Fix incorrect string format in js build script #26374
I accidentally met this small problem mentioned in https://github.com/opencv/opencv/pull/25084#discussion_r1710838120 when play with wasm build. It seems https://github.com/EDVTAZ didn't fix it yet, so I create this tiny pr.
Additionally, I remove a redundant argument in `add_argument` call. `'store_true'` already set the default, see https://docs.python.org/3/library/argparse.html#action.
### 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
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
Features2d cleanup: Move several feature detectors and descriptors to opencv_contrib #25292
features2d cleanup: #24999
The PR moves KAZE, AKAZE, AgastFeatureDetector, BRISK and BOW to opencv_contrib/xfeatures2d.
Related PR: opencv/opencv_contrib#3709
RISC-V/AArch64: disable CPU features detection #25901
This PR is the first step in fixing current issues with NEON/RVV, FP16, BF16 and other CPU features on AArch64 and RISC-V platforms.
On AArch64 and RISC-V platforms we usually have the platform set by default in the toolchain when we compile it or in the cmake toolchain file or in CMAKE_CXX_FLAGS by user. Then, there are two ways to set platform options: a) "-mcpu=<some_cpu>" ; b) "-march=<arch description>" (e.g. "rv64gcv"). Furthermore, there are no similar "levels" of optimizations as for x86_64, instead we have features (RVV, FP16,...) which can be enabled or disabled. So, for example, if a user has "rv64gc" set by the toolchain and we want to enable RVV. Then we need to somehow parse their current feature set and append "v" (vector optimizations) to this string. This task is quite hard and the whole procedure is prone to errors.
I propose to use "CPU_BASELINE=DETECT" by default on AArch64 and RISC-V platforms. And somehow remove other features or make them read-only/detect-only, so that OpenCV wouldn't add any extra "-march" flags to the default configuration. We would rely only on the flags provided by the compiler and cmake toolchain file. We can have some predefined configurations in our cmake toolchain files.
Changes made by this PR:
- `CMakeLists.txt`:
- use `CMAKE_CROSSCOMPILING` instead of `CMAKE_TOOLCHAIN_FILE` to detect cross-compilation. This might be useful in cases of native compilation with a toolchain file
- removed obsolete variables `ENABLE_NEON` and `ENABLE_VFPV3`, the first one have been turned ON by default on AArch64 platform which caused setting `CPU_BASELINE=NEON`
- raise minimum cmake version allowed to 3.7 to allow using `CMAKE_CXX_FLAGS_INIT` in toolchain files
- added separate files with arch flags for native compilation on AArch64 and RISC-V, these files will be used in our toolchain files and in regular cmake
- use `DETECT` as default value for `CPU_BASELINE` also allow `NATIVE`, warn user if other values were used (only for AArch64 and RISC-V)
- for each feature listed in `CPU_DISPATCH` check if corresponding `CPU_${opt}_FLAGS_ON` has been provided, warn user if it is empty (only for AArch64 and RISC-V)
- use `CPU_BASELINE_DISABLE` variable to actually turn off macros responsible for corresponding features even if they are enabled by compiler
- removed Aarch64 feature merge procedure (it didn't support `-mcpu` and built-in `-march`)
- reworked AArch64 and two RISC-V cmake toolchain files (does not affect Android/OSX/iOS/Win):
- use `CMAKE_CXX_FLAGS_INIT` to set compiler flags
- use variables `ENABLE_BF16`, `ENABLE_DOTPROD`, `ENABLE_RVV`, `ENABLE_FP16` to control `-march`
- AArch64: removed other compiler and linker flags
- `-fdata-sections`, `-fsigned-char`, `-Wl,--no-undefined`, `-Wl,--gc-sections` - already set by OpenCV
- `-Wa,--noexecstack`, `-Wl,-z,noexecstack`, `-Wl,-z,relro`, `-Wl,-z,now` - can be enabled by OpenCV via `ENABLE_HARDENING`
- `-Wno-psabi` - this option used to disable some warnings on older ARM platforms, shouldn't harm
- ARM: removed same common flags as for AArch64, but left `-mthumb` and `--fix-cortex-a8`, `-z nocopyreloc`
Split Javascript white-list to support contrib modules #25986
Single whitelist converted to several per-module json files. They are concatenated automatically and can be overriden by user config.
