.. note::
    :class: sphx-glr-download-link-note

    Click :ref:`here <sphx_glr_download_auto_examples_viz_roi_contour.py>` to download the full example code
.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_viz_roi_contour.py:


======================================================
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.



.. code-block:: python


    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.tracking.streamline import Streamlines
    from fury import actor, window
    from fury.colormap import line_colors







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


.. code-block:: python


    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 = Streamlines(streamlines)





.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    Dataset is already in place. If you want to fetch it again please first remove the folder /Users/koudoro/.dipy/stanford_hardi 
    Dataset is already in place. If you want to fetch it again please first remove the folder /Users/koudoro/.dipy/stanford_hardi 
    Dataset is already in place. If you want to fetch it again please first remove the folder /Users/koudoro/.dipy/stanford_hardi


We will create a streamline actor from the streamlines.


.. code-block:: python


    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.


.. code-block:: python


    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.


.. code-block:: python


    ren = window.Renderer()
    ren.add(streamlines_actor)
    ren.add(seedroi_actor)







If you uncomment the following line, the rendering will pop up in an
interactive window.


.. code-block:: python


    interactive = False
    if interactive:
        window.show(ren)

    ren.zoom(1.5)
    ren.reset_clipping_range()

    window.record(ren, out_path='contour_from_roi_tutorial.png', size=(1200, 900),
                  reset_camera=False)



.. image:: /auto_examples/images/sphx_glr_viz_roi_contour_001.png
    :class: sphx-glr-single-img




**Total running time of the script:** ( 0 minutes  17.608 seconds)


.. _sphx_glr_download_auto_examples_viz_roi_contour.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. container:: sphx-glr-download

     :download:`Download Python source code: viz_roi_contour.py <viz_roi_contour.py>`



  .. container:: sphx-glr-download

     :download:`Download Jupyter notebook: viz_roi_contour.ipynb <viz_roi_contour.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_