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
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)