- 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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- [x] The feature is well documented and sample code can be built with the project CMake
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
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
Add yolov5n to tests #24553
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [ X] I agree to contribute to the project under Apache 2 License.
- [ X] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [ X] The PR is proposed to the proper branch
- [ X] There is a reference to the original bug report and related work
- [ X] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
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Fix 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.
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
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Fix out of image corners in cv::cornerSubPix #24527
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
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- [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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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
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
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Patch to opencv_extra has the same branch name.
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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
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
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Patch to opencv_extra has the same branch name.
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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.
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
Bugfix/qrcode version estimator #24364
Fixes https://github.com/opencv/opencv/issues/24366
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
Fast gemm for einsum #24509
## This PR adds performance tests for Einsum Layer with FastGemm. See below results of performance test on different inputs
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
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"));
```
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [ ] I agree to contribute to the project under Apache 2 License.
- [ ] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [ ] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
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
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
Enable softmax layer vectorization on RISC-V RVV #24510
Related: https://github.com/opencv/opencv/pull/24466
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
Fix some of the broken urls in docs #24521
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
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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
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
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Patch to opencv_extra has the same branch name.
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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
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
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Patch to opencv_extra has the same branch name.
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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
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [X] I agree to contribute to the project under Apache 2 License.
- [X] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [X] The PR is proposed to the proper branch
- [-] There is a reference to the original bug report and related work => Reproducible test provided
- [-] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [X] The feature is well documented and sample code can be built with the project CMake
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
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
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)
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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
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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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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.
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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.
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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
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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.
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Encode QR code data to UTF-8 #24350
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**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.
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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!
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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
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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.
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- [ ] The PR is proposed to the proper branch
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- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
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
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
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
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
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
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
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
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [ ] I agree to contribute to the project under Apache 2 License.
- [ ] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [ ] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
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
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
* 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.
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
Rewrite Universal Intrinsic code: ImgProc (CV_SIMD_WIDTH related Part) #24166
Related PR: #24058, #24132. The goal of this series of PRs is to modify the SIMD code blocks in the opencv/modules/imgproc folder by using the new Universal Intrinsic API.
The modification of this PR mainly focuses on the code that uses the `CV_SIMD_WIDTH` macro. This macro is sometimes used for loop tail processing, such as `box_filter.simd.hpp` and `morph.simd.hpp`.
```cpp
#if CV_SIMD
int i = 0;
for (i < n - v_uint16::nlanes; i += v_uint16::nlanes) {
// some universal intrinsic code
// e.g. v_uint16...
}
#if CV_SIMD_WIDTH > 16
for (i < n - v_uint16x8::nlanes; i += v_uint16x8::nlanes) {
// handle loop tail by 128 bit SIMD
// e.g. v_uint16x8
}
#endif //CV_SIMD_WIDTH
#endif// CV_SIMD
```
The main contradiction is that the variable-length Universal Intrinsic backend cannot use 128bit fixed-length data structures. Therefore, this PR uses the scalar loop to handle the loop tail.
This PR is marked as draft because the modification of the `box_filter.simd.hpp` file caused a compilation error. The cause of the error is initially believed to be due to an internal error in the GCC compiler.
```bash
box_filter.simd.hpp:1162:5: internal compiler error: Segmentation fault
1162 | }
| ^
0xe03883 crash_signal
/wafer/share/gcc/gcc/toplev.cc:314
0x7ff261c4251f ???
./signal/../sysdeps/unix/sysv/linux/x86_64/libc_sigaction.c:0
0x6bde48 hash_set<rtl_ssa::set_info*, false, default_hash_traits<rtl_ssa::set_info*> >::iterator::operator*()
/wafer/share/gcc/gcc/hash-set.h:125
0x6bde48 extract_single_source
/wafer/share/gcc/gcc/config/riscv/riscv-vsetvl.cc:1184
0x6bde48 extract_single_source
/wafer/share/gcc/gcc/config/riscv/riscv-vsetvl.cc:1174
0x119ad9e pass_vsetvl::propagate_avl() const
/wafer/share/gcc/gcc/config/riscv/riscv-vsetvl.cc:4087
0x119ceaf pass_vsetvl::execute(function*)
/wafer/share/gcc/gcc/config/riscv/riscv-vsetvl.cc:4344
0x119ceaf pass_vsetvl::execute(function*)
/wafer/share/gcc/gcc/config/riscv/riscv-vsetvl.cc:4325
Please submit a full bug report, with preprocessed source (by using -freport-bug).
Please include the complete backtrace with any bug report.
```
This PR can be compiled with Clang 16, and `opencv_test_imgproc` is passed on QEMU.
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [ ] I agree to contribute to the project under Apache 2 License.
- [ ] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [ ] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
Python: support tuple src for cv::add()/subtract()/... #24074
fix https://github.com/opencv/opencv/issues/24057
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [ x The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
Rewrite Universal Intrinsic code by using new API: ImgProc module Part 2 #24132
The goal of this series of PRs is to modify the SIMD code blocks guarded by CV_SIMD macro in the opencv/modules/imgproc folder: rewrite them by using the new Universal Intrinsic API.
This is the second part of the modification to the Imgproc module ( Part 1: #24058 ), And I tested this patch on RVV (QEMU) and AVX devices, `opencv_test_imgproc` is passed.
