def visualize_pdf(pdf, gtab=None):
signal = pdf
s = signal.shape
grid_s = math.ceil((signal.shape[-1])**(1 / 3))
signal = signal.reshape(*s[:-1], grid_s, grid_s, grid_s)
sphere = get_sphere('repulsion724')
if gtab:
dsmodel = DiffusionSpectrumModel(gtab)
signal_xq = np.fft.fftn(signal, axes=[-3, -2, -1])
dsfit = dsmodel.fit(signal_xq.reshape(*s[:2], -1))
odfs = dsfit.odf(sphere)
else:
signal = signal.reshape(-1, grid_s, grid_s, grid_s)
odfs = np.stack([
get_odf(signal[vid], sphere) for vid in range(signal.shape[0])
]).reshape(*s[:-1], -1)
if len(odfs.shape) == 3:
odfs = odfs[:, :, np.newaxis]
# visualize
r = window.Scene()
sfu = actor.odf_slicer(odfs, sphere=sphere, colormap='plasma', scale=0.5)
sfu.display(z=0)
r.add(sfu)
window.show(r)
def visualize(evals,evecs,viz_scale=0.5, fname='tensor_ellipsoids.png', size=(1000,1000)):
# Do vizualisation
interactive = True
ren = window.Scene()
from dipy.data import get_sphere
#sphere = get_sphere('symmetric362')
#sphere = get_sphere('repulsion724')
sphere = get_sphere('symmetric642')
# Calculate the colors. See dipy documentation.
from dipy.reconst.dti import fractional_anisotropy, color_fa
FA = fractional_anisotropy(evals)
#print(FA)
FA[np.isnan(FA)] = 0
FA = np.clip(FA, 0, 1)
RGB = color_fa(FA, evecs)
k=0
cfa = RGB[:, :, k:k+1]
# Normalizing like this increases the contrast, but this will make the contrast different across plots
#cfa /= cfa.max()
# imgplot = plt.imshow(FA, cmap='gray')
# plt.show()
ren.add(actor.tensor_slicer(evals, evecs, sphere=sphere, scalar_colors=cfa, scale=viz_scale, norm=False))
if interactive:
window.show(ren)
window.record(ren, n_frames=1, out_path=fname, size=(1000, 1000))
def plot_directions(peak_dirs, peak_values, x_angle, y_angle, size=(300, 300)):
"""
Opens a 3-d fury window of the maximum peaks visualized
To show a slice, provide a sliced volume of the peak_dirs and peak_values and adjust the x_angle and y_angle
See Tractography Directional Field QA Tutorial for examples
Parameters
-----------
peak_dirs: np array
peak directional vector (x,y,z)
peak_values: np array
peak values/magnitude
x_angle: int
angle to rotate image along x axis
y_angle: int
angle to rotate image along y axis
size: tuple
size of fury window
"""
centers, directions, directions_colors, heights = generate_3_d_directions(
peak_dirs, peak_values
)
scene = window.Scene()
arrow_actor = actor.arrow(centers, directions, directions_colors, heights)
scene.add(arrow_actor)
scene.roll(x_angle)
scene.pitch(y_angle)
window.show(scene, size=size)
def show_weighted_tractography(folder_name,
vec_vols,
s_list,
bundle_short,
direction,
downsamp=1):
s_img = rf'{folder_name}\streamlines\ax_fa_corr_{bundle_short}_{direction}.png'
if downsamp != 1:
vec_vols = vec_vols[::downsamp]
s_list = s_list[::downsamp]
vec_vols.append(1)
vec_vols.append(-1)
cmap = create_colormap(np.asarray(vec_vols), name='seismic')
vec_vols = vec_vols[:-2]
cmap = cmap[:-2]
print(min(vec_vols), max(vec_vols))
#w_actor = actor.line(s_list, vec_vols, linewidth=1.2, lookup_colormap=cmap)
w_actor = actor.line(s_list, cmap, linewidth=1.2)
r = window.Scene()
#r.SetBackground(*window.colors.white)
r.add(w_actor)
#r.add(bar)
window.show(r)
r.set_camera(r.camera_info())
window.record(r, out_path=s_img, size=(800, 800))
def calculate_fodf(gtab,
images,
name,
sphere=default_sphere,
radius=10,
fa_threshold=0.7):
response, ratio = auto_response(gtab,
images,
roi_radius=radius,
fa_thr=fa_threshold)
csd_model = ConstrainedSphericalDeconvModel(gtab, response)
csd_fit = csd_model.fit(images)
csd_odf = csd_fit.odf(sphere)
fodf_spheres = actor.odf_slicer(csd_odf,
sphere=sphere,
scale=0.9,
norm=False,
colormap='plasma')
ren = window.Scene()
ren.add(fodf_spheres)
print('Saving illustration as csd_odfs_{}.png'.format(name))
window.record(ren,
out_path='results/csd_odfs_{}.png'.format(name),
size=(600, 600))
return csd_fit
def visualize_roi(roi,
affine_or_mapping=None,
static_img=None,
roi_affine=None,
static_affine=None,
reg_template=None,
scene=None,
color=np.array([1, 0, 0]),
opacity=1.0,
inline=False,
interact=False):
"""
Render a region of interest into a VTK viz as a volume
"""
if not isinstance(roi, np.ndarray):
if isinstance(roi, str):
roi = nib.load(roi).get_fdata()
else:
roi = roi.get_fdata()
if affine_or_mapping is not None:
if isinstance(affine_or_mapping, np.ndarray):
# This is an affine:
if (static_img is None or roi_affine is None
or static_affine is None):
raise ValueError(
"If using an affine to transform an ROI, "
"need to also specify all of the following",
"inputs: `static_img`, `roi_affine`, ", "`static_affine`")
roi = reg.resample(roi, static_img, roi_affine, static_affine)
else:
# Assume it is a mapping:
if (isinstance(affine_or_mapping, str)
or isinstance(affine_or_mapping, nib.Nifti1Image)):
if reg_template is None or static_img is None:
raise ValueError(
"If using a mapping to transform an ROI, need to ",
"also specify all of the following inputs: ",
"`reg_template`, `static_img`")
affine_or_mapping = reg.read_mapping(affine_or_mapping,
static_img, reg_template)
roi = auv.patch_up_roi(
affine_or_mapping.transform_inverse(
roi, interpolation='nearest')).astype(bool)
if scene is None:
scene = window.Scene()
roi_actor = actor.contour_from_roi(roi, color=color, opacity=opacity)
scene.add(roi_actor)
if inline:
tdir = tempfile.gettempdir()
fname = op.join(tdir, "fig.png")
window.snapshot(scene, fname=fname)
display.display_png(display.Image(fname))
return _inline_interact(scene, inline, interact)
def build_scene(self):
self.mem = GlobalHorizon()
scene = window.Scene()
self.add_cluster_actors(scene, self.tractograms,
self.cluster_thr,
enable_callbacks=False)
return scene
def test_scene(self):
xyz = 10 * np.random.rand(100, 3)
colors = np.random.rand(100, 4)
radii = np.random.rand(100) + 0.5
sphere_actor = actor.sphere(centers=xyz, colors=colors, radii=radii)
scene = window.Scene()
scene.add(sphere_actor)
self.assertEqual((0, 0), scene.GetSize())
def plotTract(tractIn):
import numpy as np
from dipy.viz import window, actor
renderer = window.Scene()
stream_actor = actor.line(tractIn)
#renderer.set_camera(position=(-176.42, 118.52, 128.20),
# focal_point=(113.30, 128.31, 76.56),
# view_up=(0.18, 0.00, 0.98))
get_ipython().run_line_magic('matplotlib', 'inline')
renderer.add(stream_actor)
window.show(renderer, size=(600, 600), reset_camera=True)
def plot_streamlines(streamlines):
if has_fury:
# Prepare the display objects.
color = colormap.line_colors(streamlines)
streamlines_actor = actor.line(streamlines,
colormap.line_colors(streamlines))
