def show_atlas_target_graph(atlas, target, out_path, interactive=True):
ren = window.Scene()
ren.SetBackground(1, 1, 1)
ren.add(actor.line(atlas, colors=(1, 0, 1))) # Magenta
ren.add(actor.line(target, colors=(1, 1, 0))) # Yellow
#window.record(ren, out_path=out_path, size=(600, 600))
if interactive:
window.show(ren)
def test_streamtube_and_line_actors():
scene = window.Scene()
line1 = np.array([[0, 0, 0], [1, 1, 1], [2, 2, 2.]])
line2 = line1 + np.array([0.5, 0., 0.])
lines = [line1, line2]
colors = np.array([[1, 0, 0], [0, 0, 1.]])
c = actor.line(lines, colors, linewidth=3)
scene.add(c)
c = actor.line(lines, colors, spline_subdiv=5, linewidth=3)
scene.add(c)
# create streamtubes of the same lines and shift them a bit
c2 = actor.streamtube(lines, colors, linewidth=.1)
c2.SetPosition(2, 0, 0)
scene.add(c2)
arr = window.snapshot(scene)
report = window.analyze_snapshot(arr,
colors=[(255, 0, 0), (0, 0, 255)],
find_objects=True)
npt.assert_equal(report.objects, 4)
npt.assert_equal(report.colors_found, [True, True])
# as before with splines
c2 = actor.streamtube(lines, colors, spline_subdiv=5, linewidth=.1)
c2.SetPosition(2, 0, 0)
scene.add(c2)
arr = window.snapshot(scene)
report = window.analyze_snapshot(arr,
colors=[(255, 0, 0), (0, 0, 255)],
find_objects=True)
npt.assert_equal(report.objects, 4)
npt.assert_equal(report.colors_found, [True, True])
c3 = actor.line(lines, colors, depth_cue=True, fake_tube=True)
VTK_9_PLUS = window.vtk.vtkVersion.GetVTKMajorVersion() >= 9
shader_obj = c3.GetShaderProperty() if VTK_9_PLUS else c3.GetMapper()
mapper_code = shader_obj.GetGeometryShaderCode()
file_code = shaders.load("line.geom")
npt.assert_equal(mapper_code, file_code)
npt.assert_equal(c3.GetProperty().GetRenderLinesAsTubes(), True)
def main():
parser = _build_arg_parser()
args = parser.parse_args()
assert_inputs_exist(parser, [args.tractogram])
assert_outputs_exist(parser, args, [], [args.save])
tracts_format = detect_format(args.tractogram)
if tracts_format is not TrkFile:
raise ValueError("Invalid input streamline file format " +
"(must be trk): {0}".format(args.tractogram_filename))
# Load files and data. TRKs can have 'same' as reference
tractogram = load_tractogram(args.tractogram, 'same')
# Streamlines are saved in RASMM but seeds are saved in VOX
# This might produce weird behavior with non-iso
tractogram.to_vox()
streamlines = tractogram.streamlines
if 'seeds' not in tractogram.data_per_streamline:
parser.error('Tractogram does not contain seeds')
seeds = tractogram.data_per_streamline['seeds']
# Make display objects
streamlines_actor = actor.line(streamlines)
points = actor.dots(seeds, color=(1., 1., 1.))
# Add display objects to canvas
s = window.Scene()
s.add(streamlines_actor)
s.add(points)
# Show and record if needed
if args.save is not None:
window.record(s, out_path=args.save, size=(1000, 1000))
window.show(s)
def load_and_add_reference_tractogram(self, path_to_tractogram):
"""
Load tractogram from given path and add all streamlines to the scene.
The camera setup is determined based on this tractogram. The streamlines are visualised
with transparency.
Parameters
----------
path_to_tractogram : str
path to tractogram file (.trk)
Returns
-------
None
"""
# get streamlines in voxel space
tracto = nib.streamlines.load(path_to_tractogram)
streamlines = transform_streamlines(tracto.streamlines,
np.linalg.inv(tracto.affine))
# add streamlines to the scene
sl_actor = actor.line(streamlines, opacity=0.3)
self.scene.add(sl_actor)
# update camera settings
self.apply_camera_update(streamlines, w=1)
self.scene.reset_camera()
def main():
parser = _build_args_parser()
args = parser.parse_args()
assert_inputs_exist(parser, [args.tractogram])
assert_outputs_exist(parser, args, [], [args.save])
tracts_format = detect_format(args.tractogram)
if tracts_format is not TrkFile:
raise ValueError("Invalid input streamline file format " +
"(must be trk): {0}".format(args.tractogram_filename))
# Load files and data
trk = TrkFile.load(args.tractogram)
tractogram = trk.tractogram
streamlines = tractogram.streamlines
if 'seeds' not in tractogram.data_per_streamline:
parser.error('Tractogram does not contain seeds')
seeds = tractogram.data_per_streamline['seeds']
# Make display objects
streamlines_actor = actor.line(streamlines)
points = actor.dots(seeds, color=(1., 1., 1.))
# Add display objects to canvas
r = window.Renderer()
r.add(streamlines_actor)
r.add(points)
# Show and record if needed
if args.save is not None:
window.record(r, out_path=args.save, size=(1000, 1000))
window.show(r)
def timer_callback(_obj, _event):
global pts, time, incre_time, coor_1
time += incre_time
cnt = next(counter)
x = initial_velocity*time + 0.5*acc*(time**2)
y = np.sin(10*angular_frq*time + phase_angle)
z = np.cos(10*angular_frq*time + phase_angle)
pts = np.array([[x, y, z]])
vertices[:] = initial_vertices + \
np.repeat(pts, no_vertices_per_point, axis=0)
utils.update_actor(charge_actor)
# Plotting the path followed by the particle
coor_2 = np.array([x, y, z])
coors = np.array([coor_1, coor_2])
coors = [coors]
line_actor = actor.line(coors, window.colors.cyan, linewidth=3)
scene.add(line_actor)
coor_1 = coor_2
showm.render()
# to end the animation
if cnt == end:
showm.exit()
def show_model_reco_bundles(model,
recognized_bundle,
folder_name,
file_bundle_name,
interactive=True):
ren = window.Scene()
ren.SetBackground(1, 1, 1)
ren.add(actor.line(model, colors=(.1, .7, .26))) #green
ren.add(actor.line(recognized_bundle, colors=(.1, .1, 6))) #blue
if interactive:
window.show(ren)
ren.set_camera(ren.camera_info())
window.record(ren,
out_path=pjoin(folder_name, file_bundle_name) + '.png',
size=(600, 600))
def test_streamtube_and_line_actors():
renderer = window.renderer()
line1 = np.array([[0, 0, 0], [1, 1, 1], [2, 2, 2.]])
