Class for extracting keypoints and computing descriptors using the Scale Invariant Feature Transform (SIFT) algorithm by D. Lowe . Show ` include <opencv2/features2d.hpp>` Public Member Functionsvirtual String () const Static Public Member Functionsstatic Ptr< SIFT > (int nfeatures=0, int nOctaveLayers=3, double contrastThreshold=0.04, double edgeThreshold=10, double sigma=1.6) static Ptr< SIFT > (int nfeatures, int nOctaveLayers, double contrastThreshold, double edgeThreshold, double sigma, int ) Create SIFT with specified descriptorType. Class for extracting keypoints and computing descriptors using the Scale Invariant Feature Transform (SIFT) algorithm by D. Lowe . create() [1/2] static Ptr<SIFT> cv::SIFT::create ( int nfeatures = 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. NoteThe 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.create() [2/2]static Ptr<SIFT> cv::SIFT::create ( int nfeatures, int nOctaveLayers, double contrastThreshold, double edgeThreshold, double sigma, int descriptorType )staticPython:cv.SIFT.create([, nfeatures[, nOctaveLayers[, contrastThreshold[, edgeThreshold[, sigma]]]]]) -> retvalcv.SIFT.create(nfeatures, nOctaveLayers, contrastThreshold, edgeThreshold, sigma, descriptorType) -> retvalcv.SIFT_create([, nfeatures[, nOctaveLayers[, contrastThreshold[, edgeThreshold[, sigma]]]]]) -> retvalcv.SIFT_create(nfeatures, nOctaveLayers, contrastThreshold, edgeThreshold, sigma, descriptorType) -> 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. NoteThe 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.getDefaultName()virtual String cv::SIFT::getDefaultName ( ) constvirtualPython: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. |
