---
layout: page
title: "TensorFlow"
description: "Detect and recognize objects with TensorFlow."
date: 2018-10-24 00:00
sidebar: true
comments: false
sharing: true
footer: true
logo: tensorflow.png
ha_category: Image Processing
ha_iot_class: "Local Polling"
ha_release: 0.82
---
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.
<p class='note warning'>
The following packages must be installed on Hassbian after following the setup for the component to work:
`$ sudo apt-get install libatlas-base-dev libopenjp2-7 libtiff5`
</p>
## {% linkable_title Setup %}
You need to install the `tensorflow` Python packages with: `$ pip3 install tensorflow==1.11.0`. The wheel is not available for all platforms. See [the official install guide](https://www.tensorflow.org/install/) for other options. Hass.io has this package pre-installed.
This component requires files to be downloaded, compiled on your computer, and added to the Home Assistant configuration directory. These steps can be performed using the sample script at [this gist](https://gist.github.com/hunterjm/6f9332f92b60c3d5e448ad936d7353c3). Alternatively, if you wish to perform the process manually, the process is as follows:
- Clone [tensorflow/models](https://github.com/tensorflow/models/tree/master/research/object_detection)
- Compile protobuf models located in `research/object_detection/protos` with `protoc`
- Create the following directory structure inside your config directory:
```bash
|- {config_dir}
| - tensorflow/
|- object_detection/
|- __init__.py
```
- Copy required object_detection dependancies to the `object_detection` folder inside of the `tensorflow` folder:
- `research/object_detection/data`
- `research/object_detection/utils`
- `research/object_detection/protos`
## {% linkable_title 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/detection_model_zoo.md).
The trade-off between the different models is accuracy vs speed. Users with a decent CPU should start with the `faster_rcnn_inception_v2_coco` model. If you are running on an ARM device like a Raspberry Pi, start with the `ssd_mobilenet_v2_coco` model.
Whichever model you choose, download it and place the `frozen_inference_graph.pb` file in the `tensorflow` folder in your configuration directory.
## {% linkable_title 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: /home/homeassistant/.homeassistant/tensorflow/frozen_inference_graph.pb
```
{% 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 component 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 `frozen_inference_graph.pb`.
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
model_dir:
description: Full path to tensorflow models directory.
required: false
type: string
default: /tensorflow inside config
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:
```yaml
# Example advanced configuration.yaml entry
image_processing:
- platform: tensorflow
source:
- entity_id: camera.driveway
- entity_id: camera.backyard
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"
model:
graph: /home/homeassistant/.homeassistant/tensorflow/frozen_inference_graph.pb
categories:
- category: person
area:
# Exclude top 10% of image
top: 0.1
# Exclude right 15% of image
right: 0.85
- car
- truck
```
## {% linkable_title Optimising resources %}
[Image processing components](https://www.home-assistant.io/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.