def genren_AGG(sls,
sls2=None,
niidata=None,
roi1=None,
roi2=None,
roi3=None,
aff=np.eye(4),
putpath='test.png',
showme=False,
showaxes=False):
renderer = window.Renderer()
renderer.set_camera(position=(-606.93, -153.23, 28.70),
focal_point=(2.78, 11.06, 15.66),
view_up=(0, 0, 1))
stream_actor = actor.line(sls)
renderer.add(stream_actor)
if sls2 is not None:
stream_actor2 = actor.line(sls2, colors=(1, 1, 1))
renderer.add(stream_actor2)
if roi1 is not None:
contour_actor1 = actor.contour_from_roi(roi1,
affine=aff,
color=(1., 1., 0.),
opacity=0.5)
renderer.add(contour_actor1)
if roi2 is not None:
contour_actor2 = actor.contour_from_roi(roi2,
affine=aff,
color=(1., 0., 0.),
opacity=0.5)
renderer.add(contour_actor2)
if roi3 is not None:
contour_actor3 = actor.contour_from_roi(roi3,
affine=aff,
color=(0., 0., 1.),
opacity=0.5)
renderer.add(contour_actor3)
if niidata is not None:
slice_actor = actor.slicer(niidata, affine=aff)
renderer.add(slice_actor)
if showaxes:
axes = actor.axes()
renderer.add(axes)
if showme:
window.show(renderer, size=(500, 500), reset_camera=False)
window.record(renderer, out_path=putpath, size=(500, 500))
# renderer.camera_info()
del renderer
return putpath
def window_show_test(bundles, mask_roi, anat, interactive=True, outpath=None):
"""
:param bundles:
:param mask_roi:
:param anat:
:param interactive:
:param outpath:
:return:
"""
candidate_streamlines_actor = actor.streamtube(
bundles, cmap.line_colors(candidate_sl))
ROI_actor = actor.contour_from_roi(mask_roi,
color=(1., 1., 0.),
opacity=0.5)
ren = window.Renderer()
if anat:
vol_actor = actor.slicer(anat)
vol_actor.display(x=40)
vol_actor2 = vol_actor.copy()
vol_actor2.display(z=35)
# Add display objects to canvas
ren.add(candidate_streamlines_actor)
ren.add(ROI_actor)
ren.add(vol_actor)
ren.add(vol_actor2)
if outpath is not None:
window.record(ren, n_frames=1, out_path=outpath, size=(800, 800))
if interactive:
window.show(ren)
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 create_roi_actor(roi_list, affine, opacity=0.8):
roi_actor_list = []
for i in xrange(len(roi_list)):
random = np.random.RandomState(i)
color = random.uniform(0., 1., size=3)
print color
i_actor = actor.contour_from_roi(roi_list[i],
affine=affine,
color=color,
opacity=opacity)
roi_actor_list.append(i_actor)
return roi_actor_list
def left_button_clicked(i_ren, obj, file_select_text):
""" A callback to handle left click for this UI element.
Parameters
----------
i_ren: :class:`CustomInteractorStyle`
obj: :class:`vtkActor`
The picked actor
file_select_text: :class:`FileSelectMenuText2D`
"""
if file_select_text.file_type == "directory":
file_select_text.file_select.select_file(file_name="")
file_select_text.file_select.window_offset = 0
file_select_text.file_select.current_directory = os.path.abspath(
os.path.join(file_select_text.file_select.current_directory,
file_select_text.text_actor.message))
file_select_text.file_select.window = 0
file_select_text.file_select.fill_text_actors()
print "----------------------"
else:
file_select_text.file_select.select_file(
file_name=file_select_text.file_name)
file_select_text.file_select.fill_text_actors()
file_select_text.mark_selected()
print "+++++++++++++++++++++++"
data_path = os.path.join(
file_select_text.file_select.current_directory,
file_select_text.file_name)
suffix = os.path.split(data_path)[-1].split('.')[-1]
print
if suffix == 'tck':
fibs = nib.streamlines.tck.TckFile.load(data_path)
ac = actor.line(fibs.streamlines)
ren.add(ac)
if suffix == 'gz':
img = nib.load(data_path)
im = actor.contour_from_roi(img.get_data(), affine=img.affine)
ren.add(im)
i_ren.force_render()
i_ren.event.abort() # Stop propagating the event.
