Common Interfaces of Feature Detectors

Feature detectors in OpenCV have wrappers with common interface that enables to switch easily between different algorithms solving the same problem. All objects that implement keypoint detectors inherit :func:`FeatureDetector` interface.

KeyPoint

Data structure for salient point detectors.

class KeyPoint
{
public:
    // the default constructor
    KeyPoint() : pt(0,0), size(0), angle(-1), response(0), octave(0),
                 class_id(-1) {}
    // the full constructor
    KeyPoint(Point2f _pt, float _size, float _angle=-1,
            float _response=0, int _octave=0, int _class_id=-1)
            : pt(_pt), size(_size), angle(_angle), response(_response),
              octave(_octave), class_id(_class_id) {}
    // another form of the full constructor
    KeyPoint(float x, float y, float _size, float _angle=-1,
            float _response=0, int _octave=0, int _class_id=-1)
            : pt(x, y), size(_size), angle(_angle), response(_response),
              octave(_octave), class_id(_class_id) {}
    // converts vector of keypoints to vector of points
    static void convert(const std::vector<KeyPoint>& keypoints,
                        std::vector<Point2f>& points2f,
                        const std::vector<int>& keypointIndexes=std::vector<int>());
    // converts vector of points to the vector of keypoints, where each
    // keypoint is assigned the same size and the same orientation
    static void convert(const std::vector<Point2f>& points2f,
                        std::vector<KeyPoint>& keypoints,
                        float size=1, float response=1, int octave=0,
                        int class_id=-1);

    // computes overlap for pair of keypoints;
    // overlap is a ratio between area of keypoint regions intersection and
    // area of keypoint regions union (now keypoint region is circle)
    static float overlap(const KeyPoint& kp1, const KeyPoint& kp2);

    Point2f pt; // coordinates of the keypoints
    float size; // diameter of the meaningfull keypoint neighborhood
    float angle; // computed orientation of the keypoint (-1 if not applicable)
    float response; // the response by which the most strong keypoints
                    // have been selected. Can be used for the further sorting
                    // or subsampling
    int octave; // octave (pyramid layer) from which the keypoint has been extracted
    int class_id; // object class (if the keypoints need to be clustered by
                  // an object they belong to)
};

// writes vector of keypoints to the file storage
void write(FileStorage& fs, const string& name, const vector<KeyPoint>& keypoints);
// reads vector of keypoints from the specified file storage node
void read(const FileNode& node, CV_OUT vector<KeyPoint>& keypoints);

FeatureDetector

Abstract base class for 2D image feature detectors.

class CV_EXPORTS FeatureDetector
{
public:
    virtual ~FeatureDetector();

    void detect( const Mat& image, vector<KeyPoint>& keypoints,
                 const Mat& mask=Mat() ) const;

    void detect( const vector<Mat>& images,
                 vector<vector<KeyPoint> >& keypoints,
                 const vector<Mat>& masks=vector<Mat>() ) const;

    virtual void read(const FileNode&);
    virtual void write(FileStorage&) const;

    static Ptr<FeatureDetector> create( const string& detectorType );

protected:
...
};

FeatureDetector::detect

FeatureDetector::read

FeatureDetector::write

FeatureDetector::create

:func:`FeatureDetector` .. c:function:: Ptr<FeatureDetector> FeatureDetector::create( const string& detectorType )

Feature detector factory that creates of given type with default parameters (rather using default constructor).

param detectorType:	Feature detector type.

Now the following detector types are supported: "FAST" -- :func:`FastFeatureDetector`,"STAR" -- :func:`StarFeatureDetector`,"SIFT" -- :func:`SiftFeatureDetector`,"SURF" -- :func:`SurfFeatureDetector`,"MSER" -- :func:`MserFeatureDetector`,"GFTT" -- :func:`GfttFeatureDetector`,"HARRIS" -- :func:`HarrisFeatureDetector` . Also combined format is supported: feature detector adapter name ( "Grid" -- :func:`GridAdaptedFeatureDetector`,``"Pyramid"`` -- :func:`PyramidAdaptedFeatureDetector` ) + feature detector name (see above), e.g. "GridFAST",``"PyramidSTAR"`` , etc.

