def test_fvtk_functions():
# Create a renderer
r = fvtk.ren()
# Create 2 lines with 2 different colors
lines = [np.random.rand(10, 3), np.random.rand(20, 3)]
colors = np.random.rand(2, 3)
c = fvtk.line(lines, colors)
fvtk.add(r, c)
# create streamtubes of the same lines and shift them a bit
c2 = fvtk.streamtube(lines, colors)
c2.SetPosition(2, 0, 0)
fvtk.add(r, c2)
# Create a volume and return a volumetric actor using volumetric rendering
vol = 100 * np.random.rand(100, 100, 100)
vol = vol.astype('uint8')
r = fvtk.ren()
v = fvtk.volume(vol)
fvtk.add(r, v)
# Remove all objects
fvtk.rm_all(r)
# Put some text
l = fvtk.label(r, text='Yes Men')
fvtk.add(r, l)
# Slice the volume
fvtk.add(r, fvtk.slicer(vol, plane_i=[50]))
# Change the position of the active camera
fvtk.camera(r, pos=(0.6, 0, 0), verbose=False)
def plot_tract(bundle, affine, num_class=1, pred=[], ignore=[]):
# Visualize the results
from dipy.viz import fvtk, actor
from dipy.tracking.streamline import transform_streamlines
# Create renderer
r = fvtk.ren()
if len(pred) > 0:
colors = get_spaced_colors(num_class)
for i in range(num_class):
if i in ignore:
continue
idx = np.argwhere(pred == i).squeeze()
bundle_native = transform_streamlines(
bundle[idx], np.linalg.inv(affine))
if len(bundle_native) == 0:
continue
lineactor = actor.line(
bundle_native, colors[i], linewidth=0.2)
fvtk.add(r, lineactor)
elif num_class == 1:
bundle_native = transform_streamlines(bundle, np.linalg.inv(affine))
lineactor = actor.line(bundle_native, linewidth=0.2)
fvtk.add(r, lineactor)
# Show original fibers
fvtk.camera(r, pos=(-264, 285, 155), focal=(0, -14, 9),
viewup=(0, 0, 1), verbose=False)
fvtk.show(r)
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 test_fvtk_functions():
# This tests will fail if any of the given actors changed inputs or do
# not exist
# Create a renderer
r = fvtk.ren()
# Create 2 lines with 2 different colors
lines = [np.random.rand(10, 3), np.random.rand(20, 3)]
colors = np.random.rand(2, 3)
c = fvtk.line(lines, colors)
fvtk.add(r, c)
# create streamtubes of the same lines and shift them a bit
c2 = fvtk.streamtube(lines, colors)
c2.SetPosition(2, 0, 0)
fvtk.add(r, c2)
# Create a volume and return a volumetric actor using volumetric rendering
vol = 100 * np.random.rand(100, 100, 100)
vol = vol.astype('uint8')
r = fvtk.ren()
v = fvtk.volume(vol)
fvtk.add(r, v)
# Remove all objects
fvtk.rm_all(r)
# Put some text
l = fvtk.label(r, text='Yes Men')
fvtk.add(r, l)
# Slice the volume
slicer = fvtk.slicer(vol)
slicer.display(50, None, None)
fvtk.add(r, slicer)
# Change the position of the active camera
fvtk.camera(r, pos=(0.6, 0, 0), verbose=False)
fvtk.clear(r)
# Peak directions
p = fvtk.peaks(np.random.rand(3, 3, 3, 5, 3))
fvtk.add(r, p)
p2 = fvtk.peaks(np.random.rand(3, 3, 3, 5, 3),
np.random.rand(3, 3, 3, 5),
colors=(0, 1, 0))
fvtk.add(r, p2)
def test_fvtk_functions():
# This tests will fail if any of the given actors changed inputs or do
# not exist
# Create a renderer
r = fvtk.ren()
# Create 2 lines with 2 different colors
lines = [np.random.rand(10, 3), np.random.rand(20, 3)]
