generic-library/opencv
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Merge branch 'bug_3172' of https://github.com/elmarb/opencv into bfmatcher_fix;

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use different fix for the problem, embedded right into the BFMatcher.
This commit is contained in:
Vadim Pisarevsky
2014-09-19 15:31:21 +04:00
parent d82b918a7b bdb82d181f
commit 3516e14e05
3 changed files with 29 additions and 65 deletions
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70
modules/core/src/stat.cpp
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@@ -2460,21 +2460,32 @@ struct BatchDistInvoker : public ParallelLoopBody
BatchDistFunc func;
};
static void batchDistanceImpl( InputArray _src1, InputArray _src2,
InputOutputArray _dist, int dtype, InputOutputArray _nidx,
int normType, int K, InputArray _mask,
int update, bool crosscheck )
}
void cv::batchDistance( InputArray _src1, InputArray _src2,
OutputArray _dist, int dtype, OutputArray _nidx,
int normType, int K, InputArray _mask,
int update, bool crosscheck )
{
Mat src1 = _src1.getMat(), src2 = _src2.getMat(), mask = _mask.getMat();
int type = src1.type();
CV_Assert( type == src2.type() && src1.cols == src2.cols &&
(type == CV_32F || type == CV_8U));
(type == CV_32F || type == CV_8U));
CV_Assert( _nidx.needed() == (K > 0) );
if( dtype == -1 )
{
dtype = normType == NORM_HAMMING || normType == NORM_HAMMING2 ? CV_32S : CV_32F;
}
CV_Assert( (type == CV_8U && dtype == CV_32S) || dtype == CV_32F);
K = std::min(K, src2.rows);
_dist.create(src1.rows, (K > 0 ? K : src2.rows), dtype);
Mat dist = _dist.getMat(), nidx;
if( _nidx.needed() )
{
_nidx.create(dist.size(), CV_32S);
nidx = _nidx.getMat();
}
@@ -2562,55 +2573,6 @@ static void batchDistanceImpl( InputArray _src1, InputArray _src2,
BatchDistInvoker(src1, src2, dist, nidx, K, mask, update, func));
}
}
void cv::batchDistance( InputArray _src1, InputArray _src2,
OutputArray _dist, int dtype, OutputArray _nidx,
int normType, int K, InputArray _mask,
int update, bool crosscheck )
{
if( dtype == -1 )
{
dtype = normType == NORM_HAMMING || normType == NORM_HAMMING2 ? CV_32S : CV_32F;
}
// K == 0: return all matches; K > 0: return K best matches, but never more than the number of candidates in _src2
CV_Assert( _nidx.needed() == (K > 0) );
int candidates = _src2.size().height;
K = std::min( K, candidates );
_dist.create( _src1.size().height, (K > 0 ? K : candidates), dtype );
if( _nidx.needed() )
{
_nidx.create( _dist.size(), CV_32S );
}
batchDistanceImpl( _src1, _src2, _dist, dtype, _nidx, normType, K, _mask, update, crosscheck );
}
void cv::batchDistanceForBFMatcher( InputArray _src1, InputArray _src2,
InputOutputArray _dist, int dtype, InputOutputArray _nidx,
int normType, int K, InputArray _mask,
int update, bool crosscheck )
{
if( dtype == -1 )
{
dtype = normType == NORM_HAMMING || normType == NORM_HAMMING2 ? CV_32S : CV_32F;
}
// always work with K matches (unlike cv::batchDistance), even if _src2 has fewer candidates
// if this function is called in a loop, then the other loop iterations may require all K
CV_Assert( K > 0 && _nidx.needed() );
cv::Size size( K, _src1.size().height );
CV_Assert( update == 0 || (_dist.size() == size && _nidx.size() == size) );
_dist.create( size, dtype );
_nidx.create( size, CV_32S );
batchDistanceImpl( _src1, _src2, _dist, dtype, _nidx, normType, K, _mask, update, crosscheck );
CV_Assert( _dist.size() == size && _nidx.size() == size );
}
void cv::findNonZero( InputArray _src, OutputArray _idx )
{