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_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_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_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_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_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)