"""
We will create a streamline actor from the streamlines.
"""
streamlines_actor = actor.line(streamlines, cmap.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 ''Renderer'' object and add both actors
to the rendering.
"""
ren = window.ren()
ren.add(streamlines_actor)
ren.add(seedroi_actor)
"""
If you uncomment the following line, the rendering will pop up in an
interactive window.
"""
interactive = False
if interactive:
"""
We can use some of DIPY_'s visualization tools to display the ROI we targeted
above and all the streamlines that pass though that ROI. The ROI is the yellow
region near the center of the axial image.
"""
from dipy.viz import window, actor, colormap as cmap
# Enables/disables interactive visualization
interactive = False
# Make display objects
color = cmap.line_colors(cc_streamlines)
cc_streamlines_actor = actor.line(cc_streamlines,
cmap.line_colors(cc_streamlines))
cc_ROI_actor = actor.contour_from_roi(cc_slice, color=(1., 1., 0.),
opacity=0.5)
vol_actor = actor.slicer(t1_data)
vol_actor.display(x=40)
vol_actor2 = vol_actor.copy()
vol_actor2.display(z=35)
# Add display objects to canvas
r = window.Renderer()
r.add(vol_actor)
r.add(vol_actor2)
r.add(cc_streamlines_actor)
r.add(cc_ROI_actor)
# Save figures
# Make a corpus callosum seed mask for tracking
seed_mask = labels == 2
seeds = utils.seeds_from_mask(seed_mask, density=[1, 1, 1], affine=affine)
# Make a streamline bundle model of the corpus callosum ROI connectivity
streamlines = LocalTracking(csa_peaks, classifier, seeds, affine,
step_size=2)
streamlines = Streamlines(streamlines)
# Visualize the streamlines and the Path Length Map base ROI
# (in this case also the seed ROI)
streamlines_actor = actor.line(streamlines, cmap.line_colors(streamlines))
surface_opacity = 0.5
surface_color = [0, 1, 1]
seedroi_actor = actor.contour_from_roi(seed_mask, affine,
surface_color, surface_opacity)
ren = window.Renderer()
ren.add(streamlines_actor)
ren.add(seedroi_actor)
"""
If you set interactive to True (below), the rendering will pop up in an
interactive window.
"""
interactive = False
if interactive:
window.show(ren)
window.record(ren, n_frames=1, out_path='plm_roi_sls.png',
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)
"""
We can use some of dipy_'s visualization tools to display the ROI we targeted
above and all the streamlines that pass though that ROI. The ROI is the yellow
region near the center of the axial image.
"""
from dipy.viz import window, actor
from dipy.viz.colormap import line_colors
# Enables/disables interactive visualization
interactive = False
# Make display objects
color = line_colors(cc_streamlines)
cc_streamlines_actor = actor.line(cc_streamlines, line_colors(cc_streamlines))
cc_ROI_actor = actor.contour_from_roi(cc_slice, color=(1., 1., 0.),
opacity=0.5)
vol_actor = actor.slicer(t1_data)
vol_actor.display(x=40)
vol_actor2 = vol_actor.copy()
vol_actor2.display(z=35)
# Add display objects to canvas
r = window.Renderer()
r.add(vol_actor)
r.add(vol_actor2)
r.add(cc_streamlines_actor)
r.add(cc_ROI_actor)
# Save figures
def build_show(self, scene):
title = 'Horizon ' + horizon_version
self.show_m = window.ShowManager(
scene,
title=title,
size=(1200, 900),
order_transparent=self.order_transparent,
reset_camera=False)
self.show_m.initialize()
if self.cluster and self.tractograms:
lengths = np.array([self.cla[c]['length'] for c in self.cla])
szs = [self.cla[c]['size'] for c in self.cla]
sizes = np.array(szs)
# global self.panel2, slider_length, slider_size
self.panel2 = ui.Panel2D(size=(320, 200),
