nareshmlx/opencv-pr huggingface dataset:
**File:** modules/python/src2/cv2_convert.cpp
**Change Type:** modified
**Context:** PR #23702: Python binding for RotatedRect
**Review Line:** 757

**Code Changes:**
```diff
 {
     if (!obj || obj == Py_None)
     {
         return true;
     }
+    // This is a workaround for compatibility with an initialization from tuple.
+    // Allows import RotatedRect as an object.
+    if (convertToRotatedRect(obj, dst))
+    {
+        return true;
+    }
```

Review comment:
> Templated version of `pyopencv_to` uses `PyOpenCV_Converter<T>::to` method if there is no specialization of `pyopencv_to` (`cv::RotatedRect` has it), so you can call `PyOpenCV_Converter<cv::RotatedRect>::to` and check against successful conversion. This way there is a compile time problem: ``` error: ‘to’ is not a member of ‘PyOpenCV_Converter<cv::RotatedRect>’ 743 | return PyOpenCV_Converter<cv::RotatedRect>::to(obj, dst, info); ``` However, the struct can be defined locally (see [e406cbde36466c37bd741db758f6e7ca0696efe8](https://github.com/dkurt/opencv/commit/e406cbde36466c37bd741db758f6e7ca0696efe8)). Another problem is with `PYTHON3_LIMITED_API=ON` (Working on it): ``` rror: invalid use of incomplete type ‘struct _typeobject’ 739 | if (std::string(Py_TYPE(obj)->tp_name) == "cv2.RotatedRect") ```
**File:** modules/gapi/src/backends/ov/govbackend.cpp
**Change Type:** modified
**Context:** PR #23799: G-API: Implement InferROI, InferList, InferList2 for OpenVINO backend
**Review Line:** 621

**Code Changes:**
```diff
+            auto output = ctx->output(i);
+            ctx->out.meta(output, ctx->getMeta());
+            ctx->out.post(std::move(output), ctx->eptr);
+        }
+    }
+}
+
 namespace cv {
 namespace gimpl {
 namespace ov {
@@ -594,6 +667,34 @@ cv::optional<V> lookUp(const std::map<K, V> &map, const K& key) {
```

Review comment:
Does it make sense to unify code between the two?
**File:** modules/gapi/src/backends/ov/govbackend.cpp
**Change Type:** modified
**Context:** PR #23799: G-API: Implement InferROI, InferList, InferList2 for OpenVINO backend
**Review Line:** 1011

**Code Changes:**
```diff
+            ppp.cfgLayouts(input_name);
+            ppp.cfgPreProcessing(input_name, mm, true /*disable_img_resize*/);
+            ppp.cfgScaleMean(input_name);
+            ppp.cfgPostProcessing();
+            ppp.finalize();
+        }
+
+        for (const auto &out_name : uu.params.output_names) {
+            cv::GMatDesc outm;
+            if (cv::util::holds_alternative<ParamDesc::Model>(uu.params.kind)) {
+                const auto &out = uu.model->output(out_name);
```

Review comment:
else it is a blob? should this case be handled? some checks be made? how preprocessing would work for the compiled blobs?
**File:** modules/core/include/opencv2/core/optim.hpp
**Change Type:** modified
**Context:** PR #23827: (4.x) Merge 3.4 - #22798 from lamm45:distransform-large #23664 from asmorkalov:as/solveLP_infeasible #23692 from asmorkalov:as/ffmpeg_fps_3...

