# Saqr — PPE Safety Detection on Unitree G1

Real-time PPE compliance (helmet, vest, boots, gloves, goggles) using YOLO11n,
designed to run on a Unitree G1 humanoid with an Intel RealSense D435I. On
UNSAFE the robot speaks a warning and plays the `reject` arm action.

## Layout

```
saqr/           # python package
  core/         # detection + tracking + events (shared by CLI/GUI/bridge)
  apps/         # CLI entry points (saqr, detect, train, manager, view_stream)
  gui/          # PySide6 desktop GUI
  robot/        # G1 bridge + DDS controller
  utils/        # logger
scripts/        # deploy.sh, start_saqr.sh, run_local.sh, run_robot.sh, systemd unit
config/         # logging.json
data/           # dataset/, models/   (gitignored)
runtime/        # captures/, logs/, runs/   (gitignored)
docs/           # DEPLOY.md, start.md, use_case_catalogue.pdf
```

## Quick start

```bash
# Install the package (editable)
pip install -e .

# Local dev run (webcam)
saqr --source 0

# PySide6 GUI
pip install -e ".[gui]"
saqr-gui

# On the Unitree G1 (bridge owns the R2+X / R2+Y flow)
saqr-bridge --iface eth0 --source realsense --headless -- --stream 8080
```

Without installing, everything still works via `python -m`:

```bash
python -m saqr.apps.saqr_cli --source 0
python -m saqr.robot.bridge --iface eth0 --source realsense --headless
```

## Docs

- [docs/DEPLOY.md](docs/DEPLOY.md) — full deploy + robot setup.
- [docs/start.md](docs/start.md) — systemd auto-start workflow.
- [docs/use_case_catalogue.pdf](docs/use_case_catalogue.pdf) — PPE use-case spec.

## Data & Models

The `data/` and `runtime/` directories are excluded from git (too large).
Download them separately before training or running inference.

### `data/` — dataset and pre-trained weights

Expected contents:

```
data/
  dataset/
    train/{images,labels}/
    valid/{images,labels}/
    test/{images,labels}/
    data.yaml
  models/
    saqr_best.pt       # Saqr YOLO11n fine-tuned on PPE
    saqr_last.pt
    yolo11n.pt         # base YOLO11n
    yolo26n.pt         # base YOLO26n
```

Download:

- **Dataset** (PPE, Roboflow): [testcasque/ppe-detection-qlq3d](https://universe.roboflow.com/testcasque/ppe-detection-qlq3d)
  Open the Roboflow link → *Download Dataset* → format **YOLOv11** → unzip into `data/dataset/`.
- **Base YOLO weights**: [Ultralytics assets releases](https://github.com/ultralytics/assets/releases)
  Grab `yolo11n.pt` (and optionally `yolo26n.pt`) into `data/models/`.
- **Saqr fine-tuned weights** (`saqr_best.pt`, `saqr_last.pt`):
  Produced by training — see "Training" below. Or request from the maintainer.

Place everything under `data/` so the tree matches above.

### `runtime/` — training output (optional)

Auto-generated when you run training. Not required for inference.
Contains confusion matrices, PR curves, batch previews, and the raw weights
under `runtime/runs/train/saqr_det/weights/`.

### Training

```bash
# after placing the dataset in data/dataset/ and base weights in data/models/
python -m saqr.apps.train_cli --data data/dataset/data.yaml --weights data/models/yolo11n.pt
```

Outputs land in `runtime/runs/train/saqr_det/`. Copy the best checkpoint to
`data/models/saqr_best.pt` to use it at inference time.