__all__ = ['weingarten_image_curvature']
import numpy as np
import ants
from ants.decorators import image_method
from ants.internal import get_lib_fn
[docs]
@image_method
def weingarten_image_curvature(image, sigma=1.0, opt='mean'):
"""
Uses the weingarten map to estimate image mean or gaussian curvature
ANTsR function: `weingartenImageCurvature`
Arguments
---------
image : ANTsImage
image from which curvature is calculated
sigma : scalar
smoothing parameter
opt : string
mean by default, otherwise `gaussian` or `characterize`
Returns
-------
ANTsImage
Example
-------
>>> import ants
>>> image = ants.image_read(ants.get_ants_data('mni')).resample_image((3,3,3))
>>> imagecurv = ants.weingarten_image_curvature(image)
"""
if image.dimension not in {2,3}:
raise ValueError('image must be 2D or 3D')
if image.dimension == 2:
d = image.shape
temp = np.zeros(list(d)+[10])
for k in range(1,7):
voxvals = image[:d[0],:d[1]].numpy()
temp[:d[0],:d[1],k] = voxvals
temp = ants.from_numpy(temp)
myspc = image.spacing
myspc = list(myspc) + [min(myspc)]
temp.set_spacing(myspc)
temp = temp.clone('float')
else:
temp = image.clone('float')
optnum = 0
if opt == 'gaussian':
optnum = 6
if opt == 'characterize':
optnum = 5
libfn = get_lib_fn('weingartenImageCurvature')
mykout = libfn(temp.pointer, sigma, optnum)
mykout = ants.from_pointer(mykout)
if image.dimension == 3:
return mykout
elif image.dimension == 2:
subarr = ants.from_numpy(mykout.numpy()[:,:,4])
return ants.copy_image_info(image, subarr)