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added empty() method to common features2d classes; fixed #831

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pull/13383/head
Maria Dimashova 14 years ago
parent fa446e7e35
commit 9b4c682623
7 changed files with 112 additions and 19 deletions
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  1. 42
      modules/features2d/include/opencv2/features2d/features2d.hpp
  2. 7
      modules/features2d/src/calonder.cpp
  3. 10
      modules/features2d/src/descriptors.cpp
  4. 15
      modules/features2d/src/detectors.cpp
  5. 25
      modules/features2d/src/matchers.cpp
  6. 4
      modules/features2d/src/planardetect.cpp
  7. 28
      tests/cv/src/afeatures2d.cpp

42
modules/features2d/include/opencv2/features2d/features2d.hpp
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@ -523,6 +523,7 @@ public:
virtual int operator()(const Mat& img, Point2f kpt, vector<float>& signature) const;
virtual int operator()(const Mat& patch, vector<float>& signature) const;
virtual void clear();
virtual bool empty() const;
void setVerbose(bool verbose);
int getClassCount() const;
@ -1086,6 +1087,8 @@ public:
// GetPCAFilename: get default PCA filename
static string GetPCAFilename () { return "pca.yml"; }
virtual bool empty() const { return m_train_feature_count <= 0 ? true : false; }
protected:
CvSize m_patch_size; // patch size
int m_pose_count; // the number of poses for each descriptor
@ -1212,6 +1215,9 @@ public:
// Read detector object from a file node.
virtual void write( FileStorage& ) const;
// Return true if detector object is empty
virtual bool empty() const;
// Create feature detector by detector name.
static Ptr<FeatureDetector> create( const string& detectorType );
@ -1359,18 +1365,19 @@ public:
};
SimpleBlobDetector(const SimpleBlobDetector::Params &parameters = SimpleBlobDetector::Params());
protected:
struct CV_EXPORTS Center
{
cv::Point2d location;
double radius;
double confidence;
Point2d location;
double radius;
double confidence;
};
virtual void detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;
virtual void findBlobs(const cv::Mat &image, const cv::Mat &binaryImage, std::vector<Center> &centers) const;
cv::Point2d computeGrayscaleCentroid(const cv::Mat &image, const std::vector<cv::Point> &contour) const;
Point2d computeGrayscaleCentroid(const cv::Mat &image, const std::vector<cv::Point> &contour) const;
Params params;
};
@ -1422,6 +1429,7 @@ public:
int gridRows=4, int gridCols=4 );
// TODO implement read/write
virtual bool empty() const;
protected:
virtual void detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;
@ -1442,6 +1450,7 @@ public:
PyramidAdaptedFeatureDetector( const Ptr<FeatureDetector>& detector, int levels=2 );
// TODO implement read/write
virtual bool empty() const;
protected:
virtual void detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;
@ -1500,6 +1509,8 @@ public:
*/
DynamicAdaptedFeatureDetector( const Ptr<AdjusterAdapter>& adjaster, int min_features=400, int max_features=500, int max_iters=5 );
virtual bool empty() const;
protected:
virtual void detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;
@ -1607,6 +1618,8 @@ public:
virtual int descriptorSize() const = 0;
virtual int descriptorType() const = 0;
virtual bool empty() const;
static Ptr<DescriptorExtractor> create( const string& descriptorExtractorType );
protected:
@ -1680,6 +1693,8 @@ public:
virtual int descriptorSize() const { return classifier_.classes(); }
virtual int descriptorType() const { return DataType<T>::type; }
virtual bool empty() const;
protected:
virtual void computeImpl( const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors ) const;
@ -1722,6 +1737,12 @@ template<typename T>
void CalonderDescriptorExtractor<T>::write( FileStorage& ) const
{}
template<typename T>
bool CalonderDescriptorExtractor<T>::empty() const
{
return classifier_.trees_.empty();
}
/*
* OpponentColorDescriptorExtractor
*
@ -1742,6 +1763,8 @@ public:
virtual int descriptorSize() const;
virtual int descriptorType() const;
virtual bool empty() const;
protected:
virtual void computeImpl( const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors ) const;
@ -1766,7 +1789,7 @@ public:
/// @todo read and write for brief
protected:
virtual void computeImpl(const Mat& image, std::vector<KeyPoint>& keypoints, Mat& descriptors) const;
virtual void computeImpl(const Mat& image, std::vector<KeyPoint>& keypoints, Mat& descriptors) const;
typedef void(*PixelTestFn)(const Mat&, const std::vector<KeyPoint>&, Mat&);
@ -1924,7 +1947,7 @@ public:
/*
* Return true if there are not train descriptors in collection.
*/
bool empty() const;
virtual bool empty() const;
/*
* Return true if the matcher supports mask in match methods.
*/
@ -2366,6 +2389,9 @@ public:
// Writes matcher object to a file storage
virtual void write( FileStorage& ) const;
// Return true if matching object is empty (e.g. feature detector or descriptor matcher are empty)
virtual bool empty() const;
// Clone the matcher. If emptyTrainData is false the method create deep copy of the object, i.e. copies
// both parameters and train data. If emptyTrainData is true the method create object copy with current parameters
// but with empty train data.
@ -2473,6 +2499,8 @@ public:
virtual void read( const FileNode &fn );
virtual void write( FileStorage& fs ) const;
virtual bool empty() const;
virtual Ptr<GenericDescriptorMatcher> clone( bool emptyTrainData=false ) const;
protected:
@ -2540,6 +2568,7 @@ public:
virtual void read( const FileNode &fn );
virtual void write( FileStorage& fs ) const;
virtual bool empty() const;
virtual Ptr<GenericDescriptorMatcher> clone( bool emptyTrainData=false ) const;
@ -2586,6 +2615,7 @@ public:
virtual void read( const FileNode& fn );
virtual void write( FileStorage& fs ) const;
virtual bool empty() const;
virtual Ptr<GenericDescriptorMatcher> clone( bool emptyTrainData=false ) const;