position=(870, 520),
color=(1, 1, 1),
opacity=0.1,
align="right")
cluster_panel_label = build_label(text="Cluster panel", bold=True)
slider_label_threshold = build_label(text="Threshold")
print("Cluster threshold", self.cluster_thr)
slider_threshold = ui.LineSlider2D(min_value=5,
max_value=25,
initial_value=self.cluster_thr,
text_template="{value:.0f}",
length=140,
shape='square')
_color_slider(slider_threshold)
slider_label_length = build_label(text="Length")
slider_length = ui.LineSlider2D(
min_value=lengths.min(),
max_value=np.percentile(lengths, 98),
initial_value=np.percentile(lengths, 25),
text_template="{value:.0f}",
length=140)
_color_slider(slider_length)
slider_label_size = build_label(text="Size")
slider_size = ui.LineSlider2D(min_value=sizes.min(),
max_value=np.percentile(sizes, 98),
initial_value=np.percentile(
sizes, 50),
text_template="{value:.0f}",
length=140)
_color_slider(slider_size)
# global self.length_min, size_min
self.size_min = sizes.min()
self.length_min = lengths.min()
def change_threshold(istyle, obj, slider):
sv = np.round(slider.value, 0)
self.remove_cluster_actors(scene)
self.add_cluster_actors(scene, self.tractograms, threshold=sv)
# TODO need to double check if this section is still needed
lengths = np.array([self.cla[c]['length'] for c in self.cla])
szs = [self.cla[c]['size'] for c in self.cla]
sizes = np.array(szs)
slider_length.min_value = lengths.min()
slider_length.max_value = lengths.max()
slider_length.value = lengths.min()
slider_length.update()
slider_size.min_value = sizes.min()
slider_size.max_value = sizes.max()
slider_size.value = sizes.min()
slider_size.update()
self.length_min = min(lengths)
self.size_min = min(sizes)
self.show_m.render()
slider_threshold.handle_events(slider_threshold.handle.actor)
slider_threshold.on_left_mouse_button_released = change_threshold
def hide_clusters_length(slider):
self.length_min = np.round(slider.value)
for k in self.cla:
if (self.cla[k]['length'] < self.length_min
or self.cla[k]['size'] < self.size_min):
self.cla[k]['centroid_actor'].SetVisibility(0)
if k.GetVisibility() == 1:
k.SetVisibility(0)
else:
self.cla[k]['centroid_actor'].SetVisibility(1)
self.show_m.render()
def hide_clusters_size(slider):
self.size_min = np.round(slider.value)
for k in self.cla:
if (self.cla[k]['length'] < self.length_min
or self.cla[k]['size'] < self.size_min):
self.cla[k]['centroid_actor'].SetVisibility(0)
if k.GetVisibility() == 1:
k.SetVisibility(0)
else:
self.cla[k]['centroid_actor'].SetVisibility(1)
self.show_m.render()
slider_length.on_change = hide_clusters_length
# Clustering panel
self.panel2.add_element(slider_label_threshold, coords=(0.1, 0.15))
self.panel2.add_element(slider_threshold, coords=(0.42, 0.15))
self.panel2.add_element(slider_label_length, coords=(0.1, 0.4))
self.panel2.add_element(slider_length, coords=(0.42, 0.4))
slider_size.on_change = hide_clusters_size
self.panel2.add_element(slider_label_size, coords=(0.1, 0.65))
self.panel2.add_element(slider_size, coords=(0.42, 0.65))
self.panel2.add_element(cluster_panel_label, coords=(0.05, 0.85))
scene.add(self.panel2)
# Information panel
text_block = build_label(HELP_MESSAGE, 18)
text_block.message = HELP_MESSAGE
self.help_panel = ui.Panel2D(size=(320, 200),
position=(10, 10),
color=(0.8, 0.8, 1),
opacity=0.2,
align="left")
self.help_panel.add_element(text_block, coords=(0.05, 0.1))
scene.add(self.help_panel)
if len(self.images) > 0:
# Only first non-binary image loading supported for now
first_img = True