# Create the 3D display.
scene = window.Scene()
scene.add(streamlines_actor)
# Save still images for this static example. Or for interactivity use
window.show(scene)
def show_template_bundles(bundles, static, show=True, fname=None):
scene = window.Scene()
template_actor = actor.slicer(static)
scene.add(template_actor)
lines_actor = actor.streamtube(bundles,
window.colors.orange,
linewidth=0.3)
scene.add(lines_actor)
if show:
window.show(scene)
if fname is not None:
window.record(scene, n_frames=1, out_path=fname, size=(900, 900))
def show_both_bundles(bundles, colors=None, show=True, fname=None):
scene = window.Scene()
scene.SetBackground(1., 1, 1)
for (i, bundle) in enumerate(bundles):
color = colors[i]
streamtube_actor = actor.streamtube(bundle, color, linewidth=0.3)
streamtube_actor.RotateX(-90)
streamtube_actor.RotateZ(90)
scene.add(streamtube_actor)
if show:
window.show(scene)
elif fname is not None:
window.record(scene, out_path=fname, size=(900, 900))
def viewclusters(clusters,streamlines, outpath=None, interactive=False):
#Linked to viewing clusters. If outpath given, will save info to right location, if interactive, will show window
colormap = actor.create_colormap(np.ravel(clusters.centroids))
colormap_full = np.ones((len(streamlines), 3))
for cluster, color in zip(clusters, colormap):
colormap_full[cluster.indices] = color
scene = window.Scene()
scene.SetBackground(1, 1, 1)
scene.add(actor.streamtube(streamlines, colormap_full))
window.record(scene, out_path=outpath, size=(600, 600))
# Enables/disables interactive visualization
if interactive:
window.show(scene)
def visualize_streamline(darray,
score,
save_able=False,
save_name='default.png',
control_par=1,
hue=[0.5, 1]):
data_evl = darray
streamlines_evl = Streamlines()
for i in range(np.shape(data_evl)[0]):
tmp = data_evl[i]
tmp = zero_remove(tmp)
#tmp = tmp[~np.all(tmp == 0, axis=-1)]
#tmp = np.around(tmp, decimals=0)
streamlines_evl.append(tmp)
mse_nor = score
# Visualize the streamlines, colored by cci
ren = window.Scene()
saturation = [0.0, 1.0]
lut_cmap = actor.colormap_lookup_table(
scale_range=(min(mse_nor), max(mse_nor) / control_par),
hue_range=hue,
saturation_range=saturation)
bar3 = actor.scalar_bar(lut_cmap)
ren.add(bar3)
stream_actor = actor.line(streamlines_evl,
mse_nor,
linewidth=0.1,
lookup_colormap=lut_cmap)
ren.add(stream_actor)
if not save_able:
interactive = True
if interactive:
window.show(ren)
if save_able:
window.record(ren, n_frames=1, out_path=save_name, size=(800, 800))
def show_tracts(hue, saturation, scale, streamlines, mean_vol_per_tract,
folder_name, fig_type):
from dipy.viz import window, actor
lut_cmap = actor.colormap_lookup_table(hue_range=hue,
saturation_range=saturation,
scale_range=scale)
streamlines_actor = actor.streamtube(streamlines,
mean_vol_per_tract,
linewidth=0.5,
lookup_colormap=lut_cmap)
bar = actor.scalar_bar(lut_cmap)
r = window.Scene()
r.add(streamlines_actor)
r.add(bar)
mean_pasi_weighted_img = f'{folder_name}{os.sep}streamlines{os.sep}mean_pasi_weighted{fig_type}.png'
window.show(r)
r.set_camera(r.camera_info())
window.record(r, out_path=mean_pasi_weighted_img, size=(800, 800))
def calculate_odf(gtab, data, sh_order=4):
csamodel = CsaOdfModel(gtab, sh_order)
data_small = data[30:65, 40:75, 39:40]
csa_odf = csamodel.fit(data_small).odf(default_sphere)
csa_odf = np.clip(csa_odf, 0, np.max(csa_odf, -1)[..., None])
odf_spheres = actor.odf_slicer(csa_odf,
sphere=default_sphere,
scale=0.9,
norm=False,
colormap='plasma')
ren = window.Scene()
ren.add(odf_spheres)
print('Saving illustration as csa_odfs_{}.png'.format(data.shape[-1] - 1))
window.record(ren,
out_path='results/csa_odfs_{}.png'.format(data.shape[-1] -
1),
size=(600, 600))
return csa_odf
def qball(gtab, data, name, sh_order=4):
qballmodel = QballModel(gtab, sh_order)
data_small = data[:, :, 39:40]
qball_fit = qballmodel.fit(data_small)
qball_odf = qball_fit.odf(default_sphere)
odf_spheres = actor.odf_slicer(qball_odf,
sphere=default_sphere,
scale=0.9,
norm=False,
colormap='plasma')
ren = window.Scene()
ren.add(odf_spheres)
print('Saving illustration as qball_odfs_{}.png'.format(
name)) #data.shape[-1] - 1))
window.record(ren,
out_path='results/qball_odfs_{}.png'.format(name),
size=(600, 600))
return qball_odf, qball_fit.shm_coeff
def visualize(self,
out_path='out/',
outer_box=True,
axes=True,
clip_neg=False,
azimuth=0,
elevation=0,
n_frames=1,
mag=1,
video=False,
viz_type='ODF',
mask=None,
mask_roi=None,
skip_n=1,
skip_n_roi=1,
scale=1,
roi_scale=1,
zoom_start=1.0,
zoom_end=1.0,
top_zoom=1,
interact=False,
save_parallels=False,
my_cam=None,
compress=True,
roi=None,
corner_text='',
scalemap=None,
titles_on=True,
scalebar_on=True,
invert=False,
flat=False,
colormap='bwr',
global_cm=True,
camtilt=False,
axes_on=False,
colors=None,
arrows=None,
arrow_color=np.array([0, 0, 0]),
linewidth=0.1,
mark_slices=None,
z_shift=0,
profiles=[],
markers=[],
marker_colors=[],
marker_scale=1,
normalize_glyphs=True,
gamma=1,
density_max=1):
log.info('Preparing to render ' + out_path)
# Handle scalemap
if scalemap is None:
scalemap = util.ScaleMap(min=np.min(self.f[..., 0]),
max=np.max(self.f[..., 0]))
# Prepare output
util.mkdir(out_path)
# Setup vtk renderers
renWin = vtk.vtkRenderWindow()
if not interact:
renWin.SetOffScreenRendering(1)
if isinstance(viz_type, str):
viz_type = [viz_type]
# Rows and columns
cols = len(viz_type)
if roi is None:
rows = 1
else:
rows = 2
renWin.SetSize(np.int(500 * mag * cols), np.int(500 * mag * rows))
# Select background color
if save_parallels:
bg_color = [1, 1, 1]
line_color = np.array([0, 0, 0])
line_bcolor = np.array([1, 1, 1])
else:
if not invert:
bg_color = [0, 0, 0]
line_color = np.array([1, 1, 1])
line_bcolor = np.array([0, 0, 0])
else:
bg_color = [1, 1, 1]
line_color = np.array([0, 0, 0])
line_bcolor = np.array([1, 1, 1])
# For each viz_type
rens = []
zoom_start = []
zoom_end = []
for row in range(rows):
for col in range(cols):
# Render
ren = window.Scene()
rens.append(ren)
if viz_type[col] is 'Density':
ren.background([0, 0, 0])
line_color = np.array([1, 1, 1])
else:
ren.background(bg_color)
ren.SetViewport(col / cols, (rows - row - 1) / rows,
(col + 1) / cols, (rows - row) / rows)
renWin.AddRenderer(ren)
iren = vtk.vtkRenderWindowInteractor()
iren.SetRenderWindow(renWin)
# Mask
if mask is None:
mask = np.ones((self.X, self.Y, self.Z), dtype=np.bool)
if mask_roi is None:
mask_roi = mask
# Main vs roi
if row == 0:
data = self.f
skip_mask = np.zeros(mask.shape, dtype=np.bool)
skip_mask[::skip_n, ::skip_n, ::skip_n] = 1
my_mask = np.logical_and(mask, skip_mask)
scale = scale