The patch is partially auto-generated by using the [rewriter](https://github.com/hanliutong/rewriter).
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [ ] I agree to contribute to the project under Apache 2 License.
- [ ] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [ ] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
In the previous code, there was a memory leak issue where the
previously allocated memory was not freed upon a failed realloc
operation. This commit addresses the problem by releasing the old
memory before setting the pointer to NULL in case of a realloc failure.
This ensures that memory is properly managed and avoids potential
memory leaks.
Skip test on SkipTestException at fixture's constructor (version 2) #24250
### Pull Request Readiness Checklist
Another version of https://github.com/opencv/opencv/pull/24186 (reverted by https://github.com/opencv/opencv/pull/24223). Current implementation cannot handle skip exception at `static void SetUpTestCase` but works on `virtual void SetUp`.
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
Rewrite Universal Intrinsic code by using new API: ImgProc module. #24058
The goal of this series of PRs is to modify the SIMD code blocks guarded by CV_SIMD macro in the `opencv/modules/imgproc` folder: rewrite them by using the new Universal Intrinsic API.
For easier review, this PR includes a part of the rewritten code, and another part will be brought in the next PR (coming soon). I tested this patch on RVV (QEMU) and AVX devices, `opencv_test_imgproc` is passed.
The patch is partially auto-generated by using the [rewriter](https://github.com/hanliutong/rewriter), related PR https://github.com/opencv/opencv/pull/23885 and https://github.com/opencv/opencv/pull/23980.
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [ ] I agree to contribute to the project under Apache 2 License.
- [ ] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [ ] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake
Fix undefined behavior arithmetic in copyMakeBorder and adjustROI. #24260
This is due to the undefined: negative int multiplied by size_t pointer increment.
To test, compile with:
```
mkdir build
cd build
cmake ../ -DCMAKE_C_FLAGS_INIT="-fsanitize=undefined" -DCMAKE_CXX_FLAGS_INIT="-fsanitize=undefined" -DCMAKE_C_COMPILER="/usr/bin/clang" -DCMAKE_CXX_COMPILER="/usr/bin/clang++" -DCMAKE_SHARED_LINKER_FLAGS="-fsanitize=undefined -lubsan"
```
And run:
```
make -j opencv_test_core && ./bin/opencv_test_core --gtest_filter=*UndefinedBehavior*
```
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
Added default dimension value to tensorflow ArgMax and ArgMin layers #24266
Added default dimension value to tensorflow ArgMax and ArgMin layers.
Added exception when accessing layer's input with out of range index.
Fixes https://bugs.chromium.org/p/oss-fuzz/issues/detail?id=48452
Modify the outputVideoFormat after changing the output format in MSMF backend #24142
After changing the output format, need to modify the outputVideoFormat, otherwise the outputVideoFormat is always CV_CAP_MODE_BGR, and an error will occur when converting the format in retrieveVideoFrame(), and will always enter "case CV_CAP_MODE_BGR:" process.
### Pull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
- [ ] I agree to contribute to the project under Apache 2 License.
- [ ] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [ ] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
Patch to opencv_extra has the same branch name.
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Co-authored-by: 李龙 <lilong@sobey.com>
Use ngraph::Output in OpenVINO backend wrapper #24196
### Pull Request Readiness Checklist
resolves https://github.com/opencv/opencv/issues/24102
* Use `ngraph::Output<ngraph::Node>>` insead of `std::shared_ptr<ngraph::Node>` as a backend wrapper. It lets access to multi-output nodes: 588ddf1b18/modules/dnn/src/net_openvino.cpp (L501-L504)
* All layers can be customizable with OpenVINO >= 2022.1. nGraph reference code used for default layer implementation does not required CPU plugin also (might be tested by commenting CPU plugin at `/opt/intel/openvino/runtime/lib/intel64/plugins.xml`).
* Correct inference if only intermediate blobs requested.
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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Properly preserve chi_table license as mandated by BSD-3-Clause #24204
Amend reference to online hosted file with the full license quotation as mandated by the original license.
Fix distanceTransform for inputs with large step and height #24214
### Pull Request Readiness Checklist
resolves https://github.com/opencv/opencv/issues/23895
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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Minor optimization of two lines intersection #24216
### Pull Request Readiness Checklist
Not significant, but we can reduce number of multiplications while compute two lines intersection. Both methods are used heavily in their modules.
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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The address sanitizer highlighted this issue in our code base. It
looks like the code is currently grabbing a pointer to a temporary
object and then performing operations on it.
I printed some information right before the asan crash:
eigensolver address: 0x7f0ad95032f0
eigensolver size: 4528
eig_vecs_ ptr: 0x7f0ad95045e0
eig_vecs_ offset: 4848
This shows that `eig_vecs_` points past the end of `eigensolver`. In
other words, it points at the temporary object created by the
`eigensolver.eigenvectors()` call.
Compare the docs for `.eigenvalues()`:
https://eigen.tuxfamily.org/dox/classEigen_1_1EigenSolver.html#a0f507ad7ab14797882f474ca8f2773e7
to the docs for `.eigenvectors()`:
https://eigen.tuxfamily.org/dox/classEigen_1_1EigenSolver.html#a66288022802172e3ee059283b26201d7
The difference in return types is interesting. `.eigenvalues()`
returns a reference. But `.eigenvectors()` returns a matrix.
