wyzely-detect

Wyzely Detect

Recognize faces/objects in a video stream (from a webcam or a security camera) and send notifications to your devices

Features

• Recognize objects
• Recognize faces
• Send notifications to your phone (or other devices) using ntfy
• Optionally, run headless with Docker
• Either use a webcam or an RTSP feed
  • Use mrlt8/docker-wyze-bridge to get RTSP feeds from Wyze Cams

Prerequisites

Python

• Camera, either a webcam or a Wyze Cam
  • All RTSP feeds should work, however.
  • WSL, by default, does not support USB devices. It is recommended to natively run this, but it is possible to use it on WSL with streams or some workarounds.
• Python 3.10 or 3.11
• Poetry (optional)
• Windows or Linux
  • I've tested this on MacOS - it works on my 2014 MacBook Air but not a 2011 MacBook Pro
  • Both were upgraded with OpenCore, with the MacBook Air running Monterey and the MacBook Pro running a newer version of MacOS, which may have been the problem

Docker

• A Wyze Cam
  • Any other RTSP feed should work, as mentioned above
• Docker
• Docker Compose

What's not required

• A Wyze subscription

Usage

Installation

Cloning the repository is not required when installing from PyPi but is required when installing from source

1. Clone this repo with git clone https://github.com/slashtechno/wyzely-detect
2. cd into the cloned repository
3. Then, either install with Poetry or run with Docker

Installing from PyPi with pip (recommended)

This assumes you have Python 3.10 or 3.11 installed

1. pip install wyzely-detect
   a. You may need to use pip3 instead of pip
2. wyzely-detect

Poetry (best for GPU support)

1. poetry install
   a. For GPU support, use poetry install -E cuda --with gpu
2. poetry run -- wyzely-detect

Docker

Running with Docker has the benefit of having easier configuration, the ability to run headlessly, and easy setup of Ntfy and mrlt8/docker-wyze-bridge. However, for now, CPU-only is supported. Contributions are welcome to add GPU support. In addition, Docker is tested a less-tested method of running this program.

1. Modify to docker-compose.yml to achieve desired configuration
2. Run in the background with docker compose up -d

Configuration

The following are some basic CLI options. Most flags have environment variable equivalents which can be helpful when using Docker.

• For face recognition, put images of faces in subdirectories ./faces (this can be changed with --faces-directory)
  • Keep in mind, on the first run, face rec
• By default, notifications are sent for all objects. This can be changed with one or more occurrences of --detect-object to specify which objects to detect
  • Currently, all classes in the COCO dataset can be detected
• To specify where notifications are sent, specify a ntfy URL with --ntfy-url
• To configure the program when using Docker, edit docker-compose.yml and/or set environment variables.
• For further information, use --help

How to uninstall

• If you used Docker, run docker-compose down --rmi all in the cloned repository
• If you used Poetry, just delete the virtual environment and then the cloned repository