wyzely-detect/README.md

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.
• 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

Docker

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

Installing from PyPi with pip

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

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

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