Home Assistant custom components for using Deepstack object detection. Deepstack is a service which runs in a docker container and exposes deep-learning models via a REST API. Deepstack object detection can identify 80 different kinds of objects, including people (person) and animals. There is no cost for using Deepstack, although you will need a machine with 8 GB RAM. On your machine with docker, pull the latest image (approx. 2GB):
docker pull deepquestai/deepstack
OR
docker pull deepquestai/deepstack:noavx
Recommended OS Deepstack docker containers are optimised for Linux or Windows 10 Pro. Mac and regular windows users my experience performance issues. You can also run deepstack on a Raspberry pi if you own an Intel NCS (Movidius) stick (approx $70).
GPU users Note that if your machine has an Nvidia GPU you can get a 5 x 20 times performance boost by using the GPU.
Legacy machine users If you are using a machine that doesn't support avx or you are having issues with making requests, Deepstack has a specific build for these systems. Use deepquestai/deepstack:noavx instead of deepquestai/deepstack when you are installing or running Deepstack. I expect many users will be using noavx mode so I will use it in the examples below.
Before you get started, you will need to activate the Deepstack API. First, go to www.deepstack.cc and sign up for an account. Choose the basic plan which will give us unlimited access for one installation. You will then see an activation key in your portal.
On your machine with docker, run Deepstack (noavx mode) without any recognition so you can activate the API on port 5000:
docker run -v localstorage:/datastore -p 5000:5000 deepquestai/deepstack:noavx
Now go to http://YOUR_SERVER_IP_ADDRESS:5000/ on another computer or the same one running Deepstack. Input your activation key from your portal into the text box below "Enter New Activation Key" and press enter. Now stop your docker container, and restart and run Deepstack (noavx mode) with the object detection service active on port 5000:
docker run -e VISION-DETECTION=True -e API-KEY="Mysecretkey" -v localstorage:/datastore -p 5000:5000 --name deepstack -d deepquestai/deepstack:noavx
The deepstack_object component adds an image_processing entity where the state of the entity is the total number of target objects that are above a confidence threshold which has a default value of 80%. The time of the last detection of the target object is in the last detection attribute. The type and number of objects (of any confidence) is listed in the summary attributes. Optionally the processed image can be saved to disk. If save_file_folder is configured an image with filename of format deepstack_object_{source name}_latest_{target}.jpg is over-written on each new detection of the target. Optionally this image can also be saved with a timestamp in the filename, if save_timestamped_file is configred as True. An event image_processing.object_detected is fired for each object detected. If you are a power user with advanced needs such as zoning detections or you want to track multiple object types, you will need to use the image_processing.object_detected events.
Note that by default the component will not automatically scan images, but requires you to call the image_processing.scan service e.g. using an automation triggered by motion. Alternativley, periodic scanning can be enabled by configuring a scan_interval. The use of scan_interval is described here.
Place the custom_components folder in your configuration directory (or add its contents to an existing custom_components folder). Then configure object detection. Important: It is necessary to configure only a single camera per deepstack_object entity. If you want to process multiple cameras, you will therefore need multiple deepstack_object image_processing entities.
Add to your Home-Assistant config:
image_processing:
- platform: deepstack_object
ip_address: localhost
port: 5000
api_key: Mysecretkey
# scan_interval: 30 # Optional, in seconds
save_file_folder: /config/www/
save_timestamped_file: True
source:
- entity_id: camera.local_file
name: deepstack_person_detectorConfiguration variables:
- ip_address: the ip address of your deepstack instance.
- port: the port of your deepstack instance.
- api_key: (Optional) Any API key you have set.
- timeout: (Optional, default 10 seconds) The timout for requests to deepstack.
- save_file_folder: (Optional) The folder to save processed images to. Note that folder path should be added to whitelist_external_dirs
- save_timestamped_file: (Optional, default
False, requiressave_file_folderto be configured) Save the processed image with the time of detection in the filename. - source: Must be a camera.
- target: The target object class, default
person. - confidence: (Optional) The confidence (in %) above which detected targets are counted in the sensor state. Default value: 80
- name: (Optional) A custom name for the the entity.
If save_file_folder is configured, an new image will be saved with bounding boxes of detected target objects, and the filename will include the time of the image capture. On saving this image a image_processing.file_saved event is fired, with a payload that includes:
entity_id: the entity id responsible for the eventfile: the full path to the saved file
An example automation using the image_processing.file_saved event is given below, which sends a Telegram message with the saved file:
- action:
- data_template:
caption: "Captured {{ trigger.event.data.file }}"
file: "{{ trigger.event.data.file }}"
service: telegram_bot.send_photo
alias: New person alert
condition: []
id: '1120092824611'
trigger:
- platform: event
event_type: image_processing.file_savedAn event image_processing.object_detected is fired for each object detected above the configured confidence threshold. This is the recommended way to check the confidence of detections, and to keep track of objects that are not configured as the target (configure logger level to debug to observe events in the Home Assistant logs). An example use case for event is to get an alert when some rarely appearing object is detected, or to increment a counter. The image_processing.object_detected event payload includes:
entity_id: the entity id responsible for the eventobject: the object detectedconfidence: the confidence in detection in the range 0 - 1 where 1 is 100% confidence.box: the bounding box of the objectcentroid: the centre point of the object
An example automation using the image_processing.object_detected event is given below:
- action:
- data_template:
title: "New object detection"
message: "{{ trigger.event.data.object }} with confidence {{ trigger.event.data.confidence }}"
service: notify.pushbullet
alias: Object detection automation
condition: []
id: '1120092824622'
trigger:
- platform: event
event_type: image_processing.object_detected
event_data:
object: personThe box coordinates and the box center (centroid) can be used to determine whether an object falls within a defined region-of-interest (ROI). This can be useful to include/exclude objects by their location in the image.
- The
boxis defined by the tuple(y_min, x_min, y_max, x_max)(equivalent to image top, left, bottom, right) where the coordinates are floats in the range[0.0, 1.0]and relative to the width and height of the image. - The centroid is in
(x,y)coordinates where(0,0)is the top left hand corner of the image and(1,1)is the bottom right corner of the image.
It easy to display the deepstack_object_{source name}_latest_{target}.jpg image with a local_file camera. An example configuration is:
camera:
- platform: local_file
file_path: /config/www/deepstack_object_local_file_latest_person.jpg
name: deepstack_latest_personFor face recognition with Deepstack use https://github.com/robmarkcole/HASS-Deepstack-face
If you have a Google Coral USB stick you can use it as a drop in replacement for Deepstack object detection by using the coral-pi-rest-server. Note that the predictions may differ from those provided by Deepstack.
For code related issues such as suspected bugs, please open an issue on this repo. For general chat or to discuss Home Assistant specific issues related to configuration or use cases, please use this thread on the Home Assistant forums.
Add the -d flag to run the container in background, thanks @arsaboo.
To check Deepstack is functioning, run without an api_key and make a request using cURL from the command line:
curl -X POST -F image=@development/test-image3.jpg 'http://localhost:5000/v1/vision/detection'
This should return the predictions for that image.
Q1: I get the following warning, is this normal?
2019-01-15 06:37:52 WARNING (MainThread) [homeassistant.loader] You are using a custom component for image_processing.deepstack_face which has not been tested by Home Assistant. This component might cause stability problems, be sure to disable it if you do experience issues with Home Assistant.
A1: Yes this is normal
Q2: Will Deepstack always be free, if so how do these guys make a living?
A2: I'm informed there will always be a basic free version with preloaded models, while there will be an enterprise version with advanced features such as custom models and endpoints, which will be subscription based.
Q3: What are the minimum hardware requirements for running Deepstack?
A3. Based on my experience, I would allow 0.5 GB RAM per model.
Q4: Can object detection be configured to detect car/car colour?
A4: The list of detected object classes is at the end of the page here. There is no support for detecting the colour of an object.
Q5: I am getting an error from Home Assistant: Platform error: image_processing - Integration deepstack_object not found
A5: This can happen when you are running in Docker/Hassio, and indicates that one of the dependencies isn't installed. It is necessary to reboot your Hassio device, or rebuild your Docker container. Note that just restarting Home Assistant will not resolve this.
https://github.com/sponsors/robmarkcole
If you or your business find this work useful please consider becoming a sponsor at the link above, this really helps justify the time I invest in maintaining this repo. As we say in England, 'every little helps' - thanks in advance!