def test_simple_usage(self):
# Given
cs = tvtk.ConeSource()
ef = tvtk.ElevationFilter(input_connection=cs.output_port)
# When
p = PipelineBrowser(root_object=[ef])
# Then
self.assertEqual(len(p._root.children), 1)
kids = list(p._root.children)
self.assertEqual(kids[0].name, 'ElevationFilter')
gk = list(kids[0].children)
self.assertEqual(len(gk), 1)
self.assertEqual(gk[0].name, 'ConeSource')
ggk = list(gk[0].children)
self.assertEqual(len(ggk), 0)
# Check if the default traits_view returns correctly.
p.default_traits_view()
# When
# Check if editing a selected object fires a ui change.
self.count = 0
def callback():
self.count += 1
p.on_trait_change(callback, 'object_edited')
p._on_select(gk[0])
cs.height = 2.0
# Then
self.assertTrue(self.count > 0)
def _create_lhs(self, parent):
""" Creates the left hand side or top depending on the style. """
self._create_scene(parent)
self.browser = PipelineBrowser(self.scene)
if parent is not None:
parent.show()
self.browser.show(parent=parent)
return self.browser._ui.control
def create_control(self, parent):
""" Create the toolkit-specific control that represents the view. """
from tvtk.pipeline.browser import PipelineBrowser
self.browser = PipelineBrowser()
self.browser.show(parent=parent)
return self.browser.ui.control
connect_ = tvtk.PolyDataConnectivityFilter(extraction_mode=4)
connect = mlab.pipeline.user_defined(smooth, filter=connect_)
# Compute normals for shading the surface
compute_normals = mlab.pipeline.poly_data_normals(connect)
compute_normals.filter.feature_angle = 80
surf = mlab.pipeline.surface(compute_normals,
color=(0.9, 0.72, 0.62))
#----------------------------------------------------------------------
# Display a cut plane of the raw data
ipw = mlab.pipeline.image_plane_widget(src, colormap='bone',
plane_orientation='z_axes',
slice_index=55)
mlab.view(-165, 32, 350, [143, 133, 73])
mlab.roll(180)
fig.scene.disable_render = False
#----------------------------------------------------------------------
# To make the link between the Mayavi pipeline and the much more
# complex VTK pipeline, we display both:
mlab.show_pipeline(rich_view=False)
from tvtk.pipeline.browser import PipelineBrowser
browser = PipelineBrowser(fig.scene)
browser.show()
mlab.show()
def isosurfacing(data):
"""data should be a 3d array with channel last."""
# Heuristic for finding the threshold for the brain
# Exctract the percentile 20 and 80 (without using
# scipy.stats.scoreatpercentile)
# sorted_data = np.sort(data.ravel())
# l = len(sorted_data)
# lower_thr = sorted_data[int(0.2 * l)]
# upper_thr = sorted_data[int(0.8 * l)]
# The white matter boundary: find the densest part of the upper half
# of histogram, and take a value 10% higher, to cut _in_ the white matter
# hist, bins = np.histogram(data[data > np.mean(data)], bins=50)
# brain_thr_idx = np.argmax(hist)
# brain_thr = bins[brain_thr_idx + 4]
# del hist, bins, brain_thr_idx
# Display the data #############################################################
fig = mlab.figure(bgcolor=(0, 0, 0), size=(400, 500))
# to speed things up
fig.scene.disable_render = True
src = mlab.pipeline.scalar_field(data)
# Our data is not equally spaced in all directions:
src.spacing = [1, 1, 20]
src.update_image_data = True
#----------------------------------------------------------------------
# Brain extraction pipeline
# In the following, we create a Mayavi pipeline that strongly
# relies on VTK filters. For this, we make heavy use of the
# mlab.pipeline.user_defined function, to include VTK filters in
# the Mayavi pipeline.
# Apply image-based filters to clean up noise
# thresh_filter = tvtk.ImageThreshold()
# thresh_filter.threshold_between(lower_thr, upper_thr)
# thresh = mlab.pipeline.user_defined(src, filter=thresh_filter)
median_filter = tvtk.ImageMedian3D()
median_filter.kernel_size = [3, 3, 3]
median = mlab.pipeline.user_defined(src, filter=median_filter)
diffuse_filter = tvtk.ImageAnisotropicDiffusion3D(
diffusion_factor=1.0,
diffusion_threshold=100.0,
number_of_iterations=5, )
diffuse = mlab.pipeline.user_defined(median, filter=diffuse_filter)
# Extract brain surface
contour = mlab.pipeline.contour(diffuse, )
contour.filter.contours = [0.5, ]
# Apply mesh filter to clean up the mesh (decimation and smoothing)
dec = mlab.pipeline.decimate_pro(mlab.pipeline.triangle_filter(contour))
dec.filter.feature_angle = 60.
dec.filter.target_reduction = 0.5
smooth_ = tvtk.SmoothPolyDataFilter(
number_of_iterations=10,
relaxation_factor=0.1,
feature_angle=60,
feature_edge_smoothing=False,
boundary_smoothing=False,
convergence=0.,
)
smooth = mlab.pipeline.user_defined(dec, filter=smooth_)
# Get the largest connected region
connect_ = tvtk.PolyDataConnectivityFilter(extraction_mode=4)
connect = mlab.pipeline.user_defined(smooth, filter=connect_)
# Compute normals for shading the surface
compute_normals = mlab.pipeline.poly_data_normals(connect)
compute_normals.filter.feature_angle = 80
surf = mlab.pipeline.surface(compute_normals,
color=(1, 1, 1))
#----------------------------------------------------------------------
# Display a cut plane of the raw data
ipw = mlab.pipeline.image_plane_widget(src, colormap='bone',
plane_orientation='z_axes',
slice_index=55)
# mlab.view(-165, 32, 350, [143, 133, 73])
# mlab.roll(180)
fig.scene.disable_render = False
#----------------------------------------------------------------------
# To make the link between the Mayavi pipeline and the much more
# complex VTK pipeline, we display both:
mlab.show_pipeline(rich_view=False)
from tvtk.pipeline.browser import PipelineBrowser
browser = PipelineBrowser(fig.scene)
browser.show()
mlab.show()
def _browser_default(self):
b = PipelineBrowser()
b.on_trait_change(self._fire_data_changed, 'object_edited')
return b