**Code Changes:**
```diff
@@ -256,6 +256,7 @@ class CV_EXPORTS ConjGradSolver : public MinProblemSolver
 //! return codes for cv::solveLP() function
 enum SolveLPResult
 {
+    SOLVELP_LOST   = -3, //!< problem is feasible, but solver lost solution due to floating-point arithmetic errors
     SOLVELP_UNBOUNDED    = -2, //!< problem is unbounded (target function can achieve arbitrary high values)
     SOLVELP_UNFEASIBLE    = -1, //!< problem is unfeasible (there are no points that satisfy all the constraints imposed)
     SOLVELP_SINGLE    = 0, //!< there is only one maximum for target function
@@ -291,8 +292,12 @@ in the latter case it is understood to correspond to \f$c^T\f$.
 and the remaining to \f$A\f$. It should contain 32- or 64-bit floating point numbers.
```

Review comment:
Do we need the overload for 4.x or default value for eps is enough?
**File:** modules/core/include/opencv2/core/optim.hpp
**Change Type:** modified
**Context:** PR #23827: (4.x) Merge 3.4 - #22798 from lamm45:distransform-large #23664 from asmorkalov:as/solveLP_infeasible #23692 from asmorkalov:as/ffmpeg_fps_3...

**Code Changes:**
```diff
@@ -256,6 +256,7 @@ class CV_EXPORTS ConjGradSolver : public MinProblemSolver
 //! return codes for cv::solveLP() function
 enum SolveLPResult
 {
+    SOLVELP_LOST   = -3, //!< problem is feasible, but solver lost solution due to floating-point arithmetic errors
     SOLVELP_UNBOUNDED    = -2, //!< problem is unbounded (target function can achieve arbitrary high values)
     SOLVELP_UNFEASIBLE    = -1, //!< problem is unfeasible (there are no points that satisfy all the constraints imposed)
     SOLVELP_SINGLE    = 0, //!< there is only one maximum for target function
@@ -291,8 +292,12 @@ in the latter case it is understood to correspond to \f$c^T\f$.
 and the remaining to \f$A\f$. It should contain 32- or 64-bit floating point numbers.
```

Review comment:
If we want to preserve binary compatibility with 4.7 then we need the overload. I vote for "yes" :slightly_smiling_face:
**File:** modules/core/include/opencv2/core/optim.hpp
**Change Type:** modified
**Context:** PR #23827: (4.x) Merge 3.4 - #22798 from lamm45:distransform-large #23664 from asmorkalov:as/solveLP_infeasible #23692 from asmorkalov:as/ffmpeg_fps_3...

**Code Changes:**
```diff
@@ -256,6 +256,7 @@ class CV_EXPORTS ConjGradSolver : public MinProblemSolver
 //! return codes for cv::solveLP() function
 enum SolveLPResult
 {
+    SOLVELP_LOST   = -3, //!< problem is feasible, but solver lost solution due to floating-point arithmetic errors
     SOLVELP_UNBOUNDED    = -2, //!< problem is unbounded (target function can achieve arbitrary high values)
     SOLVELP_UNFEASIBLE    = -1, //!< problem is unfeasible (there are no points that satisfy all the constraints imposed)
     SOLVELP_SINGLE    = 0, //!< there is only one maximum for target function
@@ -291,8 +292,12 @@ in the latter case it is understood to correspond to \f$c^T\f$.
 and the remaining to \f$A\f$. It should contain 32- or 64-bit floating point numbers.
```

Review comment:
`CV_EXPORTS` => `CV_EXPORTS_W`
**File:** modules/core/src/lpsolver.cpp
**Change Type:** modified
**Context:** PR #23827: (4.x) Merge 3.4 - #22798 from lamm45:distransform-large #23664 from asmorkalov:as/solveLP_infeasible #23692 from asmorkalov:as/ffmpeg_fps_3...

**Code Changes:**
```diff
@@ -90,7 +90,7 @@ static void swap_columns(Mat_<double>& A,int col1,int col2);
 #define SWAP(type,a,b) {type tmp=(a);(a)=(b);(b)=tmp;}
 
 //return codes:-2 (no_sol - unbdd),-1(no_sol - unfsbl), 0(single_sol), 1(multiple_sol=>least_l2_norm)
-int solveLP(InputArray Func_, InputArray Constr_, OutputArray z_)
+int solveLP(InputArray Func_, InputArray Constr_, OutputArray z_, double constr_eps)
 {
     dprintf(("call to solveLP\n"));
 
@@ -143,9 +143,25 @@ int solveLP(InputArray Func_, InputArray Constr_, OutputArray z_)
```

Review comment:
> `CV_EXPORTS_W` Should not be used in .cpp files. Doesn't make any sense.
**File:** modules/core/src/lpsolver.cpp
**Change Type:** modified
**Context:** PR #23827: (4.x) Merge 3.4 - #22798 from lamm45:distransform-large #23664 from asmorkalov:as/solveLP_infeasible #23692 from asmorkalov:as/ffmpeg_fps_3...

