# Pickle Vision

Real-time referee system for pickleball with 2 CSI cameras on Jetson.

## Current Product Scope

Three tabs in the web UI:

1. `Camera` - live feeds from both cameras.
2. `Calibration` - step-by-step court calibration flow (ROI -> lines -> intersections -> template match -> camera pose).
3. `Trajectory` - 3D court + ball trajectory + VAR overlay.

Dagster pipeline has been removed from active project runtime.

## Run

### Local (Jetson)

```bash
python3 jetson/main.py --port 8080
```

Open: `http://<jetson-ip>:8080`

### Docker

```bash
docker-compose up --build
```

Open: `http://localhost:8080`

## Calibration Flow

Calibration is launched from the `Calibration` tab with `Run Calibration Flow`.

Per camera, UI shows each step status:

1. Court ROI (green mask)
2. White line segments detection
3. Segment merge into court lines
4. Line intersections
5. Template point match
6. Camera pose solve (PnP)
7. Geometry overlay

Output is stored in:

- `jetson/config/cam0_calibration.json`
- `jetson/config/cam1_calibration.json`

## Main Runtime Files

- `jetson/main.py` - dual-camera loop, calibration, detection, VAR events
- `src/web/app.py` - Flask API + tab endpoints
- `src/web/templates/index.html` - 3-tab UI
- `src/calibration/camera_calibrator.py` - camera geometry / projection
- `src/physics/trajectory.py` - trajectory model
- `src/physics/event_detector.py` - close-call logic

## Dependencies

Install from `requirements.txt`.

```bash
pip install -r requirements.txt
```

## Notes

- Calibration is required before trajectory/VAR logic becomes fully useful.
- Each camera is calibrated independently against known half-court geometry.