Real-time Object Detection

Table of contents

Description

This project demonstrates a real-time object detection system using a C++ WebSocket server with YOLO model for object detection, and a React client with Webcam integration for capturing and sending video frames. The server uses OpenCV to process frames and the YOLO model for detecting objects in the video stream.
Source code can be found here.

Running Locally

Prerequisites

• C++ compiler (Clang or GCC)
• Node.js & npm (for React client)

Steps

1. Clone the repository

   git clone https://github.com/martishin/react-cpp-object-detection
   cd react-cpp-object-detection

2. Install and configure vcpkg
   Navigate to the server folder and install vcpkg

    cd server
    git clone https://github.com/microsoft/vcpkg.git
    cd vcpkg
    ./bootstrap-vcpkg.sh
    cd ..
    export VCPKG_ROOT=$(pwd)/vcpkg

3. Install server dependencies

    vcpkg install

4. Build and run the WebSocket server (C++)

   make run-server

5. Running the React client
   Navigate to the client folder and install dependencies:

   cd ../client
   npm install
   npm run dev

   The client will be running at http://localhost:5173/.

Technologies Used

• C++ (Server): For real-time frame processing and object detection using OpenCV and YOLO.
• React (Client): For capturing video frames and displaying object detection results.
• WebSockets: For efficient, low-latency, real-time communication between the client and the server.
• YOLO: A state-of-the-art object detection model.
• OpenCV: A popular computer vision library for image and video processing.