Features matcher similar to cv::detail::BestOf2NearestMatcher which finds two best matches for each feature and leaves the best one only if the ratio between descriptor distances is greater than the threshold match_conf.
More...
#include <opencv2/stitching/detail/matchers.hpp>
|
| AffineBestOf2NearestMatcher (bool full_affine=false, bool try_use_gpu=false, float match_conf=0.3f, int num_matches_thresh1=6) |
| Constructs a "best of 2 nearest" matcher that expects affine transformation between images.
|
|
| BestOf2NearestMatcher (bool try_use_gpu=false, float match_conf=0.3f, int num_matches_thresh1=6, int num_matches_thresh2=6, double matches_confindece_thresh=3.) |
| Constructs a "best of 2 nearest" matcher.
|
|
void | collectGarbage () CV_OVERRIDE |
| Frees unused memory allocated before if there is any.
|
|
virtual | ~FeaturesMatcher () |
|
bool | isThreadSafe () const |
|
void | operator() (const ImageFeatures &features1, const ImageFeatures &features2, MatchesInfo &matches_info) |
|
void | operator() (const std::vector< ImageFeatures > &features, std::vector< MatchesInfo > &pairwise_matches, const cv::UMat &mask=cv::UMat()) |
| Performs images matching.
|
|
|
void | match (const ImageFeatures &features1, const ImageFeatures &features2, MatchesInfo &matches_info) CV_OVERRIDE |
| This method must implement matching logic in order to make the wrappers detail::FeaturesMatcher::operator()_ work.
|
|
void | match (const ImageFeatures &features1, const ImageFeatures &features2, MatchesInfo &matches_info) CV_OVERRIDE |
| This method must implement matching logic in order to make the wrappers detail::FeaturesMatcher::operator()_ work.
|
|
| FeaturesMatcher (bool is_thread_safe=false) |
|
virtual void | match (const std::vector< ImageFeatures > &features, std::vector< MatchesInfo > &pairwise_matches, const cv::UMat &mask=cv::UMat()) |
| This method implements logic to match features between arbitrary number of features. By default this checks every pair of inputs in the input, but the behaviour can be changed by subclasses.
|
|
|
static Ptr< BestOf2NearestMatcher > | create (bool try_use_gpu=false, float match_conf=0.3f, int num_matches_thresh1=6, int num_matches_thresh2=6, double matches_confindece_thresh=3.) |
|
Features matcher similar to cv::detail::BestOf2NearestMatcher which finds two best matches for each feature and leaves the best one only if the ratio between descriptor distances is greater than the threshold match_conf.
Unlike cv::detail::BestOf2NearestMatcher this matcher uses affine transformation (affine transformation estimate will be placed in matches_info).
- See also
- cv::detail::FeaturesMatcher cv::detail::BestOf2NearestMatcher
◆ AffineBestOf2NearestMatcher()
cv::detail::AffineBestOf2NearestMatcher::AffineBestOf2NearestMatcher |
( |
bool |
full_affine = false , |
|
|
bool |
try_use_gpu = false , |
|
|
float |
match_conf = 0.3f , |
|
|
int |
num_matches_thresh1 = 6 |
|
) |
| |
|
inline |
Python: |
---|
| cv.detail.AffineBestOf2NearestMatcher( | [, full_affine[, try_use_gpu[, match_conf[, num_matches_thresh1]]]] | ) -> | <detail_AffineBestOf2NearestMatcher object> |
Constructs a "best of 2 nearest" matcher that expects affine transformation between images.
- Parameters
-
full_affine | whether to use full affine transformation with 6 degress of freedom or reduced transformation with 4 degrees of freedom using only rotation, translation and uniform scaling |
try_use_gpu | Should try to use GPU or not |
match_conf | Match distances ration threshold |
num_matches_thresh1 | Minimum number of matches required for the 2D affine transform estimation used in the inliers classification step |
- See also
- cv::estimateAffine2D cv::estimateAffinePartial2D
◆ match()
This method must implement matching logic in order to make the wrappers detail::FeaturesMatcher::operator()_ work.
- Parameters
-
features1 | first image features |
features2 | second image features |
matches_info | found matches |
Implements cv::detail::FeaturesMatcher.
◆ full_affine_
bool cv::detail::AffineBestOf2NearestMatcher::full_affine_ |
|
protected |
The documentation for this class was generated from the following file: