# 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
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"description": "# Wyzely Detect \nRecognize faces/objects in a video stream (from a webcam or a security camera) and send notifications to your devices \n\n### Features \n- Recognize objects \n- Recognize faces \n- Send notifications to your phone (or other devices) using [ntfy](https://ntfy.sh/) \n- Optionally, run headless with Docker \n- Either use a webcam or an RTSP feed \n - Use [mrlt8/docker-wyze-bridge](https://github.com/mrlt8/docker-wyze-bridge) to get RTSP feeds from Wyze Cams \n\n\n## Prerequisites \n### Python \n- Camera, either a webcam or a Wyze Cam \n - All RTSP feeds _should_ work, however. \n - **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. \n- Python 3.10 or 3.11 \n- Poetry (optional) \n- Windows or Linux \n - I've tested this on MacOS - it works on my 2014 MacBook Air but not a 2011 MacBook Pro \n - 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 \n\n### Docker \n- A Wyze Cam \n - Any other RTSP feed _should_ work, as mentioned above \n- Docker\n- Docker Compose\n\n\n## What's not required \n- A Wyze subscription \n\n## Usage \n### Installation \nCloning the repository is not required when installing from PyPi but is required when installing from source \n1. Clone this repo with `git clone https://github.com/slashtechno/wyzely-detect` \n2. `cd` into the cloned repository \n3. Then, either install with [Poetry](https://python-poetry.org/) or run with Docker \n\n\n#### Installing from PyPi with pip (recommended) \nThis assumes you have Python 3.10 or 3.11 installed \n1. `pip install wyzely-detect` \n a. You may need to use `pip3` instead of `pip` \n2. `wyzely-detect` \n\n#### Poetry (best for GPU support)\n1. `poetry install` \n a. For GPU support, use `poetry install -E cuda --with gpu`\n2. `poetry run -- wyzely-detect` \n\n#### Docker \nRunning 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. \n\n1. Modify to `docker-compose.yml` to achieve desired configuration \n2. Run in the background with `docker compose up -d`\n\n### Configuration \nThe following are some basic CLI options. Most flags have environment variable equivalents which can be helpful when using Docker. \n\n- For face recognition, put images of faces in subdirectories `./faces` (this can be changed with `--faces-directory`) \n - Keep in mind, on the first run, face rec\n- 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\n - Currently, all classes in the [COCO](https://cocodataset.org/) dataset can be detected\n- To specify where notifications are sent, specify a [ntfy](https://ntfy.sh/) URL with `--ntfy-url`\n- To configure the program when using Docker, edit `docker-compose.yml` and/or set environment variables.\n- **For further information, use `--help`**\n\n### How to uninstall \n- If you used Docker, run `docker-compose down --rmi all` in the cloned repository\n- If you used Poetry, just delete the virtual environment and then the cloned repository\n",
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