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https://github.com/slashtechno/wyzely-detect
Recognize faces/objects in a video stream (from a webcam or a security camera) and send notifications to your devices
https://github.com/slashtechno/wyzely-detect
computer-vision hacktoberfest object-detection opencv wyze wyzecam
Last synced: 19 days ago
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Recognize faces/objects in a video stream (from a webcam or a security camera) and send notifications to your devices
- Host: GitHub
- URL: https://github.com/slashtechno/wyzely-detect
- Owner: slashtechno
- License: agpl-3.0
- Created: 2022-12-15T22:59:27.000Z (almost 2 years ago)
- Default Branch: master
- Last Pushed: 2024-05-12T20:48:37.000Z (6 months ago)
- Last Synced: 2024-10-11T02:31:59.221Z (about 1 month ago)
- Topics: computer-vision, hacktoberfest, object-detection, opencv, wyze, wyzecam
- Language: Python
- Homepage:
- Size: 793 KB
- Stars: 11
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
Awesome Lists containing this project
README
# 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](https://ntfy.sh/)
- Optionally, run headless with Docker
- Either use a webcam or an RTSP feed
- Use [mrlt8/docker-wyze-bridge](https://github.com/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](https://python-poetry.org/) 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](https://github.com/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](https://cocodataset.org/) dataset can be detected
- To specify where notifications are sent, specify a [ntfy](https://ntfy.sh/) 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