def test_threshold_li_image_automatic():
numpy.random.seed(73)
data = numpy.random.rand(10, 10)
workspace, module = make_workspace(data)
image = workspace.image_set.get_image(INPUT_IMAGE_NAME)
module.threshold_scope.value = cellprofiler.modules.threshold.TS_GLOBAL
module.global_operation.value = cellprofiler.modules.threshold.TM_LI
module.threshold_range.maximum = 0.0 # expected to be ignored
t_local, t_global, _ = module.get_threshold(image, workspace, automatic=True)
expected = skimage.filters.threshold_li(data)
assert t_local != 0.0
assert t_global != 0.0
numpy.testing.assert_almost_equal(t_local, expected)
numpy.testing.assert_almost_equal(t_global, expected)
def test_threshold_li_adaptive_image():
numpy.random.seed(73)
data = numpy.random.rand(10, 10)
workspace, module = make_workspace(data)
image = workspace.image_set.get_image(INPUT_IMAGE_NAME)
module.threshold_scope.value = cellprofiler.modules.threshold.TS_ADAPTIVE
module.local_operation.value = cellprofiler.modules.threshold.TM_LI
module.adaptive_window_size.value = 3
t_local, t_global, t_guide = module.get_threshold(image, workspace)
t_guide_expected = skimage.filters.threshold_li(data)
t_local_expected = module._get_adaptive_threshold(data,
skimage.filters.threshold_li)
t_local_expected = module._correct_local_threshold(t_local_expected, t_guide_expected)
numpy.testing.assert_almost_equal(t_guide, t_guide_expected)
numpy.testing.assert_almost_equal(t_local, t_local_expected)
def test_threshold_otsu_partial_mask_uniform_data():
numpy.random.seed(73)
data = numpy.random.rand(10, 10)
mask = numpy.zeros_like(data, dtype=numpy.bool)
mask[2:5, 2:5] = True
data[mask] = 0.2
workspace, module = make_workspace(data, mask=mask)
image = workspace.image_set.get_image(INPUT_IMAGE_NAME)
module.threshold_scope.value = cellprofiler.modules.threshold.TS_ADAPTIVE
module.global_operation.value = centrosome.threshold.TM_OTSU
module.two_class_otsu.value = cellprofiler.modules.threshold.O_TWO_CLASS
module.adaptive_window_size.value = 3
t_local, t_global = module.get_threshold(image, workspace)
t_global_expected = 0.2
t_local_expected = 0.7 * t_global_expected * numpy.ones_like(data)
numpy.testing.assert_array_almost_equal(t_local, t_local_expected)
numpy.testing.assert_almost_equal(t_global, t_global_expected)
def test_threshold_robust_background_median_sd_volume():
numpy.random.seed(73)
data = numpy.random.rand(10, 10, 10)
workspace, module = make_workspace(data, dimensions=3)
image = workspace.image_set.get_image(INPUT_IMAGE_NAME)
module.threshold_scope.value = cellprofiler.modules.threshold.TS_GLOBAL
module.global_operation.value = cellprofiler.modules.threshold.TM_ROBUST_BACKGROUND
module.averaging_method.value = cellprofiler.modules.threshold.RB_MEDIAN
module.variance_method.value = cellprofiler.modules.threshold.RB_SD
t_local, t_global, _ = module.get_threshold(image, workspace)
t_local_expected, t_global_expected = centrosome.threshold.get_threshold(
centrosome.threshold.TM_ROBUST_BACKGROUND,
cellprofiler.modules.threshold.TS_GLOBAL,
data,
threshold_range_min=0.0,
threshold_range_max=1.0,
threshold_correction_factor=1.0,
lower_outlier_fraction=0.05,
upper_outlier_fraction=0.05,
deviations_above_average=2,
average_fn=numpy.median,
variance_fn=numpy.std,
)
numpy.testing.assert_almost_equal(t_local, t_local_expected)
def test_threshold_robust_background_adaptive():
numpy.random.seed(73)
data = numpy.random.rand(10, 10)
workspace, module = make_workspace(data, dimensions=2)
image = workspace.image_set.get_image(INPUT_IMAGE_NAME)
module.threshold_scope.value = cellprofiler.modules.threshold.TS_ADAPTIVE
module.adaptive_window_size.value = 3
module.local_operation.value = cellprofiler.modules.threshold.TM_ROBUST_BACKGROUND
module.averaging_method.value = cellprofiler.modules.threshold.RB_MEAN
module.variance_method.value = cellprofiler.modules.threshold.RB_SD
t_local, t_uncorrected, t_guide = module.get_threshold(image, workspace)
