(5.x) Merge 4.x

[asmorkalov]

Contrib: opencv/opencv_contrib#3578
Extra: opencv/opencv_extra#1109

#23109 from seanm:misc-warnings
#23987 from dkurt:openvino_int8_backend
#24233 from jvuillaumier:rotate_flip_hal_hooks
#24234 from dkurt:distanceTransform_max_dist
#24240 from asmorkalov:as/vl4_convert_rgb
#24298 from WanliZhong:extend_perf_net_test
#24315 from fengyuentau:gemm_fixes
#24323 from georgthegreat:akaze-variadic
#24324 from hanliutong:rewrite-fluid
#24325 from hanliutong:rewrite
#24333 from definitelyuncertain:CvtRGB2YUV422
#24337 from mshabunin:bump-ade-012c
#24341 from emmanuel-ferdman:wip
#24342 from asmorkalov:as/java_test_status
#24343 from mshabunin:fix-test-writes
#24344 from asmorkalov:as/einsum_fallback_fix
#24349 from AleksandrPanov:aruco_check_board_separation
#24350 from dkurt:py_return_non_utf8_string
#24351 from sergiomb2:master
#24353 from alexlyulkov:al/fixed-cumsum-layer
#24354 from asmorkalov:as/charuco_ub
#24355 from AleksandrPanov:dynamic_window_in_aruco_cornerRefinement
#24356 from VadimLevin:dev/vlevin/typing-re-export
#24359 from asmorkalov:as/FastNeuralStyle_eccv16_tuning
#24362 from dkurt:enable_ov_2023_tests
#24368 from mshabunin:rvv-clang-17
#24370 from LiuPeiqiCN:4.x
#24386 from fengyuentau:fix_dtype_nary_eltwise
#24401 from mshabunin:enable-riscv-ci

Previous "Merge 4.x": #24338

[opencv-alalek]

I expect to have these extra changes:

diff --git a/modules/3d/src/precomp.hpp b/modules/3d/src/precomp.hpp
index 7d96a4becc..ef6bf01297 100755
--- a/modules/3d/src/precomp.hpp
+++ b/modules/3d/src/precomp.hpp
@@ -89,7 +89,7 @@ namespace cv {
  * @param ep outlier ratio
  * @param modelPoints number of model points required for estimation
  * @param maxIters maximum number of iterations
- * @return
+ * @return The number of iterations according to the formula
  * \f[
  * \frac{\ln(1-p)}{\ln\left(1-(1-ep)^\mathrm{modelPoints}\right)}
  * \f]
diff --git a/modules/core/include/opencv2/core/matx.hpp b/modules/core/include/opencv2/core/matx.hpp
index e7a7694e9f..fae4526584 100644
--- a/modules/core/include/opencv2/core/matx.hpp
+++ b/modules/core/include/opencv2/core/matx.hpp
@@ -1002,8 +1002,6 @@ Matx<_Tp, m, n> MatxCommaInitializer<_Tp, m, n>::operator *() const
     return *dst;
 }
 
-
-
 /////////////////////////////////// Vec Implementation ///////////////////////////////////
 
 template<typename _Tp, int cn> inline
diff --git a/modules/core/src/arithm.simd.hpp b/modules/core/src/arithm.simd.hpp
index 2c26680b51..3f2f3f10a1 100644
--- a/modules/core/src/arithm.simd.hpp
+++ b/modules/core/src/arithm.simd.hpp
@@ -104,10 +104,6 @@ namespace cv { namespace hal {
 
