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 run(self, workspace):
image = workspace.image_set.get_image(self.image_name.value,
must_be_grayscale=True)
orig_pixels = image.pixel_data
if image.has_mask:
mask = image.mask
else:
mask = np.ones(orig_pixels.shape, bool)
if self.method == M_SOBEL:
if self.direction == E_ALL:
output_pixels = sobel(orig_pixels, mask)
elif self.direction == E_HORIZONTAL:
output_pixels = hsobel(orig_pixels, mask)
elif self.direction == E_VERTICAL:
output_pixels = vsobel(orig_pixels, mask)
else:
raise NotImplementedError(
"Unimplemented direction for Sobel: %s",
self.direction.value)
elif self.method == M_LOG:
sigma = self.get_sigma()
size = int(sigma * 4) + 1
output_pixels = laplacian_of_gaussian(orig_pixels, mask, size,
sigma)
elif self.method == M_PREWITT:
if self.direction == E_ALL:
output_pixels = prewitt(orig_pixels)
elif self.direction == E_HORIZONTAL:
output_pixels = hprewitt(orig_pixels, mask)
elif self.direction == E_VERTICAL:
output_pixels = vprewitt(orig_pixels, mask)
else:
raise NotImplementedError(
"Unimplemented direction for Prewitt: %s",
self.direction.value)
elif self.method == M_CANNY:
high_threshold = self.manual_threshold.value
low_threshold = self.low_threshold.value
if (self.wants_automatic_low_threshold.value
or self.wants_automatic_threshold.value):
sobel_image = sobel(orig_pixels, mask)
low, high = otsu3(sobel_image[mask])
if self.wants_automatic_low_threshold.value:
low_threshold = low * self.threshold_adjustment_factor.value
if self.wants_automatic_threshold.value:
high_threshold = high * self.threshold_adjustment_factor.value
output_pixels = canny(orig_pixels, mask, self.get_sigma(),
low_threshold, high_threshold)
elif self.method == M_ROBERTS:
output_pixels = roberts(orig_pixels, mask)
else:
raise NotImplementedError(
"Unimplemented edge detection method: %s" % self.method.value)
output_image = cpi.Image(output_pixels, parent_image=image)
workspace.image_set.add(self.output_image_name.value, output_image)
if self.show_window:
workspace.display_data.orig_pixels = orig_pixels
workspace.display_data.output_pixels = output_pixels
def run(self, workspace):
image = workspace.image_set.get_image(self.image_name.value,
must_be_grayscale=True)
orig_pixels = image.pixel_data
if image.has_mask:
mask = image.mask
else:
mask = np.ones(orig_pixels.shape, bool)
if self.method == M_SOBEL:
if self.direction == E_ALL:
output_pixels = sobel(orig_pixels, mask)
elif self.direction == E_HORIZONTAL:
output_pixels = hsobel(orig_pixels, mask)
elif self.direction == E_VERTICAL:
output_pixels = vsobel(orig_pixels, mask)
else:
raise NotImplementedError("Unimplemented direction for Sobel: %s", self.direction.value)
elif self.method == M_LOG:
sigma = self.get_sigma()
size = int(sigma * 4) + 1
output_pixels = laplacian_of_gaussian(orig_pixels, mask, size, sigma)
elif self.method == M_PREWITT:
if self.direction == E_ALL:
output_pixels = prewitt(orig_pixels)
elif self.direction == E_HORIZONTAL:
output_pixels = hprewitt(orig_pixels, mask)
elif self.direction == E_VERTICAL:
output_pixels = vprewitt(orig_pixels, mask)
else:
raise NotImplementedError("Unimplemented direction for Prewitt: %s", self.direction.value)
elif self.method == M_CANNY:
high_threshold = self.manual_threshold.value
low_threshold = self.low_threshold.value
if (self.wants_automatic_low_threshold.value or
self.wants_automatic_threshold.value):
sobel_image = sobel(orig_pixels, mask)
low, high = otsu3(sobel_image[mask])
if self.wants_automatic_low_threshold.value:
low_threshold = low * self.threshold_adjustment_factor.value
if self.wants_automatic_threshold.value:
high_threshold = high * self.threshold_adjustment_factor.value
output_pixels = canny(orig_pixels, mask, self.get_sigma(),
low_threshold,
high_threshold)
elif self.method == M_ROBERTS:
output_pixels = roberts(orig_pixels, mask)
elif self.method == M_KIRSCH:
output_pixels = kirsch(orig_pixels)
else:
raise NotImplementedError("Unimplemented edge detection method: %s" %
self.method.value)
output_image = cpi.Image(output_pixels, parent_image=image)
workspace.image_set.add(self.output_image_name.value, output_image)
if self.show_window:
workspace.display_data.orig_pixels = orig_pixels
workspace.display_data.output_pixels = output_pixels
def test_08_otsu3(self):
'''Test 3-class Otsu 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 = otsu3(data)
self.assertTrue(threshold1 > .2)
self.assertTrue(threshold1 < .5)
self.assertTrue(threshold2 > .5)
self.assertTrue(threshold2 < .8)
def test_08_otsu3(self):
'''Test 3-class Otsu 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 = otsu3(data)
self.assertTrue(threshold1 > .2)
self.assertTrue(threshold1 < .5)
self.assertTrue(threshold2 > .5)
self.assertTrue(threshold2 < .8)
def test_08_otsu3(self):
"""Test 3-class Otsu 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 = otsu3(data)
self.assertTrue(threshold1 > 0.2)
self.assertTrue(threshold1 < 0.5)
self.assertTrue(threshold2 > 0.5)
self.assertTrue(threshold2 < 0.8)
def test_06_01_canny(self):
'''Test the canny method'''
i, j = np.mgrid[-20:20, -20:20]
image = np.logical_and(i > j, i ** 2 + j ** 2 < 300).astype(np.float32)
np.random.seed(0)
image = image * .5 + np.random.uniform(size=image.shape) * .3
image = np.ascontiguousarray(image, np.float32)
workspace, module = self.make_workspace(image)
module.method.value = F.M_CANNY
module.wants_automatic_threshold.value = True
module.wants_automatic_low_threshold.value = True
module.wants_automatic_sigma.value = True
module.run(workspace)
output = workspace.image_set.get_image(OUTPUT_IMAGE_NAME)
t1, t2 = otsu3(FIL.sobel(image))
result = FIL.canny(image, np.ones(image.shape, bool), 1.0, t1, t2)
self.assertTrue(np.all(output.pixel_data == result))
def test_06_01_canny(self):
'''Test the canny method'''
i, j = np.mgrid[-20:20, -20:20]
image = np.logical_and(i > j, i**2 + j**2 < 300).astype(np.float32)
np.random.seed(0)
image = image * .5 + np.random.uniform(size=image.shape) * .3
image = np.ascontiguousarray(image, np.float32)
workspace, module = self.make_workspace(image)
module.method.value = F.M_CANNY
module.wants_automatic_threshold.value = True
module.wants_automatic_low_threshold.value = True
module.wants_automatic_sigma.value = True
module.run(workspace)
output = workspace.image_set.get_image(OUTPUT_IMAGE_NAME)
t1, t2 = otsu3(FIL.sobel(image))
result = FIL.canny(image, np.ones(image.shape, bool), 1.0, t1, t2)
self.assertTrue(np.all(output.pixel_data == result))
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_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)