**Code Changes:**
```diff
@@ -90,7 +90,7 @@ static void swap_columns(Mat_<double>& A,int col1,int col2);
 #define SWAP(type,a,b) {type tmp=(a);(a)=(b);(b)=tmp;}
 
 //return codes:-2 (no_sol - unbdd),-1(no_sol - unfsbl), 0(single_sol), 1(multiple_sol=>least_l2_norm)
-int solveLP(InputArray Func_, InputArray Constr_, OutputArray z_)
+int solveLP(InputArray Func_, InputArray Constr_, OutputArray z_, double constr_eps)
 {
     dprintf(("call to solveLP\n"));
 
@@ -143,9 +143,25 @@ int solveLP(InputArray Func_, InputArray Constr_, OutputArray z_)
```

Review comment:
> `Func_` We don't need underscore suffixes here.
**File:** modules/imgproc/test/test_imgwarp.cpp
**Change Type:** modified
**Context:** PR #23754: Keep inliers for linear remap with BORDER_TRANSPARENT

**Code Changes:**
```diff
@@ -1658,5 +1658,21 @@ TEST(Imgproc_warpPolar, identity)
 #endif
 }
 
+TEST(Imgproc_Remap, issue_23562)
+{
+    cv::RNG rng(17);
+    Mat_<float> mapx({3, 3}, {0, 1, 2, 0, 1, 2, 0, 1, 2});
+    Mat_<float> mapy({3, 3}, {0, 0, 0, 1, 1, 1, 2, 2, 2});
+    for (int cn = 1; cn <= 4; ++cn) {
```

Review comment:
Please use `std::initializer_list` instead of `MatCommaInitializer`.
**File:** modules/imgproc/src/imgwarp.cpp
**Change Type:** modified
**Context:** PR #23754: Keep inliers for linear remap with BORDER_TRANSPARENT

**Code Changes:**
```diff
@@ -757,13 +757,6 @@ static void remapBilinear( const Mat& _src, Mat& _dst, const Mat& _xy,
             }
             else
             {
-                if( borderType == BORDER_TRANSPARENT && cn != 3 )
-                {
-                    D += (X1 - dx)*cn;
-                    dx = X1;
-                    continue;
-                }
```

Review comment:
> Code condition is strange too: borderType == BORDER_TRANSPARENT && cn != 3. Why is cn used here? Removed that limitation and modified test correspondingly
**File:** modules/js/test/test_objdetect.js
**Change Type:** modified
**Context:** PR #23797: Barcode js bindings

**Code Changes:**
```diff
@@ -221,6 +221,27 @@ QUnit.test('Aruco-based QR code detect', function (assert) {
         mat.delete();
     }
 });
+QUnit.test('Bar code detect', function (assert) {
+    {
+        let detector = new cv.barcode_BarcodeDetector();
+        let mat = cv.Mat.ones(800, 600, cv.CV_8U);
+        assert.ok(mat);
+
```

Review comment:
Should be `codeContent` here.
**File:** modules/core/src/matrix_transform.cpp
**Change Type:** modified
**Context:** PR #22898: dnn: support ONNX Slice with negative steps by adding and using cv::flipND

**Code Changes:**
```diff
@@ -6,6 +6,8 @@
 #include "opencl_kernels_core.hpp"
 #include "opencv2/core/detail/dispatch_helper.impl.hpp"
 
+#include <algorithm> // std::swap_ranges
+
 namespace cv {
 
 ////////////////////////////////////// transpose /////////////////////////////////////////
@@ -812,6 +814,49 @@ void flip( InputArray _src, OutputArray _dst, int flip_mode )
```

Review comment:
Can you please narrow down the scope of the variables?
**File:** modules/core/src/matrix_transform.cpp
**Change Type:** modified
**Context:** PR #22898: dnn: support ONNX Slice with negative steps by adding and using cv::flipND

**Code Changes:**
```diff
@@ -6,6 +6,8 @@
 #include "opencl_kernels_core.hpp"
 #include "opencv2/core/detail/dispatch_helper.impl.hpp"
 
+#include <algorithm> // std::swap_ranges
+
 namespace cv {
 
 ////////////////////////////////////// transpose /////////////////////////////////////////
@@ -812,6 +814,49 @@ void flip( InputArray _src, OutputArray _dst, int flip_mode )
```

Review comment:
I think we should do the copy only if src == dst.
**File:** modules/core/src/matrix_transform.cpp
**Change Type:** modified
**Context:** PR #22898: dnn: support ONNX Slice with negative steps by adding and using cv::flipND

**Code Changes:**
```diff
@@ -6,6 +6,8 @@
 #include "opencl_kernels_core.hpp"
 #include "opencv2/core/detail/dispatch_helper.impl.hpp"
 
+#include <algorithm> // std::swap_ranges
+
 namespace cv {
 
 ////////////////////////////////////// transpose /////////////////////////////////////////
@@ -812,6 +814,49 @@ void flip( InputArray _src, OutputArray _dst, int flip_mode )
```

Review comment:
We need this loop if the matrix is non continuous, right? If we want to allocate memory for output either way, we could copy input to output and then do in-place operation with assumption that the memory is continuous(hence no need for this loop), but if we don't, it's fine as it is.
**File:** modules/dnn/src/layers/slice_layer.cpp
**Change Type:** modified
**Context:** PR #22898: dnn: support ONNX Slice with negative steps by adding and using cv::flipND

**Code Changes:**
```diff
@@ -84,6 +84,34 @@ Range normalizeRange(const Range& input_range, int n)
     return range;
 }
 
+// TODO: support cv::Range with steps and negative steps to get rid of this transformation
+void tranformForNegSteps(const MatShape& inpShape, std::vector<std::vector<Range> >& sliceRanges, std::vector<std::vector<int> >& sliceSteps)
+{
+    // in case of negative steps,
+    // x of shape [5, 10], x[5:0:-1, 10:1:-3] <=> np.flip(x[1:5:1, 2:10:3], aixs=(0, 1))
+    // new_end_i = start_i + 1 > dim_i ? dim_i : start_i + 1
```

Review comment:
Why is this a template?
**File:** modules/dnn/src/layers/slice_layer.cpp
**Change Type:** modified
**Context:** PR #22898: dnn: support ONNX Slice with negative steps by adding and using cv::flipND

**Code Changes:**
```diff
@@ -84,6 +84,34 @@ Range normalizeRange(const Range& input_range, int n)
     return range;
 }
 
+// TODO: support cv::Range with steps and negative steps to get rid of this transformation
+void tranformForNegSteps(const MatShape& inpShape, std::vector<std::vector<Range> >& sliceRanges, std::vector<std::vector<int> >& sliceSteps)
+{
+    // in case of negative steps,
+    // x of shape [5, 10], x[5:0:-1, 10:1:-3] <=> np.flip(x[1:5:1, 2:10:3], aixs=(0, 1))
+    // new_end_i = start_i + 1 > dim_i ? dim_i : start_i + 1
```

Review comment:
We can save only negative dims here. Or use smaller type - `uint8_t` or even `bool`(`bool` needs testing).
**File:** modules/dnn/src/layers/slice_layer.cpp
**Change Type:** modified
**Context:** PR #22898: dnn: support ONNX Slice with negative steps by adding and using cv::flipND

