openpilot_comma/tools

openpilot tools

tools to facilitate development and debugging of openpilot

Table of Contents

• Requirements
• Setup
• Tool examples
  • Replay driving data
  • Debug car controls
  • Stream replayed CAN messages to EON
  • Stream EON video data to a PC
• Welcomed contributions

Requirements

openpilot tools and the following setup steps are developed and tested on Ubuntu 16.04, MacOS 10.14.2 and Python 3.7.3.

Setup

1. Run ubuntu_setup.sh, make sure everything completed correctly

2. Compile openpilot by running scons in the openpilot directory

3. Add some folders to root

   sudo mkdir /data
   sudo mkdir /data/params
   sudo chown $USER /data/params
   

4. Try out some tools!

Tool examples

Replay driving data

Hardware needed: none

unlogger.py replays data collected with chffrplus or openpilot.

Unlogger with remote data:

# Log in via browser
python lib/auth.py

# Start unlogger
python replay/unlogger.py <route-name>
#Example:
#python replay/unlogger.py '3533c53bb29502d1|2019-12-10--01-13-27'

# In another terminal you can run a debug visualizer:
python replay/ui.py   # Define the environmental variable HORIZONTAL is the ui layout is too tall

Unlogger with local data downloaded from device or https://my.comma.ai:

python replay/unlogger.py <route-name> <path-to-data-directory>

#Example:

#python replay/unlogger.py '99c94dc769b5d96e|2018-11-14--13-31-42' /home/batman/unlogger_data

#Within /home/batman/unlogger_data:
#  99c94dc769b5d96e|2018-11-14--13-31-42--0--fcamera.hevc
#  99c94dc769b5d96e|2018-11-14--13-31-42--0--rlog.bz2
#  ...

LogReader with remote data

from tools.lib.logreader import LogReader
from tools.lib.route import Route
route = Route('3533c53bb29502d1|2019-12-10--01-13-27')
log_paths = route.log_paths()
events_seg0 = list(LogReader(log_paths[0]))
print(len(events_seg0), 'events logged in first segment')

Debug car controls

Hardware needed: panda, giraffe, joystick

Use the panda's OBD-II port to connect with your car and a usb cable to connect the panda to your pc. Also, connect a joystick to your pc.

joystickd.py runs a deamon that reads inputs from a joystick and publishes them over zmq. boardd sends the CAN messages from your pc to the panda. debug_controls is a mocked version of controlsd.py and uses input from a joystick to send controls to your car.

Make sure the conditions are met in the panda to allow controls (e.g. cruise control engaged). You can also make a modification to the panda code to always allow controls.

Usage:

python carcontrols/joystickd.py

# In another terminal:
PARAMS_PATH=persist/params selfdrive/boardd/boardd

# In another terminal:
python carcontrols/debug_controls.py

Stream replayed CAN messages to EON

Hardware needed: 2 x panda, debug board, EON.

It is possible to replay CAN messages as they were recorded and forward them to a EON.  Connect 2 pandas to the debug board. A panda connects to the PC, the other panda connects to the EON.

Usage:

# With MOCK=1 boardd will read logged can messages from a replay and send them to the panda.
MOCK=1 selfdrive/boardd/tests/boardd_old.py

# In another terminal:
python replay/unlogger.py <route-name> <path-to-data-directory>

Stream EON video data to a PC

Hardware needed: EON, comma Smays.

You can connect your EON to your pc using the Ethernet cable provided with the comma Smays and you'll be able to stream data from your EON, in real time, with low latency. A useful application is being able to stream the raw video frames at 20fps, as captured by the EON's camera.

Usage:

# ssh into the eon and run loggerd with the flag "--stream". In ../selfdrive/manager.py you can change:
# ...
# "loggerd": ("selfdrive/loggerd", ["./loggerd"]),
# ...
# with:
# ...
# "loggerd": ("selfdrive/loggerd", ["./loggerd", "--stream"]),
# ...

# On the PC:
# To receive frames from the EON and re-publish them. Set PYGAME env variable if you want to display the video stream
python streamer/streamerd.py

Welcomed contributions

• Documentation: code comments, better tutorials, etc..
• Support for other platforms other than Ubuntu 16.04.
• Performance improvements: the tools have been developed on high-performance workstations (12+ logical cores with 32+ GB of RAM), so they are not optimized for running efficiently. For example, ui.py might not be able to run real-time on most PCs.
• More tools: anything that you think might be helpful to others.