first_roi = True
if self.roi_images:
roi_color = self.roi_colors
for img in self.images:
img_data, img_affine = img
dim = np.unique(img_data).shape[0]
if dim == 2:
if 'rois' in self.random_colors:
roi_color = next(self.color_gen)
roi_actor = actor.contour_from_roi(
img_data,
affine=img_affine,
color=roi_color,
opacity=self.mem.roi_opacity)
self.mem.slicer_roi_actor.append(roi_actor)
scene.add(roi_actor)
if first_roi:
self.panel3 = ui.Panel2D(size=(320, 100),
position=(870, 730),
color=(1, 1, 1),
opacity=0.1,
align="right")
rois_panel_label = build_label(text="ROIs panel",
bold=True)
slider_label_opacity = build_label(text="Opacity")
slider_opacity = ui.LineSlider2D(
min_value=0.0,
max_value=1.0,
initial_value=self.mem.roi_opacity,
length=140,
text_template="{ratio:.0%}")
_color_slider(slider_opacity)
def change_opacity(slider):
roi_opacity = slider.value
self.mem.roi_opacity = roi_opacity
for contour in self.mem.slicer_roi_actor:
contour.GetProperty().SetOpacity(
roi_opacity)
slider_opacity.on_change = change_opacity
self.panel3.add_element(slider_label_opacity,
coords=(0.1, 0.3))
self.panel3.add_element(slider_opacity,
coords=(0.42, 0.3))
self.panel3.add_element(rois_panel_label,
coords=(0.05, 0.7))
scene.add(self.panel3)
first_roi = False
else:
if first_img:
data, affine = img
self.vox2ras = affine
if len(self.pams) > 0:
pam = self.pams[0]
else:
pam = None
self.panel = slicer_panel(scene,
self.show_m.iren,
data,
affine,
self.world_coords,
pam=pam,
mem=self.mem)
first_img = False
else:
data, affine = self.images[0]
self.vox2ras = affine
if len(self.pams) > 0:
pam = self.pams[0]
else:
pam = None
self.panel = slicer_panel(scene,
self.show_m.iren,
data,
affine,
self.world_coords,
pam=pam,
mem=self.mem)
else:
data = None
affine = None
pam = None
self.win_size = scene.GetSize()
def win_callback(obj, event):
if self.win_size != obj.GetSize():
size_old = self.win_size
self.win_size = obj.GetSize()
size_change = [self.win_size[0] - size_old[0], 0]
if data is not None:
self.panel.re_align(size_change)
if self.cluster:
self.panel2.re_align(size_change)
self.help_panel.re_align(size_change)
if self.roi_images:
self.panel3.re_align(size_change)
self.show_m.initialize()
self.hide_centroids = True
self.select_all = False
def hide():
if self.hide_centroids:
for ca in self.cea:
if (self.cea[ca]['length'] >= self.length_min
or self.cea[ca]['size'] >= self.size_min):
if self.cea[ca]['selected'] == 0:
ca.VisibilityOff()
else:
for ca in self.cea:
if (self.cea[ca]['length'] >= self.length_min
and self.cea[ca]['size'] >= self.size_min):
if self.cea[ca]['selected'] == 0:
ca.VisibilityOn()
self.hide_centroids = not self.hide_centroids
self.show_m.render()
def invert():
for ca in self.cea:
if (self.cea[ca]['length'] >= self.length_min
and self.cea[ca]['size'] >= self.size_min):
self.cea[ca]['selected'] = \
not self.cea[ca]['selected']
cas = self.cea[ca]['cluster_actor']
self.cla[cas]['selected'] = \
self.cea[ca]['selected']
self.show_m.render()
def save():
saving_streamlines = Streamlines()
for bundle in self.cla.keys():
if bundle.GetVisibility():
t = self.cla[bundle]['tractogram']
c = self.cla[bundle]['cluster']
indices = self.tractogram_clusters[t][c]
saving_streamlines.extend(Streamlines(indices))
print('Saving result in tmp.trk')
# Using the header of the first of the tractograms
sft_new = StatefulTractogram(saving_streamlines,
self.tractograms[0], Space.RASMM)
save_tractogram(sft_new, 'tmp.trk', bbox_valid_check=False)
print('Saved!')