**Code Changes:**
```diff
@@ -84,6 +84,34 @@ Range normalizeRange(const Range& input_range, int n)
     return range;
 }
 
+// TODO: support cv::Range with steps and negative steps to get rid of this transformation
+void tranformForNegSteps(const MatShape& inpShape, std::vector<std::vector<Range> >& sliceRanges, std::vector<std::vector<int> >& sliceSteps)
+{
+    // in case of negative steps,
+    // x of shape [5, 10], x[5:0:-1, 10:1:-3] <=> np.flip(x[1:5:1, 2:10:3], aixs=(0, 1))
+    // new_end_i = start_i + 1 > dim_i ? dim_i : start_i + 1
```

Review comment:
nit: we could invert the condition to reduce nesting
**File:** modules/core/src/matrix_transform.cpp
**Change Type:** modified
**Context:** PR #22898: dnn: support ONNX Slice with negative steps by adding and using cv::flipND

**Code Changes:**
```diff
@@ -6,6 +6,8 @@
 #include "opencl_kernels_core.hpp"
 #include "opencv2/core/detail/dispatch_helper.impl.hpp"
 
+#include <algorithm> // std::swap_ranges
+
 namespace cv {
 
 ////////////////////////////////////// transpose /////////////////////////////////////////
@@ -812,6 +814,49 @@ void flip( InputArray _src, OutputArray _dst, int flip_mode )
```

Review comment:
Done with checking if `src.ptr() == dst.ptr()`. Do you have another better way to check whether src == dst?
**File:** modules/core/src/matrix_transform.cpp
**Change Type:** modified
**Context:** PR #22898: dnn: support ONNX Slice with negative steps by adding and using cv::flipND

**Code Changes:**
```diff
@@ -6,6 +6,8 @@
 #include "opencl_kernels_core.hpp"
 #include "opencv2/core/detail/dispatch_helper.impl.hpp"
 
+#include <algorithm> // std::swap_ranges
+
 namespace cv {
 
 ////////////////////////////////////// transpose /////////////////////////////////////////
@@ -812,6 +814,49 @@ void flip( InputArray _src, OutputArray _dst, int flip_mode )
```

Review comment:
Yes, we need this loop for non-continuous mat. But if we are doing in-place operation, do you know an efficient way to swap two pieces of memory? I use `std::memcpy` to copy from src to dst to avoid the in-place swapping problem.
**File:** modules/dnn/src/layers/slice_layer.cpp
**Change Type:** modified
**Context:** PR #22898: dnn: support ONNX Slice with negative steps by adding and using cv::flipND

**Code Changes:**
```diff
@@ -84,6 +84,34 @@ Range normalizeRange(const Range& input_range, int n)
     return range;
 }
 
+// TODO: support cv::Range with steps and negative steps to get rid of this transformation
+void tranformForNegSteps(const MatShape& inpShape, std::vector<std::vector<Range> >& sliceRanges, std::vector<std::vector<int> >& sliceSteps)
+{
+    // in case of negative steps,
+    // x of shape [5, 10], x[5:0:-1, 10:1:-3] <=> np.flip(x[1:5:1, 2:10:3], aixs=(0, 1))
+    // new_end_i = start_i + 1 > dim_i ? dim_i : start_i + 1
```

Review comment:
Yes, it is needed since we cannot change values in `getMemoryShapes`. The reason why I want to change the values after the constructor is I want to preserve the original starts, ends and steps, so that we might be able to support other backends if they support negative steps. We can avoid this if `cv::Range` can support steps and negative steps, but that should be another pull request.
**File:** modules/dnn/src/layers/slice_layer.cpp
**Change Type:** modified
**Context:** PR #22898: dnn: support ONNX Slice with negative steps by adding and using cv::flipND

