def show_tract(segmented_tract, color):
ren = fvtk.ren()
fvtk.add(
ren,
fvtk.line(segmented_tract.tolist(),
colors=color,
linewidth=2,
opacity=0.3))
fvtk.show(ren)
fvtk.clear(ren)
def show_tract(segmented_tract, color):
"""Visualization of the segmented tract.
"""
ren = fvtk.ren()
fvtk.add(ren, fvtk.line(segmented_tract.tolist(),
colors=color,
linewidth=2,
opacity=0.3))
fvtk.show(ren)
fvtk.clear(ren)
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 show_odfs(fpng, odf_sh, invB, sphere):
ren = fvtk.ren()
ren.SetBackground(1, 1, 1.0)
odf = np.dot(odf_sh, invB)
print(odf.shape)
odf = odf[14:24, 22, 23:33]
# odf = odf[:, 22, :]
# odf = odf[:, 0, :]
sfu = fvtk.sphere_funcs(odf[:, None, :], sphere, norm=True)
sfu.RotateX(-90)
fvtk.add(ren, sfu)
fvtk.show(ren)
fvtk.record(ren, n_frames=1, out_path=fpng, size=(900, 900))
fvtk.clear(ren)
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 show_tracts(estimated_target_tract, target_tract):
"""Visualization of the tracts.
"""
ren = fvtk.ren()
fvtk.add(
ren,
fvtk.line(estimated_target_tract,
fvtk.colors.green,
linewidth=1,
opacity=0.3))
fvtk.add(
ren,
fvtk.line(target_tract, fvtk.colors.white, linewidth=2, opacity=0.3))
fvtk.show(ren)
fvtk.clear(ren)
def show_tract(segmented_tract_positive, color_positive, color_negative,
segmented_tract_negative):
"""Visualization of the segmented tract.
"""
ren = fvtk.ren()
fvtk.add(
ren,
fvtk.line(segmented_tract_positive.tolist(),
colors=color_positive,
linewidth=2,
opacity=0.3))
# fvtk.add(ren, fvtk.line(segmented_tract_negative.tolist(),
# colors=color_negative,
# linewidth=2,
# opacity=0.3))
fvtk.show(ren)
fvtk.clear(ren)
def renderCentroids(streamlines, clusters):
from dipy.viz import fvtk
import numpy as np
ren = fvtk.ren()
ren.SetBackground(0, 0, 0)
colormap = fvtk.create_colormap(np.arange(len(clusters)))
colormap_full = np.ones((len(streamlines), 3))
for cluster in clusters:
colormap_full[cluster.indices] = np.random.rand(3)
#fvtk.add(ren, fvtk.streamtube(streamlines, fvtk.colors.white, opacity=0.05))
fvtk.add(ren, fvtk.line(clusters.centroids, linewidth=0.4, opacity=1))
#fvtk.record(ren, n_frames=1, out_path='fornix_centroids.png', size=(600, 600))
fvtk.show(ren)
fvtk.clear(ren)
def renderBundles(streamlines, clusters):
from dipy.viz import fvtk
import numpy as np
ren = fvtk.ren()
ren.SetBackground(0, 0, 0)
colormap = fvtk.create_colormap(np.arange(len(clusters)))
colormap_full = np.ones((len(streamlines), 3))
for cluster in clusters:
colormap_full[cluster.indices] = np.random.rand(3)
fvtk.add(ren, fvtk.line(streamlines, colormap_full))
#fvtk.record(ren, n_frames=1, out_path='fornix_clusters.png', size=(600, 600))
fvtk.show(ren)
fvtk.clear(ren)
def renderCentroids(clusters):
from dipy.viz import fvtk
import numpy as np
ren = fvtk.ren()
ren.SetBackground(0, 0, 0)
colormap = fvtk.create_colormap(np.arange(len(clusters)))
colormap_full = np.ones((len(streamlines), 3))
for cluster in clusters:
colormap_full[cluster.indices] = np.random.rand(3)
#fvtk.add(ren, fvtk.streamtube(streamlines, fvtk.colors.white, opacity=0.05))
fvtk.add(ren, fvtk.line(clusters.centroids, linewidth=0.4, opacity=1))
#fvtk.record(ren, n_frames=1, out_path='fornix_centroids.png', size=(600, 600))
fvtk.show(ren)
fvtk.clear(ren)
def renderBundles(clusters):
from dipy.viz import fvtk
import numpy as np
ren = fvtk.ren()
ren.SetBackground(0, 0, 0)
colormap = fvtk.create_colormap(np.arange(len(clusters)))
colormap_full = np.ones((len(streamlines), 3))
for cluster in clusters:
colormap_full[cluster.indices] = np.random.rand(3)
fvtk.add(ren, fvtk.line(streamlines, colormap_full))
#fvtk.record(ren, n_frames=1, out_path='fornix_clusters.png', size=(600, 600))
fvtk.show(ren)
fvtk.clear(ren)
def show_peak_directions(fpng, peaks, scale=0.3, x=10, y=0, z=10):
r = fvtk.ren()
for index in ndindex(peaks.shape[:-1]):
peak = peaks[index]
directions = peak.reshape(peak.shape[0] / 3, 3)
# pos = np.array(index)
for i in xrange(directions.shape[0]):