FastFeatureDetector

Wrapping class for feature detection using :func:`FAST` method.

class FastFeatureDetector : public FeatureDetector
{
public:
    FastFeatureDetector( int threshold=1, bool nonmaxSuppression=true );
    virtual void read( const FileNode& fn );
    virtual void write( FileStorage& fs ) const;
protected:
    ...
};

GoodFeaturesToTrackDetector

Wrapping class for feature detection using :func:`goodFeaturesToTrack` function.

class GoodFeaturesToTrackDetector : public FeatureDetector
{
public:
    class Params
    {
    public:
        Params( int maxCorners=1000, double qualityLevel=0.01,
                double minDistance=1., int blockSize=3,
                bool useHarrisDetector=false, double k=0.04 );
        void read( const FileNode& fn );
        void write( FileStorage& fs ) const;

        int maxCorners;
        double qualityLevel;
        double minDistance;
        int blockSize;
        bool useHarrisDetector;
        double k;
    };

    GoodFeaturesToTrackDetector( const GoodFeaturesToTrackDetector::Params& params=
                                            GoodFeaturesToTrackDetector::Params() );
    GoodFeaturesToTrackDetector( int maxCorners, double qualityLevel,
                                 double minDistance, int blockSize=3,
                                 bool useHarrisDetector=false, double k=0.04 );
    virtual void read( const FileNode& fn );
    virtual void write( FileStorage& fs ) const;
protected:
    ...
};

MserFeatureDetector

Wrapping class for feature detection using :func:`MSER` class.

class MserFeatureDetector : public FeatureDetector
{
public:
    MserFeatureDetector( CvMSERParams params=cvMSERParams() );
    MserFeatureDetector( int delta, int minArea, int maxArea,
                         double maxVariation, double minDiversity,
                         int maxEvolution, double areaThreshold,
                         double minMargin, int edgeBlurSize );
    virtual void read( const FileNode& fn );
    virtual void write( FileStorage& fs ) const;
protected:
    ...
};

StarFeatureDetector

Wrapping class for feature detection using :func:`StarDetector` class.

class StarFeatureDetector : public FeatureDetector
{
public:
    StarFeatureDetector( int maxSize=16, int responseThreshold=30,
                         int lineThresholdProjected = 10,
                         int lineThresholdBinarized=8, int suppressNonmaxSize=5 );
    virtual void read( const FileNode& fn );
    virtual void write( FileStorage& fs ) const;
protected:
    ...
};

SiftFeatureDetector

Wrapping class for feature detection using :func:`SIFT` class.

class SiftFeatureDetector : public FeatureDetector
{
public:
    SiftFeatureDetector(
        const SIFT::DetectorParams& detectorParams=SIFT::DetectorParams(),
        const SIFT::CommonParams& commonParams=SIFT::CommonParams() );
    SiftFeatureDetector( double threshold, double edgeThreshold,
                         int nOctaves=SIFT::CommonParams::DEFAULT_NOCTAVES,
                         int nOctaveLayers=SIFT::CommonParams::DEFAULT_NOCTAVE_LAYERS,
                         int firstOctave=SIFT::CommonParams::DEFAULT_FIRST_OCTAVE,
                         int angleMode=SIFT::CommonParams::FIRST_ANGLE );
    virtual void read( const FileNode& fn );
    virtual void write( FileStorage& fs ) const;
protected:
    ...
};

SurfFeatureDetector

Wrapping class for feature detection using :func:`SURF` class.

class SurfFeatureDetector : public FeatureDetector
{
public:
    SurfFeatureDetector( double hessianThreshold = 400., int octaves = 3,
                         int octaveLayers = 4 );
    virtual void read( const FileNode& fn );
    virtual void write( FileStorage& fs ) const;
protected:
    ...
};

GridAdaptedFeatureDetector

Adapts a detector to partition the source image into a grid and detect points in each cell.

class GridAdaptedFeatureDetector : public FeatureDetector
{
public:
    /*
     * detector            Detector that will be adapted.
     * maxTotalKeypoints   Maximum count of keypoints detected on the image.
     *                     Only the strongest keypoints will be keeped.
     * gridRows            Grid rows count.
     * gridCols            Grid column count.
     */
    GridAdaptedFeatureDetector( const Ptr<FeatureDetector>& detector,
                                int maxTotalKeypoints, int gridRows=4,
                                int gridCols=4 );
    virtual void read( const FileNode& fn );
    virtual void write( FileStorage& fs ) const;
protected:
    ...
};

PyramidAdaptedFeatureDetector

Adapts a detector to detect points over multiple levels of a Gaussian pyramid. Useful for detectors that are not inherently scaled.

class PyramidAdaptedFeatureDetector : public FeatureDetector
{
public:
    PyramidAdaptedFeatureDetector( const Ptr<FeatureDetector>& detector,
                                   int levels=2 );
    virtual void read( const FileNode& fn );
    virtual void write( FileStorage& fs ) const;
protected:
    ...
};

DynamicAdaptedFeatureDetector

If the detector is persisted, it will "remember" the parameters used on the last detection. In this way, the detector may be used for consistent numbers of keypoints in a sets of images that are temporally related such as video streams or panorama series.