scalemap = scalemap
if np.sum(my_mask) == 0:
my_mask[0, 0, 0] = True
else:
data = self.f[roi[0][0]:roi[1][0], roi[0][1]:roi[1][1],
roi[0][2]:roi[1][2], :]
roi_mask = mask_roi[roi[0][0]:roi[1][0],
roi[0][1]:roi[1][1],
roi[0][2]:roi[1][2]]
skip_mask = np.zeros(roi_mask.shape, dtype=np.bool)
skip_mask[::skip_n_roi, ::skip_n_roi, ::skip_n_roi] = 1
my_mask = np.logical_and(roi_mask, skip_mask)
scale = roi_scale
scalemap = scalemap
# Add visuals to renderer
if viz_type[col] == "ODF":
renWin.SetMultiSamples(4)
log.info('Rendering ' + str(np.sum(my_mask)) + ' ODFs')
fodf_spheres = viz.odf_sparse(data,
self.Binv,
sphere=self.sphere,
scale=skip_n * scale * 0.5,
norm=False,
colormap=colormap,
mask=my_mask,
global_cm=global_cm,
scalemap=scalemap,
odf_sphere=False,
flat=flat,
normalize=normalize_glyphs)
ren.add(fodf_spheres)
elif viz_type[col] == "ODF Sphere":
renWin.SetMultiSamples(4)
log.info('Rendering ' + str(np.sum(my_mask)) + ' ODFs')
fodf_spheres = viz.odf_sparse(data,
self.Binv,
sphere=self.sphere,
scale=skip_n * scale * 0.5,
norm=False,
colormap=colormap,
mask=my_mask,
global_cm=global_cm,
scalemap=scalemap,
odf_sphere=True,
flat=flat)
ren.add(fodf_spheres)
elif viz_type[col] == "Ellipsoid":
renWin.SetMultiSamples(4)
log.info(
'Warning: scaling is not implemented for ellipsoids')
log.info('Rendering ' + str(np.sum(my_mask)) +
' ellipsoids')
fodf_peaks = viz.tensor_slicer_sparse(data,
sphere=self.sphere,
scale=skip_n *
scale * 0.5,
mask=my_mask)
ren.add(fodf_peaks)
elif viz_type[col] == "Peak":
renWin.SetMultiSamples(4)
log.info('Rendering ' + str(np.sum(my_mask)) + ' peaks')
fodf_peaks = viz.peak_slicer_sparse(
data,
self.Binv,
self.sphere.vertices,
linewidth=linewidth,
scale=skip_n * scale * 0.5,
colors=colors,
mask=my_mask,
scalemap=scalemap,
normalize=normalize_glyphs)
fodf_peaks.GetProperty().LightingOn()
fodf_peaks.GetProperty().SetDiffuse(
0.4) # Doesn't work (VTK bug I think)
fodf_peaks.GetProperty().SetAmbient(0.15)
fodf_peaks.GetProperty().SetSpecular(0)
fodf_peaks.GetProperty().SetSpecularPower(0)
ren.add(fodf_peaks)
elif viz_type[col] == "Principal":
log.info(
'Warning: scaling is not implemented for principals')
log.info('Rendering ' + str(np.sum(my_mask)) +
' principals')
fodf_peaks = viz.principal_slicer_sparse(
data,
self.Binv,
self.sphere.vertices,
scale=skip_n * scale * 0.5,
mask=my_mask)
ren.add(fodf_peaks)
elif viz_type[col] == "Density":
renWin.SetMultiSamples(0) # Must be zero for smooth
# renWin.SetAAFrames(4) # Slow antialiasing for volume renders
log.info('Rendering density')
gamma_corr = np.where(data[..., 0] > 0,
data[..., 0]**gamma, data[..., 0])
scalemap.max = density_max * scalemap.max**gamma
volume = viz.density_slicer(gamma_corr, scalemap)
ren.add(volume)
X = np.float(data.shape[0])
Y = np.float(data.shape[1])
Z = np.float(data.shape[2]) - z_shift
# Titles
if row == 0 and titles_on:
viz.add_text(ren, viz_type[col], 0.5, 0.96, mag)
# Scale bar
if col == cols - 1 and not save_parallels and scalebar_on:
yscale = 1e-3 * self.vox_dim[1] * data.shape[1]
yscale_label = '{:.2g}'.format(yscale) + ' um'
viz.add_text(ren, yscale_label, 0.5, 0.03, mag)
viz.draw_scale_bar(ren, X, Y, Z, [1, 1, 1])
# Corner text
if row == rows - 1 and col == 0 and titles_on:
viz.add_text(ren, corner_text, 0.03, 0.03, mag, ha='left')
# Draw boxes
Nmax = np.max([X, Y, Z])
if outer_box:
if row == 0:
viz.draw_outer_box(
ren,
np.array([[0, 0, 0], [X, Y, Z]]) - 0.5, line_color)
if row == 1:
viz.draw_outer_box(
ren,
np.array([[0, 0, 0], [X, Y, Z]]) - 0.5, [0, 1, 1])
# Add colored axes
if axes:
viz.draw_axes(ren, np.array([[0, 0, 0], [X, Y, Z]]) - 0.5)
# Add custom arrows
if arrows is not None:
for i in range(arrows.shape[0]):
viz.draw_single_arrow(ren,
arrows[i, 0, :],
arrows[i, 1, :],
color=arrow_color)
viz.draw_unlit_line(ren, [
np.array([arrows[i, 0, :], [X / 2, Y / 2, Z / 2]])
], [arrow_color],
lw=0.3,
scale=1.0)
# Draw roi box
if row == 0 and roi is not None:
maxROI = np.max([
roi[1][0] - roi[0][0], roi[1][1] - roi[0][1],
roi[1][2] - roi[0][2]
])
maxXYZ = np.max([self.X, self.Y, self.Z])
viz.draw_outer_box(ren,
roi, [0, 1, 1],
lw=0.3 * maxXYZ / maxROI)
viz.draw_axes(ren, roi, lw=0.3 * maxXYZ / maxROI)
# Draw marked slices
if mark_slices is not None:
for slicen in mark_slices:
md = np.max((X, Z))
frac = slicen / data.shape[1]
rr = 0.83 * md
t1 = 0
t2 = np.pi / 2
t3 = np.pi
t4 = 3 * np.pi / 2
points = [
np.array([[
X / 2 + rr * np.cos(t1), frac * Y,
Z / 2 + rr * np.sin(t1)
],
[
X / 2 + rr * np.cos(t2), frac * Y,
Z / 2 + rr * np.sin(t2)
],
[
X / 2 + rr * np.cos(t3), frac * Y,
Z / 2 + rr * np.sin(t3)
],
[
X / 2 + rr * np.cos(t4), frac * Y,
Z / 2 + rr * np.sin(t4)
],
[
X / 2 + rr * np.cos(t1), frac * Y,
Z / 2 + rr * np.sin(t1)
],
[
X / 2 + rr * np.cos(t2), frac * Y,
Z / 2 + rr * np.sin(t2)
]])
]
viz.draw_unlit_line(ren,
points,
6 * [line_color + 0.6],
lw=0.3,
scale=1.0)
# Draw markers
for i, marker in enumerate(markers):
# Draw sphere
source = vtk.vtkSphereSource()
source.SetCenter(marker)
source.SetRadius(marker_scale)
source.SetThetaResolution(30)
source.SetPhiResolution(30)
# mapper
mapper = vtk.vtkPolyDataMapper()
mapper.SetInputConnection(source.GetOutputPort())
# actor
actor = vtk.vtkActor()
actor.SetMapper(mapper)
actor.GetProperty().SetColor(marker_colors[i, :])
actor.GetProperty().SetLighting(0)
ren.AddActor(actor)
# Draw profile lines
colors = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
for i, profile in enumerate(profiles):
import pdb
pdb.set_trace()
n_seg = profile.shape[0]
viz.draw_unlit_line(ren, [profile],
n_seg * [colors[i, :]],
lw=0.5,
scale=1.0)
# Draw sphere
source = vtk.vtkSphereSource()
source.SetCenter(profile[0])
source.SetRadius(1)
source.SetThetaResolution(30)
source.SetPhiResolution(30)
# mapper
mapper = vtk.vtkPolyDataMapper()
mapper.SetInputConnection(source.GetOutputPort())
# actor
actor = vtk.vtkActor()
actor.SetMapper(mapper)
# actor.GetProperty().SetColor(colors[i,:])
actor.GetProperty().SetLighting(0)
# assign actor to the renderer
ren.AddActor(actor)
# Setup cameras
Rmax = np.linalg.norm([Z / 2, X / 2, Y / 2])
Rcam_rad = Rmax / np.tan(np.pi / 12)
Ntmax = np.max([X, Y])
ZZ = Z
if ZZ > Ntmax:
Rcam_edge = np.max([X / 2, Y / 2])
else:
Rcam_edge = np.min([X / 2, Y / 2])
Rcam = Rcam_edge + Rcam_rad
if my_cam is None:
cam = ren.GetActiveCamera()
if camtilt:
cam.SetPosition(
((X - 1) / 2, (Y - 1) / 2, (Z - 1) / 2 + Rcam))
cam.SetViewUp((-1, 0, 1))
if axes_on:
max_dim = np.max((X, Z))
viz.draw_unlit_line(ren, [
np.array([[(X - max_dim) / 2, Y / 2, Z / 2],
[X / 2, Y / 2, +Z / 2],
[X / 2, Y / 2, (Z + max_dim) / 2]])
],
3 * [line_color],
