Class for extracting keypoints and computing descriptors using the Scale Invariant Feature Transform (SIFT) algorithm by D. Lowe [79] .
More...
#include "nonfree.hpp"
|
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). More...
|
|
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). More...
|
|
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 |
| Return true if detector object is empty. More...
|
|
| Algorithm () |
|
virtual | ~Algorithm () |
|
virtual void | clear () |
| Clears the algorithm state. More...
|
|
virtual String | getDefaultName () const |
|
virtual void | read (const FileNode &fn) |
| Reads algorithm parameters from a file storage. More...
|
|
virtual void | save (const String &filename) const |
|
virtual void | write (FileStorage &fs) const |
| Stores algorithm parameters in a file storage. More...
|
|
Class for extracting keypoints and computing descriptors using the Scale Invariant Feature Transform (SIFT) algorithm by D. Lowe [79] .
static Ptr<SIFT> cv::xfeatures2d::SIFT::create |
( |
int |
nfeatures = 0 , |
|
|
int |
nOctaveLayers = 3 , |
|
|
double |
contrastThreshold = 0.04 , |
|
|
double |
edgeThreshold = 10 , |
|
|
double |
sigma = 1.6 |
|
) |
| |
|
static |
- Parameters
-
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:
- /builds/master-contrib_docs-mac/opencv_contrib/modules/xfeatures2d/include/opencv2/xfeatures2d/nonfree.hpp