.. pyCarDisplay documentation master file, created by
   sphinx-quickstart on Thu Apr  8 23:23:10 2021.
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Welcome to pyCarDisplay's documentation!
========================================

.. image:: ../../img/example.png

Python Library for Simulating Autonomous Vehicle: pyCarDisplay.
pyCarDisplay is developed to read the Kitti dataset, and simulate an automated car.
It can perform object detection, depth detection, IMU sensor simulation, Kalman Filtering,
and display the results on a GUI.


Prerequisites
========================================
* Python >= v3.8.5
* Download the `pre-trained PyTorch object detection model <https://drive.google.com/open?id=1bvJfF6r_zYl2xZEpYXxgb7jLQHFZ01Qe>`_ (Provided by `sgrvinod <https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Object-Detection>`_).
* Download the `pre-trained PyTorch depth detection model <https://github.com/intel-isl/MiDaS/releases/download/v2_1/model-f6b98070.pt>`_ (Provided by `OniroAI <https://github.com/OniroAI/MonoDepth-PyTorch>`_).
* Download a set of raw sampels from the `Kitti dataset <http://www.cvlibs.net/datasets/kitti/raw_data.php>`_.


Installation
========================================
.. code-block:: shell

   pip install pyCarDisplay

or

.. code-block:: shell

    git clone https://github.com/MaksimEkin/pyCarDisplay
    cd pyCarDisplay
    python setup.py install

The simulator also needs, at minimum, the pre-trained models and Kitti dataset
samples. Please see the prerequisites.


Example Usage
========================================
.. code-block:: python

    from pyCarDisplay.pyCarDisplay import CarDisplay

    display = CarDisplay(
        # Kitti dataset:
        # https://s3.eu-central-1.amazonaws.com/avg-kitti/raw_data/2011_09_26_drive_0005/2011_09_26_drive_0005_sync.zip
        car_images_path="2011_09_26/2011_09_26_drive_0005_sync/image_02/data/",
        imu_sensor_path="2011_09_26/2011_09_26_drive_0005_sync/oxts/data/",
        # Object detection model downloaded from:
        # https://drive.google.com/open?id=1bvJfF6r_zYl2xZEpYXxgb7jLQHFZ01Qe
        object_detection_model_path="checkpoint_ssd300.pth.tar",
        # Depth detection model downloaded from:
        # https://github.com/intel-isl/MiDaS/releases/download/v2_1/model-f6b98070.pt
        depth_detection_model_path="model-f6b98070.pt",
        verbose=True,
        device="cpu"
    )

    display.start()


Dependencies
========================================
.. code-block:: shell

    numpy>=1.20.1
    pandas>=1.2.3
    Pillow>=8.1.2
    PySimpleGUI>=4.37.0
    torch>=1.8.0
    torchaudio>=0.8.0
    torchvision>=0.9.0
    matplotlib>=3.4.1
    opencv-python>=4.5.1
    termcolor>=1.1.0


How to Cite pyCarDisplay?
========================================
.. code-block:: console

    @electronic{cmsc611_2021_umbc,
      author = {R. {Barron} and M. E. {Eren} and C. {Varga} and W. {Wang}},
      title = {pyCarDisplay},
      url = "https://github.com/MaksimEkin/pyCarDisplay"
    }



References
========================================
* Vinodababu, S. (n.d.). A-PyTorch-Tutorial-to-Object-Detection. https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Object-Detection
* “MiDaS,” Pytorch.org. [Online]. Available: https://pytorch.org/hub/intelisl_midas_v2/. Accessed: 27-Mar-2021.
* R. E. Kalman, A New Approach to Linear Filtering and Prediction Problems, Research Institute for Advanced Study, 2 Baltimore, Md. https://www.cs.unc.edu/~welch/kalman/media/pdf/Kalman1960.pdf
* A Geiger, P Lenz, C Stiller, and R Urtasun. 2013. Vision meets robotics: The KITTI dataset. Int. J. Rob. Res. 32, 11 (September 2013), 1231–1237. DOI:https://doi.org/10.1177/0278364913491297
* Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., … Chintala, S. (2019). PyTorch: An Imperative Style, High-Performance Deep Learning Library. In Advances in Neural Information Processing Systems 32 (pp. 8024–8035). Curran Associates, Inc. Retrieved from http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf



.. toctree::
   :maxdepth: 2
   :caption: Contents:

   modules


Indices and tables
==================

* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`