__all__ = ["fit_bspline_displacement_field"]
import numpy as np
from ..core import ants_image as iio
from .. import core
from .. import utils
[docs]def fit_bspline_displacement_field(displacement_field=None,
displacement_weight_image=None,
displacement_origins=None,
displacements=None,
displacement_weights=None,
origin=None,
spacing=None,
size=None,
direction=None,
number_of_fitting_levels=4,
mesh_size=1,
spline_order=3,
enforce_stationary_boundary=True,
estimate_inverse=False,
rasterize_points=False):
"""
Fit a b-spline object to a dense displacement field image and/or a set of points
with associated displacements and smooths them using B-splines. The inverse
can also be estimated.. This is basically a wrapper for the ITK filter
https://itk.org/Doxygen/html/classitk_1_1DisplacementFieldToBSplineImageFilter.html}
which, in turn is a wrapper for the ITK filter used for the function
fit_bspline_object_to_scattered_data.
ANTsR function: `fitBsplineToDisplacementField`
Arguments
---------
displacement_field : ANTs image
Input displacement field. Either this and/or the points must be specified.
displacement_weight_image : ANTs image
Input image defining weighting of the voxelwise displacements in the displacement_field. I
If None, defaults to identity weighting for each displacement. Default = None.
displacement_origins : 2-D numpy array
Matrix (number_of_points x dimension) defining the origins of the input
displacement points. Default = None.
displacements : 2-D numpy array
Matrix (number_of_points x dimension) defining the displacements of the input
displacement points. Default = None.
displacement_weights : 1-D numpy array
Array defining the individual weighting of the corresponding scattered data value.
Default = None meaning all values are weighted the same.
origin : n-D tuple
Defines the physical origin of the B-spline object.
spacing : n-D tuple
Defines the physical spacing of the B-spline object.
size : n-D tuple
Defines the size (length) of the B-spline object. Note that the length of the
B-spline object in dimension d is defined as
spacing[d] * size[d]-1.
direction : 2-D numpy array
Booleans defining whether or not the corresponding parametric dimension is
closed (e.g., closed loop). Default = None.
number_of_fitting_levels : integer
Specifies the number of fitting levels.
mesh_size : n-D tuple
Defines the mesh size at the initial fitting level.
spline_order : integer
Spline order of the B-spline object. Default = 3.
enforce_stationary_boundary : boolean
Ensure no displacements on the image boundary. Default = True.
estimate_inverse : boolean
Estimate the inverse displacement field. Default = False.
rasterize_points : boolean
Use nearest neighbor rasterization of points for estimating the
field (potential speed-up). Default = False.
Returns
-------
Returns an ANTsImage.
Example
-------
>>> # Perform 2-D fitting
>>>
>>> import ants, numpy
>>>
>>> points = numpy.array([[-50, -50]])
>>> deltas = numpy.array([[10, 10]])
>>>
>>> bspline_field = ants.fit_bspline_displacement_field(
>>> displacement_origins=points, displacements=deltas,
>>> origin=[0.0, 0.0], spacing=[1.0, 1.0], size=[100, 100],
>>> direction=numpy.array([[-1, 0], [0, -1]]),
>>> number_of_fitting_levels=4, mesh_size=(1, 1))
"""
if displacement_field is None and (displacement_origins is None or displacements is None):
raise ValueError("Missing input. Either a displacement field or input point set (origins + displacements) needs to be specified.")
if displacement_field is None:
if origin is None or spacing is None or size is None or direction is None:
raise ValueError("If the displacement field is not specified, one must fully specify the input physical domain.")
if displacement_field is not None and displacement_weight_image is None:
displacement_weight_image = core.make_image(displacement_field.shape, voxval=1,
spacing=displacement_field.spacing, origin=displacement_field.origin,
direction=displacement_field.direction, has_components=False, pixeltype='float')
if displacement_field is not None:
if origin is None:
origin = displacement_field.origin
if spacing is None:
spacing = displacement_field.spacing
if direction is None:
direction = displacement_field.direction
if size is None:
size = displacement_field.shape
dimensionality = None
if displacement_field is not None:
dimensionality = displacement_field.dimension
else:
dimensionality = displacement_origins.shape[1]
if displacements.shape[1] != dimensionality:
raise ValueError("Dimensionality between origins and displacements does not match.")
if displacement_origins is not None:
if displacement_weights is not None and (len(displacement_weights) != displacement_origins.shape[0]):
raise ValueError("Length of displacement weights must match the number of displacement points.")
else:
displacement_weights = np.ones(displacement_origins.shape[0])
if isinstance(mesh_size, int) == False and len(mesh_size) != dimensionality:
raise ValueError("Incorrect specification for mesh_size.")
if origin is not None and len(origin) != dimensionality:
raise ValueError("Origin is not of length dimensionality.")
if spacing is not None and len(spacing) != dimensionality:
raise ValueError("Spacing is not of length dimensionality.")
if size is not None and len(size) != dimensionality:
raise ValueError("Size is not of length dimensionality.")
if direction is not None and (direction.shape[0] != dimensionality and direction.shape[1] != dimensionality):
raise ValueError("Direction is not of shape dimensionality x dimensionality.")
# It would seem that pybind11 doesn't really play nicely when the
# arguments are 'None'
if origin is None:
origin = np.empty(0)
if spacing is None:
spacing = np.empty(0)
if size is None:
size = np.empty(0)
if direction is None:
direction = np.empty((0, 0))
if displacement_origins is None:
displacement_origins = np.empty((0, 0))
displacement_weights = np.empty(0)
else:
if displacement_weights is None:
displacement_weights = np.repeat(1.0, displacement_origins.shape[0])
number_of_control_points = list(np.array(mesh_size) + np.repeat(spline_order, dimensionality))
bspline_field = None
if displacement_field is not None:
libfn = utils.get_lib_fn("fitBsplineDisplacementFieldD%i" % (dimensionality))
bspline_field = libfn(displacement_field.pointer, displacement_weight_image.pointer,
displacement_origins, displacements, displacement_weights,
origin, spacing, size, direction,
number_of_fitting_levels, number_of_control_points, spline_order,
enforce_stationary_boundary, estimate_inverse)
elif displacement_field is None and displacements is not None:
libfn = utils.get_lib_fn("fitBsplineDisplacementFieldToScatteredDataD%i" % (dimensionality))
bspline_field = libfn(displacement_origins, displacements, displacement_weights,
origin, spacing, size, direction,
number_of_fitting_levels, number_of_control_points, spline_order,
enforce_stationary_boundary, estimate_inverse, rasterize_points)
bspline_displacement_field = iio.ANTsImage(pixeltype='float',
dimension=dimensionality, components=dimensionality,
pointer=bspline_field).clone('float')
return bspline_displacement_field