line2 = line1 + np.array([0.5, 0., 0.])
lines = [line1, line2]
colors = np.array([[1, 0, 0], [0, 0, 1.]])
c = actor.line(lines, colors, linewidth=3)
window.add(renderer, c)
c = actor.line(lines, colors, spline_subdiv=5, linewidth=3)
window.add(renderer, c)
# create streamtubes of the same lines and shift them a bit
c2 = actor.streamtube(lines, colors, linewidth=.1)
c2.SetPosition(2, 0, 0)
window.add(renderer, c2)
arr = window.snapshot(renderer)
report = window.analyze_snapshot(arr,
colors=[(255, 0, 0), (0, 0, 255)],
find_objects=True)
npt.assert_equal(report.objects, 4)
npt.assert_equal(report.colors_found, [True, True])
# as before with splines
c2 = actor.streamtube(lines, colors, spline_subdiv=5, linewidth=.1)
c2.SetPosition(2, 0, 0)
window.add(renderer, c2)
arr = window.snapshot(renderer)
report = window.analyze_snapshot(arr,
colors=[(255, 0, 0), (0, 0, 255)],
find_objects=True)
npt.assert_equal(report.objects, 4)
npt.assert_equal(report.colors_found, [True, True])
def execute(self, iren, event):
renderer.RemoveAllViewProps()
# streamlines
#current_location = pts[self.iterations] # change this to read in real time
current_location = np.loadtxt("data/update_pts.txt")
mask = np.zeros(handknob_left.shape)
location = tuple(dipy.tracking._utils._to_voxel_coordinates([current_location], lin_T, offset)[0])
mask[location]=1
##size of the mask
mask = scipy.ndimage.morphology.binary_dilation(mask, iterations=15)
endlabels = mask[i, j, k]
idx = np.logical_or(endlabels[0,:]==1, endlabels[1,:]==1)
if np.sum(idx)>0:
active_stream = streamlines[idx]
stream_actor = actor.line(active_stream)
renderer.add(stream_actor)
# Stimulation Location
roiActor = actor.contour_from_roi(mask,
affine=handknob_right.affine,
color=np.array([1, 1, 1]),
opacity=0.5)
renderer.AddActor(roiActor)
# ROI
roiActor = actor.contour_from_roi(handknob_left.get_data(),
affine=handknob_left.affine,
color=np.array([1, 0, 0]),
opacity=0.5)
renderer.AddActor(roiActor)
roiActor = actor.contour_from_roi(handknob_right.get_data(),
affine=handknob_right.affine,
color=np.array([0, 0, 1]),
opacity=0.5)
renderer.AddActor(roiActor)
#stream_actor = actor.line([streamlines[self.iterations]])
#self.renderer.add(stream_actor)
iren.GetRenderWindow().Render()
if self.iterations < len(self.pts)-1:
self.iterations += 1
def particle(colors,
origin=[0, 0, 0],
num_total_steps=300,
total_time=5,
delta=1.8,
path_thickness=3):
origin = np.asarray(origin, dtype=float)
position = np.tile(origin, (num_total_steps, 1))
path_actor = actor.line([position], colors, linewidth=path_thickness)
path_actor.position = position
path_actor.delta = delta
path_actor.num_total_steps = num_total_steps
path_actor.time_step = total_time / num_total_steps
path_actor.vertices = utils.vertices_from_actor(path_actor)
path_actor.no_vertices_per_point = \
len(path_actor.vertices) / num_total_steps
path_actor.initial_vertices = path_actor.vertices.copy() - \
np.repeat(position, path_actor.no_vertices_per_point, axis=0)
return path_actor
def save_views_imgs(lines, size=(500, 500), interactive=False, ext='jpg'):
"""
Function to save view images when the input file does fulfill the
requirements of the validator.
:param lines: Streamlines-like object.
:param size: A 2-tuple, containing (width, height) in pixels.
:param interactive: (Boolean) If True, launches a window. Useful for
developing/debugging options.
:param ext: (String) Extension of the output files.
"""
# Start virtual display
if has_xvfbwrapper:
print('Starting Xvfb')
vdisplay = Xvfb()
vdisplay.start()
# Create streamlines actor
streamlines_actor = actor.line(lines)
# Set renderer window
scene = window.Scene()
# Add streamlines to renderer
scene.add(streamlines_actor)
# Loop through views
for param in _VIEW_PARAMS:
# Set camera
scene.set_camera(position=param['cam_pos'],
focal_point=param['focal_pnt'],
view_up=param['view_up'])
if interactive:
window.show(scene, size=size)
# Save imgs
out_file = os.path.join('secondary', param['view'] + '.' + ext)
print('Saving: {}'.format(out_file))
window.record(scene, out_path=out_file, size=size)
# Stop virtual display
if has_xvfbwrapper:
vdisplay.stop()
def add_and_render(self, streamlines, w):
"""
Add given streamlines to the scene and render the update.
