https://github.com/flash3388/flash-vision-perocessing
Vision using the Raspberry PI image for FRC used in 2019
https://github.com/flash3388/flash-vision-perocessing
frc frc-java java raspberry-pi vision
Last synced: 2 months ago
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Vision using the Raspberry PI image for FRC used in 2019
- Host: GitHub
- URL: https://github.com/flash3388/flash-vision-perocessing
- Owner: Flash3388
- Created: 2020-01-16T15:37:03.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2020-03-06T22:03:03.000Z (over 6 years ago)
- Last Synced: 2025-06-14T03:44:17.407Z (about 1 year ago)
- Topics: frc, frc-java, java, raspberry-pi, vision
- Language: Java
- Homepage:
- Size: 19.2 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# frcvision-rpi
Testing vision using the raspberry PI image for FRC
Using the example provided with the image, we set up a server which performs a simple processing on an image from a camera, and uploads it to the camera server.
# What does the code do?
The code will start a camera server, using pre-defined configuration, and run a vision processing thread on the first
defined camera.
The vision processing will take an image from the camera, grayscale it, and upload it to the camera server into a different stream.
# Configuration
The configuration is defined in `/boot/frc.json` by default, and contains the following information:
- team number
- status of the network tables instance used (for communicating with the robot - server or client)
- settings for cameras to display in the camera server
```JSON
{
"team": ,
"ntmode": <"client" or "server", "client" if unspecified>
"cameras": [
{
"name":
"path":
"pixel format": <"MJPEG", "YUYV", etc> // optional
"width": // optional
"height": // optional
"fps": // optional
"brightness": // optional
"white balance": <"auto", "hold", value> // optional
"exposure": <"auto", "hold", value> // optional
"properties": [ // optional
{
"name":
"value":
}
],
"fov":
}
],
"templateMatchingMethod": ,
"visionTemplatePath": ,
"templateMatchingScaleFactor":
}
```
# Building
To build the code run the gradle wrapper: `./gradlew build` for UNIX systems and `gradlew.bat build` for Windows systems.
# Deploy
To deploy the code to the Raspberry PI, simple run `./deploy.sh`. This script will use `scp` to copy both the compiled code and the `runCamera` script.
# Running
Using `ssh` run the `runInteractive` script in `/home/pi`.