Fix Aruco marker incorrect detection near image edge #26968
### Pull Request Readiness Checklist
Fix#26922
As I understood the algorithm, at the first stage we search for the contours of the marker several times (adaptive threshold with different windows sizes). Therefore, for the same marker, we get several contours (inner and outer with different sizes due to the different windows sizes). In the second stage, we group the contours for the same marker into one group, from which we take the largest contour as the best candidate (which should best match the border of the marker).
The problem is that using the `minDistanceToBorder` parameter, we discard contours at the first stage. Thus, we discard the best candidates most appropriate to the marker border, and inner contours may remain, representing a significantly smaller marker border (which we observe in the issue).
But if we use the `minDistanceToBorder` parameter to discard the best candidate of the group at the second stage, then there will be no such problems and we will completely discard markers located too close to the border of the image.
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
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Patch to opencv_extra has the same branch name.
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Use map to manage unique marker size candidate trees.
Avoid code duplication.
Add a test to show double detection with overlapping dictionaries.
Generalize to marker sizes of not only predefined dictionaries.
Fix rotated aruco marker board generation #26753
### Issue : [25884](https://github.com/opencv/opencv/issues/25884)
### Pull Request Readiness Checklist
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- [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
Use size_t when calculating size of all_points #26650Closes: #26642
Asan log
```
=================================================================
==41401==ERROR: AddressSanitizer: heap-buffer-overflow on address 0x7fc55a02a3fc at pc 0x7fc58e304131 bp 0x7ffd54787b00 sp 0x7ffd54787af8
WRITE of size 4 at 0x7fc55a02a3fc thread T0
#0 0x7fc58e304130 in cv::QRDetectMulti::checkSets(std::vector<std::vector<cv::Point_<float>, std::allocator<cv::Point_<float> > >, std::allocator<std::vector<cv::Point_<float>, std::allocator<cv::Point_<float> > > > >&, std::vector<std::vector<cv::Point_<float>, std::allocator<cv::Point_<float> > >, std::allocator<std::vector<cv::Point_<float>, std::allocator<cv::Point_<float> > > > >&, std::vector<cv::Point_<float>, std::allocator<cv::Point_<float> > >&) /home/fanta/source/opencv/modules/objdetect/src/qrcode.cpp:3726
#1 0x7fc58e3054b0 in cv::QRDetectMulti::localization() /home/fanta/source/opencv/modules/objdetect/src/qrcode.cpp:3829
#2 0x7fc58e308020 in cv::ImplContour::detectMulti(cv::_InputArray const&, cv::_OutputArray const&) const /home/fanta/source/opencv/modules/objdetect/src/qrcode.cpp:3987
#3 0x7fc58e30b5b1 in cv::ImplContour::detectAndDecodeMulti(cv::_InputArray const&, std::vector<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::allocator<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > >&, cv::_OutputArray const&, cv::_OutputArray const&) const /home/fanta/source/opencv/modules/objdetect/src/qrcode.cpp:4176
#4 0x7fc58e28922f in cv::GraphicalCodeDetector::detectAndDecodeMulti(cv::_InputArray const&, std::vector<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::allocator<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > >&, cv::_OutputArray const&, cv::_OutputArray const&) const /home/fanta/source/opencv/modules/objdetect/src/graphical_code_detector.cpp:42
#5 0x5954e8 in Body /home/fanta/source/opencv/modules/objdetect/test/test_qrcode.cpp:48
#6 0x594fc0 in TestBody /home/fanta/source/opencv/modules/objdetect/test/test_qrcode.cpp:42
#7 0x67ee6a in void testing::internal::HandleSehExceptionsInMethodIfSupported<testing::Test, void>(testing::Test*, void (testing::Test::*)(), char const*) /home/fanta/source/opencv/modules/ts/src/ts_gtest.cpp:3919
#8 0x6734a4 in void testing::internal::HandleExceptionsInMethodIfSupported<testing::Test, void>(testing::Test*, void (testing::Test::*)(), char const*) /home/fanta/source/opencv/modules/ts/src/ts_gtest.cpp:3955
#9 0x641fe8 in testing::Test::Run() /home/fanta/source/opencv/modules/ts/src/ts_gtest.cpp:3993
#10 0x6431ac in testing::TestInfo::Run() /home/fanta/source/opencv/modules/ts/src/ts_gtest.cpp:4169
#11 0x643d15 in testing::TestCase::Run() /home/fanta/source/opencv/modules/ts/src/ts_gtest.cpp:4287
