Wrapping class for feature detection using the goodFeaturesToTrack function. :
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#include "features2d.hpp"
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virtual int | getBlockSize () const =0 |
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virtual String | getDefaultName () const CV_OVERRIDE |
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virtual bool | getHarrisDetector () const =0 |
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virtual double | getK () const =0 |
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virtual int | getMaxFeatures () const =0 |
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virtual double | getMinDistance () const =0 |
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virtual double | getQualityLevel () const =0 |
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virtual void | setBlockSize (int blockSize)=0 |
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virtual void | setHarrisDetector (bool val)=0 |
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virtual void | setK (double k)=0 |
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virtual void | setMaxFeatures (int maxFeatures)=0 |
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virtual void | setMinDistance (double minDistance)=0 |
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virtual void | setQualityLevel (double qlevel)=0 |
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virtual | ~Feature2D () |
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virtual void | compute (InputArray image, std::vector< KeyPoint > &keypoints, OutputArray descriptors) |
| Computes the descriptors for a set of keypoints detected in an image (first variant) or image set (second variant). More...
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virtual void | compute (InputArrayOfArrays images, std::vector< std::vector< KeyPoint > > &keypoints, OutputArrayOfArrays descriptors) |
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virtual int | defaultNorm () const |
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virtual int | descriptorSize () const |
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virtual int | descriptorType () const |
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virtual void | detect (InputArray image, std::vector< KeyPoint > &keypoints, InputArray mask=noArray()) |
| Detects keypoints in an image (first variant) or image set (second variant). More...
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virtual void | detect (InputArrayOfArrays images, std::vector< std::vector< KeyPoint > > &keypoints, InputArrayOfArrays masks=noArray()) |
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virtual void | detectAndCompute (InputArray image, InputArray mask, std::vector< KeyPoint > &keypoints, OutputArray descriptors, bool useProvidedKeypoints=false) |
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virtual bool | empty () const CV_OVERRIDE |
| Return true if detector object is empty. More...
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void | read (const String &fileName) |
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virtual void | read (const FileNode &) CV_OVERRIDE |
| Reads algorithm parameters from a file storage. More...
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void | write (const String &fileName) const |
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virtual void | write (FileStorage &) const CV_OVERRIDE |
| Stores algorithm parameters in a file storage. More...
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void | write (const Ptr< FileStorage > &fs, const String &name=String()) const |
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| Algorithm () |
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virtual | ~Algorithm () |
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virtual void | clear () |
| Clears the algorithm state. More...
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virtual void | save (const String &filename) const |
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void | write (const Ptr< FileStorage > &fs, const String &name=String()) const |
| simplified API for language bindings This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts. More...
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static Ptr< GFTTDetector > | create (int maxCorners=1000, double qualityLevel=0.01, double minDistance=1, int blockSize=3, bool useHarrisDetector=false, double k=0.04) |
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static Ptr< GFTTDetector > | create (int maxCorners, double qualityLevel, double minDistance, int blockSize, int gradiantSize, bool useHarrisDetector=false, double k=0.04) |
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template<typename _Tp > |
static Ptr< _Tp > | load (const String &filename, const String &objname=String()) |
| Loads algorithm from the file. More...
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template<typename _Tp > |
static Ptr< _Tp > | loadFromString (const String &strModel, const String &objname=String()) |
| Loads algorithm from a String. More...
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template<typename _Tp > |
static Ptr< _Tp > | read (const FileNode &fn) |
| Reads algorithm from the file node. More...
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Wrapping class for feature detection using the goodFeaturesToTrack function. :
§ create() [1/2]
static Ptr<GFTTDetector> cv::GFTTDetector::create |
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int |
maxCorners = 1000 , |
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double |
qualityLevel = 0.01 , |
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double |
minDistance = 1 , |
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int |
blockSize = 3 , |
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bool |
useHarrisDetector = false , |
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double |
k = 0.04 |
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) |
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static |
Python: |
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| retval | = | cv.GFTTDetector_create( | [, maxCorners[, qualityLevel[, minDistance[, blockSize[, useHarrisDetector[, k]]]]]] | ) |
| retval | = | cv.GFTTDetector_create( | maxCorners, qualityLevel, minDistance, blockSize, gradiantSize[, useHarrisDetector[, k]] | ) |
§ create() [2/2]
static Ptr<GFTTDetector> cv::GFTTDetector::create |
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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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int |
gradiantSize, |
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bool |
useHarrisDetector = false , |
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double |
k = 0.04 |
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) |
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static |
Python: |
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| retval | = | cv.GFTTDetector_create( | [, maxCorners[, qualityLevel[, minDistance[, blockSize[, useHarrisDetector[, k]]]]]] | ) |
| retval | = | cv.GFTTDetector_create( | maxCorners, qualityLevel, minDistance, blockSize, gradiantSize[, useHarrisDetector[, k]] | ) |
§ getBlockSize()
virtual int cv::GFTTDetector::getBlockSize |
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const |
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pure virtual |
Python: |
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| retval | = | cv.GFTTDetector.getBlockSize( | | ) |
§ getDefaultName()
virtual String cv::GFTTDetector::getDefaultName |
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const |
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virtual |
Python: |
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| retval | = | cv.GFTTDetector.getDefaultName( | | ) |
Returns the algorithm string identifier. This string is used as top level xml/yml node tag when the object is saved to a file or string.
Reimplemented from cv::Feature2D.
§ getHarrisDetector()
virtual bool cv::GFTTDetector::getHarrisDetector |
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const |
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pure virtual |
Python: |
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| retval | = | cv.GFTTDetector.getHarrisDetector( | | ) |
§ getK()
virtual double cv::GFTTDetector::getK |
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const |
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pure virtual |
Python: |
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| retval | = | cv.GFTTDetector.getK( | | ) |
§ getMaxFeatures()
virtual int cv::GFTTDetector::getMaxFeatures |
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const |
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pure virtual |
Python: |
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| retval | = | cv.GFTTDetector.getMaxFeatures( | | ) |
§ getMinDistance()
virtual double cv::GFTTDetector::getMinDistance |
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const |
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pure virtual |
Python: |
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| retval | = | cv.GFTTDetector.getMinDistance( | | ) |
§ getQualityLevel()
virtual double cv::GFTTDetector::getQualityLevel |
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const |
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pure virtual |
Python: |
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| retval | = | cv.GFTTDetector.getQualityLevel( | | ) |
§ setBlockSize()
virtual void cv::GFTTDetector::setBlockSize |
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int |
blockSize | ) |
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pure virtual |
Python: |
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| None | = | cv.GFTTDetector.setBlockSize( | blockSize | ) |
§ setHarrisDetector()
virtual void cv::GFTTDetector::setHarrisDetector |
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bool |
val | ) |
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pure virtual |
Python: |
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| None | = | cv.GFTTDetector.setHarrisDetector( | val | ) |
§ setK()
virtual void cv::GFTTDetector::setK |
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double |
k | ) |
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pure virtual |
Python: |
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| None | = | cv.GFTTDetector.setK( | k | ) |
§ setMaxFeatures()
virtual void cv::GFTTDetector::setMaxFeatures |
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int |
maxFeatures | ) |
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pure virtual |
Python: |
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| None | = | cv.GFTTDetector.setMaxFeatures( | maxFeatures | ) |
§ setMinDistance()
virtual void cv::GFTTDetector::setMinDistance |
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double |
minDistance | ) |
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pure virtual |
Python: |
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| None | = | cv.GFTTDetector.setMinDistance( | minDistance | ) |
§ setQualityLevel()
virtual void cv::GFTTDetector::setQualityLevel |
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double |
qlevel | ) |
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pure virtual |
Python: |
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| None | = | cv.GFTTDetector.setQualityLevel( | qlevel | ) |
The documentation for this class was generated from the following file: