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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"

namespace cv {
namespace detail {

//! @addtogroup stitching_match
//! @{

/** @brief Structure containing image keypoints and descriptors. */
struct CV_EXPORTS_W_SIMPLE ImageFeatures
{
    CV_PROP_RW int img_idx;
    CV_PROP_RW Size img_size;
    std::vector<KeyPoint> keypoints;
    CV_PROP_RW UMat descriptors;
    CV_WRAP std::vector<KeyPoint> getKeypoints() { return keypoints; };
};
/** @brief

@param featuresFinder
@param images
@param features
@param masks
*/
CV_EXPORTS_W void computeImageFeatures(
    const Ptr<Feature2D> &featuresFinder,
    InputArrayOfArrays  images,
    CV_OUT std::vector<ImageFeatures> &features,
    InputArrayOfArrays masks = noArray());

/** @brief

@param featuresFinder
@param image
@param features
@param mask
*/
CV_EXPORTS_AS(computeImageFeatures2) void computeImageFeatures(
    const Ptr<Feature2D> &featuresFinder,
    InputArray image,
    CV_OUT ImageFeatures &features,
    InputArray mask = noArray());

/** @brief Structure containing information about matches between two images.

It's assumed that there is a transformation between those images. Transformation may be
homography or affine transformation based on selected matcher.

@sa detail::FeaturesMatcher
*/
struct CV_EXPORTS_W_SIMPLE MatchesInfo
{
    MatchesInfo();
    MatchesInfo(const MatchesInfo &other);
    MatchesInfo& operator =(const MatchesInfo &other);

    CV_PROP_RW int src_img_idx;
    CV_PROP_RW int dst_img_idx;       //!< Images indices (optional)
    std::vector<DMatch> matches;
    std::vector<uchar> inliers_mask;    //!< Geometrically consistent matches mask
    CV_PROP_RW int num_inliers;                    //!< Number of geometrically consistent matches
    CV_PROP_RW Mat H;                              //!< Estimated transformation
    CV_PROP_RW double confidence;                  //!< Confidence two images are from the same panorama
    CV_WRAP std::vector<DMatch> getMatches() { return matches; };
    CV_WRAP std::vector<uchar> getInliers() { return inliers_mask; };
};

/** @brief Feature matchers base class. */
class CV_EXPORTS_W FeaturesMatcher
{
public:
    CV_WRAP virtual ~FeaturesMatcher() {}

    /** @overload
    @param features1 First image features
    @param features2 Second image features
    @param matches_info Found matches
    */
    CV_WRAP_AS(apply) void operator ()(const ImageFeatures &features1, const ImageFeatures &features2,
                     CV_OUT 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
    */
    CV_WRAP_AS(apply2) void operator ()(const std::vector<ImageFeatures> &features, CV_OUT 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
    */
   CV_WRAP bool isThreadSafe() const { return is_thread_safe_; }

    /** @brief Frees unused memory allocated before if there is any.
    */
   CV_WRAP 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_W 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
     */
    CV_WRAP BestOf2NearestMatcher(bool try_use_gpu = false, float match_conf = 0.3f, int num_matches_thresh1 = 6,
                          int num_matches_thresh2 = 6);

    CV_WRAP void collectGarbage() CV_OVERRIDE;
    CV_WRAP static Ptr<BestOf2NearestMatcher> create(bool try_use_gpu = false, float match_conf = 0.3f, int num_matches_thresh1 = 6,
        int num_matches_thresh2 = 6);

protected:

    void match(const ImageFeatures &features1, const ImageFeatures &features2, MatchesInfo &matches_info) CV_OVERRIDE;
    int num_matches_thresh1_;
    int num_matches_thresh2_;
    Ptr<FeaturesMatcher> impl_;
};

class CV_EXPORTS_W BestOf2NearestRangeMatcher : public BestOf2NearestMatcher
{
public:
    CV_WRAP 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_;
};

/** @brief 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).

@sa cv::detail::FeaturesMatcher cv::detail::BestOf2NearestMatcher
 */
class CV_EXPORTS_W AffineBestOf2NearestMatcher : public BestOf2NearestMatcher
{
public:
    /** @brief Constructs a "best of 2 nearest" matcher that expects affine transformation
    between images

    @param 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
    @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 affine transform
    estimation used in the inliers classification step

    @sa cv::estimateAffine2D cv::estimateAffinePartial2D
     */
    CV_WRAP AffineBestOf2NearestMatcher(bool full_affine = false, bool try_use_gpu = false,
                                float match_conf = 0.3f, int num_matches_thresh1 = 6) :
        BestOf2NearestMatcher(try_use_gpu, match_conf, num_matches_thresh1, num_matches_thresh1),
        full_affine_(full_affine) {}

protected:
    void match(const ImageFeatures &features1, const ImageFeatures &features2, MatchesInfo &matches_info) CV_OVERRIDE;

    bool full_affine_;
};

//! @} stitching_match

} // namespace detail
} // namespace cv

#endif // OPENCV_STITCHING_MATCHERS_HPP
