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/src/matrix_expressions.cpp

@@ -1679,7 +1679,7 @@ void MatOp_Initializer::assign(const MatExpr& e, Mat& m, int _type) const
     if( _type == -1 )
         _type = e.a.type();
 
-    m.create(e.a.dims, e.a.size, _type);
+    m.create(e.a.size, _type);
 
     if( e.flags == 'I' && e.a.dims <= 2 )
         setIdentity(m, Scalar(e.alpha));
@@ -1767,6 +1767,15 @@ MatExpr Mat::zeros(int ndims, const int* sizes, int type)
     return e;
 }
 
+MatExpr Mat::zeros(const MatShape& shape, int type)
+{
+    CV_INSTRUMENT_REGION();
+
+    MatExpr e;
+    MatOp_Initializer::makeExpr(e, '0', shape.dims, shape.p, type);
+    return e;
+}
+
 MatExpr Mat::ones(int rows, int cols, int type)
 {
     CV_INSTRUMENT_REGION();
@@ -1794,6 +1803,15 @@ MatExpr Mat::ones(int ndims, const int* sizes, int type)
     return e;
 }
 
+MatExpr Mat::ones(const MatShape& shape, int type)
+{
+    CV_INSTRUMENT_REGION();
+
+    MatExpr e;
+    MatOp_Initializer::makeExpr(e, '1', shape.dims, shape.p, type);
+    return e;
+}
+
 MatExpr Mat::eye(int rows, int cols, int type)
 {
     CV_INSTRUMENT_REGION();