PyOpenCV - A Python wrapper for OpenCV 2.x using Boost.Python and NumPy
PyOpenCV brings Willow Garage’s Open Source Computer Vision Library (OpenCV) verion 2.x to Python. The package takes a completely new and different approach in wrapping OpenCV from traditional swig-based and ctypes-based approaches. It is intended to be a successor of ctypes-opencv and to provide Python bindings for OpenCV 2.x. Ctypes-based approaches like ctypes-opencv, while being very flexible at wrapping functions and structures, are weak at wrapping OpenCV’s C++ interface. On the other hand, swig-based approaches flatten C++ classes and create countless memory management issues. In PyOpenCV, we use Boost.Python, a C++ library which enables seamless interoperability between C++ and Python. PyOpenCV will offer a better solution than both ctypes-based and swig-based wrappers. Its main features include:
A Python interface similar to the new C++ interface of OpenCV 2.x, including features that are available in the existing C interface but not yet in the C++ interface.
Access to C++ data structures in Python.
Elimination of memory management issues. The user never has to worry about memory management.
Ability to convert between OpenCV’s Mat and arrays used in wxWidgets, PyGTK, and PIL.
OpenCV extensions: classes DifferentialImage, IntegralImage, and IntegralHistogram.
To the best of our knowledge, PyOpenCV is the largest wrapper among existing Python wrappers for OpenCV. It exposes to Python 200+ classes and 500+ free functions of OpenCV 2.x, including those instantiated from templates.
In addition, we use NumPy to provide fast indexing and slicing functionality to OpenCV’s dense data types like Vec-like, Point-like, Rect-like, Size-like, Scalar, Mat, and MatND, and to offer the user an option to work with their multi-dimensional arrays in NumPy. It is well-known that NumPy is one of the best packages (if not the best) for dealing with multi-dimensional arrays in Python. OpenCV 2.x provides a new C++ generic programming approach for matrix manipulation (i.e. MatExpr). It is a good attempt in C++. However, in Python, a package like NumPy is without a doubt a better solution. By incorporating NumPy into PyOpenCV to replace OpenCV 2.x’s MatExpr approach, we seek to bring OpenCV and NumPy closer together, and offer a package that inherits the best of both world: fast computer vision functionality (OpenCV) and fast multi-dimensional array computation (NumPy).
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