---
title: TensorFlow
description: Detect and recognize objects with TensorFlow.
ha_category:
- Image processing
ha_iot_class: Local Polling
ha_release: 0.82
ha_domain: tensorflow
ha_integration_type: integration
---
The TensorFlow image processing platform 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.
<div class='note'>
This integration is only available on Home Assistant Core installation types. Unfortunately, it cannot be used with Home Assistant OS, Supervised or Container.
</div>
## Prerequisites
The following packages must be installed on Debian before following the setup for the integration to work:
`sudo apt-get install libatlas-base-dev libopenjp2-7 libtiff5`
It is possible that Home Assistant is unable to install the Python TensorFlow bindings. If that is the case,
you'll need to install those manually using: `pip install tensorflow==2.2.0`, as the Python wheel is
not available for all platforms.
See [the official install guide](https://www.tensorflow.org/install/) for other options.
Furthermore, the official Python TensorFlow wheels by Google, require your CPU to support the `avx` extension.
If your CPU lacks those capabilities, Home Assistant will crash when using TensorFlow, without any message.
## Preparation
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:
Create the following folder structure in your configuration directory.
```bash
|- {config_dir}
|- tensorflow/
|- models/
```
Follow these steps (Linux) to compile the object detection library.
```bash
# Clone tensorflow/models
git clone https://github.com/tensorflow/models.git
# Compile Protobuf (apt-get install protobuf-compiler)
cd models/research
protoc object_detection/protos/*.proto --python_out=.
# Copy object_detection to {config_dir}
cp -r object_detection {config_dir}/tensorflow
```
Your final folder structure should look as follows
```bash
|- {config_dir}
|- tensorflow/
|- models/
|- object_detection/
|- ...
```
## Model Selection
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).
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.
Whichever model you choose, download it and extract in to the `tensorflow/models` folder in your configuration directory.
## Configuration
To enable this platform in your installation, add the following to your `configuration.yaml` file:
```yaml
# Example configuration.yaml entry
image_processing:
- platform: tensorflow
source:
- entity_id: camera.local_file
model:
graph: /config/tensorflow/models/efficientdet_d0_coco17_tpu-32/
```
{% 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
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
model:
description: Information about the TensorFlow model.
required: true
type: map
keys:
graph:
description: Full path to the base model directory.
required: true
type: string
labels:
description: Full path to a `*label_map.pbtext`.
required: false
type: string
default: tensorflow/object_detection/data/mscoco_label_map.pbtxt
label_offset:
description: Offset for mapping label ID to a name (only use for custom models)
required: false
type: integer
default: 1
model_dir:
description: Full path to TensorFlow models directory.
required: false
type: string
default: "`/tensorflow` inside configuration"
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
categories:
description: List of categories to include in object detection. Can be seen in the file provided to `labels`.
type: list
required: false
{% endconfiguration %}
`categories` can also be defined as dictionary providing an `area` for each category as seen in the advanced configuration below:
{% raw %}
```yaml
# Example advanced configuration.yaml entry
image_processing:
- platform: tensorflow
source:
- entity_id: camera.driveway
- entity_id: camera.backyard
file_out:
- "/tmp/{{ camera_entity.split('.')[1] }}_latest.jpg"
- "/tmp/{{ camera_entity.split('.')[1] }}_{{ now().strftime('%Y%m%d_%H%M%S') }}.jpg"
model:
graph: /config/tensorflow/models/efficientdet_d0_coco17_tpu-32/
categories:
- category: person
area:
# Exclude top 10% of image
top: 0.1
# Exclude right 15% of image
right: 0.85
- car
- truck
```
{% endraw %}
## Optimizing resources
[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` service when you actually want to perform processing.
```yaml
# Example advanced configuration.yaml entry
image_processing:
- platform: tensorflow
scan_interval: 10000
source:
- entity_id: camera.driveway
- entity_id: camera.backyard
```
```yaml
# Example advanced automations.yaml entry
- alias: "TensorFlow scanning"
trigger:
- platform: state
entity_id:
- binary_sensor.driveway
action:
- service: image_processing.scan
target:
entity_id: camera.driveway
```