Common Interfaces of Feature Detectors

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

Note

• An example explaining keypoint detection can be found at opencv_source_code/samples/cpp/descriptor_extractor_matcher.cpp

KeyPoint

class KeyPoint

Data structure for salient point detectors.

Point2f pt

coordinates of the keypoint

float size

diameter of the meaningful keypoint neighborhood

float angle

computed orientation of the keypoint (-1 if not applicable). Its possible values are in a range [0,360) degrees. It is measured relative to image coordinate system (y-axis is directed downward), ie in clockwise.

float response

the response by which the most strong keypoints have been selected. Can be used for further sorting or subsampling

int octave

octave (pyramid layer) from which the keypoint has been extracted

int class_id

object id that can be used to clustered keypoints by an object they belong to

KeyPoint::KeyPoint

The keypoint constructors

C++: KeyPoint::KeyPoint()

C++: KeyPoint::KeyPoint(Point2f _pt, float _size, float _angle=-1, float _response=0, int _octave=0, int _class_id=-1)

C++: KeyPoint::KeyPoint(float x, float y, float _size, float _angle=-1, float _response=0, int _octave=0, int _class_id=-1)

Python: cv2.KeyPoint([x, y, _size[, _angle[, _response[, _octave[, _class_id]]]]]) → <KeyPoint object>

Parameters:

• x – x-coordinate of the keypoint
• y – y-coordinate of the keypoint
• _pt – x & y coordinates of the keypoint
• _size – keypoint diameter
• _angle – keypoint orientation
• _response – keypoint detector response on the keypoint (that is, strength of the keypoint)
• _octave – pyramid octave in which the keypoint has been detected
• _class_id – object id

FeatureDetector

class FeatureDetector : public Algorithm

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

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

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

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

Parameters:

• image – Image.
• images – Image set.
• keypoints – The detected keypoints. In the second variant of the method keypoints[i] is a set of keypoints detected in images[i] .
• mask – Mask specifying where to look for keypoints (optional). It must be a 8-bit integer matrix with non-zero values in the region of interest.
• masks – Masks for each input image specifying where to look for keypoints (optional). masks[i] is a mask for images[i].

FeatureDetector::create

Creates a feature detector by its name.

C++: Ptr<FeatureDetector> FeatureDetector::create(const string& detectorType)

Parameters:

• detectorType – Feature detector type.

The following detector types are supported:

• "FAST" – FastFeatureDetector
• "STAR" – StarFeatureDetector
• "SIFT" – SIFT (nonfree module)
• "SURF" – SURF (nonfree module)
• "ORB" – ORB
• "BRISK" – BRISK
• "MSER" – MSER
• "GFTT" – GoodFeaturesToTrackDetector
• "HARRIS" – GoodFeaturesToTrackDetector with Harris detector enabled
• "Dense" – DenseFeatureDetector
• "SimpleBlob" – SimpleBlobDetector

Also a combined format is supported: feature detector adapter name ( "Grid" – GridAdaptedFeatureDetector, "Pyramid" – PyramidAdaptedFeatureDetector ) + feature detector name (see above), for example: "GridFAST", "PyramidSTAR" .

Note

When using the SIFT and SURF detector types be sure to add the following lines to your code

• Add an extra include: #include <opencv2/nonfree/nonfree.hpp>
• Add an initialisation function at the start of your code: initModules_nonfree();

FastFeatureDetector

class FastFeatureDetector : public FeatureDetector

Wrapping class for feature detection using the FAST() method.

class FastFeatureDetector : public FeatureDetector
{
public:
    FastFeatureDetector( int threshold=1, bool nonmaxSuppression=true, type=FastFeatureDetector::TYPE_9_16 );
    virtual void read( const FileNode& fn );
    virtual void write( FileStorage& fs ) const;
protected:
    ...
};

GoodFeaturesToTrackDetector

class GoodFeaturesToTrackDetector : public FeatureDetector

Wrapping class for feature detection using the 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

class MserFeatureDetector : public FeatureDetector

Wrapping class for feature detection using the 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

class StarFeatureDetector : public FeatureDetector

The class implements the keypoint detector introduced by [Agrawal08], synonym of StarDetector.

