Coloquei da forma que indicou, mas continua dando erro.
Mas o erro mudou?
Vê se assim dá erro:
cameras:
sala:
ffmpeg:
output_args:
record: -f segment -segment_time 300 -segment_format mp4 -reset_timestamps 1 -strftime 1 -c:v copy -c:a aac
inputs:
- path: rtsp://admin:FXXXXXXX@192.168.1.45:554/cam/realmonitor?channel=1&subtype=0&unicast=true&proto=Onvif
roles:
- detect
- rtmp
- record
[s6-init] making user provided files available at /var/run/s6/etc...exited 0.
[s6-init] ensuring user provided files have correct perms...exited 0.
[fix-attrs.d] applying ownership & permissions fixes...
[fix-attrs.d] done.
[cont-init.d] executing container initialization scripts...
[cont-init.d] done.
[services.d] starting services
[services.d] done.
[2023-01-11 17:29:11] frigate.app INFO : Starting Frigate (0.11.1-2eada21)
*************************************************************
*************************************************************
*** Your config file is not valid! ***
*** Please check the docs at ***
*** https://docs.frigate.video/configuration/index ***
*************************************************************
*************************************************************
*** Config Validation Errors ***
*************************************************************
'inputs'
Traceback (most recent call last):
File "/opt/frigate/frigate/app.py", line 332, in start
self.init_config()
File "/opt/frigate/frigate/app.py", line 82, in init_config
user_config = FrigateConfig.parse_file(config_file)
File "/opt/frigate/frigate/config.py", line 942, in parse_file
return cls.parse_obj(config)
File "pydantic/main.py", line 521, in pydantic.main.BaseModel.parse_obj
File "pydantic/main.py", line 339, in pydantic.main.BaseModel.__init__
File "pydantic/main.py", line 1056, in pydantic.main.validate_model
File "pydantic/fields.py", line 859, in pydantic.fields.ModelField.validate
File "pydantic/fields.py", line 994, in pydantic.fields.ModelField._validate_mapping_like
File "pydantic/fields.py", line 1067, in pydantic.fields.ModelField._validate_singleton
File "pydantic/fields.py", line 857, in pydantic.fields.ModelField.validate
File "pydantic/fields.py", line 1074, in pydantic.fields.ModelField._validate_singleton
File "pydantic/fields.py", line 1121, in pydantic.fields.ModelField._apply_validators
File "pydantic/class_validators.py", line 313, in pydantic.class_validators._generic_validator_basic.lambda12
File "pydantic/main.py", line 704, in pydantic.main.BaseModel.validate
File "/opt/frigate/frigate/config.py", line 584, in __init__
if len(config["ffmpeg"]["inputs"]) == 1:
KeyError: 'inputs'
*************************************************************
*** End Config Validation Errors ***
*************************************************************
[cmd] python3 exited 1
[cont-finish.d] executing container finish scripts...
[cont-finish.d] done.
[s6-finish] waiting for services.
[s6-finish] sending all processes the TERM signal.
cameras:
sala:
ffmpeg:
inputs:
- path: rtsp://admin:FXXXXXXX@192.168.1.45:554/cam/realmonitor?channel=1&subtype=0&unicast=true&proto=Onvif
roles:
- detect
- rtmp
- record
mqtt:
host: 192.168.1.150
port: 1883
topic_prefix: frigate
client_id: frigate
user: mqttmarcelo
password: MXXXX
cameras:
sala:
ffmpeg:
inputs:
- path: rtsp://admin:FXXXXXXX@192.168.1.45:554/cam/realmonitor?channel=1&subtype=0&unicast=true&proto=Onvif
roles:
- detect
- rtmp
- record
detect:
width: 1280
height: 720
# fps: 5
# objects:
# track:
# - person
# - cat
# - cellphone
Desta forma funcionou, quando coloco essa parte de objetos dá erro sei que pode ser identação… tentei varias… pode me ajudar
Estou instalando aqui pra testar em umas cameras…
A indentação (espaços no inicio da linha) vai ser sempre de no mínimo a quantidade de espaço do nome da camera mais 2 espaços.
