web_port: 5000

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 -- Uncomment for use
#  password: password # Optional -- Uncomment for use

#################
# 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:
#     - -vf
#     - mpdecimate
#     - -f
#     - rawvideo
#     - -pix_fmt
#     - rgb24

####################
# Global object configuration. Applies to all cameras and regions
# unless overridden at the camera/region 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.
####################
objects:
  person:
    min_area: 5000
    max_area: 100000
    threshold: 0.5

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: []

    ################
    ## Optional mask. Must be the same dimensions as your video feed.
    ## 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.
    ################
    # 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

    objects:
      person:
        min_area: 5000
        max_area: 100000
        threshold: 0.5
    
    ################
    # size: size of the region in pixels
    # x_offset/y_offset: position of the upper left corner of your region (top left of image is 0,0)
    # min_person_area (optional): minimum width*height of the bounding box for the detected person
    # max_person_area (optional): maximum width*height of the bounding box for the detected person
    # threshold (optional): The minimum decimal percentage (50% hit = 0.5) for the confidence from tensorflow
    # Tips: All regions are resized to 300x300 before detection because the model is trained on that size.
    #       Resizing regions takes CPU power. Ideally, all regions should be as close to 300x300 as possible.
    #       Defining a region that goes outside the bounds of the image will result in errors.
    ################
    regions:
      - size: 350
        x_offset: 0
        y_offset: 300
        objects:
          car:
            threshold: 0.2
      - size: 400
        x_offset: 350
        y_offset: 250
        objects:
          person:
            min_area: 2000
      - size: 400
        x_offset: 750
        y_offset: 250
        objects:
          person:
            min_area: 2000