public BFMatcher(OpenCVForUnity.Features2dModule.BFMatcher nativeObj) : base(nativeObj)
{
}
/**
* Brute-force matcher create method.
* param normType One of NORM_L1, NORM_L2, NORM_HAMMING, NORM_HAMMING2. L1 and L2 norms are
* preferable choices for SIFT and SURF descriptors, NORM_HAMMING should be used with ORB, BRISK and
* BRIEF, NORM_HAMMING2 should be used with ORB when WTA_K==3 or 4 (see ORB::ORB constructor
* description).
* nearest neighbors for each query descriptor. If crossCheck==true, then the knnMatch() method with
* k=1 will only return pairs (i,j) such that for i-th query descriptor the j-th descriptor in the
* matcher's collection is the nearest and vice versa, i.e. the BFMatcher will only return consistent
* pairs. Such technique usually produces best results with minimal number of outliers when there are
* enough matches. This is alternative to the ratio test, used by D. Lowe in SIFT paper.
* return automatically generated
*/
public static new BFMatcher create(int normType)
{
return(BFMatcher.__fromPtr__(features2d_BFMatcher_create_11(normType)));
}
/**
* Brute-force matcher create method.
* preferable choices for SIFT and SURF descriptors, NORM_HAMMING should be used with ORB, BRISK and
* BRIEF, NORM_HAMMING2 should be used with ORB when WTA_K==3 or 4 (see ORB::ORB constructor
* description).
* nearest neighbors for each query descriptor. If crossCheck==true, then the knnMatch() method with
* k=1 will only return pairs (i,j) such that for i-th query descriptor the j-th descriptor in the
* matcher's collection is the nearest and vice versa, i.e. the BFMatcher will only return consistent
* pairs. Such technique usually produces best results with minimal number of outliers when there are
* enough matches. This is alternative to the ratio test, used by D. Lowe in SIFT paper.
* return automatically generated
*/
public static BFMatcher create()
{
return(BFMatcher.__fromPtr__(features2d_BFMatcher_create_12()));
}
//
// C++: static Ptr_BFMatcher cv::BFMatcher::create(int normType = NORM_L2, bool crossCheck = false)
//
/**
* Brute-force matcher create method.
* param normType One of NORM_L1, NORM_L2, NORM_HAMMING, NORM_HAMMING2. L1 and L2 norms are
* preferable choices for SIFT and SURF descriptors, NORM_HAMMING should be used with ORB, BRISK and
* BRIEF, NORM_HAMMING2 should be used with ORB when WTA_K==3 or 4 (see ORB::ORB constructor
* description).
* param crossCheck If it is false, this is will be default BFMatcher behaviour when it finds the k
* nearest neighbors for each query descriptor. If crossCheck==true, then the knnMatch() method with
* k=1 will only return pairs (i,j) such that for i-th query descriptor the j-th descriptor in the
* matcher's collection is the nearest and vice versa, i.e. the BFMatcher will only return consistent
* pairs. Such technique usually produces best results with minimal number of outliers when there are
* enough matches. This is alternative to the ratio test, used by D. Lowe in SIFT paper.
* return automatically generated
*/
public static BFMatcher create(int normType, bool crossCheck)
{
return(BFMatcher.__fromPtr__(features2d_BFMatcher_create_10(normType, crossCheck)));
}
