__all__ = ['affine_initializer']
import warnings
from tempfile import mktemp
from ants.internal import get_lib_fn, process_arguments
def affine_initializer(fixed_image, moving_image, search_factor=20,
radian_fraction=0.1, use_principal_axis=False,
local_search_iterations=10, mask=None, txfn=None ):
"""
A multi-start optimizer for affine registration
Searches over the sphere to find a good initialization for further
registration refinement, if needed. This is a wrapper for the ANTs
function antsAffineInitializer.
ANTsR function: `affineInitializer`
Arguments
---------
fixed_image : ANTsImage
the fixed reference image
moving_image : ANTsImage
the moving image to be mapped to the fixed space
search_factor : scalar
degree of increments on the sphere to search
radian_fraction : scalar
between zero and one, defines the arc to search over
use_principal_axis : boolean
boolean to initialize by principal axis
local_search_iterations : scalar
gradient descent iterations
mask : ANTsImage (optional)
optional mask to restrict registration
txfn : string (optional)
filename for the transformation
Returns
-------
ndarray
transformation matrix
Example
-------
>>> import ants
>>> fi = ants.image_read(ants.get_ants_data('r16'))
>>> mi = ants.image_read(ants.get_ants_data('r27'))
>>> txfile = ants.affine_initializer( fi, mi )
>>> tx = ants.read_transform(txfile, dimension=2)
"""
if txfn is None:
txfn = mktemp(suffix='.mat')
veccer = [fixed_image.dimension, fixed_image, moving_image, txfn,
search_factor, radian_fraction, int(use_principal_axis),
local_search_iterations]
if mask is not None:
veccer.append(mask)
xxx = process_arguments(veccer)
libfn = get_lib_fn('antsAffineInitializer')
retval = libfn(xxx)
if retval != 0:
warnings.warn('ERROR: Non-zero exit status!')
return txfn
