HomeVision

Welcome to HomeVision, unlock the full potential of your security cameras with YOLOv8 and WebRTC!

Transform your live video feeds into intelligent streams that detect, analyze, and respond to critical events in real-time.

Perfect for home security, commercial surveillance, and beyond. It empowers you to monitor your property like never before.

Features

• High-quality video streaming: Enjoy seamless, high-definition multiple source video streaming with minimal lag and interruption.

• Cross-platform compatibility: The package is designed to work with a variety of platforms and devices, including web browsers, mobile devices, and more.

• Real-time processing: Pre-configured with popular computer vision solutions such as object detection and tracking, face recognition, and more.

• Scalability: Our package can easily be scaled to meet the needs of any project, from small-scale applications to large-scale enterprise deployments.

• Customizable: Enables developers to test and experiment with computer vision solutions on real-time RTSP streams without the hassle of setting up their own infrastructure.

Dependencies

• Linux/Windows
• Python 3.9
• WebRTC: aiortc
• Web Framework: FastAPI, aiohttp
• Testing: Pytest
• Supported Algorithms: YOLOv8, YOLOvX (ONNX)

(Note: These algorithm are just for demostration purposes, the package is mainly about to create a framework that you can easily integrate any computer vision algorithms and run with your home sercurity cameras.)

Installation

To install HomeVision, follow these steps:

1. Clone the latest repo
2. Install dependencies by: pip install -r requirements.txt pip install -e . or poetry install

Usage

First download models by running ./download_models.sh

Restream RTSP

Once you have installed HomeVision, run python solution_manager/main.py and follow UI to run the computer vision algorithm on the RTSP stream you want.

Add camera from add camera button on UI or change the solution_manager.yaml config file.

API doc follow the http://localhost:5555/docs

Run local video file

run python demo/run_video.py

Docker

• git clone https://github.com/microsoft/onnxruntime.git
• Follow instructions here to build image onnxruntime-cuda
• run sh docker.sh to build and run docker

Testing

run pytest