 #ifdef ARITHM_DEFINITIONS_ONLY
 
-#if !(CV_SIMD_64F || CV_SIMD_SCALABLE_64F)
-typedef int v_float64; // dummy
-#endif
-
 //=======================================
 // Utility
 //=======================================
diff --git a/modules/core/test/test_io.cpp b/modules/core/test/test_io.cpp
index 71994d1cd6..c08e6ba17a 100644
--- a/modules/core/test/test_io.cpp
+++ b/modules/core/test/test_io.cpp
@@ -690,6 +690,7 @@ static void test_filestorage_basic(int write_flags, const char* suffix_name, boo
             }
             std::cout << "Storage size: " << sz << std::endl;
             EXPECT_LE(sz, (size_t)6000);
+
         }
         {   /* read */
             cv::FileStorage fs(name, cv::FileStorage::READ + (useMemory ? cv::FileStorage::MEMORY : 0));
@@ -768,10 +769,10 @@ static void test_filestorage_basic(int write_flags, const char* suffix_name, boo
         if (useMemory) {
             EXPECT_EQ(0, cv::norm(_rd_in, _rd_out, NORM_INF));
         }
-        if (testReadWrite && !useMemory && !generateTestData) {
+        if (testReadWrite && !useMemory && !generateTestData)
+        {
             EXPECT_EQ(0, remove(name.c_str()));
         }
-
     }
 }
 
diff --git a/modules/dnn/perf/perf_net.cpp b/modules/dnn/perf/perf_net.cpp
index a8ad06a30d..46484e82c5 100644
--- a/modules/dnn/perf/perf_net.cpp
+++ b/modules/dnn/perf/perf_net.cpp
@@ -269,7 +269,8 @@ PERF_TEST_P_(DNNTestNetwork, Inception_v2_Faster_RCNN)
         (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16))
         throw SkipTestException("");
     processNet("dnn/faster_rcnn_inception_v2_coco_2018_01_28.pb",
-               "dnn/faster_rcnn_inception_v2_coco_2018_01_28.pbtxt", cv::Size(800, 600));
+               "dnn/faster_rcnn_inception_v2_coco_2018_01_28.pbtxt",
+               cv::Size(800, 600));
 }
 
 PERF_TEST_P_(DNNTestNetwork, EfficientDet)
diff --git a/modules/stereo/src/precomp.hpp b/modules/stereo/src/precomp.hpp
index a786466ae6..24f8f675b8 100644
--- a/modules/stereo/src/precomp.hpp
+++ b/modules/stereo/src/precomp.hpp
@@ -65,7 +65,7 @@ namespace cv
  * @param ep outlier ratio
  * @param modelPoints number of model points required for estimation
  * @param maxIters maximum number of iterations
- * @return
+ * @return The number of iterations according to the formula
  * \f[
  * \frac{\ln(1-p)}{\ln\left(1-(1-ep)^\mathrm{modelPoints}\right)}
  * \f]
diff --git a/samples/dnn/README.md b/samples/dnn/README.md
index 8c06b262ee..c99b735a1f 100644
--- a/samples/dnn/README.md
+++ b/samples/dnn/README.md
@@ -50,14 +50,14 @@ python your_script.py
 **Note** that you can provide a directory using **save_dir** parameter or via **OPENCV_SAVE_DIR** environment variable.
 
 #### Face detection
-[An origin model](https://github.com/opencv/opencv/tree/4.x/samples/dnn/face_detector)
+[An origin model](https://github.com/opencv/opencv/tree/5.x/samples/dnn/face_detector)
 with single precision floating point weights has been quantized using [TensorFlow framework](https://www.tensorflow.org/).
 To achieve the best accuracy run the model on BGR images resized to `300x300` applying mean subtraction
 of values `(104, 177, 123)` for each blue, green and red channels correspondingly.
 
 The following are accuracy metrics obtained using [COCO object detection evaluation
 tool](http://cocodataset.org/#detections-eval) on [FDDB dataset](http://vis-www.cs.umass.edu/fddb/)
-(see [script](https://github.com/opencv/opencv/blob/4.x/modules/dnn/misc/face_detector_accuracy.py))
+(see [script](https://github.com/opencv/opencv/blob/5.x/modules/dnn/misc/face_detector_accuracy.py))
 applying resize to `300x300` and keeping an origin images' sizes.

