generic-library/opencv
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

Added distance threshold-based matching

Browse Source
This commit is contained in:
Ilya Lysenkov
2010-06-16 08:56:53 +00:00
parent f4dba4686d
commit 276f3b8862
3 changed files with 154 additions and 20 deletions
Download Patch File Download Diff File Expand all files Collapse all files

52
modules/features2d/src/descriptors.cpp
View File

@@ -41,6 +41,8 @@
#include "precomp.hpp"
//#define _KDTREE
using namespace std;
namespace cv
{
@@ -439,21 +441,22 @@ void OneWayDescriptorMatch::match( const Mat& image, vector<KeyPoint>& points, v
match( image, points, matchings );
for( size_t i = 0; i < points.size(); i++ )
indices[i] = matchings[i].index;
indices[i] = matchings[i].indexTrain;
}
void OneWayDescriptorMatch::match( const Mat& image, vector<KeyPoint>& points, vector<DMatch>& matchings )
void OneWayDescriptorMatch::match( const Mat& image, vector<KeyPoint>& points, vector<DMatch>& matches )
{
matchings.resize( points.size() );
matches.resize( points.size() );
IplImage _image = image;
for( size_t i = 0; i < points.size(); i++ )
{
int poseIdx = -1;
DMatch matching;
matching.index = -1;
base->FindDescriptor( &_image, points[i].pt, matching.index, poseIdx, matching.distance );
matchings[i] = matching;
DMatch match;
match.indexQuery = i;
match.indexTrain = -1;
base->FindDescriptor( &_image, points[i].pt, match.indexTrain, poseIdx, match.distance );
matches[i] = match;
}
}
@@ -744,18 +747,45 @@ void FernDescriptorMatch::match( const Mat& image, vector<KeyPoint>& keypoints,
}
}
void FernDescriptorMatch::match( const Mat& image, vector<KeyPoint>& keypoints, vector<DMatch>& matchings )
void FernDescriptorMatch::match( const Mat& image, vector<KeyPoint>& keypoints, vector<DMatch>& matches )
{
trainFernClassifier();
matchings.resize( keypoints.size() );
matches.resize( keypoints.size() );
vector<float> signature( (size_t)classifier->getClassCount() );
for( size_t pi = 0; pi < keypoints.size(); pi++ )
{
calcBestProbAndMatchIdx( image, keypoints[pi].pt, matchings[pi].distance, matchings[pi].index, signature );
matches[pi].indexQuery = pi;
calcBestProbAndMatchIdx( image, keypoints[pi].pt, matches[pi].distance, matches[pi].indexTrain, signature );
//matching[pi].distance is log of probability so we need to transform it
matchings[pi].distance = -matchings[pi].distance;
matches[pi].distance = -matches[pi].distance;
}
}
void FernDescriptorMatch::match( const Mat& image, vector<KeyPoint>& keypoints, vector<vector<DMatch> >& matches, float threshold )
{
trainFernClassifier();
matches.resize( keypoints.size() );
vector<float> signature( (size_t)classifier->getClassCount() );
for( size_t pi = 0; pi < keypoints.size(); pi++ )
{
(*classifier)( image, keypoints[pi].pt, signature);
DMatch match;
match.indexQuery = pi;
for( size_t ci = 0; ci < (size_t)classifier->getClassCount(); ci++ )
{
if( -signature[ci] < threshold )
{
match.distance = -signature[ci];
match.indexTrain = ci;
matches[pi].push_back( match );
}
}
}
}