visioncore-frc 2.5.9.17

FRC vision pipeline for custom, hardware accelerated object detection.

VisionCore

FRC vision pipeline for object detection and field mapping — runs on Orange Pi with Rockchip NPU, supports RKNN, ONNX, OpenVINO, TFLite, and CoreML backends.

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What It Does

VisionCore is a plug-and-play computer vision system for FRC robots. You point a camera at the field, it detects game pieces, converts pixel positions into field-relative coordinates, and sends them to your robot over NetworkTables — all automatically.

• Detects objects with a YOLO model (any size, any format)
• Converts detections to real-world field coordinates using camera calibration
• Tracks objects across frames with EMA smoothing and DBSCAN clustering
• Publishes positions and diagnostics to NetworkTables
• Streams live annotated video over a local web server
• Auto-selects the fastest model format for whatever hardware you're running on
• Survives crashes via a watchdog that restarts the pipeline automatically

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Hardware

Recommended deploy target: Orange Pi 5 / 5 Pro (RK3588 NPU)

Also runs on:

• Any aarch64 Linux board (Raspberry Pi, Jetson) — uses TFLite
• x86 Linux — uses ONNX or OpenVINO
• macOS (Apple Silicon) — uses CoreML
• Windows — uses ONNX

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Quick Start — Flash and Go

This is the zero-config path. Flash the pre-built image, plug in ethernet, power on.

1. Download the image

Go to the Releases page and download the latest orangepi.img file.

2. Flash it

Use balenaEtcher or dd:

sudo dd if=orangepi.img of=/dev/sdX bs=4M status=progress

3. Power on with ethernet connected

The board will boot, connect to the internet, clone this repo, install all dependencies, and start the vision pipeline automatically. Watch progress over serial or SSH:

journalctl -u first-boot -f

Once complete, the pipeline runs as a systemd service on every boot:

journalctl -u visioncore -f   # live logs
systemctl restart visioncore  # restart
systemctl stop visioncore     # stop

4. Configure

Edit /etc/visioncore/config.json on the board, then restart the service. See Configuration below.

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Manual Install (No Image)

If you have a board already running Ubuntu/Debian:

git clone https://github.com/aidan-j532/VisionCore-Deploy
cd VisionCore-Deploy
chmod +x install-deploy.sh
./install-deploy.sh

Or run the full provisioner in one line:

curl -fsSL https://raw.githubusercontent.com/aidan-j532/VisionCore-Deploy/main/Image/provision.sh | bash

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Dev Setup (x86 / Laptop)

Use this to train models, convert formats, or modify the pipeline on a regular computer.

git clone https://github.com/aidan-j532/VisionCore-Deploy
cd VisionCore-Deploy
chmod +x install-dev.sh
./install-dev.sh

Run the pipeline locally (uses a webcam or image file):

visioncore-run

Run the boot sequence (downloads a default model, sets up service):

visioncore-boot

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Configuration

The config file lives at Config/config.json (or /etc/visioncore/config.json on deployed boards).

{
    "vision_model": {
        "file_path": "YoloModels/pytorch/nano/your_model.pt",
        "input_size": [640, 640],
        "min_conf": 0.5,
        "margin": 10
    },
    "unit": "meter",
    "auto_opt": true,
    "debug_mode": false,
    "use_network_tables": true,
    "network_tables_ip": "10.TE.AM.2",
    "stale_threshold": 1.0,
    "distance_threshold": 0.5,
    "dbscan": {
        "elipson": 0.3,
        "min_samples": 3
    },
    "camera_configs": {
        "front_cam": {
            "name": "front_cam",
            "source": "/dev/video0",
            "pipeline": "object_detection",
            "fps_cap": 30,
            "yaw": 0,
            "pitch": 0,
            "height": 0.5,
            "x": 0.2,
            "y": 0,
            "subsystem": "field",
            "calibration": {
                "distance": 1.0,
                "game_piece_size": 3.5,
                "size": 120,
                "fov": 70
            }
        }
    }
}

Key settings

Key	What it does
auto_opt	Automatically converts your .pt model to the fastest format for the current hardware
unit	Output coordinate unit: meter, inch, foot, centimeter
network_tables_ip	Robot IP — typically 10.TE.AM.2 where TEAM is your 4-digit team number
stale_threshold	Seconds before a detection is considered stale (default 1.0)
distance_threshold	Merge radius for the object tracker in your chosen unit (default 0.5)
debug_mode	Draws bounding boxes and FPS on the video feed
margin	Pixels to ignore at image edges (filters partial detections)

