592 lines
20 KiB
ReStructuredText
592 lines
20 KiB
ReStructuredText
Common Interfaces of Feature Detectors
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======================================
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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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--------
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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
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------------------
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The keypoint constructors
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.. ocv:function:: KeyPoint::KeyPoint()
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.. ocv:function:: KeyPoint::KeyPoint(Point2f _pt, float _size, float _angle=-1, float _response=0, int _octave=0, int _class_id=-1)
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.. 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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---------------
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.. ocv:class:: FeatureDetector
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Abstract base class for 2D image feature detectors. ::
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class CV_EXPORTS FeatureDetector
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{
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public:
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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,
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vector<vector<KeyPoint> >& keypoints,
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const vector<Mat>& masks=vector<Mat>() ) const;
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virtual void read(const FileNode&);
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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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...
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};
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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]`` .
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: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::read
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-------------------------
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Reads a feature detector object from a file node.
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.. ocv:function:: void FeatureDetector::read( const FileNode& fn )
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:param fn: File node from which the detector is read.
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FeatureDetector::write
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--------------------------
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Writes a feature detector object to a file storage.
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.. ocv:function:: void FeatureDetector::write( FileStorage& fs ) const
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:param fs: File storage where the detector is written.
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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`
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* ``"STAR"`` -- :ocv:class:`StarFeatureDetector`
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* ``"SIFT"`` -- :ocv:class:`SiftFeatureDetector`
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* ``"SURF"`` -- :ocv:class:`SurfFeatureDetector`
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* ``"ORB"`` -- :ocv:class:`OrbFeatureDetector`
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* ``"MSER"`` -- :ocv:class:`MserFeatureDetector`
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* ``"GFTT"`` -- :ocv:class:`GoodFeaturesToTrackDetector`
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* ``"HARRIS"`` -- :ocv:class:`GoodFeaturesToTrackDetector` with Harris detector enabled
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* ``"Dense"`` -- :ocv:class:`DenseFeatureDetector`
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* ``"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"`` --
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: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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-------------------
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.. ocv:class:: FastFeatureDetector
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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
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{
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public:
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FastFeatureDetector( int threshold=1, bool nonmaxSuppression=true );
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virtual void read( const FileNode& fn );
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virtual void write( FileStorage& fs ) const;
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protected:
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...
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};
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GoodFeaturesToTrackDetector
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---------------------------
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.. ocv:class:: GoodFeaturesToTrackDetector
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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
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{
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public:
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class Params
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{
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public:
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Params( int maxCorners=1000, double qualityLevel=0.01,
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double minDistance=1., int blockSize=3,
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bool useHarrisDetector=false, double k=0.04 );
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void read( const FileNode& fn );
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void write( FileStorage& fs ) const;
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int maxCorners;
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double qualityLevel;
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double minDistance;
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int blockSize;
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bool useHarrisDetector;
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double k;
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};
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GoodFeaturesToTrackDetector( const GoodFeaturesToTrackDetector::Params& params=
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GoodFeaturesToTrackDetector::Params() );
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GoodFeaturesToTrackDetector( int maxCorners, double qualityLevel,
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double minDistance, int blockSize=3,
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bool useHarrisDetector=false, double k=0.04 );
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virtual void read( const FileNode& fn );
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virtual void write( FileStorage& fs ) const;
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protected:
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...
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};
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MserFeatureDetector
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-------------------
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.. ocv:class:: MserFeatureDetector
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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
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{
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public:
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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 );
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virtual void read( const FileNode& fn );
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virtual void write( FileStorage& fs ) const;
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protected:
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...
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};
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StarFeatureDetector
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-------------------
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.. ocv:class:: StarFeatureDetector
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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
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{
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public:
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StarFeatureDetector( int maxSize=16, int responseThreshold=30,
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int lineThresholdProjected = 10,
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int lineThresholdBinarized=8, int suppressNonmaxSize=5 );
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virtual void read( const FileNode& fn );
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virtual void write( FileStorage& fs ) const;
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protected:
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...
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};
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SiftFeatureDetector
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-------------------
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.. ocv:class:: SiftFeatureDetector
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Wrapping class for feature detection using the
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:ocv:class:`SIFT` class. ::
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class SiftFeatureDetector : public FeatureDetector
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{
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public:
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SiftFeatureDetector(
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const SIFT::DetectorParams& detectorParams=SIFT::DetectorParams(),
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const SIFT::CommonParams& commonParams=SIFT::CommonParams() );
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SiftFeatureDetector( double threshold, double edgeThreshold,
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int nOctaves=SIFT::CommonParams::DEFAULT_NOCTAVES,
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int nOctaveLayers=SIFT::CommonParams::DEFAULT_NOCTAVE_LAYERS,
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int firstOctave=SIFT::CommonParams::DEFAULT_FIRST_OCTAVE,
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int angleMode=SIFT::CommonParams::FIRST_ANGLE );
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virtual void read( const FileNode& fn );
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virtual void write( FileStorage& fs ) const;
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protected:
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...
