YOLOv4-Qt-OpenCV-Detection 🚀🔍

A Python application that utilizes YOLOv4 for object detection and face detection with the help of Qt for GUI and OpenCV for image processing 📚💻.

Overview 📖

This project aims to demonstrate the real-time object and face detection capabilities using the powerful YOLOv4 algorithm. The application provides a graphical user interface (GUI) using the Qt framework and leverages OpenCV for additional image processing tasks 🛠️🔧.

Features 🌟

• Real-time object detection using YOLOv4 🚀
• Real-time face detection 🔍
• GUI for easy interaction 📱
• Customizable detection parameters ⚙️

Requirements 📋

• Python 3.x
• Qt 5 or higher
• OpenCV 4.x
• NumPy
• YOLOv4 pre-trained weights

Installation 📦

1. Clone the repository:

   git clone https://github.com/yourusername/YOLOv4-Qt-OpenCV-Detection.git
   

2. Install the required packages:

   pip install -r requirements.txt
   

3. Download the YOLOv4 pre-trained weights from the official YOLO website and place them in the weights directory.

Usage 🎨

1. Run the application:

   python main.py
   

2. The GUI will open, allowing you to select an image or video stream for detection.
3. The detected objects and faces will be highlighted in real-time.

Screenshots 📷

• Object Detection Example

  

• Face Detection Example

  

Contributing 👥

Contributions are welcome! For bug reports or requests, please submit an issue or open a pull request.

License 📜

This project is licensed under the MIT License.

Acknowledgments 🎗️

• YOLOv4 by AlexeyAB for the YOLOv4 implementation.
• Qt for Python (PyQt5) for the GUI framework.
• OpenCV for image processing capabilities.