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Merge branch 'opencv:4.x' into update_mcc_CCHecker
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‎.github/workflows/PR-4.x.yaml

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macOS-X64:
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uses: opencv/ci-gha-workflow/.github/workflows/OCV-Contrib-PR-4.x-macOS-x86_64.yaml@main
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Linux-RISC-V-Clang:
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uses: opencv/ci-gha-workflow/.github/workflows/OCV-Contrib-PR-4.x-RISCV.yaml@main
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openEuler2203-x64:
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if: "${{ contains(github.event.pull_request.labels.*.name, 'category: cann') }}"
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uses: opencv/ci-gha-workflow/.github/workflows/OCV-Contrib-PR-4.x-O22-CANN.yaml@main

‎modules/README.md

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- **saliency**: Saliency API -- Where humans would look in a scene. Has routines for static, motion and "objectness" saliency.
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- **signal**: Signal processing algorithms
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- **sfm**: Structure from Motion -- This module contains algorithms to perform 3d reconstruction from 2d images. The core of the module is a light version of Libmv.
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- **shape**: Shape Distance and Matching

‎modules/alphamat/README.md

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# Computer Vision based Alpha Matting
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![Trimap Alphamat Example from Askoy et al 2017](https://github.com/opencv/opencv_contrib/assets/810997/5cafae51-18e1-4e75-9035-6b40c9a064cb)
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This project was part of the Google Summer of Code 2019.
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####Student: Muskaan Kularia
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####Mentor: Sunita Nayak
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#### Student: Muskaan Kularia
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#### Mentor: Sunita Nayak
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***
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Alphamatting is the problem of extracting the foreground from an image. Given the input of an image and its corresponding trimap, we try to extract the foreground from the background.
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[4] Qifeng Chen, Dingzeyu Li, Chi-Keung Tang, "[KNN Matting](http://dingzeyu.li/files/knn-matting-tpami.pdf)", IEEE TPAMI, 2013.
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[5] Yagiz Aksoy, "[Affinity Based Matting Toolbox](https://github.com/yaksoy/AffinityBasedMattingToolbox)".
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[5] Yagiz Aksoy, "[Affinity Based Matting Toolbox](https://github.com/yaksoy/AffinityBasedMattingToolbox)".

‎modules/aruco/CMakeLists.txt

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set(the_description "ArUco Marker Detection")
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ocv_define_module(aruco opencv_core opencv_imgproc opencv_calib3d opencv_objdetect WRAP python java objc js)
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ocv_include_directories(${CMAKE_CURRENT_BINARY_DIR})
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ocv_add_testdata(samples/ contrib/aruco
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FILES_MATCHING PATTERN "*yml"
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)
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ocv_add_testdata(tutorials/aruco_detection/images/ contrib/aruco
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FILES_MATCHING PATTERN "singlemarkersoriginal.jpg"
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)
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ocv_add_testdata(tutorials/aruco_board_detection/images/ contrib/aruco
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FILES_MATCHING PATTERN "gboriginal.png"
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)
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ocv_add_testdata(tutorials/charuco_detection/images/ contrib/aruco
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FILES_MATCHING PATTERN "choriginal.jpg"
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)
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ocv_add_testdata(tutorials/charuco_detection/images/ contrib/aruco
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FILES_MATCHING PATTERN "chocclusion_original.jpg"
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)
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ocv_add_testdata(tutorials/charuco_diamond_detection/images/ contrib/aruco
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FILES_MATCHING PATTERN "diamondmarkers.png"
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)
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ocv_add_testdata(tutorials/aruco_calibration/images/ contrib/aruco
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FILES_MATCHING REGEX "img_[0-9]+.jpg"
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)

‎modules/aruco/README.md

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**ArUco**
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![markers](https://github.com/opencv/opencv_contrib/assets/810997/8d587456-f27f-49e4-9540-28a0477d43fc)
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ArUco markers are easy to detect pattern grids that yield up to 1024 different patterns. They were built for augmented reality and later used for camera calibration. Since the grid uniquely orients the square, the detection algorithm can determing the pose of the grid.
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**ChArUco**
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![screen_charuco](https://github.com/opencv/opencv_contrib/assets/810997/64610da1-ee06-406c-a19b-006b02ac44fd)
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ArUco markers were improved by interspersing them inside a checkerboard called ChArUco. Checkerboard corner intersections provide more stable corners because the edge location bias on one square is countered by the opposite edge orientation in the connecting square. By interspersing ArUco markers inside the checkerboard, each checkerboard corner gets a label which enables it to be used in complex calibration or pose scenarios where you cannot see all the corners of the checkerboard.
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The smallest ChArUco board is 5 checkers and 4 markers called a "Diamond Marker".
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The smallest ChArUco board is 5 checkers and 4 markers called a "Diamond Marker".

‎modules/aruco/samples/create_marker.cpp

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