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Common Interfaces of Feature Detectors
======================================
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.. highlight :: cpp
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Feature detectors in OpenCV have wrappers with a common interface that enables you to easily switch
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between different algorithms solving the same problem. All objects that implement keypoint detectors
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inherit the
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:ocv:class: `FeatureDetector` interface.
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KeyPoint
--------
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.. ocv:class :: KeyPoint
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Data structure for salient point detectors.
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.. ocv:member :: Point2f pt
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coordinates of the keypoint
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.. ocv:member :: float size
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diameter of the meaningful keypoint neighborhood
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.. ocv:member :: float angle
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computed orientation of the keypoint (-1 if not applicable)
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.. ocv:member :: float response
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the response by which the most strong keypoints have been selected. Can be used for further sorting or subsampling
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.. ocv:member :: int octave
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octave (pyramid layer) from which the keypoint has been extracted
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.. ocv:member :: int class_id
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object id that can be used to clustered keypoints by an object they belong to
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KeyPoint::KeyPoint
------------------
The keypoint constructors
.. ocv:function :: KeyPoint::KeyPoint()
.. ocv:function :: KeyPoint::KeyPoint(Point2f _pt, float _size, float _angle=-1, float _response=0, int _octave=0, int _class_id=-1)
.. ocv:function :: KeyPoint::KeyPoint(float x, float y, float _size, float _angle=-1, float _response=0, int _octave=0, int _class_id=-1)
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.. ocv:pyfunction :: cv2.KeyPoint([x, y, _size[, _angle[, _response[, _octave[, _class_id]]]]]) -> <KeyPoint object>
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:param x: x-coordinate of the keypoint
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:param y: y-coordinate of the keypoint
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:param _pt: x & y coordinates of the keypoint
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:param _size: keypoint diameter
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:param _angle: keypoint orientation
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:param _response: keypoint detector response on the keypoint (that is, strength of the keypoint)
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:param _octave: pyramid octave in which the keypoint has been detected
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:param _class_id: object id
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FeatureDetector
---------------
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.. ocv:class :: FeatureDetector : public Algorithm
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Abstract base class for 2D image feature detectors. ::
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class CV_EXPORTS FeatureDetector
{
public:
virtual ~FeatureDetector();
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void detect( const Mat& image, vector<KeyPoint>& keypoints,
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const Mat& mask=Mat() ) const;
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void detect( const vector<Mat>& images,
vector<vector<KeyPoint> >& keypoints,
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const vector<Mat>& masks=vector<Mat>() ) const;
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virtual void read(const FileNode&);
virtual void write(FileStorage&) const;
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static Ptr<FeatureDetector> create( const string& detectorType );
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protected:
...
};
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FeatureDetector::detect
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---------------------------
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Detects keypoints in an image (first variant) or image set (second variant).
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.. ocv:function :: void FeatureDetector::detect( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask=Mat() ) const
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.. ocv:function :: void FeatureDetector::detect( const vector<Mat>& images, vector<vector<KeyPoint> >& keypoints, const vector<Mat>& masks=vector<Mat>() ) const
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:param image: Image.
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:param images: Image set.
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:param keypoints: The detected keypoints. In the second variant of the method ``keypoints[i]`` is a set of keypoints detected in ``images[i]`` .
:param 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.
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:param masks: Masks for each input image specifying where to look for keypoints (optional). ``masks[i]`` is a mask for ``images[i]``.
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FeatureDetector::create
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---------------------------
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Creates a feature detector by its name.
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.. ocv:function :: Ptr<FeatureDetector> FeatureDetector::create( const string& detectorType )
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:param detectorType: Feature detector type.
