GPUPixel: A High-Performance Image and Video Filter Library
Introduction
GPUPixel is a cutting-edge, high-performance library designed for real-time image and video filtering. Built with efficiency in mind, GPUPixel boasts an easy compilation process and seamless integration due to its compact size. The library is written in C++11 and leverages OpenGL/ES for graphics processing, offering a range of commercial-grade beauty filters.
Supported Platforms
GPUPixel is versatile and supports a wide array of platforms, including iOS, Android, Mac, Windows, and Linux. Thanks to its foundation on OpenGL/ES, GPUPixel can theoretically be adapted to any platform supporting OpenGL/ES.
Key Features and Filters
GPUPixel separates itself from other filter libraries by supporting an impressive array of filters and features:
- Beauty Filters: Achieve smooth, white skin, slimming face, bigger eyes, lipstick, and blush effects, which make it a powerful tool for beauty enhancements.
- Input and Output Formats: It can handle various input formats like YUV420P(I420), RGBA, JPEG, and PNG. Planned support for NV21 on Android further broadens its utility. Output formats include RGBA, with potential future support for YUV420P(I420).
- Platform Support: It provides complete support across multiple operating systems, ensuring a consistent experience.
Performance
GPUPixel delivers impressive performance across different devices. For instance, on an iPhone 8, GPUPixel only utilizes about 5% of the CPU and completes processing in approximately 4ms. Similar results can be observed on various Android devices, ensuring smooth operation without draining device resources.
Library Size
One of GPUPixel's strengths is its small library size, making it highly efficient and easy to integrate. The iOS framework, for example, is just 2.4 MB, while the Android library is only 2.1 MB, proving that powerful tools do not need to be bulky.
Getting Started
For new users looking to integrate GPUPixel, the documentation offers a comprehensive guide to help you:
- Understand and build the library.
- Explore examples to see GPUPixel in action.
- Integrate it into your projects with ease.
Contributing
GPUPixel is open source, inviting developers and users alike to contribute to its ongoing development. Whether it's joining discussions, reporting issues, or submitting pull requests, community involvement is highly encouraged.
Acknowledgments
GPUPixel draws inspiration from several notable projects like GPUImage, CainCamera, AwemeLike, and VNN, reflecting a pool of best practices in image processing.
Licensing
GPUPixel is available under the MIT License, allowing for flexibility and freedom in its use and distribution.
To explore more about GPUPixel, see examples, or start your integration, visit the documentation and community pages. Join the conversation and help in enhancing GPUPixel's future development!