ratio = 100
step_size = 2
peak_processes=1
get_params=False
doprune = False
#mask, _ = getmask(os.path.dirname(input_diff_file), subject, masktype, verbose)
#check_dif_ratio(outpath, subject, strproperty, ratio)
if os.path.exists(output_trk_file) and overwrite is False:
print("The tract creation of subject " + subject + " is already done")
if verbose:
print('Running the ' + subject + ' file')
diff_data, affine, vox_size, header, ref_info = extract_nii_info(input_diff_file, verbose)
gtab = getgtab(os.path.dirname(input_diff_file), subject, bvec_orient)
if np.size(np.shape(mask)) == 1:
mask = mask[0]
if np.size(np.shape(mask)) == 4:
mask = mask[:, :, :, 0]
print("Mask shape is " + str(np.shape(mask)))
#if classifier == "FA":
# outpathbmfa, mask = make_tensorfit(diff_data ,mask ,gtab ,affine ,subject ,outpath=diffpath ,verbose=verbose)
print(verbose)
if verbose:
txt = ("The QCSA Tractmake is ready to launch for subject " + subject)
print(txt)
affine=np.eye(4), verbose=verbose)
affine_mat_path = os.path.join(path_transforms, f'{subj}_affine.txt')
affine_mat_s = read_affine_txt(affine_mat_path)
affine_mat = np.eye(4)
affine_mat[:3, :3] = affine_mat_s
#streamlines_postrigid, header_postrigid = unload_trk(trk_preprocess_postrigid)
streamlines_postrigidaffine = transform_streamlines(streamlines_postrigid, np.linalg.inv(affine_mat))
if (not os.path.exists(trk_preprocess_postrigid_affine) or overwrite) and save_temp_files:
save_trk_header(filepath=trk_preprocess_postrigid_affine, streamlines=streamlines_postrigidaffine, header=header,
affine=np.eye(4), verbose=verbose)
#streamlines_postrigidaffine, header_postrigidaffine = unload_trk(trk_preprocess_postrigid_affine)
warp, affine, vox_size, header_warp, ref_info = extract_nii_info(runno_to_MDT)
warp = warp[:,:,:,0,:]
vox_size = tuple(np.linalg.eigvals(affine[:3,:3]))
vox_size = vox_size[0]
target_isocenter = np.diag(np.array([-vox_size, vox_size, vox_size, 1]))
streamlines_post_warp = deform_streamlines(
streamlines_postrigidaffine, deform_field=warp,
stream_to_current_grid=target_isocenter,
current_grid_to_world=np.eye(4), stream_to_ref_grid=target_isocenter,
ref_grid_to_world=np.eye(4))
if (not os.path.exists(trk_MDT_space) or overwrite):
save_trk_header(filepath=trk_MDT_space, streamlines=streamlines_post_warp, header=header,
affine=np.eye(4), verbose=verbose)
else:
def header_superpose_trk(target_path, origin_path, outpath=None):
if not isinstance(origin_path, str):
origin_trk = origin_path
else:
origin_trk = load_trk(origin_path, 'same')
target_data, target_affine, vox_size, target_header, target_ref_info = extract_nii_info(
target_path)
if outpath is None:
if isinstance(origin_path, str):
warnings.warn("Will copy over old trkfile, if this what you want?")
permission = input("enter yes or y if you are ok with this")
if permission.lower() == "yes" or permission.lower() == "y":
outpath = origin_trk
else:
raise Exception("Will not copy over old trk file")
else:
raise Exception("Need to specify a output path of some kind")
trk_header = origin_trk.space_attributes
trk_affine = origin_trk._affine
trkstreamlines = origin_trk.streamlines
if np.any(trk_header[1][0:3] != np.shape(target_data)[0:3]):
raise TypeError(
'Size of the originating matrix are difference, recalculation not implemented'
)
if np.any(trk_affine != target_affine):
test = 3
if test == 1:
trk_header = list(trk_header)
trk_header[0] = target_affine
trk_header = tuple(trk_header)
myheader = create_tractogram_header(outpath, *trk_header)
trk_sl = lambda: (s for s in trkstreamlines)
save_trk_heavy_duty(outpath,
streamlines=trk_sl,
affine=target_affine,
header=myheader)
elif test == 2:
transform_matrix = (
np.inverse(np.transpose(trk_affine) * trk_affine) *
np.transpose(trk_affine)) * target_affine
from dipy.tracking.streamline import transform_streamlines
myheader = create_tractogram_header(outpath, *trk_header)
new_streamlines = transform_streamlines(trkstreamlines,
transform_matrix)
trk_sl = lambda: (s for s in new_streamlines)
save_trk_heavy_duty(outpath,
streamlines=trkstreamlines,
affine=trk_affine,
header=myheader)
elif test == 3:
myheader = create_tractogram_header(outpath, *target_ref_info)
trk_sl = lambda: (s for s in trkstreamlines)
save_trk_heavy_duty(outpath,
streamlines=trk_sl,
affine=target_affine,
header=myheader)
else:
print("No need to change affine, bring to new path")
if isinstance(origin_path, str):
copyfile(origin_path, outpath)
else:
myheader = create_tractogram_header(outpath, *trk_header)
save_trk_heavy_duty(outpath,
streamlines=trkstreamlines,
affine=target_affine,
header=myheader)