Frigate et CodeProject association

Bonjour,
J’ai installé Frigate et brancher un Google Coral le tout sur mon RPi5.
Cela fonctionne, mais la détection des objets n’étant pas top, j’ai tenté d’installer CodeProject.
Je l’ai installé sur une VM sur mon petit serveur.
CodeProject tourne et j’ai pu installer l’Object Detection (Coral).
Dans les logs de CodeProject, il se passe bien quelque chose.


Mais sur Frigate, cela ne semble pas communiqué.

Voici la config de Frigate :

## Connection à MQTT ##
mqtt:
  host: 192.168.1.35
  port: Port
  client_id: frigate
  topic_prefix: frigate
  user: XxX
  password: XxX


## Choix des caméras ##
go2rtc:
  streams:
    r4252_smart_outdoor_camera:
    - echo:bash /config/custom_components/expose_camera_stream_source/get_stream.sh
      camera.r4252_smart_outdoor_camera

# if you want to decode a h265 (hevc) stream
ffmpeg:
  hwaccel_args: preset-rpi-64-h264

## Liste des caméras avec leurs adresses de connection ##
cameras:
  r4252_smart_outdoor_camera:
    ffmpeg:
      inputs:
      - path: rtsp://127.0.0.1:8554/r4252_smart_outdoor_camera?video
        input_args: preset-rtsp-restream-low-latency
        roles:
        - detect

#    motion: ## Création des zones à ignorer ##
#    motion:
#      mask:

## Choix des options de détection et d'enregistrement ##
record:
  enabled: true
  retain: # Durée de rétention des enregistrements
    days: 7
    mode: all
  events:
# Seconds d'enregistrement avant et après l'èvenement
    pre_capture: 60
    post_capture: 60
    retain:
      default: 15 #Durée de rétention en J
#      mode: motion ## Conserve les vidéos avec mouvements seulement ## ## active_objects ## Conserve les vidéos avec objets seulement ##
      mode: all ## Conserve les vidéos avec détection d'objets ##
      objects:
        dog: 7 ## Durée de conservation par objets ##
        cat: 7
        person: 7

#Objets à suivre
objects:
  # Optional: list of objects to track from labelmap.txt (default: shown below)
  track:
  - person
  - dog
  - cat
  # 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: 0.7
      # Optional: minimum decimal percentage for tracked object's computed score to be considered a true positive (default: shown below)
      threshold: 0.85
#    dog:
      # 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
    cat:
      # 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: 0.4
      # Optional: minimum decimal percentage for tracked object's computed score to be considered a true positive (default: shown below)
      threshold: 0.5

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: 30

# Optional
ui:
  # Optional: Set the default live mode for cameras in the UI (default: shown below)
  live_mode: mse
  # Optional: Set a timezone to use in the UI (default: use browser local time)
  timezone: Europe/Paris
  # Optional: Use an experimental recordings / camera view UI (default: shown below)
#  use_experimental: False
  # Optional: Set the time format used.
  # Options are browser, 12hour, or 24hour (default: shown below)
  #time_format: browser
  # Optional: Set the date style for a specified length.
  # Options are: full, long, medium, short
  # Examples:
  #    short: 2/11/23
  #    medium: Feb 11, 2023
  #    full: Saturday, February 11, 2023
  # (default: shown below).
#  date_style: short
  # Optional: Set the time style for a specified length.
  # Options are: full, long, medium, short
  # Examples:
  #    short: 8:14 PM
  #    medium: 8:15:22 PM
  #    full: 8:15:22 PM Mountain Standard Time
  # (default: shown below).
# time_style: medium
  # Optional: Ability to manually override the date / time styling to use strftime format
  # https://www.gnu.org/software/libc/manual/html_node/Formatting-Calendar-Time.html
  # possible values are shown above (default: not set)
# strftime_fmt: "%Y/%m/%d %H:%M"

# Detecteur Google Coral
detectors:
  coral:
    type: edgetpu
    device: usb
  deepstack:
    api_url: http://192.168.1.136:32168/v1/vision/detection
    type: deepstack
    api_timeout: 0.1 # seconds


snapshots:
  enabled: true

Frigate n’arrive pas à appeler l’API de CodeProject, mais je ne sais pas pourquoi.