OpenCV  4.10.0
Open Source Computer Vision
Loading...
Searching...
No Matches
Public Member Functions | Static Public Member Functions | List of all members
cv::SIFT Class Referenceabstract

Class for extracting keypoints and computing descriptors using the Scale Invariant Feature Transform (SIFT) algorithm by D. Lowe [173] . More...

#include <opencv2/features2d.hpp>

Collaboration diagram for cv::SIFT:

Public Member Functions

virtual double getContrastThreshold () const =0
 
virtual String getDefaultName () const CV_OVERRIDE
 
virtual double getEdgeThreshold () const =0
 
virtual int getNFeatures () const =0
 
virtual int getNOctaveLayers () const =0
 
virtual double getSigma () const =0
 
virtual void setContrastThreshold (double contrastThreshold)=0
 
virtual void setEdgeThreshold (double edgeThreshold)=0
 
virtual void setNFeatures (int maxFeatures)=0
 
virtual void setNOctaveLayers (int nOctaveLayers)=0
 
virtual void setSigma (double sigma)=0
 
- Public Member Functions inherited from cv::Feature2D
virtual ~Feature2D ()
 
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).
 
virtual void compute (InputArrayOfArrays images, std::vector< std::vector< KeyPoint > > &keypoints, OutputArrayOfArrays descriptors)
 
virtual int defaultNorm () const
 
virtual int descriptorSize () const
 
virtual int descriptorType () const
 
virtual void detect (InputArray image, std::vector< KeyPoint > &keypoints, InputArray mask=noArray())
 Detects keypoints in an image (first variant) or image set (second variant).
 
virtual void detect (InputArrayOfArrays images, std::vector< std::vector< KeyPoint > > &keypoints, InputArrayOfArrays masks=noArray())
 
virtual void detectAndCompute (InputArray image, InputArray mask, std::vector< KeyPoint > &keypoints, OutputArray descriptors, bool useProvidedKeypoints=false)
 
virtual bool empty () const CV_OVERRIDE
 Return true if detector object is empty.
 
virtual void read (const FileNode &) CV_OVERRIDE
 Reads algorithm parameters from a file storage.
 
void read (const String &fileName)
 
void write (const Ptr< FileStorage > &fs, const String &name) const
 
void write (const String &fileName) const
 
virtual void write (FileStorage &) const CV_OVERRIDE
 Stores algorithm parameters in a file storage.
 
void write (FileStorage &fs, const String &name) const
 
- Public Member Functions inherited from cv::Algorithm
 Algorithm ()
 
virtual ~Algorithm ()
 
virtual void clear ()
 Clears the algorithm state.
 
virtual void save (const String &filename) const
 
void write (const Ptr< FileStorage > &fs, const String &name=String()) const
 
void write (FileStorage &fs, const String &name) const
 

Static Public Member Functions

static Ptr< SIFTcreate (int nfeatures, int nOctaveLayers, double contrastThreshold, double edgeThreshold, double sigma, int descriptorType, bool enable_precise_upscale=false)
 Create SIFT with specified descriptorType.
 
static Ptr< SIFTcreate (int nfeatures=0, int nOctaveLayers=3, double contrastThreshold=0.04, double edgeThreshold=10, double sigma=1.6, bool enable_precise_upscale=false)
 
- Static Public Member Functions inherited from cv::Algorithm
template<typename _Tp >
static Ptr< _Tpload (const String &filename, const String &objname=String())
 Loads algorithm from the file.
 
template<typename _Tp >
static Ptr< _TploadFromString (const String &strModel, const String &objname=String())
 Loads algorithm from a String.
 
template<typename _Tp >
static Ptr< _Tpread (const FileNode &fn)
 Reads algorithm from the file node.
 

Additional Inherited Members

- Protected Member Functions inherited from cv::Algorithm
void writeFormat (FileStorage &fs) const
 

Detailed Description

Class for extracting keypoints and computing descriptors using the Scale Invariant Feature Transform (SIFT) algorithm by D. Lowe [173] .

Member Function Documentation

◆ create() [1/2]

static Ptr< SIFT > cv::SIFT::create ( int  nfeatures,
int  nOctaveLayers,
double  contrastThreshold,
double  edgeThreshold,
double  sigma,
int  descriptorType,
bool  enable_precise_upscale = false 
)
static
Python:
cv.SIFT.create([, nfeatures[, nOctaveLayers[, contrastThreshold[, edgeThreshold[, sigma[, enable_precise_upscale]]]]]]) -> retval
cv.SIFT.create(nfeatures, nOctaveLayers, contrastThreshold, edgeThreshold, sigma, descriptorType[, enable_precise_upscale]) -> retval
cv.SIFT_create([, nfeatures[, nOctaveLayers[, contrastThreshold[, edgeThreshold[, sigma[, enable_precise_upscale]]]]]]) -> retval
cv.SIFT_create(nfeatures, nOctaveLayers, contrastThreshold, edgeThreshold, sigma, descriptorType[, enable_precise_upscale]) -> retval

Create SIFT with specified descriptorType.

