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_hasnonzero.cpp

@@ -160,23 +160,26 @@ TEST_P(HasNonZeroNd, hasNonZeroNd)
       std::vector<size_t> steps(ndims);
       std::vector<int> sizes(ndims);
       size_t totalBytes = 1;
-      for(int dim = 0 ; dim<ndims ; ++dim)
+      for(int dim = ndims-1; dim >= 0; --dim)
       {
           const bool isFirstDim = (dim == 0);
           const bool isLastDim = (dim+1 == ndims);
           const int length = rng.uniform(1, 64);
-          steps[dim] = (isLastDim ? 1 : static_cast<size_t>(length))*CV_ELEM_SIZE(type);
+          steps[dim] = isLastDim ? CV_ELEM_SIZE(type) : sizes[dim+1]*steps[dim+1];
           sizes[dim] = (isFirstDim || continuous) ? length : rng.uniform(1, length);
-          totalBytes *= steps[dim]*static_cast<size_t>(sizes[dim]);
+          totalBytes *= steps[dim];
       }
+      totalBytes *= sizes[0];
 
-      std::vector<unsigned char> buffer(totalBytes);
-      void* data = buffer.data();
+      unsigned char magicval = 153;
+      size_t border = 128;
+      std::vector<unsigned char> buffer(totalBytes+border*2, magicval);
+      void* data = buffer.data() + border;
 
       Mat m = Mat(ndims, sizes.data(), type, data, steps.data());
 
       std::vector<Range> nzRange(ndims);
-      for(int dim = 0 ; dim<ndims ; ++dim)
+      for(int dim = 0; dim < ndims ; ++dim)
       {
         const int pos = rng.uniform(0, sizes[dim]);
         nzRange[dim] = Range(pos, pos+1);
@@ -187,6 +190,10 @@ TEST_P(HasNonZeroNd, hasNonZeroNd)
 
       const int nzCount = countNonZero(m);
       EXPECT_EQ((nzCount>0), hasNonZero(m));
+      for (size_t j = 0; j < border; j++) {
+          ASSERT_EQ(buffer[j], magicval);
+          ASSERT_EQ(buffer[border + totalBytes + j], magicval);
+      }
     }
 }