Frigate - NVR With Realtime Object Detection for IP Cameras

A complete and local NVR designed for HomeAssistant with AI object detection. Uses OpenCV and Tensorflow to perform realtime object detection locally for IP cameras.

Use of a Google Coral Accelerator is optional, but highly recommended. The Coral will outperform even the best CPUs and can process 100+ FPS with very little overhead.

• Tight integration with HomeAssistant via a custom component
• Designed to minimize resource use and maximize performance by only looking for objects when and where it is necessary
• Leverages multiprocessing heavily with an emphasis on realtime over processing every frame
• Uses a very low overhead motion detection to determine where to run object detection
• Object detection with TensorFlow runs in separate processes for maximum FPS
• Communicates over MQTT for easy integration into other systems
• 24/7 recording
• Re-streaming via RTMP to reduce the number of connections to your camera

Screenshots

Media Browser Integration Entities Live Detection View

Documentation

• How Frigate Works
• Recommended Hardware
• Installing
• Configuration File
• Setting Up Camera Inputs
• Optimizing Performance
• Detectors
• Object Filters
• Masks
• Zones
• Recording Clips
• 24/7 Recordings
• RTMP Streams
• Integration with HomeAssistant
• MQTT Topics
• HTTP Endpoints
• Custom Models
• Troubleshooting

Recommended Hardware

Name	Inference Speed	Notes
Atomic Pi	16ms	Good option for a dedicated low power board with a small number of cameras. Can leverage Intel QuickSync for stream decoding.
Intel NUC NUC7i3BNK	8-10ms	Great performance. Can handle many cameras at 5fps depending on typical amounts of motion.
BMAX B2 Plus	10-12ms	Good balance of performance and cost. Also capable of running many other services at the same time as frigate.
Minisforum GK41	9-10ms	Great alternative to a NUC with dual Gigabit NICs. Easily handles several 1080p cameras.
Raspberry Pi 3B (32bit)	60ms	Can handle a small number of cameras, but the detection speeds are slow due to USB 2.0.
Raspberry Pi 4 (32bit)	15-20ms	Can handle a small number of cameras. The 2GB version runs fine.
Raspberry Pi 4 (64bit)	10-15ms	Can handle a small number of cameras. The 2GB version runs fine.

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Installing

HassOS Addon

HassOS users can install via the addon repository. Frigate requires that an MQTT server be running.

1. Navigate to Supervisor > Add-on Store > Repositories
2. Add https://github.com/blakeblackshear/frigate-hass-addons
3. Setup your configuration in the Configuration tab
4. Start the addon container

Docker

Make sure you choose the right image for your architecture:

Arch	Image Name
amd64	blakeblackshear/frigate:stable-amd64
armv7	blakeblackshear/frigate:stable-armv7
aarch64	blakeblackshear/frigate:stable-aarch64

It is recommended to run with docker-compose:

  frigate:
    container_name: frigate
    restart: unless-stopped
    privileged: true
    image: blakeblackshear/frigate:stable-amd64
    volumes:
      - /dev/bus/usb:/dev/bus/usb
      - /etc/localtime:/etc/localtime:ro
      - <path_to_config>:/config
      - <path_to_directory_for_clips>:/media/frigate/clips      
      - <path_to_directory_for_recordings>:/media/frigate/recordings      
      - type: tmpfs # Optional: 1GB of memory, reduces SSD/SD Card wear
        target: /tmp/cache
        tmpfs:
          size: 100000000
    ports:
      - "5000:5000"
      - "1935:1935" # RTMP feeds
    environment:
      FRIGATE_RTSP_PASSWORD: "password"
    healthcheck:
      test: ["CMD", "wget" , "-q", "-O-", "http://localhost:5000"]
      interval: 30s
      timeout: 10s
      retries: 5
      start_period: 3m

If you can't use docker compose, you can run the container with something similar to this:

docker run --rm \
--name frigate \
--privileged \
-v /dev/bus/usb:/dev/bus/usb \
-v <path_to_config_dir>:/config:ro \
-v /etc/localtime:/etc/localtime:ro \
-p 5000:5000 \
-e FRIGATE_RTSP_PASSWORD='password' \
blakeblackshear/frigate:stable-amd64

Kubernetes

Use the helm chart.