Parameters
nfeaturesThe number of best features to retain. The features are ranked by their scores (measured in SIFT algorithm as the local contrast)
nOctaveLayersThe number of layers in each octave. 3 is the value used in D. Lowe paper. The number of octaves is computed automatically from the image resolution.
contrastThresholdThe contrast threshold used to filter out weak features in semi-uniform (low-contrast) regions. The larger the threshold, the less features are produced by the detector.
Note
The contrast threshold will be divided by nOctaveLayers when the filtering is applied. When nOctaveLayers is set to default and if you want to use the value used in D. Lowe paper, 0.03, set this argument to 0.09.
Parameters
edgeThresholdThe threshold used to filter out edge-like features. Note that the its meaning is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are filtered out (more features are retained).
sigmaThe sigma of the Gaussian applied to the input image at the octave #0. If your image is captured with a weak camera with soft lenses, you might want to reduce the number.
descriptorTypeThe type of descriptors. Only CV_32F and CV_8U are supported.
enable_precise_upscaleWhether to enable precise upscaling in the scale pyramid, which maps index \(\texttt{x}\) to \(\texttt{2x}\). This prevents localization bias. The option is disabled by default.

◆ create() [2/2]

static Ptr< SIFT > cv::SIFT::create ( int  nfeatures = 0,
int  nOctaveLayers = 3,
double  contrastThreshold = 0.04,
double  edgeThreshold = 10,
double  sigma = 1.6,
bool  enable_precise_upscale = false 
)
static
Python:
cv.SIFT.create([, nfeatures[, nOctaveLayers[, contrastThreshold[, edgeThreshold[, sigma[, enable_precise_upscale]]]]]]) -> retval
cv.SIFT.create(nfeatures, nOctaveLayers, contrastThreshold, edgeThreshold, sigma, descriptorType[, enable_precise_upscale]) -> retval
cv.SIFT_create([, nfeatures[, nOctaveLayers[, contrastThreshold[, edgeThreshold[, sigma[, enable_precise_upscale]]]]]]) -> retval
cv.SIFT_create(nfeatures, nOctaveLayers, contrastThreshold, edgeThreshold, sigma, descriptorType[, enable_precise_upscale]) -> retval
Parameters
nfeaturesThe number of best features to retain. The features are ranked by their scores (measured in SIFT algorithm as the local contrast)
nOctaveLayersThe number of layers in each octave. 3 is the value used in D. Lowe paper. The number of octaves is computed automatically from the image resolution.
contrastThresholdThe contrast threshold used to filter out weak features in semi-uniform (low-contrast) regions. The larger the threshold, the less features are produced by the detector.
Note
The contrast threshold will be divided by nOctaveLayers when the filtering is applied. When nOctaveLayers is set to default and if you want to use the value used in D. Lowe paper, 0.03, set this argument to 0.09.
Parameters
edgeThresholdThe threshold used to filter out edge-like features. Note that the its meaning is different from the contrastThreshold, i.e. the larger the edgeThreshold, the less features are filtered out (more features are retained).
sigmaThe sigma of the Gaussian applied to the input image at the octave #0. If your image is captured with a weak camera with soft lenses, you might want to reduce the number.
enable_precise_upscaleWhether to enable precise upscaling in the scale pyramid, which maps index \(\texttt{x}\) to \(\texttt{2x}\). This prevents localization bias. The option is disabled by default.

◆ getContrastThreshold()

virtual double cv::SIFT::getContrastThreshold ( ) const
pure virtual
Python:
cv.SIFT.getContrastThreshold() -> retval

◆ getDefaultName()

virtual String cv::SIFT::getDefaultName ( ) const
virtual
Python:
cv.SIFT.getDefaultName() -> retval

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.

◆ getEdgeThreshold()

virtual double cv::SIFT::getEdgeThreshold ( ) const
pure virtual
Python:
cv.SIFT.getEdgeThreshold() -> retval

◆ getNFeatures()

virtual int cv::SIFT::getNFeatures ( ) const
pure virtual
Python:
cv.SIFT.getNFeatures() -> retval

◆ getNOctaveLayers()

virtual int cv::SIFT::getNOctaveLayers ( ) const
pure virtual
Python:
cv.SIFT.getNOctaveLayers() -> retval

◆ getSigma()

virtual double cv::SIFT::getSigma ( ) const
pure virtual
Python:
cv.SIFT.getSigma() -> retval

◆ setContrastThreshold()

virtual void cv::SIFT::setContrastThreshold ( double  contrastThreshold)
pure virtual
Python:
cv.SIFT.setContrastThreshold(contrastThreshold) -> None

◆ setEdgeThreshold()

virtual void cv::SIFT::setEdgeThreshold ( double  edgeThreshold)
pure virtual
Python:
cv.SIFT.setEdgeThreshold(edgeThreshold) -> None

◆ setNFeatures()

virtual void cv::SIFT::setNFeatures ( int  maxFeatures)
pure virtual
Python:
cv.SIFT.setNFeatures(maxFeatures) -> None

◆ setNOctaveLayers()

virtual void cv::SIFT::setNOctaveLayers ( int  nOctaveLayers)
pure virtual
Python:
cv.SIFT.setNOctaveLayers(nOctaveLayers) -> None

◆ setSigma()

virtual void cv::SIFT::setSigma ( double  sigma)
pure virtual
Python:
cv.SIFT.setSigma(sigma) -> None

The documentation for this class was generated from the following file: