This change replaces references to a number of deprecated NumPy
type aliases (np.bool, np.int, np.float, np.complex, np.object,
np.str) with their recommended replacement (bool, int, float,
complex, object, str).
Those types were deprecated in 1.20 and are removed in 1.24,
cf https://github.com/numpy/numpy/pull/22607.
Parallelize implementation of HDR MergeMertens.
* Parallelize MergeMertens.
* Added performance tests for HDR.
* Ran clang-format.
* Optimizations.
* Fix data path for Windows.
* Remove compiiation warning on Windows.
* Remove clang-format for existing file.
* Addressing reviewer comments.
* Ensure correct summation order.
* Add test for determinism.
* Move result pyramid into sync struct.
* Reuse sync for first loop as well.
* Use OpenCV's threading primitives.
* Remove cout.
Add Python bindings for VideoCapture::waitAny #21826
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
Replaced sprintf with safer snprintf
* Straightforward replacement of sprintf with safer snprintf
* Trickier replacement of sprintf with safer snprintf
Some functions were changed to take another parameter: the size of the buffer, so that they can pass that size on to snprintf.
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.
* feat: OpenCV extension with pure Python modules
* feat: cv2 is now a Python package instead of extension module
Python package cv2 now can handle both Python and C extension modules
properly without additional "subfolders" like "_extra_py_code".
* feat: can call native function from its reimplementation in Python
`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.
Vadim Levin
Alexander Alekhin
Vincent Rabaud
Marco Feuerstein
Sergei Shutov
赵楚洋
cudawarped
Sean McBride
Andrey Senyaev
Tomoaki Teshima
huangziqing
Alexander Smorkalov
Andrey Senyaev
Maksim Shabunin
Dale Phurrough
Alexander Panov
Duong Dac
berak