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added tests for some detectors; made features2d object create functions as static classes methods; fixed OpponentColorDescriptorExtractor, BriefDescriptorExtractor (on rgb); renamed DynamicDetector

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This commit is contained in:
Maria Dimashova
2010-11-25 15:59:37 +00:00
parent 9ad7a1c927
commit 7e5c11a920
13 changed files with 400 additions and 322 deletions
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92
modules/features2d/include/opencv2/features2d/features2d.hpp
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@@ -1250,11 +1250,14 @@ public:
*/
void detect( const vector<Mat>& images, vector<vector<KeyPoint> >& keypoints, const vector<Mat>& masks=vector<Mat>() ) const;
// Read detector object from a file node
// Read detector object from a file node.
virtual void read( const FileNode& );
// Read detector object from a file node
// Read detector object from a file node.
virtual void write( FileStorage& ) const;
// Create feature detector by detector name.
static Ptr<FeatureDetector> create( const string& detectorType );
protected:
virtual void detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const = 0;
/*
@@ -1416,7 +1419,7 @@ public:
* gridRows Grid rows count.
* gridCols Grid column count.
*/
GridAdaptedFeatureDetector( const Ptr<FeatureDetector>& detector, int maxTotalKeypoints,
GridAdaptedFeatureDetector( const Ptr<FeatureDetector>& detector, int maxTotalKeypoints=1000,
int gridRows=4, int gridCols=4 );
// TODO implement read/write
@@ -1448,19 +1451,15 @@ protected:
int levels;
};
/*
* Dynamic Feature Detectors
*/
/** \brief A feature detector parameter adjuster, this is used by the DynamicDetector
/** \brief A feature detector parameter adjuster, this is used by the DynamicAdaptedFeatureDetector
* and is a wrapper for FeatureDetector that allow them to be adjusted after a detection
*/
class CV_EXPORTS AdjusterAdapter: public FeatureDetector {
public:
class CV_EXPORTS AdjusterAdapter: public FeatureDetector
{
public:
/** pure virtual interface
*/
virtual ~AdjusterAdapter() {
}
virtual ~AdjusterAdapter() {}
/** too few features were detected so, adjust the detector params accordingly
* \param min the minimum number of desired features
* \param n_detected the number previously detected
@@ -1475,6 +1474,8 @@ public:
* \return false if the parameters can't be adjusted any more
*/
virtual bool good() const = 0;
static Ptr<AdjusterAdapter> create( const string& detectorType );
};
/** \brief an adaptively adjusting detector that iteratively detects until the desired number
* of features are detected.
@@ -1485,24 +1486,24 @@ public:
* sample usage:
//will create a detector that attempts to find 100 - 110 FAST Keypoints, and will at most run
//FAST feature detection 10 times until that number of keypoints are found
Ptr<FeatureDetector> detector(new DynamicDetector (100, 110, 10,new FastAdjuster(20,true)));
Ptr<FeatureDetector> detector(new DynamicAdaptedFeatureDetector(new FastAdjuster(20,true),100, 110, 10));
*/
class CV_EXPORTS DynamicDetector: public FeatureDetector {
class CV_EXPORTS DynamicAdaptedFeatureDetector: public FeatureDetector
{
public:
/** \param min_features the minimum desired features
/** \param adjaster an AdjusterAdapter that will do the detection and parameter adjustment
* \param max_features the maximum desired number of features
* \param max_iters the maximum number of times to try to adjust the feature detector params
* for the FastAdjuster this can be high, but with Star or Surf this can get time consuming
* \param a an AdjusterAdapter that will do the detection and parameter adjustment
* \param min_features the minimum desired features
*/
DynamicDetector(int min_features, int max_features, int max_iters,
const Ptr<AdjusterAdapter>& a);
DynamicAdaptedFeatureDetector( const Ptr<AdjusterAdapter>& adjaster, int min_features=400, int max_features=500, int max_iters=5 );
protected:
virtual void detectImpl(const cv::Mat& image,
std::vector<cv::KeyPoint>& keypoints, const cv::Mat& mask =
cv::Mat()) const;
virtual void detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;
private:
int escape_iters_;
int min_features_, max_features_;
@@ -1512,7 +1513,8 @@ private:
/**\brief an adjust for the FAST detector. This will basically decrement or increment the
* threshhold by 1
*/
class CV_EXPORTS FastAdjuster: public AdjusterAdapter {
class CV_EXPORTS FastAdjuster: public AdjusterAdapter
