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DDD20 End-to-End Event Camera Driving Dataset

See https://sites.google.com/view/davis-driving-dataset-2020/home for details.

view.py

Software released as part of the publication

  • Hu, Y., Binas, J., Neil, D., Liu, S.-C., and Delbruck, T. (2020). "DDD20 End-to-End Event Camera Driving Dataset: Fusing Frames and Events with Deep Learning for Improved Steering Prediction". Special session Beyond Traditional Sensing for Intelligent Transportation, The 23rd IEEE International Conference on Intelligent Transportation Systems, September 20 – 23, 2020, Rhodes, Greece. arXiv [cs.CV]. arXiv. http://arxiv.org/abs/2005.08605

  • Binas, J., Neil, D., Liu, S.-C., and Delbruck, T. (2017). DDD17: End-To-End DAVIS Driving Dataset. in ICML’17 Workshop on Machine Learning for Autonomous Vehicles (MLAV 2017), Sydney, Australia. Available at: arXiv:1711.01458 [cs] http://arxiv.org/abs/1711.01458

See https://github.com/SensorsINI/ddd20-itsc20 for the code used for the Hu paper above.

Installation instructions using conda and Python 2.7

This project currently works with Python 2.7 under linux. Lasted tested working view.py November 2024.

(Don't try to run this code in python 3; it depends on some cryptic multiprocessing code that is not portable to python3! Trust us, we tried to port it.)

  1. First, create an Python 2.7 environment

    conda create -n ddd20 python=2.7
conda activate ddd20

  2. Install all dependencies:

    pip install future
pip install numpy h5py opencv-python==4.2.0.32 openxc==0.15.0

  3. There is no step 3, have fun! 🎉

Usage:

See https://sites.google.com/view/davis-driving-dataset-2020/home for details

viewing

  • Play a file from the beginning

    $ python view.py <recorded_file.hdf5>

  • Print usage

    $ python view.py --help
usage: view.py [-h] [--start START] [--rotate ROTATE] filename

positional arguments:
filename

optional arguments:
-h, --help            show this help message and exit
--start START, -s START
                        Examples:
                        -s 50% - play file starting at 50%
                        -s 66s - play file starting at 66s
--rotate ROTATE, -r ROTATE
                        Rotate the scene 180 degrees if True, Otherwise False

While viewing, hit ? or h for help in console:

space pause
b brighter
d darker
s slower
f faster
i toggle/rotate info
r rotate 180 deg

Exporting raw data into standard data types

The DDD20 recordings are recorded using a custom data structure in HDF5. This design choice made the batch processing restricted without reformatting/exporting.

We prepared a script that can convert the original HDF5 recording into a nicer data strcture that user can directly work on. However, this file will not contain the car CAN bus steering/throttle/GPS, etc.

$ python export_ddd20_hdf.py [-h] [--rotate ROTATE] filename

The newly exported file is an HDF5 file that is called filename.exported.hdf5. This file is saved at the same folder of the filename. This HDF5 file has a very simple structure, it has three datasets:

event: (N events x 4)  # each row is an event.
frame: (M frames x 260 x 346)
frame_ts: (M frames x 1)

Added now is option to turn off the display (thanks youkaichao) so that issue #4 can be resolved by simply adding the option:

python export_ddd20_hdf.py filename --display 0

Exporting to frame-based representation

$ python export.py [-h] [--tstart TSTART] [--tstop TSTOP] [--binsize BINSIZE]
                 [--update_prog_every UPDATE_PROG_EVERY]
                 [--export_aps EXPORT_APS] [--export_dvs EXPORT_DVS] [--display 0]
                 [--out_file OUT_FILE]
                 filename

License

This software is released under the GNU LESSER GENERAL PUBLIC LICENSE Version 3.