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_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_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)
