def test_ignore_zeros():
np.seterr(all='raise')
np.random.seed(22)
A = np.zeros((1024, 24), np.uint8)
A[:24, :24] = np.random.random_integers(100, 200, size=(24, 24))
assert rc(A) < 100
assert otsu(A) < 100
assert rc(A, ignore_zeros=1) > 100
assert otsu(A, ignore_zeros=1) > 100
def test_ignore_zeros():
np.seterr(all='raise')
np.random.seed(22)
A = np.zeros((1024,24), np.uint8)
A[:24,:24] = np.random.randint(100, 200, size=(24,24))
assert rc(A) < 100
assert otsu(A) < 100
assert rc(A, ignore_zeros=1) > 100
assert otsu(A, ignore_zeros=1) > 100
def threshold(img,thresh):
'''
T = threshold(img, thresh)
thresh can be:
* None: returns -1
* a number: returns thresh
* a function: returns thresh(img)
* a string:
one of ('otsu','rc','murphy_rc','mean')
'''
if thresh is None:
return -1
if type(thresh) is str:
if thresh == 'otsu':
return otsu(img)
if thresh == 'rc':
return rc(img)
if thresh == 'murphy_rc':
return murphy_rc(img)
if thresh == 'mean':
return img.mean()
raise ValueError("pyslic.threshold: Cannot handle argument '%s'" % thresh)
if callable(thresh):
return thresh(img)
return thresh
def murphy_rc(img,ignore_zeros=False):
"""
T = murphy_rc(img)
Calculate a threshold according to Murphy's adaptation of the RC method.
@param ignore_zeros: Whether to ignore zero valued pixels (default: False)
Murphy's Matlab implementation always ignores zero valued pixels.
"""
pmax = img.max()
return pmax-rc(pmax-img, ignore_zeros=ignore_zeros)
def extract1(img, solution):
labeled, _ = solution
binary = (labeled > 0)
bg = img[~binary].ravel()
objects = img[binary].ravel()
bstd = bg.std()
if bstd == 0:
bstd = 1
separation = (bg.mean()-objects.mean())/bstd
corrcoefs = []
for T in (img.mean(), img.mean() + img.std(), otsu(img), rc(img)):
corrcoefs.append(_corrcoef(binary, img > T))
return np.concatenate( ([separation], corrcoefs) )
def oversegment(img):
overseg = sobel_segment(img)
for t in (img.mean(), otsu(img), rc(img)):
overseg = intersec(overseg, segment(img, t))
return overseg
def test_nozeros():
np.seterr(all='raise')
np.random.seed(22)
A = (np.random.rand(100,100)*50).astype(np.uint8)+201
assert rc(A) > 200
assert otsu(A) > 200
def test_nozeros():
np.seterr(all='raise')
np.random.seed(22)
A = (np.random.rand(100,100)*50).astype(np.uint8)+201
assert rc(A) > 200
assert otsu(A) > 200