**Code Changes:**
```diff
@@ -84,6 +84,34 @@ Range normalizeRange(const Range& input_range, int n)
     return range;
 }
 
+// TODO: support cv::Range with steps and negative steps to get rid of this transformation
+void tranformForNegSteps(const MatShape& inpShape, std::vector<std::vector<Range> >& sliceRanges, std::vector<std::vector<int> >& sliceSteps)
+{
+    // in case of negative steps,
+    // x of shape [5, 10], x[5:0:-1, 10:1:-3] <=> np.flip(x[1:5:1, 2:10:3], aixs=(0, 1))
+    // new_end_i = start_i + 1 > dim_i ? dim_i : start_i + 1
```

Review comment:
Done with saving only dims that have neg steps.
**File:** modules/dnn/src/layers/slice_layer.cpp
**Change Type:** modified
**Context:** PR #22898: dnn: support ONNX Slice with negative steps by adding and using cv::flipND

**Code Changes:**
```diff
@@ -84,6 +84,34 @@ Range normalizeRange(const Range& input_range, int n)
     return range;
 }
 
+// TODO: support cv::Range with steps and negative steps to get rid of this transformation
+void tranformForNegSteps(const MatShape& inpShape, std::vector<std::vector<Range> >& sliceRanges, std::vector<std::vector<int> >& sliceSteps)
+{
+    // in case of negative steps,
+    // x of shape [5, 10], x[5:0:-1, 10:1:-3] <=> np.flip(x[1:5:1, 2:10:3], aixs=(0, 1))
+    // new_end_i = start_i + 1 > dim_i ? dim_i : start_i + 1
```

Review comment:
I don't really understand how inverting the condition here can reduce nesting. Could you give me an example?
**File:** modules/dnn/src/layers/slice_layer.cpp
**Change Type:** modified
**Context:** PR #22898: dnn: support ONNX Slice with negative steps by adding and using cv::flipND

**Code Changes:**
```diff
@@ -84,6 +84,34 @@ Range normalizeRange(const Range& input_range, int n)
     return range;
 }
 
+// TODO: support cv::Range with steps and negative steps to get rid of this transformation
+void tranformForNegSteps(const MatShape& inpShape, std::vector<std::vector<Range> >& sliceRanges, std::vector<std::vector<int> >& sliceSteps)
+{
+    // in case of negative steps,
+    // x of shape [5, 10], x[5:0:-1, 10:1:-3] <=> np.flip(x[1:5:1, 2:10:3], aixs=(0, 1))
+    // new_end_i = start_i + 1 > dim_i ? dim_i : start_i + 1
```

Review comment:
Done with: ```cpp if (step > 0) continue; // ... ``` I guess this can help reduce nesting?
**File:** modules/dnn/src/layers/slice_layer.cpp
**Change Type:** modified
**Context:** PR #22898: dnn: support ONNX Slice with negative steps by adding and using cv::flipND

**Code Changes:**
```diff
@@ -84,6 +84,34 @@ Range normalizeRange(const Range& input_range, int n)
     return range;
 }
 
+// TODO: support cv::Range with steps and negative steps to get rid of this transformation
+void tranformForNegSteps(const MatShape& inpShape, std::vector<std::vector<Range> >& sliceRanges, std::vector<std::vector<int> >& sliceSteps)
+{
+    // in case of negative steps,
+    // x of shape [5, 10], x[5:0:-1, 10:1:-3] <=> np.flip(x[1:5:1, 2:10:3], aixs=(0, 1))
+    // new_end_i = start_i + 1 > dim_i ? dim_i : start_i + 1
```

Review comment:
Yup, that's what I meant.
**File:** modules/dnn/src/layers/slice_layer.cpp
**Change Type:** modified
**Context:** PR #22898: dnn: support ONNX Slice with negative steps by adding and using cv::flipND

**Code Changes:**
```diff
@@ -84,6 +84,34 @@ Range normalizeRange(const Range& input_range, int n)
     return range;
 }
 
+// TODO: support cv::Range with steps and negative steps to get rid of this transformation
+void tranformForNegSteps(const MatShape& inpShape, std::vector<std::vector<Range> >& sliceRanges, std::vector<std::vector<int> >& sliceSteps)
+{
+    // in case of negative steps,
+    // x of shape [5, 10], x[5:0:-1, 10:1:-3] <=> np.flip(x[1:5:1, 2:10:3], aixs=(0, 1))
+    // new_end_i = start_i + 1 > dim_i ? dim_i : start_i + 1
```

