def test_crop():
shape = (4, 6, 2)
data = np.ones(shape)
padded = pad_img(data, 3, 2, 4, 4, 5, 7)
padd_nii = Nifti1Image(padded, np.eye(4))
cropped = crop_img(padd_nii, pad=False)
np.testing.assert_equal(cropped.get_data(), data)
def test_crop():
# Testing that padding of arrays and cropping of images work symmetrically
shape = (4, 6, 2)
data = np.ones(shape)
padded = _pad_array(data, [3, 2, 4, 4, 5, 7])
padd_nii = Nifti1Image(padded, np.eye(4))
cropped = crop_img(padd_nii, pad=False)
np.testing.assert_equal(cropped.get_data(), data)
def test_crop_threshold_tolerance():
"""Check to see whether crop can skip values that are extremely
close to zero in a relative sense and will crop them away"""
data = np.zeros([10, 14, 12])
data[3:7, 3:7, 5:9] = 1.
active_shape = (4 + 2, 4 + 2, 4 + 2) # add padding
# add an infinitesimal outside this block
data[3, 3, 3] = 1e-12
affine = np.eye(4)
img = nibabel.Nifti1Image(data, affine=affine)
cropped_img = image.crop_img(img)
assert_true(cropped_img.shape == active_shape)
def test_crop_img():
data = np.zeros((5, 6, 7))
data[2:4, 1:5, 3:6] = 1
affine = np.diag((4, 3, 2, 1))
img = nibabel.Nifti1Image(data, affine=affine)
cropped_img = image.crop_img(img)
# correction for padding with "-1"
new_origin = np.array((4, 3, 2)) * np.array((2 - 1, 1 - 1, 3 - 1))
# check that correct part was extracted:
# This also corrects for padding
assert_true((cropped_img.get_data()[1:-1, 1:-1, 1:-1] == 1).all())
assert_true(cropped_img.shape == (2 + 2, 4 + 2, 3 + 2))