lw=max_dim / 250,
scale=1.0)
else:
cam.SetPosition(
((X - 1) / 2 + Rcam, (Y - 1) / 2, (Z - 1) / 2))
cam.SetViewUp((0, 0, 1))
cam.SetFocalPoint(((X - 1) / 2, (Y - 1) / 2, (Z - 1) / 2))
#ren.reset_camera()
else:
ren.set_camera(*my_cam)
ren.azimuth(azimuth)
ren.elevation(elevation)
# Set zooming
if save_parallels:
zoom_start.append(1.7)
zoom_end.append(1.7)
else:
if row == 0:
zoom_start.append(1.3 * top_zoom)
zoom_end.append(1.3 * top_zoom)
else:
zoom_start.append(1.3)
zoom_end.append(1.3)
# Setup writer
writer = vtk.vtkTIFFWriter()
if not compress:
writer.SetCompressionToNoCompression()
# Execute renders
az = 90
naz = np.ceil(360 / n_frames)
log.info('Rendering ' + out_path)
if save_parallels:
# Parallel rendering for summaries
filenames = ['yz', 'xy', 'xz']
zooms = [zoom_start[0], 1.0, 1.0]
azs = [90, -90, 0]
els = [0, 0, 90]
ren.projection(proj_type='parallel')
ren.reset_camera()
for i in tqdm(range(3)):
ren.zoom(zooms[i])
ren.azimuth(azs[i])
ren.elevation(els[i])
ren.reset_clipping_range()
renderLarge = vtk.vtkRenderLargeImage()
renderLarge.SetMagnification(1)
renderLarge.SetInput(ren)
renderLarge.Update()
writer.SetInputConnection(renderLarge.GetOutputPort())
writer.SetFileName(out_path + filenames[i] + '.tif')
writer.Write()
else:
# Rendering for movies
for j, ren in enumerate(rens):
ren.zoom(zoom_start[j])
for i in tqdm(range(n_frames)):
for j, ren in enumerate(rens):
ren.zoom(1 + ((zoom_end[j] - zoom_start[j]) / n_frames))
ren.azimuth(az)
ren.reset_clipping_range()
renderLarge = vtk.vtkRenderLargeImage()
renderLarge.SetMagnification(1)
renderLarge.SetInput(ren)
renderLarge.Update()
writer.SetInputConnection(renderLarge.GetOutputPort())
if n_frames != 1:
writer.SetFileName(out_path + str(i).zfill(3) + '.tif')
else:
writer.SetFileName(out_path + '.tif')
writer.Write()
az = naz
# Interactive
if interact:
window.show(ren)
# Generate video (requires ffmpeg)
if video:
log.info('Generating video from frames')
fps = np.ceil(n_frames / 12)
subprocess.call([
'ffmpeg', '-nostdin', '-y', '-framerate',
str(fps), '-loglevel', 'panic', '-i',
out_path + '%03d' + '.png', '-pix_fmt', 'yuvj420p', '-vcodec',
'mjpeg', out_path[:-1] + '.avi'
])
# subprocess.call(['rm', '-r', out_path])
return my_cam
def visualize_roi(roi,
affine_or_mapping=None,
static_img=None,
roi_affine=None,
static_affine=None,
reg_template=None,
name='ROI',
figure=None,
color=np.array([1, 0, 0]),
flip_axes=None,
opacity=1.0,
inline=False,
interact=False):
"""
Render a region of interest into a VTK viz as a volume
Parameters
----------
roi : str or Nifti1Image
The ROI information
affine_or_mapping : ndarray, Nifti1Image, or str, optional
An affine transformation or mapping to apply to the ROIs before
visualization. Default: no transform.
static_img: str or Nifti1Image, optional
Template to resample roi to.
Default: None
roi_affine: ndarray, optional
Default: None
static_affine: ndarray, optional
Default: None
reg_template: str or Nifti1Image, optional
Template to use for registration.
Default: None
name: str, optional
Name of ROI for the legend.
Default: 'ROI'
color : ndarray, optional
RGB color for ROI.
Default: np.array([1, 0, 0])
flip_axes : None
This parameter is to conform fury and plotly APIs.
opacity : float, optional
Opacity of ROI.
Default: 1.0
figure : fury Scene object, optional
If provided, the visualization will be added to this Scene. Default:
Initialize a new Scene.
interact : bool
Whether to provide an interactive VTK window for interaction.
Default: False
inline : bool
Whether to embed the visualization inline in a notebook. Only works
in the notebook context. Default: False.
Returns
-------
Fury Scene object
"""
roi = vut.prepare_roi(roi, affine_or_mapping, static_img, roi_affine,
static_affine, reg_template)
if figure is None:
figure = window.Scene()
roi_actor = actor.contour_from_roi(roi, color=color, opacity=opacity)
figure.add(roi_actor)
return _inline_interact(figure, inline, interact)
def viewstreamlines_anat(streamlines_full,
anat_path,
affine,
ratio=1,
threshold=10.,
verbose=False):
scene = window.Scene()
scene.SetBackground(1, 1, 1)
#colors = ['white', 'cadmium_red_deep', 'misty_rose', 'slate_grey_dark', 'ivory_black', 'chartreuse']
colors = [
window.colors.white, window.colors.cadmium_red_deep,
window.colors.misty_rose, window.colors.slate_grey_dark,
window.colors.ivory_black, window.colors.chartreuse
]
streamline_cut = []
i = 0
if ratio != 1:
for streamline in streamlines_full:
if i % ratio == 0:
streamline_cut.append(streamline)
i += 1
else:
streamline_cut = streamlines_full
qb = QuickBundles(threshold=threshold)
clusters = qb.cluster(streamline_cut)
if verbose:
print("Nb. clusters:", len(clusters))
print("Cluster sizes:", map(len, clusters))
print("Small clusters:", clusters < 10)
print("Streamlines indices of the first cluster:\n",
clusters[0].indices)
print("Centroid of the last clustker:\n", clusters[-1].centroid)
j = 0
scene = window.Scene()
scene.add(actor.streamtube(streamline_cut, colors[j]))
slicer_opacity = 0.6
j += 1
if isinstance(anat_path, str) and os.path.exists(anat_path):
anat_nifti = load_nifti(anat_path)
try:
data = anat_nifti.data
except AttributeError:
data = anat_nifti[0]
if affine is None:
try:
affine = anat_nifti.affine
except AttributeError:
affine = anat_nifti[1]
else:
data = anat_path
shape = np.shape(data)
if np.size(shape) == 4:
data = data[:, :, :, 0]
image_actor_z = actor.slicer(data, affine)
image_actor_z.opacity(slicer_opacity)
image_actor_x = image_actor_z.copy()
x_midpoint = int(np.round(shape[0] / 2))
image_actor_x.display_extent(x_midpoint, x_midpoint, 0, shape[1] - 1, 0,
shape[2] - 1)
image_actor_y = image_actor_z.copy()
y_midpoint = int(np.round(shape[1] / 2))
image_actor_y.display_extent(0, shape[0] - 1, y_midpoint, y_midpoint, 0,
shape[2] - 1)
scene.add(image_actor_z)
scene.add(image_actor_x)
scene.add(image_actor_y)
global size
size = scene.GetSize()
show_m = window.ShowManager(scene, size=(1200, 900))
show_m.initialize()
interactive = True
interactive = False
if interactive:
show_m.add_window_callback(win_callback)
show_m.render()
show_m.start()
else:
window.record(scene,
out_path='bundles_and_3_slices.png',
size=(1200, 900),
reset_camera=False)
def connective_streamlines_figuremaker(allstreamlines,
ROI_streamlines,
ROI_names,
anat_path,
threshold=10.,
verbose=False):
#streamlines = Streamlines(res['af.left'])
#streamlines.extend(res['cst.right'])
#streamlines.extend(res['cc_1'])
world_coords = True
# Cluster sizes: [64, 191, 47, 1]
# Small clusters: array([False, False, False, True], dtype=bool)
scene = window.Scene()
