def test_resample_from_to():
# Test resampling from image to image / image space
data = np.arange(24).reshape((2, 3, 4))
affine = np.diag([-4, 5, 6, 1])
img = Nifti1Image(data, affine)
img.header['descrip'] = 'red shirt image'
out = resample_from_to(img, img)
assert_almost_equal(img.dataobj, out.dataobj)
assert_array_equal(img.affine, out.affine)
# Check resampling reverses effect of flipping axes
# This will also test translations
flip_ornt = np.array([[0, 1], [1, 1], [2, 1]])
for axis in (0, 1, 2):
ax_flip_ornt = flip_ornt.copy()
ax_flip_ornt[axis, 1] = -1
aff_flip_i = inv_ornt_aff(ax_flip_ornt, (2, 3, 4))
flipped_img = Nifti1Image(flip_axis(data, axis),
np.dot(affine, aff_flip_i))
out = resample_from_to(flipped_img, ((2, 3, 4), affine))
assert_almost_equal(img.dataobj, out.dataobj)
assert_array_equal(img.affine, out.affine)
# A translation of one voxel on each axis
trans_aff = from_matvec(np.diag([-4, 5, 6]), [4, -5, -6])
trans_img = Nifti1Image(data, trans_aff)
out = resample_from_to(trans_img, img)
exp_out = np.zeros_like(data)
exp_out[:-1, :-1, :-1] = data[1:, 1:, 1:]
assert_almost_equal(out.dataobj, exp_out)
out = resample_from_to(img, trans_img)
trans_exp_out = np.zeros_like(data)
trans_exp_out[1:, 1:, 1:] = data[:-1, :-1, :-1]
assert_almost_equal(out.dataobj, trans_exp_out)
# Test mode with translation of first axis only
# Default 'constant' mode first
trans1_aff = from_matvec(np.diag([-4, 5, 6]), [4, 0, 0])
trans1_img = Nifti1Image(data, trans1_aff)
out = resample_from_to(img, trans1_img)
exp_out = np.zeros_like(data)
exp_out[1:, :, :] = data[:-1, :, :]
assert_almost_equal(out.dataobj, exp_out)
# Then 'nearest' mode
out = resample_from_to(img, trans1_img, mode='nearest')
exp_out[0, :, :] = exp_out[1, :, :]
assert_almost_equal(out.dataobj, exp_out)
# Test order
trans_p_25_aff = from_matvec(np.diag([-4, 5, 6]), [1, 0, 0])
trans_p_25_img = Nifti1Image(data, trans_p_25_aff)
# Suprising to me, but all points outside are set to 0, even with NN
out = resample_from_to(img, trans_p_25_img, order=0)
exp_out = np.zeros_like(data)
exp_out[1:, :, :] = data[1, :, :]
assert_almost_equal(out.dataobj, exp_out)
out = resample_from_to(img, trans_p_25_img)
exp_out = spnd.affine_transform(data, [1, 1, 1], [-0.25, 0, 0], order=3)
assert_almost_equal(out.dataobj, exp_out)
# Test cval
out = resample_from_to(img, trans_img, cval=99)
exp_out = np.zeros_like(data) + 99
exp_out[1:, 1:, 1:] = data[:-1, :-1, :-1]
assert_almost_equal(out.dataobj, exp_out)
# Out class
out = resample_from_to(img, trans_img)
assert out.__class__ == Nifti1Image
# By default, type of from_img makes no difference
n1_img = Nifti2Image(data, affine)
out = resample_from_to(n1_img, trans_img)
assert out.__class__ == Nifti1Image
# Passed as keyword arg
out = resample_from_to(img, trans_img, out_class=Nifti2Image)
assert out.__class__ == Nifti2Image
# If keyword arg is None, use type of from_img
out = resample_from_to(n1_img, trans_img, out_class=None)
assert out.__class__ == Nifti2Image
# to_img type irrelevant in all cases
n1_trans_img = Nifti2Image(data, trans_aff)
out = resample_from_to(img, n1_trans_img, out_class=None)
assert out.__class__ == Nifti1Image
# From 2D to 3D, error, the fixed affine is not invertible
img_2d = Nifti1Image(data[:, :, 0], affine)
with pytest.raises(AffineError):
resample_from_to(img_2d, img)
# 3D to 2D, we don't need to invert the fixed matrix
out = resample_from_to(img, img_2d)
assert_array_equal(out.dataobj, data[:, :, 0])
# Same for tuple as to_img imput
out = resample_from_to(img, (img_2d.shape, img_2d.affine))
assert_array_equal(out.dataobj, data[:, :, 0])
# 4D input and output also OK
data_4d = np.arange(24 * 5).reshape((2, 3, 4, 5))
img_4d = Nifti1Image(data_4d, affine)
out = resample_from_to(img_4d, img_4d)
assert_almost_equal(data_4d, out.dataobj)
assert_array_equal(img_4d.affine, out.affine)
# Errors trying to match 3D to 4D
with pytest.raises(ValueError):
resample_from_to(img_4d, img)
with pytest.raises(ValueError):
resample_from_to(img, img_4d)
