The new C++ code is copy/pasted from OpenCV5:
- functions initIntrinsicParams2D, subMatrix (the first 160 lines)
- function prepareDistCoeffs
- the different asserts
Not all the API/code is ported to C++ yet to ease the review.
HAL added for add(array, scalar) #25624
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int64 data type support for FileStorage. 1d and empty Mat with exact dimensions #26434
### Pull Request Readiness Checklist
Port of https://github.com/opencv/opencv/pull/26399 to 4.x branch
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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Backport C++ stereo/stereo_geom.cpp:5.x to calib3d/stereo_geom.cpp:4.x #26437
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Fix incorrect string format in js build script #26374
I accidentally met this small problem mentioned in https://github.com/opencv/opencv/pull/25084#discussion_r1710838120 when play with wasm build. It seems https://github.com/EDVTAZ didn't fix it yet, so I create this tiny pr.
Additionally, I remove a redundant argument in `add_argument` call. `'store_true'` already set the default, see https://docs.python.org/3/library/argparse.html#action.
### Pull Request Readiness Checklist
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Use LMUL=2 in the RISC-V Vector (RVV) backend of Universal Intrinsic. #26318
The modification of this patch involves the RVV backend of Universal Intrinsic, replacing `LMUL=1` with `LMUL=2`.
Now each Universal Intrinsic type actually corresponds to two RVV vector registers, and each Intrinsic function also operates two vector registers. Considering that algorithms written using Universal Intrinsic usually do not use the maximum number of registers, this can help the RVV backend utilize more register resources without modifying the algorithm implementation
This patch is generally beneficial in performance.
We compiled OpenCV with `Clang-19.1.1` and `GCC-14.2.0` , ran it on `CanMV-k230` and `Banana-Pi F3`. Then we have four scenarios on combinations of compilers and devices. In `opencv_perf_core`, there are 3363 cases, of which:
- 901 (26.8%) cases achieved more than `5%` performance improvement in all four scenarios, and the average speedup of these test cases (compared to scalar) increased from `3.35x` to `4.35x`
- 75 (2.2%) cases had more than `5%` performance loss in all four scenarios, indicating that these cases are better with `LMUL=1` instead of `LMUL=2`. This involves `Mat_Transform`, `hasNonZero`, `KMeans`, `meanStdDev`, `merge` and `norm2`. Among them, `Mat_Transform` only has performance degradation in a few cases (`8UC3`), and the actual execution time of `hasNonZero` is so short that it can be ignored. For `KMeans`, `meanStdDev`, `merge` and `norm2`, we should be able to use the HAL to optimize/restore their performance. (In fact, we have already done this for `merge` #26216 )
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Patch to opencv_extra has the same branch name.
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