def affine_resample(source,
target,
T,
toresample='source',
dtype=None,
order=3,
use_scipy=False):
"""
Image resampling using spline interpolation.
Parameters
----------
source : image
target : image
T : source-to-target affine transform
"""
Tv = np.dot(np.linalg.inv(target.get_affine()), np.dot(T, source.get_affine()))
if use_scipy or not order==3:
use_scipy = True
from scipy.ndimage import affine_transform
if toresample == 'target':
if not use_scipy:
data = cspline_resample(target.get_data(),
source.get_shape(),
Tv,
dtype=dtype)
else:
data = affine_transform(target.get_data(),
Tv[0:3,0:3], offset=Tv[0:3,3],
output_shape=source.get_shape(),
order=order)
return Image(data, source.get_affine())
else:
if not use_scipy:
data = cspline_resample(source.get_data(),
target.get_shape(),
np.linalg.inv(Tv),
dtype=dtype)
else:
Tv = np.linalg.inv(Tv)
data = affine_transform(source.get_data(),
Tv[0:3,0:3], offset=Tv[0:3,3],
output_shape=target.get_shape(),
order=order)
return Image(data, target.get_affine())
def resampling(Tv):
"""
Adding this new test to check whether resample
may be replaced with scipy.ndimage.affine_transform
"""
I = Image(make_data_int16())
t0 = time.clock()
I1 = cspline_resample(I.array, I.array.shape, Tv)
dt1 = time.clock()-t0
t0 = time.clock()
I2 = affine_transform(I.array, Tv[0:3,0:3], offset=Tv[0:3,3], output_shape=I.array.shape)
dt2 = time.clock()-t0
assert_almost_equal(I1, I2)
print('3d array resampling')
print(' using nipy.neurospin: %f sec' % dt1)
print(' using scipy.ndimage: %f sec' % dt2)
