libjpeg upgrade to version 9f #25092
Upgrade libjpeg dependency from version 9d to 9f.
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The parallel code works out how many CPUs are on the system by checking
the quota it has been assigned in the Linux cgroup. The existing code
works under cgroups v1 but the file structure changed in cgroups v2.
From [1]:
"cpu.cfs_quota_us" and "cpu.cfs_period_us" are replaced by "cpu.max"
which contains both quota and period.
This commit add support to parallel so it will read from the cgroups v2
location. v1 support is still retained.
Resolves#25284
[1] 0d5936344f
Speed up adaptive threshold in findChessboardCorners #25177
### Pull Request Readiness Checklist
If `block_size` hasn't been changed between iterations for same `k`, then all `adaptiveThreshold` arguments will be same and we can reuse result from previous iteration.
I tested this PR with benchmark
```
python3 objdetect_benchmark.py --configuration=generate_run --board_x=7 --path=res_chessboard --synthetic_object=chessboard
```
PR speed up chessboards detection by `7.5/17%` without any changes in detected chessboards number:
```
cell_img_size = 100 (default)
before
category detected chessboard total detected chessboard total chessboard average detected error chessboard
all 0.904167 13020 14400 0.600512
Total detected time: 107.27875600000003 sec
after
category detected chessboard total detected chessboard total chessboard average detected error chessboard
all 0.904167 13020 14400 0.600512
Total detected time: 99.0223499999999 sec
----------------------------------------------------------------------------------------------------------------------------------------------
cell_img_size = 10
before
category detected chessboard total detected chessboard total chessboard average detected error chessboard
all 0.539792 7773 14400 4.209964
Total detected time: 2.989205999999999 sec
after
category detected chessboard total detected chessboard total chessboard average detected error chessboard
all 0.539792 7773 14400 4.209964
Total detected time: 2.4802350000000013 sec
```
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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Added in-place support for cartToPolar and polarToCart #24893
- a fused hal::cartToPolar[32|64]f() is used instead of sequential hal::magnitude[32|64]f/hal::fastAtan[32|64]f
- ipp_polarToCart is skipped for in-place processing (it seems not to support it correctly)
relates to #24891
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0D test for split layer #25205
This PR introduces parametrized `0/1D` input support test for `Split` layer.
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dnn: avoid const layer forwarding in layer norm layer and attention layer #25238
While profiling ViTs with dnn, I found `ConstLayer` can take a proportion of the inference time, which is weird. This comes from the data copy during the inference of `ConstLayer`. There is a chance that we can improve the efficiency of data copying but the easiest and most convenient way is to avoid `ConstLayer`. This PR change the way how we handle constants in layer normalization layer and attention layer, which is storing in the layer blobs instead of making constant layers for them.
Checklists:
- [x] Backend compatibility in layer normalization layer.
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doc: add note on handling of spaces in CommandLineParser #25237
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Added note that this class will not work properly if tabs and other whitespace characters are included in the key.
The support of whitespace characters by istringstream, etc. is on hold because the future of this class is not clear compared to implementations in Python and other languages.
Allowed int types in Tile and Reduce layers #25218
Allowed any Mat type in Tile layer.
Allowed int64 type in Reduce layer.
ONNX tests with int32 and int64 inputs will be added later in a separate PR
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dnn (CANN): Fix incorrect shape of 1d bias in Gemm #25166
Gemm layer was refactored some time ago. Users found that the mobilenet example in https://github.com/opencv/opencv/wiki/Huawei-CANN-Backend does not work because of incorrect shape set for 1d bias in Gemm. This PR resolves this issue.
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Release convolution weightsMat after usage #25181
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related (but not resolved): https://github.com/opencv/opencv/issues/24134
Minor memory footprint improvement. Also, adds a test for VmHWM.
RAM top memory usage (-230MB)
| YOLOv3 (237MB file) | 4.x | PR |
|---------------------|---------|---------|
| no winograd | 808 MB | 581 MB |
| winograd | 1985 MB | 1750 MB |
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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Normaly, we sets IMWRITE_* flags for imwrite() params.
But imgcodecs expects to use some TIFFTAG_* directory.
This patch introduce IMWRITE_TIFF_ROWSPERSTRIP and
IMWRITE_TIFF_PREDICTOR instead of TIFFTAG_*.
* libtiff upgrade to version 4.6.0
* fix tiffvers.h cmake generation
* temp: force build 3rd party deps from source
* remove libport.h and spintf.c
* cmake fixes
* don't use tiff_dummy_namespace on windows
* introduce numeric_types namespace alias
* include cstdint
* uint16_t is not a numeric_types type
* fix uint16 and uint32 type defs
* use standard c++ types
* remove unused files
* remove more unused files
* revert build 3rd party code from source
---------
Co-authored-by: Misha Klatis <misha.klatis@autodesk.com>
G-API: A quick value-initialization support GMat #25055
This PR enables `GMat` objects to be value-initialized in the same way as it was done for `GScalar`s (and, possibly, other types).
- Added some helper methods in backends to distinguish if a certain G-type value initialization is supported or not;
- Added tests, including negative.
Where it is needed:
- Further extension of the OVCV backend (#24379 - will be refreshed soon);
- Further experiments with DNN module;
- Further experiments with "G-API behind UMat" sort of aggregation.
In the current form, PR can be reviewed & merged (@TolyaTalamanov please have a look)
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calib3d: doc: remove C API link (For 4.x) #25141
Related to #25140 (for 4.x)
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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
C-API cleanup: apps, imgproc_c and some constants #25075
Merge with https://github.com/opencv/opencv_contrib/pull/3642
* Removed obsolete apps - traincascade and createsamples (please use older OpenCV versions if you need them). These apps relied heavily on C-API
* removed all mentions of imgproc C-API headers (imgproc_c.h, types_c.h) - they were empty, included core C-API headers
* replaced usage of several C constants with C++ ones (error codes, norm modes, RNG modes, PCA modes, ...) - most part of this PR (split into two parts - all modules and calib+3d - for easier backporting)
* removed imgproc C-API headers (as separate commit, so that other changes could be backported to 4.x)
Most of these changes can be backported to 4.x.
Fixed ReduceMean layer behaviour #25120
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a93c31e3c9/onnxruntime/core/providers/cpu/reduction/reduction_ops.cc (L433-L443)
Use std::priority_queue in inpaint function for performance improvement #25122
In `cv::inpaint` implementation, it uses a priority queue with O(n) time linear search. For large images it is very slow.
I replaced it with C++'s standard library `std::priority_queue`, that uses O(log(n)) algorithm.
In my use case, it is x10 faster than the original.
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Added int32, int64 support and type inference to dnn #24411
**Added a type inference to dnn similar to the shape inference, added int32 and int64 support.**
- Added getTypes method for layers that calculates layer outputs types and internals types from inputs types (Similar to getMemoryShapes). By default outputs and internals types = input[0] type
- Added type inference pipeline similar to shape inference pipeline. LayersShapes struct (that is used in shape inference pipeline) now contains both shapes and types
- All layers output blobs are now allocated using the calculated types from the type inference.
- Inputs and constants with int32 and int64 types are not automatically converted into float32 now.
- Added int32 and int64 support for all the layers with indexing and for all the layers required in tests.
Added int32 and int64 support for CUDA:
- Added host<->device data moving for int32 and int64
- Added int32 and int64 support for several layers (just slightly modified CUDA C++ templates)
Passed all the accuracy tests on CPU, OCL, OCL_FP16, CUDA, CUDA_FP16. (except RAFT model)
**CURRENT PROBLEMS**:
- ONNX parser always converts int64 constants and layers attributes to int32, so some models with int64 constants doesn't work (e.g. RAFT). The solution is to disable int64->int32 conversion and fix attributes reading in a lot of ONNX layers parsers (https://github.com/opencv/opencv/issues/25102)
- I didn't add type inference and int support to VULCAN, so it doesn't work at all now.
- Some layers don't support int yet, so some unknown models may not work.
**CURRENT WORKAROUNDS**:
- CPU arg_layer indides are implemented in int32 followed by a int32->int64 conversion (the master branch has the same workaround with int32->float conversion)
- CPU and OCL pooling_layer indices are implemented in float followed by a float->int64 conversion
- CPU gather_layer indices are implemented in int32, so int64 indices are converted to int32 (the master branch has the same workaround with float->int32 conversion)
**DISABLED TESTS**:
- RAFT model
**REMOVED TESTS**:
- Greater_input_dtype_int64 (because it doesn't fit ONNX rules, the whole test is just comparing float tensor with int constant)
**TODO IN NEXT PULL REQUESTS**:
- Add int64 support for ONNX parser
- Add int support for more layers
- Add int support for OCL (currently int layers just run on CPU)
- Add int tests
- Add int support for other backends
[GSoC] Update octree methods and create frames for PCC #23985
## PR for GSoC Point Cloud Compression
[Issue for GSoC 2023](https://github.com/opencv/opencv/issues/23624)
* We are **updating the Octree method create() by using OctreeKey**: Through voxelization, directly calculate the leaf nodes that the point cloud belongs to, and omit the judgment whether the point cloud is in the range when inserted. The index of the child node is calculated by bit operation.
* We are also **introducing a new header file pcc.h (Point Cloud Compression) with API framework**.
* We added tests for restoring point clouds from an octree.
* Currently, the features related to octree creation and point cloud compression are part of the internal API, which means they are not directly accessible to users. However, our plan for the future is to **include only the 'PointCloudCompression' class in the 'opencv2/3d.hpp' header file**. This will provide an interface for utilizing the point cloud compression functionality.
The previous PR of this was closed due to repo name conflicts, therefore we resubmitted in this PR.
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First proposal of cv::remap with relative displacement field (#24603) #24621
Implements #24603
Currently, `remap()` is applied as `dst(x, y) <- src(mapX(x, y), mapY(x, y))` It means that the maps must be filled with absolute coordinates.
However, if one wants to remap something according to a displacement field ("warp"), the operation should be `dst(x, y) <- src(x+displacementX(x, y), y+displacementY(x, y))`
It is trivial to build a mapping from a displacement field, but it is an undesirable overhead for CPU and memory.
This PR implements the feature as an experimental option, through the optional flag WARP_RELATIVE_MAP than can be ORed to the interpolation mode.
Since the xy maps might be const, there is no attempt to add the coordinate offset to those maps, and everything is postponed on-the-fly to the very last coordinate computation before fetching `src`. Interestingly, this let `cv::convertMaps()` unchanged since the fractional part of interpolation does not care of the integer coordinate offset.