This patch here fixes the problem by saving the temporary object and
then grabbing a pointer into it.
This is a curated snippet of the original asan failure:
==12==ERROR: AddressSanitizer: stack-use-after-scope on address 0x7fc633704640 at pc 0x7fc64f7f1593 bp 0x7ffe8875fc90 sp 0x7ffe8875fc88
READ of size 8 at 0x7fc633704640 thread T0
#0 0x7fc64f7f1592 in cv::usac::EssentialMinimalSolverStewenius5ptsImpl::estimate(std::__1::vector<int, std::__1::allocator<int> > const&, std::__1::vector<cv::Mat, std::__1::allocator<cv::Mat> >&) const /proc/self/cwd/external/com_github_opencv_opencv/modules/calib3d/src/usac/essential_solver.cpp:181:48
#1 0x7fc64f915d92 in cv::usac::EssentialEstimatorImpl::estimateModels(std::__1::vector<int, std::__1::allocator<int> > const&, std::__1::vector<cv::Mat, std::__1::allocator<cv::Mat> >&) const /proc/self/cwd/external/com_github_opencv_opencv/modules/calib3d/src/usac/estimator.cpp:110:46
#2 0x7fc64fa74fb0 in cv::usac::Ransac::run(cv::Ptr<cv::usac::RansacOutput>&) /proc/self/cwd/external/com_github_opencv_opencv/modules/calib3d/src/usac/ransac_solvers.cpp:152:58
#3 0x7fc64fa6cd8e in cv::usac::run(cv::Ptr<cv::usac::Model const> const&, cv::_InputArray const&, cv::_InputArray const&, int, cv::Ptr<cv::usac::RansacOutput>&, cv::_InputArray const&, cv::_InputArray const&, cv::_InputArray const&, cv::_InputArray const&) /proc/self/cwd/external/com_github_opencv_opencv/modules/calib3d/src/usac/ransac_solvers.cpp:1010:16
#4 0x7fc64fa6fb46 in cv::usac::findEssentialMat(cv::_InputArray const&, cv::_InputArray const&, cv::_InputArray const&, int, double, double, cv::_OutputArray const&) /proc/self/cwd/external/com_github_opencv_opencv/modules/calib3d/src/usac/ransac_solvers.cpp:527:9
#5 0x7fc64f3b5522 in cv::findEssentialMat(cv::_InputArray const&, cv::_InputArray const&, cv::_InputArray const&, int, double, double, int, cv::_OutputArray const&) /proc/self/cwd/external/com_github_opencv_opencv/modules/calib3d/src/five-point.cpp:437:16
#6 0x7fc64f3b7e00 in cv::findEssentialMat(cv::_InputArray const&, cv::_InputArray const&, cv::_InputArray const&, int, double, double, cv::_OutputArray const&) /proc/self/cwd/external/com_github_opencv_opencv/modules/calib3d/src/five-point.cpp:486:12
...
Address 0x7fc633704640 is located in stack of thread T0 at offset 17984 in frame
#0 0x7fc64f7ed4ff in cv::usac::EssentialMinimalSolverStewenius5ptsImpl::estimate(std::__1::vector<int, std::__1::allocator<int> > const&, std::__1::vector<cv::Mat, std::__1::allocator<cv::Mat> >&) const /proc/self/cwd/external/com_github_opencv_opencv/modules/calib3d/src/usac/essential_solver.cpp:36
This frame has 63 object(s):
[32, 56) 'coefficients' (line 38)
[96, 384) 'ee' (line 55)
...
[13040, 17568) 'eigensolver' (line 142)
[17824, 17840) 'ref.tmp518' (line 143)
[17856, 17872) 'ref.tmp523' (line 144)
[17888, 19488) 'ref.tmp524' (line 144) <== Memory access at offset 17984 is inside this variable
[19616, 19640) 'ref.tmp532' (line 169)
...
The crash report says that we're accessing a temporary object from
line 144 when we shouldn't be. Line 144 looks like this:
https://github.com/opencv/opencv/blob/4.6.0/modules/calib3d/src/usac/essential_solver.cpp#L144
const auto * const eig_vecs_ = (double *) eigensolver.eigenvectors().real().data();
We are using version 4.6.0 for this, but the problem is present on the
4.x branch.
Note that I am dropping the .real() call here. I think that is safe because
of the code further down (line 277 in the most recent version):
const int eig_i = 20 * i + 12; // eigen stores imaginary values too
The code appears to expect to have to skip doubles for the imaginary parts
of the complex numbers.
Admittedly, I couldn't find a test case that exercised this code path to
validate correctness.
Fix crash in ap3p #23607
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G-API: Introduce a Queue Source #24178
- Added a new IStreamSource class: in fact, a wrapper over a concurrent queue;
- Added minimal example on how it can be used;
- Extended IStreamSource with optional "halt" interface to break the blocking calls in the emitter threads when required to stop.
- Introduced a QueueInput class which allows to pass the whole graph's input vector at once. In fact it is a thin wrapper atop of individual Queue Sources.
