/Objectify-YOLOv5

This project is a real-time object detection system that leverages the YOLOv5 model for detecting objects in a video stream from a webcam or other video input. The system is built using a Flask web application to serve the video feed, providing an interactive, real-time experience directly in a browser.

Primary LanguagePython

Real-Time Object Detection System Using YOLOv5 and Flask

This project is a real-time object detection system that leverages the YOLOv5 model for detecting objects in a video stream from a webcam or other video input. The system is built using a Flask web application to serve the video feed, providing an interactive, real-time experience directly in a browser. YOLOv5's efficiency and speed make it ideal for real-time applications where both performance and accuracy are critical.

Features

  • Real-Time Object Detection: Detects and classifies objects in real-time using YOLOv5.
  • Web-Based Interface: Access video feed with detections via a browser-based interface built with HTML and CSS.
  • Responsive UI: An intuitive and modern UI design provides a user-friendly experience.

How It Works

  • Video Capture: Captures frames from a webcam or other video input.
  • Object Detection: Each frame is passed through the YOLOv5 model to detect objects.
  • Annotation: Detected objects are labeled and bounded in the frames.
  • Streaming to Web Interface: The Flask app streams these annotated frames in real-time to a web page, accessible from any browser.

Installation

  1. Clone the repository

    git clone https://github.com/letsdoitbycode/Objectify-YOLOv5
cd Objectify-YOLOv5

  2. Create a virtual environment and activate it:

    python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`

  3. Install the required packages:

    pip install torch torchvision opencv-python matplotlib
pip install requirements.txt   #else you can do this directly

  4. Set Up YOLOv5 in the Project:

    git clone https://github.com/ultralytics/yolov5
cd yolov5
pip install -r requirements.txt

  5. Return to the main project folder:

    cd..

  6. Run the Flask app:

    python app.py

Project Structure

Objectify-YOLOv5/
│
├── templates/
│   └── index.html          # HTML file for UI 
├── static/
│   └── style.css          # CSS file for styling
├── venv                    # Virtual environment
├── yolov5                  # Model YOLOv5 
├── README.md               # This README file
├── app.py                  # Main Flask application
├── yolov5s.pt              # mirror of the YOLOv5 project
└── requirements.txt        # requirement file

Acknowledgements

  • Ultralytics for the YOLOv5 model: YOLOv5 GitHub Repository
  • OpenCV for video processing
  • Flask for powering the web interface

Contributing

Contributions are welcome! Please open an issue or submit a pull request for any changes or improvements.

DEMP APPLICATION

Screenshot (14)