* attempt to add 0d/1d mat support to OpenCV
* revised the patch; now 1D mat is treated as 1xN 2D mat rather than Nx1.
* a step towards 'green' tests
* another little step towards 'green' tests
* calib test failures seem to be fixed now
* more fixes _core & _dnn
* another step towards green ci; even 0D mat's (a.k.a. scalars) are now partly supported!
* * fixed strange bug in aruco/charuco detector, not sure why it did not work
* also fixed a few remaining failures (hopefully) in dnn & core
* disabled failing GAPI tests - too complex to dig into this compiler pipeline
* hopefully fixed java tests
* trying to fix some more tests
* quick followup fix
* continue to fix test failures and warnings
* quick followup fix
* trying to fix some more tests
* partly fixed support for 0D/scalar UMat's
* use updated parseReduce() from upstream
* trying to fix the remaining test failures
* fixed [ch]aruco tests in Python
* still trying to fix tests
* revert "fix" in dnn's CUDA tensor
* trying to fix dnn+CUDA test failures
* fixed 1D umat creation
* hopefully fixed remaining cuda test failures
* removed training whitespaces
Modify the outputVideoFormat after changing the output format in MSMF backend #24142
After changing the output format, need to modify the outputVideoFormat, otherwise the outputVideoFormat is always CV_CAP_MODE_BGR, and an error will occur when converting the format in retrieveVideoFrame(), and will always enter "case CV_CAP_MODE_BGR:" process.
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Co-authored-by: 李龙 <lilong@sobey.com>
Use ngraph::Output in OpenVINO backend wrapper #24196
### Pull Request Readiness Checklist
resolves https://github.com/opencv/opencv/issues/24102
* Use `ngraph::Output<ngraph::Node>>` insead of `std::shared_ptr<ngraph::Node>` as a backend wrapper. It lets access to multi-output nodes: 588ddf1b18/modules/dnn/src/net_openvino.cpp (L501-L504)
* All layers can be customizable with OpenVINO >= 2022.1. nGraph reference code used for default layer implementation does not required CPU plugin also (might be tested by commenting CPU plugin at `/opt/intel/openvino/runtime/lib/intel64/plugins.xml`).
* Correct inference if only intermediate blobs requested.
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Properly preserve chi_table license as mandated by BSD-3-Clause #24204
Amend reference to online hosted file with the full license quotation as mandated by the original license.
Fix distanceTransform for inputs with large step and height #24214
### Pull Request Readiness Checklist
resolves https://github.com/opencv/opencv/issues/23895
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Minor optimization of two lines intersection #24216
### Pull Request Readiness Checklist
Not significant, but we can reduce number of multiplications while compute two lines intersection. Both methods are used heavily in their modules.
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The address sanitizer highlighted this issue in our code base. It
looks like the code is currently grabbing a pointer to a temporary
object and then performing operations on it.
I printed some information right before the asan crash:
eigensolver address: 0x7f0ad95032f0
eigensolver size: 4528
eig_vecs_ ptr: 0x7f0ad95045e0
eig_vecs_ offset: 4848
This shows that `eig_vecs_` points past the end of `eigensolver`. In
other words, it points at the temporary object created by the
`eigensolver.eigenvectors()` call.
Compare the docs for `.eigenvalues()`:
https://eigen.tuxfamily.org/dox/classEigen_1_1EigenSolver.html#a0f507ad7ab14797882f474ca8f2773e7
to the docs for `.eigenvectors()`:
https://eigen.tuxfamily.org/dox/classEigen_1_1EigenSolver.html#a66288022802172e3ee059283b26201d7
The difference in return types is interesting. `.eigenvalues()`
returns a reference. But `.eigenvectors()` returns a matrix.
This patch here fixes the problem by saving the temporary object and
then grabbing a pointer into it.