Virtualization

For ideal performance, Frigate needs access to underlying hardware for the Coral and GPU devices for ffmpeg decoding. Running Frigate in a VM on top of Proxmox, ESXi, Virtualbox, etc. is not recommended. The virtualization layer typically introduces a sizable amount of overhead for communication with Coral devices.

Proxmox

Some people have had success running Frigate in LXC directly with the following config:

arch: amd64
cores: 2
features: nesting=1
hostname: FrigateLXC
memory: 4096
net0: name=eth0,bridge=vmbr0,firewall=1,hwaddr=2E:76:AE:5A:58:48,ip=dhcp,ip6=auto,type=veth
ostype: debian
rootfs: local-lvm:vm-115-disk-0,size=12G
swap: 512
lxc.cgroup.devices.allow: c 189:385 rwm
lxc.mount.entry: /dev/dri/renderD128 dev/dri/renderD128 none bind,optional,create=file
lxc.mount.entry: /dev/bus/usb/004/002 dev/bus/usb/004/002 none bind,optional,create=file
lxc.apparmor.profile: unconfined
lxc.cgroup.devices.allow: a
lxc.cap.drop:

Calculating shm-size

The default shm-size of 64m is fine for setups with 3 or less 1080p cameras. If frigate is exiting with "Bus error" messages, it could be because you have too many high resolution cameras and you need to specify a higher shm size.

You can calculate the necessary shm-size for each camera with the following formula:

(width * height * 1.5 * 7 + 270480)/1048576 = <shm size in mb>

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Configuration

HassOS users can manage their configuration directly in the addon Configuration tab. For other installations, the default location for the config file is /config/config.yml. This can be overridden with the CONFIG_FILE environment variable. Camera specific ffmpeg parameters are documented here.

It is recommended to start with a minimal configuration and add to it:

mqtt:
  host: mqtt.server.com
cameras:
  back:
    ffmpeg:
      inputs:
        - path: rtsp://viewer:{FRIGATE_RTSP_PASSWORD}@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
          roles:
            - detect
            - rtmp
    height: 720
    width: 1280
    fps: 5

Here are all the configuration options:

# Optional: Logging configuration
logger:
  # Optional: default log level (default: shown below)
  default: info
  # Optional: module by module log level configuration
  logs:
    frigate.mqtt: error

# Optional: detectors configuration
# USB Coral devices will be auto detected with CPU fallback
detectors:
  # Required: name of the detector
  coral:
    # Required: type of the detector
    # Valid values are 'edgetpu' (requires device property below) and 'cpu'.
    type: edgetpu
    # Optional: device name as defined here: https://coral.ai/docs/edgetpu/multiple-edgetpu/#using-the-tensorflow-lite-python-api
    device: usb

# Required: mqtt configuration
mqtt:
  # Required: host name
  host: mqtt.server.com
  # Optional: port (default: shown below)
  port: 1883
  # Optional: topic prefix (default: shown below)
  # WARNING: must be unique if you are running multiple instances
  topic_prefix: frigate
  # Optional: client id (default: shown below)
  # WARNING: must be unique if you are running multiple instances
  client_id: frigate
  # Optional: user
  user: mqtt_user
  # Optional: password
  # NOTE: Environment variables that begin with 'FRIGATE_' may be referenced in {}. 
  #       eg. password: '{FRIGATE_MQTT_PASSWORD}'
  password: password

# Optional: Global configuration for saving clips
save_clips:
  # Optional: Maximum length of time to retain video during long events. (default: shown below)
  # NOTE: 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
  # Optional: Retention settings for clips (default: shown below)
  retain:
    # Required: Default retention days (default: shown below)
    default: 10
    # Optional: Per object retention days
    objects:
      person: 15