colors = np.random.rand(2, 3)
c = fvtk.line(lines, colors)
fvtk.add(r, c)
# create streamtubes of the same lines and shift them a bit
c2 = fvtk.streamtube(lines, colors)
c2.SetPosition(2, 0, 0)
fvtk.add(r, c2)
# Create a volume and return a volumetric actor using volumetric rendering
vol = 100 * np.random.rand(100, 100, 100)
vol = vol.astype('uint8')
r = fvtk.ren()
v = fvtk.volume(vol)
fvtk.add(r, v)
# Remove all objects
fvtk.rm_all(r)
# Put some text
l = fvtk.label(r, text='Yes Men')
fvtk.add(r, l)
# Slice the volume
slicer = fvtk.slicer(vol)
slicer.display(50, None, None)
fvtk.add(r, slicer)
# Change the position of the active camera
fvtk.camera(r, pos=(0.6, 0, 0), verbose=False)
fvtk.clear(r)
# Peak directions
p = fvtk.peaks(np.random.rand(3, 3, 3, 5, 3))
fvtk.add(r, p)
p2 = fvtk.peaks(np.random.rand(3, 3, 3, 5, 3),
np.random.rand(3, 3, 3, 5),
colors=(0, 1, 0))
fvtk.add(r, p2)
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
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)
# 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
:align: center
**Corpus Callosum Axial**
.. include:: ../links_names.inc
.. figure:: corpuscallosum_sagittal.png
:align: center
# Original lines colored by LFBC
lineactor = actor.line(fbc_sl_orig, clrs_orig, linewidth=0.2)
fvtk.add(ren, lineactor)
# Horizontal (axial) slice of T1 data
vol_actor1 = fvtk.slicer(t1_data, affine=affine)
vol_actor1.display(None, None, 20)
fvtk.add(ren, vol_actor1)
# Vertical (sagittal) slice of T1 data
vol_actor2 = fvtk.slicer(t1_data, affine=affine)
vol_actor2.display(35, None, None)
fvtk.add(ren, vol_actor2)
# Show original fibers
fvtk.camera(ren, pos=(-264, 285, 155), focal=(0, -14, 9), viewup=(0, 0, 1),
verbose=False)
fvtk.record(ren, n_frames=1, out_path='OR_before.png', size=(900, 900))
# Show thresholded fibers
fvtk.rm(ren, lineactor)
fvtk.add(ren, actor.line(fbc_sl_thres, clrs_thres, linewidth=0.2))
fvtk.record(ren, n_frames=1, out_path='OR_after.png', size=(900, 900))
"""
.. figure:: OR_before.png
:align: center
The optic radiation obtained through probabilistic tractography colored by
local fiber to bundle coherence.
.. figure:: OR_after.png
# Original lines colored by LFBC
lineactor = actor.line(fbc_sl_orig, clrs_orig, linewidth=0.2)
fvtk.add(ren, lineactor)
# Horizontal (axial) slice of T1 data
vol_actor1 = fvtk.slicer(t1_data, affine=affine)
vol_actor1.display(None, None, 20)
fvtk.add(ren, vol_actor1)
# Vertical (sagittal) slice of T1 data
vol_actor2 = fvtk.slicer(t1_data, affine=affine)
vol_actor2.display(35, None, None)
fvtk.add(ren, vol_actor2)
# Show original fibers
fvtk.camera(ren, pos=(-264, 285, 155), focal=(0, -14, 9), viewup=(0, 0, 1),
verbose=False)
fvtk.record(ren, n_frames=1, out_path='OR_before.png', size=(900, 900))
# Show thresholded fibers
fvtk.rm(ren, lineactor)
fvtk.add(ren, actor.line(fbc_sl_thres, clrs_thres, linewidth=0.2))
fvtk.record(ren, n_frames=1, out_path='OR_after.png', size=(900, 900))
"""
.. figure:: OR_before.png
:align: center
The optic radiation obtained through probabilistic tractography colored by
local fiber to bundle coherence.
