Added ORB detection test for Java and Android

core: FileStorage: detect invalid attribute value

python: prefer cv::Mat over cv::UMat in python binding

modules/js/perf/perf_helpfunc.js and target tests, e.g. perf_gaussianBlur.js contained "const isNodeJs", leading to re-definition when using associated *.html files.

fix: js/html perf tests

[GSoC] dnn: Blockwise quantization support opencv#25644

This PR introduces blockwise quantization in DNN allowing the parsing of ONNX models quantized in blockwise style. In particular it modifies the `Quantize` and `Dequantize` operations. The related PR opencv/opencv_extra#1181 contains the test data.

Additional notes:
- The original quantization issue has been fixed. Previously, for 1D scale and zero-point, the operation applied was  $y = int8(x/s - z)$ instead of $y = int8(x/s + z)$. Note that the operation was already correctly implemented when the scale and zero-point were scalars. The previous implementation failed the ONNX test cases, but now all have passed successfully.  [Reference](https://github.com/onnx/onnx/blob/main/docs/Operators.md#QuantizeLinear)
- the function `block_repeat` broadcasts scale and zero-point to the input shape. It repeats all the elements of a given axis n times. This function generalizes the behavior of `repeat` from the core module which is defined just for 2 axis assuming `Mat` has 2 dimensions. If appropriate and useful, you might consider moving `block_repeat` to the core module.
- Now, the scale and zero-point can be taken as layer inputs. This increases the ONNX layers' coverage and enables us to run the ONNX test cases (previously disabled) being fully compliant with ONNX standards. Since they are now supported, I have enabled the test cases for: `test_dequantizelinear`, `test_dequantizelinear_axis`, `test_dequantizelinear_blocked`, `test_quantizelinear`, `test_quantizelinear_axis`, `test_quantizelinear_blocked` just in CPU backend. All of them pass successfully.
   
### 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
- [ ] 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

To be on par with `cv::Mat`, let's add `cv::cuda::GpuMat::getStdAllocator()`
This is useful anyway, because when a user wants to use custom allocators, he might want to resort to the standard default allocator behaviour, not some other allocator that could have been set by `setDefaultAllocator()`

Improved error handling in image codecs.

…e_try_detect_feature_without_flags

Current code using CMAKE_SOURCE_DIR and it works well if opencv is standalone CMake project,
but in case of building OpenCV as part of a larger CMake project (e.g. one that includes
opencv and opencv_contrib) this path is incorrect, unlike OpenCV_SOURCE_DIR

Fix path to 3rdparty cmake.

related: opencv#25199

fix compilation errors caused by namespace

Adding getStdAllocator() to cv::cuda::GpuMat

Set and check allocator pointer for all cv::Mat instances

…tching-in-ChessBoardDetector-NeighborsFinder-findCornerNeighbor

Improve corners matching in ChessBoardDetector::NeighborsFinder::findCornerNeighbor opencv#25991

### Pull Request Readiness Checklist

Idea was mentioned in `Section III-B. New Heuristic for Quadrangle Linking` of `Rufli, Martin & Scaramuzza, Davide & Siegwart, Roland. (2008). Automatic Detection of Checkerboards on Blurred and Distorted Images. 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS. 3121-3126. 10.1109/IROS.2008.4650703` (https://rpg.ifi.uzh.ch/docs/IROS08_scaramuzza_b.pdf):
![Снимок экрана от 2024-08-05 09-51-27](https://github.com/user-attachments/assets/7a090ccc-c24c-4dfb-b0dd-259c8709eb72)
```
* For each candidate pair, focus on the quadrangles they belong to and draw two straight lines passing through the midsections of the respective quadrangle edges (see Fig. 6).
* If the candidate corner and the source corner are on the same side of every of the four straight lines drawn this way (this corresponds to the yellow shaded area in Fig. 6), then the corners are successfully matched.
```

By improving corners matching, we can increase the search radius (`thresh_scale`).

I tested this PR with benchmark
```
python3 objdetect_benchmark.py --configuration=generate_run --board_x=7 --path=res_chessboard --synthetic_object=chessboard
```
PR increases detected chessboards number by `3/7%`:
```
cell_img_size = 100 (default)

before
                                 category  detected chessboard  total detected chessboard  total chessboard  average detected error chessboard
                                      all             0.910417                      13110             14400                           0.599746
Total detected time:  147.50906700000002 sec

after
                                 category  detected chessboard  total detected chessboard  total chessboard  average detected error chessboard
                                      all             0.941667                      13560             14400                           0.596726
Total detected time:  136.68963200000007 sec

----------------------------------------------------------------------------------------------------------------------------------------------

cell_img_size = 10

before
                                 category  detected chessboard  total detected chessboard  total chessboard  average detected error chessboard
                                      all             0.539792                       7773             14400                           4.208237
Total detected time:  2.668964 sec

after
                                 category  detected chessboard  total detected chessboard  total chessboard  average detected error chessboard
                                      all             0.579167                       8340             14400                           4.198448
Total detected time:  2.535998999999999 sec
```

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
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake

3rdparty: NDSRVP - Part 1.5: New Interfaces

…ting-file-tests

replace lena.jpg in find-existing-file tests

Added fisheye::distortPoints with non-identity projection matrix

Added xxxApprox overloads for YUV color conversions in HAL and AlgorithmHint to cvtColor opencv#25932

The xxxApprox to implement HAL functions with less bits for arithmetic of FP.

The hint was introduced in opencv#25792 and opencv#25911

### 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