There is a hidden trap found with our type system as described in https://github.com/orgs/g-api-org/discussions/2
While it works even in this form, it should be addressed somewhere in the 5.0 timeframe.
### Pull Request Readiness Checklist
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* add broadcast_to with tests
* change name
* fix test
* fix implicit type conversion
* replace type of shape with InputArray
* add perf test
* add perf tests which takes care of axis
* v2 from ficus expand
* rename to broadcast
* use randu in place of declare
* doc improvement; smaller scale in perf
* capture get_index by reference
If building with -mcpu=native or any other setting which implies the current
CPU has FP16 but with intrinsics disabled, we mistakenly try to use it even
though convolution.hpp conditionally defines it correctly based on whether
we should *use it*. convolution.cpp on the other hand was mismatched and
trying to use it if the CPU supported it, even if not enabled in the build
system.
Make the guards match.
Bug: https://bugs.gentoo.org/913031
Signed-off-by: Sam James <sam@gentoo.org>
Skip test on SkipTestException at fixture's constructor
* Skip test on SkipTestException at fixture's constructor
* Add warning supression
* Skip Python tests if no test file found
* Skip instances of test fixture with exception at SetUpTestCase
* Skip test with exception at SetUp method
* Try remove warning disable
* Add CV_NORETURN
* Remove FAIL assertion
* Use findDataFile to throw Skip exception
* Throw exception conditionally
* core:add OPENCV_IPP_MEAN/MINMAX/SUM option to enable IPP optimizations
* fix: to use guard HAVE_IPP and ocv_append_source_file_compile_definitions() macro.
* support OPENCV_IPP_ENABLE_ALL
* add document for OPENCV_IPP_ENABLE_ALL
* fix OPENCV_IPP_ENABLE_ALL comment
Fixed an off-by-1 buffer resize, the space for the null termination was forgotten.
Prefer snprintf, which can never overflow (if given the right size).
In one case I cheated and used strcpy, because I cannot figure out the buffer size at that point in the code.
OCL_FP16 MatMul with large batch
* Workaround FP16 MatMul with large batch
* Fix OCL reinitialization
* Higher thresholds for INT8 quantization
* Try fix gemm_buffer_NT for half (columns)
* Fix GEMM by rows
* Add batch dimension to InnerProduct layer test
* Fix Test_ONNX_conformance.Layer_Test/test_basic_conv_with_padding
* Batch 16
* Replace all vload4
* Version suffix for MobileNetSSD_deploy Caffe model
Rewrite Universal Intrinsic code by using new API: Core module. #23980
The goal of this PR is to match and modify all SIMD code blocks guarded by `CV_SIMD` macro in the `opencv/modules/core` folder and rewrite them by using the new Universal Intrinsic API.
The patch is almost auto-generated by using the [rewriter](https://github.com/hanliutong/rewriter), related PR #23885.
Most of the files have been rewritten, but I marked this PR as draft because, the `CV_SIMD` macro also exists in the following files, and the reasons why they are not rewrited are:
1. ~~code design for fixed-size SIMD (v_int16x8, v_float32x4, etc.), need to manually rewrite.~~ Rewrited
- ./modules/core/src/stat.simd.hpp
- ./modules/core/src/matrix_transform.cpp
- ./modules/core/src/matmul.simd.hpp
2. Vector types are wrapped in other class/struct, that are not supported by the compiler in variable-length backends. Can not be rewrited directly.
- ./modules/core/src/mathfuncs_core.simd.hpp
```cpp
struct v_atan_f32
{
explicit v_atan_f32(const float& scale)
{
...
}
v_float32 compute(const v_float32& y, const v_float32& x)
{
...
}
...
v_float32 val90; // sizeless type can not used in a class
v_float32 val180;
v_float32 val360;
v_float32 s;
};
```
3. The API interface does not support/does not match
- ./modules/core/src/norm.cpp
Use `v_popcount`, ~~waiting for #23966~~ Fixed
- ./modules/core/src/has_non_zero.simd.hpp
Use illegal Universal Intrinsic API: For float type, there is no logical operation `|`. Further discussion needed
```cpp
/** @brief Bitwise OR
Only for integer types. */
template<typename _Tp, int n> CV_INLINE v_reg<_Tp, n> operator|(const v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b);
template<typename _Tp, int n> CV_INLINE v_reg<_Tp, n>& operator|=(v_reg<_Tp, n>& a, const v_reg<_Tp, n>& b);
```
```cpp
#if CV_SIMD
typedef v_float32 v_type;
const v_type v_zero = vx_setzero_f32();
constexpr const int unrollCount = 8;
int step = v_type::nlanes * unrollCount;
int len0 = len & -step;
const float* srcSimdEnd = src+len0;
int countSIMD = static_cast<int>((srcSimdEnd-src)/step);
while(!res && countSIMD--)
{
v_type v0 = vx_load(src);
src += v_type::nlanes;
v_type v1 = vx_load(src);
src += v_type::nlanes;
....
src += v_type::nlanes;
v0 |= v1; //Illegal ?
....
