def test_06_NaN(self):
"""Regression test of Otsu with NaN in input (issue #624)"""
r = np.random.RandomState()
r.seed(6)
data = r.uniform(size=100)
data[r.uniform(size=100) > 0.8] = np.NaN
self.assertEqual(otsu(data), otsu(data[~np.isnan(data)]))
self.assertEqual(entropy(data), entropy(data[~np.isnan(data)]))
self.assertEqual(otsu3(data), otsu3(data[~np.isnan(data)]))
self.assertEqual(entropy3(data), entropy3(data[~np.isnan(data)]))
def test_06_NaN(self):
"""Regression test of Otsu with NaN in input (issue #624)"""
r = np.random.RandomState()
r.seed(6)
data = r.uniform(size=100)
data[r.uniform(size=100) > .8] = np.NaN
self.assertEqual(otsu(data), otsu(data[~np.isnan(data)]))
self.assertEqual(entropy(data), entropy(data[~np.isnan(data)]))
self.assertEqual(otsu3(data), otsu3(data[~np.isnan(data)]))
self.assertEqual(entropy3(data), entropy3(data[~np.isnan(data)]))
def get_otsu_threshold(image,
mask=None,
two_class_otsu=True,
use_weighted_variance=True,
assign_middle_to_foreground=True):
if not mask is None:
image = image[mask]
else:
image = np.array(image.flat)
image = image[image >= 0]
if len(image) == 0:
return 1
image, d = log_transform(image)
if two_class_otsu:
if use_weighted_variance:
threshold = otsu(image)
else:
threshold = entropy(image)
else:
if use_weighted_variance:
t1, t2 = otsu3(image)
else:
t1, t2 = entropy3(image)
threshold = t1 if assign_middle_to_foreground else t2
threshold = inverse_log_transform(threshold, d)
return threshold
def get_otsu_threshold(image, mask = None,
two_class_otsu = True,
use_weighted_variance = True,
assign_middle_to_foreground = True):
if not mask is None:
image = image[mask]
else:
image = np.array(image.flat)
image = image[image >= 0]
if len(image) == 0:
return 1
image, d = log_transform(image)
if two_class_otsu:
if use_weighted_variance:
threshold = otsu(image)
else:
threshold = entropy(image)
else:
if use_weighted_variance:
t1, t2 = otsu3(image)
else:
t1,t2 = entropy3(image)
threshold = t1 if assign_middle_to_foreground else t2
threshold = inverse_log_transform(threshold, d)
return threshold
def test_07_entropy(self):
"""Test entropy with two normal distributions"""
r = np.random.RandomState()
r.seed(7)
x1 = r.normal(0.2, 0.1, 10000)
x2 = r.normal(0.5, 0.25, 5000)
data = np.hstack((x1, x2))
data = data[(data > 0) & (data < 1)]
threshold = entropy(data)
self.assertTrue(threshold > 0.2)
self.assertTrue(threshold < 0.5)
def test_07_entropy(self):
'''Test entropy with two normal distributions'''
r = np.random.RandomState()
r.seed(7)
x1 = r.normal(.2, .1, 10000)
x2 = r.normal(.5, .25, 5000)
data = np.hstack((x1, x2))
data = data[(data > 0) & (data < 1)]
threshold = entropy(data)
self.assertTrue(threshold > .2)
self.assertTrue(threshold < .5)
def test_05_02_otsu_entropy(self):
'''Test the entropy version of Otsu'''
np.random.seed(0)
image = np.hstack((np.random.exponential(1.5,size=600),
np.random.poisson(15,size=300)))
image.shape=(30,30)
image = stretch(image)
limage, d = T.log_transform(image)
threshold = entropy(limage)
threshold = T.inverse_log_transform(threshold, d)
expected = image > threshold
workspace, module = self.make_workspace(image)
module.binary.value = A.BINARY
module.threshold_method.value = T.TM_OTSU_GLOBAL
module.use_weighted_variance.value = I.O_ENTROPY
module.two_class_otsu.value = I.O_TWO_CLASS
module.run(workspace)
output = workspace.image_set.get_image(OUTPUT_IMAGE_NAME)
self.assertTrue(np.all(output.pixel_data == expected))
def test_05_02_otsu_entropy(self):
'''Test the entropy version of Otsu'''
np.random.seed(0)
image = np.hstack((np.random.exponential(1.5,size=600),
np.random.poisson(15,size=300)))
image.shape=(30,30)
image = stretch(image)
limage, d = T.log_transform(image)
threshold = entropy(limage)
threshold = T.inverse_log_transform(threshold, d)
expected = image > threshold
workspace, module = self.make_workspace(image)
module.binary.value = A.BINARY
module.threshold_method.value = T.TM_OTSU_GLOBAL
module.use_weighted_variance.value = I.O_ENTROPY
module.two_class_otsu.value = I.O_TWO_CLASS
module.run(workspace)
output = workspace.image_set.get_image(OUTPUT_IMAGE_NAME)
self.assertTrue(np.all(output.pixel_data == expected))