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dnn: try improving performance of Attention layer #25076
Checklist:
- [x] Use `Mat` over `Mat::zeros` for temporary buffer in forward
- [x] Use layer internal buffer over temporary Mat buffer
- [x] Try a single fastGemmBatch on the Q/K/V calculation
Performance:
Performance test case is `Layer_Attention.VisionTransformer/0`, which has input of shape {1, 197, 768}, weight of shape {768, 2304} and bias {2304}.
Data is in millisecond.
| | macOS 14.2.1, Apple M1 | Ubuntu 22.04.2, Intel i7 12700K |
| - | - | - |
| Current | 10.96 | 1.58 |
| w/ Mat | 6.27 | 1.41 |
| w/ Internals | 5.87 | 1.38 |
| w/ fastGemmBatch | 6.12 | 2.14 |
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Fix issue #25077#25100
Fixes https://github.com/opencv/opencv/issues/25077
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Fixes#25056 : Optimising postProcess(const std::vector<Mat>& output_blobs) #25091
Like mentioned in the issue #25056 , I think checking the condition with `scoreThreshold` and then assigning the bounding boxes can optimize the function pretty well. By doing this, we prevent allocating boxes to faces with scores below the threshold. It also reduces the amount of data that needs to be processed during the subsequent NMS step. Builds and passed locally.
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Co-authored-by: Dhanwanth1803 <dhanwanthvarala@gmail,com>
Handle degenerate cases in RQDecomp3x3 #25050
The point of the Givens rotations here is to iteratively set the lower left matrix entries to zero. If an element is zero already, we don't need to do anything. This resolves#24330.
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RegisterCameras function for heterogenious cameras pair #25061
Credits to Linfei Pan
Extracted from https://github.com/opencv/opencv/pull/24052
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---------
Co-authored-by: lpanaf <linpan@student.ethz.ch>
Move Aruco tutorials and samples to main repo #23018
merge with https://github.com/opencv/opencv_contrib/pull/3401
merge with https://github.com/opencv/opencv_extra/pull/1143
### Pull Request Readiness Checklist
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---------
Co-authored-by: AleksandrPanov <alexander.panov@xperience.ai>
Co-authored-by: Alexander Smorkalov <alexander.smorkalov@xperience.ai>
Compensate edge length in ChessBoardDetector::generateQuads (attempt 2) #25090
### Pull Request Readiness Checklist
New attempt for #24833, which was reverted as #25036.
Locally I fixed `Calib3d_StereoCalibrate_CPP.regression` test by corners refinement using `cornerSubPix` function
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Add compatibility with latest (3.1.54) emsdk version #25084
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- [ ] The PR is proposed to the proper branch
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Patch to opencv_extra has the same branch name.
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### 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!
G-API: Make test execution lighter (first attempt) #25060
### Background
G-API tests look running longer than tests for the rest of modules (e.g., 5m), and the analysis show that there's several outliers in G-API test suite which take a lot of time but don't improve the testing quality much:
In this PR I will cut the execution time to something reasonable.
### Contents
- Marked some outliers as `verylong`:
- OneVPL barrier test - pure brute force
- Stateful test in stream - in fact BG Sub accuracy test clone
- Restructured parameters instantiation in Streaming tests to generate less test configurations (54 -> 36)
### Pull Request Readiness Checklist
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Fix very slow compilation of five-point algorithm on some platforms (e.g. Qualcomm) #25064
Thanks to our big friend and long-term contributor for the patch!
### Pull Request Readiness Checklist
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Primitive 1D Tests #24977
This PR is designed to add tests for 1D inputs for layer, which is required after introducing 1d support in 5.x. Currently tests are written for following layers:
- [x] `Add`, `Sub`
- [x] `Product`, `Div`
- [x] `Min`, `Max`
- [x] `Argmin`, `Argmax`
- [x] `Gather`
This list is to be extended for more layer such `gemm`, `conv` etc.
### Pull Request Readiness Checklist
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G-API: Lower supported IE backend version #25054
Related to https://github.com/opencv/opencv/issues/25053
### Pull Request Readiness Checklist
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Replace legacy __ARM_NEON__ by __ARM_NEON #25024
Even ACLE 1.1 referes to __ARM_NEON
https://developer.arm.com/documentation/ihi0053/b/?lang=en
### Pull Request Readiness Checklist
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Triangle rasterization function #24459#24065 reopened since the previous one was automatically closed after rebase
Connected PR with ground truth data: [#1113@extra](https://github.com/opencv/opencv_extra/pull/1113)
### Pull Request Readiness Checklist
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dnn cleanup: On-fly-quantization removal #2498
On-fly-quantization is first introduced via https://github.com/opencv/opencv/pull/20228.
We decided to remove it but keep int8 layers implementation because on-fly-quantization
is less practical given the fact that there has been so many dedicated tools for model
quantization.
### Pull Request Readiness Checklist
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Fixes#24974 support HardSwishInt8 #24985
As given very clearly in the issue #24974 I made the required 2 changes to implement HardSwish Layer in INT8. Requesting comments.
resolves https://github.com/opencv/opencv/issues/24974
- [X] I agree to contribute to the project under Apache 2 License.
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Co-authored-by: Dhanwanth1803 <dhanwanthvarala@gmail,com>
solvePnP implementation for Fisheye camera model #25028
Credits to Linfei Pan
Extracted from https://github.com/opencv/opencv/pull/24052
**Warning:** The patch changes Obj-C generator behaviour and adds "fisheye_" prefix for all ObjC functions from namespace.
### Pull Request Readiness Checklist
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Co-authored-by: lpanaf <linpan@student.ethz.ch>
Co-authored-by: Vadim Levin <vadim.levin@xperience.ai>
Fix barcode detectAndDecode #25035
The method `detectAndDecode()` in the `BarcodeDetector` class doesn't return the barcode corners.
This PR fixes the and add test for `detectAndDecode`.
### Pull Request Readiness Checklist
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Remove bypass for ABI check in warpPointBackward #24989
### Pull Request Readiness Checklist
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Patch to opencv_extra has the same branch name.~~
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### What?
In my [PR](d31b6c3480) regarding `warpPointBackward`, I was asked by alalek to [add a bypass](https://github.com/opencv/opencv/pull/18607/files#r508423486) to python wrapper class, presumably to ship the changes in the newest patch release (4.5.1).
The bypass was not removed in 4.6.0 release, so please remove it in either 4.10.0 or 4.11.0. Thanks!
Co-authored-by: Martin Stefanak <martin.stefanak@codasip.com>
Co-authored-by: Alexander Smorkalov <alexander.smorkalov@xperience.ai>
Fix qrcode bugs #25026
This PR fixes#22892, #24011 and #24450 and adds regression tests using the images provided. I've also verified with the [benchmark](https://github.com/opencv/opencv_benchmarks/tree/develop/python_benchmarks/qr_codes) that this doesn't break anything there.
resolves#22892resolves#24011resolves#24450
Replaces #23802
Requires extra: https://github.com/opencv/opencv_extra/pull/1148
### Pull Request Readiness Checklist
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PLY mesh support #24961
**Warning:** The PR changes exising API.
Fixes#24960
Connected PR: [#1145@extra](https://github.com/opencv/opencv_extra/pull/1145)
### Changes
* Adds faces loading from and saving to PLY files
* Fixes incorrect PLY loading (see issue)
* Adds per-vertex color loading / saving
### Pull Request Readiness Checklist
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bug fix infinite loop #24987Fixes#24967
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Bugfix to #24967
* started adding support for new types (16f, 16bf, 32u, 64u, 64s) to arithmetic functions
* fixed several tests; refactored and extended sum(), extended inRange().
* extended countNonZero(), mean(), meanStdDev(), minMaxIdx(), norm() and sum() to support new types (F16, BF16, U32, U64, S64)
* put missing CV_DEPTH_MAX to some function dispatcher tables
* extended findnonzero, hasnonzero with the new types support
* extended mixChannels() to support new types
* minor fix
* fixed a few compile errors on Linux and a few failures in core tests
* fixed a few more warnings and test failures
* trying to fix the remaining warnings and test failures. The test `MulTestGPU.MathOpTest` was disabled - not clear whether to set tolerance - it's not bit-exact operation, as possibly assumed by the test, due to the use of scale and possibly limited accuracy of the intermediate floating-point calculations.
* found that in the current snapshot G-API produces incorrect results in Mul, Div and AddWeighted (at least when using OpenCL on Windows x64 or MacOS x64). Disabled the respective tests.
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.
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Fix bug in ChessBoardDetector::findQuadNeighbors #24779
### Pull Request Readiness Checklist
`corners` and `neighbors` indices means not filling order, but relative position. So, for example if `quad->count = 2`, it doesn't mean that `quad->neighbors[0]` and `quad->neighbors[1]` are filled. And we should should iterate over all four `neighbors`.
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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QR codes Structured Append decoding mode #24548
### Pull Request Readiness Checklist
resolves https://github.com/opencv/opencv/issues/23245
Merge after https://github.com/opencv/opencv/pull/24299
Current proposal is to use `detectAndDecodeMulti` or `decodeMulti` for structured append mode decoding. 0-th QR code in a sequence gets a full message while the rest of codes will correspond to empty strings.
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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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
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G-API: Implement concurrent executor #24845
## Overview
This PR introduces the new G-API executor called `GThreadedExecutor` which can be selected when the `GComputation` is compiled in `serial` mode (a.k.a `GComputation::compile(...)`)
### ThreadPool
`cv::gapi::own::ThreadPool` has been introduced in order to abstract usage of threads in `GThreadedExecutor`.
`ThreadPool` is implemented by using `own::concurrent_bounded_queue`
`ThreadPool` has only as single method `schedule` that will push task into the queue for the further execution.
The **important** notice is that if `Task` executed in `ThreadPool` throws exception - this is `UB`.
### GThreadedExecutor
The `GThreadedExecutor` is mostly copy-paste of `GExecutor`, should we extend `GExecutor` instead?
#### Implementation details
1. Build the dependency graph for `Island` nodes.
2. Store the tasks that don't have dependencies into separate `vector` in order to run them first.
3. at the `GThreadedExecutor::run()` schedule the tasks that don't have dependencies that will schedule their dependents and wait for the completion.
