Before starting the program, you must install OpenCV >= v3.1.x along with the pip dependencies:
$ pip install -r requirements.txt
Then, you can run the program using:
$ python run.py
usage: run.py [-h] [-i IMAGE] [-s SOURCE] [-t TEAM] [-d] [-na MIN_AREA] [-xa MAX_AREA] [-nf MIN_FULL] [-xf MAX_FULL] [-l LOWER_COLOR [LOWER_COLOR ...]] [-u UPPER_COLOR [UPPER_COLOR ...]] [-tn] [-v]
If you are using Docker, the installation process is much easier.
Note: due to the sizes of the images that are installed as part of the build process, it is recommended to have a few GB of hard disk space available when building.
cd to this repository and then run
docker build . -t vision. All existing config files will be used in the build process to make the image.
This will produce a docker image which can be run using:
$ docker run --device /dev/video0 vision
Make sure that you have a camera connected before running this!
SERT's vision software comes with a connection status GUI to help debug connection issues. This GUI can be displayed on a monitor connected to the co-processor. We use an Adafruit 5" screen.
RADIOchecks connection to the robot's radio (at
ROBOTchecks connection to the roboRIO (at
NTABLchecks connection to the NetworkTables server on the roboRIO
|Connection is good and system is ready.||Some connections are down.||No connection. Ensure ethernet is plugged in.|
All command-line arguments may be configured in the
config/config.ini). For example, the
argument may be edited using the
lower-rgb line in the
optional arguments: -h, --help show this help message and exit -i IMAGE, --image IMAGE path to image -s SOURCE, --source SOURCE video source (default=0) -t TEAM, --team TEAM the team of the target roboRIO -d, --display display results of processing in a new window -na MIN_AREA, --min-area MIN_AREA minimum area for blobs -xa MAX_AREA, --max-area MAX_AREA maximum area for blobs -nf MIN_FULL, --min-full MIN_FULL minimum fullness of blobs -xf MAX_FULL, --max-full MAX_FULL maximum fullness of blobs -l LOWER_COLOR [LOWER_COLOR ...], --lower-color LOWER_COLOR [LOWER_COLOR ...] lower color threshold in HSV -u UPPER_COLOR [UPPER_COLOR ...], --upper-color UPPER_COLOR [UPPER_COLOR ...] upper color threshold in HSV -tn, --tuning open in tuning mode -v, --verbose for debugging, prints useful values
For use with the Microsoft Lifecam 3000, the camera's exposure should be set manually because the Lifecam will auto-adjust otherwise, making thresholding difficult. This can be done with V4L:
$ sudo apt-get install v4l-utils $ v4l-ctl -d /dev/video0 -c exposure_auto=1 # 1=DISABLED, 3=ENABLED $ v4l-ctl -d /dev/video0 -c exposure_absolute=50