Introduce option to generate Java code with finalize() or Cleaners interface #28159
Closes https://github.com/opencv/opencv/issues/22260
Replaces https://github.com/opencv/opencv/pull/23467
The PR introduce configuration option to generate Java code with Cleaner interface for Java 9+ and old-fashion finalize() method for old Java and Android. Mat class and derivatives are manually written. The PR introduce 2 base classes for it depending on the generator configuration.
Pros:
1. No need to implement complex and error prone cleaner on library side.
2. No new CMake templates, easier to modify code in IDE.
Cons:
1. More generator branches and different code for modern desktop and Android.
TODO:
- [x] Add Java version check to cmake
- [x] Use Cleaners for ANDROID API 33+
### 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
- Added vector_vector_Mat to gen_dict.json
- Implemented Mat_to_vector_vector_Mat and vector_vector_Mat_to_Mat conversion functions in converters.h/cpp and Converters.java
- Added DnnForwardAndRetrieve.java test to verify List<List<Mat>> conversion : Reference: C++ test in modules/dnn/test/test_misc.cpp - TEST(Net, forwardAndRetrieve)
New dnn engine #26056
This is the 1st PR with the new engine; CI is green and PR is ready to be merged, I think.
Merge together with https://github.com/opencv/opencv_contrib/pull/3794
---
**Known limitations:**
* [solved] OpenVINO is temporarily disabled, but is probably easy to restore (it's not a deal breaker to merge this PR, I guess)
* The new engine does not support any backends nor any targets except for the default CPU implementation. But it's possible to choose the old engine when loading a model, then all the functionality is available.
* [Caffe patch is here: #26208] The new engine only supports ONNX. When a model is constructed manually or is loaded from a file of different format (.tf, .tflite, .caffe, .darknet), the old engine is used.
* Even in the case of ONNX some layers are not supported by the new engine, such as all quantized layers (including DequantizeLinear, QuantizeLinear, QLinearConv etc.), LSTM, GRU, .... It's planned, of course, to have full support for ONNX by OpenCV 5.0 gold release. When a loaded model contains unsupported layers, we switch to the old engine automatically (at ONNX parsing time, not at `forward()` time).
* Some layers , e.g. Expat, are only partially supported by the new engine. In the case of unsupported flavours it switches to the old engine automatically (at ONNX parsing time, not at `forward()` time).
* 'Concat' graph optimization is disabled. The optimization eliminates Concat layer and instead makes the layers that generate tensors to be concatenated to write the outputs to the final destination. Of course, it's only possible when `axis=0` or `axis=N=1`. The optimization is not compatible with dynamic shapes since we need to know in advance where to store the tensors. Because some of the layer implementations have been modified to become more compatible with the new engine, the feature appears to be broken even when the old engine is used.
* Some `dnn::Net` API is not available with the new engine. Also, shape inference may return false if some of the output or intermediate tensors' shapes cannot be inferred without running the model. Probably this can be fixed by a dummy run of the model with zero inputs.
* Some overloads of `dnn::Net::getFLOPs()` and `dnn::Net::getMemoryConsumption()` are not exposed any longer in wrapper generators; but the most useful overloads are exposed (and checked by Java tests).
* [in progress] A few Einsum tests related to empty shapes have been disabled due to crashes in the tests and in Einsum implementations. The code and the tests need to be repaired.
* OpenCL implementation of Deconvolution is disabled. It's very bad and very slow anyway; need to be completely revised.
* Deconvolution3D test is now skipped, because it was only supported by CUDA and OpenVINO backends, both of which are not supported by the new engine.
* Some tests, such as FastNeuralStyle, checked that the in the case of CUDA backend there is no fallback to CPU. Currently all layers in the new engine are processed on CPU, so there are many fallbacks. The checks, therefore, have been temporarily disabled.
---
- [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
Add sample for GPT2 inference #25868
### Pull Request Readiness Checklist
This PR adds sample for inferencing GPT-2 model. More specificly implementation of GPT-2 from [this repository](https://github.com/karpathy/build-nanogpt). Currently inference in OpenCV is only possible to do with fixed window size due to not supported dynamic shapes.
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.
- [x] The feature is well documented and sample code can be built with the project CMake
* attempt to add 0d/1d mat support to OpenCV
* revised the patch; now 1D mat is treated as 1xN 2D mat rather than Nx1.
* a step towards 'green' tests
* another little step towards 'green' tests
* calib test failures seem to be fixed now
* more fixes _core & _dnn
* another step towards green ci; even 0D mat's (a.k.a. scalars) are now partly supported!
* * fixed strange bug in aruco/charuco detector, not sure why it did not work
* also fixed a few remaining failures (hopefully) in dnn & core
* disabled failing GAPI tests - too complex to dig into this compiler pipeline
* hopefully fixed java tests
* trying to fix some more tests
* quick followup fix
* continue to fix test failures and warnings
* quick followup fix
* trying to fix some more tests
* partly fixed support for 0D/scalar UMat's
* use updated parseReduce() from upstream
* trying to fix the remaining test failures
* fixed [ch]aruco tests in Python
* still trying to fix tests
* revert "fix" in dnn's CUDA tensor
* trying to fix dnn+CUDA test failures
* fixed 1D umat creation
* hopefully fixed remaining cuda test failures
* removed training whitespaces
* Expose more C++ functionality in the Java wrapper of the Mat class
In particular expose methods for handling Mat with more than 2 dimensions
* add constructors taking an array of dimension sizes
* add constructor taking an existing Mat and an array of Ranges
* add override of the create method taking an array of dimension sizes
* add overrides of the ones and zeros methods taking an array of dimension sizes
* add override of the submat method taking an array of ranges
* add overrides of put and get taking arrays of indices
* add wrapper for copySize method
* fix crash in the JNI wrapper of the reshape(int cn, int[] newshape) method
* add test for each method added to Mat.java
* Fix broken test
Adding capability to parse subsections of a byte array in Java bindings (#10489)
* Adding capability to parse subsections of a byte array in Java bindings. (Because Java lacks pointers. Therefore, reading images within a subsection of a byte array is impossible by Java's nature and limitations. Because of this, many IO functions in Java require additional parameters offset and length to define, which section of an array to be read.)
* Corrected according to the review. Previous interfaces were restored, instead internal interfaces were modified to provide subsampling of java byte arrays.
* Adding tests and test related files.
* Adding missing files for the test.
* Simplified the test
* Check was corrected according to discussion. An OutOfRangeException will be thrown instead of returning.
* java: update MatOfByte implementation checks / tests
Alexander Smorkalov
Alexander Smorkalov
utibenkei
WU Jia
Vadim Pisarevsky
Abduragim Shtanchaev
Giles Payne
Alexander Alekhin
Alexander Alekhin
Jan Starzynski
Giles Payne
berak
Dmitry Kurtaev
Hamdi Sahloul
luz.paz
SarenT
Florian Echtler
Aleksandr Rybnikov
abratchik
Maksim Shabunin
Muto Masayuki
Alexander Alekhin
Roman Donchenko
Andrey Pavlenko