config.example.yml

web_port: 5000

################
## Tell frigate to look for a specific EdgeTPU device. Useful if you want to run multiple instances of frigate
## on the same machine with multiple EdgeTPUs. https://coral.ai/docs/edgetpu/multiple-edgetpu/#using-the-tensorflow-lite-python-api
################
tensorflow_device: usb

mqtt:
  host: mqtt.server.com
  topic_prefix: frigate
  # client_id: frigate # Optional -- set to override default client id of 'frigate' if running multiple instances
  # user: username # Optional
  #################
  ## Environment variables that begin with 'FRIGATE_' may be referenced in {}.
  ##   password: '{FRIGATE_MQTT_PASSWORD}'
  #################
  # password: password # Optional

################
# Global configuration for saving clips
################
save_clips:
  ###########
  # Maximum length of time to retain video during long events.
  # If an object is being tracked for longer than this amount of time, the cache  
  # will begin to expire and the resulting clip will be the last x seconds of the event.
  ###########
  max_seconds: 300

#################
# Default ffmpeg args. Optional and can be overwritten per camera.
# Should work with most RTSP cameras that send h264 video
# Built from the properties below with:
# "ffmpeg" + global_args + input_args + "-i" + input + output_args
#################
# ffmpeg:
#   global_args:
#     - -hide_banner
#     - -loglevel
#     - panic
#   hwaccel_args: []
#   input_args:
#     - -avoid_negative_ts
#     - make_zero
#     - -fflags
#     - nobuffer
#     - -flags
#     - low_delay
#     - -strict
#     - experimental
#     - -fflags
#     - +genpts+discardcorrupt
#     - -vsync
#     - drop
#     - -rtsp_transport
#     - tcp
#     - -stimeout
#     - '5000000'
#     - -use_wallclock_as_timestamps
#     - '1'
#   output_args:
#     - -f
#     - rawvideo
#     - -pix_fmt
#     - rgb24

####################
# Global object configuration. Applies to all cameras
# unless overridden at the camera levels.
# Keys must be valid labels. By default, the model uses coco (https://dl.google.com/coral/canned_models/coco_labels.txt).
# All labels from the model are reported over MQTT. These values are used to filter out false positives.
# min_area (optional): minimum width*height of the bounding box for the detected object
# max_area (optional): maximum width*height of the bounding box for the detected object
# min_score (optional): minimum score for the object to initiate tracking
# threshold (optional): The minimum decimal percentage for tracked object's computed score to considered a true positive
####################
objects:
  track:
    - person
    - car
    - truck
  filters:
    person:
      min_area: 5000
      max_area: 100000
      min_score: 0.5
      threshold: 0.85

cameras:
  back:
    ffmpeg:
      ################
      # Source passed to ffmpeg after the -i parameter. Supports anything compatible with OpenCV and FFmpeg.
      # Environment variables that begin with 'FRIGATE_' may be referenced in {}
      ################
      input: rtsp://viewer:{FRIGATE_RTSP_PASSWORD}@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
      #################
      # These values will override default values for just this camera
      #################
      # global_args: []
      # hwaccel_args: []
      # input_args: []
      # output_args: []
    
    ################
    ## Optionally specify the resolution of the video feed. Frigate will try to auto detect if not specified
    ################
    # height: 1280
    # width: 720

    ################
    ## Specify the framerate of your camera
    ##
    ## NOTE: This should only be set in the event ffmpeg is unable to determine your camera's framerate
    ##       on its own and the reported framerate for your camera in frigate is well over what is expected.
    ################
    # fps: 5

    ################
    ## Optional mask. Must be the same aspect ratio as your video feed. Value is any of the following:
    ##   - name of a file in the config directory
    ##   - base64 encoded image prefixed with 'base64,' eg. 'base64,asfasdfasdf....'
    ##   - polygon of x,y coordinates prefixed with 'poly,' eg. 'poly,0,900,1080,900,1080,1920,0,1920'
    ## 
    ## The mask works by looking at the bottom center of the bounding box for the detected
    ## person in the image. If that pixel in the mask is a black pixel, it ignores it as a
    ## false positive. In my mask, the grass and driveway visible from my backdoor camera 
    ## are white. The garage doors, sky, and trees (anywhere it would be impossible for a 
    ## person to stand) are black.
    ## 
    ## Masked areas are also ignored for motion detection.
    ################
    # mask: back-mask.bmp

    ################
    # Allows you to limit the framerate within frigate for cameras that do not support
    # custom framerates. A value of 1 tells frigate to look at every frame, 2 every 2nd frame, 
    # 3 every 3rd frame, etc.
    ################
    take_frame: 1

    ################
    # The number of seconds to retain the highest scoring image for the best.jpg endpoint before allowing it
    # to be replaced by a newer image. Defaults to 60 seconds.
    ################
    best_image_timeout: 60

    ################
    # MQTT settings
    ################
    # mqtt:
    #   crop_to_region: True
    #   snapshot_height: 300

    ################
    # Zones
    ################
    zones:
      #################
      # Name of the zone
      ################
      front_steps:
        ####################
        # A list of x,y coordinates to define the polygon of the zone. The top 
        # left corner is 0,0. Can also be a comma separated string of all x,y coordinates combined.
        # The same zone name can exist across multiple cameras if they have overlapping FOVs.
        # An object is determined to be in the zone based on whether or not the bottom center
        # of it's bounding box is within the polygon. The polygon must have at least 3 points.
        # Coordinates can be generated at https://www.image-map.net/
        ####################
        coordinates:
          - 545,1077
          - 747,939
          - 788,805
        ################
        # Zone level object filters. These are applied in addition to the global and camera filters
        # and should be more restrictive than the global and camera filters. The global and camera
        # filters are applied upstream.
        ################
        filters:
          person:
            min_area: 5000
            max_area: 100000
            threshold: 0.8

    ################
    # This will save a clip for each tracked object by frigate along with a json file that contains
    # data related to the tracked object. This works by telling ffmpeg to write video segments to /cache
    # from the video stream without re-encoding. Clips are then created by using ffmpeg to merge segments
    # without re-encoding. The segments saved are unaltered from what frigate receives to avoid re-encoding.
    # They do not contain bounding boxes. These are optimized to capture "false_positive" examples for improving frigate.
    #
    # NOTE: This feature does not work if you have "-vsync drop" configured in your input params. 
    #       This will only work for camera feeds that can be copied into the mp4 container format without
    #       encoding such as h264. It may not work for some types of streams.
    ################
    save_clips:
      enabled: False
      #########
      # Number of seconds before the event to include in the clips
      #########
      pre_capture: 30
      #########
      # Objects to save clips for. Defaults to all tracked object types.
      #########
      # objects:
      #   - person      

    ################
    # Configuration for the snapshots in the debug view and mqtt
    ################
    snapshots:
      show_timestamp: True
      draw_zones: False

    ################
    # Camera level object config. This config is merged with the global config above.
    ################
    objects:
      track:
        - person
      filters:
        person:
          min_area: 5000
          max_area: 100000
          min_score: 0.5
          threshold: 0.85