customvalues.template.yaml

videoSource:
  logLevel: DEBUG   # Options: ERROR, WARNING, INFO, DEBUG
  configs: 
  - id: video1        # The name of the video stream. Will be used as Redis stream key and as camera_id in database output.
    uri: 'uri'        # Where to retrieve the video from. Must be in a format that OpenCV VideoCapture understands. RTSP stream makes the most sense.
    max_fps: 5        # Effectively sets a lower bound on the time between to frames. 0 means off. Integer fractions of original frame rate make the most sense (i.e. 15, 10, 5 for a 30fps source).
    scale_width: 0    # If > 0, scale the image to the given width (preserving aspect ratio)
    jpeg_quality: 80  # JPEG quality 0 - 100 (100 is lossless and big); Reasonable starting points (for image height): 2160 = 80, 1080 = 90, <720 = 95

objectDetector:
  config:
    model:
      size: "n"                   # Which Ultralytics model to use. Options: n (nano), s (small), m (medium), l (large), x (xlarge)
      device: cpu                 # Options: cpu, cuda (uses gpu; needs proper setup; see README)
    inferenceSize: [ 640, 640 ]   # What resolution the image will be downscaled to before object-detection inference (should be square and a multiple of 32)
    classes: [ 2 ]                # Which object classes to detect (refer to coco object classes)
    logLevel: DEBUG               # Options: ERROR, WARNING, INFO, DEBUG
    redis:
      streamIds:
        - video1                  # On which video streams to detect objects on. Mostly all video source stream ids.

objectTracker:
  logLevel: DEBUG                             # Options: ERROR, WARNING, INFO, DEBUG
  needsGpu: False                             # Whether the algorithm needs gpu support (needs proper setup; see README)
  streamIds:                                  # On which video streams to track objects (the same as object-detector stream ids above)
    - video1
  config:                                     # Tracker specific config (see object-tracker repo / Boxmot for details)
    tracker_algorithm: OCSORT
    tracker_config:
      det_thresh: 0.2
      max_age: 30
      min_hits: 3
      asso_threshold: 0.3
      delta_t: 3
      asso_func: 'iou'
      inertia: 0.2
      use_byte: False

geoMapper:
  enabled: false                   # If the geomapper should be deployed (by default it reads from "objecttracker:*"" and outputs into "geomapper:*")
  config:
    cameras:                       # Parameters have to be specified for each camera (it makes no sense to run this without correct-ish parameters)
      - stream_id: video1          # This must match one of the existing camera streams
        passthrough: false         # If the stream should be passed through without geo mapping (all other parameters are ignored if true)
        focallength_mm: 20         # view_*_deg and image dimensions are equivalent. Sensor size is harder to obtain and therefore only second choice.
        sensor_height_mm: 16       # See above
        sensor_width_mm: 9         # See above
        view_x_deg: 90
        view_y_deg: 55
        image_width_px: 1920
        image_height_px: 1080
        elevation_m: 10
        tilt_deg: 45
        pos_lat: 52
        pos_lon: 10
        heading_deg: 90
        abc_distortion_a: 0
        abc_distortion_b: 0
        abc_distortion_c: 0

databaseWriter:
  enabled: true                               # If the database-writer should be deployed
  config:
    redisStreamIds:
      - video1                                # Which video streams to read (and extract detection and tracking data from)
    db:
      jdbcUrl: jdbc:postgresql://host:port/   # JDBC URL. Must be a Postgres database. Does not have to be a Timescale (although the latter makes most sense)
      schema: schema
      username: username
      password: password
      hypertable: tablename                   # Table name. Does not have to be a Hypertable.