scene.SetBackground(1, 1, 1)
colors = [
'white', 'cadmium_red_deep', 'misty_rose', 'slate_grey_dark',
'ivory_black', 'chartreuse'
]
colors = [
window.colors.white, window.colors.cadmium_red_deep,
window.colors.misty_rose, window.colors.slate_grey_dark,
window.colors.ivory_black, window.colors.chartreuse
]
i = 0
for ROI in ROI_streamlines:
ROI_streamline = allstreamlines[ROI]
qb = QuickBundles(threshold=threshold)
clusters = qb.cluster(ROI_streamline)
if verbose:
print("Nb. clusters:", len(clusters))
print("Cluster sizes:", map(len, clusters))
print("Small clusters:", clusters < 10)
print("Streamlines indices of the first cluster:\n",
clusters[0].indices)
print("Centroid of the last cluster:\n", clusters[-1].centroid)
#if not world_coords:
# from dipy.tracking.streamline import transform_streamlines
# streamlines = transform_streamlines(ROI_streamline, np.linalg.inv(affine))
scene = window.Scene()
#stream_actor = actor.line(ROI_streamline)
#scene.add(actor.streamtube(ROI_streamline, window.colors.misty_rose))
scene.add(actor.streamtube(ROI_streamline, colors[i]))
#if not world_coords:
# image_actor_z = actor.slicer(data, affine=np.eye(4))
#else:
# image_actor_z = actor.slicer(data, affine)
slicer_opacity = 0.6
i = i + 1
anat_nifti = load_nifti(anat_path)
try:
data = anat_nifti.data
except AttributeError:
data = anat_nifti[0]
try:
affine = anat_nifti.affine
except AttributeError:
affine = anat_nifti[1]
shape = np.shape(data)
image_actor_z = actor.slicer(data[:, :, :, 0], affine)
image_actor_z.opacity(slicer_opacity)
image_actor_x = image_actor_z.copy()
x_midpoint = int(np.round(shape[0] / 2))
image_actor_x.display_extent(x_midpoint, x_midpoint, 0, shape[1] - 1, 0,
shape[2] - 1)
image_actor_y = image_actor_z.copy()
y_midpoint = int(np.round(shape[1] / 2))
image_actor_y.display_extent(0, shape[0] - 1, y_midpoint, y_midpoint, 0,
shape[2] - 1)
scene.add(image_actor_z)
scene.add(image_actor_x)
scene.add(image_actor_y)
global size
size = scene.GetSize()
show_m = window.ShowManager(scene, size=(1200, 900))
show_m.initialize()
interactive = True
interactive = False
if interactive:
show_m.add_window_callback(win_callback)
show_m.render()
show_m.start()
else:
window.record(scene,
out_path='bundles_and_3_slices.png',
size=(1200, 900),
reset_camera=False)
def plot_bundles_with_metric(bundle_path,
endings_path,
brain_mask_path,
bundle,
metrics,
output_path,
tracking_format="trk_legacy",
show_color_bar=True):
import seaborn as sns # import in function to avoid error if not installed (this is only needed in this function)
from dipy.viz import actor, window
from tractseg.libs import vtk_utils
def _add_extra_point_to_last_streamline(sl):
# Coloring broken as soon as all streamlines have same number of points -> why???
# Add one number to last streamline to make it have a different number
sl[-1] = np.append(sl[-1], [sl[-1][-1]], axis=0)
return sl
# Settings
NR_SEGMENTS = 100
ANTI_INTERPOL_MULT = 1 # increase number of points to avoid interpolation to blur the colors
algorithm = "distance_map" # equal_dist | distance_map | cutting_plane
# colors = np.array(sns.color_palette("coolwarm", NR_SEGMENTS)) # colormap blue to red (does not fit to colorbar)
colors = np.array(sns.light_palette(
"red", NR_SEGMENTS)) # colormap only red, which fits to color_bar
img_size = (1000, 1000)
# Tractometry skips first and last element. Therefore we only have 98 instead of 100 elements.
# Here we duplicate the first and last element to get back to 100 elements
metrics = list(metrics)
metrics = np.array([metrics[0]] + metrics + [metrics[-1]])
metrics_max = metrics.max()
metrics_min = metrics.min()
if metrics_max == metrics_min:
metrics = np.zeros(len(metrics))
else:
metrics = img_utils.scale_to_range(
metrics,
range=(0, 99)) # range needs to be same as segments in colormap
orientation = dataset_specific_utils.get_optimal_orientation_for_bundle(
bundle)
# Load mask
beginnings_img = nib.load(endings_path)
beginnings = beginnings_img.get_fdata().astype(np.uint8)
for i in range(1):
beginnings = binary_dilation(beginnings)
# Load trackings
if tracking_format == "trk_legacy":
streams, hdr = trackvis.read(bundle_path)
streamlines = [s[0] for s in streams]
else:
sl_file = nib.streamlines.load(bundle_path)
streamlines = sl_file.streamlines
# Reduce streamline count
streamlines = streamlines[::2]
# Reorder to make all streamlines have same start region
streamlines = fiber_utils.add_to_each_streamline(streamlines, 0.5)
streamlines_new = []
for idx, sl in enumerate(streamlines):
startpoint = sl[0]
# Flip streamline if not in right order
if beginnings[int(startpoint[0]),
int(startpoint[1]),
int(startpoint[2])] == 0:
sl = sl[::-1, :]
streamlines_new.append(sl)
streamlines = fiber_utils.add_to_each_streamline(streamlines_new, -0.5)
if algorithm == "distance_map" or algorithm == "equal_dist":
streamlines = fiber_utils.resample_fibers(
streamlines, NR_SEGMENTS * ANTI_INTERPOL_MULT)
elif algorithm == "cutting_plane":
streamlines = fiber_utils.resample_to_same_distance(
streamlines,
max_nr_points=NR_SEGMENTS,
ANTI_INTERPOL_MULT=ANTI_INTERPOL_MULT)
# Cut start and end by percentage
# streamlines = FiberUtils.resample_fibers(streamlines, NR_SEGMENTS * ANTI_INTERPOL_MULT)
# remove = int((NR_SEGMENTS * ANTI_INTERPOL_MULT) * 0.15) # remove X% in beginning and end
# streamlines = np.array(streamlines)[:, remove:-remove, :]
# streamlines = list(streamlines)
if algorithm == "equal_dist":
segment_idxs = []
for i in range(len(streamlines)):
segment_idxs.append(list(range(NR_SEGMENTS * ANTI_INTERPOL_MULT)))
segment_idxs = np.array(segment_idxs)
elif algorithm == "distance_map":
metric = AveragePointwiseEuclideanMetric()
qb = QuickBundles(threshold=100., metric=metric)
clusters = qb.cluster(streamlines)
centroids = Streamlines(clusters.centroids)
_, segment_idxs = cKDTree(centroids.data, 1,
copy_data=True).query(streamlines, k=1)
elif algorithm == "cutting_plane":
streamlines_resamp = fiber_utils.resample_fibers(
streamlines, NR_SEGMENTS * ANTI_INTERPOL_MULT)
metric = AveragePointwiseEuclideanMetric()
qb = QuickBundles(threshold=100., metric=metric)
clusters = qb.cluster(streamlines_resamp)
centroid = Streamlines(clusters.centroids)[0]
# index of the middle cluster
middle_idx = int(NR_SEGMENTS / 2) * ANTI_INTERPOL_MULT
middle_point = centroid[middle_idx]
segment_idxs = fiber_utils.get_idxs_of_closest_points(
streamlines, middle_point)
# Align along the middle and assign indices
segment_idxs_eqlen = []
for idx, sl in enumerate(streamlines):
sl_middle_pos = segment_idxs[idx]
before_elems = sl_middle_pos
after_elems = len(sl) - sl_middle_pos