t_guide_expected = module.get_threshold_robust_background(data)
t_guide_expected = module._correct_global_threshold(t_guide_expected)
t_local_expected = module._get_adaptive_threshold(data,
module.get_threshold_robust_background)
t_local_expected = module._correct_local_threshold(t_local_expected, t_guide_expected)
numpy.testing.assert_almost_equal(t_guide, t_guide_expected)
numpy.testing.assert_almost_equal(t_local, t_local_expected)
def test_threshold_li_image_log():
numpy.random.seed(73)
data = numpy.random.rand(10, 10)
mask = numpy.zeros_like(data, dtype=bool)
mask[1:-1, 1:-1] = True
workspace, module = make_workspace(data, mask=mask)
image = workspace.image_set.get_image(INPUT_IMAGE_NAME)
module.threshold_scope.value = cellprofiler.modules.threshold.TS_GLOBAL
module.global_operation.value = cellprofiler.modules.threshold.TM_LI
module.log_transform.value = True
t_local, t_global, t_guide = module.get_threshold(image, workspace)
transformed_data, d = centrosome.threshold.log_transform(data)
t_expected = skimage.filters.threshold_li(transformed_data[mask])
t_expected = centrosome.threshold.inverse_log_transform(t_expected, d)
t_expected = module._correct_global_threshold(
t_expected
)
numpy.testing.assert_almost_equal(t_global, t_expected, decimal=5)
def test_threshold_otsu_uniform_data():
data = numpy.ones((10, 10), dtype=numpy.float32)
data *= 0.2
workspace, module = make_workspace(data)
image = workspace.image_set.get_image(INPUT_IMAGE_NAME)
module.threshold_scope.value = cellprofiler.modules.threshold.TS_ADAPTIVE
module.global_operation.value = centrosome.threshold.TM_OTSU
module.two_class_otsu.value = cellprofiler.modules.threshold.O_TWO_CLASS
module.adaptive_window_size.value = 3
t_local, t_global, t_guide = module.get_threshold(image, workspace)
t_guide_expected = 0.2
t_local_expected = module._get_adaptive_threshold(data,
skimage.filters.threshold_otsu)
t_local_expected = module._correct_local_threshold(t_local_expected, t_guide_expected)
numpy.testing.assert_array_almost_equal(t_local, t_local_expected)
numpy.testing.assert_almost_equal(t_guide, t_guide_expected)
def test_threshold_otsu_full_mask():
numpy.random.seed(73)
data = numpy.random.rand(10, 10)
mask = numpy.zeros_like(data, dtype=numpy.bool)
workspace, module = make_workspace(data, mask=mask)
image = workspace.image_set.get_image(INPUT_IMAGE_NAME)
module.threshold_scope.value = cellprofiler.modules.threshold.TS_ADAPTIVE
module.global_operation.value = centrosome.threshold.TM_OTSU
module.two_class_otsu.value = cellprofiler.modules.threshold.O_TWO_CLASS
module.adaptive_window_size.value = 3
t_local, t_global, _ = module.get_threshold(image, workspace)
t_local_expected = numpy.zeros_like(data)
t_global_expected = 0.0
numpy.testing.assert_array_equal(t_local, t_local_expected)
assert numpy.all(t_global == t_global_expected)
assert numpy.all(t_local == t_local_expected)
def run_local_otsu_2(self, image, workspace, module):
module.threshold_scope.value = "Adaptive"
module.global_operation.value = "Otsu"
module.two_class_otsu.value = "Two classes"
module.adaptive_window_size.value = self.adaptive_window_size.value
t_final, t_orig = module.get_threshold(image, workspace)
return t_final, t_orig
def run_global_otsu_3b(self, image, workspace, module):
module.threshold_scope.value = "Global"
module.global_operation.value = "Otsu"
module.two_class_otsu.value = "Three classes"
module.assign_middle_to_foreground.value = "Background"
t_final, t_orig = module.get_threshold(image, workspace)
return t_final, t_orig
def test_threshold_otsu_image():
numpy.random.seed(73)
data = numpy.random.rand(10, 10)
mask = numpy.zeros_like(data, dtype=numpy.bool)
mask[1:-1, 1:-1] = True
workspace, module = make_workspace(data, mask=mask)
image = workspace.image_set.get_image(INPUT_IMAGE_NAME)
module.threshold_scope.value = cellprofiler.modules.threshold.TS_ADAPTIVE
module.local_operation.value = cellprofiler.modules.threshold.TM_OTSU
