Geometry C++ sample combining other shape detection samples #25304
Clean Samples #25006
This PR removes adds a new cpp sample (geometry) which combines different methods of finding and drawing shapes in an image. It makes separate samples for convexHull, fitellipse, minAreaRect, minAreaCircle redudant. Shapes can be changed using hotkeys after running the program
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Fully connected 0D test. #25208
This PR introduces parametrized `0/1D` input support test for `Fullyconnected` layer.
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imgproc: fix unaligned memory access in filters and Gaussian blur #25364
* filter/SIMD: removed parts which casted 8u pointers to int causing unaligned memory access on RISC-V platform.
* GaussianBlur/fixed_point: replaced casts from s16 to u32 with union operations
Performance comparison:
- [x] check performance on x86_64 - (4 threads, `-DCPU_BASELINE=AVX2`, GCC 11.4, Ubuntu 22) - [report_imgproc_x86_64.ods](https://github.com/opencv/opencv/files/14904702/report_x86_64.ods)
- [x] check performance on AArch64 - (4 cores of RK3588, GCC 11.4 aarch64, Raspbian) - [report_imgproc_aarch64.ods](https://github.com/opencv/opencv/files/14908437/report_aarch64.ods)
Note: for some reason my performance results are quite unstable, unaffected functions show speedups and slowdowns in many cases. Filter2D and GaussianBlur seem to be OK.
Slightly related PR: https://github.com/opencv/ci-gha-workflow/pull/165
Added and tested yolov8m model. #25357
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- [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.
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Below is evidence of the test:
![yolov8m](https://github.com/opencv/opencv/assets/675645/f9bfe2c6-fe4a-42fc-93a6-17e4da5c9bb5)
Added int support to padding layer #25241
Added int32 and int64 support to padding layer (CPU and CUDA).
ONNX parser doesn't convert non-zero padding value to float now.
### Pull Request Readiness Checklist
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- [x] The PR is proposed to the proper branch
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Patch to opencv_extra has the same branch name.
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Reworked findContours to reduce C-API usage #25146
What is done:
* rewritten `findContours` and `icvApproximateChainTC89` using C++ data structures
* extracted LINK_RUNS mode to separate new public functions - `findContoursLinkRuns` (it uses completely different algorithm)
* ~added new public `cv::approximateChainTC89`~ - **❌ decided to hide it**
* enabled chain code output (method = 0, no public enum value for this in C++ yet)
* kept old function as `findContours_old` (exported, but not exposed to user)
* added more tests for findContours (`test_contours_new.cpp`), some tests compare results of old function with new one. Following tests have been added:
* contours of random rectangle
* contours of many small (1-2px) blobs
* contours of random noise
* backport of old accuracy test
* separate test for LINK RUNS variant
What is left to be done (can be done now or later):
* improve tests:
* some tests have limited verification (e.g. only verify contour sizes)
* perhaps reference data can be collected and stored
* maybe more test variants can be added (?)
* add enum value for chain code output and a method of returning starting points (e.g. first 8 elements of returned `vector<uchar>` can represent 2 int point coordinates)
* add documentation for new functions - **✔️ DONE**
* check and improve performance (my experiment showed 0.7x-1.1x some time ago)
* remove old functions completely (?)
* change contour return order (BFS) or allow to select it (?)
* return result tree as-is (?) (new data structures should be exposed, bindings should adapt)
core: doc: add note for countNonZero, hasNonZero and findNonZero #25356Close#25345
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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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- [x] I agree to contribute to the project under Apache 2 License.
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- [x] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
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Patch to opencv_extra has the same branch name.
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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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- [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