def test_edt32_indices_wrong_format(shape: Tuple[int, ...], dtype: np.dtype):
x = np.random.random((10, )) > 0.5
indices = np.zeros((x.ndim, ) + shape, dtype=dtype)
with pytest.raises(RuntimeError):
stats.distance_transform_edt_float32(input=x,
sampling=[1.2],
indices=indices)
def test_distance_transform_edt01(type_):
# euclidean distance transform (edt)
data = np.array(
[
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 1, 1, 0, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 0, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
],
type_,
)
out, ft = stats.distance_transform_edt_float32(data, return_indices=True)
bf = ndimage.distance_transform_bf(data, "euclidean")
assert_array_almost_equal(bf, out)
dt = ft - np.indices(ft.shape[1:], dtype=ft.dtype)
dt = dt.astype(np.float64)
np.multiply(dt, dt, dt)
dt = np.add.reduce(dt, axis=0)
np.sqrt(dt, dt)
assert_array_almost_equal(bf, dt)
def test_distance_transform_edt4(type_):
data = np.array(
[
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 1, 1, 0, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 0, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
],
type_,
)
ref = ndimage.distance_transform_bf(data, "euclidean", sampling=[2, 1])
out = stats.distance_transform_edt_float32(data, sampling=[2, 1])
assert_array_almost_equal(ref, out)
def test_distance_transform_edt5():
# Ticket #954 regression test
out = stats.distance_transform_edt_float32(False)
assert_array_almost_equal(out, [0.])
def test_distance_transform_edt02(type_):
data = np.array(
[
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 1, 1, 0, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 0, 0],
[0, 0, 0, 1, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0],
],
type_,
)
tdt, tft = stats.distance_transform_edt_float32(data, return_indices=True)
dts = []
fts = []
dt = np.zeros(data.shape, dtype=np.float32)
stats.distance_transform_edt_float32(data, distances=dt)
dts.append(dt)
ft = stats.distance_transform_edt_float32(data,
return_distances=0,
return_indices=True)
fts.append(ft)
ft = np.indices(data.shape, dtype=np.int32)
stats.distance_transform_edt_float32(data,
return_distances=False,
return_indices=True,
indices=ft)
fts.append(ft)
dt, ft = stats.distance_transform_edt_float32(data, return_indices=True)
dts.append(dt)
fts.append(ft)
dt = np.zeros(data.shape, dtype=np.float32)
ft = stats.distance_transform_edt_float32(data,
distances=dt,
return_indices=True)
dts.append(dt)
fts.append(ft)
ft = np.indices(data.shape, dtype=np.int32)
dt = stats.distance_transform_edt_float32(data,
return_indices=True,
indices=ft)
dts.append(dt)
fts.append(ft)
dt = np.zeros(data.shape, dtype=np.float32)
ft = np.indices(data.shape, dtype=np.int32)
stats.distance_transform_edt_float32(data,
distances=dt,
return_indices=True,
indices=ft)
dts.append(dt)
fts.append(ft)
for dt in dts:
assert_array_almost_equal(tdt, dt)
for ft in fts:
assert_array_almost_equal(tft, ft)