Parameters
----------
streamlines : nib.streamlines.array_sequence.ArraySequence
streamlines to update the scene with
w : float
weighting factor for the new camera settings
Returns
-------
None
"""
# remove oldest bunch of streamlines
if self.q.full():
self.scene.rm(self.q.get())
if self.show:
sleep(StreamlineVisualiser.TIME_INC)
# add new streamlines to the scene and the queue
sl_actor = actor.line(streamlines)
self.scene.add(sl_actor)
self.q.put(sl_actor)
# update camera settings
if self.update_camera:
self.apply_camera_update(streamlines, w)
if self.reset_camera:
self.scene.reset_camera()
# render updated scene or collect 2D snapshot of the scene
if self.show:
self.sm.render()
else:
self.frames.append(
window.snapshot(self.scene, size=self.plot_size)[::-1, ::-1])
def __init__(self,
colors,
origin=[0, 0, 0],
num_total_steps=300,
total_time=5,
delta=1.8,
path_thickness=3):
origin = np.asarray(origin, dtype=float)
self.position = np.tile(origin, (num_total_steps, 1))
self.colors = colors
self.delta = delta
self.num_total_steps = num_total_steps
self.time_step = total_time / num_total_steps
self.path_actor = actor.line([self.position],
colors,
linewidth=path_thickness)
self.vertices = utils.vertices_from_actor(self.path_actor)
self.vcolors = utils.colors_from_actor(self.path_actor, 'colors')
self.no_vertices_per_point = len(self.vertices) / num_total_steps
nvpp = self.no_vertices_per_point
self.initial_vertices = self.vertices.copy() - np.repeat(
self.position, nvpp, axis=0)
def show_both_bundles(bundles, colors=None, show=True, fname=None):
if colors is None:
colors = [window.colors.orange, window.colors.red]
scene = window.Scene()
#scene.SetBackground(1., 1, 1)
# scene.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))
for (i, bundle) in enumerate(bundles):
color = colors[i]
lines_actor = actor.line(bundle, color, linewidth=0.3)
#lines_actor.RotateX(-90)
#lines_actor.RotateZ(90)
scene.add(lines_actor)
if show:
window.show(scene)
if fname is not None:
sleep(1)
window.record(scene, n_frames=1, out_path=fname, size=(900, 900))
###############################################################################
# If we want to see the objects in native space we need to make sure that all
# objects which are currently in world coordinates are transformed back to
# native space using the inverse of the affine.
if not world_coords:
from dipy.tracking.streamline import transform_streamlines
streamlines = transform_streamlines(streamlines, np.linalg.inv(affine))
###############################################################################
# Now we create, a ``Renderer`` object and add the streamlines using the
# ``line`` function and an image plane using the ``slice`` function.
ren = window.Renderer()
stream_actor = actor.line(streamlines)
if not world_coords:
image_actor_z = actor.slicer(data, affine=np.eye(4))
else:
image_actor_z = actor.slicer(data, affine)
###############################################################################
# We can also change also the opacity of the slicer.
slicer_opacity = 0.6
image_actor_z.opacity(slicer_opacity)
###############################################################################
# We can add additonal slicers by copying the original and adjusting the
# ``display_extent``.
def test_bundle_maps():
scene = window.Scene()
bundle = simulated_bundle(no_streamlines=10, waves=False)
metric = 100 * np.ones((200, 200, 200))
# add lower values
metric[100, :, :] = 100 * 0.5
# create a nice orange-red colormap
lut = actor.colormap_lookup_table(scale_range=(0., 100.),
hue_range=(0., 0.1),
saturation_range=(1, 1),
value_range=(1., 1))
line = actor.line(bundle, metric, linewidth=0.1, lookup_colormap=lut)
scene.add(line)
scene.add(actor.scalar_bar(lut, ' '))
report = window.analyze_scene(scene)
npt.assert_almost_equal(report.actors, 1)
# window.show(scene)
scene.clear()
nb_points = np.sum([len(b) for b in bundle])
values = 100 * np.random.rand(nb_points)
# values[:nb_points/2] = 0
line = actor.streamtube(bundle, values, linewidth=0.1, lookup_colormap=lut)
scene.add(line)
# window.show(scene)
report = window.analyze_scene(scene)
npt.assert_equal(report.actors_classnames[0], 'vtkLODActor')
scene.clear()
colors = np.random.rand(nb_points, 3)
# values[:nb_points/2] = 0
line = actor.line(bundle, colors, linewidth=2)
scene.add(line)
# window.show(scene)
report = window.analyze_scene(scene)
npt.assert_equal(report.actors_classnames[0], 'vtkLODActor')
# window.show(scene)
arr = window.snapshot(scene)
report2 = window.analyze_snapshot(arr)
npt.assert_equal(report2.objects, 1)
# try other input options for colors
scene.clear()
actor.line(bundle, (1., 0.5, 0))
actor.line(bundle, np.arange(len(bundle)))
actor.line(bundle)
colors = [np.random.rand(*b.shape) for b in bundle]
actor.line(bundle, colors=colors)
atlas = sft_atlas.streamlines
atlas_header = create_tractogram_header(atlas_file, *sft_atlas.space_attribute)
sft_target = load_trk(target_file, "same", bbox_valid_check=False)
target = sft_target.streamlines
target_header = create_tractogram_header(atlas_file,
*sft_atlas.space_attribute)
"""
let's visualize atlas tractogram and target tractogram before registration
"""
interactive = False
ren = window.Renderer()
ren.SetBackground(1, 1, 1)
ren.add(actor.line(atlas, colors=(1, 0, 1)))
ren.add(actor.line(target, colors=(1, 1, 0)))
window.record(ren, out_path='tractograms_initial.png', size=(600, 600))
if interactive:
window.show(ren)
"""
.. figure:: tractograms_initial.png
:align: center
Atlas and target before registration.