def test_resample_from_to():
# Test resampling from image to image / image space
data = np.arange(24).reshape((2, 3, 4))
affine = np.diag([-4, 5, 6, 1])
img = Nifti1Image(data, affine)
img.header['descrip'] = 'red shirt image'
out = resample_from_to(img, img)
assert_almost_equal(img.dataobj, out.dataobj)
assert_array_equal(img.affine, out.affine)
# Check resampling reverses effect of flipping axes
# This will also test translations
flip_ornt = np.array([[0, 1], [1, 1], [2, 1]])
for axis in (0, 1, 2):
ax_flip_ornt = flip_ornt.copy()
ax_flip_ornt[axis, 1] = -1
aff_flip_i = inv_ornt_aff(ax_flip_ornt, (2, 3, 4))
flipped_img = Nifti1Image(flip_axis(data, axis),
np.dot(affine, aff_flip_i))
out = resample_from_to(flipped_img, ((2, 3, 4), affine))
assert_almost_equal(img.dataobj, out.dataobj)
assert_array_equal(img.affine, out.affine)
# A translation of one voxel on each axis
trans_aff = from_matvec(np.diag([-4, 5, 6]), [4, -5, -6])
trans_img = Nifti1Image(data, trans_aff)
out = resample_from_to(trans_img, img)
exp_out = np.zeros_like(data)
exp_out[:-1, :-1, :-1] = data[1:, 1:, 1:]
assert_almost_equal(out.dataobj, exp_out)
out = resample_from_to(img, trans_img)
trans_exp_out = np.zeros_like(data)
trans_exp_out[1:, 1:, 1:] = data[:-1, :-1, :-1]
assert_almost_equal(out.dataobj, trans_exp_out)
# Test mode with translation of first axis only
# Default 'constant' mode first
trans1_aff = from_matvec(np.diag([-4, 5, 6]), [4, 0, 0])
trans1_img = Nifti1Image(data, trans1_aff)
out = resample_from_to(img, trans1_img)
exp_out = np.zeros_like(data)
exp_out[1:, :, :] = data[:-1, :, :]
assert_almost_equal(out.dataobj, exp_out)
# Then 'nearest' mode
out = resample_from_to(img, trans1_img, mode='nearest')
exp_out[0, :, :] = exp_out[1, :, :]
assert_almost_equal(out.dataobj, exp_out)
# Test order
trans_p_25_aff = from_matvec(np.diag([-4, 5, 6]), [1, 0, 0])
trans_p_25_img = Nifti1Image(data, trans_p_25_aff)
# Suprising to me, but all points outside are set to 0, even with NN
out = resample_from_to(img, trans_p_25_img, order=0)
exp_out = np.zeros_like(data)
exp_out[1:, :, :] = data[1, :, :]
assert_almost_equal(out.dataobj, exp_out)
out = resample_from_to(img, trans_p_25_img)
exp_out = spnd.affine_transform(data, [1, 1, 1], [-0.25, 0, 0], order=3)
assert_almost_equal(out.dataobj, exp_out)
# Test cval
out = resample_from_to(img, trans_img, cval=99)
exp_out = np.zeros_like(data) + 99
exp_out[1:, 1:, 1:] = data[:-1, :-1, :-1]
assert_almost_equal(out.dataobj, exp_out)
# Out class
out = resample_from_to(img, trans_img)
assert_equal(out.__class__, Nifti1Image)
# By default, type of from_img makes no difference
n1_img = Nifti2Image(data, affine)
out = resample_from_to(n1_img, trans_img)
assert_equal(out.__class__, Nifti1Image)
# Passed as keyword arg
out = resample_from_to(img, trans_img, out_class=Nifti2Image)
assert_equal(out.__class__, Nifti2Image)
# If keyword arg is None, use type of from_img
out = resample_from_to(n1_img, trans_img, out_class=None)
assert_equal(out.__class__, Nifti2Image)
# to_img type irrelevant in all cases
n1_trans_img = Nifti2Image(data, trans_aff)
out = resample_from_to(img, n1_trans_img, out_class=None)
assert_equal(out.__class__, Nifti1Image)
# From 2D to 3D, error, the fixed affine is not invertible
img_2d = Nifti1Image(data[:, :, 0], affine)
assert_raises(AffineError, resample_from_to, img_2d, img)
# 3D to 2D, we don't need to invert the fixed matrix
out = resample_from_to(img, img_2d)
assert_array_equal(out.dataobj, data[:, :, 0])
# Same for tuple as to_img imput
out = resample_from_to(img, (img_2d.shape, img_2d.affine))
assert_array_equal(out.dataobj, data[:, :, 0])
# 4D input and output also OK
data_4d = np.arange(24 * 5).reshape((2, 3, 4, 5))
img_4d = Nifti1Image(data_4d, affine)
out = resample_from_to(img_4d, img_4d)
assert_almost_equal(data_4d, out.dataobj)
assert_array_equal(img_4d.affine, out.affine)
# Errors trying to match 3D to 4D
assert_raises(ValueError, resample_from_to, img_4d, img)
assert_raises(ValueError, resample_from_to, img, img_4d)