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Remove deprecated tensorflow docs (#39295)
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Robert Resch
Franck Nijhof
parent
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commit
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---
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title: TensorFlow
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description: Detect and recognize objects with TensorFlow.
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ha_category:
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- Image processing
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ha_iot_class: Local Polling
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ha_release: 0.82
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ha_domain: tensorflow
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ha_integration_type: integration
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related:
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- docs: /docs/configuration/
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title: Configuration file
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ha_quality_scale: legacy
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---
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The TensorFlow image processing {% term integration %} allows you to detect and recognize objects in a camera image using [TensorFlow](https://www.tensorflow.org/). 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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{% important %}
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This integration is only available on Home Assistant Core installation types. Unfortunately, it cannot be used with Home Assistant OS, Supervised or Container.
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{% endimportant %}
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## Prerequisites
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The following packages must be installed on Debian before following the setup for the integration to work:
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`sudo apt-get install libatlas-base-dev libopenjp2-7 libtiff5`
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It is possible that Home Assistant is unable to install the Python TensorFlow bindings. If that is the case,
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you'll need to install those manually using: `pip install tensorflow==2.2.0`, as the Python wheel is
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not available for all platforms.
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See [the official install guide](https://www.tensorflow.org/install/) for other options.
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Furthermore, the official Python TensorFlow wheels by Google, require your CPU to support the `avx` extension.
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If your CPU lacks those capabilities, Home Assistant will crash when using TensorFlow, without any message.
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## Preparation
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This integration requires files to be downloaded, compiled on your computer, and added to the Home Assistant configuration directory. These steps can be performed by cloning [this repository](https://github.com/hunterjm/hass-tensorflow) into your configuration directory. Alternatively, if you wish to perform the process manually, the process is as follows:
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Create the following folder structure in your configuration directory.
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```bash
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|- {config_dir}
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|- tensorflow/
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|- models/
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```
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Follow these steps (Linux) to compile the object detection library.
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```bash
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# Clone tensorflow/models
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git clone https://github.com/tensorflow/models.git
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# Compile Protobuf (apt-get install protobuf-compiler)
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cd models/research
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protoc object_detection/protos/*.proto --python_out=.
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# Copy object_detection to {config_dir}
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cp -r object_detection {config_dir}/tensorflow
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```
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Your final folder structure should look as follows
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```bash
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|- {config_dir}
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|- tensorflow/
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|- models/
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|- object_detection/
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|- ...
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```
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## Model Selection
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Lastly, it is time to pick a model. It is recommended to start with one of the COCO models available in the [Model Detection Zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2_detection_zoo.md).
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The trade-off between the different models is accuracy vs speed. Users with a decent CPU should start with one of the `EfficientDet` models. If you are running on an ARM device like a Raspberry Pi, start with the `SSD MobileNet v2 320x320` model.
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Whichever model you choose, download it and extract in to the `tensorflow/models` folder in your configuration directory.
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## Configuration
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To enable this {% term integration %} in your installation, add the following to your {% term "`configuration.yaml`" %} file.
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{% include integrations/restart_ha_after_config_inclusion.md %}
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```yaml
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# Example configuration.yaml entry
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image_processing:
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- platform: tensorflow
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source:
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- entity_id: camera.local_file
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model:
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graph: /config/tensorflow/models/efficientdet_d0_coco17_tpu-32/
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```
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{% configuration %}
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source:
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description: The list of image sources.
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required: true
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type: map
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keys:
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entity_id:
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description: A camera entity id to get picture from.
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required: true
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type: string
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name:
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description: This parameter allows you to override the name of your `image_processing` entity.
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required: false
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type: string
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file_out:
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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.
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required: false
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type: list
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model:
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description: Information about the TensorFlow model.
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required: true
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type: map
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keys:
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graph:
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description: Full path to the base model directory.
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required: true
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type: string
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labels:
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description: Full path to a `*label_map.pbtext`.
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required: false
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type: string
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default: tensorflow/object_detection/data/mscoco_label_map.pbtxt
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label_offset:
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description: Offset for mapping label ID to a name (only use for custom models)
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required: false
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type: integer
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default: 1
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model_dir:
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description: Full path to TensorFlow models directory.
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required: false
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type: string
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default: "`/tensorflow` inside configuration"
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area:
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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.
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required: false
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type: map
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keys:
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top:
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description: Top line defined as % from top of image.
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required: false
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type: float
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default: 0
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left:
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description: Left line defined as % from left of image.
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required: false
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type: float
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default: 0
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bottom:
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description: Bottom line defined as % from top of image.
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required: false
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type: float
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default: 1
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right:
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description: Right line defined as % from left of image.
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required: false
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type: float
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default: 1
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categories:
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description: List of categories to include in object detection. Can be seen in the file provided to `labels`.
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type: list
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required: false
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{% endconfiguration %}
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`categories` can also be defined as dictionary providing an `area` for each category as seen in the advanced configuration below:
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{% raw %}
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```yaml
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# Example advanced configuration.yaml entry
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image_processing:
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- platform: tensorflow
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source:
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- entity_id: camera.driveway
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- entity_id: camera.backyard
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file_out:
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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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model:
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graph: /config/tensorflow/models/efficientdet_d0_coco17_tpu-32/
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categories:
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- category: person
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area:
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# Exclude top 10% of image
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top: 0.1
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# Exclude right 15% of image
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right: 0.85
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- car
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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](/integrations/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` action 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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image_processing:
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- platform: tensorflow
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scan_interval: 10000
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source:
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- entity_id: camera.driveway
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- entity_id: camera.backyard
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```
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```yaml
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# Example advanced automations.yaml entry
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- alias: "TensorFlow scanning"
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triggers:
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- trigger: state
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entity_id:
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- binary_sensor.driveway
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actions:
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- action: image_processing.scan
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target:
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entity_id: camera.driveway
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```
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@ -637,6 +637,7 @@ layout: null
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/integrations/sytadin /more-info/removed-integration 301
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/integrations/sunweg /more-info/removed-integration 301
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/integrations/tahoma /more-info/removed-integration 301
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/integrations/tensorflow /more-info/removed-integration 301
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/integrations/teksavvy /more-info/removed-integration 301
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/integrations/tesla /more-info/removed-integration 301
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/integrations/tof /more-info/removed-integration 301
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