All classes are registered in the scope that corresponds to C++
namespace or exported class.
Example:
`cv::ml::Boost` is exported as `cv.ml.Boost`
`cv::SimpleBlobDetector::Params` is exported as
`cv.SimpleBlobDetector.Params`
For backward compatibility all classes are registered in the global
module with their mangling name containing scope information.
Example:
`cv::ml::Boost` has `cv.ml_Boost` alias to `cv.ml.Boost` type
4.x: submodule or a class scope for exported classes
* feature: submodule or a class scope for exported classes
All classes are registered in the scope that corresponds to C++
namespace or exported class.
Example:
`cv::ml::Boost` is exported as `cv.ml.Boost`
`cv::SimpleBlobDetector::Params` is exported as
`cv.SimpleBlobDetector.Params`
For backward compatibility all classes are registered in the global
module with their mangling name containing scope information.
Example:
`cv::ml::Boost` has `cv.ml_Boost` alias to `cv.ml.Boost` type
* refactor: remove redundant GAPI aliases
* fix: use explicit string literals in CVPY_TYPE macro
* fix: add handling for class aliases
- Add special case handling when submodule has the same name as parent
- `PyDict_SetItemString` doesn't steal reference, so reference count
should be explicitly decremented to transfer object life-time
ownership
- Add sanity checks for module registration input
- Add Python 2 and Python 3 reference counting handling
- Add special case handling when submodule has the same name as parent
- `PyDict_SetItemString` doesn't steal reference, so reference count
should be explicitly decremented to transfer object life-time
ownership
- Add sanity checks for module registration input
Comment from Python documentation:
Unlike other functions that steal references, `PyModule_AddObject()` only
decrements the reference count of value on success.
This means that its return value must be checked, and calling code must
`Py_DECREF()` value manually on error.
`PyObject*` to `std::vector<T>` conversion logic:
- If user passed Numpy Array
- If array is planar and T is a primitive type (doesn't require
constructor call) that matches with the element type of array, then
copy element one by one with the respect of the step between array
elements. If compiler is lucky (or brave enough) copy loop can be
vectorized.
For classes that require constructor calls this path is not
possible, because we can't begin an object lifetime without hacks.
- Otherwise fall-back to general case
- Otherwise - execute the general case:
If PyObject* corresponds to Sequence protocol - iterate over the
sequence elements and invoke the appropriate `pyopencv_to` function.
`std::vector<T>` to `PyObject*` conversion logic:
- If `std::vector<T>` is empty - return empty tuple.
- If `T` has a corresponding `Mat` `DataType` than return
Numpy array instance of the matching `dtype` e.g.
`std::vector<cv::Rect>` is returned as `np.ndarray` of shape `Nx4` and
`dtype=int`.
This branch helps to optimize further evaluations in user code.
- Otherwise - execute the general case:
Construct a tuple of length N = `std::vector::size` and insert
elements one by one.
Unnecessary functions were removed and code was rearranged to allow
compiler select the appropriate conversion function specialization.
G-API: Wrap render functionality to python
* Wrap render Rect prim
* Add all primitives and tests
* Cover mosaic and image
* Handle error in pyopencv_to(Prim)
* Move Mosaic and Rect ctors wrappers to shadow file
* Use GAPI_PROP_RW
* Fix indent
this corrects bug #16592 where a Stream is created at
each GpuMat::load(arr,stream) call
a correct solution would have been to add a default to GpuMat::load
but due to circular dependence between Stream and GpuMat, this is not possible
add test_cuda_upload_download_stream to test_cuda.py
[G-API] Introduce cv.gin/cv.descr_of for python
* Implement cv.gin/cv.descr_of
* Fix macos build
* Fix gcomputation tests
* Add test
* Add using to a void exceeded length for windows build
* Add using to a void exceeded length for windows build
* Fix comments to review
* Fix comments to review
* Update from latest master
* Avoid graph compilation to obtain in/out info
* Fix indentation
* Fix comments to review
* Avoid using default in switches
* Post output meta for giebackend
[G-API] Introduce GOpaque and GArray for python
* Introduce GOpaque and GArray for python
* Fix ctor
* Avoid code duplication by using macros
* gapi: move Python-specific files to misc/python
* Fix windows build
Co-authored-by: Alexander Alekhin <alexander.a.alekhin@gmail.com>
they might be thrown from third-party code (notably Ogre in the ovis
module).