### Pull Request Readiness Checklist
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Vulkan backend for NaryEltwiseLayer in DNN module #24768
We improve Vulkan backend for ``NaryEltwiseLayer`` in DNN module by:
- add a basic framework for Vulkan backend in ``NaryEltwiseLayer``
- add a compute shader for binary forwarding (an imitation of what has been done in native OpenCV backend including broadcasting and eltwise-operation)
- typo fixed:
- Wrong info output in ``context.cpp``
Currently, our implementation (or all layers supporting Vulkan backend) runs pretty slow on discrete GPUs basically due to IO cost in function ``copyToHost``, and we are going to fix that by
- find out the best ``VkMemoryProperty`` for various discrete GPUs
- prevent ``copyToHost`` in middle layers during forwarding, (i.e keep data in GPU memory)
### Pull Request Readiness Checklist
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Co-authored-by: IskXCr <IskXCr@outlook.com>
Handle warnings in loongson-related code #24925
See https://github.com/fengyuentau/opencv/actions/runs/7665377694/job/20891162958#step:14:16
Warnings needs to be handled before we add the loongson server to our CI.
### Pull Request Readiness Checklist
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core(OpenCL): optimize convertTo() with CV_16F (convertFp16() replacement) #24918
relates #24909
relates #24917
relates #24892
Performance changes:
- [x] 12700K (1 thread) + Intel iGPU
|Name of Test|noOCL|convertFp16|convertTo BASE|convertTo PATCH|
|---|:-:|:-:|:-:|:-:|
|ConvertFP16FP32MatMat::OCL_Core|3.130|3.152|3.127|3.136|
|ConvertFP16FP32MatUMat::OCL_Core|3.030|3.996|3.007|2.671|
|ConvertFP16FP32UMatMat::OCL_Core|3.010|3.101|3.056|2.854|
|ConvertFP16FP32UMatUMat::OCL_Core|3.016|3.298|2.072|2.061|
|ConvertFP32FP16MatMat::OCL_Core|2.697|2.652|2.723|2.721|
|ConvertFP32FP16MatUMat::OCL_Core|2.752|4.268|2.662|2.947|
|ConvertFP32FP16UMatMat::OCL_Core|2.706|2.601|2.603|2.528|
|ConvertFP32FP16UMatUMat::OCL_Core|2.704|3.215|1.999|1.988|
Patched version is not worse than convertFp16 and convertTo baseline (except MatUMat 32->16, baseline uses CPU code+dst buffer map).
There are still gaps against noOpenCL(CPU only) mode due to T-API implementation issues (unnecessary synchronization).
- [x] 12700K + AMD dGPU
|Name of Test|noOCL|convertFp16 dGPU|convertTo BASE dGPU|convertTo PATCH dGPU|
|---|:-:|:-:|:-:|:-:|
|ConvertFP16FP32MatMat::OCL_Core|3.130|3.133|3.172|3.087|
|ConvertFP16FP32MatUMat::OCL_Core|3.030|1.713|9.559|1.729|
|ConvertFP16FP32UMatMat::OCL_Core|3.010|6.515|6.309|4.452|
|ConvertFP16FP32UMatUMat::OCL_Core|3.016|0.242|23.597|0.170|
|ConvertFP32FP16MatMat::OCL_Core|2.697|2.641|2.713|2.689|
|ConvertFP32FP16MatUMat::OCL_Core|2.752|4.076|6.483|4.191|
|ConvertFP32FP16UMatMat::OCL_Core|2.706|9.042|16.481|1.834|
|ConvertFP32FP16UMatUMat::OCL_Core|2.704|0.229|15.730|0.176|
convertTo-baseline can't compile OpenCL kernel for FP16 properly - FIXED.
dGPU has much more power, so results are x16-17 better than single cpu core.
Patched version is not worse than convertFp16 and convertTo baseline.
There are still gaps against noOpenCL(CPU only) mode due to T-API implementation issues (unnecessary synchronization) and required memory transfers.
Co-authored-by: Alexander Alekhin <alexander.a.alekhin@gmail.com>
Removed all pre-C++11 code, workarounds, and branches #23736
This removes a bunch of pre-C++11 workrarounds that are no longer necessary as C++11 is now required.
It is a nice clean up and simplification.
* No longer unconditionally #include <array> in cvdef.h, include explicitly where needed
* Removed deprecated CV_NODISCARD, already unused in the codebase
* Removed some pre-C++11 workarounds, and simplified some backwards compat defines
* Removed CV_CXX_STD_ARRAY
* Removed CV_CXX_MOVE_SEMANTICS and CV_CXX_MOVE
* Removed all tests of CV_CXX11, now assume it's always true. This allowed removing a lot of dead code.
* Updated some documentation consequently.
* Removed all tests of CV_CXX11, now assume it's always true
* Fixed links.
---------
Co-authored-by: Maksim Shabunin <maksim.shabunin@gmail.com>
Co-authored-by: Alexander Smorkalov <alexander.smorkalov@xperience.ai>
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
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- 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>
Make \epsilon parameter accessible in VariationalRefinement #24852Resolves#24847
I believe this is necessary to expose \epsilon parameter.
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python: accept path-like objects wherever file names are expected #24773
Merry Christmas, all 🎄
Implements #15731
Support is enabled for all arguments named `filename` or `filepath` (case-insensitive), or annotated with `CV_WRAP_FILE_PATH`.
Support is based on `PyOS_FSPath`, which is available in Python 3.6+. When running on older Python versions the arguments must have a `str` value as before.
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dnn onnx: add group norm layer #24610
dnn onnx: add group norm layer
Todo:
- [x] speed up by multi-threading
- [x] add perf
- [x] add backend: OpenVINO
- [x] add backend: CUDA
- [x] add backend: OpenCL (no fp16)
- [ ] add backend: CANN
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Replace interactive batched Matrix Multiply. #24812
This PR replaces iterative batch matrix multiplication which `FastGemmBatch` in Einsum layer.
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dnn: no layer norm fusion if axes.back() is not the axis of last dimension #24808
Merge with https://github.com/opencv/opencv_extra/pull/1137
Resolves https://github.com/opencv/opencv/issues/24797
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dnn onnx: add mod #24765
Resolves https://github.com/opencv/opencv/issues/23174
TODO:
- [x] enable some conformance tests
- [x] add backends
- [x] CANN
- [x] OpenVINO
- [x] CUDA
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FreeBSD does not have the /proc file system. FreeBSD was added to the code path
for aarch64 before the use of the /proc file system with f7b4b750d8
but then /proc usage was added not long after with b3269b08a1
- 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
dnn onnx: support constaint inputs in einsum importer #24753
Merge with https://github.com/opencv/opencv_extra/pull/1132.
Resolves https://github.com/opencv/opencv/issues/24697
Credits to @LaurentBerger.
---
This is a workaround. I suggest to get input shapes and calculate the output shapes in `getMemoryShapes` so as to keep the best compatibility. It is not always robust getting shapes during the importer stage and we should avoid that as much as possible.
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Fix to convert float32 to int32/uint32 with rounding to nearest (ties to even). #24271
Fix https://github.com/opencv/opencv/issues/24163
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(carotene is BSD)
Fix mismatch and simplify code in ChessBoardDetector::findQuadNeighbors #24667
### Pull Request Readiness Checklist
Сode doesn't match comment.
If we want check `1:4` edges ratio and `edge_len` is squared edge length, then we should check
```
ediff > 15*edge_len
```
with constant `15`, not `32`, because
```
ediff > 15*edge_len2 <=> edge_len1 - edge_len2 > 15*edge_len2 <=> edge_len1 > 16*edge_len2 <=> 1:4 edges ratio
```
But for me it's better and simpler to directly check `edge_len1 > 16*edge_len2`
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Currently, if `PNG_FOUND`, cmake scripts will check include and parse
header while we can use `PNG_VERSION_STRING` conveniently. If
`BUILD_PNG`, parse version from `PNG_LIBPNG_VER_STRING` directly is more
convenient than parsing major, minor and patch and concatenate them.
The comment of png.h also supports this.
```
/* These should match the first 3 components of PNG_LIBPNG_VER_STRING: */
```
https://github.com/glennrp/libpng/blob/libpng16/png.h#L287
This patch also modifies `ocv_parse_header_version` macro to receive
another parameter to make it more general.
The reason why changing `PNG_VERSION` to `PNG_VERSION_STRING` is to be
consistent with cmake's FindPNG.
This patch removes `HAVE_LIBPNG_PNG_H` variable because `PNG_INCLUDE_DIR`
is where to find png.h, etc according to
https://cmake.org/cmake/help/latest/module/FindPNG.html.
This patch also removes `PNG_PNG_INCLUDE_DIR` variable which is an
advanced variable used in cmake's FindPNG and is not used in opencv.
Fixes#22747. Support [crop] configuration for DarkNet #24384
Request for comments. This is my first PR.
**Merge with extra**: https://github.com/opencv/opencv_extra/pull/1112
resolves https://github.com/opencv/opencv/issues/22747
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Try to enable Winograd by default in FP32 mode and disable it by default in FP16 mode #24709
Hopefully, it will resolve regressions since 4.8.1 (see also https://github.com/opencv/opencv/pull/24587)
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dnn: add attention layer #24476Resolves#24609
Merge with: https://github.com/opencv/opencv_extra/pull/1128.
Attention operator spec from onnxruntime: https://github.com/microsoft/onnxruntime/blob/v1.16.1/docs/ContribOperators.md#com.microsoft.Attention.
TODO:
- [x] benchmark (before this PR vs. with this PR vs. ORT).
- [x] Layer fusion: Take care Slice with end=INT64_MAX.
- [x] Layer fusion: match more potential attention (VIT) patterns.
- [x] Single-head attention is supported.
- [x] Test AttentionSubgraph fusion.
- [x] Add acc tests for VIT_B_32 and VitTrack
- [x] Add perf tests for VIT_B_32 and VitTrack
## Benchmarks
Platform: Macbook Air M1.
### Attention Subgraph
Input scale: [1, 197, 768].
| | mean (ms) | median (ms) | min (ms) |
| ---------------------- | --------- | ----------- | -------- |
| w/ Attention (this PR) | 3.75 | 3.68 | 3.22 |
| w/o Attention | 9.06 | 9.01 | 8.24 |
| ORT (python) | 4.32 | 2.63 | 2.50 |
### ViTs
All data in millisecond (ms).
| ViTs | With Attention | Without Attention | ORT |
| -------- | -------------- | ----------------- | ------ |
| vit_b_16 | 302.77 | 365.35 | 109.70 |
| vit_b_32 | 89.92 | 116.22 | 30.36 |
| vit_l_16 | 1593.32 | 1730.74 | 419.92 |
| vit_l_32 | 468.11 | 577.41 | 134.12 |
| VitTrack | 3.80 | 3.87 | 2.25 |
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Check Checkerboard Corners #24546
What I did was get you to pull out of findChessboardCorners cornres the whole part that "checks" and sorts the corners of the checkerboard if present.
The main reason for this is that findChessboardCorners is often very slow to find the corners and this depends in that the size the contrast etc of the checkerboards can be very different from each other and writing a function that works on all kinds of images is complicated.
