__all__ = ['create_warped_grid']
import numpy as np
from ..core import ants_image as iio
from ..core import ants_image_io as iio2
from .apply_transforms import apply_transforms
def create_grid_source(size=(250, 250),
sigma=(0.5, 0.5),
grid_spacing=(5.0, 5.0),
grid_offset=(0.0, 0.0),
spacing=(0.2, 0.2),
pixeltype='float'):
pass
[docs]def create_warped_grid(image, grid_step=10, grid_width=2, grid_directions=(True, True),
fixed_reference_image=None, transform=None, foreground=1, background=0):
"""
Deforming a grid is a helpful way to visualize a deformation field.
This function enables a user to define the grid parameters
and apply a deformable map to that grid.
ANTsR function: `createWarpedGrid`
Arguments
---------
image : ANTsImage
input image
grid_step : scalar
width of grid blocks
grid_width : scalar
width of grid lines
grid_directions : tuple of booleans
directions in which to draw grid lines, boolean vector
fixed_reference_image : ANTsImage (optional)
reference image space
transform : list/tuple of strings (optional)
vector of transforms
foreground : scalar
intensity value for grid blocks
background : scalar
intensity value for grid lines
Returns
-------
ANTsImage
Example
-------
>>> import ants
>>> fi = ants.image_read( ants.get_ants_data( 'r16' ) )
>>> mi = ants.image_read( ants.get_ants_data( 'r64' ) )
>>> mygr = ants.create_warped_grid( mi )
>>> mytx = ants.registration(fixed=fi, moving=mi, type_of_transform = ('SyN') )
>>> mywarpedgrid = ants.create_warped_grid( mi, grid_directions=(False,True),
transform=mytx['fwdtransforms'], fixed_reference_image=fi )
"""
if isinstance(image, iio.ANTsImage):
if len(grid_directions) != image.dimension:
grid_directions = [True]*image.dimension
garr = image.numpy() * 0 + foreground
else:
if not isinstance(image, (list, tuple)):
raise ValueError('image arg must be ANTsImage or list or tuple')
if len(grid_directions) != len(image):
grid_directions = [True]*len(image)
garr = np.zeros(image) + foreground
image = iio2.from_numpy(garr)
idim = garr.ndim
gridw = grid_width
for d in range(idim):
togrid = np.arange(-1, garr.shape[d]-1, step=grid_step)
for i in range(len(togrid)):
if (d == 0) & (idim == 3) & (grid_directions[d]):
garr[togrid[i]:(togrid[i]+gridw),...] = background
garr[0,...] = background
garr[-1,...] = background
if (d == 1) & (idim == 3) & (grid_directions[d]):
garr[:,togrid[i]:(togrid[i]+gridw),:] = background
garr[:,0,:] = background
garr[:,-1,:] = background
if (d == 2) & (idim == 3) & (grid_directions[d]):
garr[...,togrid[i]:(togrid[i]+gridw)] = background
garr[...,0] = background
garr[...,-1] = background
if (d == 0) & (idim == 2) & (grid_directions[d]):
garr[togrid[i]:(togrid[i]+gridw),:] = background
garr[0,:] = background
garr[-1,:] = background
if (d == 1) & (idim == 2) & (grid_directions[d]):
garr[:,togrid[i]:(togrid[i]+gridw)] = background
garr[:,0] = background
garr[:,-1] = background
gimage = image.new_image_like(garr)
if (transform is not None) and (fixed_reference_image is not None):
return apply_transforms( fixed=fixed_reference_image, moving=gimage,
transformlist=transform )
else:
return gimage
