[OpenCV]DMatch類和KeyPoints類：特征點匹配

 

DMatch

struct CV_EXPORTS_W_SIMPLE DMatch
{
    CV_WRAP DMatch() : queryIdx(-1), trainIdx(-1), imgIdx(-1), distance(FLT_MAX) {}//1
    CV_WRAP DMatch( int _queryIdx, int _trainIdx, float _distance ) :
            queryIdx(_queryIdx), trainIdx(_trainIdx), imgIdx(-1), distance(_distance) {}//2
    CV_WRAP DMatch( int _queryIdx, int _trainIdx, int _imgIdx, float _distance ) :
            queryIdx(_queryIdx), trainIdx(_trainIdx), imgIdx(_imgIdx), distance(_distance) {}//3

    CV_PROP_RW int queryIdx; // query descriptor index
    CV_PROP_RW int trainIdx; // train descriptor index
    CV_PROP_RW int imgIdx;   // train image index

    CV_PROP_RW float distance;

    // less is better
    bool operator<( const DMatch &m ) const
    {
        return distance < m.distance;
    }
};

1、2、3不用說，是三個構造函數。

接着, 
int queryIdx –>是測試圖像的特征點描述符（descriptor）的下標，同時也是描述符對應特征點（keypoint)的下標。

int trainIdx –> 是樣本圖像的特征點描述符的下標，同樣也是相應的特征點的下標。

int imgIdx –>當樣本是多張圖像的話有用。

float distance –>代表這一對匹配的特征點描述符（本質是向量）的歐氏距離，數值越小也就說明兩個特征點越相像。

最后， 
也就是一個小於操作符的重載，用於比較和排序。 比較的是上述的distance，當然是越小越好。

 

KeyPoints

class CV_EXPORTS_W_SIMPLE KeyPoint
{
public:
    //! the default constructor
    CV_WRAP KeyPoint() : pt(0,0), size(0), angle(-1), response(0), octave(0), class_id(-1) {}
    //! the full constructor
    KeyPoint(Point2f _pt, float _size, float _angle=-1,
            float _response=0, int _octave=0, int _class_id=-1)
            : pt(_pt), size(_size), angle(_angle),
            response(_response), octave(_octave), class_id(_class_id) {}
    //! another form of the full constructor
    CV_WRAP KeyPoint(float x, float y, float _size, float _angle=-1,
            float _response=0, int _octave=0, int _class_id=-1)
            : pt(x, y), size(_size), angle(_angle),
            response(_response), octave(_octave), class_id(_class_id) {}

    size_t hash() const;

    //! converts vector of keypoints to vector of points
    static void convert(const vector<KeyPoint>& keypoints,
                        CV_OUT vector<Point2f>& points2f,
                        const vector<int>& keypointIndexes=vector<int>());
    //! converts vector of points to the vector of keypoints, where each keypoint is assigned the same size and the same orientation
    static void convert(const vector<Point2f>& points2f,
                        CV_OUT vector<KeyPoint>& keypoints,
                        float size=1, float response=1, int octave=0, int class_id=-1);

    //! computes overlap for pair of keypoints;
    //! overlap is a ratio between area of keypoint regions intersection and
    //! area of keypoint regions union (now keypoint region is circle)
    static float overlap(const KeyPoint& kp1, const KeyPoint& kp2);

    CV_PROP_RW Point2f pt; //!< coordinates of the keypoints
    CV_PROP_RW float size; //!< diameter of the meaningful keypoint neighborhood
    CV_PROP_RW float angle; //!< computed orientation of the keypoint (-1 if not applicable);
                            //!< it's in [0,360) degrees and measured relative to
                            //!< image coordinate system, ie in clockwise.
    CV_PROP_RW float response; //!< the response by which the most strong keypoints have been selected. Can be used for the further sorting or subsampling
    CV_PROP_RW int octave; //!< octave (pyramid layer) from which the keypoint has been extracted
    CV_PROP_RW int class_id; //!< object class (if the keypoints need to be clustered by an object they belong to)
};