I’m building an automated scorer for a standard dartboard that relies on three off-the-shelf webcams. As soon as a dart sticks, the system must pinpoint its exact segment, translate that into the correct score, and push a real-time event—ideally over a WebSocket—so any client can update the match instantly. Core expectations • Camera input: three synchronized 1080p webcams positioned around the board. • Tech stack: feel free to reach for either Python/OpenCV or C++/OpenCV; whichever lets you reach millisecond-level detection with reliable accuracy. • Calibration: a quick routine that someone in a pub can run in a couple of minutes—no chessboard targets or lab lighting required. • Output: headless service only; no on-screen UI. Emit JSON events such as `{ "x":…, "y":…, "ring":"double", "number":20, "score":40 }` the moment impact is confirmed. Deliverables 1. Source code with build/run instructions. 2. Calibration workflow and any printable targets if you use them. 3. API spec for the real-time event stream plus a minimal test client. 4. Performance report: detection accuracy, average latency, and test footage. Acceptance criteria • ≥98 % hit-segment accuracy across the whole board. • End-to-end latency ≤150 ms on a mid-range PC. • Calibration completed in ≤2 minutes by a first-time user. If this sounds like your kind of challenge, let’s talk through your approach to multi-camera geometry, lighting variance, and fast image processing so we can get darts flying and scores flowing.