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#ifndef __OPENCV_STITCHING_MATCHERS_HPP__
#define __OPENCV_STITCHING_MATCHERS_HPP__
#include "opencv2/core.hpp"
#include "opencv2/features2d.hpp"
#include "opencv2/opencv_modules.hpp"
#ifdef HAVE_OPENCV_XFEATURES2D
# include "opencv2/xfeatures2d/cuda.hpp"
#endif
namespace cv {
namespace detail {
//! @addtogroup stitching_match
//! @{
/** @brief Structure containing image keypoints and descriptors. */
struct CV_EXPORTS ImageFeatures
{
int img_idx;
Size img_size;
std::vector<KeyPoint> keypoints;
UMat descriptors;
};
/** @brief Feature finders base class */
class CV_EXPORTS FeaturesFinder
{
public:
virtual ~FeaturesFinder() {}
/** @overload */
void operator ()(InputArray image, ImageFeatures &features);
/** @brief Finds features in the given image.
@param image Source image
@param features Found features
@param rois Regions of interest
@sa detail::ImageFeatures, Rect_
*/
void operator ()(InputArray image, ImageFeatures &features, const std::vector<cv::Rect> &rois);
/** @brief Frees unused memory allocated before if there is any. */
virtual void collectGarbage() {}
protected:
/** @brief This method must implement features finding logic in order to make the wrappers
detail::FeaturesFinder::operator()_ work.
@param image Source image
@param features Found features
@sa detail::ImageFeatures */
virtual void find(InputArray image, ImageFeatures &features) = 0;
};
/** @brief SURF features finder.
@sa detail::FeaturesFinder, SURF
*/
class CV_EXPORTS SurfFeaturesFinder : public FeaturesFinder
{
public:
SurfFeaturesFinder(double hess_thresh = 300., int num_octaves = 3, int num_layers = 4,
int num_octaves_descr = /*4*/3, int num_layers_descr = /*2*/4);
private:
void find(InputArray image, ImageFeatures &features);
Ptr<FeatureDetector> detector_;
Ptr<DescriptorExtractor> extractor_;
Ptr<Feature2D> surf;
};
/** @brief ORB features finder. :
@sa detail::FeaturesFinder, ORB
*/
class CV_EXPORTS OrbFeaturesFinder : public FeaturesFinder
{
public:
OrbFeaturesFinder(Size _grid_size = Size(3,1), int nfeatures=1500, float scaleFactor=1.3f, int nlevels=5);
private:
void find(InputArray image, ImageFeatures &features);
Ptr<ORB> orb;
Size grid_size;
};
#ifdef HAVE_OPENCV_XFEATURES2D
class CV_EXPORTS SurfFeaturesFinderGpu : public FeaturesFinder
{
public:
SurfFeaturesFinderGpu(double hess_thresh = 300., int num_octaves = 3, int num_layers = 4,
int num_octaves_descr = 4, int num_layers_descr = 2);
void collectGarbage();
private:
void find(InputArray image, ImageFeatures &features);
cuda::GpuMat image_;
cuda::GpuMat gray_image_;
cuda::SURF_CUDA surf_;
cuda::GpuMat keypoints_;
cuda::GpuMat descriptors_;
int num_octaves_, num_layers_;
int num_octaves_descr_, num_layers_descr_;
};
#endif
/** @brief Structure containing information about matches between two images.
It's assumed that there is a homography between those images.
*/
struct CV_EXPORTS MatchesInfo
{
MatchesInfo();
MatchesInfo(const MatchesInfo &other);
const MatchesInfo& operator =(const MatchesInfo &other);
int src_img_idx, dst_img_idx; //!< Images indices (optional)
std::vector<DMatch> matches;
std::vector<uchar> inliers_mask; //!< Geometrically consistent matches mask
int num_inliers; //!< Number of geometrically consistent matches
Mat H; //!< Estimated homography
double confidence; //!< Confidence two images are from the same panorama
};
/** @brief Feature matchers base class. */
class CV_EXPORTS FeaturesMatcher
{
public:
virtual ~FeaturesMatcher() {}
/** @overload
@param features1 First image features
@param features2 Second image features
@param matches_info Found matches
*/
void operator ()(const ImageFeatures &features1, const ImageFeatures &features2,
MatchesInfo& matches_info) { match(features1, features2, matches_info); }
/** @brief Performs images matching.
@param features Features of the source images
@param pairwise_matches Found pairwise matches
@param mask Mask indicating which image pairs must be matched
The function is parallelized with the TBB library.
@sa detail::MatchesInfo
*/
void operator ()(const std::vector<ImageFeatures> &features, std::vector<MatchesInfo> &pairwise_matches,
const cv::UMat &mask = cv::UMat());
/** @return True, if it's possible to use the same matcher instance in parallel, false otherwise
*/
bool isThreadSafe() const { return is_thread_safe_; }
/** @brief Frees unused memory allocated before if there is any.
*/
virtual void collectGarbage() {}
protected:
FeaturesMatcher(bool is_thread_safe = false) : is_thread_safe_(is_thread_safe) {}
/** @brief This method must implement matching logic in order to make the wrappers
detail::FeaturesMatcher::operator()_ work.
@param features1 first image features
@param features2 second image features
@param matches_info found matches
*/
virtual void match(const ImageFeatures &features1, const ImageFeatures &features2,
MatchesInfo& matches_info) = 0;
bool is_thread_safe_;
};
/** @brief Features matcher 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
@sa detail::FeaturesMatcher
*/
class CV_EXPORTS BestOf2NearestMatcher : public FeaturesMatcher
{
public:
/** @brief Constructs a "best of 2 nearest" matcher.
@param try_use_gpu Should try to use GPU or not
@param match_conf Match distances ration threshold
@param num_matches_thresh1 Minimum number of matches required for the 2D projective transform
estimation used in the inliers classification step
@param num_matches_thresh2 Minimum number of matches required for the 2D projective transform
re-estimation on inliers
*/
BestOf2NearestMatcher(bool try_use_gpu = false, float match_conf = 0.3f, int num_matches_thresh1 = 6,
int num_matches_thresh2 = 6);
void collectGarbage();
protected:
void match(const ImageFeatures &features1, const ImageFeatures &features2, MatchesInfo &matches_info);
int num_matches_thresh1_;
int num_matches_thresh2_;
Ptr<FeaturesMatcher> impl_;
};
class CV_EXPORTS BestOf2NearestRangeMatcher : public BestOf2NearestMatcher
{
public:
BestOf2NearestRangeMatcher(int range_width = 5, bool try_use_gpu = false, float match_conf = 0.3f,
int num_matches_thresh1 = 6, int num_matches_thresh2 = 6);
void operator ()(const std::vector<ImageFeatures> &features, std::vector<MatchesInfo> &pairwise_matches,
const cv::UMat &mask = cv::UMat());
protected:
int range_width_;
};
//! @} stitching_match
} // namespace detail
} // namespace cv
#endif // __OPENCV_STITCHING_MATCHERS_HPP__