.. figure:: OR_after.png
scms.append(scm)
smoments = [0, 6]
lambdas = [0, 0.001, 0.01, 0.1, 1, 2]
for smoment in smoments:
for (k, lambd) in enumerate(lambdas):
for (i, radial_order) in enumerate(radial_orders):
scm = scms[i]
scm.lambd = lambd
for (j, angle) in enumerate(angles):
print(radial_order, angle)
odfs[i + 1, j] = scm.fit(sim_data[j]).odf(sphere, smoment=smoment)
odfs = odfs[:, None, :]
ren = fvtk.ren()
fvtk.add(ren, fvtk.sphere_funcs(odfs, sphere))
fvtk.camera(ren, [0, -5, 0], [0, 0, 0], viewup=[-1, 0, 0])
#fvtk.show(ren)
fname = 'shore_cart_odfs_snr_' + str(SNR) + '_s_' + str(smoment) + '_' + str(k) + '_l_' + str(lambd) + '.png'
fvtk.record(ren, n_frames=1, out_path=fname, size=(1000, 1000))
odfs = np.squeeze(odfs)
ren = fvtk.ren()
# convolve kernel with delta spike
spike = np.zeros((7, 7, 7, k.get_orientations().shape[0]), dtype=np.float64)
spike[3, 3, 3, 0] = 1
spike_shm_conv = convolve(sf_to_sh(spike, k.get_sphere(), sh_order=8), k,
sh_order=8, test_mode=True)
sphere = get_sphere('symmetric724')
spike_sf_conv = sh_to_sf(spike_shm_conv, sphere, sh_order=8)
model_kernel = fvtk.sphere_funcs((spike_sf_conv * 6)[3,:,:,:],
sphere,
norm=False,
radial_scale=True)
fvtk.add(ren, model_kernel)
fvtk.camera(ren, pos=(30, 0, 0), focal=(0, 0, 0), viewup=(0, 0, 1), verbose=False)
fvtk.record(ren, out_path='kernel.png', size=(900, 900))
"""
.. figure:: kernel.png
:align: center
Visualization of the contour enhancement kernel.
"""
"""
Shift-twist convolution is applied on the noisy data
"""
# Perform convolution
csd_shm_enh = convolve(csd_shm_noisy, k, sh_order=8)
ren = fvtk.ren() ren.SetBackground(*fvtk.colors.white) bundle_actor = fvtk.streamtube(bundle, fvtk.colors.red, linewidth=0.3) fvtk.add(ren, bundle_actor) bundle_actor2 = fvtk.streamtube(bundle_downsampled, fvtk.colors.red, linewidth=0.3) bundle_actor2.SetPosition(0, 40, 0) bundle_actor3 = fvtk.streamtube(bundle_downsampled2, fvtk.colors.red, linewidth=0.3) bundle_actor3.SetPosition(0, 80, 0) fvtk.add(ren, bundle_actor2) fvtk.add(ren, bundle_actor3) fvtk.camera(ren, pos=(0, 0, 0), focal=(30, 0, 0)) fvtk.record(ren, out_path="simulated_cosine_bundle.png", size=(900, 900)) """ .. figure:: simulated_cosine_bundle.png :align: center **Initial bundle (down), downsampled at 12 equidistant points (middle), downsampled not equidistantly(up)** From the figure above we can see that all 3 bundles look quite similar. However, when we plot the histogram of the number of points used for each streamline, it becomes obvious that we have managed to reduce in a great amount the size of the initial dataset. """ import matplotlib.pyplot as plt
fvtk.add(ren, bundle_actor)
bundle_actor2 = fvtk.streamtube(bundle_downsampled,
fvtk.colors.red,
linewidth=0.3)
bundle_actor2.SetPosition(0, 40, 0)
bundle_actor3 = fvtk.streamtube(bundle_downsampled2,
fvtk.colors.red,
linewidth=0.3)
bundle_actor3.SetPosition(0, 80, 0)
fvtk.add(ren, bundle_actor2)
fvtk.add(ren, bundle_actor3)
fvtk.camera(ren, pos=(0, 0, 0), focal=(30, 0, 0))
fvtk.record(ren, out_path='simulated_cosine_bundle.png', size=(900, 900))
"""
.. figure:: simulated_cosine_bundle.png
:align: center
Initial bundle (down), downsampled at 12 equidistant points (middle),
downsampled not equidistantly (up).
From the figure above we can see that all 3 bundles look quite similar. However,
when we plot the histogram of the number of points used for each streamline, it
becomes obvious that we have managed to reduce in a great amount the size of the
initial dataset.