# Optional: Global ffmpeg args
# Args may be provided as a string or an array
# "ffmpeg" + global_args + input_args + "-i" + input + output_args
ffmpeg:
  # Optional: global ffmpeg args (default: shown below)
  global_args: -hide_banner -loglevel fatal
  # Optional: global hwaccel args (default: shown below)
  # NOTE: See hardware acceleration docs for your specific device
  hwaccel_args: []
  # Optional: global input args (default: shown below)
  input_args: -avoid_negative_ts make_zero -fflags +genpts+discardcorrupt -rtsp_transport tcp -stimeout 5000000 -use_wallclock_as_timestamps 1
  # Optional: global output args
  output_args: 
    # Optional: output args for detect streams (default: shown below)
    detect: -f rawvideo -pix_fmt yuv420p
    # Optional: output args for record streams (default: shown below)
    record: -f segment -segment_time 60 -segment_format mp4 -reset_timestamps 1 -strftime 1 -c copy -an
    # Optional: output args for clips streams (default: shown below)
    clips: -f segment -segment_time 10 -segment_format mp4 -reset_timestamps 1 -strftime 1 -c copy -an
    # Optional: output args for rtmp streams (default: shown below)
    rtmp: -c copy -f flv

# Optional: Global object filters for all cameras.
# NOTE: can be overridden at the camera level
objects:
  # Optional: list of objects to track from labelmap.txt (default: shown below)
  track:
    - person
  # Optional: filters to reduce false positives for specific object types
  filters:
    person:
      # Optional: minimum width*height of the bounding box for the detected object (default: 0)
      min_area: 5000
      # Optional: maximum width*height of the bounding box for the detected object (default: 24000000)
      max_area: 100000
      # Optional: minimum score for the object to initiate tracking (default: shown below)
      min_score: 0.5
      # Optional: minimum decimal percentage for tracked object's computed score to be considered a true positive (default: shown below)
      threshold: 0.85

# Required: configuration section for cameras
cameras:
  # Required: name of the camera
  back:
    # Required: ffmpeg settings for the camera
    ffmpeg:
      # Required: A list of input streams for the camera. See documentation for more information.
      inputs: 
          # Required: the path to the stream
          # NOTE: Environment variables that begin with 'FRIGATE_' may be referenced in {}
        - path: rtsp://viewer:{FRIGATE_RTSP_PASSWORD}@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
          # Required: list of roles for this stream. valid values are: detect,record,clips,rtmp
          roles:
            - detect
            - rtmp
          # Optional: stream specific global args (default: inherit)
          global_args:
          # Optional: stream specific hwaccel args (default: inherit)
          hwaccel_args:
          # Optional: stream specific input args (default: inherit)
          input_args:
        
      # Optional: camera specific global args (default: inherit)
      global_args:
      # Optional: camera specific hwaccel args (default: inherit)
      hwaccel_args:
      # Optional: camera specific input args (default: inherit)
      input_args:
      # Optional: camera specific output args (default: inherit)
      output_args:
    
    # Required: height of the frame
    # NOTE: Recommended to set this value, but frigate will attempt to autodetect.
    height: 720
    # Required: width of the frame
    # NOTE: Recommended to set this value, but frigate will attempt to autodetect.
    width: 1280
    # Optional: desired fps for your camera
    # NOTE: Recommended value of 5. Ideally, try and reduce your FPS on the camera.
    #       Frigate will attempt to autodetect if not specified.
    fps: 5

    # Optional: motion mask
    # NOTE: see docs for more detailed info on creating masks
    mask: poly,0,900,1080,900,1080,1920,0,1920

    # Optional: timeout for highest scoring image before allowing it
    # to be replaced by a newer image. (default: shown below)
    best_image_timeout: 60

    # Optional: camera specific mqtt settings
    mqtt:
      # Optional: crop the camera frame to the detection region of the object (default: False)
      crop_to_region: True
      # Optional: resize the image before publishing over mqtt
      snapshot_height: 175