//res = v_check_any(((v0 | v4) != v_zero));//beware : (NaN != 0) returns "false" since != is mapped to _CMP_NEQ_OQ and not _CMP_NEQ_UQ
res = !v_check_all(((v0 | v4) == v_zero));
}
v_cleanup();
#endif
```
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dnn: cleanup of tengine backend #24122🚀 Cleanup for OpenCV 5.0. Tengine backend is added for convolution layer speedup on ARM CPUs, but it is not maintained and the convolution layer on our default backend has reached similar performance to that of Tengine.
Tengine backend related PRs:
- https://github.com/opencv/opencv/pull/16724
- https://github.com/opencv/opencv/pull/18323
### Pull Request Readiness Checklist
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Invalid memory access fix for ONNX split layer parser #24076#24101
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- [x] The PR is proposed to the proper branch
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TFLite models on different backends (tests and improvements) #24039
### Pull Request Readiness Checklist
* MaxUnpooling with OpenVINO
* Fully connected with transposed inputs/weights with OpenVINO
* Enable backends tests for TFLite (related to https://github.com/opencv/opencv/issues/23992#issuecomment-1640691722)
* Increase existing tests thresholds
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* started working on adding 32u, 64u, 64s, bool and 16bf types to OpenCV
* core & imgproc tests seem to pass
* fixed a few compile errors and test failures on macOS x86
* hopefully fixed some compile problems and test failures
* fixed some more warnings and test failures
* trying to fix small deviations in perf_core & perf_imgproc by revering randf_64f to exact version used before
* trying to fix behavior of the new OpenCV with old plugins; there is (quite strong) assumption that video capture would give us frames with depth == CV_8U (0) or CV_16U (2). If depth is > 7 then it means that the plugin is built with the old OpenCV. It needs to be recompiled, of course and then this hack can be removed.
* try to repair the case when target arch does not have FP64 SIMD
* 1. fixed bug in itoa() found by alalek
2. restored ==, !=, > and < univ. intrinsics on ARM32/ARM64.
Resolve uncovered CUDA dnn layer #24080
### Pull Request Readiness Checklist
* Gelu activation layer on CUDA
* Try to relax GEMM from ONNX
resolves https://github.com/opencv/opencv/issues/24064
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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Remove legacy nGraph logic #24072
### Pull Request Readiness Checklist
TODO:
- [x] Test with OpenVINO 2021.4 (tested locally)
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DetectionOutput layer on OpenVINO without limitations #24069
### Pull Request Readiness Checklist
required for https://github.com/opencv/opencv/pull/23987
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G-API: Support CUDA & TensoRT Execution Providers for ONNXRT Backend #24059
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Merge usac to 5.x #24005
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Base branch is PR #23979. Merging PR #23078, 23900 and PR #23806 to 5.x
PReLU with element-wise scales #24056
### Pull Request Readiness Checklist
resolves https://github.com/opencv/opencv/issues/24051
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Update opencv dnn to support cann version >=6.3 #23936
1.modify the search path of "libopsproto.so" in OpenCVFindCANN.cmake
2.add the search path of "libgraph_base.so" in OpenCVFindCANN.cmake
3.automatic check Ascend socVersion,and test on Ascend310/Ascend310B/Ascend910B well
Python typing refinement for dnn_registerLayer/dnn_unregisterLayer functions #24066
This patch introduces typings generation for `dnn_registerLayer`/`dnn_unregisterLayer` manually defined in [`cv2/modules/dnn/misc/python/pyopencv_dnn.hpp`](https://github.com/opencv/opencv/blob/4.x/modules/dnn/misc/python/pyopencv_dnn.hpp)
Updates:
- Add `LayerProtocol` to `cv2/dnn/__init__.pyi`:
```python
class LayerProtocol(Protocol):
def __init__(
self, params: dict[str, DictValue],
blobs: typing.Sequence[cv2.typing.MatLike]
) -> None: ...
def getMemoryShapes(
self, inputs: typing.Sequence[typing.Sequence[int]]
) -> typing.Sequence[typing.Sequence[int]]: ...
def forward(
self, inputs: typing.Sequence[cv2.typing.MatLike]
) -> typing.Sequence[cv2.typing.MatLike]: ...
```
- Add `dnn_registerLayer` function to `cv2/__init__.pyi`:
```python
def dnn_registerLayer(layerTypeName: str,
layerClass: typing.Type[LayerProtocol]) -> None: ...
```
- Add `dnn_unregisterLayer` function to `cv2/__init__.pyi`:
```python
def dnn_unregisterLayer(layerTypeName: str) -> None: ...
```
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Fix harmless ASAN error. #24042
For an empty radius, &v[0] would be accessed (though the called functions would not use it due to v.size() being 0). Also add checks for emptyness and fix the first element checks, in case we get INT_MAX to compare to.
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G-API: Support DirectML Execution Provider for ONNXRT Backend #24045
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GSoC: Modified PLY reader to support color attribute read/write #23805
* Modified PLY reader to support color attribute read/write
* Fix bugs & Modified OBJ reader for color IO
* Replace with correct test file
* Fix I/O of property [w] in OBJ files
### Pull Request Readiness Checklist
**Merged with https://github.com/opencv/opencv_extra/pull/1075**
[Issue for GSoC 2023](https://github.com/opencv/opencv/issues/23624)
The ply loader in 3D module doesn't support color attribute reading. I modified that to support color attribute reading & writing for the color attribute compression as described in proposal.
