def test_different_types(
self, tmp_path, monkeypatch, img_grayscale_uint, img_grayscale_float
):
"""Make sure that the registration does not depend on the type of input images.
Notes
-----
Marked as `todo` because we did not find a way how to force ANTsPY to be always reproducible.
"""
monkeypatch.setenv("ANTS_RANDOM_SEED", "4")
monkeypatch.setenv("ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS", "1")
size = img_grayscale_uint.shape
p = img_grayscale_uint / np.sum(img_grayscale_uint)
df = DisplacementField.generate(size, approach="paper", p=p)
moving_img_uint = df.warp(img_grayscale_uint)
moving_img_float = df.warp(img_grayscale_float)
df_final1, meta1 = antspy_registration(
img_grayscale_uint, moving_img_uint, path=tmp_path, verbose=False
)
df_final2, meta2 = antspy_registration(
img_grayscale_float, moving_img_float, path=tmp_path, verbose=False
)
assert np.allclose(df_final1.delta_x, df_final2.delta_x, atol=0.1)
assert np.allclose(df_final1.delta_y, df_final2.delta_y, atol=0.1)
def test_displacement_field(self, tmp_path, img_grayscale):
"""Make sure that the displacement extracted can reproduce the registered moving image.
Done by comparing with the image contained in the registration output.
"""
fixed_img = img_grayscale
size = fixed_img.shape
df = DisplacementField.generate(
size, approach="affine_simple", translation_x=20, translation_y=20
)
moving_img = df.warp(img_grayscale)
df_final, meta = antspy_registration(fixed_img, moving_img, path=tmp_path)
img1 = meta["warpedmovout"].numpy()
img2 = df_final.warp(
moving_img, interpolation="linear", border_mode="constant", c=0
)
if img_grayscale.dtype == "uint8":
epsilon = 1
else:
epsilon = 0.005
assert abs(img1 - img2).mean() < epsilon
def test_different_results(self, tmp_path, img_grayscale_uint, registration_type):
"""Make sure that the registration is not reproducible if some environment variables not set.
The environment variables are ANTS_RANDOM_SEED and ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS.
"""
size = img_grayscale_uint.shape
df = DisplacementField.generate(
size, approach="affine_simple", translation_x=20
)
moving_img = df.warp(img_grayscale_uint)
df_final1, meta1 = antspy_registration(
img_grayscale_uint, moving_img, path=tmp_path
)
df_final2, meta2 = antspy_registration(
img_grayscale_uint, moving_img, path=tmp_path
)
assert not np.allclose(df_final1.delta_x, df_final2.delta_x, atol=0.1)
assert not np.allclose(df_final1.delta_y, df_final2.delta_y, atol=0.1)
def test_same_results(
self, tmp_path, monkeypatch, img_grayscale, registration_type
):
"""Make sure that the registration is reproducible.
Done by checking if the displacement field extracted are equal if we run the registration with
the same parameters.
Notes
-----
Marked as `todo` because we did not find a way how to force ANTsPY to be always reproducible.
"""
monkeypatch.setenv("ANTS_RANDOM_SEED", "1")
monkeypatch.setenv("ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS", "1")
size = img_grayscale.shape
p = img_grayscale / np.sum(img_grayscale)
df = DisplacementField.generate(size, approach="paper", p=p, random_state=1)
moving_img = df.warp(img_grayscale)
df_final1, meta1 = antspy_registration(
img_grayscale,
moving_img,
registration_type=registration_type,
path=tmp_path,
verbose=True,
)
df_final2, meta2 = antspy_registration(
img_grayscale,
moving_img,
registration_type=registration_type,
path=tmp_path,
verbose=True,
)
assert np.allclose(df_final1.delta_x, df_final2.delta_x, atol=1)
assert np.allclose(df_final1.delta_y, df_final2.delta_y, atol=1)
def test_each_type(self, tmp_path, img_grayscale, registration_type):
"""Make sure that every type of registration gives a valid DisplacementField as output."""
moving_img = img_grayscale
fixed_img = img_grayscale
reg_iterations = (4, 2, 0)
df_final, meta = antspy_registration(
fixed_img,
moving_img,
registration_type=registration_type,
reg_iterations=reg_iterations,
path=tmp_path,
)
assert isinstance(df_final, DisplacementField)
assert df_final.is_valid