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Merge pull request #12908 from alexevans:Issue11855

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This commit is contained in:
Alexander Alekhin 2018-11-16 20:42:54 +00:00
commit e580061b74
2 changed files with 30 additions and 2 deletions
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13
modules/core/src/batch_distance.cpp
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@ -297,19 +297,21 @@ void cv::batchDistance( InputArray _src1, InputArray _src2,
nidx = Scalar::all(-1);
}
if( crosscheck )
{
CV_Assert( K == 1 && update == 0 && mask.empty() );
CV_Assert(!nidx.empty());
Mat tdist, tidx;
Mat tdist, tidx, sdist, sidx;
batchDistance(src2, src1, tdist, dtype, tidx, normType, K, mask, 0, false);
batchDistance(src1, src2, sdist, dtype, sidx, normType, K, mask, 0, false);
// if an idx-th element from src1 appeared to be the nearest to i-th element of src2,
// we update the minimum mutual distance between idx-th element of src1 and the whole src2 set.
// As a result, if nidx[idx] = i*, it means that idx-th element of src1 is the nearest
// to i*-th element of src2 and i*-th element of src2 is the closest to idx-th element of src1.
// If nidx[idx] = -1, it means that there is no such ideal couple for it in src2.
// This O(N) procedure is called cross-check and it helps to eliminate some false matches.
// This O(2N) procedure is called cross-check and it helps to eliminate some false matches.
if( dtype == CV_32S )
{
for( int i = 0; i < tdist.rows; i++ )
@ -336,6 +338,13 @@ void cv::batchDistance( InputArray _src1, InputArray _src2,
}
}
}
for( int i = 0; i < sdist.rows; i++ )
{
if( tidx.at<int>(sidx.at<int>(i)) != i )
{
nidx.at<int>(i) = -1;
}
}
return;
}

19
modules/features2d/test/test_matchers_algorithmic.cpp
View File

@ -558,4 +558,23 @@ TEST( Features2d_DMatch, read_write )
ASSERT_NE( strstr(str.c_str(), "4.5"), (char*)0 );
}
TEST(Features2d_DMatch, issue_11855)
{
Mat sources = (Mat_<uchar>(2, 3) << 1, 1, 0,
1, 1, 1);
Mat targets = (Mat_<uchar>(2, 3) << 1, 1, 1,
0, 0, 0);
Ptr<BFMatcher> bf = BFMatcher::create(NORM_HAMMING, true);
vector<vector<DMatch> > match;
bf->knnMatch(sources, targets, match, 1, noArray(), true);
ASSERT_EQ((size_t)1, match.size());
ASSERT_EQ((size_t)1, match[0].size());
EXPECT_EQ(1, match[0][0].queryIdx);
EXPECT_EQ(0, match[0][0].trainIdx);
EXPECT_EQ(0.0f, match[0][0].distance);
}
}} // namespace