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

    Click :ref:`here <sphx_glr_download_auto_examples_plot_1_getting_started.py>` to download the full example code
.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_plot_1_getting_started.py:


Get started with some basics
============================



1. Read a video, given its path
-------------------------------



.. code-block:: python


    # Import
    from mydia import Videos

    # Initialize video path
    video_path = r"./sample_video/bigbuckbunny.mp4"

    # Create a reader object
    reader = Videos()

    # Call the 'read()' function to get the video tensor
    video = reader.read(video_path)

    # a tensor of shape (1, 132, 720, 1280, 3)
    print("The shape of the tensor:", video.shape)





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

 Out:

 .. code-block:: none

    The shape of the tensor: (1, 132, 720, 1280, 3)


The tensor can be interpreted as -

* 1 video
* Having 132 frames,
* Dimension (width x height) of each frame: 1280x720 pixels
* ``3`` denotes that the video is RGB

2. Read multiple videos
-----------------------

.. code-block:: python

   from mydia import Videos

   video_paths = [
       "path/to/video_1",
       "path/to/video_2",
       "path/to/video_3",
       ...,
   ]          # list of path of videos

   reader = Videos()
   video = reader.read(video_paths)

3. Use multiple workers for reading the videos in parallel
----------------------------------------------------------

.. code-block:: python
   :emphasize-lines: 11

   from mydia import Videos

   video_paths = [
       "path/to/video_1",
       "path/to/video_2",
       "path/to/video_3",
       ...,
   ]          # list of path of videos

   reader = Videos()
   video = reader.read(video_paths, workers=4)

**The code above will use 4 CPUs to read the videos in parallel,
which could result in a significant speed up, depending on the
videos to be read and the performance of the CPU.**

4. Use a python generator for multiple videos
---------------------------------------------

.. code-block:: python

   from mydia import Videos

   video_paths = [
       "path/to/video_1",
       "path/to/video_2",
       "path/to/video_3",
       ...,
   ]          # list of path of videos

   reader = Videos()

   def generate_video():
       for path in video_paths:
           video = reader.read(path)
           yield video

   for i in range(len(video_paths)):
       vid = next(generate_video())
       # Do something

*For information on the parameters available, read the examples ahead
and also refer to the documentation of the class* :class:`mydia.Videos`.


Saving the loaded video tensor
------------------------------

.. important:: Once the videos have been processed, they could be
 saved as :obj:`numpy.ndarray` (in `.npz` or `.npy` format). For
 further details, view the documentation of:

 * :obj:`numpy.save`: for saving in `.npy` format

 * :obj:`numpy.savez`: for saving in `.npz` format

 * :obj:`numpy.load`: for loading back the saved numpy tensors

 Since the whole reading process is time consuming, this could turn
 out to be a useful way to store and reload the video tensors.


**Total running time of the script:** ( 0 minutes  1.035 seconds)


.. _sphx_glr_download_auto_examples_plot_1_getting_started.py:


.. only :: html

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



  .. container:: sphx-glr-download

     :download:`Download Python source code: plot_1_getting_started.py <plot_1_getting_started.py>`



  .. container:: sphx-glr-download

     :download:`Download Jupyter notebook: plot_1_getting_started.ipynb <plot_1_getting_started.ipynb>`


.. only:: html

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

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