"""
import matplotlib.pyplot as plt
# 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
:align: center
**Corpus Callosum Axial**
.. include:: ../links_names.inc
.. figure:: corpuscallosum_sagittal.png
:align: center
**Corpus Callosum Sagittal**
hdr['voxel_order'] = 'LAS' hdr['dim'] = FA.shape tensor_streamlines_trk = ((sl,None, None) for sl in tensor_streamlines) ten_sl_fname = sys.argv[3]+'_streamlines.trk' nib.trackvis.write(ten_sl_fname, tensor_streamlines_trk, hdr, points_space='voxel') from dipy.viz import fvtk ren = fvtk.ren() position_1=(5.35,59.07,362.79) focal_point_1=(55.35,59.07,29.54) viewup_1=(0.00,1.00,0.00) camera1=fvtk.vtk.vtkCamera() camera1.SetPosition(position_1) camera1.SetFocalPoint(focal_point_1) position_2=(50.35,59.07,362.79) focal_point_2=(55.35,59.07,29.54) viewup_2=(0.00,1.00,0.00) camera2=fvtk.vtk.vtkCamera() camera2.SetPosition(position_2) camera2.SetFocalPoint(focal_point_2) ren.SetActiveCamera(camera1) ren.SetActiveCamera(camera2) from dipy.viz.colormap import line_colors fvtk.add(ren,fvtk.streamtube(tensor_streamlines, line_colors(tensor_streamlines))) ren.SetBackground(1,1,1) camera=fvtk.camera(ren) fvtk.record(ren,n_frames=1, out_path=sys.argv[3]+'_tracks.png',size=(600,600)) fvtk.show(ren)
tensor_streamlines_trk,
hdr,
points_space='voxel')
from dipy.viz import fvtk
ren = fvtk.ren()
position_1 = (5.35, 59.07, 362.79)
focal_point_1 = (55.35, 59.07, 29.54)
viewup_1 = (0.00, 1.00, 0.00)
camera1 = fvtk.vtk.vtkCamera()
camera1.SetPosition(position_1)
camera1.SetFocalPoint(focal_point_1)
position_2 = (50.35, 59.07, 362.79)
focal_point_2 = (55.35, 59.07, 29.54)
viewup_2 = (0.00, 1.00, 0.00)
camera2 = fvtk.vtk.vtkCamera()
camera2.SetPosition(position_2)
camera2.SetFocalPoint(focal_point_2)
ren.SetActiveCamera(camera1)
ren.SetActiveCamera(camera2)
from dipy.viz.colormap import line_colors
fvtk.add(ren,
fvtk.streamtube(tensor_streamlines, line_colors(tensor_streamlines)))
ren.SetBackground(1, 1, 1)
camera = fvtk.camera(ren)
fvtk.record(ren,
n_frames=1,
out_path=sys.argv[3] + '_tracks.png',
size=(600, 600))
fvtk.show(ren)
ren = fvtk.ren()
# convolve kernel with delta spike
spike = np.zeros((7, 7, 7, k.get_orientations().shape[0]), dtype=np.float64)
spike[3, 3, 3, 0] = 1
spike_shm_conv = convolve(sf_to_sh(spike, k.get_sphere(), sh_order=8), k,
sh_order=8, test_mode=True)
sphere = get_sphere('symmetric724')
spike_sf_conv = sh_to_sf(spike_shm_conv, sphere, sh_order=8)
model_kernel = fvtk.sphere_funcs((spike_sf_conv * 6)[3,:,:,:],
sphere,
norm=False,
radial_scale=True)
fvtk.add(ren, model_kernel)
fvtk.camera(ren, pos=(30, 0, 0), focal=(0, 0, 0), viewup=(0, 0, 1), verbose=False)
fvtk.record(ren, out_path='kernel.png', size=(900, 900))
"""
.. figure:: kernel.png
:align: center
Visualization of the contour enhancement kernel.
"""
"""
Shift-twist convolution is applied on the noisy data
"""
# Perform convolution
csd_shm_enh = convolve(csd_shm_noisy, k, sh_order=8)
centroids = qb.centroids clusters = qb.clusters() colormap = np.random.rand(len(centroids),3) #print npts #ren = vtk.vtkRenderer() #renwin = vtk.vtkRenderWindow() #renwin.AddRenderer(ren) #c1 = clusters[0] #print c1['hidden'] ren = fvtk.ren() cam = fvtk.camera(ren, pos=(0,0,-1), viewup=(0,1,0)) fvtk.clear(ren) fvtk.add(ren, fvtk.add(ren, fvtk.streamtube(streamlines, fvtk.colors.white, opacity=0.05))) #fvtk.add(ren, fvtk.add(ren, fvtk.dots(c1['hidden'], fvtk.colors.red, opacity=0.5, dot_size=1))) fvtk.add(ren, fvtk.add(ren, fvtk.streamtube(centroids, colormap, linewidth=0.4))) #mapper = vtk.vtkPolyDataMapper() #mapper.SetInput(reader.GetOutput()) #actor=vtk.vtkActor() #actor.SetMapper(mapper) #ren.AddActor(actor)