cameras: #sem espaço
sala: # 2 espaços no inicio da linha
ffmpeg: # 4 espaços no inicio da linha
cozinha: # 2 espaços no inicio da linha
ffmpeg: # 4 espaços no inicio da linha
Testa ai:
mqtt:
host: 192.168.1.150
port: 1883
topic_prefix: frigate
client_id: frigate
user: mqttmarcelo
password: MXXXX
cameras:
sala:
ffmpeg:
inputs:
- path: rtsp://admin:FXXXXXXX@192.168.1.45:554/cam/realmonitor?channel=1&subtype=0&unicast=true&proto=Onvif
roles:
- detect
- rtmp
- record
detect:
width: 1280
height: 720
objects:
track:
- person
- cellphone
- cat
Da uma olhada aqui:
Aqui são todas as opções segundo a documentação:
mqtt:
# Required: host name
host: mqtt.server.com
# Optional: port (default: shown below)
port: 1883
# Optional: topic prefix (default: shown below)
# NOTE: must be unique if you are running multiple instances
topic_prefix: frigate
# Optional: client id (default: shown below)
# NOTE: must be unique if you are running multiple instances
client_id: frigate
# Optional: user
user: mqtt_user
# Optional: password
# NOTE: MQTT password can be specified with an environment variables that must begin with 'FRIGATE_'.
# e.g. password: '{FRIGATE_MQTT_PASSWORD}'
password: password
# Optional: tls_ca_certs for enabling TLS using self-signed certs (default: None)
tls_ca_certs: /path/to/ca.crt
# Optional: tls_client_cert and tls_client key in order to use self-signed client
# certificates (default: None)
# NOTE: certificate must not be password-protected
# do not set user and password when using a client certificate
tls_client_cert: /path/to/client.crt
tls_client_key: /path/to/client.key
# Optional: tls_insecure (true/false) for enabling TLS verification of
# the server hostname in the server certificate (default: None)
tls_insecure: false
# Optional: interval in seconds for publishing stats (default: shown below)
stats_interval: 60
# Optional: Detectors configuration. Defaults to a single CPU detector
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
# Optional: num_threads value passed to the tflite.Interpreter (default: shown below)
# This value is only used for CPU types
num_threads: 3
# Optional: Database configuration
database:
# The path to store the SQLite DB (default: shown below)
path: /media/frigate/frigate.db
# Optional: model modifications
model:
# Optional: path to the model (default: automatic based on detector)
path: /edgetpu_model.tflite
# Optional: path to the labelmap (default: shown below)
labelmap_path: /labelmap.txt
# Required: Object detection model input width (default: shown below)
width: 320
# Required: Object detection model input height (default: shown below)
height: 320
# Optional: Label name modifications. These are merged into the standard labelmap.
labelmap:
2: vehicle
# Optional: logger verbosity settings
logger:
# Optional: Default log verbosity (default: shown below)
default: info
# Optional: Component specific logger overrides
logs:
frigate.event: debug
# Optional: set environment variables
environment_vars:
EXAMPLE_VAR: value
# Optional: birdseye configuration
# NOTE: Can (enabled, mode) be overridden at the camera level
birdseye:
# Optional: Enable birdseye view (default: shown below)
enabled: True
# Optional: Width of the output resolution (default: shown below)
width: 1280
# Optional: Height of the output resolution (default: shown below)
height: 720
# Optional: Encoding quality of the mpeg1 feed (default: shown below)
# 1 is the highest quality, and 31 is the lowest. Lower quality feeds utilize less CPU resources.
quality: 8
# Optional: Mode of the view. Available options are: objects, motion, and continuous
# objects - cameras are included if they have had a tracked object within the last 30 seconds
# motion - cameras are included if motion was detected in the last 30 seconds
# continuous - all cameras are included always
mode: objects
# Optional: ffmpeg configuration
ffmpeg:
# Optional: global ffmpeg args (default: shown below)
global_args: -hide_banner -loglevel warning
# 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 -timeout 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 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: Detect configuration
# NOTE: Can be overridden at the camera level
detect:
# Optional: width of the frame for the input with the detect role (default: shown below)
width: 1280
# Optional: height of the frame for the input with the detect role (default: shown below)
height: 720
# Optional: desired fps for your camera for the input with the detect role (default: shown below)
# NOTE: Recommended value of 5. Ideally, try and reduce your FPS on the camera.
fps: 5
# Optional: enables detection for the camera (default: True)
# This value can be set via MQTT and will be updated in startup based on retained value
enabled: True
# Optional: Number of frames without a detection before frigate considers an object to be gone. (default: 5x the frame rate)
max_disappeared: 25
# Optional: Configuration for stationary object tracking
stationary:
# Optional: Frequency for confirming stationary objects (default: shown below)
# When set to 0, object detection will not confirm stationary objects until movement is detected.
# If set to 10, object detection will run to confirm the object still exists on every 10th frame.
interval: 0
# Optional: Number of frames without a position change for an object to be considered stationary (default: 10x the frame rate or 10s)
threshold: 50
# Optional: Define a maximum number of frames for tracking a stationary object (default: not set, track forever)
# This can help with false positives for objects that should only be stationary for a limited amount of time.
# It can also be used to disable stationary object tracking. For example, you may want to set a value for person, but leave
# car at the default.
# WARNING: Setting these values overrides default behavior and disables stationary object tracking.
# There are very few situations where you would want it disabled. It is NOT recommended to
# copy these values from the example config into your config unless you know they are needed.
max_frames:
# Optional: Default for all object types (default: not set, track forever)
default: 3000
# Optional: Object specific values
objects:
person: 1000
# Optional: Object configuration
# NOTE: Can be overridden at the camera level
objects:
# Optional: list of objects to track from labelmap.txt (default: shown below)
track:
- person
# Optional: mask to prevent all object types from being detected in certain areas (default: no mask)
# Checks based on the bottom center of the bounding box of the object.
# NOTE: This mask is COMBINED with the object type specific mask below
mask: 0,0,1000,0,1000,200,0,200
# 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 width/height of the bounding box for the detected object (default: 0)
min_ratio: 0.5
# Optional: maximum width/height of the bounding box for the detected object (default: 24000000)
max_ratio: 2.0
# 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: 0.7
# Optional: mask to prevent this object type from being detected in certain areas (default: no mask)
# Checks based on the bottom center of the bounding box of the object
mask: 0,0,1000,0,1000,200,0,200
# Optional: Motion configuration
# NOTE: Can be overridden at the camera level
motion:
# Optional: The threshold passed to cv2.threshold to determine if a pixel is different enough to be counted as motion. (default: shown below)
# Increasing this value will make motion detection less sensitive and decreasing it will make motion detection more sensitive.
# The value should be between 1 and 255.
threshold: 25
# Optional: Minimum size in pixels in the resized motion image that counts as motion (default: 30)
# Increasing this value will prevent smaller areas of motion from being detected. Decreasing will
# make motion detection more sensitive to smaller moving objects.
# As a rule of thumb:
# - 15 - high sensitivity
# - 30 - medium sensitivity
# - 50 - low sensitivity
contour_area: 30
# Optional: Alpha value passed to cv2.accumulateWeighted when averaging the motion delta across multiple frames (default: shown below)
# Higher values mean the current frame impacts the delta a lot, and a single raindrop may register as motion.
# Too low and a fast moving person wont be detected as motion.
delta_alpha: 0.2
# Optional: Alpha value passed to cv2.accumulateWeighted when averaging frames to determine the background (default: shown below)
# Higher values mean the current frame impacts the average a lot, and a new object will be averaged into the background faster.
