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 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 test_09_entropy3(self):
'''Test 3-class entropy with three normal distributions'''
r = np.random.RandomState()
r.seed(8)
x1 = r.normal(.2, .1, 10000)
x2 = r.normal(.5, .25, 5000)
x3 = r.normal(.8, .1, 5000)
data = np.hstack((x1, x2, x3))
data = data[(data > 0) & (data < 1)]
threshold1, threshold2 = entropy3(data)
self.assertTrue(threshold1 > .2)
self.assertTrue(threshold1 < .5)
self.assertTrue(threshold2 > .5)
self.assertTrue(threshold2 < .8)
def test_09_entropy3(self):
'''Test 3-class entropy with three normal distributions'''
r = np.random.RandomState()
r.seed(8)
x1 = r.normal(.2, .1, 10000)
x2 = r.normal(.5, .25, 5000)
x3 = r.normal(.8, .1, 5000)
data = np.hstack((x1, x2, x3))
data = data[(data > 0) & (data < 1)]
threshold1, threshold2 = entropy3(data)
self.assertTrue(threshold1 > .2)
self.assertTrue(threshold1 < .5)
self.assertTrue(threshold2 > .5)
self.assertTrue(threshold2 < .8)
def test_09_entropy3(self):
"""Test 3-class entropy with three normal distributions"""
r = np.random.RandomState()
r.seed(8)
x1 = r.normal(0.2, 0.1, 10000)
x2 = r.normal(0.5, 0.25, 5000)
x3 = r.normal(0.8, 0.1, 5000)
data = np.hstack((x1, x2, x3))
data = data[(data > 0) & (data < 1)]
threshold1, threshold2 = entropy3(data)
self.assertTrue(threshold1 > 0.2)
self.assertTrue(threshold1 < 0.5)
self.assertTrue(threshold2 > 0.5)
self.assertTrue(threshold2 < 0.8)
def test_05_06_otsu3_entropy_high(self):
'''Test the three-class otsu, entropy, middle = background'''
np.random.seed(0)
image = np.hstack((np.random.exponential(1.5, size=300),
np.random.poisson(15, size=300),
np.random.poisson(30, size=300)))
image.shape = (30, 30)
image = stretch(image)
limage, d = T.log_transform(image)
t1, t2 = entropy3(limage)
threshold = T.inverse_log_transform(t1, d)
expected = image > threshold
workspace, module = self.make_workspace(image)
module.binary.value = A.BINARY
module.threshold_scope.value = I.TS_GLOBAL
module.threshold_method.value = T.TM_OTSU
module.use_weighted_variance.value = I.O_ENTROPY
module.two_class_otsu.value = I.O_THREE_CLASS
module.assign_middle_to_foreground.value = I.O_FOREGROUND
module.run(workspace)
output = workspace.image_set.get_image(OUTPUT_IMAGE_NAME)
self.assertTrue(np.all(output.pixel_data == expected))
def test_05_06_otsu3_entropy_high(self):
'''Test the three-class otsu, entropy, middle = background'''
np.random.seed(0)
image = np.hstack(
(np.random.exponential(1.5, size=300),
np.random.poisson(15, size=300), np.random.poisson(30, size=300)))
image.shape = (30, 30)
image = stretch(image)
limage, d = T.log_transform(image)
t1, t2 = entropy3(limage)
threshold = T.inverse_log_transform(t1, d)
expected = image > threshold
workspace, module = self.make_workspace(image)
module.binary.value = A.BINARY
module.threshold_scope.value = I.TS_GLOBAL
module.threshold_method.value = T.TM_OTSU
module.use_weighted_variance.value = I.O_ENTROPY
module.two_class_otsu.value = I.O_THREE_CLASS
module.assign_middle_to_foreground.value = I.O_FOREGROUND
module.run(workspace)
output = workspace.image_set.get_image(OUTPUT_IMAGE_NAME)
self.assertTrue(np.all(output.pixel_data == expected))
def test_05_05_otsu3_entropy_low(self):
'''Test the three-class otsu, entropy, middle = background'''
np.random.seed(0)
image = np.hstack((np.random.exponential(1.5, size=300),
np.random.poisson(15, size=300),
np.random.poisson(30, size=300)))
image.shape = (30, 30)
image = stretch(image)
limage, d = T.log_transform(image)
t1, t2 = entropy3(limage)
threshold = T.inverse_log_transform(t2, d)
workspace, module = self.make_workspace(image)
module.binary.value = A.BINARY
module.threshold_scope.value = I.TS_GLOBAL
module.threshold_method.value = T.TM_OTSU
module.use_weighted_variance.value = I.O_ENTROPY
module.two_class_otsu.value = I.O_THREE_CLASS
module.assign_middle_to_foreground.value = I.O_BACKGROUND
module.run(workspace)
output = workspace.image_set.get_image(OUTPUT_IMAGE_NAME)
m = workspace.measurements
m_threshold = m[cpmeas.IMAGE, I.FF_ORIG_THRESHOLD % module.get_measurement_objects_name()]
self.assertAlmostEqual(m_threshold, threshold)
def test_05_05_otsu3_entropy_low(self):
'''Test the three-class otsu, entropy, middle = background'''
np.random.seed(0)
image = np.hstack((np.random.exponential(1.5,size=300),
np.random.poisson(15,size=300),
np.random.poisson(30,size=300)))
image.shape=(30,30)
image = stretch(image)
limage, d = T.log_transform(image)
t1,t2 = entropy3(limage)
threshold = T.inverse_log_transform(t2, d)
workspace, module = self.make_workspace(image)
module.binary.value = A.BINARY
module.threshold_scope.value = I.TS_GLOBAL
module.threshold_method.value = T.TM_OTSU
module.use_weighted_variance.value = I.O_ENTROPY
module.two_class_otsu.value = I.O_THREE_CLASS
module.assign_middle_to_foreground.value = I.O_BACKGROUND
module.run(workspace)
output = workspace.image_set.get_image(OUTPUT_IMAGE_NAME)
m = workspace.measurements
m_threshold = m[cpmeas.IMAGE, I.FF_ORIG_THRESHOLD % module.get_measurement_objects_name()]
self.assertAlmostEqual(m_threshold, threshold)