base_idx = 1000 # use higher index to avoid negative numbers for area below middle
r = range((base_idx - before_elems), (base_idx + after_elems))
segment_idxs_eqlen.append(r)
segment_idxs = segment_idxs_eqlen
# Add extra point otherwise coloring BUG
streamlines = _add_extra_point_to_last_streamline(streamlines)
renderer = window.Scene()
colors_all = [] # final shape will be [nr_streamlines, nr_points, 3]
for jdx, sl in enumerate(streamlines):
colors_sl = []
for idx, p in enumerate(sl):
if idx >= len(segment_idxs[jdx]):
seg_idx = segment_idxs[jdx][idx - 1]
else:
seg_idx = segment_idxs[jdx][idx]
m = metrics[int(seg_idx / ANTI_INTERPOL_MULT)]
color = colors[int(m)]
colors_sl.append(color)
colors_all.append(
colors_sl
) # this can not be converted to numpy array because last element has one more elem
sl_actor = actor.streamtube(streamlines,
colors=colors_all,
linewidth=0.2,
opacity=1)
renderer.add(sl_actor)
# plot brain mask
mask = nib.load(brain_mask_path).get_fdata().astype(np.uint8)
cont_actor = vtk_utils.contour_from_roi_smooth(
mask,
affine=beginnings_img.affine,
color=[.9, .9, .9],
opacity=.2,
smoothing=50)
renderer.add(cont_actor)
if show_color_bar:
lut_cmap = actor.colormap_lookup_table(scale_range=(metrics_min,
metrics_max),
hue_range=(0.0, 0.0),
saturation_range=(0.0, 1.0))
renderer.add(actor.scalar_bar(lut_cmap))
if orientation == "sagittal":
renderer.set_camera(position=(-412.95, -34.38, 80.15),
focal_point=(102.46, -16.96, -11.71),
view_up=(0.1806, 0.0, 0.9835))
elif orientation == "coronal":
renderer.set_camera(position=(-48.63, 360.31, 98.37),
focal_point=(-20.16, 92.89, 36.02),
view_up=(-0.0047, -0.2275, 0.9737))
elif orientation == "axial":
pass
else:
raise ValueError("Invalid orientation provided")
# Use this to interatively get new camera angle
# window.show(renderer, size=img_size, reset_camera=False)
# print(renderer.get_camera())
window.record(renderer, out_path=output_path, size=img_size)
def launch_quickbundles(streamlines,
outpath,
ROIname="all",
threshold=10.,
labelmask=None,
affine=np.eye(4),
interactive=False):
#qb = QuickBundles(threshold=10.)
qb = QuickBundles(threshold=threshold)
clusters = qb.cluster(streamlines)
print("Nb. clusters:", len(clusters))
print("Cluster sizes:", map(len, clusters))
print("Small clusters:", clusters < 10)
print("Streamlines indices of the first cluster:\n", clusters[0].indices)
print("Centroid of the last cluster:\n", clusters[-1].centroid)
# Cluster sizes: [64, 191, 47, 1]
# Small clusters: array([False, False, False, True], dtype=bool)
scene = window.Scene()
scene.SetBackground(1, 1, 1)
scene.add(actor.streamtube(streamlines, window.colors.misty_rose))
if labelmask is not None:
shape = labelmask.shape
image_actor_z = actor.slicer(labelmask, affine)
slicer_opacity = 0.6
image_actor_z.opacity(slicer_opacity)
image_actor_x = image_actor_z.copy()
x_midpoint = int(np.round(shape[0] / 2))
image_actor_x.display_extent(x_midpoint, x_midpoint, 0, shape[1] - 1,
0, shape[2] - 1)
image_actor_y = image_actor_z.copy()
y_midpoint = int(np.round(shape[1] / 2))
image_actor_y.display_extent(0, shape[0] - 1, y_midpoint, y_midpoint,
0, shape[2] - 1)
scene.add(image_actor_z)
scene.add(image_actor_x)
scene.add(image_actor_y)
window.record(scene,
out_path=outpath + ROIname + '_initial.png',
size=(600, 600))
if interactive:
window.show(scene)
colormap = actor.create_colormap(np.arange(len(clusters)))
scene.clear()
scene.SetBackground(1, 1, 1)
scene.add(actor.streamtube(streamlines, window.colors.white, opacity=0.05))
scene.add(actor.streamtube(clusters.centroids, colormap, linewidth=0.4))
if labelmask is not None:
image_actor_z = actor.slicer(labelmask, affine)
window.record(scene,
out_path=outpath + ROIname + '_centroids.png',
size=(600, 600))
if interactive:
window.show(scene)
colormap_full = np.ones((len(streamlines), 3))
for cluster, color in zip(clusters, colormap):
colormap_full[cluster.indices] = color
scene.clear()
scene.SetBackground(1, 1, 1)
scene.add(actor.streamtube(streamlines, colormap_full))
window.record(scene,
out_path=outpath + ROIname + '_clusters.png',
size=(600, 600))
if interactive:
window.show(scene)
#mean_vol_per_tract.append(np.nanmedian(s))
hue = [0.25, -0.05]
saturation = [0, 1]
scale = [3, 12]
if downsamp != 1:
mean_vol_per_tract = mean_vol_per_tract[::downsamp]
str1 = str1[::downsamp]
mean_pasi_weighted_img = f'{folder_name}{os.sep}streamlines{os.sep}CC_3-12_Exp_DTI_PreReg_1_ds2_tube0p3.png'
lut_cmap = actor.colormap_lookup_table(hue_range=hue,
saturation_range=saturation,
scale_range=scale)
#str2 = transform_streamlines(str1, np.linalg.inv(affine2))
streamlines_actor = actor.streamtube(str1,
mean_vol_per_tract,
linewidth=0.3,
lookup_colormap=lut_cmap)
bar = actor.scalar_bar(lut_cmap)
r = window.Scene()
r.add(streamlines_actor)
r.add(bar)
#r.SetBackground(*window.colors.white)
window.show(r)
r.set_camera(r.camera_info())
window.record(r, out_path=mean_pasi_weighted_img, size=(800, 800))
def visualize_bundles(sft,
affine=None,
n_points=None,
bundle_dict=None,
bundle=None,
colors=None,
color_by_volume=None,
cbv_lims=[None, None],
figure=None,
background=(1, 1, 1),
interact=False,
inline=False,
flip_axes=None):
"""
Visualize bundles in 3D using VTK
Parameters
----------
sft : Stateful Tractogram, str
A Stateful Tractogram containing streamline information
or a path to a trk file
In order to visualize individual bundles, the Stateful Tractogram
must contain a bundle key in it's data_per_streamline which is a list
of bundle `'uid'`.
affine : ndarray, optional
An affine transformation to apply to the streamlines before
visualization. Default: no transform.
n_points : int or None
n_points to resample streamlines to before plotting. If None, no
resampling is done.
bundle_dict : dict, optional
Keys are names of bundles and values are dicts that should include
a key `'uid'` with values as integers for selection from the sft
metadata. Default: bundles are either not identified, or identified
only as unique integers in the metadata.
bundle : str or int, optional
The name of a bundle to select from among the keys in `bundle_dict`
or an integer for selection from the sft metadata.
colors : dict or list
If this is a dict, keys are bundle names and values are RGB tuples.
If this is a list, each item is an RGB tuple. Defaults to a list
with Tableau 20 RGB values if bundle_dict is None, or dict from
bundles to Tableau 20 RGB values if bundle_dict is not None.
color_by_volume : ndarray or str, optional
3d volume use to shade the bundles. If None, no shading
is performed. Only works when using the plotly backend.
