def load_params(indf):
""" Takes a pandas.DataFrame generated by grism and reads it into an
lmfit.Parameters object.
"""
df = indf.copy()
p = lf.Parameters()
p.clear()
# print('DEBUG LMFITWRAPPER: POS', df.Pos)
df.set_value('Contin', 'Identifier', 'x')
if not 'SigMin' in df.columns:
df['SigMin'] = 0.1
if not 'SigMax' in df.columns:
df['SigMax'] = 30.
if not 'AmpMin' in df.columns:
df['AmpMin'] = 0.001
if not 'AmpMax' in df.columns:
df['AmpMax'] = 5000.
if not 'WavMin' in df.columns:
df['WavMin'] = df.Pos - 10.
if not 'WavMax' in df.columns:
df['WavMax'] = df.Pos + 10.
# print('DEBUG LMFITWRAPPER: POS', df.Pos)
# print('DEBUG LMFITWRAPPER: WAVMIN', df.WavMin)
df.Pos += df['Line center']
# print df
for comp in df.index:
if comp == 'Contin':
varval = df.ix[comp]['Ampl'] + .0001
p.add('Contin_Ampl', value=varval, min=-10., max=10000.,
vary=sp.invert(df.loc[comp]['Lock']))
continue
else:
for col in df.columns:
if '_stddev' in col:
continue
if col in ['Ampl', 'Sigma', 'Pos', 'Gamma',]:
#name = df.ix[comp]['Identifier']+'_'+col
name = comp + '_' + col
value = df.ix[comp][col]
if col == 'Pos':
varmin = df.loc[comp]['Line center'] + \
df.loc[comp]['WavMin'] # value - 10
varmax = df.loc[comp]['Line center'] + \
df.loc[comp]['WavMax'] # value + 10
vary = sp.invert(df.loc[comp]['Lock'][0])
elif col == 'Sigma':
varmin = df.loc[comp]['SigMin']
varmax = df.loc[comp]['SigMax']
vary = sp.invert(df.loc[comp]['Lock'][1])
elif col == 'Ampl':
varmin = 0.001 # df.loc[comp]['AmpMin']
varmax = df.loc[comp]['AmpMax']
vary = sp.invert(df.loc[comp]['Lock'][2])
else:
varmin = None
varmax = None
p.add(name, value, min=varmin, max=varmax, vary=vary)
return p
def load_exps():
_FEATURES = None
exp_data = dict()
exp_labs = dict()
for mpath in mat_paths:
exp = format_exp(mpath)
matobj = sio.loadmat(mpath)
feats = format_featnames(matobj)
data = format_data(matobj)
mask = sp.asarray([sp.invert(has_complex(arr)) for arr in data])
feats = feats[mask]
order = sp.argsort(feats)
feats = [f.lower() for f in feats[order]]
data = data[mask][order].real
labels = format_labels(matobj)
if _FEATURES is None:
_FEATURES = feats
assert feats == _FEATURES, 'Features do not match! %s\n%s' % (
' '.join(feats), ' '.join(_FEATURES))
assert data.shape[1] == len(
labels), 'Labels (%d) do not match time points (%d)' % (
len(labels), data.shape[1])
exp_data[exp] = data
exp_labs[exp] = labels
return exp_data, exp_labs, _FEATURES
def load_params(indf):
df = indf.copy()
p = lf.Parameters()
p.clear()
df.Pos += df['Line center']
df.set_value('Contin', 'Identifier', 'x')
for comp in df.index:
print comp
if comp == 'Contin':
varval = df.ix[comp]['Ampl'] + .0001
p.add('Contin_Ampl', value=varval, min=0.,
vary=sp.invert(df.loc[comp]['Lock']))
continue
else:
for col in df.columns:
if col in ['Ampl', 'Sigma', 'Pos', 'Gamma',]:
#name = df.ix[comp]['Identifier']+'_'+col
name = comp + '_' + col
value = df.ix[comp][col]
if col == 'Pos':
varmin = value - 10
varmax = value + 10
vary = sp.invert(df.loc[comp]['Lock'][0])
elif col == 'Sigma':
varmin = 1e-8
varmax = 1e8
vary = sp.invert(df.loc[comp]['Lock'][1])
elif col == 'Ampl':
varmin = 0.
varmax = None
vary = sp.invert(df.loc[comp]['Lock'][2])
else:
varmin = None
varmax = None
p.add(name, value, min=varmin, max=varmax, vary=vary)
return p
def fit(self, X, y):
num_tasks = X.shape[0]
X = np.array(X)
y = np.array(y)
X = [item for task in X for item in task]
sample_per_task = [task.shape[0] for task in X]
K = self.kernel.compute(X, X)
Q = np.multiply(np.multiply(X, y), y.T)
delta = np.repeat(np.array(range(0, num_tasks)), sample_per_task)
for i in delta:
for j in delta:
if i == j:
Q[i, j] = Q[i, j] * (1 + num_tasks / self.mu + 1)
alpha = scipy.invert(Q) * np.ones(y.shape[0])
pass
def tri_flat(array):
R = array.shape[0]
mask = SP.asarray(SP.invert(SP.tri(R, R, dtype=bool)), dtype=float)
x, y = mask.nonzero()
return array[x, y]
def herm_sqrt_inv(x,
zero_tol=1E-15,
sanity_checks=False,
return_rank=False,
sc_data=''):
if isinstance(x, mm.eyemat):
x_sqrt = x
x_sqrt_i = x
rank = x.shape[0]
else:
try:
ev = x.diag #simple_diag_matrix
EV = None
except AttributeError:
ev, EV = la.eigh(x)
zeros = ev <= zero_tol #throw away negative results too!
ev_sqrt = sp.sqrt(ev)
err = sp.seterr(divide='ignore', invalid='ignore')
try:
ev_sqrt_i = 1 / ev_sqrt
ev_sqrt[zeros] = 0
ev_sqrt_i[zeros] = 0
finally:
sp.seterr(divide=err['divide'], invalid=err['invalid'])
if EV is None:
x_sqrt = mm.simple_diag_matrix(ev_sqrt, dtype=x.dtype)
x_sqrt_i = mm.simple_diag_matrix(ev_sqrt_i, dtype=x.dtype)
else:
B = mm.mmul_diag(ev_sqrt, EV.conj().T)
x_sqrt = EV.dot(B)
B = mm.mmul_diag(ev_sqrt_i, EV.conj().T)
x_sqrt_i = EV.dot(B)
rank = x.shape[0] - np.count_nonzero(zeros)
if sanity_checks:
if ev.min() < -zero_tol:
log.warning(
"Sanity Fail in herm_sqrt_inv(): Throwing away negative eigenvalues! %s %s",
ev.min(), sc_data)
if not np.allclose(x_sqrt.dot(x_sqrt), x):
log.warning(
"Sanity Fail in herm_sqrt_inv(): x_sqrt is bad! %s %s",
la.norm(x_sqrt.dot(x_sqrt) - x), sc_data)
if EV is None:
nulls = sp.zeros(x.shape[0])
nulls[zeros] = 1
nulls = sp.diag(nulls)
else: #if we did an EVD then we use the eigenvectors
nulls = EV.copy()
nulls[:, sp.invert(zeros)] = 0
nulls = nulls.dot(nulls.conj().T)
eye = np.eye(x.shape[0])
if not np.allclose(x_sqrt.dot(x_sqrt_i), eye - nulls):
log.warning(
"Sanity Fail in herm_sqrt_inv(): x_sqrt_i is bad! %s %s",
la.norm(x_sqrt.dot(x_sqrt_i) - eye + nulls), sc_data)
if return_rank:
return x_sqrt, x_sqrt_i, rank
else:
return x_sqrt, x_sqrt_i
matx_active = signs * matx[:, active]
u, s, vh = scipy.linalg.svd(matx_active, full_matrices=False)
matg = vh.T @ scipy.diag(s**2) @ vh
matg_inv = vh.T @ scipy.diag(scipy.reciprocal(s**2)) @ vh
vec1 = scipy.ones(len(active))