"""
"""
We will register target tractogram to model atlas' space using streamlinear
registeration (SLR) [Garyfallidis15]_
"""
def keypress_callback(obj, _):
key = obj.GetKeySym().lower()
nonlocal clusters_linewidth, background_linewidth
nonlocal curr_streamlines_actor, concat_streamlines_actor, show_curr_actor
iterator = len(accepted_streamlines) + len(rejected_streamlines)
renwin = interactor_style.GetInteractor().GetRenderWindow()
renderer = interactor_style.GetCurrentRenderer()
if key == 'c' and iterator < len(sft_accepted_on_size):
if show_curr_actor:
renderer.rm(concat_streamlines_actor)
renwin.Render()
show_curr_actor = False
logging.info('Streamlines rendering OFF')
else:
renderer.add(concat_streamlines_actor)
renderer.rm(curr_streamlines_actor)
renderer.add(curr_streamlines_actor)
renwin.Render()
show_curr_actor = True
logging.info('Streamlines rendering ON')
return
if key == 'q':
show_manager.exit()
if iterator < len(sft_accepted_on_size):
logging.warning(
'Early exit, everything remaining to be rejected.')
return
if key in ['a', 'r'] and iterator < len(sft_accepted_on_size):
if key == 'a':
accepted_streamlines.append(iterator)
choices.append('a')
logging.info('Accepted file %s',
filename_accepted_on_size[iterator])
elif key == 'r':
rejected_streamlines.append(iterator)
choices.append('r')
logging.info('Rejected file %s',
filename_accepted_on_size[iterator])
iterator += 1
if key == 'z':
if iterator > 0:
last_choice = choices.pop()
if last_choice == 'r':
rejected_streamlines.pop()
else:
accepted_streamlines.pop()
logging.info('Rewind on step.')
iterator -= 1
else:
logging.warning('Cannot rewind, first element.')
if key in ['a', 'r', 'z'] and iterator < len(sft_accepted_on_size):
renderer.rm(curr_streamlines_actor)
curr_streamlines = sft_accepted_on_size[iterator].streamlines
curr_streamlines_actor = actor.line(curr_streamlines,
opacity=0.8,
linewidth=clusters_linewidth)
renderer.add(curr_streamlines_actor)
if iterator == len(sft_accepted_on_size):
print('No more cluster, press q to exit')
renderer.rm(curr_streamlines_actor)
renwin.Render()
def test_bundle_maps():
renderer = window.renderer()
bundle = fornix_streamlines()
bundle, shift = center_streamlines(bundle)
mat = np.array([[1, 0, 0, 100],
[0, 1, 0, 100],
[0, 0, 1, 100],
[0, 0, 0, 1.]])
bundle = transform_streamlines(bundle, mat)
# metric = np.random.rand(*(200, 200, 200))
metric = 100 * np.ones((200, 200, 200))
# add lower values
metric[100, :, :] = 100 * 0.5
# create a nice orange-red colormap
lut = actor.colormap_lookup_table(scale_range=(0., 100.),
hue_range=(0., 0.1),
saturation_range=(1, 1),
value_range=(1., 1))
line = actor.line(bundle, metric, linewidth=0.1, lookup_colormap=lut)
window.add(renderer, line)
window.add(renderer, actor.scalar_bar(lut, ' '))
report = window.analyze_renderer(renderer)
npt.assert_almost_equal(report.actors, 1)
# window.show(renderer)
renderer.clear()
nb_points = np.sum([len(b) for b in bundle])
values = 100 * np.random.rand(nb_points)
# values[:nb_points/2] = 0
line = actor.streamtube(bundle, values, linewidth=0.1, lookup_colormap=lut)
renderer.add(line)
# window.show(renderer)
report = window.analyze_renderer(renderer)
npt.assert_equal(report.actors_classnames[0], 'vtkLODActor')
renderer.clear()
colors = np.random.rand(nb_points, 3)
# values[:nb_points/2] = 0
line = actor.line(bundle, colors, linewidth=2)
renderer.add(line)
# window.show(renderer)
report = window.analyze_renderer(renderer)
npt.assert_equal(report.actors_classnames[0], 'vtkLODActor')
# window.show(renderer)
arr = window.snapshot(renderer)
report2 = window.analyze_snapshot(arr)
npt.assert_equal(report2.objects, 1)
# try other input options for colors
renderer.clear()
actor.line(bundle, (1., 0.5, 0))
actor.line(bundle, np.arange(len(bundle)))
actor.line(bundle)
colors = [np.random.rand(*b.shape) for b in bundle]
actor.line(bundle, colors=colors)
###############################################################################
# It happened that this bundle is in world coordinates and therefore we need to
# transform it into native image coordinates so that it is in the same
# coordinate space as the ``fa`` image.
bundle_native = transform_streamlines(bundle, np.linalg.inv(affine))
###############################################################################
# Show every streamline with an orientation color
# ===============================================
#
# This is the default option when you are using ``line`` or ``streamtube``.
scene = window.Scene()
stream_actor = actor.line(bundle_native)
scene.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))
scene.add(stream_actor)
# Uncomment the line below to show to display the window
# window.show(scene, size=(600, 600), reset_camera=False)
window.record(scene, out_path='bundle1.png', size=(600, 600))
###############################################################################
# You may wonder how we knew how to set the camera. This is very easy. You just
# need to run ``window.show`` once see how you want to see the object and then
# close the window and call the ``camera_info`` method which prints the
def test_contour_from_roi():
# Render volume
renderer = window.renderer()
data = np.zeros((50, 50, 50))
data[20:30, 25, 25] = 1.
data[25, 20:30, 25] = 1.
affine = np.eye(4)
surface = actor.contour_from_roi(data, affine,
color=np.array([1, 0, 1]),
opacity=.5)
renderer.add(surface)
renderer.reset_camera()
renderer.reset_clipping_range()
# window.show(renderer)
# Test binarization
renderer2 = window.renderer()
data2 = np.zeros((50, 50, 50))
data2[20:30, 25, 25] = 1.
data2[35:40, 25, 25] = 1.