    # Optional: zones for this camera
    zones:
      # Required: name of the zone
      # NOTE: This must be different than any camera names, but can match with another zone on another
      #       camera.
      front_steps:
        # Required: List of x,y coordinates to define the polygon of the zone.
        # NOTE: Coordinates can be generated at https://www.image-map.net/
        coordinates: 545,1077,747,939,788,805
        # Optional: Zone level object filters.
        # NOTE: The global and camera filters are applied upstream.
        filters:
          person:
            min_area: 5000
            max_area: 100000
            threshold: 0.8

    # Optional: save clips configuration
    # NOTE: This feature does not work if you have added "-vsync drop" 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:
      # Required: enables clips for the camera (default: shown below)
      enabled: False
      # Optional: Number of seconds before the event to include in the clips (default: shown below)
      pre_capture: 30
      # Optional: Objects to save clips for. (default: all tracked objects)
      objects:
        - person
      # Optional: Camera override for retention settings (default: global values)
      retain:
        # Required: Default retention days (default: shown below)
        default: 10
        # Optional: Per object retention days
        objects:
          person: 15  

    # Optional: 24/7 recording configuration
    record:
      # Optional: Enable recording (default: global setting)
      enabled: False
      # Optional: Number of days to retain (default: global setting)
      retain_days: 30
    
    # Optional: RTMP re-stream configuration
    rtmp:
      # Required: Enable the live stream (default: True)
      enabled: True

    # Optional: Configuration for the snapshots in the debug view and mqtt
    snapshots:
      # Optional: print a timestamp on the snapshots (default: shown below)
      show_timestamp: True
      # Optional: draw zones on the debug mjpeg feed (default: shown below)
      draw_zones: False
      # Optional: draw bounding boxes on the mqtt snapshots (default: shown below)
      draw_bounding_boxes: True
      # Optional: crop the snapshot to the detection region (default: shown below)
      crop_to_region: True
      # Optional: height to resize the snapshot to (default: shown below)
      # NOTE: 175px is optimized for thumbnails in the homeassistant media browser
      height: 175

    # Optional: Camera level object filters config. If defined, this is used instead of the global config.
    objects:
      track:
        - person
        - car
      filters:
        person:
          min_area: 5000
          max_area: 100000
          min_score: 0.5
          threshold: 0.85

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Setting Up Camera Inputs

Up to 4 inputs can be configured for each camera and the role of each input can be mixed and matched based on your needs. This allows you to use a lower resolution stream for object detection, but create clips from a higher resolution stream, or vice versa.

Each role can only be assigned to one input per camera. The options for roles are as follows:

Role	Description
detect	Main feed for object detection
clips	Clips of events from objects detected in the detect feed. docs
record	Saves 60 second segments of the video feed. docs
rtmp	Broadcast as an RTMP feed for other services to consume. docs

Example:

mqtt:
  host: mqtt.server.com
cameras:
  back:
    ffmpeg:
      inputs:
        - path: rtsp://viewer:{FRIGATE_RTSP_PASSWORD}@10.0.10.10:554/cam/realmonitor?channel=1&subtype=2
          roles:
            - detect
            - rtmp
        - path: rtsp://viewer:{FRIGATE_RTSP_PASSWORD}@10.0.10.10:554/live
          roles:
            - clips
            - record
    height: 720
    width: 1280
    fps: 5

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Optimizing Performance

• Google Coral: It is strongly recommended to use a Google Coral, but Frigate will fall back to CPU in the event one is not found. Offloading TensorFlow to the Google Coral is an order of magnitude faster and will reduce your CPU load dramatically. A $60 device will outperform $2000 CPU.
• Resolution: For the detect input, choose a camera resolution where the smallest object you want to detect barely fits inside a 300x300px square. The model used by Frigate is trained on 300x300px images, so you will get worse performance and no improvement in accuracy by using a larger resolution since Frigate resizes the area where it is looking for objects to 300x300 anyway.
• FPS: 5 frames per second should be adequate. Higher frame rates will require more CPU usage without improving detections or accuracy. Reducing the frame rate on your camera will have the greatest improvement on system resources.
• Hardware Acceleration: Make sure you configure the hwaccel_args for your hardware. They provide a significant reduction in CPU usage if they are available.
• Masks: Masks can be used to ignore motion and reduce your idle CPU load. If you have areas with regular motion such as timestamps or trees blowing in the wind, frigate will constantly try to determine if that motion is from a person or other object you are tracking. Those detections not only increase your average CPU usage, but also clog the pipeline for detecting objects elsewhere. If you are experiencing high values for detection_fps when no objects of interest are in the cameras, you should use masks to tell frigate to ignore movement from trees, bushes, timestamps, or any part of the image where detections should not be wasted looking for objects.

