Open Source C++ Libraries

Cisco has taken their H.264 implementation and open-sourced it under BSD license terms. Development and maintenance will be overseen by a board from the industry and the open-source community. Furthermore, we have provided a binary form suitable for inclusion in applications across a number of different operating systems and made this binary module available for download from the Internet. We will not pass on our MPEG-LA licensing costs for this module, and based on the current licensing environment, this will effectively make H.264 free for use on supported platforms. OpenH264 is a codec library which supports H.264 encoding and decoding. It is suitable for use in real-time applications such as WebRTC. Constrained Baseline Profile up to Level 5.2 (Max frame size is 36864 macro-blocks). Arbitrary resolution, not constrained to multiples of 16x16. Rate control with adaptive quantization, or constant quantization.

ncnn is a high-performance neural network inference computing framework designed specifically for mobile platforms. It brings artificial intelligence right at your fingertips with no third-party dependencies, and speeds faster than all other known open source frameworks for mobile phone cpu. ncnn allows developers to easily deploy deep learning algorithm models to the mobile platform and create intelligent APPs. It is cross-platform and supports most commonly used CNN networks, including Classical CNN (VGG AlexNet GoogleNet Inception), Face Detection (MTCNN RetinaFace), Segmentation (FCN PSPNet UNet YOLACT), and more. ncnn is currently being used in a number of Tencent applications, namely: QQ, Qzone, WeChat, and Pitu.

A C++ high-performance regex library and Flex-compatible lexical analyzer generator with full Unicode support, new indentation anchors, lazy quantifiers, and many other modern features. Accepts Flex lexer specification syntax and is compatible with Bison/Yacc parsers. Generates reusable source code that is easy to understand. Supports fast scanning of UTF-8/16/32 files, strings, and streams. The reflex scanner generator generates clean C++ lexer class code that is thread-safe. Generates Graphviz files to visualize state machine DFAs. RE/flex works seamlessly with Bison. Language: C++ License: BSD-3 Documentation: https://www.genivia.com/doc/reflex/html/index.html Repository: https://github.com/Genivia/RE-flex Changelog: see SF-README.md

The Open Asset Import Library (short name: Assimp) is a portable Open-Source library to import various well-known 3D model formats in a uniform manner. The most recent version also knows how to export 3d files and is therefore suitable as a general-purpose 3D model converter. See the feature-list. open3mod is a Windows-based model viewer. It loads all file formats that Assimp supports and is perfectly suited to quickly inspect 3d assets. Assimp aims to provide a full asset conversion pipeline for use in game engines / realtime rendering systems of any kind, but it is not limited to this purpose. In the past, it has been used in a wide range of applications. Written in C++, it is available under a liberal BSD license. There is a C API as well as bindings to various other languages, including C#, Python and D. Assimp loads all input model formats into one straightforward data structure for further processing.

TNN, a high-performance, lightweight neural network inference framework open sourced by Tencent Youtu Lab. It also has many outstanding advantages such as cross-platform, high performance, model compression, and code tailoring. The TNN framework further strengthens the support and performance optimization of mobile devices on the basis of the original Rapidnet and ncnn frameworks. At the same time, it refers to the high performance and good scalability characteristics of the industry's mainstream open source frameworks, and expands the support for X86 and NV GPUs. On the mobile phone, TNN has been used by many applications such as mobile QQ, weishi, and Pitu. As a basic acceleration framework for Tencent Cloud AI, TNN has provided acceleration support for the implementation of many businesses. Everyone is welcome to participate in the collaborative construction to promote the further improvement of the TNN inference framework.