def gbernsen(f, se, contrast_threshold, gthresh):
'''
thresholded = gbernsen(f, se, contrast_threshold, gthresh)
Generalised Bernsen local thresholding
Parameters
----------
f : ndarray
input image
se : boolean ndarray
structuring element to use for "locality"
contrast_threshold : integer
contrast threshold
gthresh : numeric, optional
global threshold to fall back in low contrast regions
Returns
-------
thresholded : binary ndarray
See Also
--------
bernsen : function
Bernsen thresholding with a circular region
'''
from mahotas.convolve import rank_filter
fmax = rank_filter(f, se, se.sum()-1)
fmin = rank_filter(f, se, 0)
fptp = fmax - fmin
fmean = fmax/2. + fmin/2. # Do not use (fmax + fmin) as that may overflow
return np.choose(fptp < contrast_threshold, (fmean < gthresh, fmean > f))
def gbernsen(f, se, contrast_threshold, gthresh):
'''
thresholded = gbernsen(f, se, contrast_threshold, gthresh)
Generalised Bernsen local thresholding
Parameters
----------
f : ndarray
input image
se : boolean ndarray
structuring element to use for "locality"
contrast_threshold : integer
contrast threshold
gthresh : numeric, optional
global threshold to fall back in low contrast regions
Returns
-------
thresholded : binary ndarray
See Also
--------
bernsen : function
Bernsen thresholding with a circular region
'''
from mahotas.convolve import rank_filter
fmax = rank_filter(f, se, se.sum() - 1)
fmin = rank_filter(f, se, 0)
fptp = fmax - fmin
fmean = fmax / 2. + fmin / 2. # Do not use (fmax + fmin) as that may overflow
return np.choose(fptp < contrast_threshold, (fmean < gthresh, fmean > f))
def test_rank_filter():
np.random.seed(22)
A = np.random.random_integers(0,255, (32,32))
Bc = np.ones((3,3))
for r in xrange(9):
B1 = rank_filter(A,Bc,r)
B2 = _slow_rank_filter(A,r)
assert np.all(B1[1:-1,1:-1] == B2)
def test_rank_filter():
np.random.seed(22)
A = np.random.randint(0, 256, (32, 32))
Bc = np.ones((3, 3))
for r in range(9):
B1 = rank_filter(A, Bc, r, mode='constant')
B2 = _slow_rank_filter(A, r)
assert np.all(B1 == B2)
def test_rank_filter():
np.random.seed(22)
A = np.random.random_integers(0,255, (32,32))
Bc = np.ones((3,3))
for r in range(9):
B1 = rank_filter(A, Bc, r, mode='constant')
B2 = _slow_rank_filter(A,r)
assert np.all(B1 == B2)
