def test_difference_of_gaussians(s, s2):
image = cp.random.rand(10, 10)
im1 = gaussian(image, s)
im2 = gaussian(image, s2)
dog = im1 - im2
dog2 = difference_of_gaussians(image, s, s2)
assert cp.allclose(dog, dog2)
def test_auto_sigma2(s):
image = cp.random.rand(10, 10)
im1 = gaussian(image, s)
s2 = 1.6 * np.array(s)
im2 = gaussian(image, s2)
dog = im1 - im2
dog2 = difference_of_gaussians(image, s, s2)
assert cp.allclose(dog, dog2)
def test_preserve_output(dtype):
image = cp.arange(9, dtype=dtype).reshape((3, 3))
output = cp.zeros_like(image, dtype=dtype)
gaussian_image = gaussian(image,
sigma=1,
output=output,
preserve_range=True)
assert gaussian_image is output
def test_multichannel():
a = cp.zeros((5, 5, 3))
a[1, 1] = cp.arange(1, 4)
gaussian_rgb_a = gaussian(a, sigma=1, mode='reflect', multichannel=True)
# Check that the mean value is conserved in each channel
# (color channels are not mixed together)
assert cp.allclose([a[..., i].mean() for i in range(3)],
[gaussian_rgb_a[..., i].mean() for i in range(3)])
# Test multichannel = None
with expected_warnings(["multichannel"]):
gaussian_rgb_a = gaussian(a, sigma=1, mode="reflect")
# Check that the mean value is conserved in each channel
# (color channels are not mixed together)
assert cp.allclose([a[..., i].mean() for i in range(3)],
[gaussian_rgb_a[..., i].mean() for i in range(3)])
# Iterable sigma
gaussian_rgb_a = gaussian(a,
sigma=[1, 2],
mode='reflect',
multichannel=True)
assert cp.allclose([a[..., i].mean() for i in range(3)],
[gaussian_rgb_a[..., i].mean() for i in range(3)])
def test_negative_sigma():
a = cp.zeros((3, 3))
a[1, 1] = 1.0
with pytest.raises(ValueError):
gaussian(a, sigma=-1.0)
with pytest.raises(ValueError):
gaussian(a, sigma=[-1.0, 1.0])
with pytest.raises(ValueError):
gaussian(a, sigma=cp.asarray([-1.0, 1.0]))
def test_4d_ok():
img = cp.zeros((5, ) * 4)
img[2, 2, 2, 2] = 1
res = gaussian(img, 1, mode="reflect")
assert cp.allclose(res.sum(), 1)
def test_preserve_range():
img = cp.array([[10.0, -10.0], [-4, 3]], dtype=cp.float32)
gaussian(img, 1, preserve_range=True)
def test_energy_decrease():
a = cp.zeros((3, 3))
a[1, 1] = 1.0
gaussian_a = gaussian(a, sigma=1, mode="reflect")
assert gaussian_a.std() < a.std()
def test_default_sigma():
a = cp.zeros((3, 3))
a[1, 1] = 1.0
assert cp.all(gaussian(a) == gaussian(a, sigma=1))
def test_null_sigma():
a = cp.zeros((3, 3))
a[1, 1] = 1.0
assert cp.all(gaussian(a, 0) == a)
def test_output_error():
image = cp.arange(9, dtype=cp.float32).reshape((3, 3))
output = cp.zeros_like(image, dtype=cp.uint8)
with pytest.raises(ValueError):
gaussian(image, sigma=1, output=output, preserve_range=True)