affine = np.eye(4)
surface2 = actor.contour_from_roi(data2, affine,
color=np.array([0, 1, 1]),
opacity=.5)
renderer2.add(surface2)
renderer2.reset_camera()
renderer2.reset_clipping_range()
# window.show(renderer2)
arr = window.snapshot(renderer, 'test_surface.png', offscreen=True)
arr2 = window.snapshot(renderer2, 'test_surface2.png', offscreen=True)
report = window.analyze_snapshot(arr, find_objects=True)
report2 = window.analyze_snapshot(arr2, find_objects=True)
npt.assert_equal(report.objects, 1)
npt.assert_equal(report2.objects, 2)
# test on real streamlines using tracking example
from dipy.data import read_stanford_labels
from dipy.reconst.shm import CsaOdfModel
from dipy.data import default_sphere
from dipy.direction import peaks_from_model
from dipy.tracking.local import ThresholdTissueClassifier
from dipy.tracking import utils
from dipy.tracking.local import LocalTracking
from fury.colormap import line_colors
hardi_img, gtab, labels_img = read_stanford_labels()
data = hardi_img.get_data()
labels = labels_img.get_data()
affine = hardi_img.affine
white_matter = (labels == 1) | (labels == 2)
csa_model = CsaOdfModel(gtab, sh_order=6)
csa_peaks = peaks_from_model(csa_model, data, default_sphere,
relative_peak_threshold=.8,
min_separation_angle=45,
mask=white_matter)
classifier = ThresholdTissueClassifier(csa_peaks.gfa, .25)
seed_mask = labels == 2
seeds = utils.seeds_from_mask(seed_mask, density=[1, 1, 1], affine=affine)
# Initialization of LocalTracking.
# The computation happens in the next step.
streamlines = LocalTracking(csa_peaks, classifier, seeds, affine,
step_size=2)
# Compute streamlines and store as a list.
streamlines = list(streamlines)
# Prepare the display objects.
streamlines_actor = actor.line(streamlines, line_colors(streamlines))
seedroi_actor = actor.contour_from_roi(seed_mask, affine, [0, 1, 1], 0.5)
# Create the 3d display.
r = window.Renderer()
r2 = window.Renderer()
r.add(streamlines_actor)
arr3 = window.snapshot(r, 'test_surface3.png', offscreen=True)
report3 = window.analyze_snapshot(arr3, find_objects=True)
r2.add(streamlines_actor)
r2.add(seedroi_actor)
arr4 = window.snapshot(r2, 'test_surface4.png', offscreen=True)
report4 = window.analyze_snapshot(arr4, find_objects=True)
# assert that the seed ROI rendering is not far
# away from the streamlines (affine error)
npt.assert_equal(report3.objects, report4.objects)
def main():
parser = _build_arg_parser()
args = parser.parse_args()
assert_inputs_exist(parser, args.in_volume)
assert_outputs_exist(parser, args, args.out_image)
output_names = [
'axial_superior', 'axial_inferior', 'coronal_posterior',
'coronal_anterior', 'sagittal_left', 'sagittal_right'
]
for filename in args.in_bundles:
_, ext = os.path.splitext(filename)
if ext == '.tck':
tractogram = load_tractogram_with_reference(parser, args, filename)
else:
tractogram = filename
if not is_header_compatible(args.in_volume, tractogram):
parser.error('{} does not have a compatible header with {}'.format(
filename, args.in_volume))
# Delete temporary tractogram
else:
del tractogram
output_dir = os.path.dirname(args.out_image)
if output_dir:
assert_output_dirs_exist_and_empty(parser,
args,
output_dir,
create_dir=True)
_, extension = os.path.splitext(args.out_image)
# ----------------------------------------------------------------------- #
# Mosaic, column 0: orientation names and data description
# ----------------------------------------------------------------------- #
width = args.resolution_of_thumbnails
height = args.resolution_of_thumbnails
rows = 6
cols = len(args.in_bundles)
text_pos_x = 50
text_pos_y = 50
# Creates a new empty image, RGB mode
mosaic = Image.new('RGB', ((cols + 1) * width, (rows + 1) * height))
# Prepare draw and font objects to render text
draw = ImageDraw.Draw(mosaic)
font = get_font(args)
# Data of the volume used as background
ref_img = nib.load(args.in_volume)
data = ref_img.get_fdata(dtype=np.float32)
affine = ref_img.affine
mean, std = data[data > 0].mean(), data[data > 0].std()
value_range = (mean - 0.5 * std, mean + 1.5 * std)
# First column with rows description
draw_column_with_names(draw, output_names, text_pos_x, text_pos_y, height,
font)
# ----------------------------------------------------------------------- #
# Columns with bundles
# ----------------------------------------------------------------------- #
random.seed(args.random_coloring)
for idx_bundle, bundle_file in enumerate(args.in_bundles):
bundle_file_name = os.path.basename(bundle_file)
bundle_name, bundle_ext = split_name_with_nii(bundle_file_name)
i = (idx_bundle + 1) * width
if not os.path.isfile(bundle_file):
print('\nInput file {} doesn\'t exist.'.format(bundle_file))
number_streamlines = 0
view_number = 6
j = height * view_number
draw_bundle_information(draw, bundle_file_name, number_streamlines,
i + text_pos_x, j + text_pos_y, font)
else:
if args.uniform_coloring:
colors = args.uniform_coloring
elif args.random_coloring is not None:
colors = random_rgb()
# Select the streamlines to plot
if bundle_ext in ['.tck', '.trk']:
if (args.random_coloring is None
and args.uniform_coloring is None):
colors = None
bundle_tractogram_file = nib.streamlines.load(bundle_file)
streamlines = bundle_tractogram_file.streamlines
bundle_actor = actor.line(streamlines, colors)
nbr_of_elem = len(streamlines)
# Select the volume to plot
elif bundle_ext in ['.nii.gz', '.nii']:
if not args.random_coloring and not args.uniform_coloring:
colors = [1.0, 1.0, 1.0]
bundle_img_file = nib.load(bundle_file)
roi = get_data_as_mask(bundle_img_file)
