Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

• I agree to contribute to the project under Apache 2 License.
• To the best of my knowledge, the proposed patch is not based on a code under GPL or other license that is incompatible with OpenCV
• The PR is proposed to proper branch
• There is reference to original bug report and related work
• There is accuracy test, performance test and test data in opencv_extra repository, if applicable
  Patch to opencv_extra has the same branch name.
• The feature is well documented and sample code can be built with the project CMake

Overview

This PR is a part of #18900. To make review process easier decided to split this on several PR's.

Problem

To pass input arguments into GComputation::apply G-API has function cv::gin() which takes variadic number of arguments and make cv::GRunArgs from them.

Let's to consider this call:

img   = np.random.random((3,3))
array = np.random.random(5)
comp.apply(cv.gin(img, array))

Both of img and array are numpy arrays, but the first one should be represented as cv::Mat and the second as std::vector based on graph meta of course.
Another example: Both of cv::Scalar and cv::Rect are represented in python as tuple with 4 values.
The same problem for cv::descr_of.

How to implement cv::gin for python, if we don't know actual type of object and have only python representation like numpy array or tuple.

Solution

We can't unpack input PyObject* to cv::GRunArgs at the time we received it in cv::gin. But we can unpack data based on graph metadata. Let's create some functor which stores this PyObject* and implement method which will take input metadata and based on this metadata perform converting.

1. Collect input meta info during graph compilation

struct GTypeInfo
{
    cv::GShape shape;
    cv::detail::OpaqueKind kind;
    cv::detail::HostCtor ctor;
};

2. In cv.gin make the functor and return back to the python.
3. Take this functor in apply method and unpack based on collected input meta.

Build configuration

Xforce_builders_only=linux,docs
force_builders=Custom,Custom Win,Custom Mac
build_gapi_standalone:Linux x64=ade-0.1.1f
build_gapi_standalone:Win64=ade-0.1.1f
build_gapi_standalone:Mac=ade-0.1.1f
build_gapi_standalone:Linux x64 Debug=ade-0.1.1f

Xbuild_image:Custom=centos:7
Xbuildworker:Custom=linux-1
build_gapi_standalone:Custom=ade-0.1.1f

build_image:Custom=ubuntu-openvino-2021.1.0:20.04
build_image:Custom Win=openvino-2021.1.0
build_image:Custom Mac=openvino-2021.1.0

test_modules:Custom=gapi,python2,python3,java
test_modules:Custom Win=gapi,python2,python3,java
test_modules:Custom Mac=gapi,python2,python3,java

buildworker:Custom=linux-1
# disabled due high memory usage: test_opencl:Custom=ON
test_opencl:Custom=OFF
test_bigdata:Custom=1
test_filter:Custom=*