scalara = (matg_inv.sum())**(-.5)
vecw = scalara * matg_inv.sum(axis=1)
vecw = np.reshape(vecw, (vecw.shape[0], 1))
vecu = matx_active @ vecw
veca = matx.T @ vecu
if k < size_predictor - 1:
inactive = indices_predictor[scipy.invert(mask_maxc)]
arr_gamma = scipy.concatenate([(maxc - np.take(vecc, inactive)) /
(scalara - np.take(veca, inactive)),
(maxc + np.take(vecc, inactive)) /
(scalara + np.take(veca, inactive))
]).ravel()
scalargamma = arr_gamma[arr_gamma > 0].min()
else:
scalargamma = maxc / scalara
vecy_fitted += (scalargamma * vecu).T
betas[active] += scalargamma * signs
beta_lars.append(list(betas))
fig = plt.figure(figsize=(20, 20))
ax = fig.add_subplot(1, 1, 1)
def herm_sqrt_inv(x, zero_tol=1E-15, sanity_checks=False, return_rank=False, sc_data=''):
if isinstance(x, mm.eyemat):
x_sqrt = x
x_sqrt_i = x
rank = x.shape[0]
else:
try:
ev = x.diag #simple_diag_matrix
EV = None
except AttributeError:
ev, EV = la.eigh(x)
zeros = ev <= zero_tol #throw away negative results too!
ev_sqrt = sp.sqrt(ev)
err = sp.seterr(divide='ignore', invalid='ignore')
try:
ev_sqrt_i = 1 / ev_sqrt
ev_sqrt[zeros] = 0
ev_sqrt_i[zeros] = 0
finally:
sp.seterr(divide=err['divide'], invalid=err['invalid'])
if EV is None:
x_sqrt = mm.simple_diag_matrix(ev_sqrt, dtype=x.dtype)
x_sqrt_i = mm.simple_diag_matrix(ev_sqrt_i, dtype=x.dtype)
else:
B = mm.mmul_diag(ev_sqrt, EV.conj().T)
x_sqrt = EV.dot(B)
B = mm.mmul_diag(ev_sqrt_i, EV.conj().T)
x_sqrt_i = EV.dot(B)
rank = x.shape[0] - np.count_nonzero(zeros)
if sanity_checks:
if ev.min() < -zero_tol:
log.warning("Sanity Fail in herm_sqrt_inv(): Throwing away negative eigenvalues! %s %s",
ev.min(), sc_data)
if not np.allclose(x_sqrt.dot(x_sqrt), x):
log.warning("Sanity Fail in herm_sqrt_inv(): x_sqrt is bad! %s %s",
la.norm(x_sqrt.dot(x_sqrt) - x), sc_data)
if EV is None:
nulls = sp.zeros(x.shape[0])
nulls[zeros] = 1
nulls = sp.diag(nulls)
else: #if we did an EVD then we use the eigenvectors
nulls = EV.copy()
nulls[:, sp.invert(zeros)] = 0
nulls = nulls.dot(nulls.conj().T)
eye = np.eye(x.shape[0])
if not np.allclose(x_sqrt.dot(x_sqrt_i), eye - nulls):
log.warning("Sanity Fail in herm_sqrt_inv(): x_sqrt_i is bad! %s %s",
la.norm(x_sqrt.dot(x_sqrt_i) - eye + nulls), sc_data)
if return_rank:
return x_sqrt, x_sqrt_i, rank
else:
return x_sqrt, x_sqrt_i
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 tri_flat(array):
R = array.shape[0]
mask = SP.asarray(SP.invert(SP.tri(R, R, dtype=bool)), dtype=float)
x, y = mask.nonzero()
return array[x, y]
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('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.')
