Merge pull request #27757 from vpisarev:matshape_inside_mat

Use MatShape instead of MatSize inside cv::Mat/cv::UMat #27757

**Merge together with https://github.com/opencv/opencv_contrib/pull/3996**
---

This PR continues cv::Mat/cv::UMat refactoring. See #26056, where `MatShape` was introduced. Now it's put inside cv::Mat/cv::UMat instead of a weird `MatSize`. MatSize is now an alias for MatShape:

**before:**

```
struct MatShape { ... };
struct MatSize { ... };

struct Mat {
    ...
    int dims;
    int rows;
    int cols;

    ...

    MatShape shape() const { ... /* constructs MatShape out of MatSize and returns it;
                                    layout is always 'unknown', because we don't store it */ }

    MatSize size; // size is not valid without the parent cv::Mat,
                  // because size.p may point to Mat::rows or to Mat::cols,
                  // depending on the dimensionality, and dims() returns Mat::dims.
    MatStep step; // may allocate memory, depending on the dimensionality.
    ...
};
```

**after:**

```
struct MatShape { ... };
typedef MatShape MatSize; // they are now synonyms

struct Mat {
    ...
    int dims;
    int rows;
    int cols;

    ...

    MatShape shape() const { return size; } // just return the embedded shape (including the proper layout information)

    MatSize size; // size is self-contained data structure that can be used without the parent cv::Mat.
                  // size.dims is now a copy of dims; size.p[*] contains copies of Mat::rows and Mat::cols when dims <= 2.
    MatStep step; // does not allocate extra memory buffers.
    ...
};
```

There are several reasons to do that:

1. the main reason is to be able to store data layout (MatShape::layout) inside each cv::Mat/cv::UMat. This is necessary for the proper shape inference in DNN module. In particular, it's necessary for the next step of DNN inference optimization where we introduce block-layout-optimized convolution and other operations. Later on, we can use layout information to support non-interleaved images (e.g. RRR...GGG...BBB...) or even batches of such images in core/imgproc modules.
2. the other reason is to represent 3D/4D/5D etc. tensors as cv::Mat/cv::UMat instances more conveniently, without extra dynamic memory allocation. Before this patch we allocated some memory buffers dynamically to store shape & steps for more than 2D arrays. Now the whole cv::Mat/cv::UMat header can be stored completely on stack/in a container. Creating another copy of Mat/UMat header is now done more efficiently.
3. the third reason is to introduce the new coding pattern: `dst.create(src.size, <dst_type>);`. The pattern is suitable for most of element-wise (including cloning) and filtering operations. This pattern does not only look crisp and self-documenting, it will also automatically copy shape (including layout) from the source tensor into the destination matrix/tensor.
4. in the future we might add `colorspace` member to MatShape that will allow to distinguish RGB from BGR or NV12. `dst.create(src.size, <dst_type>);` will then copy the colorspace information as well.

### 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

modules/core/test/test_arithm.cpp

@@ -614,7 +614,7 @@ static void inRange(const Mat& src, const Mat& lb, const Mat& rb, Mat& dst)
 {
     CV_Assert( src.type() == lb.type() && src.type() == rb.type() &&
               src.size == lb.size && src.size == rb.size );
-    dst.create( src.dims, &src.size[0], CV_8U );
+    dst.create( src.size, CV_8U );
     const Mat *arrays[]={&src, &lb, &rb, &dst, 0};
     Mat planes[4];
 
@@ -678,7 +678,7 @@ static void inRange(const Mat& src, const Mat& lb, const Mat& rb, Mat& dst)
 
 static void inRangeS(const Mat& src, const Scalar& lb, const Scalar& rb, Mat& dst)
 {
-    dst.create( src.dims, &src.size[0], CV_8U );
+    dst.create( src.size, CV_8U );
     const Mat *arrays[]={&src, &dst, 0};
     Mat planes[2];
 
@@ -836,7 +836,7 @@ static void finiteMask_(const _Tp *src, uchar *dst, size_t total, int cn)
 
 static void finiteMask(const Mat& src, Mat& dst)
 {
-    dst.create(src.dims, &src.size[0], CV_8UC1);
+    dst.create(src.size, CV_8UC1);
 
     const Mat *arrays[]={&src, &dst, 0};
     Mat planes[2];
@@ -1019,7 +1019,7 @@ namespace reference {
 static void flip(const Mat& src, Mat& dst, int flipcode)
 {
     CV_Assert(src.dims <= 2);
-    dst.createSameSize(src, src.type());
+    dst.create(src.size, src.type());
     int i, j, k, esz = (int)src.elemSize(), width = src.cols*esz;
 
     for( i = 0; i < dst.rows; i++ )
@@ -1191,7 +1191,7 @@ struct SetZeroOp : public BaseElemWiseOp
 namespace reference {
 static void exp(const Mat& src, Mat& dst)
 {
-    dst.create( src.dims, &src.size[0], src.type() );
+    dst.create( src.size, src.type() );
     const Mat *arrays[]={&src, &dst, 0};
     Mat planes[2];
 
@@ -1220,7 +1220,7 @@ static void exp(const Mat& src, Mat& dst)
 
 static void log(const Mat& src, Mat& dst)
 {
-    dst.create( src.dims, &src.size[0], src.type() );
+    dst.create( src.size, src.type() );
     const Mat *arrays[]={&src, &dst, 0};
     Mat planes[2];
 
@@ -1312,8 +1312,8 @@ static void cartToPolar(const Mat& mx, const Mat& my, Mat& mmag, Mat& mangle, bo
 {
     CV_Assert( (mx.type() == CV_32F || mx.type() == CV_64F) &&
               mx.type() == my.type() && mx.size == my.size );
-    mmag.create( mx.dims, &mx.size[0], mx.type() );
-    mangle.create( mx.dims, &mx.size[0], mx.type() );
+    mmag.create( mx.size, mx.type() );
+    mangle.create( mx.size, mx.type() );
     const Mat *arrays[]={&mx, &my, &mmag, &mangle, 0};
     Mat planes[4];
 
@@ -1380,7 +1380,7 @@ struct CartToPolarToCartOp : public BaseArithmOp
 
         Mat msrc[] = {mag, angle, x, y};
         int pairs[] = {0, 0, 1, 1, 2, 2, 3, 3};
-        dst.create(src[0].dims, src[0].size, CV_MAKETYPE(src[0].depth(), 4));
+        dst.create(src[0].size, CV_MAKETYPE(src[0].depth(), 4));
         cv::mixChannels(msrc, 4, &dst, 1, pairs, 4);
     }
     void refop(const vector<Mat>& src, Mat& dst, const Mat&)
@@ -1389,7 +1389,7 @@ struct CartToPolarToCartOp : public BaseArithmOp
         reference::cartToPolar(src[0], src[1], mag, angle, angleInDegrees);
         Mat msrc[] = {mag, angle, src[0], src[1]};
         int pairs[] = {0, 0, 1, 1, 2, 2, 3, 3};
-        dst.create(src[0].dims, src[0].size, CV_MAKETYPE(src[0].depth(), 4));
+        dst.create(src[0].size, CV_MAKETYPE(src[0].depth(), 4));
         cv::mixChannels(msrc, 4, &dst, 1, pairs, 4);
     }
     void generateScalars(int, RNG& rng)