Related to https://github.com/opencv/opencv/pull/25656
### 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
Android SDK build script: HWAsan flags added for release mode #25746
A quick fix for #25718
### 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.
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Fix OpenCV.js tests #25732
### Pull Request Readiness Checklist
* Firefox tests passed
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
Android SDK build script: HWAsan support added #25718
### 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
- [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
3rdparty: NDSRVP - A New 3rdparty Library with Optimizations Based on RISC-V P Extension v0.5.2 - Part 1: Basic Functions #25167
# Summary
### Previous context
From PR #24556:
>> * As you wrote, the P-extension differs from RVV thus can not be easily implemented via Universal Intrinsics mechanism, but there is another HAL mechanism for lower-level CPU optimizations which is used by the [Carotene](https://github.com/opencv/opencv/tree/4.x/3rdparty/carotene) library on ARM platforms. I suggest moving all non-dnn code to similar third-party component. For example, FAST algorithm should allow such optimization-shortcut: see https://github.com/opencv/opencv/blob/4.x/modules/features2d/src/hal_replacement.hpp
>> Reference documentation is here:
>>
>> * https://docs.opencv.org/4.x/d1/d1b/group__core__hal__interface.html
>> * https://docs.opencv.org/4.x/dd/d8b/group__imgproc__hal__interface.html
>> * https://docs.opencv.org/4.x/db/d47/group__features2d__hal__interface.html
>> * Carotene library is turned on here: 8bbf08f0de/CMakeLists.txt (L906-L911)
> As a test outside of this PR, A 3rdparty component called ndsrvp is created, containing one of the non-dnn code (integral_SIMD), and it works very well.
> All the non-dnn code in this PR have been removed, currently this PR can be focused on dnn optinizations.
> This HAL mechanism is quite suitable for rvp optimizations, all the non-dnn code is expected to be moved into ndsrvp soon.
### Progress
#### Part 1 (This PR)
- [Core](https://docs.opencv.org/4.x/d1/d1b/group__core__hal__interface.html)
- [x] Element-wise add and subtract
- [x] Element-wise minimum or maximum
- [x] Element-wise absolute difference
- [x] Bitwise logical operations
- [x] Element-wise compare
- [ImgProc](https://docs.opencv.org/4.x/dd/d8b/group__imgproc__hal__interface.html)
- [x] Integral
- [x] Threshold
- [x] WarpAffine
- [x] WarpPerspective
- [Features2D](https://docs.opencv.org/4.x/db/d47/group__features2d__hal__interface.html)
#### Part 2 (Next PR)
**Rough Estimate. Todo List May Change.**
- [Core](https://docs.opencv.org/4.x/d1/d1b/group__core__hal__interface.html)
- [ImgProc](https://docs.opencv.org/4.x/dd/d8b/group__imgproc__hal__interface.html)
- smaller remap HAL interface
- AdaptiveThreshold
- BoxFilter
- Canny
- Convert
- Filter
- GaussianBlur
- MedianBlur
- Morph
- Pyrdown
- Resize
- Scharr
- SepFilter
- Sobel
- [Features2D](https://docs.opencv.org/4.x/db/d47/group__features2d__hal__interface.html)
- FAST
### Performance Tests
The optimization does not contain floating point opreations.
**Absolute Difference**
Geometric mean (ms)
|Name of Test|opencv perf core Absdiff|opencv perf core Absdiff|opencv perf core Absdiff vs opencv perf core Absdiff (x-factor)|
|---|:-:|:-:|:-:|
|Absdiff::OCL_AbsDiffFixture::(640x480, 8UC1)|23.104|5.972|3.87|
|Absdiff::OCL_AbsDiffFixture::(640x480, 32FC1)|39.500|40.830|0.97|
|Absdiff::OCL_AbsDiffFixture::(640x480, 8UC3)|69.155|15.051|4.59|
|Absdiff::OCL_AbsDiffFixture::(640x480, 32FC3)|118.715|120.509|0.99|
|Absdiff::OCL_AbsDiffFixture::(640x480, 8UC4)|93.001|19.770|4.70|
|Absdiff::OCL_AbsDiffFixture::(640x480, 32FC4)|161.136|160.791|1.00|
|Absdiff::OCL_AbsDiffFixture::(1280x720, 8UC1)|69.211|15.140|4.57|
|Absdiff::OCL_AbsDiffFixture::(1280x720, 32FC1)|118.762|119.263|1.00|
|Absdiff::OCL_AbsDiffFixture::(1280x720, 8UC3)|212.414|44.692|4.75|
|Absdiff::OCL_AbsDiffFixture::(1280x720, 32FC3)|367.512|366.569|1.00|
|Absdiff::OCL_AbsDiffFixture::(1280x720, 8UC4)|285.337|59.708|4.78|
|Absdiff::OCL_AbsDiffFixture::(1280x720, 32FC4)|490.395|491.118|1.00|
|Absdiff::OCL_AbsDiffFixture::(1920x1080, 8UC1)|158.827|33.462|4.75|
|Absdiff::OCL_AbsDiffFixture::(1920x1080, 32FC1)|273.503|273.668|1.00|
|Absdiff::OCL_AbsDiffFixture::(1920x1080, 8UC3)|484.175|100.520|4.82|
|Absdiff::OCL_AbsDiffFixture::(1920x1080, 32FC3)|828.758|829.689|1.00|
|Absdiff::OCL_AbsDiffFixture::(1920x1080, 8UC4)|648.592|137.195|4.73|
|Absdiff::OCL_AbsDiffFixture::(1920x1080, 32FC4)|1116.755|1109.587|1.01|
|Absdiff::OCL_AbsDiffFixture::(3840x2160, 8UC1)|648.715|134.875|4.81|
|Absdiff::OCL_AbsDiffFixture::(3840x2160, 32FC1)|1115.939|1113.818|1.00|
|Absdiff::OCL_AbsDiffFixture::(3840x2160, 8UC3)|1944.791|413.420|4.70|
|Absdiff::OCL_AbsDiffFixture::(3840x2160, 32FC3)|3354.193|3324.672|1.01|
|Absdiff::OCL_AbsDiffFixture::(3840x2160, 8UC4)|2594.585|553.486|4.69|
|Absdiff::OCL_AbsDiffFixture::(3840x2160, 32FC4)|4473.543|4438.453|1.01|
**Bitwise Operation**
Geometric mean (ms)
|Name of Test|opencv perf core Bit|opencv perf core Bit|opencv perf core Bit vs opencv perf core Bit (x-factor)|
|---|:-:|:-:|:-:|
|Bitwise_and::OCL_BitwiseAndFixture::(640x480, 8UC1)|22.542|4.971|4.53|
|Bitwise_and::OCL_BitwiseAndFixture::(640x480, 32FC1)|90.210|19.917|4.53|
|Bitwise_and::OCL_BitwiseAndFixture::(640x480, 8UC3)|68.429|15.037|4.55|
|Bitwise_and::OCL_BitwiseAndFixture::(640x480, 32FC3)|280.168|59.239|4.73|
|Bitwise_and::OCL_BitwiseAndFixture::(640x480, 8UC4)|90.565|19.735|4.59|