module.two_class_otsu.value = cellprofiler.modules.threshold.O_TWO_CLASS
module.adaptive_window_size.value = 3
t_local, t_global, t_guide = module.get_threshold(image, workspace)
t_guide_expected = skimage.filters.threshold_otsu(data[mask])
t_local_expected = module._get_adaptive_threshold(numpy.where(mask, data, numpy.nan),
skimage.filters.threshold_otsu,)
t_local_expected = module._correct_local_threshold(t_local_expected, t_guide_expected)
numpy.testing.assert_almost_equal(t_guide, t_guide_expected)
numpy.testing.assert_array_almost_equal(t_local, t_local_expected)
def test_threshold_sauvola_image_masked():
numpy.random.seed(73)
data = numpy.random.rand(10, 10)
mask = numpy.zeros_like(data, dtype=numpy.bool)
mask[1:3, 1:3] = True
workspace, module = make_workspace(data, mask)
image = workspace.image_set.get_image(INPUT_IMAGE_NAME)
module.threshold_scope.value = cellprofiler.modules.threshold.TS_ADAPTIVE
module.local_operation.value = cellprofiler.modules.threshold.TM_SAUVOLA
module.adaptive_window_size.value = 3
t_local, t_global, t_guide = module.get_threshold(image, workspace)
t_guide_expected = skimage.filters.threshold_li(data[mask])
t_local_expected = skimage.filters.threshold_sauvola(numpy.where(mask, data, 0), window_size=3)
t_local_expected = module._correct_local_threshold(t_local_expected, t_guide_expected)
numpy.testing.assert_almost_equal(t_guide, t_guide_expected)
numpy.testing.assert_almost_equal(t_local, t_local_expected)
def test_threshold_otsu3_full_mask():
numpy.random.seed(73)
data = numpy.random.rand(10, 10)
mask = numpy.zeros_like(data, dtype=numpy.bool)
workspace, module = make_workspace(data, mask=mask)
image = workspace.image_set.get_image(INPUT_IMAGE_NAME)
module.threshold_scope.value = cellprofiler.modules.threshold.TS_ADAPTIVE
module.global_operation.value = centrosome.threshold.TM_OTSU
module.two_class_otsu.value = cellprofiler.modules.threshold.O_THREE_CLASS
module.assign_middle_to_foreground.value = (
cellprofiler.modules.threshold.O_FOREGROUND
)
module.adaptive_window_size.value = 3
t_local, t_global, t_guide = module.get_threshold(image, workspace)
t_local_expected = numpy.zeros_like(data)
t_guide_expected = 0
numpy.testing.assert_almost_equal(t_guide, t_guide_expected)
numpy.testing.assert_array_almost_equal(t_local, t_local_expected)
def test_threshold_otsu3_volume():
numpy.random.seed(73)
data = numpy.random.rand(10, 10, 10)
mask = numpy.zeros_like(data, dtype=numpy.bool)
mask[1:-1, 1:-1, 1:-1] = True
workspace, module = make_workspace(data, mask=mask, dimensions=3)
image = workspace.image_set.get_image(INPUT_IMAGE_NAME)
module.threshold_scope.value = cellprofiler.modules.threshold.TS_ADAPTIVE
module.global_operation.value = centrosome.threshold.TM_OTSU
module.two_class_otsu.value = cellprofiler.modules.threshold.O_THREE_CLASS
module.assign_middle_to_foreground.value = (
cellprofiler.modules.threshold.O_FOREGROUND)
module.adaptive_window_size.value = 3
t_local, t_global = module.get_threshold(image, workspace)
t_global_expected = centrosome.threshold.get_otsu_threshold(
image.pixel_data,
image.mask,
two_class_otsu=False,
assign_middle_to_foreground=True,
)
t_local_expected = numpy.zeros_like(data)
for index, plane in enumerate(data):
t_local_expected[index] = centrosome.threshold.get_adaptive_threshold(
centrosome.threshold.TM_OTSU,
plane,
t_global_expected,
mask=mask[index],
adaptive_window_size=3,
two_class_otsu=False,
assign_middle_to_foreground=True,
)
t_min = t_global_expected * 0.7
t_max = min(1.0, t_global_expected * 1.5)
t_local_expected[t_local_expected < t_min] = t_min
t_local_expected[t_local_expected > t_max] = t_max
numpy.testing.assert_almost_equal(t_global, t_global_expected)
assert t_local.ndim == 3
numpy.testing.assert_array_almost_equal(t_local, t_local_expected)
def run_local_otsu_3b(self, image, workspace, module):
module.threshold_scope.value = "Adaptive"