bundle_actor = actor.contour_from_roi(roi,
bundle_img_file.affine,
colors)
nbr_of_elem = np.count_nonzero(roi)
# Render
ren = window.Scene()
zoom = args.zoom
opacity = args.opacity_background
# Structural data
slice_actor = actor.slicer(data, affine, value_range)
slice_actor.opacity(opacity)
ren.add(slice_actor)
# Streamlines
ren.add(bundle_actor)
ren.reset_camera()
ren.zoom(zoom)
view_number = 0
set_img_in_cell(mosaic, ren, view_number, width, height, i)
ren.pitch(180)
ren.reset_camera()
ren.zoom(zoom)
view_number = 1
set_img_in_cell(mosaic, ren, view_number, width, height, i)
ren.rm(slice_actor)
slice_actor2 = slice_actor.copy()
slice_actor2.display(None, slice_actor2.shape[1] // 2, None)
slice_actor2.opacity(opacity)
ren.add(slice_actor2)
ren.pitch(90)
ren.set_camera(view_up=(0, 0, 1))
ren.reset_camera()
ren.zoom(zoom)
view_number = 2
set_img_in_cell(mosaic, ren, view_number, width, height, i)
ren.pitch(180)
ren.set_camera(view_up=(0, 0, 1))
ren.reset_camera()
ren.zoom(zoom)
view_number = 3
set_img_in_cell(mosaic, ren, view_number, width, height, i)
ren.rm(slice_actor2)
slice_actor3 = slice_actor.copy()
slice_actor3.display(slice_actor3.shape[0] // 2, None, None)
slice_actor3.opacity(opacity)
ren.add(slice_actor3)
ren.yaw(90)
ren.reset_camera()
ren.zoom(zoom)
view_number = 4
set_img_in_cell(mosaic, ren, view_number, width, height, i)
ren.yaw(180)
ren.reset_camera()
ren.zoom(zoom)
view_number = 5
set_img_in_cell(mosaic, ren, view_number, width, height, i)
view_number = 6
j = height * view_number
draw_bundle_information(draw, bundle_file_name, nbr_of_elem,
i + text_pos_x, j + text_pos_y, font)
# Save image to file
mosaic.save(args.out_image)
classifier = ThresholdTissueClassifier(csa_peaks.gfa, .25)
seed_mask = labels == 2
seeds = utils.seeds_from_mask(seed_mask, density=[1, 1, 1], affine=affine)
# Initialization of LocalTracking. The computation happens in the next step.
streamlines = LocalTracking(csa_peaks, classifier, seeds, affine, step_size=2)
# Compute streamlines and store as a list.
streamlines = Streamlines(streamlines)
###############################################################################
# We will create a streamline actor from the streamlines.
streamlines_actor = actor.line(streamlines, line_colors(streamlines))
###############################################################################
# Next, we create a surface actor from the corpus callosum seed ROI. We
# provide the ROI data, the affine, the color in [R,G,B], and the opacity as
# a decimal between zero and one. Here, we set the color as blue/green with
# 50% opacity.
surface_opacity = 0.5
surface_color = [0, 1, 1]
seedroi_actor = actor.contour_from_roi(seed_mask, affine, surface_color,
surface_opacity)
###############################################################################
# Next, we initialize a ''Scene'' object and add both actors
def main():
parser = _build_arg_parser()
args = parser.parse_args()
required = [args.in_bundle, args.in_anat]
assert_inputs_exist(parser, required, args.target_template)
if args.verbose:
logging.basicConfig(level=logging.DEBUG)
output_filenames_3d = []
output_filenames_glass = []
for axis_name in ['sagittal', 'coronal', 'axial']:
if args.output_suffix:
output_filenames_3d.append(
os.path.join(
args.out_dir,
'{0}_{1}_3d.png'.format(axis_name, args.output_suffix)))
output_filenames_glass.append(
os.path.join(
args.out_dir,
'{0}_{1}_glass.png'.format(axis_name, args.output_suffix)))
else:
output_filenames_3d.append(
os.path.join(args.out_dir, '{0}_3d.png'.format(axis_name)))
output_filenames_glass.append(
os.path.join(args.out_dir, '{0}_glass.png'.format(axis_name)))
assert_outputs_exist(parser, args,
output_filenames_3d + output_filenames_glass)
if args.out_dir and not os.path.isdir(args.out_dir):
os.mkdir(args.out_dir)
if args.anat_opacity < 0.0 or args.anat_opacity > 1.0:
parser.error('Opacity must be between 0 and 1')
if args.uniform_coloring:
for val in args.uniform_coloring:
if val < 0 or val > 255:
parser.error('{0} is not a valid RGB value'.format(val))
# Get the relevant slices from the template
if args.target_template:
mni_space_img = nib.load(args.target_template)
affine = nib.load(args.target_template).affine
else:
mni_space_img = nib.load(args.in_anat)
affine = nib.load(args.in_anat).affine
x_slice = int(mni_space_img.shape[0] / 2)
y_slice = int(mni_space_img.shape[1] / 2)
z_slice = int(mni_space_img.shape[2] / 2)
slices_choice = (x_slice, y_slice, z_slice)
subject_data = prepare_data_for_actors(args.in_bundle, args.in_anat,
args.target_template)
# Create actors from each dataset for Dipy
sft, reference_data = subject_data
streamlines = sft.streamlines
volume_actor = actor.slicer(reference_data,
affine=affine,
opacity=args.anat_opacity,
interpolation='nearest')
if args.local_coloring:
colors = []
for i in streamlines:
local_color = np.gradient(i, axis=0)
local_color = np.abs(local_color)
local_color = (local_color.T / np.max(local_color, axis=1)).T
colors.append(local_color)
elif args.uniform_coloring:
colors = (args.uniform_coloring[0] / 255.0,
args.uniform_coloring[1] / 255.0,
args.uniform_coloring[2] / 255.0)
elif args.reference_coloring:
sft.to_vox()
streamlines_vox = sft.get_streamlines_copy()
sft.to_rasmm()
colors = []
normalized_data = reference_data / np.max(reference_data)
cmap = plt.get_cmap(args.reference_coloring)
for points in streamlines_vox:
values = map_coordinates(normalized_data,
points.T,
order=1,
mode='nearest')
colors.append(cmap(values)[:, 0:3])
else:
colors = None
streamlines_actor = actor.line(streamlines, colors=colors, linewidth=0.2)
