Deployment (Jetson)

Repo: https://git.proit.id/andrew/karung-counting-feedmill-semarang (git remote set-url origin <url> after the ervan → andrew transfer).

Systemd services

Unit	Runs	After
`karung-counter.service`	`/usr/bin/python3 predict.py` (cwd `/home/jetson/karung`, `QT_QPA_PLATFORM=offscreen`)	`network.target`
`karung-counter-dashboard.service`	`/usr/bin/python3 counter_dashboard.py`	`network.target` + counter
`mediamtx.service`	MediaMTX restream (see `zones.json:external_stream_url`)	—

Both app units: Restart=always, RestartSec=5, load EnvironmentFile=/home/jetson/karung/.env.

sudo systemctl enable --now karung-counter karung-counter-dashboard
sudo systemctl restart karung-counter karung-counter-dashboard
systemctl status karung-counter karung-counter-dashboard --no-pager

Deploy flow (`deploy_to_jetson.py`)

Paramiko sync of templates/{operator,monitoring,base}.html, counter_dashboard.py, predict.py, config.yaml, .env plus models/*.engine (v4-best, yolo11n-sack+box, best, truck-detector, model_karung_truk) → 192.168.192.96:/home/jetson/karung/ (creates remote models/ if missing, skips missing local files), then restarts both services and checks status + ports (5000/5721). Run from the dev machine. .pt/.onnx stay local-only (dev/export).

TensorRT export

On the Jetson (needs CUDA): python3 export_model.py models/<name>.pt exports to FP16 .engine next to the .pt (default: karung-dimuat seg model). Production loads .engine only — see models/modelREADME.md for which weights each mode uses.

Runtime data files

SQLite jetson_counter.db: batches(counting_date, batch_number, camera_name, object_label, count, start/end_time, box_*, plate, do_numbers, expected_*, net_sack, net_box), daily_summaries(...), delivery_orders(...) (DO photos).
current_batch.json (crash recovery), batch_mode.json (batch flow mode only: auto|do_manual|manual — model mode lives in config.yaml), do_settings.json (require_plate/do + ocr_engine), $OUTPUT_DIR/do_photos/YYYY-MM-DD/ (7-day retention), batch_history_folder/batch_<ts>.json + hasil_perhitungan.json (per-batch reports).
Live frame: /dev/shm/jetson-counter/live_frame.jpg (written every 2nd frame, consumed by /api/live-video MJPEG).
Helpers: check_jetson_db.py (root); retired ops scripts in archive/ (backup.py, dump_db.py, migrate_jetson_db.py, merge_batches_*.py, update_batches.py, diagnose_truck_jetson.py).

DO OCR packages

Default engine is RapidOCR (PP-OCR via onnxruntime, bundled models — offline-friendly; installed with requirements.txt):

pip install rapidocr_onnxruntime

Backups (ocr_engine — office monitoring UI toggle, no restart): Tesseract (Backup, Light):

# lighter; system package + Indonesian traineddata
sudo apt install tesseract-ocr tesseract-ocr-ind
pip install pytesseract

PaddleOCR (Accuracy, Heavy to run):

# optional; heavier — see paddleocr docs for Jetson wheels
pip install paddleocr

Missing deps → upload returns explicit error (never silent fallback); flip engine back to RapidOCR/Tesseract from the office dashboard (:5721). Operator page: http://<jetson>:5000/operator (smartphone camera capture for DO photos). Photo dir under output.dir with 7-day retention (hourly purge in dashboard process).

Dashboard (`counter_dashboard.py`)

Pages: / + /monitoring, /operator (manual start/stop; DO panel in do_manual), /history, /analytics. Key APIs: /api/live-video, /api/current-batch, /api/previous-batch, /api/batch/{start,stop,stop-preview,mode}, /api/model-modes (mode list is derived from config.yaml, so future modes appear automatically), /api/do/{upload,photo/<id>,staged,settings,retention} + PUT/DELETE /api/do/<id>, /api/summary, /api/daily-data, /api/day-detail/<date>, /api/recent-batches, /api/available-dates, /api/export-daily-csv, /api/export-day-csv/<date> (Excel via openpyxl).

Port split: mode / model_mode / require_plate / require_do / ocr_engine POSTs → office 5721 only (403 on 5000). Smartphones open http://<host>:5000/operator for camera capture.

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