Finally dropped convertFp16 function in favor of cv::Mat::convertTo() #26327
Partially address https://github.com/opencv/opencv/issues/24909
Related PR to contrib: https://github.com/opencv/opencv_contrib/pull/3812
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Fix#26322: construction of another Mat header for empty matrix #26333
The PR fixes#26322
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New dnn engine #26056
This is the 1st PR with the new engine; CI is green and PR is ready to be merged, I think.
Merge together with https://github.com/opencv/opencv_contrib/pull/3794
---
**Known limitations:**
* [solved] OpenVINO is temporarily disabled, but is probably easy to restore (it's not a deal breaker to merge this PR, I guess)
* The new engine does not support any backends nor any targets except for the default CPU implementation. But it's possible to choose the old engine when loading a model, then all the functionality is available.
* [Caffe patch is here: #26208] The new engine only supports ONNX. When a model is constructed manually or is loaded from a file of different format (.tf, .tflite, .caffe, .darknet), the old engine is used.
* Even in the case of ONNX some layers are not supported by the new engine, such as all quantized layers (including DequantizeLinear, QuantizeLinear, QLinearConv etc.), LSTM, GRU, .... It's planned, of course, to have full support for ONNX by OpenCV 5.0 gold release. When a loaded model contains unsupported layers, we switch to the old engine automatically (at ONNX parsing time, not at `forward()` time).
* Some layers , e.g. Expat, are only partially supported by the new engine. In the case of unsupported flavours it switches to the old engine automatically (at ONNX parsing time, not at `forward()` time).
* 'Concat' graph optimization is disabled. The optimization eliminates Concat layer and instead makes the layers that generate tensors to be concatenated to write the outputs to the final destination. Of course, it's only possible when `axis=0` or `axis=N=1`. The optimization is not compatible with dynamic shapes since we need to know in advance where to store the tensors. Because some of the layer implementations have been modified to become more compatible with the new engine, the feature appears to be broken even when the old engine is used.
* Some `dnn::Net` API is not available with the new engine. Also, shape inference may return false if some of the output or intermediate tensors' shapes cannot be inferred without running the model. Probably this can be fixed by a dummy run of the model with zero inputs.
* Some overloads of `dnn::Net::getFLOPs()` and `dnn::Net::getMemoryConsumption()` are not exposed any longer in wrapper generators; but the most useful overloads are exposed (and checked by Java tests).
* [in progress] A few Einsum tests related to empty shapes have been disabled due to crashes in the tests and in Einsum implementations. The code and the tests need to be repaired.
* OpenCL implementation of Deconvolution is disabled. It's very bad and very slow anyway; need to be completely revised.
* Deconvolution3D test is now skipped, because it was only supported by CUDA and OpenVINO backends, both of which are not supported by the new engine.
* Some tests, such as FastNeuralStyle, checked that the in the case of CUDA backend there is no fallback to CPU. Currently all layers in the new engine are processed on CPU, so there are many fallbacks. The checks, therefore, have been temporarily disabled.
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In that function, the floats are cast to int to be compared to 0.
But a float can be -0 or +0, hence
define CHECK_NZ_FP(x) ((x)*2 != 0)
to remove the sign bit. Except that can trigger the sanitizer:
runtime error: signed integer overflow: -1082130432 * 2 cannot be represented in type 'int'
Doing everything in uint instead of int is properly defined by the
standard.
extended Norm tests to prove that cv::norm() already supports all the types.
cv::norm() already provides enough functionality; just extended tests to prove it. See #24887
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C-API cleanup: moved cvErrorStr to new interface, minor ts changes #26101
Merge with opencv/opencv_contrib#3786
**Note:** `toString` might be too generic name (even though it is in `cv::Error::` namespace), another variant is `codeToString` (we have `typeToString` and `depthToString` in check.hpp).
**Note:** _ts_ module seem to have no other C API usage except for `ArrayTest` class which requires refactoring.
Added offset for HAL as ofs2idx expects 1-based index #26080
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Fixed the simd bugs of iPow8u and iPow16u #26061
Add the following cases in opencv_perf_core:
* OCL_PowFixture_iPow.iPow/0, where GetParam() = (640x480, 8UC1)
* OCL_PowFixture_iPow.iPow/2, where GetParam() = (640x480, 16UC1)
iPow8u and iPow16u failed to call to simd accelerating while executing.
Fix the bug by changing the input type of iPow_SIMD function.
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imgproc: add specific error code when cvtColor is used on an image with an invalid number of channels #25981close#25971
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Add support for QNX #25832
Build and test instruction for QNX:
https://github.com/chachoi-world/qnx-ports/blob/main/opencv/README.md
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To be on par with `cv::Mat`, let's add `cv::cuda::GpuMat::getStdAllocator()`
This is useful anyway, because when a user wants to use custom allocators, he might want to resort to the standard default allocator behaviour, not some other allocator that could have been set by `setDefaultAllocator()`
Mask support with CV_Bool in ts and core #25902
Partially cover https://github.com/opencv/opencv/issues/25895
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HAL for dot product added #25936
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Added flag to GaussianBlur for faster but not bit-exact implementation #25792
Rationale:
Current implementation of GaussianBlur is almost always bit-exact. It helps to get predictable results according platforms, but prohibits most of approximations and optimization tricks.
The patch converts `borderType` parameter to more generic `flags` and introduces `GAUSS_ALLOW_APPROXIMATIONS` flag to allow not bit-exact implementation. With the flag IPP and generic HAL implementation are called first. The flag naming and location is a subject for discussion.
Replaces https://github.com/opencv/opencv/pull/22073
Possibly related issue: https://github.com/opencv/opencv/issues/24135
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Mark cv::Mat(Mat&&) as noexcept #25899
This fixes https://github.com/opencv/opencv/issues/25065
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Fill mean and stdDev tails with zeros for HAL branch in meanStdDev #25789
as it's done for other branches.
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Tests added for mixed type arithmetic operations #25671
### Changes
* added accuracy tests for mixed type arithmetic operations
_Note: div-by-zero values are removed from checking since the result is implementation-defined in common case_
* added perf tests for the same cases
* fixed a typo in `getMulExtTab()` function that lead to dead code
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core: try to solve warnings caused by Apple's new LAPACK interface #24804
Resolves https://github.com/opencv/opencv/issues/24660
Apple's BLAS documentation: https://developer.apple.com/documentation/accelerate/blas?language=objc
New interface since macOS >= 13.3, iOS >= 16.4.
Todo:
- [x] Detect macOS version.
- [x] ~Detect iOS versions (major and minor version).~ No calling of Accelerate New LAPACK on iOS.
- [x] Solve calling `cblas_cgemm` and `cblas_zgemm`.
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Add support for scalar and matrix multiplication in einsum #25595
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HAL mul8x8to16 added #25506Fixes#25034
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Check range for type-dependant function tables #25598
Address https://github.com/opencv/opencv/issues/24703
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Additional fixes to 0/1D tests #25487
This has additional fixes requited for 0/1D tests.
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Transform offset to indeces for MatND in minMaxIdx HAL #25563
Address comments in https://github.com/opencv/opencv/pull/25553
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Merge pull request #25554 from savuor:rv/hal_lut
HAL for LUT added #25554
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