So I find it very useful to have the ability to write your own code to process the image and then have a function that controls or orders the corners.
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Add experimental support for Apple VisionOS platform #24136
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This is dependent on cmake support for VisionOs which is currently in progress.
Creating PR now to test that there are no regressions in iOS and macOS builds
Add blobrecttoimage #24539
### Pull Request Readiness Checklist
resolves https://github.com/opencv/opencv/issues/14659
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dnn: refactor ONNX MatMul with fastGemm #24694
Done:
- [x] add backends
- [x] CUDA
- [x] OpenVINO
- [x] CANN
- [x] OpenCL
- [x] Vulkan
- [x] add perf tests
- [x] const B case
### Benchmark
Tests are done on M1. All data is in milliseconds (ms).
| Configuration | MatMul (Prepacked) | MatMul | InnerProduct |
| - | - | - | - |
| A=[12, 197, 197], B=[12, 197, 64], trans_a=0, trans_b=0 | **0.39** | 0.41 | 1.33 |
| A=[12, 197, 64], B=[12, 64, 197], trans_a=0, trans_b=0 | **0.42** | 0.42 | 1.17 |
| A=[12, 50, 64], B=[12, 64, 50], trans_a=0, trans_b=0 | **0.13** | 0.15 | 0.33 |
| A=[12, 50, 50], B=[12, 50, 64], trans_a=0, trans_b=0 | **0.11** | 0.13 | 0.22 |
| A=[16, 197, 197], B=[16, 197, 64], trans_a=0, trans_b=0 | **0.46** | 0.54 | 1.46 |
| A=[16, 197, 64], B=[16, 64, 197], trans_a=0, trans_b=0 | **0.46** | 0.95 | 1.74 |
| A=[16, 50, 64], B=[16, 64, 50], trans_a=0, trans_b=0 | **0.18** | 0.32 | 0.43 |
| A=[16, 50, 50], B=[16, 50, 64], trans_a=0, trans_b=0 | **0.15** | 0.25 | 0.25 |
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Add support for Orbbec Gemini2 and Gemini2 XL camera #24666
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Make default axis of softmax in onnx "-1" without opset option #24613
Try to solve problem: https://github.com/opencv/opencv/pull/24476#discussion_r1404821158
**ONNX**
`opset <= 11` use 1
`else` use -1
**TensorFlow**
`TF version = 2.x` use -1
`else` use 1
**Darknet, Caffe, Torch**
use 1 by definition
G-API: Support CoreML Execution Providers for ONNXRT Backend #24068
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G-API: Get input model layout from the IR if possible in OV 2.0 backend #24658
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Classify and extend convolution and depthwise performance tests #24547
This PR aims to:
1. Extend the test cases from models: `YOLOv5`, `YOLOv8`, `EfficientNet`, `YOLOX`, `YuNet`, `SFace`, `MPPalm`, `MPHand`, `MPPose`, `ViTTrack`, `PPOCRv3`, `CRNN`, `PPHumanSeg`. (371 new test cases are added)
2. Classify the existing convolution performance test to below cases
- CONV_1x1
- CONV_3x3_S1_D1 (winograd)
- CONV
- DEPTHWISE
3. Reduce unnecessary test cases by follow 3 rules (366 test cases are pruned):
(i). For all tests, except for pad and bias related parameters, all other parameters are the same. Only one case can be reserved.
(ii). When the only difference is the channel of input shape, and other parameters are the same. Only one case can be reserved in each range `[1, 3], [4, 7], [8, 15], [16, 31], [32, 63], [64, 127], [128, 255], [256, 511], [512, 1023], [1024, 2047], [2048, 4095]`
(iii). When the only difference is the width and height of input shape, and other parameters are the same. Only one case can be reserved in each range `[1, 31], [32, 63], [64, 95]... `
> **Reproduced**: 1. follow step in https://github.com/alalek/opencv/commit/dnn_dump_conv_kernels to dump all convolution cases from new models. (declared flops may not right, need to be checked manually) 2 and 3. Use the script from python code [classify conv.txt](https://github.com/opencv/opencv/files/13522228/classify.conv.txt)
**Performance test result on Apple M2**
**Test result details**: [M2.md](https://github.com/opencv/opencv/files/13379189/M2.md)
**Additional test result details with FP16**: [m2_results_with_fp16.zip](https://github.com/opencv/opencv/files/13491070/m2_results_with_fp16.zip)
**Brief summary for 4.8.1 vs 4.7.0 or 4.6.0**:
1. `CONV_1x1_S1_D1` dropped significant with small or large input shape.
2. `DEPTHWISE_5x5 ` dropped a little compared with 4.7.0.
---
**Performance test result on [Intel Core i7-12700K](https://www.intel.com/content/www/us/en/products/sku/134594/intel-core-i712700k-processor-25m-cache-up-to-5-00-ghz/specifications.html)**: 8 Performance-cores (3.60 GHz, turbo up to 4.90 GHz), 4 Efficient-cores (2.70 GHz, turbo up to 3.80 GHz), 20 threads.
**Test result details**: [INTEL.md](https://github.com/opencv/opencv/files/13374093/INTEL.md)
**Brief summary for 4.8.1 vs 4.5.5**:
1. `CONV_5x5_S1_D1` dropped significant.
2. `CONV_1x1_S1_D1`, `CONV_3x3_S1_D1`, `DEPTHWISE_3x3_S1_D1`, `DEPTHWISW_3x3_S2_D1` dropped with small input shape.
---
TODO:
- [x] Perform tests on arm with each opencv version
- [x] Perform tests on x86 with each opencv version
- [x] Split each test classification with single test config
- [x] test enable fp16
Speed up ChessBoardDetector::findQuadNeighbors #24605
### Pull Request Readiness Checklist
Replaced brute-force algorithm with O(N^2) time complexity with kd-tree with something like O(N * log N) time complexity (maybe only in average case).
For example, on image from #23558 without quads filtering (by using `CALIB_CB_FILTER_QUADS` flag) finding chessboards corners took ~770 seconds on my laptop, of which finding quads neighbors took ~620 seconds.
Now finding chessboards corners takes ~155-160 seconds, of which finding quads neighbors takes only ~5-10 seconds.
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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Add test for YoloX Yolo v6 and Yolo v8 #24611
This PR adds test for YOLOv6 model (which was absent before)
The onnx weights for the test are located in this PR [ #1126](https://github.com/opencv/opencv_extra/pull/1126)
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dnn cuda: support Sub #24647
Related https://github.com/opencv/opencv/issues/24606#issuecomment-1837390257
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dnn onnx graph simplifier: handle optional inputs of Slice #24655
Resolves https://github.com/opencv/opencv/issues/24609
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Fix bug in ChessBoardDetector::findQuadNeighbors #24597
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I do not have more info on the platform as it is internal.
Without this fix, the error is:
core/src/arithm.simd.hpp:868:1: error: too few arguments provided to function-like macro invocation
868 | DEFINE_SIMD_ALL(cmp)
| ^
./third_party/OpenCV/public/modules/./core/src/arithm.simd.hpp:93:5: note: expanded from macro 'DEFINE_SIMD_ALL'
93 | DEFINE_SIMD_NSAT(fun, __VA_ARGS__)
| ^
./third_party/OpenCV/public/modules/./core/src/arithm.simd.hpp:89:5: note: expanded from macro 'DEFINE_SIMD_NSAT'
89 | DEFINE_SIMD_F64(fun, __VA_ARGS__)
| ^
./third_party/OpenCV/public/modules/./core/src/arithm.simd.hpp:77:9: note: expanded from macro 'DEFINE_SIMD_F64'
77 | DEFINE_NOSIMD(__CV_CAT(fun, 64f), double, __VA_ARGS__)
| ^
./third_party/OpenCV/public/modules/./core/src/arithm.simd.hpp:47:56: note: expanded from macro 'DEFINE_NOSIMD'
47 | DEFINE_NOSIMD_FUN(fun_name, c_type, __VA_ARGS__)
| ^
./third_party/OpenCV/public/modules/./core/src/arithm.simd.hpp:860:9: note: macro 'DEFINE_NOSIMD_FUN' defined here
860 | #define DEFINE_NOSIMD_FUN(fun, _T1, _Tvec, ...) \
G-API: Implement inference only mode for OV backend #24584
### Changes overview
Introduced `cv::gapi::wip::ov::benchmark_mode{}` compile argument which if enabled force `OpenVINO` backend to run only inference without populating input and copying back output tensors.
This mode is only relevant for measuring the performance of pure inference without data transfers. Similar approach is using on OpenVINO side in `benchmark_app`: https://github.com/openvinotoolkit/openvino/blob/master/samples/cpp/benchmark_app/benchmark_app.hpp#L134-L139
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Fix typo in ChessBoardDetector::generateQuads #24595
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Fix race condition in color_lab.cpp initLabTabs(). #24581
There is a race condition between when the static bool is initialized (which is thread safe) and its value check. This PR changes the static bool to a static lambda call to make it thread safe. The static_cast<void> in the end is to prevent unused variables warnings.
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Add support for custom padding in DNN preprocessing #24569
This PR add functionality for specifying value in padding.
It is required in many preprocessing pipelines in DNNs such as Yolox object detection model
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Fix graph fusion with commutative ops #24577
### Pull Request Readiness Checklist
resolves https://github.com/opencv/opencv/issues/24568
**Merge with extra**: https://github.com/opencv/opencv_extra/pull/1125
TODO:
- [x] replace recursive function to sequential
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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Add yolov5n to tests #24553
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Fix verify unsupported new mat depth for nonzero/minmax/lut #24578
`cv::LUI()`, `cv::minMaxLoc()`, `cv::minMaxIdx()`, `cv::countNonZero()`, `cv::findNonZero()` and `cv::hasNonZero()` uses depth-based function table. However, it is too short for `CV_16BF`, `CV_Bool`, `CV_64U`, `CV_64S` and `CV_32U` and it may occur out-boundary-access. This patch fix it. And If necessary, when someone extends these functions to support, please relax this test.
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Fix out of image corners in cv::cornerSubPix #24527
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dnn: add openvino, opencl and cuda backends for layer normalization layer #24552
Merge after https://github.com/opencv/opencv/pull/24544.
Todo:
- [x] openvino
- [x] opencl
- [x] cuda
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This patch change lsx to baseline feature, and lasx to dispatch
feature. Additionally, the runtime detection methods for lasx and
lsx have been modified.
