def _test_similarity_measure(simi, val):
I = Image(make_data_int16(), dummy_affine)
J = Image(I.data.copy(), dummy_affine)
regie = IconicRegistration(I, J)
regie.set_source_fov(spacing=[2,1,3])
regie.similarity = simi
assert_almost_equal(regie.eval(np.eye(4)), val)
def make_matcher():
I = Image(load(example_data.get_filename('neurospin',
'sulcal2000',
'nobias_ammon.nii.gz')))
J = Image(load(example_data.get_filename('neurospin',
'sulcal2000',
'nobias_anubis.nii.gz')))
# Create a registration instance
R = IconicRegistration(I, J)
R.set_source_fov(fixed_npoints=64**3)
R.similarity = 'llr_cc'
return R
def test_explore():
I = Image(make_data_int16(), dummy_affine)
J = Image(make_data_int16(), dummy_affine)
regie = IconicRegistration(I, J)
T = Affine()
simi, params = regie.explore(T, (0,[-1,0,1]),(1,[-1,0,1]))
array = im.data
if threshold==None:
pylab.imshow(array[:,slice,:])
else:
pylab.imshow(array[:,slice,:]>threshold)
"""
Main
"""
# Load images
I = load_image(example_data.get_filename('neurospin',
'sulcal2000',
'nobias_ammon.nii.gz'))
J = load_image(example_data.get_filename('neurospin',
'sulcal2000',
'nobias_anubis.nii.gz'))
# Create a registration instance
R = IconicRegistration(I, J)
R.set_source_fov(fixed_npoints=64**3)
R.similarity = 'llr_mi'
T = np.eye(4)
#T[0:3,3] = [4,5,6]
print R.eval(T)
import numpy as np
from nibabel import load, save
from nipy.neurospin.image import Image, asNifti1Image, transform_image
from nipy.neurospin.registration import IconicRegistration, Affine, GridTransform
from os.path import join
datadir = join('d:\\', 'home', 'Alexis', 'My Dropbox', 'example_images')
I = load(join(datadir, 'b0.nii'))
J = load(join(datadir, 'b1000.nii'))
# Create a registration instance
print('Creating matcher instance')
R = IconicRegistration(Image(I), Image(J))
R.set_source_fov(fixed_npoints=64**3)
R.similarity = 'cr'
R.interp = 'tri'
T = R.optimize(Affine())