module.global_operation.value = "Otsu"
module.two_class_otsu.value = "Three classes"
module.assign_middle_to_foreground.value = "Background"
module.adaptive_window_size.value = self.adaptive_window_size.value
t_final, t_orig = module.get_threshold(image, workspace)
return t_final, t_orig
def test_threshold_otsu3_volume_log():
numpy.random.seed(73)
data = numpy.random.rand(10, 10, 10)
mask = numpy.zeros_like(data, dtype=numpy.bool)
mask[1:-1, 1:-1, 1:-1] = True
workspace, module = make_workspace(data, mask=mask, dimensions=3)
image = workspace.image_set.get_image(INPUT_IMAGE_NAME)
module.threshold_scope.value = cellprofiler.modules.threshold.TS_ADAPTIVE
module.local_operation.value = centrosome.threshold.TM_OTSU
module.two_class_otsu.value = cellprofiler.modules.threshold.O_THREE_CLASS
module.assign_middle_to_foreground.value = (
cellprofiler.modules.threshold.O_FOREGROUND)
module.adaptive_window_size.value = 3
module.log_transform.value = True
module.log_transform.value = True
t_local, t_global, t_guide = module.get_threshold(image, workspace)
transformed_data, d = centrosome.threshold.log_transform(data)
t_guide_expected = skimage.filters.threshold_multiotsu(
transformed_data[mask], nbins=128)[0]
t_local_expected = numpy.zeros_like(data)
masked = numpy.where(mask, transformed_data, numpy.nan)
for index, plane in enumerate(masked):
t_local_expected[index] = module._get_adaptive_threshold(
plane,
skimage.filters.threshold_multiotsu,
nbins=128,
)
t_guide_expected = centrosome.threshold.inverse_log_transform(
t_guide_expected, d)
t_local_expected = centrosome.threshold.inverse_log_transform(
t_local_expected, d)
t_local_expected = module._correct_local_threshold(t_local_expected,
t_guide_expected)
numpy.testing.assert_almost_equal(t_guide, t_guide_expected, decimal=5)
assert t_local.ndim == 3
numpy.testing.assert_array_almost_equal(t_local, t_local_expected)
def run_robustbackground(self, image, workspace, module):
module.threshold_scope.value = "Global"
module.global_operation.value = "RobustBackground"
module.averaging_method.value = self.averaging_method.value
module.variance_method.value = self.variance_method.value
module.number_of_deviations.value = self.number_of_deviations.value
module.lower_outlier_fraction.value = self.lower_outlier_fraction.value
module.upper_outlier_fraction.value = self.upper_outlier_fraction.value
t_final, t_orig = module.get_threshold(image, workspace)
return t_final, t_orig
def test_threshold_li_uniform_image():
workspace, module = make_workspace(0.1 * numpy.ones((10, 10)))
image = workspace.image_set.get_image(INPUT_IMAGE_NAME)
module.threshold_scope.value = cellprofiler.modules.threshold.TS_GLOBAL
module.global_operation.value = cellprofiler.modules.threshold.TM_LI
t_local, t_global, _ = module.get_threshold(image, workspace)
numpy.testing.assert_almost_equal(t_local, 0.1)
numpy.testing.assert_almost_equal(t_global, 0.1)
def test_threshold_otsu_volume():
numpy.random.seed(73)
data = numpy.random.rand(10, 10, 10)
mask = numpy.zeros_like(data, dtype=numpy.bool)
mask[1:-1, 1:-1, 1:-1] = True
workspace, module = make_workspace(data, mask=mask, dimensions=3)
image = workspace.image_set.get_image(INPUT_IMAGE_NAME)
module.threshold_scope.value = cellprofiler.modules.threshold.TS_ADAPTIVE
module.global_operation.value = centrosome.threshold.TM_OTSU
module.two_class_otsu.value = cellprofiler.modules.threshold.O_TWO_CLASS
module.adaptive_window_size.value = 3
t_local, t_global = module.get_threshold(image, workspace)
t_global_expected = skimage.filters.threshold_otsu(data[mask])
data = skimage.img_as_ubyte(data)
t_local_expected = numpy.zeros_like(data)
for index, plane in enumerate(data):
t_local_expected[index] = skimage.filters.rank.otsu(
plane, skimage.morphology.square(3), mask=mask[index])
t_local_expected = skimage.img_as_float(t_local_expected)
t_min = 0.7 * t_global_expected
t_max = min(1.0, 1.5 * t_global_expected)
