OpenCam

Real-time object detection application using OpenCV and YOLO, with both Python and C++ implementations.

Features

• Real-time object detection using YOLOv3
• Support for multiple camera inputs
• GPU acceleration with CUDA (optional)
• Cross-platform support (Windows, Linux)
• Modern Qt-based user interface
• Portable Linux AppImage build support

Prerequisites

For C++ Version

• CMake 3.16 or higher
• C++17 compatible compiler
• Qt 5.12 or higher
• OpenCV 4.x with CUDA support (optional)
• CUDA Toolkit 10.0 or higher (optional)

For Python Version

• Python 3.8 or higher
• OpenCV-Python
• PyQt5
• NumPy

Installation

Building from Source (C++)

1. Clone the repository:

git clone https://github.com/ZockerKatze/opencam.git
cd opencam/opencamcpp

2. Build OpenCV with CUDA (optional):

chmod +x build_opencv.sh
./build_opencv.sh

3. Build the application:

mkdir build && cd build
cmake ..
make -j$(nproc)

Creating AppImage (Linux)

1. Ensure all dependencies are installed:

sudo apt-get install cmake build-essential qt5-default libopencv-dev librsvg2-bin

2. Build the AppImage:

chmod +x build_appimage.sh
./build_appimage.sh

Python Version Setup

1. Create a virtual environment:

python -m venv .venv
source .venv/bin/activate  # Linux
# or
.venv\Scripts\activate     # Windows

2. Install dependencies:

pip install opencv-python pyqt5 numpy

Usage

Running the C++ Version

./opencam

Running the Python Version

python main.py

Model Files

The application requires YOLOv3 model files:

• yolov3.weights
• yolov3.cfg
• coco.names

Download the weights file from: https://pjreddie.com/media/files/yolov3.weights

License

MiT

Contributing

1. Fork the repository
2. Create your feature branch
3. Commit your changes
4. Push to the branch
5. Create a new Pull Request