Replace double atomic in USAC #24499
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Reference to issue with atomic variable: #24281
Reference to bug with essential matrix: #24482
* add Winograd FP16 implementation
* fixed dispatching of FP16 code paths in dnn; use dynamic dispatcher only when NEON_FP16 is enabled in the build and the feature is present in the host CPU at runtime
* fixed some warnings
* hopefully fixed winograd on x64 (and maybe other platforms)
---------
Co-authored-by: Vadim Pisarevsky <vadim.pisarevsky@gmail.com>
dnn test: move layer norm tests into conformance tests #24544
Merge with https://github.com/opencv/opencv_extra/pull/1122
## Motivation
Some ONNX operators, such as `LayerNormalization`, `BatchNormalization` and so on, produce outputs for training (mean, stdev). So they have reference outputs of conformance tests for those training outputs as well. However, when it comes to inference, we do not need and produce those outputs for training here in dnn. Hence, output size does not match if we use dnn to infer those conformance models. This has become the barrier if we want to test these operators using their conformance tests.
<!--
| Operator | Inference needed | Outputs (required - total) | Optional outputs for training? |
| ----------------------- | ----------------------------------- | -------------------------- | ------------------------------ |
| BatchNormalization | Yes | 1 - 3 | Yes |
| Dropout | Maybe, can be eliminated via fusion | 1 - 2 | Yes |
| GRU | Yes | 0 - 2 | No |
| LSTM | Yes | 0 - 3 | No |
| LayerNormalization | Yes | 1 - 3 | Yes |
| MaxPool | Yes | 1 - 2 | Yes |
| RNN | Yes | 0 - 2 | No |
| SoftmaxCrossEntropyLoss | No | 1 - 2 | -- |
-->
**I checked all ONNX operators with optional outputs. Turns out there are only `BatchNormalization`, `Dropout`, `LayerNormalization` and `MaxPool` has optional outputs for training. All except `LayerNormalization` have models set for training mode and eval mode. Blame ONNX for that.**
## Solution
In this pull request, we remove graph outputs if the graph looks like the following:
```
[X] [Scale] [Bias] [X] [Scale] [Bias]
\ | / this patch \ | /
LayerNormalization -----------> LayerNormalization
/ | \ |
[Y] [Mean] [Stdev] [Y]
```
We can update conformance tests and turn on some cases as well if extending to more layers.
Notes:
1. This workaround does not solve expanded function operators if they are fused into a single operator, such as `$onnx/onnx/backend/test/data/node/test_layer_normalization_2d_axis1_expanded`, but they can be run without fusion. Note that either dnn or onnxruntime does not fuse those expanded function operators.
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Bugfix/qrcode version estimator #24364
Fixes https://github.com/opencv/opencv/issues/24366
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Fast gemm for einsum #24509
## This PR adds performance tests for Einsum Layer with FastGemm. See below results of performance test on different inputs
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G-API: Advanced device selection for ONNX DirectML Execution Provider #24060
### Overview
Extend `cv::gapi::onnx::ep::DirectML` to accept `adapter name` as `ctor` parameter in order to select execution device by `name`.
E.g:
```
pp.cfgAddExecutionProvider(cv::gapi::onnx::ep::DirectML("Intel Graphics"));
```
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Handle huge images in IPP distanceTransform #24535
### Pull Request Readiness Checklist
* Do not use IPP for huge Mat (reproduced with https://github.com/opencv/opencv/issues/23895#issuecomment-1708132367 on `DIST_MASK_5`)
I have observed two types of errors on the reproducer from the issue:
1. When `temp` is not allocated:
```
Thread 1 "app" received signal SIGSEGV, Segmentation fault.
0x00007ffff65dc755 in icv_l9_ownDistanceTransform_5x5_8u32f_C1R_21B_g9e9 () from /home/dkurtaev/opencv_install/bin/../lib/libopencv_imgproc.so.408
(gdb) bt
#0 0x00007ffff65dc755 in icv_l9_ownDistanceTransform_5x5_8u32f_C1R_21B_g9e9 () from /home/dkurtaev/opencv_install/bin/../lib/libopencv_imgproc.so.408
#1 0x00007ffff659e8df in icv_l9_ippiDistanceTransform_5x5_8u32f_C1R () from /home/dkurtaev/opencv_install/bin/../lib/libopencv_imgproc.so.408
#2 0x00007ffff5c390f0 in cv::distanceTransform (_src=..., _dst=..., _labels=..., distType=2, maskSize=5, labelType=1) at /home/dkurtaev/opencv/modules/imgproc/src/distransform.cpp:854
#3 0x00007ffff5c396ef in cv::distanceTransform (_src=..., _dst=..., distanceType=2, maskSize=5, dstType=5) at /home/dkurtaev/opencv/modules/imgproc/src/distransform.cpp:903
#4 0x000055555555669e in main (argc=1, argv=0x7fffffffdef8) at /home/dkurtaev/main.cpp:18
```
2. When we keep `temp` allocated every time:
```
OpenCV(4.8.0-dev) Error: Assertion failed (udata < (uchar*)ptr && ((uchar*)ptr - udata) <= (ptrdiff_t)(sizeof(void*)+64)) in fastFree, file /home/dkurtaev/opencv/modules/core/src/alloc.cpp, line 191
terminate called after throwing an instance of 'cv::Exception'
what(): OpenCV(4.8.0-dev) /home/dkurtaev/opencv/modules/core/src/alloc.cpp:191: error: (-215:Assertion failed) udata < (uchar*)ptr && ((uchar*)ptr - udata) <= (ptrdiff_t)(sizeof(void*)+64) in function 'fastFree'
```
* Try enable IPP for 3x3 (see https://github.com/opencv/opencv/issues/15904)
* Reduce memory footprint with IPP
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Enable softmax layer vectorization on RISC-V RVV #24510
Related: https://github.com/opencv/opencv/pull/24466
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Fix some of the broken urls in docs #24521
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finiteMask() and doubles for patchNaNs() #23098
Related to #22826
Connected PR in extra: [#1037@extra](https://github.com/opencv/opencv_extra/pull/1037)
### TODOs:
- [ ] Vectorize `finiteMask()` for 64FC3 and 64FC4
### Changes
This PR:
* adds a new function `finiteMask()`
* extends `patchNaNs()` by CV_64F support
* moves `patchNaNs()` and `finiteMask()` to a separate file
**NOTE:** now the function is called `finiteMask()` as discussed with the OpenCV core team
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Commutative rules for DNN subgraphs fusion #24483
### Pull Request Readiness Checklist
related: https://github.com/opencv/opencv/pull/24463#issuecomment-1783033931
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Fix gstreamer backend with manual pipelines #24243
- Fix broken seeking in audio/video playback
- Fix broken audio playback
- Fix unreliable seeking
- Estimate frame count if it is not available directly
- Return -1 for frame count and fps if it is not available.
- Return 0 for fps if the video has variable frame rate
- Enable and fix tests
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1. Download two test videos:
```bash
wget https://github.com/ietf-wg-cellar/matroska-test-files/raw/master/test_files/test1.mkv
wget https://test-videos.co.uk/vids/jellyfish/mkv/360/Jellyfish_360_10s_5MB.mkv
```
2. I modified a OpenCV videoio sample to demonstrate the problem, here it is the patch: http://dpaste.com//C9MAT2K6W
3. Build the sample, on Ubuntu:
```bash
g++ -g videocapture_audio_combination.cpp -I/usr/include/opencv4 `pkg-config --libs --cflags opencv4` -o videocapture_audio_combination
```
4. Play an audio stream with seeking BEFORE the fix:
```bash
$ ./videocapture_audio_combination --audio "filesrc location=test1.mkv ! queue ! matroskademux name=demux demux.audio_0 ! decodebin ! audioconvert ! appsink"[ERROR:0@0.009] global cap.cpp:164 open VIDEOIO(GSTREAMER): raised OpenCV exception:
OpenCV(4.8.0-dev) ./modules/videoio/src/cap_gstreamer.cpp:153: error: (-215:Assertion failed) ptr in function 'get'
[ WARN:0@0.009] global cap.cpp:204 open VIDEOIO(GSTREAMER): backend is generally available but can't be used to capture by name
ERROR! Can't to open file: filesrc location=test1.mkv ! queue ! matroskademux name=demux demux.audio_0 ! decodebin ! audioconvert ! appsink
```
5. Play a video stream with seeking BEFORE the fix:
```bash
$ ./videocapture_audio_combination --audio "filesrc location=Jellyfish_360_10s_5MB.mkv ! queue ! matroskademux name=demux demux.video_0 ! decodebin ! videoconvert ! video/x-raw, format=BGR ! appsink drop=1"
[ WARN:0@0.034] global cap_gstreamer.cpp:1728 open OpenCV | GStreamer warning: Cannot query video position: status=1, value=22, duration=300
CAP_PROP_AUDIO_DATA_DEPTH: CV_16S
CAP_PROP_AUDIO_SAMPLES_PER_SECOND: 44100
CAP_PROP_AUDIO_TOTAL_CHANNELS: 0
CAP_PROP_AUDIO_TOTAL_STREAMS: [ WARN:0@0.034] global cap_gstreamer.cpp:1898 getProperty OpenCV | GStreamer: CAP_PROP_AUDIO_TOTAL_STREAMS property is not supported
0
[ WARN:0@0.034] global cap_gstreamer.cpp:1817 getProperty OpenCV | GStreamer: CAP_PROP_POS_MSEC property result may be unrealiable: https://github.com/opencv/opencv/issues/19025
Timestamp: 0.6218
Timestamp: 33.1085
Timestamp: 67.1274
Timestamp: 100.1182
Timestamp: 133.1204
Timestamp: 167.1195
Timestamp: 200.1161
Timestamp: 233.1147
Timestamp: 267.1194
Timestamp: 300.1202
[ WARN:0@0.338] global cap_gstreamer.cpp:1949 setProperty OpenCV | GStreamer warning: GStreamer: unable to seek
0:00:00.338215907 3892572 0x5592899c7580 WARN basesrc gstbasesrc.c:3127:gst_base_src_loop:<filesrc0> error: Internal data stream error.
0:00:00.338235884 3892572 0x5592899c7580 WARN basesrc gstbasesrc.c:3127:gst_base_src_loop:<filesrc0> error: streaming stopped, reason not-linked (-1)
0:00:00.338264287 3892572 0x5592899c7580 WARN queue gstqueue.c:992:gst_queue_handle_sink_event:<queue0> error: Internal data stream error.
0:00:00.338270329 3892572 0x5592899c7580 WARN queue gstqueue.c:992:gst_queue_handle_sink_event:<queue0> error: streaming stopped, reason not-linked (-1)
[ WARN:0@0.339] global cap_gstreamer.cpp:2784 handleMessage OpenCV | GStreamer warning: Embedded video playback halted; module filesrc0 reported: Internal data stream error.