|Bitwise_and::OCL_BitwiseAndFixture::(640x480, 32FC4)|374.695|79.257|4.73|
|Bitwise_and::OCL_BitwiseAndFixture::(1280x720, 8UC1)|67.824|14.873|4.56|
|Bitwise_and::OCL_BitwiseAndFixture::(1280x720, 32FC1)|279.514|59.232|4.72|
|Bitwise_and::OCL_BitwiseAndFixture::(1280x720, 8UC3)|208.337|44.234|4.71|
|Bitwise_and::OCL_BitwiseAndFixture::(1280x720, 32FC3)|851.211|182.522|4.66|
|Bitwise_and::OCL_BitwiseAndFixture::(1280x720, 8UC4)|279.529|59.095|4.73|
|Bitwise_and::OCL_BitwiseAndFixture::(1280x720, 32FC4)|1132.065|244.877|4.62|
|Bitwise_and::OCL_BitwiseAndFixture::(1920x1080, 8UC1)|155.685|33.078|4.71|
|Bitwise_and::OCL_BitwiseAndFixture::(1920x1080, 32FC1)|635.253|137.482|4.62|
|Bitwise_and::OCL_BitwiseAndFixture::(1920x1080, 8UC3)|474.494|100.166|4.74|
|Bitwise_and::OCL_BitwiseAndFixture::(1920x1080, 32FC3)|1907.340|412.841|4.62|
|Bitwise_and::OCL_BitwiseAndFixture::(1920x1080, 8UC4)|635.538|134.544|4.72|
|Bitwise_and::OCL_BitwiseAndFixture::(1920x1080, 32FC4)|2552.666|556.397|4.59|
|Bitwise_and::OCL_BitwiseAndFixture::(3840x2160, 8UC1)|634.736|136.355|4.66|
|Bitwise_and::OCL_BitwiseAndFixture::(3840x2160, 32FC1)|2548.283|561.827|4.54|
|Bitwise_and::OCL_BitwiseAndFixture::(3840x2160, 8UC3)|1911.454|421.571|4.53|
|Bitwise_and::OCL_BitwiseAndFixture::(3840x2160, 32FC3)|7663.803|1677.289|4.57|
|Bitwise_and::OCL_BitwiseAndFixture::(3840x2160, 8UC4)|2543.983|562.780|4.52|
|Bitwise_and::OCL_BitwiseAndFixture::(3840x2160, 32FC4)|10211.693|2237.393|4.56|
|Bitwise_not::OCL_BitwiseNotFixture::(640x480, 8UC1)|22.341|4.811|4.64|
|Bitwise_not::OCL_BitwiseNotFixture::(640x480, 32FC1)|89.975|19.288|4.66|
|Bitwise_not::OCL_BitwiseNotFixture::(640x480, 8UC3)|67.237|14.643|4.59|
|Bitwise_not::OCL_BitwiseNotFixture::(640x480, 32FC3)|276.324|58.609|4.71|
|Bitwise_not::OCL_BitwiseNotFixture::(640x480, 8UC4)|89.587|19.554|4.58|
|Bitwise_not::OCL_BitwiseNotFixture::(640x480, 32FC4)|370.986|77.136|4.81|
|Bitwise_not::OCL_BitwiseNotFixture::(1280x720, 8UC1)|67.227|14.541|4.62|
|Bitwise_not::OCL_BitwiseNotFixture::(1280x720, 32FC1)|276.357|58.076|4.76|
|Bitwise_not::OCL_BitwiseNotFixture::(1280x720, 8UC3)|206.752|43.376|4.77|
|Bitwise_not::OCL_BitwiseNotFixture::(1280x720, 32FC3)|841.638|177.787|4.73|
|Bitwise_not::OCL_BitwiseNotFixture::(1280x720, 8UC4)|276.773|57.784|4.79|
|Bitwise_not::OCL_BitwiseNotFixture::(1280x720, 32FC4)|1127.740|237.472|4.75|
|Bitwise_not::OCL_BitwiseNotFixture::(1920x1080, 8UC1)|153.808|32.531|4.73|
