def test_addend():
"""Test adding a value to image"""
def fn(module):
module.addend.value = 0.5
module.operation.value = cellprofiler.modules.imagemath.O_NONE
numpy.random.seed(0)
image = numpy.random.uniform(size=(10, 10)) * 0.5
image = image.astype(numpy.float32)
expected = image + 0.5
output = run_imagemath([{"pixel_data": image}], fn)
check_expected(output, expected)
def test_bilateral():
"""test the smooth module with bilateral filtering"""
sigma = 16.0
sigma_range = 0.2
numpy.random.seed(0)
image = numpy.random.uniform(size=(100, 100)).astype(numpy.float32)
mask = numpy.ones(image.shape, bool)
mask[40:60, 45:65] = False
expected = skimage.restoration.denoise_bilateral(
image=image.astype(float),
multichannel=False,
sigma_color=sigma_range,
sigma_spatial=sigma,
)
workspace, module = make_workspace(image, mask)
module.smoothing_method.value = cellprofiler.modules.smooth.SMOOTH_KEEPING_EDGES
module.sigma_range.value = sigma_range
module.wants_automatic_object_size.value = False
module.object_size.value = 16.0 * 2.35
module.run(workspace)
result = workspace.image_set.get_image(OUTPUT_IMAGE_NAME)
assert not (result is None)
numpy.testing.assert_almost_equal(result.pixel_data, expected)