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det_tracking.py
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det_tracking.py
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from __future__ import division, print_function, absolute_import
import numpy as np
import nibabel as nib
from dipy.reconst.peaks import peaks_from_model, PeaksAndMetrics
from dipy.core.sphere import Sphere
from dipy.data import get_sphere
from dipy.io.gradients import read_bvals_bvecs
from dipy.core.gradients import gradient_table
from dipy.tracking import utils
from common import load_nifti, save_trk, save_peaks, load_peaks
from ipdb import set_trace
def show_results(streamlines, vol, affine):
from dipy.viz import actor, window, widget
shape = data.shape
world_coords = True
if not world_coords:
from dipy.tracking.streamline import transform_streamlines
streamlines = transform_streamlines(streamlines, np.linalg.inv(affine))
ren = window.Renderer()
stream_actor = actor.line(streamlines)
if not world_coords:
image_actor = actor.slicer(vol, affine=np.eye(4))
else:
image_actor = actor.slicer(vol, affine)
slicer_opacity = .6
image_actor.opacity(slicer_opacity)
ren.add(stream_actor)
ren.add(image_actor)
show_m = window.ShowManager(ren, size=(1200, 900))
show_m.initialize()
def change_slice(obj, event):
z = int(np.round(obj.get_value()))
image_actor.display_extent(0, shape[0] - 1,
0, shape[1] - 1, z, z)
slider = widget.slider(show_m.iren, show_m.ren,
callback=change_slice,
min_value=0,
max_value=shape[2] - 1,
value=shape[2] / 2,
label="Move slice",
right_normalized_pos=(.98, 0.6),
size=(120, 0), label_format="%0.lf",
color=(1., 1., 1.),
selected_color=(0.86, 0.33, 1.))
global size
size = ren.GetSize()
def win_callback(obj, event):
global size
if size != obj.GetSize():
slider.place(ren)
size = obj.GetSize()
show_m.initialize()
show_m.add_window_callback(win_callback)
show_m.render()
show_m.start()
# ren.zoom(1.5)
# ren.reset_clipping_range()
# window.record(ren, out_path='bundles_and_a_slice.png', size=(1200, 900),
# reset_camera=False)
del show_m
def simple_viewer(streamlines, vol, affine):
from dipy.viz import actor, window
renderer = window.Renderer()
renderer.add(actor.line(streamlines))
renderer.add(actor.slicer(vol, affine))
window.show(renderer)
dname = '/Users/ghfc/Desktop/Celine/brainhack/data/'
fdwi = dname + '2dseq_conv_32.nii.gz'
data, affine = load_nifti(fdwi)
fbval = dname + 'P64_F01.bval'
fbvec = dname + 'P64_F01.bvec'
bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
gtab = gradient_table(bvals, bvecs, b0_threshold=50)
fmask = dname + 'mask_extern.nii'
mask_vol, mask_affine = load_nifti(fmask)
sh_order = 8
if data.shape[-1] < 15:
raise ValueError('You need at least 15 unique DWI volumes to '
'compute fiber ODFs. You currently have: {0}'
' DWI volumes.'.format(data.shape[-1]))
elif data.shape[-1] < 30:
sh_order = 6
from dipy.reconst.csdeconv import (ConstrainedSphericalDeconvModel,
auto_response)
response, ratio = auto_response(gtab, data)
response = list(response)
peaks_sphere = get_sphere('symmetric362')
model = ConstrainedSphericalDeconvModel(gtab, response,
sh_order=sh_order)
peaks_csd = peaks_from_model(model=model,
data=data,
sphere=peaks_sphere,
relative_peak_threshold=.5,
min_separation_angle=25,
mask=mask_vol,
return_sh=True,
sh_order=sh_order,
normalize_peaks=True,
parallel=False)
peaks_csd.affine = affine
fpeaks = dname + 'peaks.npz'
save_peaks(fpeaks, peaks_csd)
from dipy.io.trackvis import save_trk
from dipy.tracking import utils
from dipy.tracking.local import (ThresholdTissueClassifier,
LocalTracking)
stopping_thr = 0.25
pam = load_peaks(fpeaks)
ffa = dname + 'fa.nii.gz'
fa, fa_affine = load_nifti(ffa)
classifier = ThresholdTissueClassifier(fa,
stopping_thr)
seed_density = 1
seed_mask = fa > 0.4
seeds = utils.seeds_from_mask(
seed_mask,
density=seed_density,
affine=affine)
#if use_sh:
# detmax_dg = \
# DeterministicMaximumDirectionGetter.from_shcoeff(
# pam.shm_coeff,
# max_angle=30.,
# sphere=pam.sphere)
#
# streamlines = \
# LocalTracking(detmax_dg, classifier, seeds, affine,
# step_size=.5)
#
#else:
streamlines = LocalTracking(pam, classifier,
seeds, affine=affine, step_size=.5)
# Compute streamlines and store as a list.
streamlines = list(streamlines)
ftractogram = dname + 'tractogram.trk'
save_trk(ftractogram, streamlines, affine)
show_results(streamlines[:1000], fa, fa_affine)