Consider QRCode ECI encoding #24426
### Pull Request Readiness Checklist
related: https://github.com/opencv/opencv/pull/24350#pullrequestreview-1661658421
1. Add `getEncoding` method to obtain ECI number
2. Add `detectAndDecodeBytes`, `decodeBytes`, `decodeBytesMulti`, `detectAndDecodeBytesMulti` methods in Python (return `bytes`) and Java (return `byte[]`)
3. Allow Python bytes to std::string conversion in general and add `encode(byte[] encoded_info, Mat qrcode)` in Java
Python example with Kanji encoding:
```python
img = cv.imread("test.png")
detect = cv.QRCodeDetector()
data, points, straight_qrcode = detect.detectAndDecodeBytes(img)
print(data)
print(detect.getEncoding(), cv.QRCodeEncoder_ECI_SHIFT_JIS)
print(data.decode("shift-jis"))
```
```
b'\x82\xb1\x82\xf1\x82\xc9\x82\xbf\x82\xcd\x90\xa2\x8aE'
20 20
こんにちは世界
```
source: https://github.com/opencv/opencv/blob/ba4d6c859d21536f84e0328c16f4cc3e96bf3065/modules/objdetect/test/test_qrcode_encode.cpp#L332
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
Fixed Android setCameraIndex issue. #27419
Fixed camera switching issue on Android devices: When a device has more than two cameras, the setCameraIndex method failed to switch to non-default cameras.
Root cause: The original code only considered two default camera IDs when updating camera IDs, ignoring all others. This fix ensures all camera IDs are properly handled.
### 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
Java VideoCapture buffered stream constructor #27284
### Pull Request Readiness Checklist
resolves#26809
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
Added trackers factory with pre-loaded dnn models #26875
Replaces https://github.com/opencv/opencv/pull/26295
Allows to substitute custom models or initialize tracker from in-memory model.
### 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
AndroidMediaNdkCapture pixel format enhancement #26656
### 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
- [ 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
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
Unified build.gradle files into one template #26009
Issue #24686
### 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
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
Fixed kotlin requirement in Android build.gradle #25856
Now OpenCV Android SDK doesn't always require kotlin plugin. Kotlin code is compiled only if the application uses kotlin plugin.
Fixes#24663
### 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.
- [x] The feature is well documented and sample code can be built with the project CMake
Documentation transition to fresh Doxygen #25042
* current Doxygen version is 1.10, but we will use 1.9.8 for now due to issue with snippets (https://github.com/doxygen/doxygen/pull/10584)
* Doxyfile adapted to new version
* MathJax updated to 3.x
* `@relates` instructions removed temporarily due to issue in Doxygen (to avoid warnings)
* refactored matx.hpp - extracted matx.inl.hpp
* opencv_contrib - https://github.com/opencv/opencv_contrib/pull/3638
Allow multiple flags with OPENCV_GRADLE_VERBOSE_OPTIONS #24969
### Pull Request Readiness Checklist
Merge with https://github.com/opencv/ci-gha-workflow/pull/144
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
Modified Java tests to run on Android #24910
To run the tests you need to:
1. Build OpenCV using Android pipeline. For example:
`cmake -DBUILD_TEST=ON -DANDROID=ON -DANDROID_ABI=arm64-v8a -DCMAKE_TOOLCHAIN_FILE=/usr/lib/android-sdk/ndk/25.1.8937393/build/cmake/android.toolchain.cmake -DANDROID_NDK=/usr/lib/android-sdk/ndk/25.1.8937393 -DANDROID_SDK=/usr/lib/android-sdk ../opencv`
`make`
2. Connect Android Phone
3. Run tests:
`cd android_tests`
`./gradlew tests_module:connectedAndroidTest`
Related CI pipeline: https://github.com/opencv/ci-gha-workflow/pull/138
### 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.
- [x] The feature is well documented and sample code can be built with the project CMake
Added screen rotation support to JavaCamera2View amd NativeCameraView. Fixed JavaCamera2View initialization. #24869
Added automatic image rotation to JavaCamera2View and NativeCameraView so the video preview was matched with screen orientation.
Fixed double preview initialization bug in JavaCamera2View.
Added proper cameraID parsing to NativeCameraView similar to JavaCameraView
### 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.
- [x] The feature is well documented and sample code can be built with the project CMake
- Added JavaDoc package build and publishing
- Added Source package build and publishing
- More metadata for publishing
- Disable native samples build with aar, because prefab is not complete yet
* 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
Alexander Smorkalov
Dmitry Kurtaev
FleeOvernight
Vadim Levin
Alexander Smorkalov
Rüdiger Ihle
WU Jia
Maksim Shabunin
Vadim Pisarevsky
Alexander Smorkalov
alexlyulkov
Abduragim Shtanchaev
Alexander Lyulkov
AleksandrPanov