# ZenAI KTC Counter — Edge Deployment Guide

Production deployment for **RK3588** (or compatible RKNN NPU) edge devices running a
**zone-based sack feeder counter** (1…N named zones):

| Component | Script | systemd unit |
|-----------|--------|--------------|
| RTSP counter (RKNN + ByteTrack + zones) | `counter_live_rknn.py` | `zenai-ktc-counter.service` |
| Web dashboard (Flask) | `counter_dashboard.py` | `zenai-ktc-dashboard.service` |

Both processes share a single `.env` file and read/write the same SQLite database and state JSON.

**Counting model:** `ZONE_COUNT` rectangular zones (`zone_1`…`zone_N`). A sack is counted once
after its centroid **dwells** inside a zone long enough (`ZONE_i_DWELL_SEC`).

---

## 1. Prerequisites

### Hardware & OS

- RK3588 board (or Jetson/RK device with RKNN Lite runtime)
- Linux with systemd
- Network access to the RTSP camera stream

### System packages

```bash
sudo apt update
sudo apt install -y python3 python3-venv python3-pip ffmpeg libgl1
```

`ffmpeg` is required for low-latency RTSP capture via OpenCV. `libgl1` is often needed for `opencv-python` on headless systems.

### RKNN model

Export or copy your `.rknn` model to the device, e.g.:

```text
/opt/models/your_model.rknn
```

Set `MODEL_PATH` in `.env` to match. The model class count must match `NUM_CLASSES`, and `OBJECT_CLASS_ID` must point at the class you count.

---

## 2. Directory layout

Default paths used by the service files and `env.example`:

```text
/opt/zenai-ktc-python/          # application code (this repo)
├── counter_live_rknn.py
├── counter_dashboard.py
├── counter_store.py
├── templates/
├── venv/                       # Python virtual environment (created during install)
├── .env                        # runtime config (not in git)
├── env.example                 # template — copy to .env
└── DEPLOY.md

/opt/zenai-ktc-counter/         # persistent runtime data (created automatically)
├── counter.db                  # SQLite daily records
├── current_counter.json        # live counting-day state
├── snapshots/                  # zone-entry/detect JPEGs (if enabled)
└── crossings.csv               # optional per-event CSV

/opt/models/                    # RKNN models (deploy separately)
/dev/shm/zenai-ktc-counter/     # live JPEG frame for dashboard video (tmpfs)
```

---

## 3. Install application

### 3.1 Copy code to the device

```bash
sudo mkdir -p /opt/zenai-ktc-python
sudo rsync -av --exclude venv --exclude .env --exclude __pycache__ \
  ./ /opt/zenai-ktc-python/
# Or: sudo git clone <repo-url> /opt/zenai-ktc-python
```

### 3.2 Create virtual environment and install dependencies

```bash
cd /opt/zenai-ktc-python
sudo python3 -m venv venv
sudo ./venv/bin/pip install --upgrade pip
sudo ./venv/bin/pip install -r requirements.txt
```

> `rknn-toolkit-lite2` is platform-specific. Install on the target ARM device, not on a Windows dev machine.

### 3.3 Create runtime config

```bash
cd /opt/zenai-ktc-python
sudo cp env.example .env
sudo nano .env
```

**Minimum values to edit before starting:**

| Variable | Description |
|----------|-------------|
| `SOURCE` | RTSP URL or local video file path |
| `MODEL_PATH` | Path to your `.rknn` model on device |
| `NUM_CLASSES` | Must match the exported model |
| `OBJECT_CLASS_ID` | Class index of the object being counted |
| `CLASS_OBJECT` / `OBJECT_LABEL` | Labels stored in DB (e.g. `karung`) |
| `ZONE_COUNT` / `ZONE_i_*` | Named zone rectangles, dwell, cooldown (tune per camera) |
| `SECRET_KEY` | Random string for Flask sessions |

Ensure `STATE_FILE` and `DB_PATH` both live under `/opt/zenai-ktc-counter/` so data survives reboots (avoid `/tmp` in production).

### 3.4 Create data directories (optional — app creates most paths automatically)

```bash
sudo mkdir -p /opt/zenai-ktc-counter /opt/models /dev/shm/zenai-ktc-counter
```

---

## 4. Install systemd services

```bash
cd /opt/zenai-ktc-python
sudo cp zenai-ktc-counter.service zenai-ktc-dashboard.service /etc/systemd/system/
sudo systemctl daemon-reload
sudo systemctl enable zenai-ktc-counter zenai-ktc-dashboard
sudo systemctl start zenai-ktc-counter
sudo systemctl start zenai-ktc-dashboard
```

The dashboard unit starts **after** the counter unit (`After=zenai-ktc-counter.service`).