FFmpeg Hardware Acceleration

Frigate works on Raspberry Pi 3b/4 and x86 machines. It is recommended to update your configuration to enable hardware accelerated decoding in ffmpeg. Depending on your system, these parameters may not be compatible.

Raspberry Pi 3/4 (32-bit OS):

ffmpeg:
  hwaccel_args:
    - -c:v
    - h264_mmal

Raspberry Pi 3/4 (64-bit OS)

ffmpeg:
  hwaccel_args:
    - -c:v
    - h264_v4l2m2m

Intel-based CPUs (<10th Generation) via Quicksync (https://trac.ffmpeg.org/wiki/Hardware/QuickSync)

ffmpeg:
  hwaccel_args:
    - -hwaccel
    - vaapi
    - -hwaccel_device
    - /dev/dri/renderD128
    - -hwaccel_output_format
    - yuv420p

Intel-based CPUs (>=10th Generation) via Quicksync (https://trac.ffmpeg.org/wiki/Hardware/QuickSync) Note: You also need to set LIBVA_DRIVER_NAME=iHD as an environment variable on the container.

ffmpeg:
  hwaccel_args:
    - -hwaccel
    - vaapi
    - -hwaccel_device
    - /dev/dri/renderD128

Nvidia GPU based decoding via NVDEC is supported, but requires special configuration. See the nvidia NVDEC documentation for more details.

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Detectors

By default Frigate will look for a USB Coral device and fall back to the CPU if it cannot be found. If you have PCI or multiple Coral devices, you need to configure your detector devices in the config file. When using multiple detectors, they run in dedicated processes, but pull from a common queue of requested detections across all cameras.

Frigate supports edgetpu and cpu as detector types. The device value should be specified according to the Documentation for the TensorFlow Lite Python API.

Single USB Coral:

detectors:
  coral:
    type: edgetpu
    device: usb

Multiple USB Corals:

detectors:
  coral1:
    type: edgetpu
    device: usb:0
  coral2:
    type: edgetpu
    device: usb:1

Mixing Corals:

detectors:
  coral_usb:
    type: edgetpu
    device: usb
  coral_pci:
    type: edgetpu
    device: pci

CPU Detectors (not recommended):

detectors:
  cpu1:
    type: cpu
  cpu2:
    type: cpu

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Reducing False Positives

Tune your object filters to adjust false positives: min_area, max_area, min_score, threshold.

For object filters in your configuration, any single detection below min_score will be ignored as a false positive. threshold is based on the median of the history of scores (padded to 3 values) for a tracked object. Consider the following frames when min_score is set to 0.6 and threshold is set to 0.85:

Frame	Current Score	Score History	Computed Score	Detected Object
1	0.7	0.0, 0, 0.7	0.0	No
2	0.55	0.0, 0.7, 0.0	0.0	No
3	0.85	0.7, 0.0, 0.85	0.7	No
4	0.90	0.7, 0.85, 0.95, 0.90	0.875	Yes
5	0.88	0.7, 0.85, 0.95, 0.90, 0.88	0.88	Yes
6	0.95	0.7, 0.85, 0.95, 0.90, 0.88, 0.95	0.89	Yes

In frame 2, the score is below the min_score value, so frigate ignores it and it becomes a 0.0. The computed score is the median of the score history (padding to at least 3 values), and only when that computed score crosses the threshold is the object marked as a true positive. That happens in frame 4 in the example.

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Masks

The following types of masks are supported:

• poly: (Recommended) List of x,y points like zone configuration
• base64: Base64 encoded image file
• image: Image file in the /config directory

base64 and image masks must be the same aspect ratio and resolution as your camera.