[ WARN:0@0.339] global cap_gstreamer.cpp:1199 startPipeline OpenCV | GStreamer warning: unable to start pipeline
Number of audio samples: 0
Number of video frames: 10
[ WARN:0@0.339] global cap_gstreamer.cpp:1164 isPipelinePlaying OpenCV | GStreamer warning: GStreamer: pipeline have not been created
```
6. Play an audio stream with seeking AFTER the fix:
```bash
$ ./videocapture_audio_combination --audio "filesrc location=test1.mkv ! queue ! matroskademux name=demux demux.audio_0 ! decodebin ! audioconvert ! appsink"CAP_PROP_AUDIO_DATA_DEPTH: CV_16S
CAP_PROP_AUDIO_SAMPLES_PER_SECOND: 48000
CAP_PROP_AUDIO_TOTAL_CHANNELS: 2
CAP_PROP_AUDIO_TOTAL_STREAMS: [ WARN:0@0.025] global cap_gstreamer.cpp:1903 getProperty OpenCV | GStreamer: CAP_PROP_AUDIO_TOTAL_STREAMS property is not supported
0
Timestamp: 0.0000
Timestamp: 24.0000
Timestamp: 48.0000
Timestamp: 72.0000
Timestamp: 96.0000
Timestamp: 120.0000
Timestamp: 144.0000
Timestamp: 168.0000
Timestamp: 192.0000
Timestamp: 216.0000
Timestamp: 3500.0000
Timestamp: 3504.0000
Timestamp: 3528.0000
Timestamp: 3552.0000
Timestamp: 3576.0000
Timestamp: 3600.0000
Timestamp: 3624.0000
Timestamp: 3648.0000
Timestamp: 3672.0000
Timestamp: 3696.0000
Timestamp: 3720.0000
Timestamp: 3744.0000
Timestamp: 3768.0000
Timestamp: 3792.0000
Timestamp: 3816.0000
Timestamp: 3840.0000
Timestamp: 3864.0000
Timestamp: 3888.0000
Timestamp: 3912.0000
Timestamp: 3936.0000
```
7. Play a video stream with seeking AFTER the fix:
```bash
$ ./videocapture_audio_combination --audio "filesrc location=Jellyfish_360_10s_5MB.mkv ! queue ! matroskademux name=demux demux.video_0 ! decodebin ! videoconvert ! video/x-raw, format=BGR ! appsink drop=1"
[ WARN:0@0.033] global cap_gstreamer.cpp:1746 open OpenCV | GStreamer warning: Cannot query video position: status=1, value=22, duration=300
CAP_PROP_AUDIO_DATA_DEPTH: CV_16S
CAP_PROP_AUDIO_SAMPLES_PER_SECOND: 44100
CAP_PROP_AUDIO_TOTAL_CHANNELS: 0
CAP_PROP_AUDIO_TOTAL_STREAMS: [ WARN:0@0.034] global cap_gstreamer.cpp:1903 getProperty OpenCV | GStreamer: CAP_PROP_AUDIO_TOTAL_STREAMS property is not supported
0
Timestamp: 0.0000
Timestamp: 33.0000
Timestamp: 67.0000
Timestamp: 100.0000
Timestamp: 133.0000
Timestamp: 167.0000
Timestamp: 200.0000
Timestamp: 233.0000
Timestamp: 267.0000
Timestamp: 300.0000
0:00:00.335931693 3893501 0x55bbe76ad920 WARN matroskareadcommon matroska-read-common.c:759:gst_matroska_read_common_parse_skip:<demux:sink> Unknown CueTrackPositions subelement 0xf0 - ignoring
0:00:00.335952823 3893501 0x55bbe76ad920 WARN matroskareadcommon matroska-read-common.c:759:gst_matroska_read_common_parse_skip:<demux:sink> Unknown CueTrackPositions subelement 0xf0 - ignoring
0:00:00.335988029 3893501 0x55bbe76ad920 WARN basesrc gstbasesrc.c:1742:gst_base_src_perform_seek:<filesrc0> duplicate event found 184
Timestamp: 3467.0000
Timestamp: 3500.0000
Timestamp: 3533.0000
Timestamp: 3567.0000
Timestamp: 3600.0000
Timestamp: 3633.0000
Timestamp: 3667.0000
Timestamp: 3700.0000
Timestamp: 3733.0000
Timestamp: 3767.0000
Timestamp: 3800.0000
Timestamp: 3833.0000
Timestamp: 3867.0000
Timestamp: 3900.0000
Timestamp: 3933.0000
Timestamp: 3967.0000
Timestamp: 4000.0000
Timestamp: 4033.0000
Timestamp: 4067.0000
Timestamp: 4100.0000
```
dnn (onnx): add subgraph fusion tests #24500
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dnn onnx: add instance norm layer #24378
Resolves https://github.com/opencv/opencv/issues/24377
Relates https://github.com/opencv/opencv/pull/24092#discussion_r1349841644
| Perf | multi-thread | single-thread |
| - | - | - |
| x: [2, 64, 180, 240] | 3.95ms | 11.12ms |
Todo:
- [x] speed up by multi-threading
- [x] add perf
- [x] add backend: OpenVINO
- [x] add backend: CUDA
- [x] add backend: OpenCL (no fp16)
- [ ] add backend: CANN (will be done via https://github.com/opencv/opencv/pull/24462)
### Pull Request Readiness Checklist
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```
force_builders=Linux OpenCL,Win64 OpenCL,Custom
buildworker:Custom=linux-4
build_image:Custom=ubuntu:18.04
modules_filter:Custom=none
disable_ipp:Custom=ON
```
Get the SSE2 condition match the emmintrin.h inclusion condition. #24495
### Pull Request Readiness Checklist
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* improve and refactor softmax layer
* fix building error
* compatible region layer
* fix axisStep when disable SIMD
* fix dynamic array
* try to fix error
* use nlanes from VTraits
* move axisBias to srcOffset
* fix bug caused by axisBias
* remove macro
* replace #ifdef with #if for CV_SIMD
Backport to 4.x: patchNaNs() SIMD acceleration #24480
backport from #23098
connected PR in extra: [#1118@extra](https://github.com/opencv/opencv_extra/pull/1118)
### This PR contains:
* new SIMD code for `patchNaNs()`
* CPU perf test
<details>
<summary>Performance comparison</summary>
Geometric mean (ms)
|Name of Test|noopt|sse2|avx2|sse2 vs noopt (x-factor)|avx2 vs noopt (x-factor)|
|---|:-:|:-:|:-:|:-:|:-:|
|PatchNaNs::OCL_PatchNaNsFixture::(640x480, 32FC1)|0.019|0.017|0.018|1.11|1.07|
|PatchNaNs::OCL_PatchNaNsFixture::(640x480, 32FC4)|0.037|0.037|0.033|1.00|1.10|
|PatchNaNs::OCL_PatchNaNsFixture::(1280x720, 32FC1)|0.032|0.032|0.033|0.99|0.98|
|PatchNaNs::OCL_PatchNaNsFixture::(1280x720, 32FC4)|0.072|0.072|0.070|1.00|1.03|
|PatchNaNs::OCL_PatchNaNsFixture::(1920x1080, 32FC1)|0.051|0.051|0.050|1.00|1.01|
|PatchNaNs::OCL_PatchNaNsFixture::(1920x1080, 32FC4)|0.137|0.138|0.128|0.99|1.06|
|PatchNaNs::OCL_PatchNaNsFixture::(3840x2160, 32FC1)|0.137|0.128|0.129|1.07|1.06|
|PatchNaNs::OCL_PatchNaNsFixture::(3840x2160, 32FC4)|0.450|0.450|0.448|1.00|1.01|
|PatchNaNs::PatchNaNsFixture::(640x480, 32FC1)|0.149|0.029|0.020|5.13|7.44|
|PatchNaNs::PatchNaNsFixture::(640x480, 32FC2)|0.304|0.058|0.040|5.25|7.65|
|PatchNaNs::PatchNaNsFixture::(640x480, 32FC3)|0.448|0.086|0.059|5.22|7.55|
|PatchNaNs::PatchNaNsFixture::(640x480, 32FC4)|0.601|0.133|0.083|4.51|7.23|
|PatchNaNs::PatchNaNsFixture::(1280x720, 32FC1)|0.451|0.093|0.060|4.83|7.52|
|PatchNaNs::PatchNaNsFixture::(1280x720, 32FC2)|0.892|0.184|0.126|4.85|7.06|
|PatchNaNs::PatchNaNsFixture::(1280x720, 32FC3)|1.345|0.311|0.230|4.32|5.84|
|PatchNaNs::PatchNaNsFixture::(1280x720, 32FC4)|1.831|0.546|0.436|3.35|4.20|
|PatchNaNs::PatchNaNsFixture::(1920x1080, 32FC1)|1.017|0.250|0.160|4.06|6.35|
|PatchNaNs::PatchNaNsFixture::(1920x1080, 32FC2)|2.077|0.646|0.605|3.21|3.43|
|PatchNaNs::PatchNaNsFixture::(1920x1080, 32FC3)|3.134|1.053|0.961|2.97|3.26|
|PatchNaNs::PatchNaNsFixture::(1920x1080, 32FC4)|4.222|1.436|1.288|2.94|3.28|
|PatchNaNs::PatchNaNsFixture::(3840x2160, 32FC1)|4.225|1.401|1.277|3.01|3.31|
|PatchNaNs::PatchNaNsFixture::(3840x2160, 32FC2)|8.310|2.953|2.635|2.81|3.15|
|PatchNaNs::PatchNaNsFixture::(3840x2160, 32FC3)|12.396|4.455|4.252|2.78|2.92|
|PatchNaNs::PatchNaNsFixture::(3840x2160, 32FC4)|17.174|5.831|5.824|2.95|2.95|
</details>
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Patch to opencv_extra has the same branch name.
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dnn: add shared fastNorm kernel for mvn, instance norm and layer norm #24409
Relates https://github.com/opencv/opencv/pull/24378#issuecomment-1756906570
TODO:
- [x] add fastNorm
- [x] refactor layer norm with fastNorm
- [x] refactor mvn with fastNorm
- [ ] add onnx mvn in importer (in a new PR?)
- [ ] refactor instance norm with fastNorm (in another PR https://github.com/opencv/opencv/pull/24378, need to merge this one first though)
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Extend the signature of imdecodemulti() #24405
(Edited after addressing Reviewers' comments.)
Add an argument to `imdecodemulti()` to enable optional selection of pages of multi-page images.
Be default, all pages are decoded. If used, the additional argument may specify a continuous selection of pages to decode.
