Video Frame Extractor 🎥

A high-performance Python tool for extracting frames from video files with multi-threading support and rich visualization features.

📑 Table of Contents

• Features
• Process Flow
• Prerequisites
• Virtual Environment Setup
• Installation
• Usage
  • Command Line Interface
  • Arguments
  • Jupyter Notebook
• Output
• Contributing
• License
• Acknowledgments

🚀 Features

• Multi-threaded frame extraction for optimal performance
• Real-time progress tracking with Rich console
• Detailed video metadata display
• Cross-platform compatibility
• Configurable thread count
• Robust error handling
• Jupyter Notebook integration support

🔄 Process Flow

📋 Prerequisites

• Python 3.6 or higher
• Required packages for CLI:

  pip install opencv-python rich

• Additional packages for Jupyter Notebook:

  pip install numpy matplotlib jupyter ipython

🔮 Virtual Environment Setup

1. Create a virtual environment:

   python -m venv venv

2. Activate the virtual environment:

   Windows:

   .\venv\Scripts\activate

   Linux/Mac:

   source venv/bin/activate

3. Verify activation (you should see (venv) in your terminal):

   python --version

💻 Installation

1. Clone or download this repository

   git clone https://github.com/ToolsHive/Forensi-Frame.git

2. Install all dependencies:

   pip install -r requirements.txt

🔧 Usage

Command Line Interface

python script.py <video_path> <output_directory> [--threads N]

Example:

python script.py sample.mp4 frames --threads 8

Arguments

• video_path: Path to the input video file
• output_directory: Directory where extracted frames will be saved
• --threads: Number of threads to use (default: 4)

Jupyter Notebook

You can also use the provided Jupyter notebook Video_Frame_Extractor.ipynb for an interactive experience with additional visualization features.

📊 Output

• Frames are saved as sequential JPEG images
• Naming format: frame_XXXX.jpg
• Video metadata is displayed in a formatted table
• Progress bar shows extraction status

👥 Contributing

1. Fork the repository
2. Create feature branch (git checkout -b feature/name)
3. Commit changes (git commit -am 'Add feature')
4. Push branch (git push origin feature/name)
5. Create Pull Request

📜 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• OpenCV for video processing capabilities
• Rich library for terminal visualization
• Python's concurrent.futures for multi-threading support