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added BOWTrainer::add()

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This commit is contained in:
Maria Dimashova
2010-09-30 14:21:22 +00:00
parent fb7aa43feb
commit 13b535ac19
3 changed files with 88 additions and 20 deletions
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38
modules/features2d/include/opencv2/features2d/features2d.hpp
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@@ -1356,7 +1356,7 @@ protected:
CV_EXPORTS Ptr<FeatureDetector> createFeatureDetector( const string& detectorType );
class DenseFeatureDetector : public FeatureDetector
class CV_EXPORTS DenseFeatureDetector : public FeatureDetector
{
public:
DenseFeatureDetector() : initFeatureScale(1), featureScaleLevels(1), featureScaleMul(0.1f),
@@ -1368,7 +1368,7 @@ public:
protected:
virtual void detectImpl( const Mat& image, const Mat& mask, vector<KeyPoint>& keypoints ) const = 0;
virtual void detectImpl( const Mat& image, const Mat& mask, vector<KeyPoint>& keypoints ) const;
float initFeatureScale;
int featureScaleLevels;
@@ -1379,7 +1379,6 @@ protected:
bool varyXyStepWithScale;
bool varyImgBoundWithScale;
};
/*
@@ -2240,31 +2239,47 @@ CV_EXPORTS void evaluateGenericDescriptorMatcher( const Mat& img1, const Mat& im
/*
* Abstract base class for training of a 'bag of visual words' vocabulary from a set of descriptors
*/
class BOWTrainer
class CV_EXPORTS BOWTrainer
{
public:
void add( const Mat& descriptors );
const vector<Mat>& getDescriptors() const { return descriptors; }
int descripotorsCount() const { return descriptors.empty() ? 0 : size; }
virtual void clear();
/*
* Train visual words vocabulary, that is cluster training descriptors and
* compute cluster centers.
* Returns cluster centers.
*
* descriptors Training descriptors computed on images keypoints.
* vocabulary Vocabulary is cluster centers.
*/
virtual void cluster( const Mat& descriptors, Mat& vocabulary ) = 0;
virtual Mat cluster() const = 0;
virtual Mat cluster( const Mat& descriptors ) const = 0;
protected:
vector<Mat> descriptors;
int size;
};
/*
* This is BOWTrainer using cv::kmeans to get vocabulary.
*/
class BOWKMeansTrainer : public BOWTrainer
class CV_EXPORTS BOWKMeansTrainer : public BOWTrainer
{
public:
BOWKMeansTrainer( int clusterCount, const TermCriteria& termcrit=TermCriteria(),
int attempts=3, int flags=KMEANS_PP_CENTERS );
virtual void cluster( const Mat& descriptors, Mat& vocabulary );
// Returns trained vocabulary (i.e. cluster centers).
virtual Mat cluster() const;
virtual Mat cluster( const Mat& descriptors ) const;
protected:
int clusterCount;
TermCriteria termcrit;
int attempts;
@@ -2274,14 +2289,15 @@ protected:
/*
* Class to compute image descriptor using bad of visual words.
*/
class BOWImgDescriptorExtractor
class CV_EXPORTS BOWImgDescriptorExtractor
{
public:
BOWImgDescriptorExtractor( const Ptr<DescriptorExtractor>& dextractor,
const Ptr<DescriptorMatcher>& dmatcher );
void set( const Mat& vocabulary );
void setVocabulary( const Mat& vocabulary );
const Mat& getVocabulary() const { return vocabulary; }
void compute( const Mat& image, vector<KeyPoint>& keypoints, Mat& imgDescriptor,
vector<vector<int> >& pointIdxsInClusters );
vector<vector<int> >* pointIdxsInClusters=0 ) const;
int descriptorSize() const { return vocabulary.empty() ? 0 : vocabulary.rows; }
int descriptorType() const { return CV_32FC1; }