def testPeel(self):
a = np.ones((5, 5, 5), dtype=int)
peeledA = peelVolume3D(a)
correctPeeling = np.zeros_like(a)
correctPeeling[1:-1, 1:-1, 1:-1] = 1
assert (peeledA == correctPeeling).all()
m = np.zeros((6, 6, 6), dtype=int)
m[1:-1, 1:-1, 1:-1] = 1
def testPeel(self):
a = np.ones((5, 5, 5), dtype=int)
peeledA = peelVolume3D(a)
correctPeeling = np.zeros_like(a)
correctPeeling[1:-1, 1:-1, 1:-1] = 1
assert (peeledA == correctPeeling).all()
m = np.zeros((6, 6, 6), dtype=int)
m[1:-1, 1:-1, 1:-1] = 1
def parcellate_voronoi_vol(mask, nb_parcels, seeds=None):
"""
Produce a parcellation from a Voronoi diagram built on random seeds.
The number of seeds is equal to the nb of parcels.
Seed are randomly placed within the mask, expect on edge positions
Args:
- mask (numpy.ndarray): binary 3D array of valid position to parcellate
- nb_parcels (int): the required number of parcels
- seeds: TODO
Return:
- the parcellation (numpy.ndarray): a 3D array of integers
-
"""
parcellation = np.zeros(mask.shape, dtype=int)
nvox = (mask !=0).sum()
for cc_mask in mg.split_mask_into_cc_iter(mask!=0):
# compute the required number of parcels within the current CC:
size_cc = cc_mask.sum()
cc_np = max(int(np.round(nb_parcels * size_cc / (nvox*1.))), 1)
pyhrf.verbose(2, 'Treating a connected component (CC) of %d positions' \
%cc_mask.sum())
if cc_mask.sum() < 6:
continue
if seeds is None:
# perform voronoi on random seeds
eroded_mask = peelVolume3D(cc_mask)
eroded_mask_size = eroded_mask.sum()
if eroded_mask_size < nb_parcels: #do no erode, mask too small
eroded_mask_size = nvox
eroded_mask = mask.copy()
cc_seeds = np.random.randint(0,eroded_mask_size, cc_np)
mask_for_seed = np.zeros(eroded_mask_size, dtype=int)
mask_for_seed[cc_seeds] = 1
mask_for_seed = expand_array_in_mask(mask_for_seed, eroded_mask)
else:
mask_for_seed = seeds * cc_mask
pyhrf.verbose(2, 'Nb of seeds in current CC: %d' \
%mask_for_seed.sum())
cc_parcellation = voronoi(np.vstack(np.where(cc_mask)).T,
np.vstack(np.where(mask_for_seed)).T) + 1
pyhrf.verbose(3, 'CC parcellation labels: %s' \
%str(np.unique(cc_parcellation)))
maxp = parcellation.max()
parcellation += expand_array_in_mask(cc_parcellation + maxp, cc_mask)
pyhrf.verbose(3, 'Current parcellation labels: %s' \
%str(np.unique(parcellation)))
pyhrf.verbose(1, 'voronoi parcellation: %s, %s' \
%(str(parcellation.shape), str(parcellation.dtype)))
return parcellation
def parcellate_voronoi_vol(mask, nb_parcels, seeds=None):
"""
Produce a parcellation from a Voronoi diagram built on random seeds.
The number of seeds is equal to the nb of parcels.
Seed are randomly placed within the mask, expect on edge positions
Args:
- mask (numpy.ndarray): binary 3D array of valid position to parcellate
- nb_parcels (int): the required number of parcels
- seeds: TODO
Return:
- the parcellation (numpy.ndarray): a 3D array of integers
-
"""
parcellation = np.zeros(mask.shape, dtype=int)
nvox = (mask != 0).sum()
for cc_mask in mg.split_mask_into_cc_iter(mask != 0):
# compute the required number of parcels within the current CC:
size_cc = cc_mask.sum()
cc_np = max(int(np.round(nb_parcels * size_cc / (nvox * 1.))), 1)
logger.info('Treating a connected component (CC) of %d positions',
cc_mask.sum())
if cc_mask.sum() < 6:
continue
if seeds is None:
# perform voronoi on random seeds
eroded_mask = peelVolume3D(cc_mask)
eroded_mask_size = eroded_mask.sum()
if eroded_mask_size < nb_parcels: # do no erode, mask too small
eroded_mask_size = nvox
eroded_mask = mask.copy()
cc_seeds = np.random.randint(0, eroded_mask_size, cc_np)
mask_for_seed = np.zeros(eroded_mask_size, dtype=int)
mask_for_seed[cc_seeds] = 1
mask_for_seed = expand_array_in_mask(mask_for_seed, eroded_mask)
else:
mask_for_seed = seeds * cc_mask
logger.info('Nb of seeds in current CC: %d', mask_for_seed.sum())
cc_parcellation = voronoi(
np.vstack(np.where(cc_mask)).T,
np.vstack(np.where(mask_for_seed)).T) + 1
logger.info('CC parcellation labels: %s',
str(np.unique(cc_parcellation)))
maxp = parcellation.max()
parcellation += expand_array_in_mask(cc_parcellation + maxp, cc_mask)
logger.info('Current parcellation labels: %s',
str(np.unique(parcellation)))
logger.info('voronoi parcellation: %s, %s', str(parcellation.shape),
str(parcellation.dtype))
return parcellation