The mask in the second image would limit motion detection on this camera to only the front yard and not the street.

To create a poly mask:

1. Download a camera snapshot image with the same resolution as the camera feed (/<camera_name>/latest.jpg).
2. Upload the image to https://www.image-map.net/
3. Select "shape" poly - start in the lowest left corner and place the first marker (point) and continue upwards and then to the right until the polygon shape covers the area that you want to mask out (ignore).
4. When you are finished with the polygon click "Show me the code!" and copy all coordinates (point), ie. "0,461,3,0,1919,0,1919,843,1699,492,1344,458,1346,336,973,317,869,375,866,432"
5. Adjust any -1 values to 0 and then add it all to the configuration (see the example configuration for correct indentation and placement)

Example of a finished row corresponding to the below example image:

mask: 'poly,0,461,3,0,1919,0,1919,843,1699,492,1344,458,1346,336,973,317,869,375,866,432'

You can test your mask by temporarily configuring it as a zone and enabling draw_zones in your config. Zones are visible on the MJPEG feed.

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Zones

Zones allow you to define a specific area of the frame and apply additional filters for object types so you can determine whether or not an object is within a particular area. Zones cannot have the same name as a camera. If desired, a single zone can include multiple cameras if you have multiple cameras covering the same area by configuring zones with the same name for each camera.

During testing, draw_zones should be set in the config to draw the zone on the frames so you can adjust as needed. The zone line will increase in thickness when any object enters the zone. Zones are visible on the MJPEG feed.

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Recording Clips

Note: Previous versions of frigate included -vsync drop in input parameters. This is not compatible with FFmpeg's segment feature and must be removed from your input parameters if you have overrides set.

Frigate can save video clips without any CPU overhead for encoding by simply copying the stream directly with FFmpeg. It leverages FFmpeg's segment functionality to maintain a cache of video for each camera. The cache files are written to disk at /tmp/cache and do not introduce memory overhead. When an object is being tracked, it will extend the cache to ensure it can assemble a clip when the event ends. Once the event ends, it again uses FFmpeg to assemble a clip by combining the video clips without any encoding by the CPU. Assembled clips are are saved to /media/frigate/clips. Clips are retained according to the retention settings defined on the config for each object type.

Global Configuration Options

• max_seconds: This limits the size of the cache when an object is being tracked. If an object is stationary and being tracked for a long time, the cache files will expire and this value will be the maximum clip length for the end of the event. For example, if this is set to 300 seconds and an object is being tracked for 600 seconds, the clip will end up being the last 300 seconds. Defaults to 300 seconds.

Per-camera Configuration Options

• pre_capture: Defines how much time should be included in the clip prior to the beginning of the event. Defaults to 30 seconds.
• objects: List of object types to save clips for. Object types here must be listed for tracking at the camera or global configuration. Defaults to all tracked objects.

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24/7 Recordings

Note: Previous versions of frigate included -vsync drop in input parameters. This is not compatible with FFmpeg's segment feature and must be removed from your input parameters if you have overrides set.

24/7 recordings can be enabled and are stored at /media/frigate/recordings. The folder structure for the recordings is YYYY-MM/DD/HH/<camera_name>/MM.SS.mp4. These recordings are written directly from your camera stream without re-encoding and are available in HomeAssistant's media browser. Each camera supports a configurable retention policy in the config.

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RTMP Streams

Frigate can re-stream your video feed as a RTMP feed for other applications such as HomeAssistant to utilize it. This allows you to use a video feed for detection in frigate and HomeAssistant live view at the same time without having to make two separate connections to the camera. The video feed is copied from the original video feed directly to avoid re-encoding. This feed does not include any annotation by Frigate.

