__all__ = ["simulate_displacement_field"]
import numpy as np
import ants
from ants.internal import get_lib_fn
def simulate_displacement_field(domain_image,
field_type="bspline",
number_of_random_points=1000,
sd_noise=10.0,
enforce_stationary_boundary=True,
number_of_fitting_levels=4,
mesh_size=1,
sd_smoothing=4.0):
"""
simulate displacement field using either b-spline or exponential transform
ANTsR function: `simulateDisplacementField`
Arguments
---------
domain_image : ANTsImage
Domain image
field_type : string
Either "bspline" or "exponential".
number_of_random_points : integer
Number of displacement points.
sd_noise : float
Standard deviation of the displacement field noise.
enforce_stationary_boundary : boolean
Determines fixed boundary conditions.
number_of_fitting_levels : integer
Number of fitting levels (b-spline only).
mesh_size : integer or n-D tuple
Determines fitting resolution at base level (b-spline only).
sd_smoothing : float
Standard deviation of the Gaussian smoothing in mm (exponential only).
Returns
-------
ANTs vector image.
Example
-------
>>> import ants
>>> domain = ants.image_read( ants.get_ants_data('r16'))
>>> exp_field = ants.simulate_displacement_field(domain, field_type="exponential")
>>> bsp_field = ants.simulate_displacement_field(domain, field_type="bspline")
>>> bsp_xfrm = ants.transform_from_displacement_field(bsp_field * 3)
>>> domain_warped = ants.apply_ants_transform_to_image(bsp_xfrm, domain, domain)
"""
image_dimension = domain_image.dimension
if field_type == 'bspline':
if isinstance(mesh_size, int) == False and len(mesh_size) != image_dimension:
raise ValueError("Incorrect specification for mesh_size.")
spline_order = 3
number_of_control_points = mesh_size + spline_order
if isinstance(number_of_control_points, int) == True:
number_of_control_points = np.repeat(number_of_control_points, image_dimension)
libfn = get_lib_fn("simulateBsplineDisplacementField%iD" % image_dimension)
field = libfn(domain_image.pointer, number_of_random_points, sd_noise,
enforce_stationary_boundary, number_of_fitting_levels, number_of_control_points)
bspline_field = ants.from_pointer(field).clone('float')
return bspline_field
elif field_type == 'exponential':
libfn = get_lib_fn("simulateExponentialDisplacementField%iD" % image_dimension)
field = libfn(domain_image.pointer, number_of_random_points, sd_noise,
enforce_stationary_boundary, sd_smoothing)
exp_field = ants.from_pointer(field).clone('float')
return exp_field
else:
raise ValueError("Unrecognized field type.")