|Bitwise_not::OCL_BitwiseNotFixture::(1920x1080, 32FC1)|627.765|129.990|4.83|
|Bitwise_not::OCL_BitwiseNotFixture::(1920x1080, 8UC3)|469.799|98.249|4.78|
|Bitwise_not::OCL_BitwiseNotFixture::(1920x1080, 32FC3)|1893.591|403.694|4.69|
|Bitwise_not::OCL_BitwiseNotFixture::(1920x1080, 8UC4)|627.724|129.962|4.83|
|Bitwise_not::OCL_BitwiseNotFixture::(1920x1080, 32FC4)|2529.967|540.744|4.68|
|Bitwise_not::OCL_BitwiseNotFixture::(3840x2160, 8UC1)|628.089|130.277|4.82|
|Bitwise_not::OCL_BitwiseNotFixture::(3840x2160, 32FC1)|2521.817|540.146|4.67|
|Bitwise_not::OCL_BitwiseNotFixture::(3840x2160, 8UC3)|1905.004|404.704|4.71|
|Bitwise_not::OCL_BitwiseNotFixture::(3840x2160, 32FC3)|7567.971|1627.898|4.65|
|Bitwise_not::OCL_BitwiseNotFixture::(3840x2160, 8UC4)|2531.476|540.181|4.69|
|Bitwise_not::OCL_BitwiseNotFixture::(3840x2160, 32FC4)|10075.594|2181.654|4.62|
|Bitwise_or::OCL_BitwiseOrFixture::(640x480, 8UC1)|22.566|5.076|4.45|
|Bitwise_or::OCL_BitwiseOrFixture::(640x480, 32FC1)|90.391|19.928|4.54|
|Bitwise_or::OCL_BitwiseOrFixture::(640x480, 8UC3)|67.758|14.740|4.60|
|Bitwise_or::OCL_BitwiseOrFixture::(640x480, 32FC3)|279.253|59.844|4.67|
|Bitwise_or::OCL_BitwiseOrFixture::(640x480, 8UC4)|90.296|19.802|4.56|
|Bitwise_or::OCL_BitwiseOrFixture::(640x480, 32FC4)|373.972|79.815|4.69|
|Bitwise_or::OCL_BitwiseOrFixture::(1280x720, 8UC1)|67.815|14.865|4.56|
|Bitwise_or::OCL_BitwiseOrFixture::(1280x720, 32FC1)|279.398|60.054|4.65|
|Bitwise_or::OCL_BitwiseOrFixture::(1280x720, 8UC3)|208.643|45.043|4.63|
|Bitwise_or::OCL_BitwiseOrFixture::(1280x720, 32FC3)|850.042|180.985|4.70|
|Bitwise_or::OCL_BitwiseOrFixture::(1280x720, 8UC4)|279.363|60.385|4.63|
|Bitwise_or::OCL_BitwiseOrFixture::(1280x720, 32FC4)|1134.858|243.062|4.67|
|Bitwise_or::OCL_BitwiseOrFixture::(1920x1080, 8UC1)|155.212|33.155|4.68|
|Bitwise_or::OCL_BitwiseOrFixture::(1920x1080, 32FC1)|634.985|134.911|4.71|
|Bitwise_or::OCL_BitwiseOrFixture::(1920x1080, 8UC3)|474.648|100.407|4.73|
|Bitwise_or::OCL_BitwiseOrFixture::(1920x1080, 32FC3)|1912.049|414.184|4.62|
|Bitwise_or::OCL_BitwiseOrFixture::(1920x1080, 8UC4)|635.252|132.587|4.79|
|Bitwise_or::OCL_BitwiseOrFixture::(1920x1080, 32FC4)|2544.471|560.737|4.54|
|Bitwise_or::OCL_BitwiseOrFixture::(3840x2160, 8UC1)|634.574|134.966|4.70|
|Bitwise_or::OCL_BitwiseOrFixture::(3840x2160, 32FC1)|2545.129|561.498|4.53|
|Bitwise_or::OCL_BitwiseOrFixture::(3840x2160, 8UC3)|1910.900|419.365|4.56|