# Take a snapshot of each dataset, camera settings are fixed for the
# known template, won't work with another.
if args.right:
side_pos = (300, -10, 10)
else:
side_pos = (-300, 10, 10)
display_slices(volume_actor,
slices_choice,
output_filenames_3d[0],
'sagittal',
view_position=tuple([x for x in side_pos]),
focal_point=tuple([x for x in (0, -10, 10)]),
streamlines_actor=streamlines_actor)
display_slices(volume_actor,
slices_choice,
output_filenames_3d[1],
'coronal',
view_position=tuple([x for x in (0, -300, 15)]),
focal_point=tuple([x for x in (0, 0, 15)]),
streamlines_actor=streamlines_actor)
display_slices(volume_actor,
slices_choice,
output_filenames_3d[2],
'axial',
view_position=tuple([x for x in (0, -15, 350)]),
focal_point=tuple([x for x in (0, -15, 0)]),
streamlines_actor=streamlines_actor)
plot_glass_brain(args, sft, mni_space_img, output_filenames_glass)
edges_colors = np.average(np.array(edges_colors), axis=1)
###############################################################################
# Our data preparation is ready, it is time to visualize them all. We start to
# build 2 actors that we represent our data : sphere_actor for the nodes and
# lines_actor for the edges.
sphere_actor = actor.sphere(centers=np.zeros(positions.shape),
colors=colors,
radii=radii * 0.5,
theta=8,
phi=8)
lines_actor = actor.line(np.zeros((len(edges), 2, 3)),
colors=edges_colors,
lod=False,
fake_tube=True,
linewidth=3)
###############################################################################
# Defining timer callback and layout iterator
def new_layout_timer(showm,
edges_list,
vertices_count,
max_iterations=1000,
vertex_initial_positions=None):
view_size = 500
viscosity = 0.10
alpha = 0.5
def qa_tractography(stream_path, qa_out_path, brain_path):
"""
Visualize the streamlines,
then using window.record to get nine snapshots of the 3D streamslines.
Parameters
----------
stream_path : str
Path for the input streamline.trk file(s).
qa_out_path_path : str
Path for the output QA imgae file(s)
brain_path : str
Path for the reference brain scan, in order to get the scan volume.
"""
#Use window to visualize the streamlines
r = window.renderer()
#Load the streamline.trk file
streamlines_mni_load = nib.streamlines.load(stream_path).streamlines
streamlines_mni_in = Streamlines(streamlines_mni_load)
streamlines_actor = actor.line(
streamlines_mni_in,
colormap.line_colors(streamlines_mni_in),
lod_points=10000,
depth_cue=True,
linewidth=0.2,
fake_tube=True,
opacity=0.3,
)
r.add(streamlines_actor)
#window.show(r)
showmng = window.ShowManager(r)
#window.record function can rotate the 3D-image, then get the snapshot of the specific angle.
window.record(r,
cam_pos=(70.03, 64.97, 269.80),
cam_view=(0, 1, 0),
path_numbering=True,
out_path=qa_out_path + '/Rotate_Z_axis_',
az_ang=120,
n_frames=3,
reset_camera=True,
size=(600, 600))
window.record(r,
cam_pos=(70.03, 64.97, 269.80),
cam_view=(1, 0, 0),
path_numbering=True,
out_path=qa_out_path + '/Rotate_Y_axis_',
az_ang=120,
n_frames=3,
reset_camera=True,
size=(600, 600))
window.record(r,
cam_pos=(70.03, 64.97, 269.80),
cam_view=(0, 0, 1),
path_numbering=True,
out_path=qa_out_path + '/Rotate_X_axis_',
az_ang=120,
n_frames=3,
reset_camera=True,
size=(600, 600))
showmng.exit()
combine_plot(qa_out_path, brain_path)
}
r_times = [p_data['earth_days'] for p_data in planets_data]
##############################################################################
# Here we are calculating and updating the path/orbit before animation starts.
planet_tracks = [
calculate_path(rplanet, rplanet * 85) for rplanet in r_planets
]
##############################################################################
# This is for orbit visualization. We are using line actor for orbits.
# After creating an actor we add it to the scene.
orbit_actor = actor.line(planet_tracks, colors=(1, 1, 1), linewidth=0.1)
scene.add(orbit_actor)
##############################################################################
# Define the ``timer_callback`` function, which controls what events happen
# at certain times, using the counter. Update the position of each planet
# actor using ``update_planet_position,`` assigning the x and y values of
# each planet's position with the newly calculated ones.
def timer_callback(_obj, _event):
cnt = next(counter)
showm.render()
# Rotating the sun actor
rotate_axial(sun_actor, sun_data['earth_days'], 1)
def main():
parser = _build_arg_parser()
args = parser.parse_args()
assert_inputs_exist(parser, [args.anat_reference])
assert_outputs_exist(parser, args, [args.output_name])
output_names = [
'axial_superior', 'axial_inferior', 'coronal_posterior',
'coronal_anterior', 'sagittal_left', 'sagittal_right'
]
list_of_bundles = [f for f in args.inputs]
# output_dir: where temporary files will be created
output_dir = os.path.dirname(args.output_name)
# ----------------------------------------------------------------------- #
# Mosaic, column 0: orientation names and data description
# ----------------------------------------------------------------------- #
width = args.resolution_of_thumbnails
height = args.resolution_of_thumbnails
rows = 6
cols = len(list_of_bundles)
text_pos_x = 50
text_pos_y = 50
# Creates a new empty image, RGB mode
mosaic = Image.new('RGB', ((cols + 1) * width, (rows + 1) * height))
# Prepare draw and font objects to render text
draw = ImageDraw.Draw(mosaic)
font = get_font(args)
# Data of the image used as background
ref_img = nib.load(args.anat_reference)
data = ref_img.get_data()
affine = ref_img.affine
mean, std = data[data > 0].mean(), data[data > 0].std()
value_range = (mean - 0.5 * std, mean + 1.5 * std)
# First column with rows description
draw_column_with_names(draw, output_names, text_pos_x, text_pos_y, height,
font)
# ----------------------------------------------------------------------- #
# Columns with bundles
# ----------------------------------------------------------------------- #
for idx_bundle, bundle_file in enumerate(list_of_bundles):
bundle_file_name = os.path.basename(bundle_file)
bundle_name, _ = os.path.splitext(bundle_file_name)
