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06de4338f9
* initial documentation for image_processing.tensorflow * add raw tag to fix template errors * changes per review * update documentation to reflect latest component changes and pull script out to gist * do not use deps folder as default, as it should only be managed by HA. Update to have tensorflow in root config directory * make gist a link, remove additional deps references * add additional cameras to example config, shorten model selection section, add warning at top for Hass.io * Update warning adding further Hassbian instructions * changes per PR review * fix init location in docs * fix spelling of raspberry * Update image_processing.tensorflow.markdown * Minor changes * Add class * add link to tensorflow install site for additional installation options
164 lines
6.7 KiB
Markdown
164 lines
6.7 KiB
Markdown
---
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layout: page
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title: "TensorFlow"
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description: "Detect and recognize objects with TensorFlow."
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date: 2018-10-24 00:00
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sidebar: true
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comments: false
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sharing: true
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footer: true
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logo: tensorflow.png
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ha_category: Image Processing
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ha_iot_class: "Local Polling"
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ha_release: 0.82
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---
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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.
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<p class='note warning'>
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The following packages must be installed on Hassbian after following the setup for the component to work:
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`$ sudo apt-get install libatlas-base-dev libopenjp2-7 libtiff5`
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</p>
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## {% linkable_title Setup %}
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You need to install the `tensorflow` Python packages with: `$ pip3 install tensorflow`. 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.
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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:
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- Clone [tensorflow/models](https://github.com/tensorflow/models/tree/master/research/object_detection)
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- Compile protobuf models located in `research/object_detection/protos` with `protoc`
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- Create the following directory structure inside your config directory:
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```bash
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|- {config_dir}
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| - tensorflow/
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|- object_detection/
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|- __init__.py
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```
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- Copy required object_detection dependancies to the `object_detection` folder inside of the `tensorflow` folder:
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- `research/object_detection/data`
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- `research/object_detection/utils`
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- `research/object_detection/protos`
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## {% linkable_title 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/detection_model_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 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.
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Whichever model you choose, download it and place the `frozen_inference_graph.pb` file in the `tensorflow` folder in your configuration directory.
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## {% linkable_title Configuration %}
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To enable this platform in your installation, add the following to your `configuration.yaml` file:
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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: /home/homeassistant/.homeassistant/tensorflow/frozen_inference_graph.pb
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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 component 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 `frozen_inference_graph.pb`.
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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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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 config
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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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```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/{% raw %}{{ camera_entity.split('.')[1] }}{% endraw %}_latest.jpg"
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- "/tmp/{% raw %}{{ camera_entity.split('.')[1] }}_{{ now().strftime('%Y%m%d_%H%M%S') }}{% endraw %}.jpg"
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model:
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graph: /home/homeassistant/.homeassistant/tensorflow/frozen_inference_graph.pb
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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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## {% linkable_title Optimising resources %}
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[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.
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