A Python application that utilizes YOLOv4 for object detection and face detection with the help of Qt for GUI and OpenCV for image processing ๐๐ป.
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 ๐ ๏ธ๐ง.
- Real-time object detection using YOLOv4 ๐
- Real-time face detection ๐
- GUI for easy interaction ๐ฑ
- Customizable detection parameters โ๏ธ
- Python 3.x
- Qt 5 or higher
- OpenCV 4.x
- NumPy
- YOLOv4 pre-trained weights
- Clone the repository:
git clone https://github.com/yourusername/YOLOv4-Qt-OpenCV-Detection.git
- Install the required packages:
pip install -r requirements.txt
- Download the YOLOv4 pre-trained weights from the official YOLO website and place them in the
weights
directory.
- Run the application:
python main.py
- The GUI will open, allowing you to select an image or video stream for detection.
- The detected objects and faces will be highlighted in real-time.
Contributions are welcome! For bug reports or requests, please submit an issue or open a pull request.
This project is licensed under the MIT License.
- YOLOv4 by AlexeyAB for the YOLOv4 implementation.
- Qt for Python (PyQt5) for the GUI framework.
- OpenCV for image processing capabilities.