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_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_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_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_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_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_04_otsu3_wv_high(self):
'''Test the three-class otsu, weighted variance middle = foreground'''
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 = otsu3(limage)
threshold = T.inverse_log_transform(t1, 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_WEIGHTED_VARIANCE
module.two_class_otsu.value = I.O_THREE_CLASS
module.assign_middle_to_foreground.value = I.O_FOREGROUND
module.run(workspace)
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_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_04_otsu3_wv_high(self):
'''Test the three-class otsu, weighted variance middle = foreground'''
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 = otsu3(limage)
threshold = T.inverse_log_transform(t1, 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_WEIGHTED_VARIANCE
module.two_class_otsu.value = I.O_THREE_CLASS
module.assign_middle_to_foreground.value = I.O_FOREGROUND
module.run(workspace)
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)
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)