t_local_expected[t_local_expected < t_min] = t_min
t_local_expected[t_local_expected > t_max] = t_max
numpy.testing.assert_almost_equal(t_global, t_global_expected)
numpy.testing.assert_array_almost_equal(t_local, t_local_expected)
def test_threshold_otsu3_image():
numpy.random.seed(73)
data = numpy.random.rand(10, 10)
mask = numpy.zeros_like(data, dtype=numpy.bool)
mask[1:-1, 1:-1] = True
workspace, module = make_workspace(data, mask=mask)
image = workspace.image_set.get_image(INPUT_IMAGE_NAME)
module.threshold_scope.value = cellprofiler.modules.threshold.TS_ADAPTIVE
module.global_operation.value = centrosome.threshold.TM_OTSU
module.two_class_otsu.value = cellprofiler.modules.threshold.O_THREE_CLASS
module.assign_middle_to_foreground.value = (
cellprofiler.modules.threshold.O_FOREGROUND)
module.adaptive_window_size.value = 3
t_local, t_global = module.get_threshold(image, workspace)
t_local_expected, t_global_expected = centrosome.threshold.get_threshold(
centrosome.threshold.TM_OTSU,
cellprofiler.modules.threshold.TS_ADAPTIVE,
data,
mask=mask,
adaptive_window_size=3,
threshold_range_min=0.0,
threshold_range_max=1.0,
threshold_correction_factor=1.0,
two_class_otsu=False,
assign_middle_to_foreground=True,
)
numpy.testing.assert_almost_equal(t_global, t_global_expected)
numpy.testing.assert_array_almost_equal(t_local, t_local_expected)
def test_threshold_li_volume():
numpy.random.seed(73)
data = numpy.random.rand(10, 10, 10)
workspace, module = make_workspace(data, dimensions=3)
image = workspace.image_set.get_image(INPUT_IMAGE_NAME)
module.threshold_scope.value = cellprofiler.modules.threshold.TS_GLOBAL
module.global_operation.value = cellprofiler.modules.threshold.TM_LI
t_local, t_global, _ = module.get_threshold(image, workspace)
expected = skimage.filters.threshold_li(data)
numpy.testing.assert_almost_equal(t_local, expected)
numpy.testing.assert_almost_equal(t_global, expected)
def test_threshold_li_full_mask():
numpy.random.seed(73)
data = numpy.random.rand(10, 10)
mask = numpy.zeros_like(data, dtype=numpy.bool)
workspace, module = make_workspace(data, mask)
image = workspace.image_set.get_image(INPUT_IMAGE_NAME)
module.threshold_scope.value = cellprofiler.modules.threshold.TS_GLOBAL
module.global_operation.value = cellprofiler.modules.threshold.TM_LI
t_local, t_global, _ = module.get_threshold(image, workspace)
numpy.testing.assert_almost_equal(t_local, 0.0)
numpy.testing.assert_almost_equal(t_global, 0.0)
def test_threshold_otsu3_image_log():
numpy.random.seed(73)
data = numpy.random.rand(10, 10)
mask = numpy.zeros_like(data, dtype=bool)
mask[1:-1, 1:-1] = True
workspace, module = make_workspace(data, mask=mask)
image = workspace.image_set.get_image(INPUT_IMAGE_NAME)
module.threshold_scope.value = cellprofiler.modules.threshold.TS_GLOBAL
module.global_operation.value = centrosome.threshold.TM_OTSU
module.two_class_otsu.value = cellprofiler.modules.threshold.O_THREE_CLASS
module.assign_middle_to_foreground.value = (
cellprofiler.modules.threshold.O_FOREGROUND
)
module.log_transform.value = True
t_local, t_global, t_guide = module.get_threshold(image, workspace)
transformed_data, d = centrosome.threshold.log_transform(data)
t_expected = skimage.filters.threshold_multiotsu(transformed_data[mask], nbins=128)[0]
t_expected = centrosome.threshold.inverse_log_transform(t_expected, d)
t_expected = module._correct_global_threshold(
t_expected
)
numpy.testing.assert_almost_equal(t_global, t_expected, decimal=5)
def run_global_otsu_2(self, image, workspace, module):
module.threshold_scope.value = "Global"
module.global_operation.value = "Otsu"
module.two_class_otsu.value = "Two classes"
t_final, t_orig = module.get_threshold(image, workspace)
return t_final, t_orig
def run_mce(self, image, workspace, module):
module.threshold_scope.value = "Global"
module.global_operation.value = "Minimum cross entropy"
t_final, t_orig = module.get_threshold(image, workspace)
return t_final, t_orig
