2024.11.0
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DOODS - Dedicated Open Object Detection Service

The doods image processing integrationIntegrations connect and integrate Home Assistant with your devices, services, and more. [Learn more] allows you to detect and recognize objects in a camera image using DOODS. The state of the entity is the number of objects detected and recognized objects are listed in the summary attribute along with quantity. The matches attribute provides the confidence score for recognition and the bounding box of the object for each detection category.

Setup

The DOODS software needs to be running before this integration can be used. Options to run the DOODS software:

Configuration

To enable this integration in your installation, add the following to your configuration.yamlThe configuration.yaml file is the main configuration file for Home Assistant. It lists the integrations to be loaded and their specific configurations. In some cases, the configuration needs to be edited manually directly in the configuration.yaml file. Most integrations can be configured in the UI. [Learn more] file. After changing the configuration.yamlThe configuration.yaml file is the main configuration file for Home Assistant. It lists the integrations to be loaded and their specific configurations. In some cases, the configuration needs to be edited manually directly in the configuration.yaml file. Most integrations can be configured in the UI. [Learn more] file, restart Home Assistant to apply the changes.

# Example configuration.yaml entry
image_processing:
  - platform: doods
    url: "http://<my doods server>:8080"
    detector: default
    source:
      - entity_id: camera.front_yard

Configuration Variables

Looking for your configuration file?
source map Required

The list of image sources.

entity_id string Required

A camera entity id to get picture from.

name string (Optional)

This parameter allows you to override the name of your image_processing entity.

url string Required

The URL of the DOODS server.

auth_key string (Optional)

The authentication key as set in the DOODS configuration file or as a Docker environment variable (DOODS_AUTH_KEY)

timeout integer (Optional, default: 90)

Timeout for requests (in seconds).

detector string Required

The DOODS detector to use.

confidence float (Optional)

The default confidence for any detected objects where not explicitly set.

area map (Optional)

Global detection area. Objects in this box will be reported. Top of image is 0, bottom is 1. Same left to right.

top float (Optional, default: 0)

Top line defined as % from top of image.

left float (Optional, default: 0)

Left line defined as % from left of image.

bottom float (Optional, default: 1)

Bottom line defined as % from top of image.

right float (Optional, default: 1)

Right line defined as % from left of image.

covers boolean (Optional, default: true)

If true the detection must be fully in this box. If false any part of the detection in the box will trigger.

file_out list (Optional)

A template for the integration to save processed images including bounding boxes. camera_entity is available as the entity_id string of the triggered source camera.

labels map (Optional)

Information about the selected labels model.

name string Required

The label of the object to select for detection.

confidence float (Optional)

The minimum confidence for the selected label.

area map (Optional)

Custom detection area. Only objects fully in this box will be reported. Top of image is 0, bottom is 1. Same left to right.

top float (Optional, default: 0)

Top line defined as % from top of image.

left float (Optional, default: 0)

Left line defined as % from left of image.

bottom float (Optional, default: 1)

Bottom line defined as % from top of image.

right float (Optional, default: 1)

Right line defined as % from left of image.

covers boolean (Optional, default: true)

If true the detection must be fully in this box. If false any part of the detection in the box will trigger.

Supported labels

Both detectors default and tensorflow use the labels in this file.

Sample configuration 

# Example advanced configuration.yaml entry
image_processing:
  - platform: doods
    scan_interval: 1000
    url: "http://<my doods server>:8080"
    timeout: 60
    detector: default
    auth_key: 2up3rL0ng4uthK3y
    source:
      - entity_id: camera.front_yard
    file_out:
      - "/tmp/{{ camera_entity.split('.')[1] }}_latest.jpg"
      - "/tmp/{{ camera_entity.split('.')[1] }}_{{ now().strftime('%Y%m%d_%H%M%S') }}.jpg"
    confidence: 50
    # This global detection area is required for all labels
    area:
      # Exclude top 10% of image
      top: 0.1
      # Exclude right 5% of image
      right: 0.95
      # The entire detection must be inside this box
      covers: true
    labels:
      - name: person
        confidence: 40
        area:
          # Exclude top 10% of image
          top: 0.1
          # Exclude right 15% of image
          right: 0.85
          # Any part of the detection inside this area will trigger
          covers: false
      - car
      - truck

Optimizing resources

The Image processing integration processes the image from a camera at a fixed period given by the scan_interval. This leads to excessive processing if the image on the camera hasn’t changed, as the default scan_interval is 10 seconds. You can override this by adding to your configuration scan_interval: 10000 (setting the interval to 10,000 seconds) and then call the image_processing.scan action when you actually want to perform processing.

# Example advanced configuration.yaml entry
image_processing:
  - platform: doods
    scan_interval: 10000
    source:
      - entity_id: camera.driveway
      - entity_id: camera.backyard

# Example advanced automations.yaml entry
- alias: "Doods scanning"
  triggers:
     - trigger: state
       entity_id:
         - binary_sensor.driveway
  actions:
    - action: image_processing.scan
      target:
        entity_id: image_processing.doods_camera_driveway

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