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Integration with HomeAssistant

The best way to integrate with HomeAssistant is to use the official integration. The integration will attempt to auto-discover your running frigate instance. Some setups may require manual configuration. HomeAssistant needs access to port 5000 (api) and 1935 (rtmp) for all features. The integration will setup the following entities within HomeAssistant:

Sensors:

• Stats to monitor frigate performance
• Object counts for all zones and cameras

Cameras:

• Cameras for image of the last detected object for each camera
• Camera entities with stream support (requires RTMP)

Media Browser:

• Rich UI with thumbnails for browsing event clips
• Rich UI for browsing 24/7 recordings by month, day, camera, time

API:

• Notification API with public facing endpoints for images in notifications

Notifications

Frigate publishes event information in the form of a change feed via MQTT. This allows lots of customization for notifications to meet your needs. Event changes are published with before and after information as shown here.

Here is a simple example of a notification automation of events which will update the existing notification for each change. This means the image you see in the notification will update as frigate finds a "better" image.

automation:
  - alias: Notify of events
    trigger:
      platform: mqtt
      topic: frigate/events
    action:
      - service: notify.mobile_app_pixel_3
        data_template:
          message: 'A {{trigger.payload_json["after"]["label"]}} was detected.'
          data:
            image: 'https://your.public.hass.address.com/api/frigate/notifications/{{trigger.payload_json["after"]["id"]}}.jpg?format=android'
            tag: '{{trigger.payload_json["after"]["id"]}}'

Note that the image url has ?format=android. This adjusts the aspect ratio to be idea for android notifications. For iOS optimized snapshots, no format parameter needs to be passed.

You can find some additional examples for notifications here.

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HTTP Endpoints

A web server is available on port 5000 with the following endpoints.

/<camera_name>

An mjpeg stream for debugging. Keep in mind the mjpeg endpoint is for debugging only and will put additional load on the system when in use.

You can access a higher resolution mjpeg stream by appending h=height-in-pixels to the endpoint. For example http://localhost:5000/back?h=1080. You can also increase the FPS by appending fps=frame-rate to the URL such as http://localhost:5000/back?fps=10 or both with ?fps=10&h=1000

/<camera_name>/<object_name>/best.jpg[?h=300&crop=1]

The best snapshot for any object type. It is a full resolution image by default.

Example parameters:

• h=300: resizes the image to 300 pixes tall
• crop=1: crops the image to the region of the detection rather than returning the entire image

/<camera_name>/latest.jpg[?h=300]

The most recent frame that frigate has finished processing. It is a full resolution image by default.

Example parameters:

• h=300: resizes the image to 300 pixes tall

/stats

Contains some granular debug info that can be used for sensors in HomeAssistant.

Sample response:

{
    /* Per Camera Stats */
    "back": {
        /***************
        * Frames per second being consumed from your camera. If this is higher
        * than it is supposed to be, you should set -r FPS in your input_args.
        * camera_fps = process_fps + skipped_fps
        ***************/
        "camera_fps": 5.0,
        /***************
        * Number of times detection is run per second. This can be higher than
        * your camera FPS because frigate often looks at the same frame multiple times
        * or in multiple locations
        ***************/
        "detection_fps": 1.5,
        /***************
        * PID for the ffmpeg process that consumes this camera
        ***************/
        "capture_pid": 27,
        /***************
        * PID for the process that runs detection for this camera
        ***************/
        "pid": 34,
        /***************
        * Frames per second being processed by frigate.
        ***************/
        "process_fps": 5.1,
        /***************
        * Frames per second skip for processing by frigate.
        ***************/
        "skipped_fps": 0.0
    },
    /***************
    * Sum of detection_fps across all cameras and detectors. 
    * This should be the sum of all detection_fps values from cameras.
    ***************/
    "detection_fps": 5.0,
    /* Detectors Stats */
    "detectors": {
      "coral": {
          /***************
          * Timestamp when object detection started. If this value stays non-zero and constant
          * for a long time, that means the detection process is stuck.
          ***************/
          "detection_start": 0.0,
          /***************
          * Time spent running object detection in milliseconds.
          ***************/
          "inference_speed": 10.48,
          /***************
          * PID for the shared process that runs object detection on the Coral.
          ***************/
          "pid": 25321
      }
    }
}

/config

A json representation of your configuration

/events

Events from the database. Accepts the following query string parameters:

param	Type	Description
before	int	Epoch time
after	int	Epoch time
camera	str	Camera name
label	str	Label name
zone	str	Zone name
limit	int	Limit the number of events returned

/events/summary

Returns summary data for events in the database. Used by the HomeAssistant integration.

