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Merge pull request #15971 from alalek:core_kmeans_handle_overflow

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Alexander Alekhin 2019-11-22 21:36:01 +00:00
commit 50ac880335
2 changed files with 30 additions and 0 deletions
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2
modules/core/src/kmeans.cpp
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@ -143,6 +143,8 @@ static void generateCentersPP(const Mat& data, Mat& _out_centers,
std::swap(tdist, tdist2);
}
}
if (bestCenter < 0)
CV_Error(Error::StsNoConv, "kmeans: can't update cluster center (check input for huge or NaN values)");
centers[k] = bestCenter;
sum0 = bestSum;
std::swap(dist, tdist);

28
modules/core/test/test_math.cpp
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@ -2949,6 +2949,34 @@ TEST(Core_KMeans, compactness)
}
}
TEST(Core_KMeans, bad_input)
{
const int N = 100;
const int attempts = 4;
const TermCriteria crit = TermCriteria(TermCriteria::COUNT, 5, 0); // low number of iterations
const int K = 3;
Mat data(N, 1, CV_32FC2);
cv::randu(data, Scalar(-200, -200), Scalar(200, 200));
{
SCOPED_TRACE("Huge value");
data.at<Vec2f>(10, 0) = Vec2f(1e20f, 0);
Mat labels, centers;
EXPECT_ANY_THROW(kmeans(data, K, labels, crit, attempts, KMEANS_PP_CENTERS, centers));
}
{
SCOPED_TRACE("Negative value");
data.at<Vec2f>(10, 0) = Vec2f(0, -1e20f);
Mat labels, centers;
EXPECT_ANY_THROW(kmeans(data, K, labels, crit, attempts, KMEANS_PP_CENTERS, centers));
}
{
SCOPED_TRACE("NaN");
data.at<Vec2f>(10, 0) = Vec2f(0, std::numeric_limits<float>::quiet_NaN());
Mat labels, centers;
EXPECT_ANY_THROW(kmeans(data, K, labels, crit, attempts, KMEANS_PP_CENTERS, centers));
}
}
TEST(CovariationMatrixVectorOfMat, accuracy)
{
unsigned int col_problem_size = 8, row_problem_size = 8, vector_size = 16;