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The following detector types are supported:
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* `` "FAST" `` -- :ocv:class: `FastFeatureDetector`
* `` "STAR" `` -- :ocv:class: `StarFeatureDetector`
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* `` "SIFT" `` -- :ocv:class: `SIFT` (nonfree module)
* `` "SURF" `` -- :ocv:class: `SURF` (nonfree module)
* `` "ORB" `` -- :ocv:class: `ORB`
* `` "MSER" `` -- :ocv:class: `MSER`
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* `` "GFTT" `` -- :ocv:class: `GoodFeaturesToTrackDetector`
* `` "HARRIS" `` -- :ocv:class: `GoodFeaturesToTrackDetector` with Harris detector enabled
* `` "Dense" `` -- :ocv:class: `DenseFeatureDetector`
* `` "SimpleBlob" `` -- :ocv:class: `SimpleBlobDetector`
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Also a combined format is supported: feature detector adapter name ( `` "Grid" `` --
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:ocv:class: `GridAdaptedFeatureDetector` , `` "Pyramid" `` --
:ocv:class: `PyramidAdaptedFeatureDetector` ) + feature detector name (see above),
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for example: `` "GridFAST" `` , `` "PyramidSTAR" `` .
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FastFeatureDetector
-------------------
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.. ocv:class :: FastFeatureDetector : public FeatureDetector
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Wrapping class for feature detection using the
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:ocv:func: `FAST` method. ::
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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:
...
};
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GoodFeaturesToTrackDetector
---------------------------
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.. ocv:class :: GoodFeaturesToTrackDetector : public FeatureDetector
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Wrapping class for feature detection using the
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:ocv:func: `goodFeaturesToTrack` function. ::
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class GoodFeaturesToTrackDetector : public FeatureDetector
{
public:
class Params
{
public:
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Params( int maxCorners=1000, double qualityLevel=0.01,
double minDistance=1., int blockSize=3,
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bool useHarrisDetector=false, double k=0.04 );
void read( const FileNode& fn );
void write( FileStorage& fs ) const;
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int maxCorners;
double qualityLevel;
double minDistance;
int blockSize;
bool useHarrisDetector;
double k;
};
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GoodFeaturesToTrackDetector( const GoodFeaturesToTrackDetector::Params& params=
GoodFeaturesToTrackDetector::Params() );
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GoodFeaturesToTrackDetector( int maxCorners, double qualityLevel,
double minDistance, int blockSize=3,
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bool useHarrisDetector=false, double k=0.04 );
virtual void read( const FileNode& fn );
virtual void write( FileStorage& fs ) const;
protected:
...
};
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MserFeatureDetector
-------------------
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.. ocv:class :: MserFeatureDetector : public FeatureDetector
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Wrapping class for feature detection using the
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:ocv:class: `MSER` class. ::
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class MserFeatureDetector : public FeatureDetector
{
public:
MserFeatureDetector( CvMSERParams params=cvMSERParams() );
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MserFeatureDetector( int delta, int minArea, int maxArea,
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double maxVariation, double minDiversity,
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int maxEvolution, double areaThreshold,
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double minMargin, int edgeBlurSize );
virtual void read( const FileNode& fn );
virtual void write( FileStorage& fs ) const;
protected:
...
};
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StarFeatureDetector
-------------------
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.. ocv:class :: StarFeatureDetector : public FeatureDetector
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Wrapping class for feature detection using the
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:ocv:class: `StarDetector` class. ::
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class StarFeatureDetector : public FeatureDetector
{
public:
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StarFeatureDetector( int maxSize=16, int responseThreshold=30,
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int lineThresholdProjected = 10,
int lineThresholdBinarized=8, int suppressNonmaxSize=5 );
virtual void read( const FileNode& fn );
virtual void write( FileStorage& fs ) const;
protected:
...
};
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DenseFeatureDetector
--------------------
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.. ocv:class :: DenseFeatureDetector : public FeatureDetector
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Class for generation of image features which are distributed densely and regularly over the image. ::
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class DenseFeatureDetector : public FeatureDetector
{
public:
DenseFeatureDetector( float initFeatureScale=1.f, int featureScaleLevels=1,
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float featureScaleMul=0.1f,
int initXyStep=6, int initImgBound=0,
bool varyXyStepWithScale=true,
bool varyImgBoundWithScale=false );
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protected:
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...
};
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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 `` .