#12 0x659ff3 in testing::internal::UnitTestImpl::RunAllTests() /home/fanta/source/opencv/modules/ts/src/ts_gtest.cpp:6662
#13 0x681205 in bool testing::internal::HandleSehExceptionsInMethodIfSupported<testing::internal::UnitTestImpl, bool>(testing::internal::UnitTestImpl*, bool (testing::internal::UnitTestImpl::*)(), char const*) /home/fanta/source/opencv/modules/ts/src/ts_gtest.cpp:3919
#14 0x675127 in bool testing::internal::HandleExceptionsInMethodIfSupported<testing::internal::UnitTestImpl, bool>(testing::internal::UnitTestImpl*, bool (testing::internal::UnitTestImpl::*)(), char const*) /home/fanta/source/opencv/modules/ts/src/ts_gtest.cpp:3955
#15 0x65734c in testing::UnitTest::Run() /home/fanta/source/opencv/modules/ts/src/ts_gtest.cpp:6271
#16 0x5907f0 in RUN_ALL_TESTS() /home/fanta/source/opencv/modules/ts/include/opencv2/ts/ts_gtest.h:22240
#17 0x590cdd in main (/home/fanta/source/opencv-build-4.x-clang/bin/opencv_test_objdetect+0x590cdd) (BuildId: a9363fc788d57c48225fc0559ac9199d07d415db)
#18 0x7fc58ab242ad in __libc_start_call_main (/lib64/libc.so.6+0x2a2ad) (BuildId: 03f1631dc9760d3e30311fe62e15cc4baaa89db7)
#19 0x7fc58ab24378 in __libc_start_main@@GLIBC_2.34 (/lib64/libc.so.6+0x2a378) (BuildId: 03f1631dc9760d3e30311fe62e15cc4baaa89db7)
#20 0x417014 in _start ../sysdeps/x86_64/start.S:115
0x7fc55a02a3fc is located 0 bytes after 2938510332-byte region [0x7fc4aadc8800,0x7fc55a02a3fc)
allocated by thread T0 here:
#0 0x7fc58e590298 in operator new(unsigned long) (/lib64/libasan.so.8+0xfd298) (BuildId: da72ee674d801ced58193987786b90646d94ff8d)
#1 0x7fc58e34d010 in std::__new_allocator<cv::Vec<int, 3> >::allocate(unsigned long, void const*) /usr/include/c++/14/bits/new_allocator.h:151
SUMMARY: AddressSanitizer: heap-buffer-overflow /home/fanta/source/opencv/modules/objdetect/src/qrcode.cpp:3726 in cv::QRDetectMulti::checkSets(std::vector<std::vector<cv::Point_<float>, std::allocator<cv::Point_<float> > >, std::allocator<std::vector<cv::Point_<float>, std::allocator<cv::Point_<float> > > > >&, std::vector<std::vector<cv::Point_<float>, std::allocator<cv::Point_<float> > >, std::allocator<std::vector<cv::Point_<float>, std::allocator<cv::Point_<float> > > > >&, std::vector<cv::Point_<float>, std::allocator<cv::Point_<float> > >&)
Shadow bytes around the buggy address:
0x7fc55a02a100: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
0x7fc55a02a180: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
0x7fc55a02a200: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
0x7fc55a02a280: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
0x7fc55a02a300: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
=>0x7fc55a02a380: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00[04]
0x7fc55a02a400: fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa
0x7fc55a02a480: fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa
0x7fc55a02a500: fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa
0x7fc55a02a580: fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa
0x7fc55a02a600: fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa fa
Shadow byte legend (one shadow byte represents 8 application bytes):
Addressable: 00
Partially addressable: 01 02 03 04 05 06 07
Heap left redzone: fa
Freed heap region: fd
Stack left redzone: f1
Stack mid redzone: f2
Stack right redzone: f3
Stack after return: f5
Stack use after scope: f8
Global redzone: f9
Global init order: f6
Poisoned by user: f7
Container overflow: fc
Array cookie: ac
Intra object redzone: bb
ASan internal: fe
Left alloca redzone: ca
Right alloca redzone: cb
==41401==ABORTING
```
`(true_points_group[i].size()` is 1794 and `(true_points_group[i].size() - 2 ) * (true_points_group[i].size() - 1) * true_points_group[i].size())` is 5764222464 which overflows `int`
### 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
- [ ] 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
- Implemented a new `create` method in `FaceRecognizerSF` to allow model and configuration loading from memory buffers (std::vector<uchar>), similar to the existing functionality in `FaceDetectorYN`.
- Updated `face_recognize.cpp` with a new constructor in `FaceRecognizerSFImpl` that supports buffer-based loading for both model weights and network configuration.
- Ensured compatibility with both file-based and buffer-based model loading by maintaining consistent backend and target settings across both constructors.
- This change improves flexibility, allowing FaceRecognizerSF to be instantiated from memory buffers, which is useful for dynamic model loading scenarios such as embedded systems or applications where models are loaded in-memory.