def new_window():
active_streamlines = Streamlines()
for bundle in self.cla.keys():
if bundle.GetVisibility():
t = self.cla[bundle]['tractogram']
c = self.cla[bundle]['cluster']
indices = self.tractogram_clusters[t][c]
active_streamlines.extend(Streamlines(indices))
# Using the header of the first of the tractograms
active_sft = StatefulTractogram(active_streamlines,
self.tractograms[0], Space.RASMM)
hz2 = Horizon([active_sft],
self.images,
cluster=True,
cluster_thr=self.cluster_thr / 2.,
random_colors=self.random_colors,
length_lt=np.inf,
length_gt=0,
clusters_lt=np.inf,
clusters_gt=0,
world_coords=True,
interactive=True)
ren2 = hz2.build_scene()
hz2.build_show(ren2)
def show_all():
if self.select_all is False:
for ca in self.cea:
if (self.cea[ca]['length'] >= self.length_min
and self.cea[ca]['size'] >= self.size_min):
self.cea[ca]['selected'] = 1
cas = self.cea[ca]['cluster_actor']
self.cla[cas]['selected'] = \
self.cea[ca]['selected']
self.show_m.render()
self.select_all = True
else:
for ca in self.cea:
if (self.cea[ca]['length'] >= self.length_min
and self.cea[ca]['size'] >= self.size_min):
self.cea[ca]['selected'] = 0
cas = self.cea[ca]['cluster_actor']
self.cla[cas]['selected'] = \
self.cea[ca]['selected']
self.show_m.render()
self.select_all = False
def expand():
for c in self.cea:
if self.cea[c]['selected']:
if not self.cea[c]['expanded']:
len_ = self.cea[c]['length']
sz_ = self.cea[c]['size']
if (len_ >= self.length_min and sz_ >= self.size_min):
self.cea[c]['cluster_actor']. \
VisibilityOn()
c.VisibilityOff()
self.cea[c]['expanded'] = 1
self.show_m.render()
def reset():
for c in self.cea:
if (self.cea[c]['length'] >= self.length_min
and self.cea[c]['size'] >= self.size_min):
self.cea[c]['cluster_actor'].VisibilityOff()
c.VisibilityOn()
self.cea[c]['expanded'] = 0
self.show_m.render()
def key_press(obj, event):
key = obj.GetKeySym()
if self.cluster:
# hide on/off unselected centroids
if key == 'h' or key == 'H':
hide()
# invert selection
if key == 'i' or key == 'I':
invert()
# retract help panel
if key == 'o' or key == 'O':
self.help_panel._set_position((-300, 0))
self.show_m.render()
# save current result
if key == 's' or key == 'S':
save()
if key == 'y' or key == 'Y':
new_window()
if key == 'a' or key == 'A':
show_all()
if key == 'e' or key == 'E':
expand()
if key == 'r' or key == 'R':
reset()
options = [
r'un\hide centroids', 'invert selection', r'un\select all',
'expand clusters', 'collapse clusters', 'save streamlines',
'recluster'
]
listbox = ui.ListBox2D(values=options,
position=(10, 300),
size=(200, 270),
multiselection=False,
font_size=18)
def display_element():
action = listbox.selected[0]
if action == r'un\hide centroids':
hide()
if action == 'invert selection':
invert()
if action == r'un\select all':
show_all()
if action == 'expand clusters':
expand()
if action == 'collapse clusters':
reset()
if action == 'save streamlines':
save()
if action == 'recluster':
new_window()
listbox.on_change = display_element
listbox.panel.opacity = 0.2
listbox.set_visibility(0)
self.show_m.scene.add(listbox)
def left_click_centroid_callback(obj, event):
self.cea[obj]['selected'] = not self.cea[obj]['selected']
self.cla[self.cea[obj]['cluster_actor']]['selected'] = \
self.cea[obj]['selected']
self.show_m.render()
def right_click_centroid_callback(obj, event):
for lactor in listbox._get_actors():
lactor.SetVisibility(not lactor.GetVisibility())
listbox.scroll_bar.set_visibility(False)
self.show_m.render()
def left_click_cluster_callback(obj, event):
if self.cla[obj]['selected']:
self.cla[obj]['centroid_actor'].VisibilityOn()
ca = self.cla[obj]['centroid_actor']
self.cea[ca]['selected'] = 0
obj.VisibilityOff()
self.cea[ca]['expanded'] = 0
