Reworked findContours to reduce C-API usage #25146
What is done:
* rewritten `findContours` and `icvApproximateChainTC89` using C++ data structures
* extracted LINK_RUNS mode to separate new public functions - `findContoursLinkRuns` (it uses completely different algorithm)
* ~added new public `cv::approximateChainTC89`~ - **❌ decided to hide it**
* enabled chain code output (method = 0, no public enum value for this in C++ yet)
* kept old function as `findContours_old` (exported, but not exposed to user)
* added more tests for findContours (`test_contours_new.cpp`), some tests compare results of old function with new one. Following tests have been added:
* contours of random rectangle
* contours of many small (1-2px) blobs
* contours of random noise
* backport of old accuracy test
* separate test for LINK RUNS variant
What is left to be done (can be done now or later):
* improve tests:
* some tests have limited verification (e.g. only verify contour sizes)
* perhaps reference data can be collected and stored
* maybe more test variants can be added (?)
* add enum value for chain code output and a method of returning starting points (e.g. first 8 elements of returned `vector<uchar>` can represent 2 int point coordinates)
* add documentation for new functions - **✔️ DONE**
* check and improve performance (my experiment showed 0.7x-1.1x some time ago)
* remove old functions completely (?)
* change contour return order (BFS) or allow to select it (?)
* return result tree as-is (?) (new data structures should be exposed, bindings should adapt)
core: doc: add note for countNonZero, hasNonZero and findNonZero #25356Close#25345
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[BugFix] dnn (ONNX): Foce dropping constant inputs in parseClip if they are shared #25319
Resolves https://github.com/opencv/opencv/issues/25278
Merge with https://github.com/opencv/opencv_extra/pull/1165
In Gold-YOLO ,`Div` has a constant input `B=6` which is then parsed into a `Const` layer in the ONNX importer, but `Clip` also has the shared constant input `max=6` which is already a `Const` layer and then connected to `Elementwise` layer. This should not happen because in the `forward()` of `Elementwise` layer, the legacy code goes through and apply activation to each input. More details on https://github.com/opencv/opencv/issues/25278#issuecomment-2032199630.
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Ownership check in TFLite importer #25312
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resolves https://github.com/opencv/opencv/issues/25310
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Optimize int8 layers in DNN modules by using RISC-V Vector intrinsic. #25230
This patch optimize 3 functions in the int8 layer by using RVV Native Intrinsic.
This patch was tested on QEMU using VLEN=128 and VLEN=256 on `./bin/opencv_test_dnn --gtest_filter="*Int8*"`;
On the real device (k230, VLEN=128), `EfficientDet_int8` in `opencv_perf_dnn` showed a performance improvement of 1.46x.
| Name of Test | Original | optimized | Speed-up |
| ------------------------------------------ | -------- | ---------- | -------- |
| EfficientDet_int8::DNNTestNetwork::OCV/CPU | 2843.467 | 1947.013 | 1.46 |
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imgcodecs: jpeg: re-support to read CMYK Jpeg #25280Close#25274
OpenCV Extra: https://github.com/opencv/opencv_extra/pull/1163
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Merge with https://github.com/opencv/opencv_extra/pull/1158
Todo:
- [x] Fix Attention pattern recognition.
- [x] Handle other backends.
Benchmark:
"VIT_B_32 OCV/CPU", M1, results in milliseconds.
| Model | 4.x | This PR |
| - | - | - |
| VIT_B_32 OCV/CPU | 87.66 | **83.83** |
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Orbbec Camera supports MacOS，Gemini2 and Gemini2L support Y16 format #24877
note:
1.Gemini2 and Gemini2L must use the latest firmware -- https://github.com/orbbec/OrbbecFirmware;
2.Administrator privileges are necessary to run on MacOS.
Add imread #24415
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Hello everyone,
I created this new version of the imread function and I think it can be very useful in several cases.
It is actually passed to it object on which you want to upload the image.
The advantages can be different like in case one needs to open several large images all the same in sequence.
one can use the same pointer and the system would not allocate memory each time.
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
```
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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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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.
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 |
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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
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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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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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
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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
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- [ ] 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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---------
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
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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Add compatibility with latest (3.1.54) emsdk version #25084
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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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G-API: Lower supported IE backend version #25054
Related to https://github.com/opencv/opencv/issues/25053
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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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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
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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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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
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bug fix infinite loop #24987Fixes#24967
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Bugfix to #24967
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
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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.
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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
### Pull Request Readiness Checklist
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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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Co-authored-by: fengyuentau <yuantao.feng@opencv.org.cn>
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
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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
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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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