229 lines
8.4 KiB
ReStructuredText
229 lines
8.4 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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.. note::
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* An example explaining keypoint detection can be found at opencv_source_code/samples/cpp/descriptor_extractor_matcher.cpp
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FeatureDetector
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---------------
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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
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{
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public:
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virtual ~FeatureDetector();
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void detect( InputArray image, vector<KeyPoint>& keypoints,
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InputArray mask=noArray() ) const;
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void detect( InputArrayOfArrays images,
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vector<vector<KeyPoint> >& keypoints,
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InputArrayOfArrays masks=noArray() ) 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( InputArray image, vector<KeyPoint>& keypoints, InputArray mask=noArray() ) const
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.. ocv:function:: void FeatureDetector::detect( InputArrayOfArrays images, vector<vector<KeyPoint> >& keypoints, InputArrayOfArrays masks=noArray() ) const
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.. ocv:pyfunction:: cv2.FeatureDetector_create.detect(image[, mask]) -> keypoints
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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::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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.. ocv:pyfunction:: cv2.FeatureDetector_create(detectorType) -> retval
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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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* ``"ORB"`` -- :ocv:class:`ORB`
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* ``"BRISK"`` -- :ocv:class:`BRISK`
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* ``"MSER"`` -- :ocv:class:`MSER`
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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 : 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
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{
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public:
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FastFeatureDetector( int threshold=1, bool nonmaxSuppression=true, type=FastFeatureDetector::TYPE_9_16 );
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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 : 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
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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 : 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
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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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SimpleBlobDetector
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-------------------
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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
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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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