__all__ = ['resample_image',
'resample_image_to_target']
import os
from ants.decorators import image_method
from ants.internal import get_lib_fn, process_arguments
import ants
[docs]
@image_method
def resample_image(image, resample_params, use_voxels=False, interp_type=1):
"""
Resample image by spacing or number of voxels with
various interpolators. Works with multi-channel images.
ANTsR function: `resampleImage`
Arguments
---------
image : ANTsImage
input image
resample_params : tuple/list
vector of size dimension with numeric values
use_voxels : boolean
True means interpret resample params as voxel counts
interp_type : integer
one of 0 (linear), 1 (nearest neighbor), 2 (gaussian), 3 (windowed sinc), 4 (bspline)
Returns
-------
ANTsImage
Example
-------
>>> import ants
>>> fi = ants.image_read( ants.get_ants_data("r16"))
>>> finn = ants.resample_image(fi,(50,60),True,0)
>>> filin = ants.resample_image(fi,(1.5,1.5),False,1)
>>> img = ants.image_read( ants.get_ants_data("r16"))
>>> img = ants.merge_channels([img, img])
>>> outimg = ants.resample_image(img, (128,128), True)
"""
if image.components == 1:
inimage = image.clone('float')
outimage = image.clone('float')
rsampar = 'x'.join([str(rp) for rp in resample_params])
args = [image.dimension, inimage, outimage, rsampar, int(use_voxels), interp_type]
processed_args = process_arguments(args)
libfn = get_lib_fn('ResampleImage')
libfn(processed_args)
outimage = outimage.clone(image.pixeltype)
return outimage
else:
images = ants.split_channels(image)
new_images = []
for image in images:
inimage = image.clone('float')
outimage = image.clone('float')
rsampar = 'x'.join([str(rp) for rp in resample_params])
args = [image.dimension, inimage, outimage, rsampar, int(use_voxels), interp_type]
processed_args = process_arguments(args)
libfn = get_lib_fn('ResampleImage')
libfn(processed_args)
outimage = outimage.clone(image.pixeltype)
new_images.append(outimage)
outimage = ants.merge_channels(new_images, channels_first=image.channels_first)
return outimage
[docs]
@image_method
def resample_image_to_target(image, target, interp_type='linear', imagetype=0, verbose=False, **kwargs):
"""
Resample image by using another image as target reference.
This function uses ants.apply_transform with an identity matrix
to achieve proper resampling.
ANTsR function: `resampleImageToTarget`
Arguments
---------
image : ANTsImage
image to resample
target : ANTsImage
image of reference, the output will be in this space and will have the same pixel type.
interp_type : string
Choice of interpolator. Supports partial matching.
linear
nearestNeighbor
multiLabel for label images but genericlabel is preferred
gaussian
bSpline
cosineWindowedSinc
welchWindowedSinc
hammingWindowedSinc
lanczosWindowedSinc
genericLabel use this for label images
imagetype : integer
choose 0/1/2/3 mapping to scalar/vector/tensor/time-series
verbose : boolean
print command and run verbose application of transform.
kwargs : keyword arguments
additional arugment passed to antsApplyTransforms C code
Returns
-------
ANTsImage
Example
-------
>>> import ants
>>> fi = ants.image_read(ants.get_ants_data('r16'))
>>> fi2mm = ants.resample_image(fi, (2,2), use_voxels=0, interp_type='linear')
>>> resampled = ants.resample_image_to_target(fi2mm, fi, verbose=True)
"""
fixed = target
moving = image
compose = None
transformlist = 'identity'
interpolator = interp_type
interpolator_oldoptions = ("linear", "nearestNeighbor", "gaussian", "cosineWindowedSinc", "bSpline")
if isinstance(interp_type, int):
interpolator = interpolator_oldoptions[interp_type]
accepted_interpolators = {"linear", "nearestNeighbor", "multiLabel", "gaussian",
"bSpline", "cosineWindowedSinc", "welchWindowedSinc",
"hammingWindowedSinc", "lanczosWindowedSinc", "genericLabel"}
if interpolator not in accepted_interpolators:
raise ValueError('interpolator not supported - see %s' % accepted_interpolators)
args = [fixed, moving, transformlist, interpolator]
if not isinstance(fixed, str):
if ants.is_image(fixed) and ants.is_image(moving):
inpixeltype = fixed.pixeltype
warpedmovout = moving.clone()
f = fixed.clone('float')
m = moving.clone('float')
if (moving.dimension == 4) and (fixed.dimension==3) and (imagetype==0):
raise ValueError('Set imagetype 3 to transform time series images.')
wmo = warpedmovout
mytx = ['-t', 'identity']
if compose is None:
args = ['-d', fixed.dimension, '-i', m, '-o', wmo, '-r', f, '-n', interpolator] + mytx
tfn = '%scomptx.nii.gz' % compose if compose is not None else 'NA'
if compose is not None:
mycompo = '[%s,1]' % tfn
args = ['-d', fixed.dimension, '-i', m, '-o', mycompo, '-r', f, '-n', interpolator] + mytx
myargs = process_arguments(args)
myverb = int(verbose)
processed_args = myargs + ['-z', str(1), '-v', str(myverb), '--float', str(1), '-e', str(imagetype)]
libfn = get_lib_fn('antsApplyTransforms')
libfn(processed_args)
if compose is None:
return warpedmovout.clone(inpixeltype)
else:
if os.path.exists(tfn):
return tfn
else:
return None
else:
return 1
else:
processed_args = myargs + ['-z', str(1), '--float', str(1), '-e', str(imagetype)]
libfn = get_lib_fn('antsApplyTransforms')
libfn(processed_args)