def transform(floating, T, reference=None, interp_order=3):
# Convert assumed nibabel-like input images to local image class
floating = Image(floating)
if not reference == None:
reference = Image(reference)
return asNifti1Image(floating.transform(np.asarray(T), grid_coords=False,
reference=reference, interp_order=interp_order))
T = GridTransform(I, defmodes.data.reshape(list(I.shape)+[7,3]))
# freeze mean deformation component
T._param[0] = 1
T._free_param_idx = [1,2,3,4,5,6]
if 'defrand' in TYPE:
best_param = np.load('t3_def_crl1.npy')
best_simi = 0.0
for i in range(NTRIALS):
T.param = best_param + std*np.random.normal(std.size)
T = R.optimize(T)
optimum = R.eval(T)
if optimum > best_simi:
print(' *** New best ***')
best_simi = optimum
best_param = T.param
else:
print(' *** Unbeaten optimum ***')
T.param = best_param
else:
T.param = identity
T = R.optimize(T)
# Create matcher instance
fname = 't'+str(ID)+'_'+TYPE+'_'+SIMI+'_'+INTERP
outfile = join(DATADIR, fname+'.nii')
print('Saving resampled source in: %s' % outfile)
Jt = J.transform(T, reference=I)
save(asNifti1Image(Jt), outfile)
import scipy.ndimage as nd datadir = 'D:\home\Alexis\data\delphine\zozo' path = join(datadir, 'BiasCorIm.img') # Usecase 0: load an image I = Image(load(path)) # Usecase 1: image masking I1 = I.set_mask(np.where(I.data>100)) # Usecase 2: define a regular image patch (subgridding) I2 = I[5:10:2, 5:10:2, 5:10:2] # Usecase 3: get all masked values in an image tmp = I2.values() # Usecase 4: image interpolation XYZ = 100*np.random.rand(3,10) tmp = I.values(XYZ) # Usecase 4: move an image using an affine transformation T = np.eye(4) T[0:3,3] = 10*np.random.rand(3) J = I.transform(T) # Usecase 5: set image values I2 = I2.set(I2.data**2+2) I1 = I1.set(I1.values()**2+2)
import numpy as np
from nibabel import load, save
from nipy.neurospin.image import Image, asNifti1Image
from os.path import join
from glob import glob
datadir = join('d:\\', 'home', 'Alexis', 'data', 'remy')
I = Image(load(join(datadir, '2_axial_FL_AD_ROI.nii')))
defmodes = glob(join(datadir, 'NEW_SDF', 'SDF_*.nii'))
data = np.zeros(list(I.shape)+[len(defmodes)])
for i in range(len(defmodes)):
print('image: %d (%s)' % (i, defmodes[i]))
m = Image(load(defmodes[i]))
mm = m.transform(np.eye(4), reference=I)
data[:,:,:,i] = mm.data
save(asNifti1Image(Image(data, I.affine)), join(datadir, 'deformation_modes.nii'))