# Low values will cause things like moving shadows to be detected as motion for longer.
# https://www.geeksforgeeks.org/background-subtraction-in-an-image-using-concept-of-running-average/
frame_alpha: 0.2
# Optional: Height of the resized motion frame (default: 50)
# This operates as an efficient blur alternative. Higher values will result in more granular motion detection at the expense
# of higher CPU usage. Lower values result in less CPU, but small changes may not register as motion.
frame_height: 50
# Optional: motion mask
# NOTE: see docs for more detailed info on creating masks
mask: 0,900,1080,900,1080,1920,0,1920
# Optional: improve contrast (default: shown below)
# Enables dynamic contrast improvement. This should help improve night detections at the cost of making motion detection more sensitive
# for daytime.
improve_contrast: False
# Optional: Delay when updating camera motion through MQTT from ON -> OFF (default: shown below).
mqtt_off_delay: 30
# Optional: Record configuration
# NOTE: Can be overridden at the camera level
record:
# Optional: Enable recording (default: shown below)
# WARNING: If recording is disabled in the config, turning it on via
# the UI or MQTT later will have no effect.
# WARNING: Frigate does not currently support limiting recordings based
# on available disk space automatically. If using recordings,
# you must specify retention settings for a number of days that
# will fit within the available disk space of your drive or Frigate
# will crash.
enabled: False
# Optional: Number of minutes to wait between cleanup runs (default: shown below)
# This can be used to reduce the frequency of deleting recording segments from disk if you want to minimize i/o
expire_interval: 60
# Optional: Retention settings for recording
retain:
# Optional: Number of days to retain recordings regardless of events (default: shown below)
# NOTE: This should be set to 0 and retention should be defined in events section below
# if you only want to retain recordings of events.
days: 0
# Optional: Mode for retention. Available options are: all, motion, and active_objects
# all - save all recording segments regardless of activity
# motion - save all recordings segments with any detected motion
# active_objects - save all recording segments with active/moving objects
# NOTE: this mode only applies when the days setting above is greater than 0
mode: all
# Optional: Event recording settings
events:
# Optional: Number of seconds before the event to include (default: shown below)
pre_capture: 5
# Optional: Number of seconds after the event to include (default: shown below)
post_capture: 5
# Optional: Objects to save recordings for. (default: all tracked objects)
objects:
- person
# Optional: Restrict recordings to objects that entered any of the listed zones (default: no required zones)
required_zones: []
# Optional: Retention settings for recordings of events
retain:
# Required: Default retention days (default: shown below)
default: 10
# Optional: Mode for retention. (default: shown below)
# all - save all recording segments for events regardless of activity
# motion - save all recordings segments for events with any detected motion
# active_objects - save all recording segments for event with active/moving objects
#
# NOTE: If the retain mode for the camera is more restrictive than the mode configured
# here, the segments will already be gone by the time this mode is applied.
# For example, if the camera retain mode is "motion", the segments without motion are
# never stored, so setting the mode to "all" here won't bring them back.
mode: motion
# Optional: Per object retention days
objects:
person: 15
# Optional: Configuration for the jpg snapshots written to the clips directory for each event
# NOTE: Can be overridden at the camera level
snapshots:
# Optional: Enable writing jpg snapshot to /media/frigate/clips (default: shown below)
# This value can be set via MQTT and will be updated in startup based on retained value
enabled: False
# Optional: save a clean PNG copy of the snapshot image (default: shown below)
clean_copy: True
# Optional: print a timestamp on the snapshots (default: shown below)
timestamp: False
# Optional: draw bounding box on the snapshots (default: shown below)
bounding_box: False
# Optional: crop the snapshot (default: shown below)
crop: False
# Optional: height to resize the snapshot to (default: original size)
height: 175
# Optional: Restrict snapshots to objects that entered any of the listed zones (default: no required zones)
required_zones: []
# 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: RTMP configuration
# NOTE: Can be overridden at the camera level
rtmp:
# Optional: Enable the RTMP stream (default: True)
enabled: True
# Optional: Live stream configuration for WebUI
# NOTE: Can be overridden at the camera level
live:
# Optional: Set the height of the live stream. (default: 720)
# This must be less than or equal to the height of the detect stream. Lower resolutions
# reduce bandwidth required for viewing the live stream. Width is computed to match known aspect ratio.
height: 720
# Optional: Set the encode quality of the live stream (default: shown below)
# 1 is the highest quality, and 31 is the lowest. Lower quality feeds utilize less CPU resources.
quality: 8
# Optional: in-feed timestamp style configuration
# NOTE: Can be overridden at the camera level
timestamp_style:
# Optional: Position of the timestamp (default: shown below)
# "tl" (top left), "tr" (top right), "bl" (bottom left), "br" (bottom right)
position: "tl"
# Optional: Format specifier conform to the Python package "datetime" (default: shown below)
# Additional Examples:
# german: "%d.%m.%Y %H:%M:%S"
format: "%m/%d/%Y %H:%M:%S"
# Optional: Color of font
color:
# All Required when color is specified (default: shown below)
red: 255
green: 255
blue: 255
# Optional: Line thickness of font (default: shown below)
thickness: 2
# Optional: Effect of lettering (default: shown below)
# None (No effect),
# "solid" (solid background in inverse color of font)
# "shadow" (shadow for font)
effect: None
# Required
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: path may include environment variables, which must begin with 'FRIGATE_' and 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,rtmp
# NOTICE: In addition to assigning the record, and rtmp roles,
# they must also be enabled in the camera config.
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:
# Optional: timeout for highest scoring image before allowing it
# to be replaced by a newer image. (default: shown below)
best_image_timeout: 60
# 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: Presence in a zone is evaluated only based on the bottom center of the objects bounding box.
coordinates: 545,1077,747,939,788,805
# Optional: List of objects that can trigger this zone (default: all tracked objects)
objects:
- person
# Optional: Zone level object filters.
# NOTE: The global and camera filters are applied upstream.
filters:
person:
min_area: 5000
max_area: 100000
threshold: 0.7
# Optional: Configuration for the jpg snapshots published via MQTT
mqtt:
# Optional: Enable publishing snapshot via mqtt for camera (default: shown below)
# NOTE: Only applies to publishing image data to MQTT via 'frigate/<camera_name>/<object_name>/snapshot'.
# All other messages will still be published.
enabled: True
# Optional: print a timestamp on the snapshots (default: shown below)
timestamp: True
# Optional: draw bounding box on the snapshots (default: shown below)
bounding_box: True
# Optional: crop the snapshot (default: shown below)
crop: True
# Optional: height to resize the snapshot to (default: shown below)
height: 270
# Optional: jpeg encode quality (default: shown below)
quality: 70
# Optional: Restrict mqtt messages to objects that entered any of the listed zones (default: no required zones)
required_zones: []
# Optional: Configuration for how camera is handled in the GUI.
ui:
# Optional: Adjust sort order of cameras in the UI. Larger numbers come later (default: shown below)
# By default the cameras are sorted alphabetically.
order: 0
# Optional: Whether or not to show the camera in the Frigate UI (default: shown below)
dashboard: True
1 curtida
Agora está funcionando perfeito… Não sei o motivo, mas o fps: 5 que estava gerando o erro.
Obrigado!!!
O meu deu erro tbm no fps.
Me parece que tem umas regras que podem ser usadas para todas as cameras ou individual, ai acaba confundindo um pouco.
Da uma olhada nas msg do Britto no tópico que mandei, segundo ele aquelas configurações reduzem o uso de CPU
Boa noite!
