What are the commonly used frameworks in C++?

The C framework includes: cross-platform GUI component Qt, tool library collection Boost, general data structure STL, computer vision OpenCV, linear algebra Eigen, unit test Google Test and optimization library Ceres Solver.

C Commonly used frameworks

C As a powerful programming language, it has a large number of excellent frameworks that can be used Simplify the development process and improve code quality. Here are a few commonly used C frameworks:

1. Qt

#Qt is a cross-platform C framework that provides a rich set of GUI component libraries. For building desktop, mobile and embedded applications. It is known for its powerful graphics engine and cross-platform capabilities.

2. Boost

Boost is a large collection of C libraries that provide a wide range of tools and components, from algorithms and data structures to threading and unit testing. It is known for its high performance and portability.

3. C Standard Template Library (STL)

STL is a standard C library that provides common data structures and operations such as containers, algorithms, and iterators. It is widely used in C programming to provide support for code readability, maintainability, and performance.

4. OpenCV

OpenCV is a C framework focusing on computer vision. It provides a series of visual processing algorithms and optimization functions for image processing, video analysis, machine learning and other fields.

5. Eigen

Eigen is a templated linear algebra library. It provides an efficient set of matrix and vector operations and supports a variety of compilers and platforms. It is widely used in scientific computing, image processing and machine learning.

6. Google Test

Google Test is a C framework for unit testing. It provides a flexible set of assertion macros and testing tools that simplify the writing and maintenance of unit tests.

7. Ceres Solver

Ceres Solver is a C optimization library. It provides a variety of optimization algorithms and linear algebra tools for large-scale numerical optimization problems. It is widely used in fields such as robotics, computer vision, and machine learning.

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