AI SummaryOpenDarts is a self-hosted dart application with computer vision-based auto-scoring, enabling players to track games and practice with automated score detection via their phone camera. It benefits dart enthusiasts and competitive players who want accurate scoring and game management without manual entry.
Install
Copy this and paste it into Claude Code, Cursor, or any AI assistant:
I want to add the "OpenDarts — Copilot Instructions" prompt rules to my project. Repository: https://github.com/dmall00/OpenDarts Please read the repo to find the rules/prompt file, then: 1. Download it to the correct location (.cursorrules, .windsurfrules, .github/prompts/, or project root — based on the file type) 2. If there's an existing rules file, merge the new rules in rather than overwriting 3. Confirm what was added
Description
Self hostable dart application with app for playing and practicing with auto scoring capabilities using a phone.
Three-Tier System
• React Native App (/app) - Mobile client with camera-based autoscoring • Kotlin Backend (/backend) - Spring Boot server for game orchestration and autoscore stabilization • Python AutoScore Server (/autoscore-server) - WebSocket server for YOLO-based dart detection
Critical Data Flow (AutoScore Pipeline)
` Mobile Camera → Binary WS (App→Backend) → Backend forwards → Python AutoScore Server ↓ Mobile App ← Game State Updates ← Backend Stabilizer ← Detection Results ` Key Insight: The backend acts as a stabilizer between noisy YOLO detections and confirmed game state. The AutoScoreStabilizer service uses a frame-based tracking system with pending/confirmed dart states to prevent false positives from camera jitter or lighting changes.
WebSocket Architecture
• App ↔ Backend: /ws/app/{playerId}/{gameId} - Bidirectional binary protocol for images + JSON for game state • Backend ↔ Python: Separate WebSocket client (AutoscoreWebSocketClient) forwards images to Python server • Binary Protocol: Custom format with metadata header + image data (see app/src/utils/binaryProtocol.ts)
1. Backend (from /backend)
./mvnw --pl core -amd clean compile spring-boot:run
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Works With
Any AI assistant that accepts custom rules or system prompts