self.show_m.render()
def right_click_cluster_callback(obj, event):
print('Cluster Area Selected')
self.show_m.render()
for cl in self.cla:
cl.AddObserver('LeftButtonPressEvent', left_click_cluster_callback,
1.0)
cl.AddObserver('RightButtonPressEvent',
right_click_cluster_callback, 1.0)
self.cla[cl]['centroid_actor'].AddObserver(
'LeftButtonPressEvent', left_click_centroid_callback, 1.0)
self.cla[cl]['centroid_actor'].AddObserver(
'RightButtonPressEvent', right_click_centroid_callback, 1.0)
self.mem.window_timer_cnt = 0
def timer_callback(obj, event):
self.mem.window_timer_cnt += 1
# TODO possibly add automatic rotation option
# self.show_m.scene.azimuth(0.01 * self.mem.window_timer_cnt)
# self.show_m.render()
scene.reset_camera()
scene.zoom(1.5)
scene.reset_clipping_range()
if self.interactive:
self.show_m.add_window_callback(win_callback)
self.show_m.add_timer_callback(True, 200, timer_callback)
self.show_m.iren.AddObserver('KeyPressEvent', key_press)
if self.return_showm:
return self.show_m
if self.recorded_events is None:
self.show_m.render()
self.show_m.start()
else:
# set to True if event recorded file needs updating
recording = False
recording_filename = self.recorded_events
if recording:
self.show_m.record_events_to_file(recording_filename)
else:
self.show_m.play_events_from_file(recording_filename)
else:
window.record(scene,
out_path=self.out_png,
size=(1200, 900),
reset_camera=False)
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 dipy.viz.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.get_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.ren()
r2 = window.ren()
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)
native_streamlines = "native_streamline_%s_bin-%d.vtk" % (subs_strings[s], i)
unfold_streamlines = "unfold_streamlines_%s_bin-%d.vtk" % (subs_strings[s], i)
from_unfold_streamlines = "from_unfold_streamlines_%s_bin-%d.vtk" % (
subs_strings[s], i)
roi_nii = nib.load('data/oldUnfold/U.nii.gz')
roi = roi_nii.get_fdata()
nlines = load_vtk_streamlines('data/oldUnfold/' + native_streamlines)
ulines = load_vtk_streamlines('data/oldUnfold/' + from_unfold_streamlines)
#unfoldlines=load_vtk_streamlines('data/oldUnfold/Unfolded/'+unfold_streamlines)
surface_opacity = 0.05
surface_color = [0, 1, 1]
roi_actor = actor.contour_from_roi(roi, roi_nii.affine, surface_color,
surface_opacity)
def plot_streamlines(streamlines, roi_actor):
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(roi_actor)
scene.add(streamlines_actor)
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 dipy.viz.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.get_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.ren()
r2 = window.ren()
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)
fib = nib.streamlines.tck.TckFile.load(fib_file)
streamlines = fib.streamlines
from pyfat.algorithm.fiber_selection import select_by_vol_roi
print len(streamlines)
# # create a rendering renderer
# ren = window.Renderer()
# stream_actor = actor.line(streamlines)
roi = nib.load(roi_file)
roi_data = roi.get_data()
roi1 = nib.load(roi_file1)
roi1_data = roi1.get_data()
ROI_actor = actor.contour_from_roi(roi_data,
affine=roi.affine,
color=(1., 1., 0.),
opacity=0.8)
ROI_actor1 = actor.contour_from_roi(roi1_data,
affine=roi1.affine,
color=(1., 1., 0.),
opacity=0.8)
####################################################
roi_vis = nib.load(roi_vis)
roi_vis_data = roi_vis.get_data()
label = list(set(roi_vis_data[roi_vis_data.nonzero()]))
roi_vis_data0 = np.zeros(roi_vis_data.shape)
roi_vis_data0[roi_vis_data == label[0]] = 1.0
roi_vis_data1 = np.zeros(roi_vis_data.shape)
roi_vis_data1[roi_vis_data == label[1]] = 1.0
roi_vis_data2 = np.zeros(roi_vis_data.shape)