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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feat: add cuda_Stream and cuda_GpuMat to simple types mapping #24029
This patch fixes usage of `cuda::Stream` in function arguments.
Affected modules: `cudacodec`:
[`using namespace cuda`](9dfe233020/modules/cudacodec/include/opencv2/cudacodec.hpp (L62)) in public `cudacodec.hpp` header can be removed after merge of the patch.
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Fix FLANN python bindings #24028
As a side-effect this patch improves reporting errors by FLANN `get_param`.
resolves#21642
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G-API: Support OpenVINO Execution Provider for ONNXRT Backend #24024
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[TFLite] Pack layer and other fixes for SSD from Keras #24004
### Pull Request Readiness Checklist
resolves https://github.com/opencv/opencv/issues/23992
**Merge with extra**: https://github.com/opencv/opencv_extra/pull/1076
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Details here: https://bugs.chromium.org/p/oss-fuzz/issues/detail?id=58006
runtime error: call to function (unknown) through pointer to incorrect function type 'void (*)(const unsigned char **, const int *, unsigned char **, const int *, int, int)'
Python typing magic constants #24023
This patch adds typing stubs generation for `__all__` and `__version__` constants.
Introduced `__all__` is intentionally empty for all generated modules stubs.
Type hints won't work for star imports
resolves#23950
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Fix python typing stubs generation for CUDA modules #24022resolves#23946resolves#23945resolvesopencv/opencv-python#871
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C++20 made it invalid to use simple-template-ids for constructors and destructors: https://eel.is/c++draft/diff.cpp17.class#2
GCC 11 and later throw an error on this, with the unhelpful message `expected unqualified-id before ')' token`. This PR fixes the problem.
DNN: optimize the speed of general Depth-wise #23952
Try to solve the issue: https://github.com/opencv/opencv/issues/23941
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Add V4L2_PIX_FMT_Y16_BE pixel format #18498
Address #18495
relates to #23944
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- [ ] Test using Melexis MLX90640
Fix imgwarp at borders when transparent. #23922
I believe this is a proper fix to #23562
The PR #23754 overwrites data while that should not be the case with transparent data. The original test is failing because points at the border do not get computed because they do not have 4 neighbors to be computed. Still ,we can approximate their computation with whatever neighbors that are available.
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Fix checkSignature not thread safe for AVIF. #23943
A common decoder cannot be shared with checkSignature which is used like a static function (on a static ist of decoders).
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Adds missing typing stubs:
- Matrix depths: `CV_8U`, `CV_8S` and etc.
- Matrix type constants: `CV_8UC1`, `CV_32FC3` and etc.
- Matrix type factory functions: `CV_*(channels) -> int` and `CV_MAKETYPE`
Fixing typos in usac #23900
Just read and correct some typos in `usac`
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G-API: Fix async inference for OpenVINO backend #23884
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dnn: disable warning when loading a fp16 model #23853
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Add charuco board check #23647
Added charuco board checking to avoid detection of incorrect board.
Fixes#23517
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Fix detect diamonds api #23848
`detectDiamonds` cannot be called from python, reproducer:
```
import numpy as np
import cv2 as cv
detector = cv.aruco.CharucoDetector(
cv.aruco.CharucoBoard(
(3, 3), 200.0, 100.0,
cv.aruco.getPredefinedDictionary(cv.aruco.DICT_4X4_250)
)
)
image = np.zeros((640, 480, 1), dtype=np.uint8)
res = detector.detectDiamonds(image)
print(res)
```
The error in `detectDiamonds` API fixed by replacing `InputOutputArrayOfArrays markerIds` with `InputOutputArray markerIds`.
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Python binding for RotatedRect #23702
### Pull Request Readiness Checklist
related: https://github.com/opencv/opencv/issues/23546#issuecomment-1562894602
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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G-API: Fix incorrect OpaqueKind for Kernel outputs #23843
### Pull Request Readiness Checklist
#### Overview
The PR is going to fix several problems:
1. Major: `GKernel` doesn't hold `kind` for its outputs. Since `GModelBuilder` traverse graph from outputs to inputs once it reaches any output of the operation it will use its `kind` to create `Data` meta for all operation outputs. Since it essential for `python` to know `GTypeInfo` (which is `shape` and `kind`) it will be confused.
Consider this operation:
```
@cv.gapi.op('custom.square_mean', in_types=[cv.GArray.Int], out_types=[cv.GOpaque.Float, cv.GArray.Int])
class GSquareMean:
@staticmethod
def outMeta(desc):
return cv.empty_gopaque_desc(), cv.empty_array_desc()
```
Even though `GOpaque` is `Float`, corresponding metadata might have `Int` kind because it might be taken from `cv.GArray.Int`
so it will be a problem if one of the outputs of these operation is graph output because python will cast it to the wrong type based on `Data` meta.
2. Minor: Some of the OpenVINO `IR`'s doesn't any layout information for input. It's usually true only for `IRv10` but since `OpenVINO 2.0` need this information to correctly configure resize we need to put default layout if there no such assigned in `ov::Model`.
