.. _Example 5:

Example 5
=========

Simultaneous tracking of an ant and the camera capturing its movement
with the reconstruction of the trajectory of the ant respect its
initial position. Code and multimedia resources are
available `here <https://github.com/yupidevs/yupi_examples/>`_.

The robot designed in [1] allowed to extend the study of the
motion of insects for longer times and wider regions. The robot,
keeping a proper distance from the insect, continuously moves to
preserve the insect always in the scene recorded by its camera.

However, the fact of having both, the insect and the camera,
moving at the same time, introduces additional complications
while reconstructing the trajectory from a video source. yupi
handles the motion of the camera naturally as part of the
TrackingScenario.

In this example, it is shown how to reproduce the results of
the original paper using one of the videos originally used
by the authors.


The example is structured as follows:
  | :ref:`Setup dependencies 5`
  | :ref:`Tracking tracking objects 5`
  | :ref:`Results 5`
  | :ref:`References 5`

.. note::
   You can access `the script of this example <https://github.com/yupidevs/yupi_examples/blob/master/example_005.py>`_ on the `yupi examples repository <https://github.com/yupidevs/yupi_examples>`_.

.. _Setup dependencies 5:

1. Setup dependencies
---------------------

Import all the dependencies:

.. code-block:: python

   from yupi.graphics import plot_2d
   from yupi.tracking import (
      ROI,
      CameraTracker,
      ColorMatching,
      ObjectTracker,
      RemapUndistorter,
      TrackingScenario,
   )

Set up the path to multimedia resources:

.. code-block:: python

   video_path = 'resources/videos/Serrano2019.mp4'
   camera_file = 'resources/cameras/gph3+1080-60fps-MEDIUM.npz'

.. _Tracking tracking objects 5:

2. Tracking tracking objects
----------------------------

As in the previous example, we create an instance of an Undistorter:

.. code-block:: python

   undistorter = RemapUndistorter(camera_file)


Then, we initialize a tracker for the camera:

.. code-block:: python

   camera = CameraTracker(ROI((.65, .65), ROI.CENTER_INIT_MODE))

And a tracker for the ant using ColorMatching algorithm:

.. code-block:: python

   algorithm = ColorMatching((20,20,20), (65,65,65))
   ant = ObjectTracker('ant', algorithm, ROI((120, 120), scale=0.75))

Next, we create a TrackingScenario with the ant tracker, the tracker for the camera and
the Undistorter:

.. code-block:: python

   scenario = TrackingScenario([ant], camera, undistorter, preview_scale=0.75)

Then, we track the video using the configured scenario.

.. code-block:: python

   retval, tl = scenario.track(video_path, pix_per_m=6300)

.. _Results 5:

3. Results
----------

Now, we can produce a plot quite similar to the one of the original paper [1]:

.. code-block:: python

   plot_2d(tl)

.. figure:: /images/example5.png
   :alt: Output of example5
   :align: center


.. _References 5:

4. References
--------------------------

| [1] Serrano-Muñoz, A., et al. "An autonomous robot for continuous tracking of millimetric-sized walkers." Review of Scientific Instruments 90.1 (2019): 014102.