|Bitwise_or::OCL_BitwiseOrFixture::(3840x2160, 32FC3)|7662.603|1685.812|4.55|
|Bitwise_or::OCL_BitwiseOrFixture::(3840x2160, 8UC4)|2548.971|560.787|4.55|
|Bitwise_or::OCL_BitwiseOrFixture::(3840x2160, 32FC4)|10201.407|2237.552|4.56|
|Bitwise_xor::OCL_BitwiseXorFixture::(640x480, 8UC1)|22.718|4.961|4.58|
|Bitwise_xor::OCL_BitwiseXorFixture::(640x480, 32FC1)|91.496|19.831|4.61|
|Bitwise_xor::OCL_BitwiseXorFixture::(640x480, 8UC3)|67.910|15.151|4.48|
|Bitwise_xor::OCL_BitwiseXorFixture::(640x480, 32FC3)|279.612|59.792|4.68|
|Bitwise_xor::OCL_BitwiseXorFixture::(640x480, 8UC4)|91.073|19.853|4.59|
|Bitwise_xor::OCL_BitwiseXorFixture::(640x480, 32FC4)|374.641|79.155|4.73|
|Bitwise_xor::OCL_BitwiseXorFixture::(1280x720, 8UC1)|67.704|15.008|4.51|
|Bitwise_xor::OCL_BitwiseXorFixture::(1280x720, 32FC1)|279.229|60.088|4.65|
|Bitwise_xor::OCL_BitwiseXorFixture::(1280x720, 8UC3)|208.156|44.426|4.69|
|Bitwise_xor::OCL_BitwiseXorFixture::(1280x720, 32FC3)|849.501|180.848|4.70|
|Bitwise_xor::OCL_BitwiseXorFixture::(1280x720, 8UC4)|279.642|59.728|4.68|
|Bitwise_xor::OCL_BitwiseXorFixture::(1280x720, 32FC4)|1129.826|242.880|4.65|
|Bitwise_xor::OCL_BitwiseXorFixture::(1920x1080, 8UC1)|155.585|33.354|4.66|
|Bitwise_xor::OCL_BitwiseXorFixture::(1920x1080, 32FC1)|634.090|134.995|4.70|
|Bitwise_xor::OCL_BitwiseXorFixture::(1920x1080, 8UC3)|474.931|99.598|4.77|
|Bitwise_xor::OCL_BitwiseXorFixture::(1920x1080, 32FC3)|1910.519|413.138|4.62|
|Bitwise_xor::OCL_BitwiseXorFixture::(1920x1080, 8UC4)|635.026|135.155|4.70|
|Bitwise_xor::OCL_BitwiseXorFixture::(1920x1080, 32FC4)|2560.167|560.838|4.56|
|Bitwise_xor::OCL_BitwiseXorFixture::(3840x2160, 8UC1)|634.893|134.883|4.71|
|Bitwise_xor::OCL_BitwiseXorFixture::(3840x2160, 32FC1)|2548.166|560.831|4.54|
|Bitwise_xor::OCL_BitwiseXorFixture::(3840x2160, 8UC3)|1911.392|419.816|4.55|
|Bitwise_xor::OCL_BitwiseXorFixture::(3840x2160, 32FC3)|7646.634|1677.988|4.56|
|Bitwise_xor::OCL_BitwiseXorFixture::(3840x2160, 8UC4)|2560.637|560.805|4.57|
|Bitwise_xor::OCL_BitwiseXorFixture::(3840x2160, 32FC4)|10227.044|2249.458|4.55|
### 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 component disable flag to android build #25190
Adding --disable flag to android sdk build script. The flag allows to exclude components from build by concatting -DWITH_XXX cmake flag to the build command. Example : --disable OPENEXR (uppercase).