While Linux is kind enough to print them, they cause instant termination
on Windows.
Arguably, they do not origin from OpenCV itself, but still this helps
understanding what went wrong when calling an OpenCV function.
* Implement G-API python bindings
* Fix hdr_parser
* Drop initlization with brackets using regexp
* Handle bracket initilization another way
* Add test for core operations
* Declaration and definition of View constructor now in different files
* Refactor tests
* Remove combine decorator from tests
* Fix comment to review
* Fix test
* Fix comments to review
* Remove GCompilerArgs implementation from python
Co-authored-by: Pinaev <danil.pinaev@intel.com>
- It is safe to remove `explicit` keyword for constructors with 1
argument, because it is C++ specific keyword and does not affect any of
the generated binding.
The hard-coded string value "Mat" was used in the two format strings for vector_mat and vector_mat_template, preventing UMat arguments to functions that have these types from working correctly. as noted in #12231.
Fix implicit conversion from array to scalar in python bindings
* Fix wrong conversion behavior for primitive types
- Introduce ArgTypeInfo namedtuple instead of plain tuple.
If strict conversion parameter for type is set to true, it is
handled like object argument in PyArg_ParseTupleAndKeywords and
converted to concrete type with the appropriate pyopencv_to function
call.
- Remove deadcode and unused variables.
- Fix implicit conversion from numpy array with 1 element to scalar
- Fix narrowing conversion to size_t type.
* Fix wrong conversion behavior for primitive types
- Introduce ArgTypeInfo namedtuple instead of plain tuple.
If strict conversion parameter for type is set to true, it is
handled like object argument in PyArg_ParseTupleAndKeywords and
converted to concrete type with the appropriate pyopencv_to function
call.
- Remove deadcode and unused variables.
- Fix implicit conversion from numpy array with 1 element to scalar
- Fix narrowing conversion to size_t type.·
- Enable tests with wrong conversion behavior
- Restrict passing None as value
- Restrict bool to integer/floating types conversion
* Add PyIntType support for Python 2
* Remove possible narrowing conversion of size_t
* Bindings conversion update
- Remove unused macro
- Add better conversion for types to numpy types descriptors
- Add argument name to fail messages
- NoneType treated as a valid argument. Better handling will be added
as a standalone patch
* Add descriptor specialization for size_t
* Add check for signed to unsigned integer conversion safety
- If signed integer is positive it can be safely converted
to unsigned
- Add check for plain python 2 objects
- Add check for numpy scalars
- Add simple type_traits implementation for better code style
* Resolve type "overflow" false negative in safe casting check
- Move type_traits to separate header
* Add copyright message to type_traits.hpp
* Limit conversion scope for integral numpy types
- Made canBeSafelyCasted specialized only for size_t, so
type_traits header became unused and was removed.
- Added clarification about descriptor pointer
Fix cudacodec python
* Add python bindings to cudacodec.
* Allow args with CV_OUT GpuMat& or CV_OUT cuda::GpuMat& to generate python bindings that allow the argument to be an optional output in the same way as OutputArray.
* Add wrapper flag to indicate that an OutputArray is a GpuMat.
* python: drop CV_GPU, extra checks in test
* Remove "cuda::GpuMat" check rom python parser
Vadim Levin
Alexander Alekhin
Maksim Shabunin
Alexander Smorkalov
Dale Phurrough
Duong Dac
berak
Anatoliy Talamanov
David Geldreich
Pavel Rojtberg
Daniel Motilla
Zach Lowry
Pavel Rojtberg
cudawarped
cudawarped
Alexander Alekhin