def label_streamlines(streamlines, labels, labels_Value, affine, hdr, f_name,
data_path):
cc_slice = labels == labels_Value
cc_streamlines = utils.target(streamlines, labels, affine=affine)
cc_streamlines = list(cc_streamlines)
other_streamlines = utils.target(streamlines,
cc_slice,
affine=affine,
include=False)
other_streamlines = list(other_streamlines)
assert len(other_streamlines) + len(cc_streamlines) == len(streamlines)
print("num of roi steamlines is %d", len(cc_streamlines))
# Make display objects
color = line_colors(cc_streamlines)
cc_streamlines_actor = fvtk.line(cc_streamlines,
line_colors(cc_streamlines))
cc_ROI_actor = fvtk.contour(cc_slice,
levels=[1],
colors=[(1., 1., 0.)],
opacities=[1.])
# Add display objects to canvas
r = fvtk.ren()
fvtk.add(r, cc_streamlines_actor)
fvtk.add(r, cc_ROI_actor)
# Save figures
fvtk.record(r, n_frames=1, out_path=f_name + '_roi.png', size=(800, 800))
fvtk.camera(r, [-1, 0, 0], [0, 0, 0], viewup=[0, 0, 1])
fvtk.record(r, n_frames=1, out_path=f_name + '_roi.png', size=(800, 800))
""""""
csd_streamlines_trk = ((sl, None, None) for sl in cc_streamlines)
csd_sl_fname = f_name + '_roi_streamline.trk'
nib.trackvis.write(csd_sl_fname,
csd_streamlines_trk,
hdr,
points_space='voxel')
#nib.save(nib.Nifti1Image(FA, img.get_affine()), 'FA_map2.nii.gz')
print('Saving "_roi_streamline.trk" sucessful.')
import tractconverter as tc
input_format = tc.detect_format(csd_sl_fname)
input = input_format(csd_sl_fname)
output = tc.FORMATS['vtk'].create(csd_sl_fname + ".vtk", input.hdr)
tc.convert(input, output)
return cc_streamlines
def label_streamlines(streamlines,labels,labels_Value,affine,hdr,f_name,data_path):
cc_slice=labels==labels_Value
cc_streamlines = utils.target(streamlines, labels, affine=affine)
cc_streamlines = list(cc_streamlines)
other_streamlines = utils.target(streamlines, cc_slice, affine=affine,
include=False)
other_streamlines = list(other_streamlines)
assert len(other_streamlines) + len(cc_streamlines) == len(streamlines)
print ("num of roi steamlines is %d",len(cc_streamlines))
# Make display objects
color = line_colors(cc_streamlines)
cc_streamlines_actor = fvtk.line(cc_streamlines, line_colors(cc_streamlines))
cc_ROI_actor = fvtk.contour(cc_slice, levels=[1], colors=[(1., 1., 0.)],
opacities=[1.])
# Add display objects to canvas
r = fvtk.ren()
fvtk.add(r, cc_streamlines_actor)
fvtk.add(r, cc_ROI_actor)
# Save figures
fvtk.record(r, n_frames=1, out_path=f_name+'_roi.png',
size=(800, 800))
fvtk.camera(r, [-1, 0, 0], [0, 0, 0], viewup=[0, 0, 1])
fvtk.record(r, n_frames=1, out_path=f_name+'_roi.png',
size=(800, 800))
""""""
csd_streamlines_trk = ((sl, None, None) for sl in cc_streamlines)
csd_sl_fname = f_name+'_roi_streamline.trk'
nib.trackvis.write(csd_sl_fname, csd_streamlines_trk, hdr, points_space='voxel')
#nib.save(nib.Nifti1Image(FA, img.get_affine()), 'FA_map2.nii.gz')
print('Saving "_roi_streamline.trk" sucessful.')