- [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 compatibility with latest (3.1.54) emsdk version #25084
### 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
### Details
I was following [this tutorial](https://docs.opencv.org/4.9.0/d4/da1/tutorial_js_setup.html) for building opencv with wasm target. The tutorial mentions that the last verified version of emscripten that is tested with opencv is 2.0.10, but I was curious if I could get it to work with more recent versions. I've run into a few issues with the latest version, for which fixes are included in this PR. I've found a few issues that have the same problems I encountered:
- https://github.com/opencv/opencv/issues/24620
- https://github.com/opencv/opencv/issues/20313
- https://stackoverflow.com/questions/77469603/custom-opencv-js-wasm-using-cv-matfromarray-results-in-cv-mat-is-not-a-co
- https://github.com/emscripten-core/emscripten/issues/14803
- https://github.com/opencv/opencv/issues/24572
- https://github.com/opencv/opencv/issues/19493#issuecomment-857167996
I used the docker image for building and comparing results with different emsdk versions. I tested by building with `--build_wasm` and `--build-test` flags and ran the tests in the browser. I addressed the following issues with newer versions of emscripten:
- In newer versions `EMSCRIPTEN` environemnt variable was stopped being set. I added support for deriving location based on the `EMSDK` environment variable, as suggested [here](https://github.com/emscripten-core/emscripten/issues/14803)
- In newer versions emcmake started passing `-DCMAKE...` arguments, however the opencv python script didn't know how to handle them. I added processing to the args that will forward all arguments to `cmake` that start with `-D`. I opted for this in hopes of being more futureproof, but another approach could be just ignoreing them, or explicitly forwarding them instead of matching anything starting with `-D`. These approches were suggested [here](https://github.com/opencv/opencv/issues/19493#issuecomment-855529448)
- With [version 3.1.31](https://github.com/emscripten-core/emscripten/blob/main/ChangeLog.md#3131---012623) some previously exported functions stopped being automatically exported. Because of this, `_free` and `_malloc` were no longer available and had to be explicitly exported because of breaking tests.
- With [version 3.1.42](https://github.com/emscripten-core/emscripten/compare/3.1.41...3.1.42#diff-e505aa80b2764c0197acfc9afd8179b3600f0ab5dd00ff77db01879a84515cdbL3875) the `post-js` code doesn't receive the module named as `EXPORT_NAME` anymore, but only as `moduleArg`/`Module`. This broke existing code in `helpers.js`, which was referencing exported functions through `cv.Mat`, etc. I changed all of these references to use `Module.Mat`, etc. If it is preferred, alternatively the `cv` variable could be reintroduced in `helper.js` as suggested [here](https://github.com/opencv/opencv/issues/24620)
With the above changes in place, I can successfully build and run tests with the latest emscripten/emsdk docker image (also with 2.0.10 and most of the other older tags, except for a few that contain transient issues like [this](https://github.com/emscripten-core/emscripten/issues/17700)).
This is my first time contributing to opencv, so I hope I got everything correct in this PR, but please let me know if I should change anything!
Added kotlin classes to AAR #24884
The resulting maven repo doesn't have kotlin-plugin dependency, and it works fine out of the box: Android Kotlin projects already have kotlin-plugin dependency, Android Java projects ignore kotlin classes.
Details on KGP versions: https://kotlinlang.org/docs/gradle-configure-project.html#apply-the-plugin
### 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
Co-authored-by: Alexander Lyulkov <alexander.lyulkov@opencv.ai>
Co-authored-by: Alexander Smorkalov <alexander.smorkalov@xperience.ai>
Added offline option for Android builds #24956
### 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
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
- intrinsics implementation (071) reworked to use modern RVV intrinsics syntax
- cmake toolchain file (071) now allows selecting from predefined configurations
Co-authored-by: Fang Sun <fangsun@linux.alibaba.com>
