- Implemented a new `create` method in `FaceRecognizerSF` to allow model and configuration loading from memory buffers (std::vector<uchar>), similar to the existing functionality in `FaceDetectorYN`.
- Updated `face_recognize.cpp` with a new constructor in `FaceRecognizerSFImpl` that supports buffer-based loading for both model weights and network configuration.
- Ensured compatibility with both file-based and buffer-based model loading by maintaining consistent backend and target settings across both constructors.
- This change improves flexibility, allowing FaceRecognizerSF to be instantiated from memory buffers, which is useful for dynamic model loading scenarios such as embedded systems or applications where models are loaded in-memory.
Update Documentation #26260
### 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
Update intrin_wasm.hpp #25909
See https://github.com/microsoft/vcpkg/issues/33443 for some build context when using
```vcpkg install opencv4:wasm32-emscripten```
`__EMSCRIPTEN_major__`, `__EMSCRIPTEN_minor__` and `__EMSCRIPTEN_tiny__` in `emsdk` >= 3.1.4 are in a header, as opposed to command line.
We could potentially be more aggressive with how I'm checking this property; let me know if I should make the change.
It should also be suggested that `-msimd128` is auto-included in the associated portfile for opencv, but that's a separate issue. Someone let me know if I should also make that change as well.
Special thanks to https://github.com/youar for supporting this work; please inform if applying a copyright-header is appropriate attribution.
### 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
core: C-API cleanup: RNG algorithms in core(4.x) #26259
- replace CV_RAND_UNI and NORMAL to cv::RNG::UNIFORM and cv::RNG::NORMAL.
### 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
extended Norm tests to prove that cv::norm() already supports all the types.
cv::norm() already provides enough functionality; just extended tests to prove it. See #24887
- [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
- Added function overload for the simple case
- Added CV_Bool type support for masks
- `parallel_for_` for intrinsics calibration for faster inference
- Homogenize parameters order with other calibrateXXX functions
Updated KleidiCV HAL to version 0.2. #26241
### 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
- [ ] 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
imgproc: add optimized warpAffine kernels for 8U/16U/32F + C1/C3/C4 inputs #25984
Merge wtih https://github.com/opencv/opencv_extra/pull/1198.
Merge with https://github.com/opencv/opencv_contrib/pull/3787.
### 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
* use 2 parms for now to identify the error
* Revert "use 2 parms for now to identify the error"
This reverts commit 86faf993a7.
* replace += with =
* add v_log ref
* refactor intrin_math code
* Add include guard to `intrin_math.hpp` to prevent multiple inclusions
* rename VX to V; make fp64 impl in neon be optional
* add v_setall, v_setzero for all backends; rewrite the intrin_math
* fix error on rvv_scalable
* let v_erf use v_exp_default_32f function
* 1. replaced 'v_setzero(VecType dummy)' with 'v_setzero_<VecType>()'
2. replaced 'v_setall(LaneType x, VecType dummy)' with 'v_setall_<VecType>(LaneType x)'
3. added tests for the new v_setzero_<> and v_setall_<>.
* gcc does not seem to like static_assert in functions even when they are not used
* trying to fix compile errors in Debug mode on Linux
---------
Co-authored-by: Vadim Pisarevsky <vadim.pisarevsky@gmail.com>
Reworked multiview calibration interface #26221
- Use InputArray / OutputArray
- Use enum for camera type
- Sort parameters according guidelines
- Made more outputs optional
- Introduce flags and added tests for intrinsics and extrinsics guess.
### 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
- [ ] 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
Mirror most recent changes from https://github.com/terzakig/sqpnp/pull/24
- rank revealing QR in nullspace computation
- sqrt-free Cholesky (i.e., L*D*Lt) in the SQP solution
- replaced divisions with multiplications by inverses
- simplified checks in computeRowAndNullspace()
- removed unnecessary negations
- broke some dependency chains with parentheses
- minor other changes