### Pull Request Readiness Checklist
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Refactor ObjectiveC Range class #24454
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Fix for build issue in #24405
Video tracking (dnn): set backend and target for TrackerVit #24461Resolves#24460
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Remove torch (old torch7) from dnn in 5.x #24294
Merge with https://github.com/opencv/opencv_extra/pull/1097
Completely removed torch (old torch7) from dnn:
- removed modules/dnn/src/torch directory that contained torch7 model parser
- removed readNetFromTorch() and readTorchBlob() public functions
- removed torch7 references from comments and help texts
- replaced links to t7 models by links to similar onnx models in js_style_transfer turtorial (similar to https://github.com/opencv/opencv/pull/24245/files)
videoio: Add raw encoded video stream muxing to cv::VideoWriter with CAP_FFMPEG #24363
Allow raw encoded video streams (e.g. h264[5]) to be encapsulated by `cv::VideoWriter` to video containers (e.g. mp4/mkv).
Operates in a similar way to https://github.com/opencv/opencv/pull/15290 where encapsulation is enabled by setting the `VideoWriterProperties::VIDEOWRITER_PROP_RAW_VIDEO` flag when constructing `cv::VideoWriter` e.g.
```
VideoWriter container(fileNameOut, api, fourcc, fps, { width, height }, { VideoWriterProperties::VIDEOWRITER_PROP_RAW_VIDEO, 1 });
```
and each raw encoded frame is passed as single row of a `CV_8U` `cv::Mat`.
The main reason for this PR is to allow `cudacodec::VideoWriter` to output its encoded streams to a suitable container, see https://github.com/opencv/opencv_contrib/pull/3569.
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Ellipses supported added for Einsum Layer #24322
This PR added addresses issues not covered in #24037. Namely these are:
Test case for this patch is in this PR [#1106](https://github.com/opencv/opencv_extra/pull/1106) in opencv extra
Added:
- [x] Broadcasting reduction "...ii ->...I"
- [x] Add lazy shape deduction. "...ij, ...jk->...ik"
Features to add:
- [ ] Add implicit output computation support. "bij,bjk ->" (output subscripts should be "bik")
- [ ] Add support for CUDA backend
- [ ] BatchWiseMultiply optimize
- [ ] Performance test
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Pertaining Issue: https://github.com/opencv/opencv/issues/5697
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* Optimize some function with lasx.
Optimize some function with lasx. #23929
This patch optimizes some lasx functions and reduces the runtime of opencv_test_core from 662,238ms to 633603ms on the 3A5000 platform.
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dnn: fix HAVE_TIMVX macro definition in dnn test #24425
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Native ONNX to Inference Engine backend #21066Resolves#21052
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This pointer is called unconditionally in BarcodeImpl::initDecode
assuming the size of the image is outside the specified bounds. This
seems to not cause problems on optimized builds, I assume because the
optimizer sees through the processImageScale call to see that it can be
reduced to a resize call. Leaving it as is relies on undefined
behavior.
This was the least invasive change I could make, however, it might be
worthwhile to pull up the logic for a resize so that a SuperScale does
not need to be allocated, which seems to be the most common case.
Speed up line merging in INTER_AREA #24412
This provides a 10 to 20% speed-up.
Related perf test fix: https://github.com/opencv/opencv/pull/24417
This is a split of https://github.com/opencv/opencv/pull/23525 that will be updated to only deal with column merging.
### Pull Request Readiness Checklist
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Supporting protobuf v22 and later(with abseil-cpp/C++17) #24372
fix https://github.com/opencv/opencv/issues/24369
related https://github.com/opencv/opencv/issues/23791
1. This patch supports external protobuf v22 and later, it required abseil-cpp and c++17.
Even if the built-in protobuf is upgraded to v22 or later,
the dependency on abseil-cpp and the requirement for C++17 will continue.
2. Some test for caffe required patched protobuf, so this patch disable them.
This patch is tested by following libraries.
- Protobuf: /usr/local/lib/libprotobuf.so (4.24.4)
- abseil-cpp: YES (20230125)
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* added more or less cross-platform (based on POSIX signal() semantics) method to detect various NEON extensions, such as FP16 SIMD arithmetics, BF16 SIMD arithmetics, SIMD dotprod etc. It could be propagated to other instruction sets if necessary.
* hopefully fixed compile errors
* continue to fix CI
* another attempt to fix build on Linux aarch64
* * reverted to the original method to detect special arm neon instructions without signal()
* renamed FP16_SIMD & BF16_SIMD to NEON_FP16 and NEON_BF16, respectively
* removed extra whitespaces
GSoC Add ONNX Support for GatherElements #24092
Merge with: https://github.com/opencv/opencv_extra/pull/1082
Adds support to the ONNX operator GatherElements [operator docs](https://github.com/onnx/onnx/blob/main/docs/Operators.md#GatherElements)
Added tests to opencv_extra at pull request https://github.com/opencv/opencv_extra/pull/1082
### Pull Request Readiness Checklist
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Fixed CumSum layer inplace flag #24367
When exclusive is false:
dst[i] = dst[i-1] + src[i]
When exclusive is true:
dst[i] = dst[i-1] + src[i-1]
So CumSum layer can be inplace only when exclusive flag is false.
dnn: cleanup of halide backend for 5.x #24231
Merge with https://github.com/opencv/opencv_extra/pull/1092.
### Pull Request Readiness Checklist
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Encode QR code data to UTF-8 #24350
### Pull Request Readiness Checklist
**Merge with extra**: https://github.com/opencv/opencv_extra/pull/1105
resolves https://github.com/opencv/opencv/issues/23728
This is first PR in a series. Here we just return a raw Unicode. Later I will try expand QR codes decoding methods to use ECI assignment number and return a string with proper encoding, not only UTF-8 or raw unicode.
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.
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Patch to opencv_extra has the same branch name.
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Implement color conversion from RGB to YUV422 family #24333
Related PR for extra: https://github.com/opencv/opencv_extra/pull/1104
Hi,
This patch provides CPU and OpenCL implementations of color conversions from RGB/BGR to YUV422 family (such as UYVY and YUY2).
These features would come in useful for enabling standard RGB images to be supplied as input to algorithms or networks that make use of images in YUV422 format directly (for example, on resource constrained devices working with camera images captured in YUV422).
The code, tests and perf tests are all written following the existing pattern. There is also an example `bin/example_cpp_cvtColor_RGB2YUV422` that loads an image from disk, converts it from BGR to UYVY and then back to BGR, and displays the result as a visual check that the conversion works.
The OpenCL performance for the forward conversion implemented here is the same as the existing backward conversion on my hardware. The CPU implementation, unfortunately, isn't very optimized as I am not yet familiar with the SIMD code.
Please let me know if I need to fix something or can make other modifications.
Thanks!
### Pull Request Readiness Checklist
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* remove Conformance from test names
* integrate neon optimization into default
* quick fix: define CV_NEON_AARCH64 0 for non NEON platforms
* remove var batch that leads to memory leak
* put neon code back to fast_gemm_kernels.simd
* reorganize code to reduce duplicate code
Add HAL implementation hooks to cv::flip() and cv::rotate() functions from core module #24233
Hello,
This change proposes the addition of HAL hooks for cv::flip() and cv::rotate() functions from OpenCV core module.
Flip and rotation are functions commonly available from 2D hardware accelerators. This is convenient provision to enable custom optimized implementation of image flip/rotation on systems embedding such accelerator.
Thank you
### Pull Request Readiness Checklist
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Rewrite Universal Intrinsic code: float related part #24325
The goal of this series of PRs is to modify the SIMD code blocks guarded by CV_SIMD macro: rewrite them by using the new Universal Intrinsic API.
The series of PRs is listed below:
#23885 First patch, an example
#23980 Core module
#24058 ImgProc module, part 1
#24132 ImgProc module, part 2
#24166 ImgProc module, part 3
#24301 Features2d and calib3d module
#24324 Gapi module
This patch (hopefully) is the last one in the series.
This patch mainly involves 3 parts
1. Add some modifications related to float (CV_SIMD_64F)
2. Use `#if (CV_SIMD || CV_SIMD_SCALABLE)` instead of `#if CV_SIMD || CV_SIMD_SCALABLE`,
then we can get the `CV_SIMD` module that is not enabled for `CV_SIMD_SCALABLE` by looking for `if CV_SIMD`
3. Summary of `CV_SIMD` blocks that remains unmodified: Updated comments
- Some blocks will cause test fail when enable for RVV, marked as `TODO: enable for CV_SIMD_SCALABLE, ....`
- Some blocks can not be rewrited directly. (Not commented in the source code, just listed here)
- ./modules/core/src/mathfuncs_core.simd.hpp (Vector type wrapped in class/struct)
- ./modules/imgproc/src/color_lab.cpp (Array of vector type)
- ./modules/imgproc/src/color_rgb.simd.hpp (Array of vector type)
- ./modules/imgproc/src/sumpixels.simd.hpp (fixed length algorithm, strongly ralated with `CV_SIMD_WIDTH`)
These algorithms will need to be redesigned to accommodate scalable backends.
### Pull Request Readiness Checklist
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- [ ] The PR is proposed to the proper branch
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Fix tests writing to current work dir #24343
Several tests were writing files in the current work directory and did not clean up after test. Moved all temporary files to the `/tmp` dir and added a cleanup code.
Fixed CumSum dnn layer #24353Fixes#20110
The algorithm had several errors, so I rewrote it.
Also the layer didn't work with non constant axis tensor. Fixed it.
Enabled CumSum layer tests from ONNX conformance.
OpenVINO backend for INT8 models #23987
### Pull Request Readiness Checklist
TODO:
- [x] DetectionOutput layer (https://github.com/opencv/opencv/pull/24069)
- [x] Less FP32 fallbacks (i.e. Sigmoid, eltwise sum)
- [x] Accuracy, performance tests (https://github.com/opencv/opencv/pull/24039)
- [x] Single layer tests (convolution)
- [x] ~~Fixes for OpenVINO 2022.1 (https://pullrequest.opencv.org/buildbot/builders/precommit_custom_linux/builds/100334)~~
Performace results for object detection model `coco_efficientdet_lite0_v1_1.0_quant_2021_09_06.tflite`:
| backend | performance (median time) |
|---|---|
| OpenCV | 77.42ms |
| OpenVINO 2023.0 | 10.90ms |
CPU: `11th Gen Intel(R) Core(TM) i5-1135G7 @ 2.40GHz`
Serialized model per-layer stats (note that Convolution should use `*_I8` primitives if they are quantized correctly): https://gist.github.com/dkurt/7772bbf1907035441bb5454f19f0feef
---
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
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Patch to opencv_extra has the same branch name.