import tractconverter as tc
input_format=tc.detect_format(csd_sl_fname)
input=input_format(csd_sl_fname)
output=tc.FORMATS['vtk'].create(csd_sl_fname+".vtk",input.hdr)
tc.convert(input,output)
return cc_streamlines
def createVtkPng(source, anatomical, roi):
import vtk
from dipy.viz.colormap import line_colors
from dipy.viz import fvtk
target = source.replace(".trk",".png")
roiImage= nibabel.load(roi)
anatomicalImage = nibabel.load(anatomical)
sourceImage = [s[0] for s in nibabel.trackvis.read(source, points_space='voxel')[0]]
try:
sourceActor = fvtk.streamtube(sourceImage, line_colors(sourceImage))
roiActor = fvtk.contour(roiImage.get_data(), levels=[1], colors=[(1., 1., 0.)], opacities=[1.])
anatomicalActor = fvtk.slicer(anatomicalImage.get_data(),
voxsz=(1.0, 1.0, 1.0),
plane_i=None,
plane_j=None,
plane_k=[65],
outline=False)
except ValueError:
return False
sourceActor.RotateX(-70)
sourceActor.RotateY(2.5)
sourceActor.RotateZ(185)
roiActor.RotateX(-70)
roiActor.RotateY(2.5)
roiActor.RotateZ(185)
anatomicalActor.RotateX(-70)
anatomicalActor.RotateY(2.5)
anatomicalActor.RotateZ(185)
ren = fvtk.ren()
fvtk.add(ren, sourceActor)
fvtk.add(ren, roiActor)
fvtk.add(ren, anatomicalActor)
fvtk.record(ren, out_path=target, size=(1200, 1200), n_frames=1, verbose=True, cam_pos=(90.03, 118.33, 700.59))
return target
other_streamlines = list(other_streamlines)
assert len(other_streamlines) + len(cc_streamlines) == len(streamlines)
"""
We can use some of dipy's visualization tools to display the ROI we targeted
above and all the streamlines that pass though that ROI. The ROI is the yellow
region near the center of the axial image.
"""
from dipy.viz import fvtk
from dipy.viz.colormap import line_colors
# Make display objects
color = line_colors(cc_streamlines)
cc_streamlines_actor = fvtk.line(cc_streamlines, line_colors(cc_streamlines))
cc_ROI_actor = fvtk.contour(cc_slice,
levels=[1],
colors=[(1., 1., 0.)],
opacities=[1.])
vol_actor = fvtk.slicer(t1_data)
vol_actor.display(40, None, None)
vol_actor2 = vol_actor.copy()
vol_actor2.display(None, None, 35)
# Add display objects to canvas
r = fvtk.ren()
fvtk.add(r, vol_actor)
fvtk.add(r, vol_actor2)
fvtk.add(r, cc_streamlines_actor)
fvtk.add(r, cc_ROI_actor)
other_streamlines = list(other_streamlines)
assert len(other_streamlines) + len(cc_streamlines) == len(streamlines)
"""
We can use some of dipy's visualization tools to display the ROI we targeted
above and all the streamlines that pass though that ROI. The ROI is the yellow
region near the center of the axial image.
"""
from dipy.viz import fvtk
from dipy.viz.colormap import line_colors
# Make display objects
color = line_colors(cc_streamlines)
cc_streamlines_actor = fvtk.line(cc_streamlines, line_colors(cc_streamlines))
cc_ROI_actor = fvtk.contour(cc_slice, levels=[1], colors=[(1., 1., 0.)],
opacities=[1.])
# Add display objects to canvas
r = fvtk.ren()
fvtk.add(r, cc_streamlines_actor)
fvtk.add(r, cc_ROI_actor)
# Save figures
fvtk.record(r, n_frames=1, out_path='corpuscallosum_axial.png',
size=(800, 800))
fvtk.camera(r, [-1, 0, 0], [0, 0, 0], viewup=[0, 0, 1])
fvtk.record(r, n_frames=1, out_path='corpuscallosum_sagittal.png',
size=(800, 800))
"""
.. figure:: corpuscallosum_axial.png