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:
    ...
};

[Agrawal08]

Agrawal, M., Konolige, K., & Blas, M. R. (2008). Censure: Center surround extremas for realtime feature detection and matching. In Computer Vision–ECCV 2008 (pp. 102-115). Springer Berlin Heidelberg.

DenseFeatureDetector

class DenseFeatureDetector : public FeatureDetector

Class for generation of image features which are distributed densely and regularly over the image.

    class DenseFeatureDetector : public FeatureDetector
    {
    public:
            DenseFeatureDetector( float initFeatureScale=1.f, int featureScaleLevels=1,
                          float featureScaleMul=0.1f,
                          int initXyStep=6, int initImgBound=0,
                          bool varyXyStepWithScale=true,
                          bool varyImgBoundWithScale=false );
    protected:
    ...
};

The detector generates several levels (in the amount of featureScaleLevels) of features. Features of each level are located in the nodes of a regular grid over the image (excluding the image boundary of given size). The level parameters (a feature scale, a node size, a size of boundary) are multiplied by featureScaleMul with level index growing depending on input flags, viz.:

• Feature scale is multiplied always.
• The grid node size is multiplied if varyXyStepWithScale is true.
• Size of image boundary is multiplied if varyImgBoundWithScale is true.

SimpleBlobDetector

class SimpleBlobDetector : public FeatureDetector

Class for extracting blobs from an image.

class SimpleBlobDetector : public FeatureDetector
{
public:
struct Params
{
    Params();
    float thresholdStep;
    float minThreshold;
    float maxThreshold;
    size_t minRepeatability;
    float minDistBetweenBlobs;

    bool filterByColor;
    uchar blobColor;

    bool filterByArea;
    float minArea, maxArea;

    bool filterByCircularity;
    float minCircularity, maxCircularity;

    bool filterByInertia;
    float minInertiaRatio, maxInertiaRatio;

    bool filterByConvexity;
    float minConvexity, maxConvexity;
};

SimpleBlobDetector(const SimpleBlobDetector::Params &parameters = SimpleBlobDetector::Params());

protected:
    ...
};

The class implements a simple algorithm for extracting blobs from an image:

1. Convert the source image to binary images by applying thresholding with several thresholds from minThreshold (inclusive) to maxThreshold (exclusive) with distance thresholdStep between neighboring thresholds.
2. Extract connected components from every binary image by findContours() and calculate their centers.
3. Group centers from several binary images by their coordinates. Close centers form one group that corresponds to one blob, which is controlled by the minDistBetweenBlobs parameter.
4. From the groups, estimate final centers of blobs and their radiuses and return as locations and sizes of keypoints.

This class performs several filtrations of returned blobs. You should set filterBy* to true/false to turn on/off corresponding filtration. Available filtrations:

• By color. This filter compares the intensity of a binary image at the center of a blob to blobColor. If they differ, the blob is filtered out. Use blobColor = 0 to extract dark blobs and blobColor = 255 to extract light blobs.
• By area. Extracted blobs have an area between minArea (inclusive) and maxArea (exclusive).
• By circularity. Extracted blobs have circularity () between minCircularity (inclusive) and maxCircularity (exclusive).
• By ratio of the minimum inertia to maximum inertia. Extracted blobs have this ratio between minInertiaRatio (inclusive) and maxInertiaRatio (exclusive).
• By convexity. Extracted blobs have convexity (area / area of blob convex hull) between minConvexity (inclusive) and maxConvexity (exclusive).

Default values of parameters are tuned to extract dark circular blobs.

