pyopencv 2.1.0.wr1.2.0

Description:

pyopencv 2.1.0.wr1.2.0

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).