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CodeProject.AI Home Assistant Object Detection custom component

This component is a direct port of the HASS-Deepstack-object component by Robin Cole. This component provides AI-based Object Detection capabilities using CodeProject.AI Server.

CodeProject.AI Server is a service which runs either in a Docker container or as a Windows Service and exposes various an API for many AI inferencing operations via a REST API. The Object Detection capabilities use the YOLO algorithm as implemented by Ultralytics and others. It can identify 80 different kinds of objects by default, but custom models are also available that focus on specific objects such as animals, license plates or objects typically encountered by home webcams. CodeProject.AI Server is free, locally installed, and can run without an external internet connection, is is comatible with Windows, Linux, macOS. It can run on Raspberry Pi, and supports CUDA and embedded Intel GPUs.

On the machine in which you are running CodeProject.AI server, either ensure the service is running, or if using Docker, start a Docker container.

A note on Ports

CodeProject.AI Server typically runs on port 32168, so you will need to ensure the machine hosting the server has this port open. If you need to changes ports (eg switch to port 80) thenf or Docker use the -p flag:

docker run --name CodeProject.AI-Server -d -p 80:32168 ^
 --mount type=bind,source=C:\ProgramData\CodeProject\AI\docker\data,target=/etc/codeproject/ai ^
 --mount type=bind,source=C:\ProgramData\CodeProject\AI\docker\modules,target=/app/modules ^
   codeproject/ai-server

For Windows server you will need to either set an environment variable CPAI_PORT with value 80 (on the host running CodeProject.AI Server), or edit the appsettings.json file in the C:\Program Files\CodeProject\AI folder and change the value of the CPAI_PORT environment variable in the file.

Usage of this component

Thanks again to Robin for the original write up for his component.

The codeproject_ai_object component adds an image_processing entity where the state of the entity is the total count of target objects that are above a confidence threshold which has a default value of 80%. You can have a single target object class, or multiple. The time of the last detection of any target object is in the last target detection attribute. The type and number of objects (of any confidence) is listed in the summary attributes. Optionally a region of interest (ROI) can be configured, and only objects with their center (represented by a x) within the ROI will be included in the state count. The ROI will be displayed as a green box, and objects with their center in the ROI have a red box.

Also optionally the processed image can be saved to disk, with bounding boxes showing the location of detected objects. If save_file_folder is configured, an image with filename of format codeproject_ai_object_{source name}_latest.jpg is over-written on each new detection of a target. Optionally this image can also be saved with a timestamp in the filename, if save_timestamped_file is configured as True. An event codeproject_ai.object_detected is fired for each object detected that is in the targets list, and meets the confidence and ROI criteria. 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 codeproject_ai.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.

Home Assistant setup

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 codeproject_ai_object entity. If you want to process multiple cameras, you will therefore need multiple codeproject_ai_object image_processing entities.

The component can optionally save snapshots of the processed images. If you would like to use this option, you need to create a folder where the snapshots will be stored. The folder should be in the same folder where your configuration.yaml file is located. In the example below, we have named the folder snapshots.

Add to your Home-Assistant config:

image_processing:
  - platform: codeproject_ai_object
    ip_address: localhost
    port: 32168
    # custom_model: mask
    # confidence: 80
    save_file_folder: /config/snapshots/
    save_file_format: png
    save_timestamped_file: True
    always_save_latest_file: True
    scale: 0.75
    # roi_x_min: 0.35
    roi_x_max: 0.8
    #roi_y_min: 0.4
    roi_y_max: 0.8
    crop_to_roi: True
    targets:
      - target: person
      - target: vehicle
        confidence: 60
      - target: car
        confidence: 40
    source:
      - entity_id: camera.local_file

Configuration variables:

  • ip_address: the ip address of your CodeProject.AI Server instance.
  • port: the port of your CodeProject.AI Server instance.
  • timeout: (Optional, default 10 seconds) The timeout for requests to CodeProject.AI Server.
  • custom_model: (Optional) The name of a custom model if you are using one. Don't forget to add the targets from the custom model below
  • confidence: (Optional) The confidence (in %) above which detected targets are counted in the sensor state. Default value: 80
  • save_file_folder: (Optional) The folder to save processed images to. Note that folder path should be added to whitelist_external_dirs
  • save_file_format: (Optional, default jpg, alternatively png) The file format to save images as. png generally results in easier to read annotations.
  • save_timestamped_file: (Optional, default False, requires save_file_folder to be configured) Save the processed image with the time of detection in the filename.
  • always_save_latest_file: (Optional, default False, requires save_file_folder to be configured) Always save the last processed image, even if there were no detections.
  • scale: (optional, default 1.0), range 0.1-1.0, applies a scaling factor to the images that are saved. This reduces the disk space used by saved images, and is especially beneficial when using high resolution cameras.
  • show_boxes: (optional, default True), if False bounding boxes are not shown on saved images
  • roi_x_min: (optional, default 0), range 0-1, must be less than roi_x_max
  • roi_x_max: (optional, default 1), range 0-1, must be more than roi_x_min
  • roi_y_min: (optional, default 0), range 0-1, must be less than roi_y_max
  • roi_y_max: (optional, default 1), range 0-1, must be more than roi_y_min
  • crop_to_roi: (optional, default False), crops the image to the specified roi. May improve object detection accuracy when a region-of-interest is applied
  • source: Must be a camera.
  • targets: The list of target object names and/or object_type, default person. Optionally a confidence can be set for this target, if not the default confidence is used. Note the minimum possible confidence is 10%.

