karung-counting-feedmill-semarang/config.yaml

# config.yaml — canonical configuration for the production pipeline
# (predict.py + counter_dashboard.py). Single source of truth.
#
# Secrets & deployment-specific values (RTSP_URL, dashboard host/ports,
# SECRET_KEY, SITE_NAME) stay in .env — they are NOT duplicated here.
# Zone polygons stay in zones.json. Tracker hyperparams stay in cfg/tracker.yaml.
#
# Model modes: add new entries under models.modes (E, F, ...) without code changes.
# A mode only selects engines + class filters; per-class conf/iou/min_bbox_area
# live in models.detection_params and apply to ALL modes.

stream:
  resolution: [1280, 720]
  inference_stride: 2

models:
  active_mode: "C"   # A|B|C|D (+ future E, F, ...). Dashboard switch writes here (manual restart to apply).

  # Engine file paths (relative to repo root). Mode `engines` entries reference these keys.
  paths:
    combined: "models/v4-best.engine"
    truck_only: "models/v4-best.engine"
    sack_only: "models/best.engine"
    box: "models/yolo11n-bbox-100ep-sack+box-20260909-best.engine"
    truck_detector: "models/truck-detector.engine"

  # Mode presets: engines to load + class filters. NOTHING else per mode.
  # Each engine declares the classes it contributes, so tracker roles
  # (shared vs dedicated, separate truck model) derive structurally —
  # new modes need zero Python changes.
  modes:
    A:
      description: "Combined v4 sack+truck only (legacy, no box counting)"
      engines:
        - path: combined
          classes: [truck, sack]
      class_filters:
        truck: ["truck"]
        sack: ["sack"]
        box: []
    B:
      description: "v4 truck-only + yolo11n sack+box (shared tracker, shared ID space)"
      engines:
        - path: truck_only
          classes: [truck]
        - path: box
          classes: [sack, box]
      class_filters:
        truck: ["truck"]
        sack: ["sack"]
        box: ["box"]
    C:
      description: "Combined v4 sack+truck + yolo11n box-only (dedicated tracker)"
      engines:
        - path: combined
          classes: [truck, sack]
        - path: box
          classes: [box]
      class_filters:
        truck: ["truck"]
        sack: ["sack"]
        box: ["box"]
    D:
      description: "v4 truck-only + best sack-only + yolo11n box-only (3 engines)"
      engines:
        - path: truck_only
          classes: [truck]
        - path: sack_only
          classes: [sack]
        - path: box
          classes: [box]
      class_filters:
        truck: ["truck"]
        sack: ["sack"]
        box: ["box"]

  # Shared per-class detection params (ALL modes). iou default = Ultralytics default.
  # min_bbox_area is a permissive guardrail in px^2 @1280x720 (existing
  # perspective min_valid_area filter still applies on top).
  detection_params:
    truck:
      conf: 0.35
      iou: 0.7
      min_bbox_area: 5000
    sack:
      conf: 0.3 # default 0.35
      iou: 0.7
      min_bbox_area: 1500
    box:
      conf: 0.35
      iou: 0.7
      min_bbox_area: 1500

# Polygons live in zones.json (kept separate: site calibration cadence).
zones:
  config_file: "zones.json"

# Tracker hyperparams live in cfg/tracker.yaml (kept separate: ML tuning cadence).
tracker:
  config_file: "cfg/tracker.yaml"

counting:
  confirm_delay_sec: 0.5
  exit_confirm_delay_sec: 6.0
  entry_overlap_threshold: 0.20
  exit_overlap_threshold: 0.05
  camera_noise_deadband: 50
  # Effective values below match zones.json (which previously overrode predict.py defaults).
  duplicate_circle_radius: 30
  min_valid_area: 15000
  max_reid_transit_distance: 400
  circle_stay_timeout_sec: 10.0
  jarak_toleransi_duplikat: 30
  tolerance_missing_frames: 1200
  max_reid_frames: 200
  debounce_frames: 8

batch:
  timeout_seconds: 20.0
  merge_threshold_seconds: 300   # deprecated by timeout_seconds and detecting sacks within the ROI
  daily_cutoff_time: "06:00"
  default_mode: "auto"           # auto | do_manual | manual (seed when batch_mode.json missing)

# do = Delivery Order (surat jalan). Gated manual-batch scan flow.
# Used when batch mode == do_manual (mode itself lives in batch_mode.json).
do:
  enabled: true
  require_do: true              # start needs ≥1 staged DO (do_manual mode)
  require_plate: false          # default; office UI can flip at runtime
  retention_days: 7
  photo_dir: "do_photos"        # relative to output.dir
  max_photos_per_batch: 8
  zone_warn_seconds: 3          # stop soft-warn: recent count activity
  ocr:
    engine: "tesseract"         # tesseract | paddle | none — YAML seed only
    # Runtime override: do_settings.json ocr_engine (UI toggle, both ports).

output:
  dir: "/opt/jetson-counter"
  db_name: "jetson_counter.db"
  state_file: "current_batch.json"
  # batch_mode_file is DEPRECATED: dashboard now persists the mode switch to
  # models.active_mode in this file. Key kept so old deployments can be detected.
  batch_mode_file: "batch_mode.json"
  live_frame_path: "/dev/shm/jetson-counter/live_frame.jpg"
  live_status_path: "/dev/shm/jetson-counter/live_status.json"

camera:
  name: "CC1"
  object_label: "karung-pakan"