Split Javascript white-list to support contrib modules #25986
Single whitelist converted to several per-module json files. They are concatenated automatically and can be overriden by user config.
Related to https://github.com/opencv/opencv/pull/25656
### 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
- [ ] 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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Properly check markers when none are provided. #25938
CharucoDetectorImpl::detectBoard finds temporary markers when none are provided but those are discarded when
charucoDetectorImpl::checkBoard is called.
### 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
Feature barcode detector parameters #24903
Attempt to solve #24902 without changing the default detector behaviour.
Megre with extra: https://github.com/opencv/opencv_extra/pull/1150
**Introduces new parameters and methods to `cv::barcode::BarcodeDetector`**.
### 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.
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Move Charuco/Calib tutorials and samples to main repo #25378
Merge with https://github.com/opencv/opencv_contrib/pull/3708
Move Charuco/Calib tutorials and samples to main repo:
- [x] update/fix charuco_detection.markdown and samples
- [x] update/fix charuco_diamond_detection.markdown and samples
- [x] update/fix aruco_calibration.markdown and samples
- [x] update/fix aruco_faq.markdown
- [x] move tutorials, samples and tests to main repo
- [x] remove old tutorials, samples and tests from contrib
### 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
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Patch to opencv_extra has the same branch name.
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Documentation transition to fresh Doxygen #25042
* current Doxygen version is 1.10, but we will use 1.9.8 for now due to issue with snippets (https://github.com/doxygen/doxygen/pull/10584)
* Doxyfile adapted to new version
* MathJax updated to 3.x
* `@relates` instructions removed temporarily due to issue in Doxygen (to avoid warnings)
* refactored matx.hpp - extracted matx.inl.hpp
* opencv_contrib - https://github.com/opencv/opencv_contrib/pull/3638
Fixes#25056 : Optimising postProcess(const std::vector<Mat>& output_blobs) #25091
Like mentioned in the issue #25056 , I think checking the condition with `scoreThreshold` and then assigning the bounding boxes can optimize the function pretty well. By doing this, we prevent allocating boxes to faces with scores below the threshold. It also reduces the amount of data that needs to be processed during the subsequent NMS step. Builds and passed locally.
- [X] I agree to contribute to the project under Apache 2 License.
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Patch to opencv_extra has the same branch name.
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Co-authored-by: Dhanwanth1803 <dhanwanthvarala@gmail,com>
Move Aruco tutorials and samples to main repo #23018
merge with https://github.com/opencv/opencv_contrib/pull/3401
merge with https://github.com/opencv/opencv_extra/pull/1143
### Pull Request Readiness Checklist
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- [x] I agree to contribute to the project under Apache 2 License.
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Patch to opencv_extra has the same branch name.
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---------
Co-authored-by: AleksandrPanov <alexander.panov@xperience.ai>
Co-authored-by: Alexander Smorkalov <alexander.smorkalov@xperience.ai>
Fix barcode detectAndDecode #25035
The method `detectAndDecode()` in the `BarcodeDetector` class doesn't return the barcode corners.
This PR fixes the and add test for `detectAndDecode`.
### Pull Request Readiness Checklist
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- [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
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Fix qrcode bugs #25026
This PR fixes#22892, #24011 and #24450 and adds regression tests using the images provided. I've also verified with the [benchmark](https://github.com/opencv/opencv_benchmarks/tree/develop/python_benchmarks/qr_codes) that this doesn't break anything there.
resolves#22892resolves#24011resolves#24450
Replaces #23802
Requires extra: https://github.com/opencv/opencv_extra/pull/1148
### 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
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Patch to opencv_extra has the same branch name.
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QR codes Structured Append decoding mode #24548
### Pull Request Readiness Checklist
resolves https://github.com/opencv/opencv/issues/23245
Merge after https://github.com/opencv/opencv/pull/24299
Current proposal is to use `detectAndDecodeMulti` or `decodeMulti` for structured append mode decoding. 0-th QR code in a sequence gets a full message while the rest of codes will correspond to empty strings.
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.
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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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Modified Java tests to run on Android #24910
To run the tests you need to:
1. Build OpenCV using Android pipeline. For example:
`cmake -DBUILD_TEST=ON -DANDROID=ON -DANDROID_ABI=arm64-v8a -DCMAKE_TOOLCHAIN_FILE=/usr/lib/android-sdk/ndk/25.1.8937393/build/cmake/android.toolchain.cmake -DANDROID_NDK=/usr/lib/android-sdk/ndk/25.1.8937393 -DANDROID_SDK=/usr/lib/android-sdk ../opencv`
`make`
2. Connect Android Phone
3. Run tests:
`cd android_tests`
`./gradlew tests_module:connectedAndroidTest`
Related CI pipeline: https://github.com/opencv/ci-gha-workflow/pull/138
### 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
- [ ] 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