Estou eu de volta com mais um problema, configurei 4 câmeras para usar no Frigate sendo que uma delas fica com a tela toda verde.
Pelo que olhei no Log o erro me parece algo de autenticação, mas a mesma url consigo visualizar de boa no VLC
Oi tudo bem.
tava com o mesmo problemas ai olhei a parte de:
detect:
width: 1920
height: 1080
no meu casa era a resolução da camera.
eu identifique a resolução da câmera ai saiu da tela verde.
Obrigado pela a ideia, eu tentei varias resolução da câmera, sem sucesso ainda!
Tenho esse erro no log, mas ainda nao entendi já que a url funciona no aplicativo VLC
bom dia.
posta seu code. inteiro aqui.
ai eu testo aqui no meu frigate
Wall-e:
ffmpeg:
inputs:
- path: rtsp://admin:LuccaXXXXX@192.168.1.65:554/onvif1
roles:
- detect
- rtmp
- record
# output_args:
# record: -f segment -segment_time 10 -segment_format mp4 -reset_timestamps 1 -strftime 1 -c:v copy -c:a copy
# rtmp: -c:v copy -c:a copy -f flv
objects:
track:
- person
- cat
detect:
width: 1280
height: 720
Essa câmera:
Tenho uma igual a essa. Instalei o frigate hoje mas só tá dando tela verde. Conseguiu. Resolver?
Infelizmente não achei ainda a solução. Mesmo baixando a resolução continua verde.
se alguém quiser copiar minha config, sobre a camera tentou outra proporcão tipo 4:3 800x600?
mqtt:
host:
port: 1883
topic_prefix: frigate
client_id: frigate
user:
password:record:
enabled: True
events:
retain:
default: 5snapshots:
enabled: truebirdseye:
enabled: true
mode: continuouscameras:
360_Frigate:
ffmpeg:
inputs:
- path: rtsp://192.168.0.105/live/ch00_1
roles:
- detect
- record
- rtmp
detect:
width: 800
height: 800
fps: 1objects: track: - person - dog - carFrente_Frigate:
ffmpeg:
inputs:
- path: rtsp://192.168.0.131:8554/my_camera
roles:
- detect
- record
- rtmp
detect:
width: 1280
height: 720
fps: 5objects: track: - person - dog - car
Oi tudo bem.
copiei mas deu varios erros.
tipo espaçamentos etc.
Testei aqui mas continua verde.
mqtt:
host: 192.0.0.0
user: ****
password: ****
topic_prefix: frigate
cameras:
frigate_corredor:
ffmpeg:
inputs:
- path: rtsp://admin:senha@192.0.0.0:554/onvif1
roles:
- detect
- rtmp
detect:
width: 720
height: 1280
fps: 5
objects:
track:
- person
- cat
- dog
snapshots:
enabled: true
timestamp: false
bounding_box: true
retain:
default: 2
motion:
mask:
- 640,0,640,140,490,108,507,0
detectors:
cpu1:
type: cpu
cpu2:
type: cpu
Apenas como alerta, para uma boa integração com o Frigate, a câmera precisa trabalhar com H.264 (ao invés de H.265).
As câmeras Intelbras Mibo são H.264 nos modelos que terminam com C, e H.265 nos que termina sem C. Por exemplo, iM3 C é H.264 e iM3 é H.265.
Eu tenho iM3 C integrada no meu Frigate, funciona muito bem.
Outro ponto é a instalação do Frigate via addon no Home Assistant.
Eu comecei assim, mas logo optei por usar o Frigate fora do HA em uma instalação Docker; acredito que é mais versátil (principalmente para colocar o armazenamento em drive externo). A curva de aprendizado é um pouco maior, mas acredito que vale a pena.
Estou gostando tanto que até meu Home Assistant já quero colocar em Docker também.
muito bom mesmo.
vou tentar obg