### Verify

```bash
systemctl status zenai-ktc-counter
systemctl status zenai-ktc-dashboard
journalctl -u zenai-ktc-counter -f
```

Open the dashboard in a browser:

```text
http://<device-ip>:5000
```

(Port is set by `DASHBOARD_PORT` in `.env`, default `5000`.)

---

## 5. Tuning counting zones

Zones are axis-aligned rectangles. Default is a single zone:

```ini
ZONE_COUNT=1
ZONE_1_NAME=feeder
ZONE_1_X1_FRAC=0.10
ZONE_1_Y1_FRAC=0.10
ZONE_1_X2_FRAC=0.90
ZONE_1_Y2_FRAC=1.00
ZONE_1_COOLDOWN_SEC=15
ZONE_1_DWELL_SEC=2
```

For pixel-perfect placement, set absolute coordinates instead (they override fractions):

```ini
ZONE_1_X1=40
ZONE_1_Y1=120
ZONE_1_X2=420
ZONE_1_Y2=900
```

Enable `DEBUG_TRACKING=true` temporarily for verbose inherit / dup / cooldown logs.

Each successful count always prints a Frigate-style line to the journal:

```text
Total karung di kandang_bawah_feeder: 5
```

Set `ZONE_i_NAME` (and `OBJECT_LABEL`) in `.env` to match your site.

---

## 6. Operations

### Restart after config change

```bash
sudo systemctl restart zenai-ktc-counter
sudo systemctl restart zenai-ktc-dashboard
```

### View logs

```bash
journalctl -u zenai-ktc-counter -n 100 --no-pager
journalctl -u zenai-ktc-dashboard -n 100 --no-pager
```

### Stop services

```bash
sudo systemctl stop zenai-ktc-dashboard zenai-ktc-counter
```

The counter handles `SIGTERM` gracefully — it finishes the current frame, persists state to SQLite, then exits.

### Update application code

```bash
cd /opt/zenai-ktc-python
sudo systemctl stop zenai-ktc-dashboard zenai-ktc-counter
# rsync or git pull new code
sudo ./venv/bin/pip install -r requirements.txt   # if dependencies changed
sudo systemctl start zenai-ktc-counter zenai-ktc-dashboard
```

---

## 7. Troubleshooting

| Symptom | Things to check |
|---------|-----------------|
| Counter won't start | `journalctl -u zenai-ktc-counter`; verify `MODEL_PATH` exists; RKNN drivers installed |
| No RTSP frames | Ping camera; test with `ffplay <SOURCE>`; check `OPENCV_FFMPEG_CAPTURE_OPTIONS` |
| Dashboard shows 0 count | `STATE_FILE` in `.env` must match between counter and dashboard; check file exists |
| Live video blank | `LIVE_STREAM_ENABLED=true`; path matches `LIVE_STREAM_FRAME_PATH` in both processes |
| Wrong zone / missed counts | Tune `ZONE_i_*_FRAC`, `CONF`, ByteTrack thresholds; enable `DEBUG_TRACKING=true` |
| Service keeps restarting | `journalctl -u zenai-ktc-counter -e`; often missing model, bad RTSP URL, or venv not created |

### Manual test (without systemd)

```bash
cd /opt/zenai-ktc-python
source venv/bin/activate
python counter_live_rknn.py    # terminal 1
python counter_dashboard.py    # terminal 2
```

---

## 8. Optional: reverse proxy

For HTTPS or port 80 access, put nginx in front of the dashboard:

```nginx
server {
    listen 80;
    server_name counter.example.com;

    location / {
        proxy_pass http://127.0.0.1:5000;
        proxy_http_version 1.1;
        proxy_set_header Host $host;
        proxy_set_header X-Real-IP $remote_addr;
        proxy_buffering off;        # required for /api/live-video MJPEG stream
        proxy_read_timeout 3600s;   # keep long-lived MJPEG connections open
        proxy_send_timeout 3600s;
    }
}
```

---

## 9. Security notes

- Change `SECRET_KEY` from the default before exposing the dashboard on a network.
- Services currently run as `root` for simplicity on edge devices. For hardened deployments, create a dedicated user, chown `/opt/zenai-ktc-counter`, and update the `User=` / `Group=` lines in the service files.
- Do not commit `.env` — it may contain RTSP credentials.
- Set `FLASK_DEBUG=false` in production.