home-assistant.io/source/_components/image_processing.tensorflow.markdown

layout	title	description	date	sidebar	comments	sharing	footer	logo	ha_category	ha_iot_class	ha_release
page	TensorFlow	Detect and recognize objects with TensorFlow.	2018-10-24 00:00	true	false	true	true	tensorflow.png	Image Processing	Local Polling	0.82

The tensorflow image processing platform allows you to detect and recognize objects in a camera image using TensorFlow. 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.

The following packages must be installed on Hassbian/Raspbian before following the setup for the component to work: `$ sudo apt-get install libatlas-base-dev libopenjp2-7 libtiff5`

{% 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 for other options. Hass.io is not yet supported but an addon is under development.

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. Alternatively, if you wish to perform the process manually, the process is as follows:

• Clone tensorflow/models
• Compile protobuf models located in research/object_detection/protos with protoc
• Create the following directory structure inside your config directory:

  |- {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.

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:

# 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 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:

# 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 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.