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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- Use the same tools and plugins for SDK build and AAR build
- Added script to test Gradle-based samples against local maven repo
- Various local fixes and debug prints
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
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Add yolov5n to tests #24553
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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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