G-API: New python operations API
* Reimplement test using decorators
* Custom python operation API
* Remove wip status
* python: support Python code in bindings (through loader only)
* cleanup, skip tests for Python 2.x (not supported)
* python 2.x can't skip unittest modules
* Clean up
* Clean up
* Fix segfault python3.9
Co-authored-by: Alexander Alekhin <alexander.a.alekhin@gmail.com>
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
Currently, the LOADER_DIR is set as os.path.dirname(os.path.abspath(__file__)). This does not point to the true library path if the cv2 folder is symlinked into the Python package directory such that importing cv2 under Python fails. The proposed change only resolves symbolic links correctly by calling os.path.realpath(__file__) first and does not change anything if __file__ contains no symbolic link.
* Add Python Bindings for getCacheDirectory function
* Added getCacheDirectory interop test with image codecs.
Co-authored-by: Sergey Slashchinin <sergei.slashchinin@xperience.ai>
[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.
Return accumulator value in HoughLines algorithm
* try to solve #17050
use cv_wrap_as
add python test
parameters
* review
* move wrapper to imgproc/bindings.hpp
changed OpenCV license from BSD to Apache 2 license
* as discussed and announced earlier, changed OpenCV license from BSD to Apache 2. Many files still contain old-style copyrights though
* changed wording a bit; preserve the original OpenCV BSD license
* 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>
* fixed#17044
1. fixed Python part of the tutorial about using OpenCV XML-YAML-JSON I/O functionality from C++ and Python.
2. added startWriteStruct() and endWriteStruct() methods to FileStorage
3. modifed FileStorage::write() methods to make them work well inside sequences, not only mappings.
* try to fix the doc builder
* added Python regression test for FileStorage I/O API ([TODO] iterating through long sequences can be very slow)
* fixed yaml testing
- 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
Tests for argument conversion of Python bindings generator
* Tests for parsing elemental types from Python bindings
- Add positive and negative tests for int, float, double, size_t,
const char*, bool.
- Tests with wrong conversion behavior are skipped.
* Move implicit conversion of bool to integer/floating types to wrong
conversion behavior.
* Enabled -Wnarrowing warning
* Fixed type narrowing issues
* Cast python constants
* Use long long for python constants
* Use int for python constants with fallback to long
* Update cv2.cpp
* Python wrapper for detail
* hide pyrotationwrapper
* copy code in pyopencv_rotationwarper.hpp
* move ImageFeatures MatchInfo and CameraParams in core/misc/
* add python test for detail
* move test_detail in test_stitching
* rename
[evolution] Stitching for OpenCV 4.0
* stitching: wrap Stitcher::create for bindings
* provide method for consistent stitcher usage across languages
* samples: add python stitching sample
* port cpp stitching sample to python
* stitching: consolidate Stitcher create methods
* remove Stitcher::createDefault, it returns Stitcher, not Ptr<Stitcher> -> inconsistent API
* deprecate cv::createStitcher and cv::createStitcherScans in favor of Stitcher::create
* stitching: avoid anonymous enum in Stitcher
* ORIG_RESOL should be double
* add documentatiton
* stitching: improve documentation in Stitcher
* stitching: expose estimator in Stitcher
* remove ABI hack
* stitching: drop try_use_gpu flag
* OCL will be used automatically through T-API in OCL-enable paths
* CUDA won't be used unless user sets CUDA-enabled classes manually
* stitching: drop FeaturesFinder
* use Feature2D instead of FeaturesFinder
* interoperability with features2d module
* detach from dependency on xfeatures2d
* features2d: fix compute and detect to work with UMat vectors
* correctly pass UMats as UMats to allow OCL paths
* support vector of UMats as output arg
* stitching: use nearest interpolation for resizing masks
* fix warnings
* Remove isIntel check from deep learning layers
* Remove fp16->fp32 fallbacks where it's not necessary
* Fix Kernel::run to prevent localsize > globalsize
