|
|
|
@ -1808,7 +1808,7 @@ inline void bfKnnMatchImpl( BruteForceMatcher<Distance>& matcher, |
|
|
|
|
size_t imgCount = matcher.trainDescCollection.size(); |
|
|
|
|
vector<Mat> allDists( imgCount ); // distances between one query descriptor and all train descriptors
|
|
|
|
|
for( size_t i = 0; i < imgCount; i++ ) |
|
|
|
|
allDists[i] = Mat( 1, matcher.trainDescCollection[i].rows, queryDescs.type() ); |
|
|
|
|
allDists[i] = Mat( 1, matcher.trainDescCollection[i].rows, DataType<DistanceType>::type ); |
|
|
|
|
|
|
|
|
|
for( int qIdx = 0; qIdx < queryDescs.rows; qIdx++ ) |
|
|
|
|
{ |
|
|
|
@ -1836,7 +1836,7 @@ inline void bfKnnMatchImpl( BruteForceMatcher<Distance>& matcher, |
|
|
|
|
{ |
|
|
|
|
const ValueType* d2 = (const ValueType*)(matcher.trainDescCollection[iIdx].data + |
|
|
|
|
matcher.trainDescCollection[iIdx].step*tIdx); |
|
|
|
|
allDists[iIdx].at<ValueType>(0, tIdx) = matcher.distance(d1, d2, dimension); |
|
|
|
|
allDists[iIdx].at<DistanceType>(0, tIdx) = matcher.distance(d1, d2, dimension); |
|
|
|
|
} |
|
|
|
|
} |
|
|
|
|
} |
|
|
|
@ -1847,6 +1847,7 @@ inline void bfKnnMatchImpl( BruteForceMatcher<Distance>& matcher, |
|
|
|
|
for( int k = 0; k < knn; k++ ) |
|
|
|
|
{ |
|
|
|
|
DMatch bestMatch; |
|
|
|
|
bestMatch.distance = std::numeric_limits<DistanceType>::max(); |
|
|
|
|
for( size_t iIdx = 0; iIdx < imgCount; iIdx++ ) |
|
|
|
|
{ |
|
|
|
|
double minVal; |
|
|
|
@ -1858,9 +1859,10 @@ inline void bfKnnMatchImpl( BruteForceMatcher<Distance>& matcher, |
|
|
|
|
if( bestMatch.trainIdx == -1 ) |
|
|
|
|
break; |
|
|
|
|
|
|
|
|
|
allDists[bestMatch.imgIdx].at<ValueType>(0, bestMatch.trainIdx) = std::numeric_limits<DistanceType>::max(); |
|
|
|
|
allDists[bestMatch.imgIdx].at<DistanceType>(0, bestMatch.trainIdx) = std::numeric_limits<DistanceType>::max(); |
|
|
|
|
curMatches->push_back( bestMatch ); |
|
|
|
|
} |
|
|
|
|
//TODO should already be sorted at this point?
|
|
|
|
|
std::sort( curMatches->begin(), curMatches->end() ); |
|
|
|
|
} |
|
|
|
|
} |
|
|
|
|