Camera calibration

To get accurate distances, measure these values with your actual camera and game piece:

Calibration field	How to measure
game_piece_size	Diameter or height of the game piece in inches
distance	Distance from camera to the game piece during calibration (same unit as game piece)
size	Pixel height of the game piece bounding box at that calibration distance
fov	Camera field of view in degrees (check your camera's spec sheet)

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Model Setup

Models live in YoloModels/[format]/[size]/. Example structure:

YoloModels/
  pytorch/nano/my_model.pt
  rknn/nano/my_model.rknn
  openvino/nano/my_model_openvino_model/

With auto_opt: true, VisionCore converts your .pt model at boot time and caches the result. Supported formats: rknn, onnx, openvino, tflite, coreml.

To convert manually on a dev machine:

from VisionCore.utilities.laptop.AllInOneConvert import convert_model

convert_model("my_model.pt", format="rknn", task="detect")

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Web Interface

When the pipeline is running, open a browser and go to http://<board-ip>:5000.

Endpoint	What you get
/	Live annotated camera feed
/health	System health dashboard (browser) or JSON (API)
/api/cameras	List of connected cameras
/api/camera/<name>/feed	Stream for a specific camera
/api/camera/<name>/settings	GET or POST camera settings

The health endpoint returns 200 OK when everything is healthy, 503 when degraded. Useful for robot code that wants to know if vision is alive.

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NetworkTables Output

VisionCore publishes to the VisionData table:

Key	Type	Description
VisionData/vision_data	FuelStruct[]	Array of detected object positions (x, y) in field coordinates
VisionData/fps	double	Current pipeline FPS
VisionData/num_detections	double	Number of active tracked objects
VisionData/camera_lag	double	Camera frame age in seconds
VisionData/timestamp_ms	double	Unix timestamp of last update

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Plugin System

VisionCore uses a plugin architecture. Drop a file into the right folder and it loads automatically.

Custom tracker

# VisionCore/plugins/trackers/my_tracker.py
from VisionCore.plugins.bases import TrackerBase

class MyTracker(TrackerBase):
    plugin_name = "my_tracker"

    def update(self, fuel_list, robot_x, robot_y, robot_yaw):
        # filter, smooth, or modify detections here
        return fuel_list

Then add "my_tracker" to plugins.trackers in your config.

Custom utility (Flask route, side effect, etc.)

# VisionCore/plugins/utilities/my_utility.py
from VisionCore.plugins.bases import UtilityBase

class MyUtility(UtilityBase):
    plugin_name = "my_utility"

    def __init__(self, context: dict):
        flask_app = context["flask_app"]
        if flask_app:
            flask_app.add_url_rule("/my-route", "my_route", self._route)

    def update(self, frame_data: dict):
        # called every loop with fps, detections, frame, fuel_list, etc.
        pass

    def _route(self):
        return "hello from my plugin"

Then add "my_utility" to plugins.utilities in your config.

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Validation

Run before deploying to catch config or model issues:

# Unit tests
python -m VisionCore.validations.ez

# Check model organization
python -m VisionCore.validations.model_validator check-org

# Full system validation (tests + model + config checks)
python -c "from VisionCore.validations.validate_system import validate_system; validate_system()"

# Config recommendations
python -c "from VisionCore.validations.validate_system import get_recommendations; print(get_recommendations())"

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Architecture

game_loop.py
  └── VisionCore
        ├── ObjectDetectionCamera (per camera)
        │     ├── Camera (threaded frame reader)
        │     └── GenericYolo (RKNN / ONNX / TFLite / Ultralytics)
        ├── MultipleCameraHandler (merges multi-camera detections)
        ├── Trackers (object_tracker → path_planner → your plugins)
        ├── Utilities (health_reporter, video_recorder, network_handler, your plugins)
        └── CameraApp (Flask web server)

The main loop runs at whatever FPS the camera and model allow. On an Orange Pi 5 with a nano RKNN model, expect 30–60 FPS.

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License

GPL-3.0 — see LICENSE.