get_data())
# crop input images to achieve faster execution
#crop = [slice(50, -100),
# slice(50, -100),
# slice(50, -100)]
#g_d = g_d[crop]
#l_d = l_d[crop]
#fg_d = fg_d[crop]
#bg_d = bg_d[crop]
# recompute the label ids to start from id
logger.info('Relabel input image...')
l_d = filter.relabel(l_d)
# generate graph
logger.info('Preparing graph...')
gr = graphcut.graph_from_labels(l_d,
fg_d,
bg_d,
boundary_term=graphcut.boundary_stawiaski,
boundary_term_args=g_d)
#inconsistent = gr.inconsistent()
#if inconsistent:
# logger.error('The created graph contains inconsistencies: {}'.format('\n'.join(inconsistent)))
# build graph cut graph from graph
logger.info('Generating BK_MFMC C++ graph...')
gcgraph = graphcut.GraphDouble(len(gr.get_nodes()), len(gr.get_nweights()))
gcgraph.add_node(len(gr.get_nodes()))
for node, weight in gr.get_tweights().iteritems():
gcgraph.add_tweights(int(node - 1), weight[0], weight[1])
for edge, weight in gr.get_nweights().iteritems():
gcgraph.add_edge(int(edge[0] - 1), int(edge[1] - 1), weight[0],
weight[1])
# execute min-cut
logger.info('Executing min-cut...')
maxflow = gcgraph.maxflow()
logger.debug('Maxflow is {}'.format(maxflow))
# collect
logger.info('Applying results...')
l_d = filter.relabel_map(
l_d, gcgraph.what_segment, lambda fun, rid: 0
if gcgraph.termtype.SINK == fun(int(rid) - 1) else 1)
logger.info('Saving images resulting mask...')
# save resulting mask
l_d = l_d.astype(scipy.bool_)
save(image_like(l_d, fg_i), r)
logger.info('Done!')
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)
# iterate over input images
for image in args.images:
# build output image name
image_real_name = image.split("/")[-1][:-4] + "_real" + image.split("/")[-1][-4:]
image_imag_name = image.split("/")[-1][:-4] + "_imag" + image.split("/")[-1][-4:]
# check if output images exists
if not args.force:
if os.path.exists(image_real_name):
logger.warning("The output image {} already exists. Skipping this step.".format(image_real_name))
continue
elif os.path.exists(image_imag_name):
logger.warning("The output image {} already exists. Skipping this step.".format(image_imag_name))
continue
# load image using nibabel
logger.info("Loading image {} using NiBabel...".format(image))
image_original = load(image)
# get and prepare image data
image_original_data = scipy.squeeze(image_original.get_data())
# apply the discrete fast Fourier transformation
logger.info("Executing the discrete fast Fourier transformation...")
image_fft_data = scipy.fftpack.fftn(image_original_data)
# transform to logarithmic scale
logger.info("To logarithmic space...")
image_real_data = image_fft_data.real
print image_real_data.min(), image_real_data.max()
image_real_data = image_real_data + abs(image_real_data.min())
constant = 65535.0 / (math.log(1 + image_real_data.max())) # scale by 0.0001, log and then scale to fir uint16
myfunc = lambda x: constant * math.log(1 + x * 0.0001)
new_func = numpy.vectorize(myfunc)
logger.info("Apply...")
image_real_data = new_func(image_real_data)
print image_real_data.min(), image_real_data.max()
image_imag_data = image_fft_data.imag
# save resulting images
logger.info(
"Saving resulting images real part as {} in the same format as input image, only with data-type float32...".format(
image_real_name
)
)
image_real = image_like(image_real_data, image_original)
image_real.get_header().set_data_dtype(scipy.uint16)
save(image_real, image_real_name)
logger.info(
"Saving resulting images real part as {} in the same format as input image, only with data-type float32...".format(
image_imag_name
)
)
image_imag = image_like(image_imag_data, image_original)
image_imag.get_header().set_data_dtype(scipy.float32)
save(image_imag, image_imag_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)
# build output image name
output_hdr_name = args.output + '.hdr'
output_img_name = args.output + '.img'
output_msk_name = args.output + '.msk'
# check if output image exists
if not args.force:
if os.path.exists(output_hdr_name):
logger.warning('The output header {} already exists. Breaking.'.format(output_hdr_name))
exit(1)
elif os.path.exists(output_img_name):
logger.warning('The output image {} already exists. Breaking.'.format(output_img_name))
exit(1)
elif os.path.exists(output_msk_name):
logger.warning('The output infor file {} already exists. Breaking.'.format(output_msk_name))
exit(1)
# decide on most suitable bit format
if len(args.masks) / 2 <= 8:
bit_format = scipy.uint8
elif len(args.masks) / 2 <= 16:
bit_format = scipy.uint16
elif len(args.masks) / 2 <= 32:
bit_format = scipy.uint32
elif len(args.masks) / 2 <= 64:
bit_format = scipy.uint64
else:
raise ArgumentError('It is not possible to combine more than 64 single masks.')
logger.info('Creating a Radiance® segmentation image in {} bit format...'.format(bit_format))
# loading first mask image as reference and template for saving
logger.info('Loading mask {} ({} segmentation) using NiBabel...'.format(args.masks[0], args.masks[1]))
image_mask = load(args.masks[0])
image_mask_data = scipy.squeeze(image_mask.get_data())
# prepare result image
image_radiance_data = scipy.zeros(image_mask_data.shape, dtype=bit_format)
logger.debug('Result image is of dimensions {} and type {}.'.format(image_radiance_data.shape, image_radiance_data.dtype))
# preparing .msk file
f = open(output_msk_name, 'w')
# adding first mask to result image
image_radiance_data[image_mask_data > 0] = 1
# adding first mask segmentation identifier to the .msk file
f.write('{}\t1\t{}\t{}\t{}\n'.format(args.masks[1], *__COLOURS[0%len(__COLOURS)]))
for i in range(2, len(args.masks), 2):
# loading mask image
logger.info('Loading mask {} ({} segmentation) using NiBabel...'.format(args.masks[i], args.masks[i+1]))
image_mask_data = scipy.squeeze(load(args.masks[i]).get_data())
# check if the shape of the images is consistent
if image_mask_data.shape != image_radiance_data.shape:
raise ArgumentError('Mask {} is with {} of a different shape as the first mask image (which has {}).'.format(args.masks[i], image_mask_data.shape, image_radiance_data.shape))
# adding mask to result image
image_radiance_data[image_mask_data > 0] += pow(2, i/2)
# adding mask segmentation identifier to the .msk file
f.write('{}\t{}\t{}\t{}\t{}\n'.format(args.masks[i+1], pow(2, i/2), *__COLOURS[(i/2)%len(__COLOURS)]))
logger.info('Saving Radiance® segmentation image as {}/.img/.msk...'.format(output_hdr_name))
image_mask.get_header().set_data_dtype(bit_format)
save(image_like(image_radiance_data, image_mask), output_hdr_name)
logger.info('Successfully terminated.')