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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G-API: Implement InferROI, InferList, InferList2 for OpenVINO backend #23799
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G-API: Align IE Backend with the latest OpenVINO version #23796
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Fix distransform to work with large images #22798
This attempts to fix the following bug which was caused by storing squares of large integers into 32-bit floating point variables:
https://github.com/opencv/opencv/issues/22732
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Export enums ALL_CAPS version to typing stub files #23816
- Export ALL_CAPS versions alongside from normal names for enum constants, since both versions are available in runtime
- Change enum names entries comments to documentary strings
Before patch
```python
RMat_Access_R: int
RMat_Access_W: int
RMat_Access = int # One of [R, W]
```
After patch
```python
RMat_Access_R: int
RMAT_ACCESS_R: int
RMat_Access_W: int
RMAT_ACCESS_W: int
RMat_Access = int
"""One of [RMat_Access_R, RMAT_ACCESS_R, RMat_Access_W, RMAT_ACCESS_W]"""
```
Resolves: #23776
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Build Java without ANT #23724
### Pull Request Readiness Checklist
Enables a path of building Java bindings without ANT
* Able to build OpenCV JAR and Docs without ANT
```
-- Java:
-- ant: NO
-- JNI: /usr/lib/jvm/default-java/include /usr/lib/jvm/default-java/include/linux /usr/lib/jvm/default-java/include
-- Java wrappers: YES
-- Java tests: NO
```
* Possible to build OpenCV JAR without ANT but tests still require ANT
**Merge with**: https://github.com/opencv/opencv_contrib/pull/3502
Notes:
- Use `OPENCV_JAVA_IGNORE_ANT=1` to force "Java" flow for building Java bindings
- Java tests still require Apache ANT
- JAR doesn't include `.java` source code files.
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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Keep inliers for linear remap with BORDER_TRANSPARENT #23754
Address https://github.com/opencv/opencv/issues/23562
### Pull Request Readiness Checklist
resolves https://github.com/opencv/opencv/issues/23562
I do think that this is a bug because with `INTER_CUBIC + BORDER_TRANSPARENT` the last column and row are preserved. So same should be done for `INTER_LINEAR`
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Update USAC #23078
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added Aruco MIP dictionaries #23785
added Aruco MIP dictionaries: DICT_ARUCO_MIP_16h3, DICT_ARUCO_MIP_25h7, DICT_ARUCO_MIP_36h12 from [Aruco.js](https://github.com/damianofalcioni/js-aruco2), converted in opencv format using https://github.com/damianofalcioni/js-aruco2/blob/master/src/dictionaries/utils/dic2opencv.js
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G-API: Expose explicit preprocessing for IE Backend #23786
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G-API: Refine Semantic Segmentation Demo #23766
### Overview
* Supported demo working with camera id (e.g `--input=0`)
* Supported 3d output segmentation models (e.g `deeplabv3`)
* Supported `desync` execution
* Supported higher camera resolution
* Changed the color map to pascal voc (https://cloud.githubusercontent.com/assets/4503207/17803328/1006ca80-65f6-11e6-9ff6-36b7ef5b9ac6.png)
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Although acceptible to Intel CPUs, it's still undefined behaviour according to the C++ standard.
It can be replaced with memcpy, which makes the code simpler, and it generates the same assembly code with gcc and clang with -O2 (verified with godbolt).
Also expanded the test to include other little endian CPUs by testing for __LITTLE_ENDIAN__.
Add AVIF support through libavif. #23596
This is to fix https://github.com/opencv/opencv/issues/19271
Extra: https://github.com/opencv/opencv_extra/pull/1069
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DNN: fix bug for X86 Winograd #23763
Address https://github.com/opencv/opencv/issues/23760
The patch aims to add a runtime check for X86 platform without AVX(2).
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Assertion Fix in Split Layer #23746
### Pull Request Readiness Checklist
This PR fixes issue mentioned in [#23663](https://github.com/opencv/opencv/issues/23663)
Merge with https://github.com/opencv/opencv_extra/pull/1067
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imgproc: add contour values check to IntelligentScissorsMB tests
Preparation for the #21959 changes as per @asmorkalov's https://github.com/opencv/opencv/pull/21959#issuecomment-1560511500 suggestion.
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[G-API] Implement OpenVINO 2.0 backend #23595
### Pull Request Readiness Checklist
Implemented basic functionality for `OpenVINO` 2.0 G-API backend.
#### Overview
- [x] Implement `Infer` kernel with some of essential configurable parameters + IR/Blob models format support.
- [ ] Implement the rest of kernels: `InferList`, `InferROI`, `Infer2` + other configurable params (e.g reshape)
- [x] Asyncrhonous execution support
- [ ] Remote context support
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GAPI Fluid SIMD:Add support of new several types for the Merge3
- Support of the new several types was added.
- Fixes for the Split/Merge and ConvertTo issues.
G-API: Integration branch for ONNX & Python-related changes #23597
# Changes overview
## 1. Expose ONNX backend's Normalization and Mean-value parameters in Python
* Since Python G-API bindings rely on `Generic` infer to express Inference, the `Generic` specialization of `onnx::Params` was extended with new methods to control normalization (`/255`) and mean-value; these methods were exposed in the Python bindings
* Found some questionable parts in the existing API which I'd like to review/discuss (see comments)
UPD:
1. Thanks to @TolyaTalamanov normalization inconsistencies have been identified with `squeezenet1.0-9` ONNX model itself; tests using these model were updated to DISABLE normalization and NOT using mean/value.