Review comment:
Sorry, my bad, I thought it's at the end of the function.
**File:** modules/dnn/src/layers/slice_layer.cpp
**Change Type:** modified
**Context:** PR #22898: dnn: support ONNX Slice with negative steps by adding and using cv::flipND

**Code Changes:**
```diff
@@ -84,6 +84,34 @@ Range normalizeRange(const Range& input_range, int n)
     return range;
 }
 
+// TODO: support cv::Range with steps and negative steps to get rid of this transformation
+void tranformForNegSteps(const MatShape& inpShape, std::vector<std::vector<Range> >& sliceRanges, std::vector<std::vector<int> >& sliceSteps)
+{
+    // in case of negative steps,
+    // x of shape [5, 10], x[5:0:-1, 10:1:-3] <=> np.flip(x[1:5:1, 2:10:3], aixs=(0, 1))
+    // new_end_i = start_i + 1 > dim_i ? dim_i : start_i + 1
```

Review comment:
What I meant to say is - this could be a function. EDIT: The code is diverging already: here you didn't negate the condition.
**File:** modules/dnn/src/layers/slice_layer.cpp
**Change Type:** modified
**Context:** PR #22898: dnn: support ONNX Slice with negative steps by adding and using cv::flipND

**Code Changes:**
```diff
@@ -84,6 +84,34 @@ Range normalizeRange(const Range& input_range, int n)
     return range;
 }
 
+// TODO: support cv::Range with steps and negative steps to get rid of this transformation
+void tranformForNegSteps(const MatShape& inpShape, std::vector<std::vector<Range> >& sliceRanges, std::vector<std::vector<int> >& sliceSteps)
+{
+    // in case of negative steps,
+    // x of shape [5, 10], x[5:0:-1, 10:1:-3] <=> np.flip(x[1:5:1, 2:10:3], aixs=(0, 1))
+    // new_end_i = start_i + 1 > dim_i ? dim_i : start_i + 1
```

Review comment:
I think it can be done once after ifs.
**File:** modules/dnn/src/layers/slice_layer.cpp
**Change Type:** modified
**Context:** PR #22898: dnn: support ONNX Slice with negative steps by adding and using cv::flipND

**Code Changes:**
```diff
@@ -84,6 +84,34 @@ Range normalizeRange(const Range& input_range, int n)
     return range;
 }
 
+// TODO: support cv::Range with steps and negative steps to get rid of this transformation
+void tranformForNegSteps(const MatShape& inpShape, std::vector<std::vector<Range> >& sliceRanges, std::vector<std::vector<int> >& sliceSteps)
+{
+    // in case of negative steps,
+    // x of shape [5, 10], x[5:0:-1, 10:1:-3] <=> np.flip(x[1:5:1, 2:10:3], aixs=(0, 1))
+    // new_end_i = start_i + 1 > dim_i ? dim_i : start_i + 1
```

Review comment:
We can use the fact that neg_step_dims is not empty as an indicator that `hasNegSteps` is `true`
**File:** modules/core/src/matrix_transform.cpp
**Change Type:** modified
**Context:** PR #22898: dnn: support ONNX Slice with negative steps by adding and using cv::flipND

**Code Changes:**
```diff
@@ -6,6 +6,8 @@
 #include "opencl_kernels_core.hpp"
 #include "opencv2/core/detail/dispatch_helper.impl.hpp"
 
+#include <algorithm> // std::swap_ranges
+
 namespace cv {
 
 ////////////////////////////////////// transpose /////////////////////////////////////////
@@ -812,6 +814,49 @@ void flip( InputArray _src, OutputArray _dst, int flip_mode )
```

Review comment:
we could reduce the scope of `i`