def __init__(self, input_dictionary):
"""Initialize image level tools"""
# initialize all the model objects
self.dc = data_container(input_dictionary['image_data'],
input_dictionary['times'], input_dictionary)
self._t = transformer.transformer()
self._m = matcher.matcher(input_dictionary['matcher'],
input_dictionary['window'])
self._r = regularizer.regularizer(input_dictionary['regularizer'],
input_dictionary['abcd'][0],
input_dictionary['abcd'][1],
input_dictionary['abcd'][2],
input_dictionary['abcd'][3],
self.dc.curr_vox, self.dc.curr_res)
"""
import sys
import numpy as np
import nibabel as nib
import pyrpl.image_tools.transformer as transformer
# get file paths
img_root = sys.argv[1]
txm_root = sys.argv[2]
wPath = sys.argv[3]
# image details, transformer for later
sh = (220, 220, 220)
vox = np.array([1., 1., 1.])
_t = transformer.transformer()
# grab the data
img = nib.load(img_root).get_data().squeeze()
# grab the transformation to mdt coordinates
txm = np.empty(sh + (3, ))
for i in range(3):
p = txm_root + str(i) + '.nii.gz'
txm[..., i] = nib.load(p).get_data().squeeze()
# Apply txm to data
img = _t.apply_transform(img, vox, txm)
# save results
img = nib.Nifti1Image(img, np.eye(4))
def main():
# grab the input parameters
reference_path = sys.argv[1]
write_path_root = sys.argv[2]
outer_iterations = int(sys.argv[3])
# get the reference image, make an id string for it
ref = nib.load(reference_path).get_data().squeeze()
#ref = preproc.rescale_intensity(ref, mean=1.0)
# currently assumes filenames are unique between inputs
# consider generating a unique hash here, common suffix between references
ref_str = reference_path.split('/')[-1].split('.')[0]
# create transformer for later
_t = transformer.transformer()
# establish parameters for registration of template to reference
params = {
'vox': np.array([1.0, 1.0, 1.0]),
'oIts': outer_iterations,
'pStep': 1e-5,
'iStep': 0.0,
'tStep': 1.0,
'rat': 1.0,
'its': [5],
'res': [ref.shape],
'h': 8,
'a': 1.0,
'b': 0.0,
'c': 0.05,
'd': 2.0,
'mType': 'SSD',
'rType': 'differential'
}
# number of template updates
for o in range(outer_iterations):
# keep cycling until the newest template is done
tPath = write_path_root + '/template' + str(o) + '.nii.gz'
tFound = False
while not tFound:
if os.path.exists(tPath):
time.sleep(5)
tFound = True
tmp = nib.load(tPath).get_data().squeeze()
# tmp = preproc.rescale_intensity(tmp, mean=1.0)
else:
time.sleep(2)
# Register the template to the reference image
J = np.array([tmp, ref])
T = np.array([0.0, 1.0])
# Fit geodesic
grdc, P0_mag, data_match, grad_mag = optimizer.optimize(J, T, params)
# write out the momentum and transformations
wPath = write_path_root + '/' + ref_str + '_momentum.npy'
np.save(wPath, grdc.P[0])
I0 = _t.apply_transform(ref, grdc.full_vox, grdc.uf[-1])
wPath = write_path_root + '/' + ref_str + '_I0.npy'
np.save(wPath, I0)