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 FeatureDetector() interface.

KeyPoint¶

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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¶

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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:
...
};

cv::FeatureDetector::detect¶

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

Detect keypoints in an image (first variant) or image set (second variant).

Parameters:	image – The image. keypoints – The detected keypoints. mask – Mask specifying where to look for keypoints (optional). Must be a char matrix with non-zero values in the region of interest.

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

images Images set.
keypoints Collection of keypoints detected in an input images. keypoints[i] is a set of keypoints detected in an images[i].
masks Masks for each input image specifying where to look for keypoints (optional). masks[i] is a mask for images[i].

Each element of masks vector must be a char matrix with non-zero values in the region of interest.

cv::FeatureDetector::read¶

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void FeatureDetector::read(const FileNode& fn)¶

Read feature detector object from file node.

Parameters:	fn – File node from which detector will be read.

cv::FeatureDetector::write¶

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void FeatureDetector::write(FileStorage& fs) const¶

Write feature detector object to file storage.

Parameters:	fs – File storage in which detector will be written.

cv::FeatureDetector::create¶

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FeatureDetector()

..

:param :

¶

FastFeatureDetector() ```` StarFeatureDetector() ```` SiftFeatureDetector() ```` SurfFeatureDetector() ```` MserFeatureDetector() ```` GfttFeatureDetector() ```` HarrisFeatureDetector() ```` GridAdaptedFeatureDetector() ```` PyramidAdaptedFeatureDetector() ```` ````

FastFeatureDetector¶

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FastFeatureDetector¶

Wrapping class for feature detection using 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¶

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GoodFeaturesToTrackDetector¶

Wrapping class for feature detection using 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¶

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MserFeatureDetector¶

Wrapping class for feature detection using 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¶

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StarFeatureDetector¶

Wrapping class for feature detection using 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¶

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SiftFeatureDetector¶

Wrapping class for feature detection using 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¶

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SurfFeatureDetector¶

Wrapping class for feature detection using 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¶

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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¶

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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¶

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DynamicAdaptedFeatureDetector¶

An adaptively adjusting detector that iteratively detects until the desired number of features are found.

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)));

class DynamicAdaptedFeatureDetector: public FeatureDetector
{
public:
    DynamicAdaptedFeatureDetector( const Ptr<AdjusterAdapter>& adjaster,
        int min_features=400, int max_features=500, int max_iters=5 );
    ...
};

cv::DynamicAdaptedFeatureDetector::DynamicAdaptedFeatureDetector¶

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DynamicAdaptedFeatureDetector::DynamicAdaptedFeatureDetector(const Ptr<AdjusterAdapter>& adjaster, int min_features, int max_features, int max_iters)¶

DynamicAdaptedFeatureDetector constructor.

Parameters:

adjaster – An AdjusterAdapter() that will do the detection and parameter adjustment
min_features – This minimum desired number features.
max_features – The maximum desired number of features.
max_iters – The maximum number of times to try to adjust the feature detector parameters. For the FastAdjuster() this number can be high, but with Star or Surf, many iterations can get time consuming. At each iteration the detector is rerun, so keep this in mind when choosing this value.

AdjusterAdapter¶

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AdjusterAdapter¶

A feature detector parameter adjuster interface, this is used by the DynamicAdaptedFeatureDetector() and is a wrapper for FeatureDetecto() r that allow them to be adjusted after a detection.

See FastAdjuster() , StarAdjuster() , SurfAdjuster() for concrete implementations.

class AdjusterAdapter: public FeatureDetector
{
public:
        virtual ~AdjusterAdapter() {}
        virtual void tooFew(int min, int n_detected) = 0;
        virtual void tooMany(int max, int n_detected) = 0;
        virtual bool good() const = 0;
};

cv::AdjusterAdapter::tooFew¶

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virtual void tooFew(int min, int n_detected) = 0

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_--;
}

cv::AdjusterAdapter::tooMany¶

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virtual void tooMany(int max, int n_detected) = 0: Too many features were detected so, adjust the detector parameters accordingly - so that the next

detection detects less features.

param max: This maximum desired number features.

param n_detected:

The actual number detected last run.

An example implementation of this is

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

cv::AdjusterAdapter::good¶

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virtual bool good() const = 0: Are params maxed out or still valid? Returns false if the parameters can’t be adjusted any more.

An example implementation of this is

bool FastAdjuster::good() const
{
        return (thresh_ > 1) && (thresh_ < 200);
}

FastAdjuster¶

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FastAdjuster¶

An AdjusterAdapter() for the FastFeatureDetector() . This will basically decrement or increment the threshhold by 1

class FastAdjuster FastAdjuster: public AdjusterAdapter
{
public:
        FastAdjuster(int init_thresh = 20, bool nonmax = true);
        ...
};

StarAdjuster¶

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StarAdjuster¶

An AdjusterAdapter() for the StarFeatureDetector() . This adjusts the responseThreshhold of StarFeatureDetector.

class StarAdjuster: public AdjusterAdapter
{
        StarAdjuster(double initial_thresh = 30.0);
        ...
};

SurfAdjuster¶

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SurfAdjuster¶

An AdjusterAdapter() for the SurfFeatureDetector() . This adjusts the hessianThreshold of SurfFeatureDetector.

class SurfAdjuster: public SurfAdjuster
{
        SurfAdjuster();
        ...
};

Common Interfaces of Feature Detectors¶

KeyPoint¶

FeatureDetector¶

cv::FeatureDetector::detect¶

cv::FeatureDetector::read¶

cv::FeatureDetector::write¶

cv::FeatureDetector::create¶

¶

FastFeatureDetector¶

GoodFeaturesToTrackDetector¶

MserFeatureDetector¶

StarFeatureDetector¶

SiftFeatureDetector¶

SurfFeatureDetector¶

GridAdaptedFeatureDetector¶

PyramidAdaptedFeatureDetector¶

DynamicAdaptedFeatureDetector¶

cv::DynamicAdaptedFeatureDetector::DynamicAdaptedFeatureDetector¶

AdjusterAdapter¶

cv::AdjusterAdapter::tooFew¶

cv::AdjusterAdapter::tooMany¶

cv::AdjusterAdapter::good¶

FastAdjuster¶

StarAdjuster¶

SurfAdjuster¶

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

param n_detected:
param max:	This maximum desired number features.
	The actual number detected last run.

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Common Interfaces of Feature Detectors¶

KeyPoint¶

FeatureDetector¶

cv::FeatureDetector::detect¶

cv::FeatureDetector::read¶

cv::FeatureDetector::write¶

cv::FeatureDetector::create¶

¶

FastFeatureDetector¶

GoodFeaturesToTrackDetector¶

MserFeatureDetector¶

StarFeatureDetector¶

SiftFeatureDetector¶

SurfFeatureDetector¶

GridAdaptedFeatureDetector¶

PyramidAdaptedFeatureDetector¶

DynamicAdaptedFeatureDetector¶

cv::DynamicAdaptedFeatureDetector::DynamicAdaptedFeatureDetector¶

AdjusterAdapter¶

cv::AdjusterAdapter::tooFew¶

cv::AdjusterAdapter::tooMany¶

cv::AdjusterAdapter::good¶

FastAdjuster¶

StarAdjuster¶

SurfAdjuster¶

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