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SimpleBlobDetector
-------------------
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.. ocv:class :: SimpleBlobDetector : public FeatureDetector
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Class for extracting blobs from an image. ::
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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 ¶meters = SimpleBlobDetector::Params());
protected:
...
};
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The class implements a simple algorithm for extracting blobs from an image:
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#. 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.
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#. Extract connected components from every binary image by :ocv:func: `findContours` and calculate their centers.
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#. 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.
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#. From the groups, estimate final centers of blobs and their radiuses and return as locations and sizes of keypoints.
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This class performs several filtrations of returned blobs. You should set `` filterBy* `` to true/false to turn on/off corresponding filtration. Available filtrations:
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* **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.
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* **By area** . Extracted blobs have an area between `` minArea `` (inclusive) and `` maxArea `` (exclusive).
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* **By circularity** . Extracted blobs have circularity (:math: `\frac{4*\pi*Area}{perimeter * perimeter}` ) between `` minCircularity `` (inclusive) and `` maxCircularity `` (exclusive).
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* **By ratio of the minimum inertia to maximum inertia** . Extracted blobs have this ratio between `` minInertiaRatio `` (inclusive) and `` maxInertiaRatio `` (exclusive).
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* **By convexity** . Extracted blobs have convexity (area / area of blob convex hull) between `` minConvexity `` (inclusive) and `` maxConvexity `` (exclusive).
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Default values of parameters are tuned to extract dark circular blobs.
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GridAdaptedFeatureDetector
--------------------------
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.. ocv:class :: GridAdaptedFeatureDetector : public FeatureDetector
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Class adapting a detector to partition the source image into a grid and detect points in each cell. ::
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class GridAdaptedFeatureDetector : public FeatureDetector
{
public:
/*
* detector Detector that will be adapted.
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* maxTotalKeypoints Maximum count of keypoints detected on the image.
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* Only the strongest keypoints will be kept.
* gridRows Grid row count.
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* gridCols Grid column count.
*/
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GridAdaptedFeatureDetector( const Ptr<FeatureDetector>& detector,
int maxTotalKeypoints, int gridRows=4,
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int gridCols=4 );
virtual void read( const FileNode& fn );
virtual void write( FileStorage& fs ) const;
protected:
...
};
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PyramidAdaptedFeatureDetector
-----------------------------
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.. ocv:class :: PyramidAdaptedFeatureDetector : public FeatureDetector
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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. ::
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class PyramidAdaptedFeatureDetector : public FeatureDetector
{
public:
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PyramidAdaptedFeatureDetector( const Ptr<FeatureDetector>& detector,
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int levels=2 );
virtual void read( const FileNode& fn );
virtual void write( FileStorage& fs ) const;
protected:
...
};
DynamicAdaptedFeatureDetector
-----------------------------
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.. ocv:class :: DynamicAdaptedFeatureDetector : public FeatureDetector
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Adaptively adjusting detector that iteratively detects features until the desired number is found. ::
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class DynamicAdaptedFeatureDetector: public FeatureDetector
{
public:
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DynamicAdaptedFeatureDetector( const Ptr<AdjusterAdapter>& adjuster,
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int min_features=400, int max_features=500, int max_iters=5 );
...
};
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If the detector is persisted, it "remembers" the parameters
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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
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panorama series.
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`` DynamicAdaptedFeatureDetector `` uses another detector, such as FAST or SURF, to do the dirty work,
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with the help of `` AdjusterAdapter `` .
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If the detected number of features is not large enough,
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`` AdjusterAdapter `` adjusts the detection parameters so that the next detection
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results in a bigger or smaller number of features. This is repeated until either the number of desired features are found
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or the parameters are maxed out.
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Adapters can be easily implemented for any detector via the
`` AdjusterAdapter `` interface.
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Beware that this is not thread-safe since the adjustment of parameters requires modification of the feature detector class instance.