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Optimization for parallelization when large core number #24280
**Problem description：**
When the number of cores is large, OpenCV’s thread library may reduce performance when processing parallel jobs.
**The reason for this problem:**
When the number of cores (the thread pool initialized the threads, whose number is as same as the number of cores) is large, the main thread will spend too much time on waking up unnecessary threads.
When a parallel job needs to be executed, the main thread will wake up all threads in sequence, and then wait for the signal for the job completion after waking up all threads. When the number of threads is larger than the parallel number of a job slices, there will be a situation where the main thread wakes up the threads in sequence and the awakened threads have completed the job, but the main thread is still waking up the other threads. The threads woken up by the main thread after this have nothing to do, and the broadcasts made by the waking threads take a lot of time, which reduce the performance.
**Solution：**
Reduce the time for the process of main thread waking up the worker threads through the following two methods:
• The number of threads awakened by the main thread should be adjusted according to the parallel number of a job slices. If the number of threads is greater than the number of the parallel number of job slices, the total number of threads awakened should be reduced.
• In the process of waking up threads in sequence, if the main thread finds that all parallel job slices have been allocated, it will jump out of the loop in time and wait for the signal for the job completion.
**Performance Test:**
The tests were run in the manner described by https://github.com/opencv/opencv/wiki/HowToUsePerfTests.
At core number = 160, There are big performance gain in some cases.
Take the following cases in the video module as examples:
OpticalFlowPyrLK_self::Path_Idx_Cn_NPoints_WSize_Deriv::("cv/optflow/frames/VGA_%02d.png", 2, 1, (9, 9), 11, true)
Performance improves 191%:0.185405ms ->0.0636496ms
perf::DenseOpticalFlow_VariationalRefinement::(320x240, 10, 10)
Performance improves 112%:23.88938ms -> 11.2562ms
Among all the modules, the performance improvement is greatest on module video, and there are also certain improvements on other modules.
At core number = 160, the times labeled below are the geometric mean of the average time of all cases for one module. The optimization is available on each module.
overall | time(ms) | | | | | | |
-- | -- | -- | -- | -- | -- | -- | -- | --
module name | gapi | dnn | features2d | objdetect | core | imgproc | stitching | video
original | 0.185 | 1.586 | 9.998 | 11.846 | 0.205 | 0.215 | 164.409 | 0.803
optimized | 0.174 | 1.353 | 9.535 | 11.105 | 0.199 | 0.185 | 153.972 | 0.489
Performance improves | 6% | 17% | 5% | 7% | 3% | 16% | 7% | 64%
Meanwhile, It is found that adjusting the order of test cases will have an impact on some test cases. For example, we used option --gtest-shuffle to run opencv_perf_gapi, the performance of TestPerformance::CmpWithScalarPerfTestFluid/CmpWithScalarPerfTest::(compare_f, CMP_GE, 1920x1080, 32FC1, { gapi.kernel_package }) case had 30% changes compared to the case without shuffle. I would like to ask if you have also encountered such a situation and could you share your experience?
### Pull Request Readiness Checklist
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dnn: merge tests from test_halide_layers to test_backends #24283
Context: https://github.com/opencv/opencv/pull/24231#pullrequestreview-1628649980
### Pull Request Readiness Checklist
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Rewrite Universal Intrinsic code: features2d and calib3d module. #24301
The goal of this series of PRs is to modify the SIMD code blocks guarded by CV_SIMD macro: rewrite them by using the new Universal Intrinsic API.
This is the modification to the features2d module and calib3d module.
Test with clang 16 and QEMU v7.0.0. `AP3P.ctheta1p_nan_23607` failed beacuse of a small calculation error. But this patch does not touch the relevant code, and this error always reproduce on QEMU, regardless of whether the patch is applied or not. I think we can ignore it
```
[ RUN ] AP3P.ctheta1p_nan_23607
/home/hanliutong/project/opencv/modules/calib3d/test/test_solvepnp_ransac.cpp:2319: Failure
Expected: (cvtest::norm(res.colRange(0, 2), expected, NORM_INF)) <= (3e-16), actual: 3.33067e-16 vs 3e-16
[ FAILED ] AP3P.ctheta1p_nan_23607 (26 ms)
...
[==========] 148 tests from 64 test cases ran. (1147114 ms total)
[ PASSED ] 147 tests.
[ FAILED ] 1 test, listed below:
[ FAILED ] AP3P.ctheta1p_nan_23607
```
Note: There are 2 test cases failed with GCC 13.2.1 without this patch, seems like there are someting wrong with RVV part on GCC.
```
[----------] Global test environment tear-down
[==========] 148 tests from 64 test cases ran. (1511399 ms total)
[ PASSED ] 146 tests.
[ FAILED ] 2 tests, listed below:
[ FAILED ] Calib3d_StereoSGBM.regression
[ FAILED ] Calib3d_StereoSGBM_HH4.regression
```
The patch is partially auto-generated by using the [rewriter](https://github.com/hanliutong/rewriter).
### Pull Request Readiness Checklist
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Add Support for Einsum Layer #24037
### This PR adding support for [Einsum Layer](https://pytorch.org/docs/stable/generated/torch.einsum.html) (in progress).
This PR is currently not to be merged but only reviewed. Test cases are located in [#1090](https://github.com/opencv/opencv_extra/pull/1090)RP in OpenCV extra
**DONE**:
- [x] 2-5D GMM support added
- [x] Matrix transpose support added
- [x] Reduction type comupte 'ij->j'
- [x] 2nd shape computation - during forward
**Next PRs**:
- [ ] Broadcasting reduction "...ii ->...i"
- [ ] Add lazy shape deduction. "...ij, ...jk->...ik"
- [ ] Add implicit output computation support. "bij,bjk ->" (output subscripts should be "bik")
- [ ] Add support for CUDA backend
- [ ] BatchWiseMultiply optimize
**Later in 5.x version (requires support for 1D matrices)**:
- [ ] Add 1D vector multiplication support
- [ ] Inter product "i, i" (problems with 1D shapes)
### Pull Request Readiness Checklist
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- [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
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Patch to opencv_extra has the same branch name.
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* 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
* first commit
* turned C from input to constant; force C constant in impl; better handling 0d/1d cases
* integrate with gemm from ficus nn
* fix const inputs
* adjust threshold for int8 tryQuantize
* adjust threshold for int8 quantized 2
* support batched gemm and matmul; tune threshold for rcnn_ilsvrc13; update googlenet
* add gemm perf against innerproduct
* add perf tests for innerproduct with bias
* fix perf
* add memset
* renamings for next step
* add dedicated perf gemm
* add innerproduct in perf_gemm
* remove gemm and innerproduct perf tests from perf_layer
* add perf cases for vit sizes; prepack constants
* remove batched gemm; fix wrong trans; optimize KC
* remove prepacking for const A; several fixes for const B prepacking
* add todos and gemm expression
* add optimized branch for avx/avx2
* trigger build
* update macros and signature
* update signature
* fix macro
* fix bugs for neon aarch64 & x64
* add backends: cuda, cann, inf_ngraph and vkcom
* fix cuda backend
* test commit for cuda
* test cuda backend
* remove debug message from cuda backend
* use cpu dispatcher
* fix neon macro undef in dispatcher
* fix dispatcher
* fix inner kernel for neon aarch64
* fix compiling issue on armv7; try fixing accuracy issue on other platforms
* broadcast C with beta multiplied; improve func namings
* fix bug for avx and avx2
* put all platform-specific kernels in dispatcher
* fix typos
* attempt to fix compile issues on x64
* run old gemm when neon, avx, avx2 are all not available; add kernel for armv7 neon
* fix typo
* quick fix: add macros for pack4
* quick fix: use vmlaq_f32 for armv7
* quick fix for missing macro of fast gemm pack f32 4
* disable conformance tests when optimized branches are not supported
* disable perf tests when optimized branches are not supported
* decouple cv_try_neon and cv_neon_aarch64
* drop googlenet_2023; add fastGemmBatched
* fix step in fastGemmBatched
* cpu: fix initialization ofb; gpu: support batch
* quick followup fix for cuda
* add default kernels
* quick followup fix to avoid macro redef
* optmized kernels for lasx
* resolve mis-alignment; remove comments
* tune performance for x64 platform
* tune performance for neon aarch64
* tune for armv7
* comment time consuming tests
* quick follow-up fix
VIT track(gsoc realtime object tracking model) #24201
Vit tracker(vision transformer tracker) is a much better model for real-time object tracking. Vit tracker can achieve speeds exceeding nanotrack by 20% in single-threaded mode with ARM chip, and the advantage becomes even more pronounced in multi-threaded mode. In addition, on the dataset, vit tracker demonstrates better performance compared to nanotrack. Moreover, vit trackerprovides confidence values during the tracking process, which can be used to determine if the tracking is currently lost.
opencv_zoo: https://github.com/opencv/opencv_zoo/pull/194
opencv_extra: [https://github.com/opencv/opencv_extra/pull/1088](https://github.com/opencv/opencv_extra/pull/1088)
# Performance comparison is as follows:
NOTE: The speed below is tested by **onnxruntime** because opencv has poor support for the transformer architecture for now.
ONNX speed test on ARM platform(apple M2)(ms):
| thread nums | 1| 2| 3| 4|
|--------|--------|--------|--------|--------|
| nanotrack| 5.25| 4.86| 4.72| 4.49|
| vit tracker| 4.18| 2.41| 1.97| **1.46 (3X)**|
ONNX speed test on x86 platform(intel i3 10105)(ms):
| thread nums | 1| 2| 3| 4|
|--------|--------|--------|--------|--------|
| nanotrack|3.20|2.75|2.46|2.55|
| vit tracker|3.84|2.37|2.10|2.01|
opencv speed test on x86 platform(intel i3 10105)(ms):
| thread nums | 1| 2| 3| 4|
|--------|--------|--------|--------|--------|
| vit tracker|31.3|31.4|31.4|31.4|
preformance test on lasot dataset(AUC is the most important data. Higher AUC means better tracker):
|LASOT | AUC| P| Pnorm|
|--------|--------|--------|--------|
| nanotrack| 46.8| 45.0| 43.3|
| vit tracker| 48.6| 44.8| 54.7|
[https://youtu.be/MJiPnu1ZQRI](https://youtu.be/MJiPnu1ZQRI)
In target tracking tasks, the score is an important indicator that can indicate whether the current target is lost. In the video, vit tracker can track the target and display the current score in the upper left corner of the video. When the target is lost, the score drops significantly. While nanotrack will only return 0.9 score in any situation, so that we cannot determine whether the target is lost.
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Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