The DynamicAdaptedFeatureDetector uses another detector such as FAST or SURF to do the dirty work, with the help of an AdjusterAdapter. After a detection, and an unsatisfactory number of features are detected, the AdjusterAdapter will adjust the detection parameters so that the next detection will result in more or less features. This is repeated until either the number of desired features are found or the parameters are maxed out.

Adapters can easily be implemented for any detector via the AdjusterAdapter interface.

Beware that this is not thread safe - as the adjustment of parameters breaks the const of the detection routine...

Here is a sample of how to create a DynamicAdaptedFeatureDetector.

//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 DynamicAdaptedFeatureDetector (100, 110, 10,
                              new FastAdjuster(20,true)));

DynamicAdaptedFeatureDetector::DynamicAdaptedFeatureDetector

AdjusterAdapter

See :func:`FastAdjuster`,:func:StarAdjuster,:func:SurfAdjuster for concrete implementations.

AdjusterAdapter::tooFew

Too few features were detected so, adjust the detector parameters accordingly - so that the next detection detects more features.

param min:	This minimum desired number features.
param n_detected:	The actual number detected last run.

An example implementation of this is

void FastAdjuster::tooFew(int min, int n_detected)
{
        thresh_--;
}

AdjusterAdapter::tooMany

An example implementation of this is

void FastAdjuster::tooMany(int min, int n_detected)
{
        thresh_++;
}

AdjusterAdapter::good

FastAdjuster

StarAdjuster

SurfAdjuster

FeatureDetector

FeatureDetector::detect

FeatureDetector::read

FeatureDetector::write

FeatureDetector::create

:func:`FeatureDetector` .. c:function:: Ptr<FeatureDetector> FeatureDetector::create( const string& detectorType )

Feature detector factory that creates of given type with default parameters (rather using default constructor).

param detectorType:	Feature detector type.

Now the following detector types are supported:

• "FAST" -- :func:`FastFeatureDetector`,
• "STAR" -- :func:`StarFeatureDetector`,
• "SIFT" -- :func:`SiftFeatureDetector`,
• "SURF" -- :func:`SurfFeatureDetector`,
• "MSER" -- :func:`MserFeatureDetector`,
• "GFTT" -- :func:`GfttFeatureDetector`,
• "HARRIS" -- :func:`HarrisFeatureDetector` .

Also combined format is supported: feature detector adapter name ( "Grid" -- :func:`GridAdaptedFeatureDetector`,``"Pyramid"`` -- :func:`PyramidAdaptedFeatureDetector` ) + feature detector name (see above), e.g. "GridFAST",``"PyramidSTAR"`` , etc.

FastFeatureDetector

Wrapping class for feature detection using :func:`FAST` method.

class FastFeatureDetector : public FeatureDetector
{
public:
    FastFeatureDetector( int threshold=1, bool nonmaxSuppression=true );
    virtual void read( const FileNode& fn );
    virtual void write( FileStorage& fs ) const;
protected:
    ...
};

GoodFeaturesToTrackDetector

MserFeatureDetector

StarFeatureDetector

SiftFeatureDetector

SurfFeatureDetector

GridAdaptedFeatureDetector

PyramidAdaptedFeatureDetector

DynamicAdaptedFeatureDetector

If the detector is persisted, it will "remember" the parameters used on the last detection. In this way, the detector may be used for consistent numbers of keypoints in a sets of images that are temporally related such as video streams or panorama series.

The DynamicAdaptedFeatureDetector uses another detector such as FAST or SURF to do the dirty work, with the help of an AdjusterAdapter. After a detection, and an unsatisfactory number of features are detected, the AdjusterAdapter will adjust the detection parameters so that the next detection will result in more or less features. This is repeated until either the number of desired features are found or the parameters are maxed out.

Adapters can easily be implemented for any detector via the AdjusterAdapter interface.

Beware that this is not thread safe - as the adjustment of parameters breaks the const of the detection routine...

Here is a sample of how to create a DynamicAdaptedFeatureDetector.

//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 DynamicAdaptedFeatureDetector (100, 110, 10,
                              new FastAdjuster(20,true)));

DynamicAdaptedFeatureDetector::DynamicAdaptedFeatureDetector

AdjusterAdapter

See :func:`FastAdjuster`,:func:StarAdjuster,:func:SurfAdjuster for concrete implementations.

AdjusterAdapter::tooFew

Too few features were detected so, adjust the detector parameters accordingly - so that the next detection detects more features.

param min:	This minimum desired number features.
param n_detected:	The actual number detected last run.

An example implementation of this is

void FastAdjuster::tooFew(int min, int n_detected)
{
        thresh_--;
}

AdjusterAdapter::tooMany

An example implementation of this is

void FastAdjuster::tooMany(int min, int n_detected)
{
        thresh_++;
}

AdjusterAdapter::good

FastAdjuster

StarAdjuster

SurfAdjuster