For the ROI, the (x=0,y=0) position is the top left pixel of the image, and the (x=1,y=1) position is the bottom right pixel of the image. It might seem a bit odd to have y running from top to bottom of the image, but that is the coordinate system used by pillow.

Event codeproject_ai.object_detected

An event codeproject_ai.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 (use Developer tools -> EVENTS -> :Listen to events, to monitor these events).

An example use case for event is to get an alert when some rarely appearing object is detected, or to increment a counter. The codeproject_ai.object_detected event payload includes:

  • entity_id : the entity id responsible for the event
  • name : the name of the object detected
  • object_type : the type of the object, from person, vehicle, animal or other
  • confidence : the confidence in detection in the range 0 - 100%
  • box : the bounding box of the object
  • centroid : the centre point of the object
  • saved_file : the path to the saved annotated image, which is the timestamped file if save_timestamped_file is True, or the default saved image if False

An example automation using the codeproject_ai.object_detected event is given below:

- action:
    - data_template:
        caption: "New person detection with confidence {{ trigger.event.data.confidence }}"
        file: "{{ trigger.event.data.saved_file  }}"
      service: telegram_bot.send_photo
  alias: Object detection automation
  condition: []
  id: "1120092824622"
  trigger:
    - platform: event
      event_type: codeproject_ai.object_detected
      event_data:
        name: person

Displaying the CodeProject.AI latest jpg file

It easy to display the codeproject_ai_object_{source name}_latest.jpg image with a local_file camera. An example configuration is:

camera:
  - platform: local_file
    file_path: /config/snapshots/codeproject_ai_object_local_file_latest.jpg
    name: codeproject_ai_latest_person

Info on box

The 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 box is 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.

Browsing saved images in HA

I highly recommend using the Home Assistant Media Player Browser to browse and preview processed images. Add to your config something like:

homeassistant:
.
.
  whitelist_external_dirs:
    - /config/images/
  media_dirs:
    local: /config/images/

media_source:

And configure CodeProject.AI Server to use the above directory for save_file_folder, then saved images can be browsed from the HA front end like below:

(Image courtesy of Robin Cole, and uses his original Deepstack implementation)

Face recognition

For face recognition with CodeProject.AI Server use https://github.com/codeproject/CodeProject.AI-HomeAssist-FaceDetect

Support

  • For code related issues such as suspected bugs with this integration, please open an issue on this repo.
  • For CodeProject.AI Server setup questions, please see see the CodeProject.AI Server docs
  • For bugs and suggestions related to CodeProject.AI Server, please use the CodeProject.AI forum.
  • For general chat or to discuss Home Assistant specific issues related to configuration or use cases, please use the Home Assistant forums.

Docker tips

Please view the CodeProject.AI Server docs Add the -d flag to run the container in background

FAQ

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.codeproject_ai_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

Q6: I am getting an error from Home Assistant: Platform error: image_processing - Integration codeproject_ai_object not found

A6: 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.

Objects

The following lists all valid target object names:

person,   bicycle,   car,   motorcycle,   airplane,
bus,   train,   truck,   boat,   traffic light,   fire hydrant,   stop_sign,
parking meter,   bench,   bird,   cat,   dog,   horse,   sheep,   cow,   elephant,
bear,   zebra, giraffe,   backpack,   umbrella,   handbag,   tie,   suitcase,
frisbee,   skis,   snowboard, sports ball,   kite,   baseball bat,   baseball glove,
skateboard,   surfboard,   tennis racket, bottle,   wine glass,   cup,   fork,
knife,   spoon,   bowl,   banana,   apple,   sandwich,   orange, broccoli,   carrot,
hot dog,   pizza,   donut,   cake,   chair,   couch,   potted plant,   bed, dining table,
toilet,   tv,   laptop,   mouse,   remote,   keyboard,   cell phone,   microwave,
oven,   toaster,   sink,   refrigerator,   book,   clock,   vase,   scissors,   teddy bear,
hair dryer, toothbrush.

Objects are grouped by the following object_type:

  • person: person
  • animal: bird, cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe
  • vehicle: bicycle, car, motorcycle, airplane, bus, train, truck
  • other: any object that is not in person, animal or vehicle

Development

Currently only the helper functions are tested, using pytest.

  • python3 -m venv venv
  • source venv/bin/activate
  • pip install -r requirements-dev.txt
  • venv/bin/py.test custom_components/codeproject_ai_object/tests.py -vv -p no:warnings

Videos of usage

Robin Cole has a series of videos using Deepstack with Home Asssistant which may provide some assistance.

Checkout this excellent video of usage from Everything Smart Home

Also see the video of a presentation I did to the IceVision community on deploying Deepstack on a Jetson nano.