7
modules/features2d/src/calonder.cpp
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@ -860,8 +860,11 @@ int RTreeClassifier::countNonZeroElements(float *vec, int n, double tol)
void RTreeClassifier::read(const char* file_name)
{
std::ifstream file(file_name, std::ifstream::binary);
read(file);
file.close();
if( file.is_open() )
{
read(file);
file.close();
}
}
void RTreeClassifier::read(std::istream &is)

10
modules/features2d/src/descriptors.cpp
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@ -96,6 +96,11 @@ void DescriptorExtractor::read( const FileNode& )
void DescriptorExtractor::write( FileStorage& ) const
{}
bool DescriptorExtractor::empty() const
{
return false;
}
void DescriptorExtractor::removeBorderKeypoints( vector<KeyPoint>& keypoints,
Size imageSize, int borderSize )
{
@ -361,4 +366,9 @@ int OpponentColorDescriptorExtractor::descriptorType() const
return descriptorExtractor->descriptorType();
}
bool OpponentColorDescriptorExtractor::empty() const
{
return descriptorExtractor.empty() || (DescriptorExtractor*)(descriptorExtractor)->empty();
}
}

15
modules/features2d/src/detectors.cpp
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@ -96,6 +96,11 @@ void FeatureDetector::read( const FileNode& )
void FeatureDetector::write( FileStorage& ) const
{}
bool FeatureDetector::empty() const
{
return false;
}
Ptr<FeatureDetector> FeatureDetector::create( const string& detectorType )
{
FeatureDetector* fd = 0;
@ -488,6 +493,11 @@ GridAdaptedFeatureDetector::GridAdaptedFeatureDetector( const Ptr<FeatureDetecto
: detector(_detector), maxTotalKeypoints(_maxTotalKeypoints), gridRows(_gridRows), gridCols(_gridCols)
{}
bool GridAdaptedFeatureDetector::empty() const
{
return detector.empty() || (FeatureDetector*)detector->empty();
}
struct ResponseComparator
{
bool operator() (const KeyPoint& a, const KeyPoint& b)
@ -544,6 +554,11 @@ PyramidAdaptedFeatureDetector::PyramidAdaptedFeatureDetector( const Ptr<FeatureD
: detector(_detector), levels(_levels)
{}
bool PyramidAdaptedFeatureDetector::empty() const
{
return detector.empty() || (FeatureDetector*)detector->empty();
}
void PyramidAdaptedFeatureDetector::detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask ) const
{
Mat src = image;

25
modules/features2d/src/matchers.cpp
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@ -213,7 +213,7 @@ void DescriptorMatcher::clear()
bool DescriptorMatcher::empty() const
{
return trainDescCollection.size() == 0;
return trainDescCollection.empty();
}
void DescriptorMatcher::train()
@ -848,6 +848,11 @@ void GenericDescriptorMatcher::read( const FileNode& )
void GenericDescriptorMatcher::write( FileStorage& ) const
{}
bool GenericDescriptorMatcher::empty() const
{
return true;
}
/*
* Factory function for GenericDescriptorMatch creating
*/
@ -994,13 +999,18 @@ void OneWayDescriptorMatcher::write( FileStorage& fs ) const
base->Write (fs);
}
bool OneWayDescriptorMatcher::empty() const
{
return base.empty() || base->empty();
}
Ptr<GenericDescriptorMatcher> OneWayDescriptorMatcher::clone( bool emptyTrainData ) const
{
OneWayDescriptorMatcher* matcher = new OneWayDescriptorMatcher( params );
if( !emptyTrainData )
{
CV_Error( CV_StsNotImplemented, "deep clone dunctionality is not implemented, because "
CV_Error( CV_StsNotImplemented, "deep clone functionality is not implemented, because "
"OneWayDescriptorBase has not copy constructor or clone method ");
//matcher->base;
@ -1175,6 +1185,11 @@ void FernDescriptorMatcher::write( FileStorage& fs ) const
// classifier->write(fs);
}
bool FernDescriptorMatcher::empty() const
{
return classifier.empty() || classifier->empty();
}
Ptr<GenericDescriptorMatcher> FernDescriptorMatcher::clone( bool emptyTrainData ) const
{
FernDescriptorMatcher* matcher = new FernDescriptorMatcher( params );
@ -1262,6 +1277,12 @@ void VectorDescriptorMatcher::write (FileStorage& fs) const
extractor->write (fs);
}
bool VectorDescriptorMatcher::empty() const
{
return extractor.empty() || extractor->empty() ||
matcher.empty() || matcher->empty();
}
Ptr<GenericDescriptorMatcher> VectorDescriptorMatcher::clone( bool emptyTrainData ) const
{
// TODO clone extractor

