Switch to new OpenVINO API after 2022.1 release
* Pass Layer_Test_Convolution_DLDT.Accuracy/0 test
* Pass test Test_Caffe_layers.Softmax
* Failed 136 tests
* Fix Concat. Failed 120 tests
* Custom nGraph ops. 19 failed tests
* Set and get properties from Core
* Read model from buffer
* Change MaxPooling layer output names. Restore reshape
* Cosmetic changes
* Cosmetic changes
* Override getOutputsInfo
* Fixes for OpenVINO < 2022.1
* Async inference for 2021.4 and less
* Compile model with config
* Fix serialize for 2022.1
* Asynchronous inference with 2022.1
* Handle 1d outputs
* Work with model with dynamic output shape
* Fixes with 1d output for old API
* Control outputs by nGraph function for all OpenVINO versions
* Refer inputs in PrePostProcessor by indices
* Fix cycled dependency between InfEngineNgraphNode and InfEngineNgraphNet.
Add InferRequest callback only for async inference. Do not capture InferRequest object.
* Fix tests thresholds
* Fix HETERO:GPU,CPU plugin issues with unsupported layer
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.
**Merge with contrib**: https://github.com/opencv/opencv_contrib/pull/3003
### 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 other license that is incompatible with OpenCV
- [x] The PR is proposed to proper branch
- [ ] There is reference to 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.
- [ ] The feature is well documented and sample code can be built with the project CMake
* cann backend impl v1
* cann backend impl v2: use opencv parsers to build models for cann
* adjust fc according to the new transA and transB
* put cann net in cann backend node and reuse forwardLayer
* use fork() to create a child process and compile cann model
* remove legacy code
* remove debug code
* fall bcak to CPU backend if there is one layer not supoorted by CANN backend
* fix netInput forward
G-API: replace GAPI_Assert() with 'false' and '0' to GAPI_Error()
* gapi: GAPI_Error() macro
* gapi: replace GAPI_Assert() with 'false' and '0' to GAPI_Error()
* build: eliminate 'unreachable code' after CV_Error() (MSVC 2015)
* build: eliminate 'unreachable code' warning for MSVS 2015/2017
- observed in constructors stubs with throwing exception
Megre together with https://github.com/opencv/opencv_contrib/pull/3325
1. Move aruco_detector, aruco_board, aruco_dictionary, aruco_utils to objdetect
1.1 add virtual Board::draw(), virtual ~Board()
1.2 move `testCharucoCornersCollinear` to Board classes (and rename to `checkCharucoCornersCollinear`)
1.3 add wrappers to keep the old api working
3. Reduce inludes
4. Fix java tests (add objdetect import)
5. Refactoring
### 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
```
**WIP**
force_builders=linux,win64,docs,Linux x64 Debug,Custom
Xbuild_contrib:Docs=OFF
build_image:Custom=ubuntu:22.04
build_worker:Custom=linux-1
```
videoio: add Orbbec Gemini 2 and Astra 2 camera support
### Test Result
| OS | Compiler | Camera | Result |
|-----|-----------|---------|--------|
|Windows11| (VS2022)MSVC17.3|Orbbec Gemini 2|Pass|
|Windows11| (VS2022)MSVC17.3|Orbbec Astra 2|Pass|
|Ubuntu22.04|GCC9.2|Orbbec Gemini 2|Pass|
|Ubuntu22.04|GCC9.2|Orbbec Astra 2|Pass|
### Pull Request Readiness Checklist
- [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] The feature is well documented and sample code can be built with the project CMake
Address https://github.com/opencv/opencv/issues/22868
Used the same defaults as it's done for FFmpeg
### 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
- [ ] 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
```
force_builders=Custom
build_image:Custom=gstreamer:16.04
buildworker:Custom=linux-1
```
Add Python bindings for VideoCapture::waitAny #21826
### Pull Request Readiness Checklist
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- [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
DNN: reduce the memory used in convolution layer
* reduce the memory in winograd and disabel the test when usage memory is larger than 2gb.
* remove VERY_LOG tag
[teset data in opencv_extra](https://github.com/opencv/opencv_extra/pull/1016)
NanoTrack is an extremely lightweight and fast object-tracking model.
The total size is **1.1 MB**.
And the FPS on M1 chip is **150**, on Raspberry Pi 4 is about **30**. (Float32 CPU only)
With this model, many users can run object tracking on the edge device.
The author of NanoTrack is @HonglinChu.
The original repo is https://github.com/HonglinChu/NanoTrack.
### 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
- [ ] There is a reference to the 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
The current implementation overwrites the result rotation and translation in every iteration.
If SOLVEPNP_ITERATIVE was run as a refinement it will start from the incorrect initial
transformation thus degrading the final outcome.
