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Fix configuration description and heading (#12815)
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@ -8,16 +8,15 @@ ha_release: '0.100'
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ha_domain: doods
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---
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The `doods` image processing platform allows you to detect and recognize objects in a camera image using [DOODS](https://github.com/snowzach/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.
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The `doods` image processing integration allows you to detect and recognize objects in a camera image using [DOODS](https://github.com/snowzach/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.
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## Setup
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You need to have DOODS running somewhere. It's easiest to run as a Docker container and deployment is described on Docker Hub
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[DOODS - Docker](https://hub.docker.com/r/snowzach/doods)
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You need to have DOODS running somewhere. It's easiest to run as a [Docker](https://hub.docker.com/r/snowzach/doods) container.
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## Configuration
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The configuration loosely follows the TensorFlow configuration. To enable this platform in your installation, add the following to your `configuration.yaml` file:
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To enable this integration in your installation, add the following to your `configuration.yaml` file:
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```yaml
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# Example configuration.yaml entry
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@ -44,20 +43,20 @@ source:
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required: false
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type: string
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url:
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description: The URL of the DOODS server
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description: The URL of the DOODS server.
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required: true
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type: string
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timeout:
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description: Timeout for requests (in seconds)
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description: Timeout for requests (in seconds).
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required: false
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type: integer
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default: 90
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detector:
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description: The DOODS detector to use
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description: The DOODS detector to use.
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required: true
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type: string
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confidence:
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description: The default confidence for any detected objects where not explicitly set
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description: The default confidence for any detected objects where not explicitly set.
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required: false
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type: float
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area:
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@ -104,7 +103,7 @@ labels:
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required: true
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type: string
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confidence:
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description: The minimum confidence for the selected label
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description: The minimum confidence for the selected label.
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required: false
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type: float
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area:
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@ -140,9 +139,12 @@ labels:
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{% endconfiguration %}
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## Sample configuration
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{% raw %}
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```yaml
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# Example advanced configuration.yaml entry
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# Example configuration.yaml entry
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image_processing:
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- platform: doods
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scan_interval: 1000
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@ -152,8 +154,8 @@ image_processing:
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source:
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- entity_id: camera.front_yard
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file_out:
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- "/tmp/{% raw %}{{ camera_entity.split('.')[1] }}{% endraw %}_latest.jpg"
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- "/tmp/{% raw %}{{ camera_entity.split('.')[1] }}_{{ now().strftime('%Y%m%d_%H%M%S') }}{% endraw %}.jpg"
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- "/tmp/{{ camera_entity.split('.')[1] }}_latest.jpg"
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- "/tmp/{{ camera_entity.split('.')[1] }}_{{ now().strftime('%Y%m%d_%H%M%S') }}.jpg"
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confidence: 50
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# This global detection area is required for all labels
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area:
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@ -177,9 +179,11 @@ image_processing:
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- truck
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```
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{% endraw %}
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## Optimizing resources
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[Image processing components](/components/image_processing/) process 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` service when you actually want to perform processing.
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The [Image processing integration](/components/image_processing/) process 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` service when you actually want to perform processing.
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```yaml
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# Example advanced configuration.yaml entry
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