4
modules/features2d/src/planardetect.cpp
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@ -771,6 +771,10 @@ void FernClassifier::clear()
vector<float>().swap(posteriors);
}
bool FernClassifier::empty() const
{
return features.empty();
}
int FernClassifier::getLeaf(int fern, const Mat& _patch) const
{

28
tests/cv/src/afeatures2d.cpp
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@ -73,6 +73,8 @@ protected:
void CV_FeatureDetectorTest::emptyDataTest()
{
assert( !fdetector.empty() && !fdetector->empty() );
// One image.
Mat image;
vector<KeyPoint> keypoints;
@ -172,7 +174,7 @@ void CV_FeatureDetectorTest::compareKeypointSets( const vector<KeyPoint>& validK
void CV_FeatureDetectorTest::regressionTest()
{
assert( !fdetector.empty() );
assert( !fdetector.empty() && !fdetector->empty() );
string imgFilename = string(ts->get_data_path()) + FEATURES2D_DIR + "/" + IMAGE_FILENAME;
string resFilename = string(ts->get_data_path()) + DETECTOR_DIR + "/" + string(name) + ".xml.gz";
@ -229,7 +231,7 @@ void CV_FeatureDetectorTest::regressionTest()
void CV_FeatureDetectorTest::run( int /*start_from*/ )
{
if( fdetector.empty() )
if( fdetector.empty() || fdetector->empty() )
{
ts->printf( CvTS::LOG, "Feature detector is empty.\n" );
ts->set_failed_test_info( CvTS::FAIL_INVALID_TEST_DATA );
@ -293,7 +295,7 @@ public:
CvTest( testName, "cv::DescriptorExtractor::compute" ),
maxDist(_maxDist), prevTime(_prevTime), dextractor(_dextractor), distance(d) {}
protected:
virtual void createDescriptorExtractor() {}
virtual void createDescriptorExtractor(){}
void compareDescriptors( const Mat& validDescriptors, const Mat& calcDescriptors )
{
@ -329,7 +331,7 @@ protected:
void emptyDataTest()
{
assert( !dextractor.empty() );
assert( !dextractor.empty() && !dextractor->empty() );
// One image.
Mat image;
@ -374,7 +376,7 @@ protected:
void regressionTest()
{
assert( !dextractor.empty() );
assert( !dextractor.empty() && !dextractor->empty() );
// Read the test image.
string imgFilename = string(ts->get_data_path()) + FEATURES2D_DIR + "/" + IMAGE_FILENAME;
@ -449,7 +451,7 @@ protected:
void run(int)
{
createDescriptorExtractor();
if( dextractor.empty() )
if( dextractor.empty() || dextractor->empty() )
{
ts->printf(CvTS::LOG, "Descriptor extractor is empty.\n");
ts->set_failed_test_info( CvTS::FAIL_INVALID_TEST_DATA );
@ -495,9 +497,16 @@ public:
protected:
virtual void createDescriptorExtractor()
{
string filename = string(CV_DescriptorExtractorTest<Distance>::ts->get_data_path()) +
FEATURES2D_DIR + "/calonder_classifier.rtc";
CV_DescriptorExtractorTest<Distance>::dextractor =
new CalonderDescriptorExtractor<T>( string(CV_DescriptorExtractorTest<Distance>::ts->get_data_path()) +
FEATURES2D_DIR + "/calonder_classifier.rtc");
new CalonderDescriptorExtractor<T>( filename );
if( CV_DescriptorExtractorTest<Distance>::dextractor->empty() )
{
stringstream ss; ss << "Calonder descriptor extractor can not be loaded from file" << filename<< endl;
CV_DescriptorExtractorTest<Distance>::ts->printf( CvTS::LOG, ss.str().c_str() );
CV_DescriptorExtractorTest<Distance>::ts->set_failed_test_info( CvTS::FAIL_INVALID_TEST_DATA );
}
}
};
@ -531,7 +540,8 @@ private:
void CV_DescriptorMatcherTest::emptyDataTest()
{
assert( !dmatcher.empty() );
assert( !dmatcher.empty() && !dmatcher->empty() );
Mat queryDescriptors, trainDescriptors, mask;
vector<Mat> trainDescriptorCollection, masks;
vector<DMatch> matches;

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