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Example of creating `` DynamicAdaptedFeatureDetector `` : ::
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//sample usage:
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//will create a detector that attempts to find
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//100 - 110 FAST Keypoints, and will at most run
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//FAST feature detection 10 times until that
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//number of keypoints are found
Ptr<FeatureDetector> detector(new DynamicAdaptedFeatureDetector (100, 110, 10,
new FastAdjuster(20,true)));
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DynamicAdaptedFeatureDetector::DynamicAdaptedFeatureDetector
----------------------------------------------------------------
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The constructor
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.. ocv:function :: DynamicAdaptedFeatureDetector::DynamicAdaptedFeatureDetector( const Ptr<AdjusterAdapter>& adjuster, int min_features=400, int max_features=500, int max_iters=5 )
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:param adjuster: :ocv:class:`AdjusterAdapter` that detects features and adjusts parameters.
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:param min_features: Minimum desired number of features.
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:param max_features: Maximum desired number of features.
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:param max_iters: Maximum number of times to try adjusting the feature detector parameters. For :ocv:class:`FastAdjuster` , this number can be high, but with ``Star`` or ``Surf`` many iterations can be time-consuming. At each iteration the detector is rerun.
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AdjusterAdapter
---------------
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.. ocv:class :: AdjusterAdapter : public FeatureDetector
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Class providing an interface for adjusting parameters of a feature detector. This interface is used by :ocv:class: `DynamicAdaptedFeatureDetector` . It is a wrapper for :ocv:class: `FeatureDetector` that enables adjusting parameters after feature detection. ::
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class AdjusterAdapter: public FeatureDetector
{
public:
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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 );
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};
See
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:ocv:class: `FastAdjuster` ,
:ocv:class: `StarAdjuster` , and
:ocv:class: `SurfAdjuster` for concrete implementations.
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AdjusterAdapter::tooFew
---------------------------
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Adjusts the detector parameters to detect more features.
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.. ocv:function :: void AdjusterAdapter::tooFew(int min, int n_detected)
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:param min: Minimum desired number of features.
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:param n_detected: Number of features detected during the latest run.
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Example: ::
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void FastAdjuster::tooFew(int min, int n_detected)
{
thresh_--;
}
AdjusterAdapter::tooMany
----------------------------
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Adjusts the detector parameters to detect less features.
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.. ocv:function :: void AdjusterAdapter::tooMany(int max, int n_detected)
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:param max: Maximum desired number of features.
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:param n_detected: Number of features detected during the latest run.
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Example: ::
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void FastAdjuster::tooMany(int min, int n_detected)
{
thresh_++;
}
AdjusterAdapter::good
-------------------------
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Returns false if the detector parameters cannot be adjusted any more.
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.. ocv:function :: bool AdjusterAdapter::good() const
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Example: ::
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bool FastAdjuster::good() const
{
return (thresh_ > 1) && (thresh_ < 200);
}
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AdjusterAdapter::create
-------------------------
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Creates an adjuster adapter by name
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.. ocv:function :: Ptr<AdjusterAdapter> AdjusterAdapter::create( const string& detectorType )
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Creates an adjuster adapter by name `` detectorType `` . The detector name is the same as in :ocv:func: `FeatureDetector::create` , but now supports `` "FAST" `` , `` "STAR" `` , and `` "SURF" `` only.
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FastAdjuster
------------
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.. ocv:class :: FastAdjuster : public AdjusterAdapter
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:ocv:class: `AdjusterAdapter` for :ocv:class: `FastFeatureDetector` . This class decreases or increases the threshold value by 1. ::
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class FastAdjuster FastAdjuster: public AdjusterAdapter
{
public:
FastAdjuster(int init_thresh = 20, bool nonmax = true);
...
};
StarAdjuster
------------
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.. ocv:class :: StarAdjuster : public AdjusterAdapter
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:ocv:class: `AdjusterAdapter` for :ocv:class: `StarFeatureDetector` . This class adjusts the `` responseThreshhold `` of `` StarFeatureDetector `` . ::
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class StarAdjuster: public AdjusterAdapter
{
StarAdjuster(double initial_thresh = 30.0);
...
};