visioncore-frc 0.1.0

FRC vision pipeline for object detection and field mapping

2026 Vision Testing

This repository contains vision processing code for 2026 FRC competition. The code is organized into simplified and complex implementations.

Simplified folder: detects fuel and sends positions over network tables. Good for competition and low-latency setups.

Complex folder: does path planning and plotting along with fuel detection. More features but slower.

Important Scripts

video_feed_broadcaster.py - Flask app that streams camera feed to a website yolo_with_cam.py - Runs YOLO vision model on camera (default camera 0) pt_to_wtv.py - Converts .pt files to other formats (onnx, xml, etc) map_maker.py - Interactive window to create a fuel map map_creator.py - Uses the vision model to create a birdseye view from images camera_calibration.py - Calculates focal length constants for camera calibration livestream_reader.py - Reads frames from a livestream using Camera class onnx_to_rknn.py - Converts models to RKNN format

Folder Structure

COMPLEX - path planning and plotting with fuel detection SIMPLIFIED - fuel detection and network tables sending PLOTTERS - plotting and utility experiments YOLO_MODELS - YOLO models and exports

Key Classes

Camera.py - camera configuration and capture CustomDBScan.py - point cloud filtering with DBSCAN NetworkTableHandler.py - network tables communication PathPlanner.py - path planning logic Fuel.py - fuel tracking object FuelTracker.py - fuel tracking helper

Orange Pi 5 Device

IP: 10.22.7.200 Username: ubuntu Password: 2207vision

To access the device, use SSH with the credentials above. To clone the repo on the device: git clone [e-mail]:FRC2207/2026-Vision-Testing.git

Common Commands

sudo systemctl restart vision.service - restart the vision service tmux attach -t vision - attach to vision tmux session git pull - update code from repository git reset --hard HEAD - undo local changes git clean -fd - remove untracked files

Model Performance

Model Size Latency FPS Yolov26 nano 99.5ms 10 Yolov11 nano 71.5ms 14 Yolov11 small 98.9ms 10 Yolov11 medium 235.3ms 4 Yolov8 nano 20-30ms 30-50 Yolov8 small 40-60ms 15-25 Yolov5 nano 15-25ms 40-60

Notes

The repository is functional but has rough edges and moved files during testing. When converting models, double-check export settings to avoid losing work. The simplified implementation is recommended for competition use.