1D Scatter Layer Test #25071
This PR introduces parametrized test for `Scatter` layer to test its functionality for 1D arrays
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Added int tests for CumSum, Scatter, Tile and ReduceSum dnn layers #25277
Fixed bug in tile layer.
Fixed bug in reduce layer by reimplementing the layer.
Fixed types filter in Scatter and ScatterND layers
PR for extra: https://github.com/opencv/opencv_extra/pull/1161
### Pull Request Readiness Checklist
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OBJ and PLY loaders extention to support texture coordinates and difused colors #25221
### This PR changes
* Texture coordinates support added to `loadMesh()` and `saveMesh()`
* `loadMesh()` changes its behavior: all vertex attribute arrays (vertex coordinates, colors, normals, texture coordinates) now have the same size and same-index corresponce
- This makes sense for OBJ files where vertex attribute arrays are independent from each other and are randomly accessed when defining faces
- Looks like this behavior may also happen in some PLY files; however, it is not implemented until we encounter such files in a wild nature
- At the same time `loadPointCloud()` keeps its behavior and loads vertex attributes as they are given in the file
* PLY loader supports synonyms for the properties: `diffuse_red`, `diffuse_green` and `diffuse_blue` along with `red`, `green` and `blue`
* `std::vector<cv::Vec3i>` supported as an index array type
* Colors are loaded as [0, 1] floats instead of uchars
- Since colors are usually saved as floats, internal conversion to uchar at loading significantly drops accuracy
- Performing uchar conversion does not always makes sense and can be performed by a user if they needs it
* PLY loading fixed: wrong offset ruined x coordinate
* Python tests added for `loadPointCloud` and `loadMesh`
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Concat Layer 0/1D test #25224
This PR introduces parametrized `0/1D` input support test for `Concat` layer.
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[BugFix] dnn (ONNX): Foce dropping constant inputs in parseClip if they are shared #25319
Resolves https://github.com/opencv/opencv/issues/25278
Merge with https://github.com/opencv/opencv_extra/pull/1165
In Gold-YOLO ,`Div` has a constant input `B=6` which is then parsed into a `Const` layer in the ONNX importer, but `Clip` also has the shared constant input `max=6` which is already a `Const` layer and then connected to `Elementwise` layer. This should not happen because in the `forward()` of `Elementwise` layer, the legacy code goes through and apply activation to each input. More details on https://github.com/opencv/opencv/issues/25278#issuecomment-2032199630.
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Ownership check in TFLite importer #25312
### Pull Request Readiness Checklist
resolves https://github.com/opencv/opencv/issues/25310
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Optimize int8 layers in DNN modules by using RISC-V Vector intrinsic. #25230
This patch optimize 3 functions in the int8 layer by using RVV Native Intrinsic.
This patch was tested on QEMU using VLEN=128 and VLEN=256 on `./bin/opencv_test_dnn --gtest_filter="*Int8*"`;
On the real device (k230, VLEN=128), `EfficientDet_int8` in `opencv_perf_dnn` showed a performance improvement of 1.46x.
| Name of Test | Original | optimized | Speed-up |
| ------------------------------------------ | -------- | ---------- | -------- |
| EfficientDet_int8::DNNTestNetwork::OCV/CPU | 2843.467 | 1947.013 | 1.46 |
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imgcodecs: jpeg: re-support to read CMYK Jpeg #25280Close#25274
OpenCV Extra: https://github.com/opencv/opencv_extra/pull/1163
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Merge with https://github.com/opencv/opencv_extra/pull/1158
Todo:
- [x] Fix Attention pattern recognition.
- [x] Handle other backends.
Benchmark:
"VIT_B_32 OCV/CPU", M1, results in milliseconds.
| Model | 4.x | This PR |
| - | - | - |
| VIT_B_32 OCV/CPU | 87.66 | **83.83** |
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- [x] The PR is proposed to the proper branch
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