Class for extracting keypoints and computing descriptors using the Scale Invariant Feature Transform (SIFT) algorithm by D. Lowe [126] .
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#include "nonfree.hpp"
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static Ptr< SIFT > | create (int nfeatures=0, int nOctaveLayers=3, double contrastThreshold=0.04, double edgeThreshold=10, double sigma=1.6) |
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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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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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virtual String | getDefaultName () const CV_OVERRIDE |
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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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void | writeFormat (FileStorage &fs) const |
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Class for extracting keypoints and computing descriptors using the Scale Invariant Feature Transform (SIFT) algorithm by D. Lowe [126] .
§ create()
static Ptr<SIFT> cv::xfeatures2d::SIFT::create |
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int |
nfeatures = 0 , |
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int |
nOctaveLayers = 3 , |
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double |
contrastThreshold = 0.04 , |
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double |
edgeThreshold = 10 , |
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double |
sigma = 1.6 |
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) |
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static |
Python: |
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| retval | = | cv.xfeatures2d.SIFT_create( | [, nfeatures[, nOctaveLayers[, contrastThreshold[, edgeThreshold[, sigma]]]]] | ) |
- Parameters
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nfeatures | The number of best features to retain. The features are ranked by their scores (measured in SIFT algorithm as the local contrast) |
nOctaveLayers | The 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. |
contrastThreshold | The 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. |
edgeThreshold | The 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). |
sigma | The 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. |
The documentation for this class was generated from the following file:
- /build/master-contrib_docs-lin64/opencv_contrib/modules/xfeatures2d/include/opencv2/xfeatures2d/nonfree.hpp