Default: None
cbv_lims : ndarray
Of the form (lower bound, upper bound). Shading based on
color_by_volume will only differentiate values within these bounds.
If lower bound is None, will default to 0.
If upper bound is None, will default to the maximum value in
color_by_volume.
Default: [None, None]
background : tuple, optional
RGB values for the background. Default: (1, 1, 1), which is white
background.
figure : fury Scene object, optional
If provided, the visualization will be added to this Scene. Default:
Initialize a new Scene.
interact : bool
Whether to provide an interactive VTK window for interaction.
Default: False
inline : bool
Whether to embed the visualization inline in a notebook. Only works
in the notebook context. Default: False.
flip_axes : None
This parameter is to conform fury and plotly APIs.
Returns
-------
Fury Scene object
"""
if figure is None:
figure = window.Scene()
figure.SetBackground(background[0], background[1], background[2])
for (sls, color, name, _) in vut.tract_generator(sft, affine, bundle,
bundle_dict, colors,
n_points):
sls = list(sls)
if name == "all_bundles":
color = line_colors(sls)
sl_actor = actor.line(sls, color)
figure.add(sl_actor)
sl_actor.GetProperty().SetRenderLinesAsTubes(1)
sl_actor.GetProperty().SetLineWidth(6)
return _inline_interact(figure, inline, interact)
def visualize_volume(volume,
x=None,
y=None,
z=None,
figure=None,
flip_axes=None,
opacity=0.6,
inline=True,
interact=False):
"""
Visualize a volume
Parameters
----------
volume : ndarray or str
3d volume to visualize.
figure : fury Scene object, optional
If provided, the visualization will be added to this Scene. Default:
Initialize a new Scene.
flip_axes : None
This parameter is to conform fury and plotly APIs.
opacity : float, optional
Initial opacity of slices.
Default: 0.6
interact : bool
Whether to provide an interactive VTK window for interaction.
Default: False
inline : bool
Whether to embed the visualization inline in a notebook. Only works
in the notebook context. Default: False.
Returns
-------
Fury Scene object
"""
volume = vut.load_volume(volume)
if figure is None:
figure = window.Scene()
shape = volume.shape
image_actor_z = actor.slicer(volume)
slicer_opacity = opacity
image_actor_z.opacity(slicer_opacity)
image_actor_x = image_actor_z.copy()
if x is None:
x = int(np.round(shape[0] / 2))
image_actor_x.display_extent(x, x, 0, shape[1] - 1, 0, shape[2] - 1)
image_actor_y = image_actor_z.copy()
if y is None:
y = int(np.round(shape[1] / 2))
image_actor_y.display_extent(0, shape[0] - 1, y, y, 0, shape[2] - 1)
figure.add(image_actor_z)
figure.add(image_actor_x)
figure.add(image_actor_y)
show_m = window.ShowManager(figure, size=(1200, 900))
show_m.initialize()
if interact:
line_slider_z = ui.LineSlider2D(min_value=0,
max_value=shape[2] - 1,
initial_value=shape[2] / 2,
text_template="{value:.0f}",
length=140)
line_slider_x = ui.LineSlider2D(min_value=0,
max_value=shape[0] - 1,
initial_value=shape[0] / 2,
text_template="{value:.0f}",
length=140)
line_slider_y = ui.LineSlider2D(min_value=0,
max_value=shape[1] - 1,
initial_value=shape[1] / 2,
text_template="{value:.0f}",
length=140)
opacity_slider = ui.LineSlider2D(min_value=0.0,
max_value=1.0,
initial_value=slicer_opacity,
length=140)
def change_slice_z(slider):
z = int(np.round(slider.value))
image_actor_z.display_extent(0, shape[0] - 1, 0, shape[1] - 1, z,
z)
def change_slice_x(slider):
x = int(np.round(slider.value))
image_actor_x.display_extent(x, x, 0, shape[1] - 1, 0,
shape[2] - 1)
def change_slice_y(slider):
y = int(np.round(slider.value))
image_actor_y.display_extent(0, shape[0] - 1, y, y, 0,
shape[2] - 1)
def change_opacity(slider):
slicer_opacity = slider.value
image_actor_z.opacity(slicer_opacity)
image_actor_x.opacity(slicer_opacity)
image_actor_y.opacity(slicer_opacity)
line_slider_z.on_change = change_slice_z
line_slider_x.on_change = change_slice_x
line_slider_y.on_change = change_slice_y
opacity_slider.on_change = change_opacity
def build_label(text):
label = ui.TextBlock2D()
label.message = text
label.font_size = 18
label.font_family = 'Arial'
label.justification = 'left'
label.bold = False
label.italic = False
label.shadow = False
label.background = (0, 0, 0)
label.color = (1, 1, 1)
return label
line_slider_label_z = build_label(text="Z Slice")
line_slider_label_x = build_label(text="X Slice")
line_slider_label_y = build_label(text="Y Slice")
opacity_slider_label = build_label(text="Opacity")
panel = ui.Panel2D(size=(300, 200),
color=(1, 1, 1),
opacity=0.1,
align="right")
panel.center = (1030, 120)
panel.add_element(line_slider_label_x, (0.1, 0.75))
panel.add_element(line_slider_x, (0.38, 0.75))
panel.add_element(line_slider_label_y, (0.1, 0.55))
panel.add_element(line_slider_y, (0.38, 0.55))
panel.add_element(line_slider_label_z, (0.1, 0.35))
panel.add_element(line_slider_z, (0.38, 0.35))
panel.add_element(opacity_slider_label, (0.1, 0.15))
panel.add_element(opacity_slider, (0.38, 0.15))
show_m.scene.add(panel)
global size
size = figure.GetSize()
def win_callback(obj, event):
global size
if size != obj.GetSize():
size_old = size
size = obj.GetSize()
size_change = [size[0] - size_old[0], 0]
panel.re_align(size_change)
show_m.initialize()
figure.zoom(1.5)
figure.reset_clipping_range()
if interact:
show_m.add_window_callback(win_callback)
show_m.render()
show_m.start()
return _inline_interact(figure, inline, interact)
def plot_tracts(classes,
bundle_segmentations,
affine,
out_dir,
brain_mask=None):
"""
By default this does not work on a remote server connection (ssh -X) because -X does not support OpenGL.
On the remote Server you can do 'export DISPLAY=":0"' .
(you should set the value you get if you do 'echo $DISPLAY' if you
login locally on the remote server). Then all graphics will get rendered locally and not via -X.
(important: graphical session needs to be running on remote server (e.g. via login locally))
(important: login needed, not just stay at login screen)
If running on a headless server without Display using Xvfb might help:
https://stackoverflow.com/questions/6281998/can-i-run-glu-opengl-on-a-headless-server
"""
from dipy.viz import window
from tractseg.libs import vtk_utils
SMOOTHING = 10
WINDOW_SIZE = (800, 800)
bundles = ["CST_right", "CA", "IFO_right"]
renderer = window.Scene()
renderer.projection('parallel')
rows = len(bundles)
X, Y, Z = bundle_segmentations.shape[:3]
for j, bundle in enumerate(bundles):
i = 0 #only one method
bundle_idx = dataset_specific_utils.get_bundle_names(
classes)[1:].index(bundle)
mask_data = bundle_segmentations[:, :, :, bundle_idx]
if bundle == "CST_right":
orientation = "axial"
elif bundle == "CA":
orientation = "axial"
elif bundle == "IFO_right":
orientation = "sagittal"
else:
orientation = "axial"
#bigger: more border
if orientation == "axial":
border_y = -100
else:
border_y = -100
x_current = X * i # column (width)
y_current = rows * (Y * 2 + border_y) - (
Y * 2 + border_y) * j # row (height) (starts from bottom)
plot_mask(renderer,
mask_data,
affine,
x_current,
y_current,
orientation=orientation,
smoothing=SMOOTHING,
brain_mask=brain_mask)
#Bundle label
text_offset_top = -50
text_offset_side = -100
position = (0 - int(X) + text_offset_side, y_current + text_offset_top,
50)
text_actor = vtk_utils.label(text=bundle,
pos=position,
scale=(6, 6, 6),
color=(1, 1, 1))
renderer.add(text_actor)
renderer.reset_camera()
window.record(renderer,
out_path=join(out_dir, "preview.png"),
size=(WINDOW_SIZE[0], WINDOW_SIZE[1]),
reset_camera=False,
magnification=2)
def visualize_bundles(trk,
affine=None,
bundle_dict=None,
bundle=None,
colors=None,
scene=None,
background=(1, 1, 1),
interact=False,
inline=False):
"""
Visualize bundles in 3D using VTK
Parameters
----------
trk : str, list, or Streamlines
The streamline information
affine : ndarray, optional
An affine transformation to apply to the streamlines before
visualization. Default: no transform.
bundle_dict : dict, optional
Keys are names of bundles and values are dicts that should include
a key `'uid'` with values as integers for selection from the trk
metadata. Default: bundles are either not identified, or identified
only as unique integers in the metadata.
bundle : str or int, optional
The name of a bundle to select from among the keys in `bundle_dict`
or an integer for selection from the trk metadata.
colors : dict or list
If this is a dict, keys are bundle names and values are RGB tuples.
