def parcellation_dist(p1, p2, mask=None):
"""
Compute the distance between the two parcellation p1 and p2 as the minimum
number of positions to remove in order to obtain equal partitions.
"mask" may be a binary mask to limit the distance computation to some
specific positions.
Important convention: parcel label 0 is treated as background and
corresponding positions are discarded if no mask is provided.
Return:
(distance value, parcellation overlap)
"""
assert np.issubdtype(p1.dtype, np.int)
assert np.issubdtype(p2.dtype, np.int)
assert p1.shape == p2.shape
from munkres import Munkres
if mask is None:
mask = (p1 != 0)
m = np.where(mask)
pyhrf.verbose(6,'Nb pos inside mask: %d' %len(m[0]))
fp1 = p1[m].astype(np.int32)
fp2 = p2[m].astype(np.int32)
# allocate cost matrix, assume that region labels are contiguous
# ie all labels in [1, label_max] are represented
cost_matrix = np.zeros((fp1.max()+1, fp2.max()+1), dtype=np.int32)
pyhrf.verbose(6,'Cost matrix : %s' %str(cost_matrix.shape))
compute_intersection_matrix(fp1, fp2, cost_matrix)
# discard 0-labelled parcels (background)
cost_matrix = cost_matrix[1:,1:]
# solve the assignement problem:
indexes = np.array(Munkres().compute((cost_matrix*-1).tolist()))
if 0:
print 'assignement indexes:'
print indexes
print '->'
print (indexes[:,0], indexes[:,1])
print cost_matrix[(indexes[:,0], indexes[:,1])]
assignement = cost_matrix[(indexes[:,0], indexes[:,1])].sum()
to_keep = np.zeros_like(fp1)
for s1, s2 in indexes:
to_keep += np.bitwise_and(fp1==(s1+1), fp2==(s2+1))
return fp1.size - assignement, expand_array_in_mask(to_keep, mask)
def parcellation_dist(p1, p2, mask=None):
"""
Compute the distance between the two parcellation p1 and p2 as the minimum
number of positions to remove in order to obtain equal partitions.
"mask" may be a binary mask to limit the distance computation to some
specific positions.
Important convention: parcel label 0 is treated as background and
corresponding positions are discarded if no mask is provided.
Return:
(distance value, parcellation overlap)
"""
assert np.issubdtype(p1.dtype, np.int)
assert np.issubdtype(p2.dtype, np.int)
assert p1.shape == p2.shape
from munkres import Munkres
if mask is None:
mask = (p1 != 0)
m = np.where(mask)
logger.debug('Nb pos inside mask: %d', len(m[0]))
fp1 = p1[m].astype(np.int32)
fp2 = p2[m].astype(np.int32)
# allocate cost matrix, assume that region labels are contiguous
# ie all labels in [1, label_max] are represented
cost_matrix = np.zeros((fp1.max() + 1, fp2.max() + 1), dtype=np.int32)
logger.debug('Cost matrix : %s', str(cost_matrix.shape))
compute_intersection_matrix(fp1, fp2, cost_matrix)
# discard 0-labelled parcels (background)
cost_matrix = cost_matrix[1:, 1:]
# solve the assignement problem:
indexes = np.array(Munkres().compute((cost_matrix * -1).tolist()))
if 0:
print 'assignement indexes:'
print indexes
print '->'
print(indexes[:, 0], indexes[:, 1])
print cost_matrix[(indexes[:, 0], indexes[:, 1])]
assignement = cost_matrix[(indexes[:, 0], indexes[:, 1])].sum()
to_keep = np.zeros_like(fp1)
for s1, s2 in indexes:
to_keep += np.bitwise_and(fp1 == (s1 + 1), fp2 == (s2 + 1))
return fp1.size - assignement, expand_array_in_mask(to_keep, mask)
def test_intersection_matrix(self):
from pyhrf.cparcellation import compute_intersection_matrix
im = np.zeros((self.fp1.max() + 1, self.fp2.max() + 1),
dtype=np.int32)
compute_intersection_matrix(self.fp1, self.fp2, im)
assert_array_equal(im, np.array([[0, 0, 0, 0],
[0, 2, 1, 2],
[0, 0, 5, 1],
[0, 0, 0, 3]], dtype=np.int32),
"Intersection graph not OK", 1)