/events/<id>

Returns data for a single event.

/events/<id>/snapshot.jpg

Returns a snapshot for the event id optimized for notifications. Works while the event is in progress and after completion. Passing ?format=android will convert the thumbnail to 2:1 aspect ratio.

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MQTT Topics

These are the MQTT messages generated by Frigate. The default topic_prefix is frigate, but can be changed in the config file.

frigate/available

Designed to be used as an availability topic with HomeAssistant. Possible message are: "online": published when frigate is running (on startup) "offline": published right before frigate stops

frigate/<camera_name>/<object_name>

Publishes the count of objects for the camera for use as a sensor in HomeAssistant.

frigate/<zone_name>/<object_name>

Publishes the count of objects for the zone for use as a sensor in HomeAssistant.

frigate/<camera_name>/<object_name>/snapshot

Publishes a jpeg encoded frame of the detected object type. When the object is no longer detected, the highest confidence image is published or the original image is published again.

The height and crop of snapshots can be configured in the config.

frigate/events

Message published for each changed event:

{
    "before": {
        "id": "1607123955.475377-mxklsc",
        "camera": "front_door",
        "frame_time": 1607123961.837752,
        "label": "person",
        "top_score": 0.958984375,
        "false_positive": false,
        "start_time": 1607123955.475377,
        "end_time": null,
        "score": 0.7890625,
        "box": [
            424,
            500,
            536,
            712
        ],
        "area": 23744,
        "region": [
            264,
            450,
            667,
            853
        ],
        "current_zones": [
            "driveway"
        ],
        "entered_zones": [
            "yard",
            "driveway"
        ],
        "thumbnail": null
    },
    "after": {
        "id": "1607123955.475377-mxklsc",
        "camera": "front_door",
        "frame_time": 1607123962.082975,
        "label": "person",
        "top_score": 0.958984375,
        "false_positive": false,
        "start_time": 1607123955.475377,
        "end_time": null,
        "score": 0.87890625,
        "box": [
            432,
            496,
            544,
            854
        ],
        "area": 40096,
        "region": [
            218,
            440,
            693,
            915
        ],
        "current_zones": [
            "yard",
            "driveway"
        ],
        "entered_zones": [
            "yard",
            "driveway"
        ],
        "thumbnail": null
    }
}

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Custom Models

Models for both CPU and EdgeTPU (Coral) are bundled in the image. You can use your own models with volume mounts:

• CPU Model: /cpu_model.tflite
• EdgeTPU Model: /edgetpu_model.tflite
• Labels: /labelmap.txt

Customizing the Labelmap

The labelmap can be customized to your needs. A common reason to do this is to combine multiple object types that are easily confused when you don't need to be as granular such as car/truck. You must retain the same number of labels, but you can change the names. To change:

• Download the COCO labelmap
• Modify the label names as desired. For example, change 7 truck to 7 car
• Mount the new file at /labelmap.txt in the container with an additional volume

  -v ./config/labelmap.txt:/labelmap.txt
  

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Logging

Available log levels are: debug, info, warning, error, critical

Examples of available modules are:

• frigate.app
• frigate.mqtt
• frigate.edgetpu
• frigate.zeroconf
• detector.<detector_name>
• watchdog.<camera_name>
• ffmpeg.<camera_name>.<sorted_roles> NOTE: All FFmpeg logs are sent as error level.

Troubleshooting

"ffmpeg didnt return a frame. something is wrong"

Turn on logging for the camera by overriding the global_args and setting the log level to info:

ffmpeg:
  global_args:
    - -hide_banner
    - -loglevel
    - info

"On connect called"

If you see repeated "On connect called" messages in your config, check for another instance of frigate. This happens when multiple frigate containers are trying to connect to mqtt with the same client_id.

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