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};
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SurfFeatureDetector
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-------------------
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.. ocv:class:: SurfFeatureDetector
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Wrapping class for feature detection using the
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:ocv:class:`SURF` class. ::
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class SurfFeatureDetector : public FeatureDetector
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{
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public:
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SurfFeatureDetector( double hessianThreshold = 400., int octaves = 3,
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int octaveLayers = 4 );
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virtual void read( const FileNode& fn );
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virtual void write( FileStorage& fs ) const;
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protected:
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...
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};
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OrbFeatureDetector
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-------------------
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.. ocv:class:: OrbFeatureDetector
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Wrapping class for feature detection using the
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:ocv:class:`ORB` class. ::
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class OrbFeatureDetector : public FeatureDetector
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{
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public:
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OrbFeatureDetector( size_t n_features );
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virtual void read( const FileNode& fn );
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virtual void write( FileStorage& fs ) const;
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protected:
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...
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};
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SimpleBlobDetector
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-------------------
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.. ocv:class:: SimpleBlobDetector
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Class for extracting blobs from an image. ::
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class SimpleBlobDetector : public FeatureDetector
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{
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public:
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struct Params
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{
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Params();
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float thresholdStep;
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float minThreshold;
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float maxThreshold;
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size_t minRepeatability;
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float minDistBetweenBlobs;
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bool filterByColor;
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uchar blobColor;
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bool filterByArea;
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float minArea, maxArea;
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bool filterByCircularity;
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float minCircularity, maxCircularity;
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bool filterByInertia;
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float minInertiaRatio, maxInertiaRatio;
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bool filterByConvexity;
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float minConvexity, maxConvexity;
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};
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SimpleBlobDetector(const SimpleBlobDetector::Params ¶meters = SimpleBlobDetector::Params());
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protected:
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...
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};
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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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--------------------------
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.. ocv:class:: GridAdaptedFeatureDetector
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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
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{
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public:
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/*
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* 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.
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* gridRows Grid row count.
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* gridCols Grid column count.
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*/
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GridAdaptedFeatureDetector( const Ptr<FeatureDetector>& detector,
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int maxTotalKeypoints, int gridRows=4,
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int gridCols=4 );
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virtual void read( const FileNode& fn );
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virtual void write( FileStorage& fs ) const;
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protected:
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...
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};
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PyramidAdaptedFeatureDetector
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-----------------------------
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.. ocv:class:: PyramidAdaptedFeatureDetector
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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
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{
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public:
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PyramidAdaptedFeatureDetector( const Ptr<FeatureDetector>& detector,
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int levels=2 );
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virtual void read( const FileNode& fn );
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virtual void write( FileStorage& fs ) const;
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protected:
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...
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};
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DynamicAdaptedFeatureDetector
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-----------------------------
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.. ocv:class:: DynamicAdaptedFeatureDetector
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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
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{
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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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...
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};
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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
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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
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``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
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Ptr<FeatureDetector> detector(new DynamicAdaptedFeatureDetector (100, 110, 10,
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new FastAdjuster(20,true)));
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DynamicAdaptedFeatureDetector::DynamicAdaptedFeatureDetector
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----------------------------------------------------------------
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The constructor
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.. ocv:function:: DynamicAdaptedFeatureDetector::DynamicAdaptedFeatureDetector( const Ptr<AdjusterAdapter>& adjuster, int min_features, int max_features, int max_iters )
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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-comsuming. At each iteration the detector is rerun.
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AdjusterAdapter
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---------------
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.. ocv:class:: AdjusterAdapter
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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
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{
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public:
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virtual ~AdjusterAdapter() {}
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virtual void tooFew(int min, int n_detected) = 0;
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virtual void tooMany(int max, int n_detected) = 0;
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virtual bool good() const = 0;
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virtual Ptr<AdjusterAdapter> clone() const = 0;
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static Ptr<AdjusterAdapter> create( const string& detectorType );
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};
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See
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:ocv:class:`FastAdjuster`,
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:ocv:class:`StarAdjuster`, and
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:ocv:class:`SurfAdjuster` for concrete implementations.
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AdjusterAdapter::tooFew
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---------------------------
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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)
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{
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thresh_--;
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}
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AdjusterAdapter::tooMany
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----------------------------
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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)
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{
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thresh_++;
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}
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AdjusterAdapter::good
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-------------------------
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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
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{
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return (thresh_ > 1) && (thresh_ < 200);
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}
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AdjusterAdapter::create
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-------------------------
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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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------------
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.. ocv:class:: FastAdjuster
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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
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{
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public:
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FastAdjuster(int init_thresh = 20, bool nonmax = true);
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...
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};
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StarAdjuster
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------------
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.. ocv:class:: StarAdjuster
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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
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{
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StarAdjuster(double initial_thresh = 30.0);
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...
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};
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SurfAdjuster
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------------
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.. ocv:class:: SurfAdjuster
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:ocv:class:`AdjusterAdapter` for :ocv:class:`SurfFeatureDetector`. This class adjusts the ``hessianThreshold`` of ``SurfFeatureDetector``. ::
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class SurfAdjuster: public SurfAdjuster
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{
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SurfAdjuster();
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...
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};
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