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#ifndef __OPENCV_PRECOMP_H__
#define __OPENCV_PRECOMP_H__

#include "opencv2/core/utility.hpp"

#include "opencv2/core/private.hpp"

#include "opencv2/calib3d.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/features2d.hpp"


#include "opencv2/core/ocl.hpp"

#define GET_OPTIMIZED(func) (func)


namespace cv
{

/**
 * Compute the number of iterations given the confidence, outlier ratio, number
 * of model points and the maximum iteration number.
 *
 * @param p confidence value
 * @param ep outlier ratio
 * @param modelPoints number of model points required for estimation
 * @param maxIters maximum number of iterations
 * @return
 * \f[
 * \frac{\ln(1-p)}{\ln\left(1-(1-ep)^\mathrm{modelPoints}\right)}
 * \f]
 *
 * If the computed number of iterations is larger than maxIters, then maxIters is returned.
 */
int RANSACUpdateNumIters( double p, double ep, int modelPoints, int maxIters );

class CV_EXPORTS PointSetRegistrator : public Algorithm
{
public:
    class CV_EXPORTS Callback
    {
    public:
        virtual ~Callback() {}
        virtual int runKernel(InputArray m1, InputArray m2, OutputArray model) const = 0;
        virtual void computeError(InputArray m1, InputArray m2, InputArray model, OutputArray err) const = 0;
        virtual bool checkSubset(InputArray, InputArray, int) const { return true; }
    };

    virtual void setCallback(const Ptr<PointSetRegistrator::Callback>& cb) = 0;
    virtual bool run(InputArray m1, InputArray m2, OutputArray model, OutputArray mask) const = 0;
};

CV_EXPORTS Ptr<PointSetRegistrator> createRANSACPointSetRegistrator(const Ptr<PointSetRegistrator::Callback>& cb,
                                                                    int modelPoints, double threshold,
                                                                    double confidence=0.99, int maxIters=1000 );

CV_EXPORTS Ptr<PointSetRegistrator> createLMeDSPointSetRegistrator(const Ptr<PointSetRegistrator::Callback>& cb,
                                                                   int modelPoints, double confidence=0.99, int maxIters=1000 );

template<typename T> inline int compressElems( T* ptr, const uchar* mask, int mstep, int count )
{
    int i, j;
    for( i = j = 0; i < count; i++ )
        if( mask[i*mstep] )
        {
            if( i > j )
                ptr[j] = ptr[i];
            j++;
        }
    return j;
}

static inline bool haveCollinearPoints( const Mat& m, int count )
{
    int j, k, i = count-1;
    const Point2f* ptr = m.ptr<Point2f>();

    // check that the i-th selected point does not belong
    // to a line connecting some previously selected points
    // also checks that points are not too close to each other
    for( j = 0; j < i; j++ )
    {
        double dx1 = ptr[j].x - ptr[i].x;
        double dy1 = ptr[j].y - ptr[i].y;
        for( k = 0; k < j; k++ )
        {
            double dx2 = ptr[k].x - ptr[i].x;
            double dy2 = ptr[k].y - ptr[i].y;
            if( fabs(dx2*dy1 - dy2*dx1) <= FLT_EPSILON*(fabs(dx1) + fabs(dy1) + fabs(dx2) + fabs(dy2)))
                return true;
        }
    }
    return false;
}

} // namespace cv

int checkChessboardBinary(const cv::Mat & img, const cv::Size & size);

#endif