[asmorkalov]

The test failure reason is at

opencv/modules/dnn/src/layer_internals.hpp

Line 167 in d789cb4

for (int j = 1; j < std::min(4, inputsData[i].size[1]) && singleMean; ++j)

The code expects, that all intermediate tensors (cv::Mat) have at least 2 dimensions. It's true for 4.x, but not for 5.x. The assert is enabled in debug only and the issue is not visible in release.
@vpisarev @opencv-alalek I propose to disable the failed tests for now and fix the issue with the next PR to 5.x. What do you think?

[vpisarev]

The test failure reason is at

opencv/modules/dnn/src/layer_internals.hpp

Line 167 in d789cb4

for (int j = 1; j < std::min(4, inputsData[i].size[1]) && singleMean; ++j)

The code expects, that all intermediate tensors (cv::Mat) have at least 2 dimensions. It's true for 4.x, but not for 5.x. The assert is enabled in debug only and the issue is not visible in release.
@vpisarev @opencv-alalek I propose to disable the failed tests for now and fix the issue with the next PR to 5.x. What do you think?

use inputsData[i].total() instead of inputsData[i].size[1]?

[asmorkalov]

@opencv-alalek @vpisarev I fixed the issue with shapes with small check. Please review the solution.

[opencv-alalek]

There are build issues with MSVS2019:

• legacy CI: https://pullrequest.opencv.org/buildbot/builders/precommit_opencl/builds/100232
• GHA eats warnings: https://github.com/opencv/opencv/actions/runs/6585483557/job/17891980724?pr=24416#step:12:14

But they are here since August.

[opencv-alalek]

inputsData[i].dims >=2

Why do we need that condition?

[asmorkalov]

if I understood correctly size[1] corresponds to channels in NCHW. in case of 1d input, singleMean branch should be used (it does not iterate over the second dimension. @vpisarev @dkurt Is it right assumption?

[opencv-alalek]

Problem is related to changes from here: #23473
User code goes to be broken after 4.x, mostly in a silent and unexpected way.

singleMean is true iff mean[i] = X (same value) for all i in [0..total_c]`.

1D doesn't guarantee that this is true (need to add separate test for that, because current tests pass just [0,0,0,0]).

Next, there is the same problem in OpenCL code branch few lines below.

It also raises the same exception, but it is suppressed. See FIXIT comment here: https://github.com/opencv/opencv/blame/19926e2979ef049a89dd029e2231555db40c2776/modules/dnn/src/dnn.cpp#L629

Einsum violates agreement of .setInput() API.
Test ignores the first batch number "N". Next is expected to be "C" as channels.

Accepted layouts are:

• NCHW (2d color image, dims=4)
• NCDHW (3d, e.g. "Test_TensorFlow_layers.concat_3d" test, dims=5)
• there is no 1D input expected. "NC" layout should be used instead.

There is a lack of layouts information metadata so they must be started from "NC" dimensions to keep assumption works.

BTW, there is some input with dims=3. No idea what is that. E.g. check "Test_TensorFlow_layers.l2_normalize_3d" test (--test_debug=1 could show more).

---

So I propose:

• to revert changes here.
• to disable Einsum test in this PR
• 4.x: to add CHECK(inputsData[i].dims, 2, "") check (for both CPU and OpenCL code)
• 4.x: to add check in .setInput() to "fail fast"
• 4.x: to fix Einsum tests and use "NC" layout to avoid violation of .setInput() assumptions.

[dkurt]

This feature if not very flexible, I agree. I think that adding a check that input has at least 2 dimensions is a good option because this initially designed for image processing networks that usually apply mean subtraction as a preprocessing step. I have not seen much models with 1D input that do the same. Usually, their input data is much more complicated (text tokens, audio sampling) and mean processing is not used.

[dkurt]

Can we add also a flag such as bool preprocessing which indicates if mean/scale were actually specified but not initialized by default with zero/ones?

[asmorkalov]

I disabled cumsum tests and created an issue for it. Let's "merge" merge PR and then try to handle the issue separately. I prefer to have dedicated PRs for significant changes to not hide it in merge commit with conflicts.