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How OpenCV-Python Bindings Works? {#tutorial_py_bindings_basics}
=================================
Goal
----
Learn:
- How OpenCV-Python bindings are generated?
- How to extend new OpenCV modules to Python?
How OpenCV-Python bindings are generated?
-----------------------------------------
In OpenCV, all algorithms are implemented in C++. But these algorithms can be used from different
languages like Python, Java etc. This is made possible by the bindings generators. These generators
create a bridge between C++ and Python which enables users to call C++ functions from Python. To get
a complete picture of what is happening in background, a good knowledge of Python/C API is required.
A simple example on extending C++ functions to Python can be found in official Python
documentation[1]. So extending all functions in OpenCV to Python by writing their wrapper functions
manually is a time-consuming task. So OpenCV does it in a more intelligent way. OpenCV generates
these wrapper functions automatically from the C++ headers using some Python scripts which are
located in modules/python/src2. We will look into what they do.
First, modules/python/CMakeFiles.txt is a CMake script which checks the modules to be extended to
Python. It will automatically check all the modules to be extended and grab their header files.
These header files contain list of all classes, functions, constants etc. for that particular
modules.
Second, these header files are passed to a Python script, modules/python/src2/gen2.py. This is the
Python bindings generator script. It calls another Python script modules/python/src2/hdr_parser.py.
This is the header parser script. This header parser splits the complete header file into small
Python lists. So these lists contain all details about a particular function, class etc. For
example, a function will be parsed to get a list containing function name, return type, input
arguments, argument types etc. Final list contains details of all the functions, structs, classes
etc. in that header file.
But header parser doesn't parse all the functions/classes in the header file. The developer has to
specify which functions should be exported to Python. For that, there are certain macros added to
the beginning of these declarations which enables the header parser to identify functions to be
parsed. These macros are added by the developer who programs the particular function. In short, the
developer decides which functions should be extended to Python and which are not. Details of those
macros will be given in next session.
So header parser returns a final big list of parsed functions. Our generator script (gen2.py) will
create wrapper functions for all the functions/classes/enums/structs parsed by header parser (You
can find these header files during compilation in the build/modules/python/ folder as
pyopencv_generated_\*.h files). But there may be some basic OpenCV datatypes like Mat, Vec4i,
Size. They need to be extended manually. For example, a Mat type should be extended to Numpy array,
Size should be extended to a tuple of two integers etc. Similarly, there may be some complex
structs/classes/functions etc. which need to be extended manually. All such manual wrapper functions
are placed in modules/python/src2/pycv2.hpp.
So now only thing left is the compilation of these wrapper files which gives us **cv2** module. So
when you call a function, say res = equalizeHist(img1,img2) in Python, you pass two numpy arrays and
you expect another numpy array as the output. So these numpy arrays are converted to cv::Mat and
then calls the equalizeHist() function in C++. Final result, res will be converted back into a Numpy
array. So in short, almost all operations are done in C++ which gives us almost same speed as that
of C++.
So this is the basic version of how OpenCV-Python bindings are generated.
How to extend new modules to Python?
------------------------------------
Header parser parse the header files based on some wrapper macros added to function declaration.
Enumeration constants don't need any wrapper macros. They are automatically wrapped. But remaining
functions, classes etc. need wrapper macros.
Functions are extended using CV_EXPORTS_W macro. An example is shown below.
@code{.cpp}
CV_EXPORTS_W void equalizeHist( InputArray src, OutputArray dst );
@endcode
Header parser can understand the input and output arguments from keywords like
InputArray, OutputArray etc. But sometimes, we may need to hardcode inputs and outputs. For that,
macros like CV_OUT, CV_IN_OUT etc. are used.
@code{.cpp}
CV_EXPORTS_W void minEnclosingCircle( InputArray points,
CV_OUT Point2f& center, CV_OUT float& radius );
@endcode
For large classes also, CV_EXPORTS_W is used. To extend class methods, CV_WRAP is used.
Similarly, CV_PROP is used for class fields.
@code{.cpp}
class CV_EXPORTS_W CLAHE : public Algorithm
{
public:
CV_WRAP virtual void apply(InputArray src, OutputArray dst) = 0;
CV_WRAP virtual void setClipLimit(double clipLimit) = 0;
CV_WRAP virtual double getClipLimit() const = 0;
}
@endcode
Overloaded functions can be extended using CV_EXPORTS_AS. But we need to pass a new name so that
each function will be called by that name in Python. Take the case of integral function below. Three
functions are available, so each one is named with a suffix in Python. Similarly CV_WRAP_AS can be
used to wrap overloaded methods.
@code{.cpp}
//! computes the integral image
CV_EXPORTS_W void integral( InputArray src, OutputArray sum, int sdepth = -1 );
//! computes the integral image and integral for the squared image
CV_EXPORTS_AS(integral2) void integral( InputArray src, OutputArray sum,
OutputArray sqsum, int sdepth = -1, int sqdepth = -1 );
//! computes the integral image, integral for the squared image and the tilted integral image
CV_EXPORTS_AS(integral3) void integral( InputArray src, OutputArray sum,
OutputArray sqsum, OutputArray tilted,
int sdepth = -1, int sqdepth = -1 );
@endcode
Small classes/structs are extended using CV_EXPORTS_W_SIMPLE. These structs are passed by value
to C++ functions. Examples are KeyPoint, Match etc. Their methods are extended by CV_WRAP and
fields are extended by CV_PROP_RW.
@code{.cpp}
class CV_EXPORTS_W_SIMPLE DMatch
{
public:
CV_WRAP DMatch();
CV_WRAP DMatch(int _queryIdx, int _trainIdx, float _distance);
CV_WRAP DMatch(int _queryIdx, int _trainIdx, int _imgIdx, float _distance);
CV_PROP_RW int queryIdx; // query descriptor index
CV_PROP_RW int trainIdx; // train descriptor index
CV_PROP_RW int imgIdx; // train image index
CV_PROP_RW float distance;
};
@endcode
Some other small classes/structs can be exported using CV_EXPORTS_W_MAP where it is exported to a
Python native dictionary. Moments() is an example of it.
@code{.cpp}
class CV_EXPORTS_W_MAP Moments
{
public:
//! spatial moments
CV_PROP_RW double m00, m10, m01, m20, m11, m02, m30, m21, m12, m03;
//! central moments
CV_PROP_RW double mu20, mu11, mu02, mu30, mu21, mu12, mu03;
//! central normalized moments
CV_PROP_RW double nu20, nu11, nu02, nu30, nu21, nu12, nu03;
};
@endcode
So these are the major extension macros available in OpenCV. Typically, a developer has to put
proper macros in their appropriate positions. Rest is done by generator scripts. Sometimes, there
may be an exceptional cases where generator scripts cannot create the wrappers. Such functions need
to be handled manually. But most of the time, a code written according to OpenCV coding guidelines
will be automatically wrapped by generator scripts.

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OpenCV-Python Bindings {#tutorial_py_table_of_contents_bindings}
======================
Here, you will learn how OpenCV-Python bindings are generated.
- @subpage tutorial_py_bindings_basics
Learn how OpenCV-Python bindings are generated.