# !! It creates a temporary folder to create
# the images to concatenate in the mosaic !!
output_bundle_dir = os.path.join(output_dir, bundle_name)
if not os.path.isdir(output_bundle_dir):
os.makedirs(output_bundle_dir)
output_paths = [
os.path.join(output_bundle_dir, '{}_' +
os.path.basename(output_bundle_dir)).format(name)
for name in output_names
]
i = (idx_bundle + 1) * width
if not os.path.isfile(bundle_file):
print('\nInput file {} doesn\'t exist.'.format(bundle_file))
number_streamlines = 0
view_number = 6
j = height * view_number
draw_bundle_information(draw, bundle_file_name, number_streamlines,
i + text_pos_x, j + text_pos_y, font)
else:
# Select the streamlines to plot
bundle_tractogram_file = nib.streamlines.load(bundle_file)
streamlines = bundle_tractogram_file.streamlines
tubes = actor.line(streamlines)
number_streamlines = len(streamlines)
# Render
ren = window.Renderer()
zoom = args.zoom
opacity = args.opacity_background
# Structural data
slice_actor = actor.slicer(data, affine, value_range)
slice_actor.opacity(opacity)
ren.add(slice_actor)
# Streamlines
ren.add(tubes)
ren.reset_camera()
ren.zoom(zoom)
view_number = 0
set_img_in_cell(mosaic, ren, view_number,
output_paths[view_number], width, height, i)
ren.pitch(180)
ren.reset_camera()
ren.zoom(zoom)
view_number = 1
set_img_in_cell(mosaic, ren, view_number,
output_paths[view_number], width, height, i)
ren.rm(slice_actor)
slice_actor2 = slice_actor.copy()
slice_actor2.display(None, slice_actor2.shape[1] // 2, None)
slice_actor2.opacity(opacity)
ren.add(slice_actor2)
ren.pitch(90)
ren.set_camera(view_up=(0, 0, 1))
ren.reset_camera()
ren.zoom(zoom)
view_number = 2
set_img_in_cell(mosaic, ren, view_number,
output_paths[view_number], width, height, i)
ren.pitch(180)
ren.set_camera(view_up=(0, 0, 1))
ren.reset_camera()
ren.zoom(zoom)
view_number = 3
set_img_in_cell(mosaic, ren, view_number,
output_paths[view_number], width, height, i)
ren.rm(slice_actor2)
slice_actor3 = slice_actor.copy()
slice_actor3.display(slice_actor3.shape[0] // 2, None, None)
slice_actor3.opacity(opacity)
ren.add(slice_actor3)
ren.yaw(90)
ren.reset_camera()
ren.zoom(zoom)
view_number = 4
set_img_in_cell(mosaic, ren, view_number,
output_paths[view_number], width, height, i)
ren.yaw(180)
ren.reset_camera()
ren.zoom(zoom)
view_number = 5
set_img_in_cell(mosaic, ren, view_number,
output_paths[view_number], width, height, i)
view_number = 6
j = height * view_number
draw_bundle_information(draw, bundle_file_name, number_streamlines,
i + text_pos_x, j + text_pos_y, font)
shutil.rmtree(output_bundle_dir)
# Save image to file
mosaic.save(args.output_name)
###############################################################################
# Our data preparation is ready, it is time to visualize them all. We start to
# build 2 actors that we represent our data : sphere_actor for the nodes and
# lines_actor for the edges.
sphere_actor = actor.sphere(
centers=positions,
colors=colors,
radii=radii * 0.5,
theta=8,
phi=8,
)
lines_actor = actor.line(
edgesPositions,
colors=edgesColors,
opacity=0.1,
)
###############################################################################
# All actors need to be added in a scene, so we build one and add our
# lines_actor and sphere_actor.
scene = window.Scene()
scene.add(lines_actor)
scene.add(sphere_actor)
###############################################################################
# The final step ! Visualize and save the result of our creation! Please,
# switch interactive variable to True if you want to visualize it.
# mesh_structure = mcds.get_mesh() # X_domain = np.unique(mesh_structure[0][0]) # Y_domain = np.unique(mesh_structure[1][0]) # Z_domain = np.unique(mesh_structure[2][0]) # dx = X_domain[1]-X_domain[0] # dy = Y_domain[1]-Y_domain[0] # dz = Z_domain[1]-Z_domain[0] # xlims = np.array([X_domain[0]-dx/2, X_domain[-1]+dx/2]) # ylims = np.array([Y_domain[0]-dy/2, Y_domain[-1]+dy/2]) # zlims = np.array([Z_domain[0]-dz/2, Z_domain[-1]+dz/2]) #Drawing Domain Boundaries lines = [np.array([[-2880.,-500.,-2880.],[2880.,-500.,-2880.],[2880.,500.,-2880.],[-2880.,500.,-2880.],[-2880.,-500.,-2880.],[-2880.,-500.,2880.],[-2880.,500.,2880.],[-2880.,500.,-2880.],[-2880.,500.,2880.],[2880.,500.,2880.],[2880.,500.,-2880.],[2880.,500.,-2880.],[2880.,-500.,-2880.],[2880.,-500.,2880.],[2880.,500.,2880.],[2880.,-500.,2880.],[-2880.,-500.,2880.]])] colors = np.array([0.5, 0.5, 0.5]) c = actor.line(lines, colors) scene.add(c) #Adding Dimension Labels x_label = actor.text_3d(text='x axis (micron)',position=(-750.0,-700.0,3000.0),font_size=200,justification='left') scene.add(x_label) y_label = actor.text_3d(text='y axis (micron)',position=(3000,0,3000.0),font_size=200,justification='left') scene.add(y_label) z_label = actor.text_3d(text='z axis (micron)',position=(3000,-700.0,0.0),font_size=200,justification='left') scene.add(z_label) #scene.add(actor.texture_on_sphere(image))