Added doods component documentation (#10228)

* Added doods component documentation

* Update ha_release

Co-Authored-By: Klaas Schoute <klaas_schoute@hotmail.com>

* Update for recommendations

* Removed redirect_from option
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Zach 2019-09-11 16:55:05 -04:00 committed by Alok Saboo
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---
title: "DOODS"
description: "Detect and recognize objects with DOODS."
ha_category:
- Image Processing
ha_iot_class: Local Polling
ha_release: 0.99
---
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.
## Setup
You need to have DOODS running somewhere. It's easiest to run as a docker container and deployment is described on docker hub
[DOODS - Docker](https://hub.docker.com/r/snowzach/doods)
## Configuration
The configuration loosely follows the tensorflow configuration. To enable this platform in your installation, add the following to your `configuration.yaml` file:
```yaml
# Example configuration.yaml entry
image_processing:
- platform: doods
url: "http://<my doods server>:8080"
source:
- entity_id: camera.front_yard
```
{% configuration %}
source:
description: The list of image sources.
required: true
type: map
keys:
entity_id:
description: A camera entity id to get picture from.
required: true
type: string
name:
description: This parameter allows you to override the name of your `image_processing` entity.
required: false
type: string
url:
description: The URL of the DOODS server
required: true
type: string
detector:
description: The DOODS detector to use
required: false
type: string
confidence:
description: The default confidence for any detected objects where not explicitly set
required: false
type: float
file_out:
description: A [template](/docs/configuration/templating/#processing-incoming-data) for the integration to save processed images including bounding boxes. `camera_entity` is available as the `entity_id` string of the triggered source camera.
required: false
type: list
labels:
description: Information about the selected labels model.
required: false
type: map
keys:
name:
description: The label of the object to select for detection.
required: true
type: string
confidence:
description: The minimum confidence for the selected label
required: false
type: float
area:
description: Custom detection area. Only objects fully in this box will be reported. Top of image is 0, bottom is 1. Same left to right.
required: false
type: map
keys:
top:
description: Top line defined as % from top of image.
required: false
type: float
default: 0
left:
description: Left line defined as % from left of image.
required: false
type: float
default: 0
bottom:
description: Bottom line defined as % from top of image.
required: false
type: float
default: 1
right:
description: Right line defined as % from left of image.
required: false
type: float
default: 1
{% endconfiguration %}
```yaml
# Example advanced configuration.yaml entry
# Example configuration.yaml entry
image_processing:
- platform: doods
scan_interval: 1000
url: "http://<my doods server>:8080"
detector: default
source:
- entity_id: camera.front_yard
file_out:
- "/tmp/{% raw %}{{ camera_entity.split('.')[1] }}{% endraw %}_latest.jpg"
- "/tmp/{% raw %}{{ camera_entity.split('.')[1] }}_{{ now().strftime('%Y%m%d_%H%M%S') }}{% endraw %}.jpg"
confidence: 50
labels:
- name: person
confidence: 40
area:
# Exclude top 10% of image
top: 0.1
# Exclude right 15% of image
right: 0.85
- car
- truck
```
## Optimising resources
[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 config `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.
```yaml
# Example advanced configuration.yaml entry
image_processing:
- platform: doods
scan_interval: 10000
source:
- entity_id: camera.driveway
- entity_id: camera.backyard
```
```yaml
# Example advanced automations.yaml entry
- alias: Doods scanning
trigger:
- platform: state
entity_id:
- binary_sensor.driveway
action:
- service: image_processing.scan
entity_id: camera.driveway
```