ORB Class Reference
[Feature Detection and Description]

Clears the algorithm state.

Reimplemented in DescriptorMatcher, and FlannBasedMatcher.

Definition at line 2984 of file core.hpp.

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters:

images	Image set.
keypoints	Input collection of keypoints. Keypoints for which a descriptor cannot be computed are removed. Sometimes new keypoints can be added, for example: SIFT duplicates keypoint with several dominant orientations (for each orientation).
descriptors	Computed descriptors. In the second variant of the method descriptors[i] are descriptors computed for a keypoints[i]. Row j is the keypoints (or keypoints[i]) is the descriptor for keypoint j-th keypoint.

Computes the descriptors for a set of keypoints detected in an image (first variant) or image set (second variant).

Parameters:

image	Image.
keypoints	Input collection of keypoints. Keypoints for which a descriptor cannot be computed are removed. Sometimes new keypoints can be added, for example: SIFT duplicates keypoint with several dominant orientations (for each orientation).
descriptors	Computed descriptors. In the second variant of the method descriptors[i] are descriptors computed for a keypoints[i]. Row j is the keypoints (or keypoints[i]) is the descriptor for keypoint j-th keypoint.

The ORB constructor.

Parameters:

nfeatures	The maximum number of features to retain.
scaleFactor	Pyramid decimation ratio, greater than 1. scaleFactor==2 means the classical pyramid, where each next level has 4x less pixels than the previous, but such a big scale factor will degrade feature matching scores dramatically. On the other hand, too close to 1 scale factor will mean that to cover certain scale range you will need more pyramid levels and so the speed will suffer.
nlevels	The number of pyramid levels. The smallest level will have linear size equal to input_image_linear_size/pow(scaleFactor, nlevels).
edgeThreshold	This is size of the border where the features are not detected. It should roughly match the patchSize parameter.
firstLevel	It should be 0 in the current implementation.
WTA_K	The number of points that produce each element of the oriented BRIEF descriptor. The default value 2 means the BRIEF where we take a random point pair and compare their brightnesses, so we get 0/1 response. Other possible values are 3 and 4. For example, 3 means that we take 3 random points (of course, those point coordinates are random, but they are generated from the pre-defined seed, so each element of BRIEF descriptor is computed deterministically from the pixel rectangle), find point of maximum brightness and output index of the winner (0, 1 or 2). Such output will occupy 2 bits, and therefore it will need a special variant of Hamming distance, denoted as NORM_HAMMING2 (2 bits per bin). When WTA_K=4, we take 4 random points to compute each bin (that will also occupy 2 bits with possible values 0, 1, 2 or 3).
scoreType	The default HARRIS_SCORE means that Harris algorithm is used to rank features (the score is written to KeyPoint::score and is used to retain best nfeatures features); FAST_SCORE is alternative value of the parameter that produces slightly less stable keypoints, but it is a little faster to compute.
patchSize	size of the patch used by the oriented BRIEF descriptor. Of course, on smaller pyramid layers the perceived image area covered by a feature will be larger.
fastThreshold

Detects keypoints in an image (first variant) or image set (second variant).

Parameters:

image	Image.
keypoints	The detected keypoints. In the second variant of the method keypoints[i] is a set of keypoints detected in images[i] .
mask	Mask specifying where to look for keypoints (optional). It must be a 8-bit integer matrix with non-zero values in the region of interest.

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters:

images	Image set.
keypoints	The detected keypoints. In the second variant of the method keypoints[i] is a set of keypoints detected in images[i] .
masks	Masks for each input image specifying where to look for keypoints (optional). masks[i] is a mask for images[i].

Detects keypoints and computes the descriptors.

Return true if detector object is empty.

Reimplemented from Algorithm.

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.

Loads algorithm from the file.

Parameters:

filename	Name of the file to read.
objname	The optional name of the node to read (if empty, the first top-level node will be used)

This is static template method of Algorithm. It's usage is following (in the case of SVM):

     Ptr<SVM> svm = Algorithm::load<SVM>("my_svm_model.xml");

In order to make this method work, the derived class must overwrite Algorithm::read(const FileNode& fn).

Definition at line 3027 of file core.hpp.

Loads algorithm from a String.

Parameters:

strModel	The string variable containing the model you want to load.
objname	The optional name of the node to read (if empty, the first top-level node will be used)

This is static template method of Algorithm. It's usage is following (in the case of SVM):

     Ptr<SVM> svm = Algorithm::loadFromString<SVM>(myStringModel);

Definition at line 3046 of file core.hpp.

Reads algorithm parameters from a file storage.

Reimplemented in DescriptorMatcher, and FlannBasedMatcher.

Definition at line 2992 of file core.hpp.

Reads algorithm from the file node.

This is static template method of Algorithm. It's usage is following (in the case of SVM):

     Ptr<SVM> svm = Algorithm::read<SVM>(fn);

In order to make this method work, the derived class must overwrite Algorithm::read(const FileNode& fn) and also have static create() method without parameters (or with all the optional parameters)

Reimplemented in DescriptorMatcher, and FlannBasedMatcher.

Definition at line 3008 of file core.hpp.

Saves the algorithm to a file.

In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs).

Stores algorithm parameters in a file storage.

Reimplemented in DescriptorMatcher, and FlannBasedMatcher.

Definition at line 2988 of file core.hpp.