def test_uncrop_too_small_requested_shape(self,
small_cont_1c: ImageContainer):
crops = list(small_cont_1c.generate_equal_crops(size=13))
with pytest.raises(ValueError, match=r"Requested final image shape"):
ImageContainer.uncrop(crops,
shape=(small_cont_1c.shape[0] - 1,
small_cont_1c.shape[1] - 1))
def test_equal_crops_as_array(self, small_cont: ImageContainer,
as_array: bool):
small_cont.add_img(np.random.normal(size=(small_cont.shape + (1, ))),
channel_dim="foobar",
layer="baz")
for crop in small_cont.generate_equal_crops(size=11,
as_array=as_array):
if as_array:
if isinstance(as_array, bool):
assert isinstance(crop, dict)
for key in small_cont:
assert key in crop
assert crop[key].shape == (
11, 11, small_cont[key].data.shape[-1])
elif isinstance(as_array, str):
assert isinstance(crop, np.ndarray)
assert crop.shape == (11, 11,
small_cont[as_array].data.shape[-1])
else:
assert isinstance(crop, tuple)
assert len(crop) == len(as_array)
for key, data in zip(as_array, crop):
assert isinstance(data, np.ndarray)
assert data.shape == (11, 11,
small_cont[key].data.shape[-1])
else:
assert isinstance(crop, ImageContainer)
for key in (Key.img.coords, Key.img.padding, Key.img.scale,
Key.img.mask_circle):
assert key in crop.data.attrs, key
assert crop.shape == (11, 11)
def test_uncrop_preserves_shape(self, small_cont_1c: ImageContainer):
small_cont_1c.add_img(np.random.normal(size=(small_cont_1c.shape +
(4, ))),
channel_dim="foobar",
layer="baz")
crops = list(small_cont_1c.generate_equal_crops(size=13))
uncrop = ImageContainer.uncrop(crops)
np.testing.assert_array_equal(small_cont_1c.shape, uncrop.shape)
for key in small_cont_1c:
np.testing.assert_array_equal(uncrop[key], small_cont_1c[key])
