.. AUTO-GENERATED FILE -- DO NOT EDIT!

.. _example_viz_roi_contour:


======================================================
Visualization of ROI Surface Rendered with Streamlines
======================================================

Here is a simple tutorial following the probabilistic CSA Tracking Example in
which we generate a dataset of streamlines from a corpus callosum ROI, and
then display them with the seed ROI rendered in 3D with 50% transparency.


::
  
  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.stopping_criterion import ThresholdStoppingCriterion
  from dipy.tracking import utils
  from dipy.tracking.local_tracking import LocalTracking
  from dipy.tracking.streamline import Streamlines
  from dipy.viz import actor, window, colormap as cmap
  

First, we need to generate some streamlines. For a more complete
description of these steps, please refer to the CSA Probabilistic Tracking
Tutorial.

::
  
  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)
  
  stopping_criterion = ThresholdStoppingCriterion(csa_peaks.gfa, .25)
  
  seed_mask = labels == 2
  seeds = utils.seeds_from_mask(seed_mask, affine, density=[1, 1, 1])
  
  # Initialization of LocalTracking. The computation happens in the next step.
  streamlines = LocalTracking(csa_peaks, stopping_criterion, 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, 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:
      window.show(ren)
  
  window.record(ren, out_path='contour_from_roi_tutorial.png', size=(1200, 900))
  

.. figure:: contour_from_roi_tutorial.png
   :align: center

   **A top view of corpus callosum streamlines with the blue transparent
   seed ROI in the center**.


.. admonition:: Example source code

   You can download :download:`the full source code of this example <./viz_roi_contour.py>`. This same script is also included in the dipy source distribution under the :file:`doc/examples/` directory.