pyopencv 2.1.0.wr1.2.0

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).