Documentation for the new bindings-generator features

doc/Doxyfile.in

@@ -241,6 +241,9 @@ PREDEFINED             = __cplusplus=1 \
                          CV_PROP_RW= \
                          CV_WRAP= \
                          CV_WRAP_AS(x)= \
+                         CV_WRAP_MAPPABLE(x)= \
+                         CV_WRAP_PHANTOM(x)= \
+                         CV_WRAP_DEFAULT(x)= \
                          CV_CDECL= \
                          CV_Func = \
                          CV_DO_PRAGMA(x)= \

doc/py_tutorials/py_bindings/py_bindings_basics/py_bindings_basics.markdown

@@ -20,20 +20,20 @@ A simple example on extending C++ functions to Python can be found in official P
 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.
+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
+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.
+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.
+arguments, argument types etc. Final list contains details of all the functions, enums, 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
@@ -44,15 +44,15 @@ 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
+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/cv2.cpp.
+are placed in `modules/python/src2/cv2.cpp`.
 
 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
+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
@@ -67,19 +67,19 @@ Header parser parse the header files based on some wrapper macros added to funct
 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.
+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.
+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.
+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
 {
@@ -90,9 +90,9 @@ public:
     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
+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
+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
@@ -107,9 +107,9 @@ 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.
+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
 {
@@ -125,8 +125,8 @@ public:
     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.
+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
 {
@@ -142,6 +142,41 @@ public:
 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, to do this write your own pyopencv_*.hpp extending headers and put them into
+to be handled manually, to do this write your own `pyopencv_*.hpp` extending headers and put them into
 misc/python subdirectory of your module. But most of the time, a code written according to OpenCV
-coding guidelines will be automatically wrapped by generator scripts.
+coding guidelines will be automatically wrapped by generator scripts.
+
+More advanced cases involves providing Python with additional features that does not exist
+in the C++ interface such as extra methods, type mappings, or to provide default arguments.
+We will take `UMat` datatype as an example of such cases later on.
+First, to provide Python-specific methods, `CV_WRAP_PHANTOM` is utilized in a similar manner to
+`CV_WRAP`, except that it takes the method header as its argument, and you would need to provide
+the method body in your own `pyopencv_*.hpp` extension. `UMat::queue()` and `UMat::context()` are
+an example of such phantom methods that does not exist in C++ interface, but are needed to handle
+OpenCL functionalities at the Python side.
+Second, if an already-existing datatype(s) is mappable to your class, it is highly preferable to
+indicate such capacity using `CV_WRAP_MAPPABLE` with the source type as its argument,
+rather than crafting your own binding function(s). This is the case of `UMat` which maps from `Mat`.
+Finally, if a default argument is needed, but it is not provided in the native C++ interface,
+you can provide it for Python side as the argument of `CV_WRAP_DEFAULT`. As per the `UMat::getMat`
+example below:
+@code{.cpp}
+class CV_EXPORTS_W UMat
+{
+public:
+    //! Mat is mappable to UMat.
+    // You would need to provide `static bool cv_mappable_to(const Ptr<Mat>& src, Ptr<UMat>& dst)`
+    CV_WRAP_MAPPABLE(Ptr<Mat>);
+
+    /! returns the OpenCL queue used by OpenCV UMat.
+    // You would need to provide the method body in the binder code
+    CV_WRAP_PHANTOM(static void* queue());
+
+    //! returns the OpenCL context used by OpenCV UMat
+    // You would need to provide the method body in the binder code
+    CV_WRAP_PHANTOM(static void* context());
+
+    //! The wrapped method become equvalent to `get(int flags = ACCESS_RW)`
+    CV_WRAP_AS(get) Mat getMat(int flags CV_WRAP_DEFAULT(ACCESS_RW)) const;
+};
+@endcode