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Merge remote-tracking branch 'upstream/3.4' into merge-3.4

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This commit is contained in:
Alexander Alekhin 2020-04-06 15:57:43 +00:00
commit 763a1d7392
5 changed files with 8 additions and 3 deletions
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2
doc/Doxyfile.in
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@ -246,7 +246,7 @@ PREDEFINED = __cplusplus=1 \
CV_WRAP_PHANTOM(x)= \ CV_WRAP_PHANTOM(x)= \
CV_WRAP_DEFAULT(x)= \ CV_WRAP_DEFAULT(x)= \
CV_CDECL= \ CV_CDECL= \
CV_Func = \ CV_Func= \
CV_DO_PRAGMA(x)= \ CV_DO_PRAGMA(x)= \
CV_SUPPRESS_DEPRECATED_START= \ CV_SUPPRESS_DEPRECATED_START= \
CV_SUPPRESS_DEPRECATED_END= \ CV_SUPPRESS_DEPRECATED_END= \

2
doc/py_tutorials/py_imgproc/py_houghlines/py_houghlines.markdown
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@ -80,7 +80,7 @@ Probabilistic Hough Transform
In the hough transform, you can see that even for a line with two arguments, it takes a lot of In the hough transform, you can see that even for a line with two arguments, it takes a lot of
computation. Probabilistic Hough Transform is an optimization of the Hough Transform we saw. It doesn't computation. Probabilistic Hough Transform is an optimization of the Hough Transform we saw. It doesn't
take all the points into consideration. Instead, it takes only a random subset of points which is take all the points into consideration. Instead, it takes only a random subset of points which is
sufficient for line detection. Just we have to decrease the threshold. See image below which compares sufficient for line detection. We just have to decrease the threshold. See image below which compares
Hough Transform and Probabilistic Hough Transform in Hough space. (Image Courtesy : Hough Transform and Probabilistic Hough Transform in Hough space. (Image Courtesy :
[Franck Bettinger's home page](http://phdfb1.free.fr/robot/mscthesis/node14.html) ) [Franck Bettinger's home page](http://phdfb1.free.fr/robot/mscthesis/node14.html) )

1
modules/core/include/opencv2/core/utility.hpp
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@ -622,6 +622,7 @@ void Mat::forEach_impl(const Functor& operation) {
// or (_Tp&, void*) <- in case you don't need current idx. // or (_Tp&, void*) <- in case you don't need current idx.
} }
CV_Assert(!empty());
CV_Assert(this->total() / this->size[this->dims - 1] <= INT_MAX); CV_Assert(this->total() / this->size[this->dims - 1] <= INT_MAX);
const int LINES = static_cast<int>(this->total() / this->size[this->dims - 1]); const int LINES = static_cast<int>(this->total() / this->size[this->dims - 1]);

4
modules/dnn/src/dnn.cpp
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@ -2228,7 +2228,11 @@ struct Net::Impl
auto ieInpNode = inputNodes[i].dynamicCast<InfEngineNgraphNode>(); auto ieInpNode = inputNodes[i].dynamicCast<InfEngineNgraphNode>();
CV_Assert(oid < ieInpNode->node->get_output_size()); CV_Assert(oid < ieInpNode->node->get_output_size());
#if INF_ENGINE_VER_MAJOR_GT(2020020000)
inputNodes[i] = Ptr<BackendNode>(new InfEngineNgraphNode(ieInpNode->node->get_output_as_single_output_node(oid)));
#else
inputNodes[i] = Ptr<BackendNode>(new InfEngineNgraphNode(ieInpNode->node->get_output_as_single_output_node(oid, false))); inputNodes[i] = Ptr<BackendNode>(new InfEngineNgraphNode(ieInpNode->node->get_output_as_single_output_node(oid, false)));
#endif
} }
if (layer->supportBackend(preferableBackend)) if (layer->supportBackend(preferableBackend))

2
modules/imgproc/test/ocl/test_houghlines.cpp
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@ -173,7 +173,7 @@ OCL_TEST_P(HoughLinesP, RealImage)
OCL_INSTANTIATE_TEST_CASE_P(Imgproc, HoughLines, Combine(Values(1, 0.5), // rhoStep OCL_INSTANTIATE_TEST_CASE_P(Imgproc, HoughLines, Combine(Values(1, 0.5), // rhoStep
Values(CV_PI / 180.0, CV_PI / 360.0), // thetaStep Values(CV_PI / 180.0, CV_PI / 360.0), // thetaStep
Values(80, 150))); // threshold Values(85, 150))); // threshold
OCL_INSTANTIATE_TEST_CASE_P(Imgproc, HoughLinesP, Combine(Values(100, 150), // threshold OCL_INSTANTIATE_TEST_CASE_P(Imgproc, HoughLinesP, Combine(Values(100, 150), // threshold
Values(50, 100), // minLineLength Values(50, 100), // minLineLength