MaksimEkin / pyCarDisplay

pyCarDisplay is a Python library for simulating autonomous vehicle. It 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.

pyCarDisplay

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.

Installation

pip install pyCarDisplay # TODO: Upload to PyPi

or install from source

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

Prerequisites

• Python >= v3.8.5
• Download the pre-trained PyTorch object detection model (Provided by sgrvinod).
• Download the pre-trained PyTorch depth detection model (Provided by OniroAI).
• Download a set of raw sampels from the Kitti dataset.

Usage

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

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

Documentation

The documentation of pyCarDisplay can be found here.

How to Cite pyCarDisplay?

@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