GridAdaptedFeatureDetector

class GridAdaptedFeatureDetector : public FeatureDetector

Class adapting 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 kept.
     * gridRows            Grid row 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

class PyramidAdaptedFeatureDetector : public FeatureDetector

Class adapting a detector to detect points over multiple levels of a Gaussian pyramid. Consider using this class 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

class DynamicAdaptedFeatureDetector : public FeatureDetector

Adaptively adjusting detector that iteratively detects features until the desired number is found.

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

If the detector is persisted, it “remembers” the parameters used for the last detection. In this case, the detector may be used for consistent numbers of keypoints in a set of temporally related images, such as video streams or panorama series.

DynamicAdaptedFeatureDetector uses another detector, such as FAST or SURF, to do the dirty work, with the help of AdjusterAdapter . If the detected number of features is not large enough, AdjusterAdapter adjusts the detection parameters so that the next detection results in a bigger or smaller number of features. This is repeated until either the number of desired features are found or the parameters are maxed out.

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

Beware that this is not thread-safe since the adjustment of parameters requires modification of the feature detector class instance.

Example of creating 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

The constructor

C++: DynamicAdaptedFeatureDetector::DynamicAdaptedFeatureDetector(const Ptr<AdjusterAdapter>& adjuster, int min_features=400, int max_features=500, int max_iters=5 )

Parameters:

• adjuster – AdjusterAdapter that detects features and adjusts parameters.
• min_features – Minimum desired number of features.
• max_features – Maximum desired number of features.
• max_iters – Maximum number of times to try adjusting the feature detector parameters. For FastAdjuster , this number can be high, but with Star or Surf many iterations can be time-consuming. At each iteration the detector is rerun.

AdjusterAdapter

class AdjusterAdapter : public FeatureDetector

Class providing an interface for adjusting parameters of a feature detector. This interface is used by DynamicAdaptedFeatureDetector . It is a wrapper for FeatureDetector that enables adjusting parameters after feature detection.

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;
   virtual Ptr<AdjusterAdapter> clone() const = 0;
   static Ptr<AdjusterAdapter> create( const string& detectorType );
};

See FastAdjuster, StarAdjuster, and SurfAdjuster for concrete implementations.

AdjusterAdapter::tooFew

Adjusts the detector parameters to detect more features.

C++: void AdjusterAdapter::tooFew(int min, int n_detected)

Parameters:

• min – Minimum desired number of features.
• n_detected – Number of features detected during the latest run.

Example:

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

AdjusterAdapter::tooMany

Adjusts the detector parameters to detect less features.

C++: void AdjusterAdapter::tooMany(int max, int n_detected)

Parameters:

• max – Maximum desired number of features.
• n_detected – Number of features detected during the latest run.

Example:

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

AdjusterAdapter::good

Returns false if the detector parameters cannot be adjusted any more.

C++: bool AdjusterAdapter::good() const

Example:

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

AdjusterAdapter::create

Creates an adjuster adapter by name

C++: Ptr<AdjusterAdapter> AdjusterAdapter::create(const string& detectorType)

Creates an adjuster adapter by name detectorType. The detector name is the same as in FeatureDetector::create(), but now supports "FAST", "STAR", and "SURF" only.

FastAdjuster

class FastAdjuster : public AdjusterAdapter

AdjusterAdapter for FastFeatureDetector. This class decreases or increases the threshold value by 1.

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

StarAdjuster

class StarAdjuster : public AdjusterAdapter

AdjusterAdapter for StarFeatureDetector. This class adjusts the responseThreshhold of StarFeatureDetector.

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

SurfAdjuster

class SurfAdjuster : public AdjusterAdapter

AdjusterAdapter for SurfFeatureDetector.

class CV_EXPORTS SurfAdjuster: public AdjusterAdapter
{
public:
    SurfAdjuster( double initial_thresh=400.f, double min_thresh=2, double max_thresh=1000 );

    virtual void tooFew(int minv, int n_detected);
    virtual void tooMany(int maxv, int n_detected);
    virtual bool good() const;

    virtual Ptr<AdjusterAdapter> clone() const;

    ...
};