{
public:
/**\param init_thresh the initial threshhold to start with, default = 20
* \param nonmax whether to use non max or not for fast feature detection
@@ -1521,50 +1523,50 @@ public:
virtual void tooFew(int min, int n_detected);
virtual void tooMany(int max, int n_detected);
virtual bool good() const;
protected:
virtual void detectImpl(const cv::Mat& image,
std::vector<cv::KeyPoint>& keypoints, const cv::Mat& mask =
cv::Mat()) const;
virtual void detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;
int thresh_;
bool nonmax_;
};
/** An adjuster for StarFeatureDetector, this one adjusts the responseThreshold for now
* TODO find a faster way to converge the parameters for Star - use CvStarDetectorParams
*/
struct CV_EXPORTS StarAdjuster: public AdjusterAdapter {
class CV_EXPORTS StarAdjuster: public AdjusterAdapter
{
public:
StarAdjuster(double initial_thresh = 30.0);
virtual void tooFew(int min, int n_detected);
virtual void tooMany(int max, int n_detected);
virtual bool good() const;
protected:
virtual void detectImpl(const cv::Mat& image,
std::vector<cv::KeyPoint>& keypoints, const cv::Mat& mask =
cv::Mat()) const;
virtual void detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;
double thresh_;
CvStarDetectorParams params_; //todo use these instead of thresh_
};
struct CV_EXPORTS SurfAdjuster: public AdjusterAdapter {
SurfAdjuster();
class CV_EXPORTS SurfAdjuster: public AdjusterAdapter
{
public:
SurfAdjuster();
virtual void tooFew(int min, int n_detected);
virtual void tooMany(int max, int n_detected);
virtual bool good() const;
protected:
virtual void detectImpl(const cv::Mat& image,
std::vector<cv::KeyPoint>& keypoints, const cv::Mat& mask =
cv::Mat()) const;
virtual void detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const;
double thresh_;
};
CV_EXPORTS Mat windowedMatchingMask( const vector<KeyPoint>& keypoints1, const vector<KeyPoint>& keypoints2,
float maxDeltaX, float maxDeltaY );
CV_EXPORTS Ptr<FeatureDetector> createFeatureDetector( const string& detectorType );
/****************************************************************************************\
* DescriptorExtractor *
\****************************************************************************************/
@@ -1606,6 +1608,8 @@ public:
virtual int descriptorSize() const = 0;
virtual int descriptorType() const = 0;
static Ptr<DescriptorExtractor> create( const string& descriptorExtractorType );
protected:
virtual void computeImpl( const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors ) const = 0;
@@ -1771,8 +1775,6 @@ protected:
PixelTestFn test_fn_;
};
CV_EXPORTS Ptr<DescriptorExtractor> createDescriptorExtractor( const string& descriptorExtractorType );
/****************************************************************************************\
* Distance *
\****************************************************************************************/
@@ -1981,6 +1983,7 @@ public:
// but with empty train data.
virtual Ptr<DescriptorMatcher> clone( bool emptyTrainData=false ) const = 0;
static Ptr<DescriptorMatcher> create( const string& descriptorMatcherType );
protected:
/*
* Class to work with descriptors from several images as with one merged matrix.
@@ -2265,9 +2268,6 @@ protected:
int addedDescCount;
};
CV_EXPORTS Ptr<DescriptorMatcher> createDescriptorMatcher( const string& descriptorMatcherType );
/****************************************************************************************\
* GenericDescriptorMatcher *
\****************************************************************************************/
@@ -2372,6 +2372,9 @@ public:
// but with empty train data.
virtual Ptr<GenericDescriptorMatcher> clone( bool emptyTrainData=false ) const = 0;
static Ptr<GenericDescriptorMatcher> create( const string& genericDescritptorMatcherType,
const string &paramsFilename=string() );
protected:
// In fact the matching is implemented only by the following two methods. These methods suppose
// that the class object has been trained already. Public match methods call these methods
@@ -2557,9 +2560,6 @@ protected:
int prevTrainCount;
};
CV_EXPORTS Ptr<GenericDescriptorMatcher> createGenericDescriptorMatcher( const string& genericDescritptorMatcherType,
const string &paramsFilename = string () );
/****************************************************************************************\
* VectorDescriptorMatcher *
\****************************************************************************************/