2. Questionable parts were removed and tests still pass.
### Details (taken from @TolyaTalamanov's comment):
`squeezenet1.0.*onnx` - doesn't require scaling to [0,1] and mean/std because the weights of the first convolution already scaled. ONNX documentation is broken. So the correct approach to use this models is:
1. ONNX: apply preprocessing from the documentation: https://github.com/onnx/models/blob/main/vision/classification/imagenet_preprocess.py#L8-L44 but without normalization step:
```
# DON'T DO IT:
# mean_vec = np.array([0.485, 0.456, 0.406])
# stddev_vec = np.array([0.229, 0.224, 0.225])
# norm_img_data = np.zeros(img_data.shape).astype('float32')
# for i in range(img_data.shape[0]):
# norm_img_data[i,:,:] = (img_data[i,:,:]/255 - mean_vec[i]) / stddev_vec[i]
# # add batch channel
# norm_img_data = norm_img_data.reshape(1, 3, 224, 224).astype('float32')
# return norm_img_data
# INSTEAD
return img_data.reshape(1, 3, 224, 224)
```
2. G-API: Convert image from BGR to RGB and then pass to `apply` as-is with configuring parameters:
```
net = cv.gapi.onnx.params('squeezenet', model_filename)
net.cfgNormalize('data_0', False)
```
**Note**: Results might be difference because `G-API` doesn't apply central crop but just do resize to model resolution.
---
`squeezenet1.1.*onnx` - requires scaling to [0,1] and mean/std - onnx documentation is correct.
1. ONNX: apply preprocessing from the documentation: https://github.com/onnx/models/blob/main/vision/classification/imagenet_preprocess.py#L8-L44
2. G-API: Convert image from BGR to RGB and then pass to `apply` as-is with configuring parameters:
```
net = cv.gapi.onnx.params('squeezenet', model_filename)
net.cfgNormalize('data_0', True) // default
net.cfgMeanStd('data_0', [0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
```
**Note**: Results might be difference because `G-API` doesn't apply central crop but just do resize to model resolution.
## 2. Expose Fluid & kernel package-related functionality in Python
* `cv::gapi::combine()`
* `cv::GKernelPackage::size()` (mainly for testing purposes)
* `cv::gapi::imgproc::fluid::kernels()`
Added a test for the above.
## 3. Fixed issues with Python stateful kernel handling
Fixed error message when `outMeta()` of custom python operation fails.
## 4. Fixed various issues in Python tests
1. `test_gapi_streaming.py` - fixed behavior of Desync test to avoid sporadic issues
2. `test_gapi_infer_onnx.py` - fixed model lookup (it was still using the ONNX Zoo layout but was NOT using the proper env var we use to point to one).
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better accuracy for _rotatedRectangleIntersection() (proposal for #23546) #23690
_rotatedRectangleIntersection() can be (statically) customized to use double instead of float for better accuracy
this is a proposal for experimentation around #23546
for better accuracy, _rotatedRectangleIntersection() could use double. It will still return cv::Point2f list for backward compatibility, but the inner computations are controlled by a typedef
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Re-implement named parameters bindings for Python #23705
Reverted named argument handling from #19156.
Ported new solution from #23224
The port is required to harmonize 4.x -> 5.x merges.
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imgproc: add basic IntelligentScissorsMB performance test #23698
Adding basic performance test that can be used before and after the #21959 changes etc. as per @asmorkalov's https://github.com/opencv/opencv/pull/21959#issuecomment-1565240926 comment.
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Pointer arithmetic overflow is always undefined, whether signed or unsigned.
It warned here:
`Addition of unsigned offset to 0x00017fd31b97 overflowed to 0x00017fd30c97`
Convert the offset to a signed number, so that we can offset either forward or backwards.
In my own use of OpenCV at least, this is the only case of pointer arithmetic overflow.
Import and export np.float16 in Python #23691
### Pull Request Readiness Checklist
* Also, fixes `cv::norm` with `NORM_INF` and `CV_16F`
resolves https://github.com/opencv/opencv/issues/23687
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Python typing stub generation #20370
Add stub generation to `gen2.py`, addressing #14590.
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Python bindings for CV_8UC(n) and other types macros #23679
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resolves https://github.com/opencv/opencv/issues/23628#issuecomment-1562468327
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Support ONNX operator QLinearSoftmax in dnn #23655
Resolves https://github.com/opencv/opencv/issues/23636.
Merge with https://github.com/opencv/opencv_extra/pull/1064.
This PR maps the QLinearSoftmax (from com.microsoft domain) to SoftmaxInt8 in dnn along with some speed optimization.
Todo:
- [x] support QLinearSoftmax with opset = 13
- [x] add model and test data for QLinearSoftmax with opset = 13
- [x] ensure all models have dims >= 3.
- [x] add the script to generate model and test data
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CV_MAKETYPE Python binding #23674
### Pull Request Readiness Checklist
resolves https://github.com/opencv/opencv/issues/23628
```python
import cv2 as cv
t = cv.CV_MAKETYPE(cv.CV_32F, 4)
```
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Fix truncated sentenced in boxPoints documentation #22975#23662Resolves#22975
Completed the sentence as per the suggestion given in the issue #22975
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