mirror of
https://github.com/blakeblackshear/frigate.git
synced 2024-10-15 20:07:23 +02:00
129 lines
4.2 KiB
YAML
129 lines
4.2 KiB
YAML
web_port: 5000
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mqtt:
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host: mqtt.server.com
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topic_prefix: frigate
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# client_id: frigate # Optional -- set to override default client id of 'frigate' if running multiple instances
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# user: username # Optional -- Uncomment for use
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# password: password # Optional -- Uncomment for use
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#################
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# Default ffmpeg args. Optional and can be overwritten per camera.
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# Should work with most RTSP cameras that send h264 video
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# Built from the properties below with:
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# "ffmpeg" + global_args + input_args + "-i" + input + output_args
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#################
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# ffmpeg:
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# global_args:
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# - -hide_banner
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# - -loglevel
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# - panic
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# hwaccel_args: []
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# input_args:
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# - -avoid_negative_ts
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# - make_zero
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# - -fflags
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# - nobuffer
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# - -flags
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# - low_delay
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# - -strict
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# - experimental
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# - -fflags
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# - +genpts+discardcorrupt
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# - -vsync
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# - drop
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# - -rtsp_transport
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# - tcp
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# - -stimeout
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# - '5000000'
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# - -use_wallclock_as_timestamps
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# - '1'
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# output_args:
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# - -vf
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# - mpdecimate
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# - -f
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# - rawvideo
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# - -pix_fmt
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# - rgb24
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####################
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# Global object configuration. Applies to all cameras and regions
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# unless overridden at the camera/region levels.
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# Keys must be valid labels. By default, the model uses coco (https://dl.google.com/coral/canned_models/coco_labels.txt).
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# All labels from the model are reported over MQTT. These values are used to filter out false positives.
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####################
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objects:
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person:
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min_area: 5000
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max_area: 100000
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threshold: 0.5
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cameras:
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back:
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ffmpeg:
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################
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# Source passed to ffmpeg after the -i parameter. Supports anything compatible with OpenCV and FFmpeg.
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# Environment variables that begin with 'FRIGATE_' may be referenced in {}
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################
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input: rtsp://viewer:{FRIGATE_RTSP_PASSWORD}@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
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#################
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# These values will override default values for just this camera
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#################
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# global_args: []
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# hwaccel_args: []
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# input_args: []
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# output_args: []
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################
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## Optional mask. Must be the same dimensions as your video feed.
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## The mask works by looking at the bottom center of the bounding box for the detected
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## person in the image. If that pixel in the mask is a black pixel, it ignores it as a
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## false positive. In my mask, the grass and driveway visible from my backdoor camera
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## are white. The garage doors, sky, and trees (anywhere it would be impossible for a
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## person to stand) are black.
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################
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# mask: back-mask.bmp
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################
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# Allows you to limit the framerate within frigate for cameras that do not support
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# custom framerates. A value of 1 tells frigate to look at every frame, 2 every 2nd frame,
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# 3 every 3rd frame, etc.
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################
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take_frame: 1
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objects:
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person:
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min_area: 5000
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max_area: 100000
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threshold: 0.5
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################
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# size: size of the region in pixels
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# x_offset/y_offset: position of the upper left corner of your region (top left of image is 0,0)
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# min_person_area (optional): minimum width*height of the bounding box for the detected person
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# max_person_area (optional): maximum width*height of the bounding box for the detected person
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# threshold (optional): The minimum decimal percentage (50% hit = 0.5) for the confidence from tensorflow
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# Tips: All regions are resized to 300x300 before detection because the model is trained on that size.
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# Resizing regions takes CPU power. Ideally, all regions should be as close to 300x300 as possible.
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# Defining a region that goes outside the bounds of the image will result in errors.
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################
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regions:
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- size: 350
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x_offset: 0
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y_offset: 300
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objects:
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car:
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threshold: 0.2
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- size: 400
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x_offset: 350
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y_offset: 250
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objects:
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person:
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min_area: 2000
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- size: 400
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x_offset: 750
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y_offset: 250
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objects:
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person:
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min_area: 2000
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