If this is a list, each item is an RGB tuple. Defaults to a list
with Tableau 20 RGB values
background : tuple, optional
RGB values for the background. Default: (1, 1, 1), which is white
background.
scene : fury Scene object, optional
If provided, the visualization will be added to this Scene. Default:
Initialize a new Scene.
interact : bool
Whether to provide an interactive VTK window for interaction.
Default: False
inline : bool
Whether to embed the visualization inline in a notebook. Only works
in the notebook context. Default: False.
Returns
-------
Fury Scene object
"""
if isinstance(trk, str):
trk = nib.streamlines.load(trk)
tg = trk.tractogram
else:
# Assume these are streamlines (as list or Streamlines object):
tg = nib.streamlines.Tractogram(trk)
if affine is not None:
tg = tg.apply_affine(np.linalg.inv(affine))
streamlines = tg.streamlines
if scene is None:
scene = window.Scene()
scene.SetBackground(background[0], background[1], background[2])
if colors is None:
# Use the color dict provided
colors = color_dict
def _color_selector(bundle_dict, colors, b):
"""Helper function """
if bundle_dict is None:
# We'll choose a color from a rotating list:
if isinstance(colors, list):
color = colors[np.mod(len(colors), int(b))]
else:
color_list = colors.values()
color = color_list[np.mod(len(colors), int(b))]
else:
# We have a mapping from UIDs to bundle names:
for b_name_iter, b_iter in bundle_dict.items():
if b_iter['uid'] == b:
b_name = b_name_iter
break
color = colors[b_name]
return color
if list(tg.data_per_streamline.keys()) == []:
# There are no bundles in here:
streamlines = list(streamlines)
# Visualize all the streamlines with directionally assigned RGB:
sl_actor = actor.line(streamlines, line_colors(streamlines))
scene.add(sl_actor)
sl_actor.GetProperty().SetRenderLinesAsTubes(1)
sl_actor.GetProperty().SetLineWidth(6)
else:
# There are bundles:
if bundle is None:
# No selection: visualize all of them:
for b in np.unique(tg.data_per_streamline['bundle']):
idx = np.where(tg.data_per_streamline['bundle'] == b)[0]
this_sl = list(streamlines[idx])
color = _color_selector(bundle_dict, colors, b)
sl_actor = actor.line(this_sl, color)
sl_actor.GetProperty().SetRenderLinesAsTubes(1)
sl_actor.GetProperty().SetLineWidth(6)
scene.add(sl_actor)
else:
# Select just one to visualize:
if isinstance(bundle, str):
# We need to find the UID:
uid = bundle_dict[bundle]['uid']
else:
# It's already a UID:
uid = bundle
idx = np.where(tg.data_per_streamline['bundle'] == uid)[0]
this_sl = list(streamlines[idx])
color = _color_selector(bundle_dict, colors, uid)
sl_actor = actor.line(this_sl, color)
sl_actor.GetProperty().SetRenderLinesAsTubes(1)
sl_actor.GetProperty().SetLineWidth(6)
scene.add(sl_actor)
return _inline_interact(scene, inline, interact)
fod = csd_fit.odf(small_sphere)
pmf = fod.clip(min=0)
prob_dg = ProbabilisticDirectionGetter.from_pmf(pmf,
max_angle=30.,
sphere=small_sphere)
streamline_generator = LocalTracking(prob_dg,
stopping_criterion,
seeds,
affine,
step_size=.5)
streamlines = Streamlines(streamline_generator)
sft = StatefulTractogram(streamlines, hardi_img, Space.RASMM)
save_trk(sft, "tractogram_probabilistic_dg_pmf.trk")
if has_fury:
scene = window.Scene()
scene.add(actor.line(streamlines, colormap.line_colors(streamlines)))
window.record(scene,
out_path='tractogram_probabilistic_dg_pmf.png',
size=(800, 800))
if interactive:
window.show(scene)
"""
.. figure:: tractogram_probabilistic_dg_pmf.png
:align: center
**Corpus Callosum using probabilistic direction getter from PMF**
"""
"""
One disadvantage of using a discrete PMF to represent possible tracking
directions is that it tends to take up a lot of memory (RAM). The size of the
def dMRI2ODF_DTI(PATH):
'''
Input the dMRI data
return the ODF
'''
dMRI_path = PATH + 'data.nii.gz'
mask_path = PATH + 'nodif_brain_mask.nii.gz'
dMRI_img = nib.load(dMRI_path)
dMRI_data = dMRI_img.get_fdata()
mask_img = nib.load(mask_path)
mask = mask_img.get_fdata()
########## subsample ##########
# dMRI_data = dMRI_data[45:-48,50:-65,51:-54,...]
# mask = mask[45:-48,50:-65,51:-54]
# breakpoint()
dMRI_data = dMRI_data[:, 87, ...]
mask = mask[:, 87, ...]
for cnt in range(10):
fig = plt.imshow(dMRI_data[:, :, cnt].transpose(1, 0),
cmap='Greys',
interpolation='nearest')
plt.axis('off')
# plt.imshow(dMRI_data[:,15,:,cnt].transpose(1,0),cmap='Greys')
plt.savefig(str(cnt) + '.png',
bbox_inches='tight',
dpi=300,
transparent=True,
pad_inches=0)
# breakpoint()
bval = PATH + "bvals"
bvec = PATH + "bvecs"
radial_order = 6
zeta = 700
lambdaN = 1e-8
lambdaL = 1e-8
gtab = gradient_table(bvals=bval, bvecs=bvec)
asm = ShoreModel(gtab,
radial_order=radial_order,
zeta=zeta,
lambdaN=lambdaN,
lambdaL=lambdaL)
asmfit = asm.fit(dMRI_data, mask=mask)
sphere = get_sphere('symmetric362')
dMRI_odf = asmfit.odf(sphere)
dMRI_odf[dMRI_odf <= 0] = 0
tenmodel = dti.TensorModel(gtab)
tenfit = tenmodel.fit(dMRI_data, mask)
dMRI_dti = tenfit.quadratic_form
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
FA = np.clip(FA, 0, 1)
RGB = color_fa(FA, tenfit.evecs)
evals = tenfit.evals + 1e-20
evecs = tenfit.evecs
cfa = RGB + 1e-20
cfa /= cfa.max()
evals = np.expand_dims(evals, 2)
evecs = np.expand_dims(evecs, 2)
cfa = np.expand_dims(cfa, 2)
ren = window.Scene()
sphere = get_sphere('symmetric362')
ren.add(
actor.tensor_slicer(evals,
evecs,
scalar_colors=cfa,
sphere=sphere,
scale=0.5))
window.record(ren,
n_frames=1,
out_path='../data/tensor.png',
size=(5000, 5000))
odf_ = dMRI_odf
ren = window.Scene()
sfu = actor.odf_slicer(np.expand_dims(odf_, 2),
sphere=sphere,
colormap="plasma",
scale=0.5)
ren.add(sfu)
window.record(ren,
n_frames=1,
out_path='../data/odfs.png',
size=(5000, 5000))
return None