This is a curated snippet of the original asan failure:
==12==ERROR: AddressSanitizer: stack-use-after-scope on address 0x7fc633704640 at pc 0x7fc64f7f1593 bp 0x7ffe8875fc90 sp 0x7ffe8875fc88
READ of size 8 at 0x7fc633704640 thread T0
#0 0x7fc64f7f1592 in cv::usac::EssentialMinimalSolverStewenius5ptsImpl::estimate(std::__1::vector<int, std::__1::allocator<int> > const&, std::__1::vector<cv::Mat, std::__1::allocator<cv::Mat> >&) const /proc/self/cwd/external/com_github_opencv_opencv/modules/calib3d/src/usac/essential_solver.cpp:181:48
#1 0x7fc64f915d92 in cv::usac::EssentialEstimatorImpl::estimateModels(std::__1::vector<int, std::__1::allocator<int> > const&, std::__1::vector<cv::Mat, std::__1::allocator<cv::Mat> >&) const /proc/self/cwd/external/com_github_opencv_opencv/modules/calib3d/src/usac/estimator.cpp:110:46
#2 0x7fc64fa74fb0 in cv::usac::Ransac::run(cv::Ptr<cv::usac::RansacOutput>&) /proc/self/cwd/external/com_github_opencv_opencv/modules/calib3d/src/usac/ransac_solvers.cpp:152:58
#3 0x7fc64fa6cd8e in cv::usac::run(cv::Ptr<cv::usac::Model const> const&, cv::_InputArray const&, cv::_InputArray const&, int, cv::Ptr<cv::usac::RansacOutput>&, cv::_InputArray const&, cv::_InputArray const&, cv::_InputArray const&, cv::_InputArray const&) /proc/self/cwd/external/com_github_opencv_opencv/modules/calib3d/src/usac/ransac_solvers.cpp:1010:16
#4 0x7fc64fa6fb46 in cv::usac::findEssentialMat(cv::_InputArray const&, cv::_InputArray const&, cv::_InputArray const&, int, double, double, cv::_OutputArray const&) /proc/self/cwd/external/com_github_opencv_opencv/modules/calib3d/src/usac/ransac_solvers.cpp:527:9
#5 0x7fc64f3b5522 in cv::findEssentialMat(cv::_InputArray const&, cv::_InputArray const&, cv::_InputArray const&, int, double, double, int, cv::_OutputArray const&) /proc/self/cwd/external/com_github_opencv_opencv/modules/calib3d/src/five-point.cpp:437:16
#6 0x7fc64f3b7e00 in cv::findEssentialMat(cv::_InputArray const&, cv::_InputArray const&, cv::_InputArray const&, int, double, double, cv::_OutputArray const&) /proc/self/cwd/external/com_github_opencv_opencv/modules/calib3d/src/five-point.cpp:486:12
...
Address 0x7fc633704640 is located in stack of thread T0 at offset 17984 in frame
#0 0x7fc64f7ed4ff in cv::usac::EssentialMinimalSolverStewenius5ptsImpl::estimate(std::__1::vector<int, std::__1::allocator<int> > const&, std::__1::vector<cv::Mat, std::__1::allocator<cv::Mat> >&) const /proc/self/cwd/external/com_github_opencv_opencv/modules/calib3d/src/usac/essential_solver.cpp:36
This frame has 63 object(s):
[32, 56) 'coefficients' (line 38)
[96, 384) 'ee' (line 55)
...
[13040, 17568) 'eigensolver' (line 142)
[17824, 17840) 'ref.tmp518' (line 143)
[17856, 17872) 'ref.tmp523' (line 144)
[17888, 19488) 'ref.tmp524' (line 144) <== Memory access at offset 17984 is inside this variable
[19616, 19640) 'ref.tmp532' (line 169)
...
The crash report says that we're accessing a temporary object from
line 144 when we shouldn't be. Line 144 looks like this:
https://github.com/opencv/opencv/blob/4.6.0/modules/calib3d/src/usac/essential_solver.cpp#L144
const auto * const eig_vecs_ = (double *) eigensolver.eigenvectors().real().data();
We are using version 4.6.0 for this, but the problem is present on the
4.x branch.
Note that I am dropping the .real() call here. I think that is safe because
of the code further down (line 277 in the most recent version):
const int eig_i = 20 * i + 12; // eigen stores imaginary values too
The code appears to expect to have to skip doubles for the imaginary parts
of the complex numbers.
Admittedly, I couldn't find a test case that exercised this code path to
validate correctness.
Fix crash in ap3p #23607
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G-API: Introduce a Queue Source #24178
- Added a new IStreamSource class: in fact, a wrapper over a concurrent queue;
- Added minimal example on how it can be used;
- Extended IStreamSource with optional "halt" interface to break the blocking calls in the emitter threads when required to stop.
- Introduced a QueueInput class which allows to pass the whole graph's input vector at once. In fact it is a thin wrapper atop of individual Queue Sources.
There is a hidden trap found with our type system as described in https://github.com/orgs/g-api-org/discussions/2
While it works even in this form, it should be addressed somewhere in the 5.0 timeframe.
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* add broadcast_to with tests
* change name
* fix test
* fix implicit type conversion
* replace type of shape with InputArray
* add perf test
* add perf tests which takes care of axis
* v2 from ficus expand
* rename to broadcast
* use randu in place of declare
* doc improvement; smaller scale in perf
* capture get_index by reference
If building with -mcpu=native or any other setting which implies the current
CPU has FP16 but with intrinsics disabled, we mistakenly try to use it even
though convolution.hpp conditionally defines it correctly based on whether
we should *use it*. convolution.cpp on the other hand was mismatched and
trying to use it if the CPU supported it, even if not enabled in the build
system.
Make the guards match.
Bug: https://bugs.gentoo.org/913031
Signed-off-by: Sam James <sam@gentoo.org>
