def smooth_image(img, fwhm):
""" Smooth image `img` by FWHM `fwhm`
"""
# Make sure time axis is last
img = as_xyz_image(img)
fwhm = np.asarray(fwhm)
if fwhm.size == 1:
fwhm = np.ones(3, ) * fwhm
# 4x4 affine
affine = xyz_affine(img)
# Voxel sizes in the three spatial axes
RZS = affine[:3, :3]
vox = np.sqrt(np.sum(RZS**2))
# Smoothing in terms of voxels
vox_fwhm = fwhm / vox
vox_sd = fwhm2sigma(vox_fwhm)
# Do the smoothing
data = img.get_data()
sm_data = snd.gaussian_filter(data, list(vox_sd) + [0])
return Image(sm_data, img.coordmap)
def smooth_image(img, fwhm):
""" Smooth image `img` by FWHM `fwhm`
"""
# Make sure time axis is last
img = as_xyz_image(img)
fwhm = np.asarray(fwhm)
if fwhm.size == 1:
fwhm = np.ones(3,) * fwhm
# 4x4 affine
affine = xyz_affine(img)
# Voxel sizes in the three spatial axes
RZS = affine[:3, :3]
vox = np.sqrt(np.sum(RZS ** 2))
# Smoothing in terms of voxels
vox_fwhm = fwhm / vox
vox_sd = fwhm2sigma(vox_fwhm)
# Do the smoothing
data = img.get_data()
sm_data = snd.gaussian_filter(data, list(vox_sd) + [0])
return Image(sm_data, img.coordmap)
def test_sigma_fwhm():
# ensure that fwhm2sigma and sigma2fwhm are inverses of each other
fwhm = np.arange(1.0, 5.0, 0.1)
sigma = np.arange(1.0, 5.0, 0.1)
yield assert_true, np.allclose(sigma2fwhm(fwhm2sigma(fwhm)), fwhm)
yield assert_true, np.allclose(fwhm2sigma(sigma2fwhm(sigma)), sigma)