if norm(directions[i]) != 0:
line_actor = fvtk.line(
index + scale * np.vstack((-directions[i], directions[i])), abs(directions[i] / norm(directions[i]))
)
line_actor.RotateX(-90)
fvtk.add(r, line_actor)
fvtk.show(r)
fvtk.record(r, out_path=fpng, size=(900, 900))
fvtk.clear(r)
def show(T,A,IND,VERTS,scale):
r=fvtk.ren()
fvtk.clear(r)
fvtk.add(r,fvtk.line(T,fvtk.red))
fvtk.show(r)
Td=[downsample(t,20) for t in T]
C=local_skeleton_clustering(Td,3)
fvtk.clear(r)
lent=float(len(T))
for c in C:
color=np.random.rand(3)
virtual=C[c]['hidden']/float(C[c]['N'])
if length(virtual)> virtual_thr:
linewidth=100*len(C[c]['indices'])/lent
if linewidth<1.:
linewidth=1
#fvtk.add(r,fvtk.line(virtual,color,linewidth=linewidth))
#fvtk.add(r,fvtk.label(r,str(len(C[c]['indices'])),pos=virtual[0],scale=3,color=color ))
#print C[c]['hidden'].shape
print A.shape
print IND.shape
print VERTS.shape
all,allo=fvtk.crossing(A,IND,VERTS,scale,True)
colors=np.zeros((len(all),3))
for (i,a) in enumerate(all):
if allo[i][0]==0 and allo[i][1]==0 and allo[i][2]==1:
pass
else:
colors[i]=cm.boys2rgb(allo[i])
fvtk.add(r,fvtk.line(all,colors))
fvtk.show(r)
**CSD ODFs**.
In Dipy we also provide tools for finding the peak directions (maxima) of the
ODFs. For this purpose we recommend using ``peaks_from_model``.
"""
from dipy.direction import peaks_from_model
csd_peaks = peaks_from_model(model=csd_model,
data=data_small,
sphere=sphere,
relative_peak_threshold=.5,
min_separation_angle=25,
parallel=True)
fvtk.clear(ren)
fodf_peaks = fvtk.peaks(csd_peaks.peak_dirs, csd_peaks.peak_values, scale=1.3)
fvtk.add(ren, fodf_peaks)
print('Saving illustration as csd_peaks.png')
fvtk.record(ren, out_path='csd_peaks.png', size=(600, 600))
"""
.. figure:: csd_peaks.png
:align: center
**CSD Peaks**.
We can finally visualize both the ODFs and peaks in the same space.
"""
all_streamlines_threshold_classifier = LocalTracking(dg,
threshold_classifier,
seeds,
affine,
step_size=.5,
return_all=True)
save_trk("deterministic_threshold_classifier_all.trk",
all_streamlines_threshold_classifier,
affine,
labels.shape)
streamlines = [sl for sl in all_streamlines_threshold_classifier]
fvtk.clear(ren)
fvtk.add(ren, fvtk.line(streamlines, line_colors(streamlines)))
fvtk.record(ren, out_path='all_streamlines_threshold_classifier.png',
size=(600, 600))
"""
.. figure:: all_streamlines_threshold_classifier.png
:align: center
**Deterministic tractography using a thresholded fractional anisotropy.**
"""
"""
Binary Tissue Classifier
------------------------
**Deterministic streamlines with EuDX on ODF peaks field modulated by GFA**.
It is also possible to use EuDX with multiple ODF peaks, which is very helpful when
tracking in crossing areas.
"""
eu = EuDX(csapeaks.peak_values,
csapeaks.peak_indices,
seeds=10000,
odf_vertices=sphere.vertices,
ang_thr=20.,
a_low=0.6)
csa_streamlines_mult_peaks = [streamline for streamline in eu]
fvtk.clear(r)
fvtk.add(
r,
fvtk.line(csa_streamlines_mult_peaks,
line_colors(csa_streamlines_mult_peaks)))
print('Saving illustration as csa_tracking_mpeaks.png')
fvtk.record(r, n_frames=1, out_path='csa_tracking_mpeaks.png', size=(600, 600))
"""
.. figure:: csa_tracking_mpeaks.png
:align: center
**Deterministic streamlines with EuDX on multiple ODF peaks**.
fvtk.add(r,fvtk.line(T,fvtk.white,opacity=1))
#fvtk.show(r)
fvtk.record(r,n_frames=1,out_path='fornix_initial',size=(600,600))
"""
.. figure:: fornix_initial1000000.png
:align: center
**Initial Fornix dataset**.
"""
"""
Show the *Fornix* after clustering (with random bundle colors):
"""
fvtk.clear(r)
colors=np.zeros((len(T),3))
for c in C:
color=np.random.rand(1,3)
for i in C[c]['indices']:
colors[i]=color
fvtk.add(r,fvtk.line(T,colors,opacity=1))
#fvtk.show(r)
fvtk.record(r,n_frames=1,out_path='fornix_clust',size=(600,600))
"""
.. figure:: fornix_clust1000000.png
:align: center
**Showing the different clusters with random colors**.