opencv/modules/python/src2/opencv_extra_api.hpp

#ifndef _OPENCV_API_EXTRA_HPP_
#define _OPENCV_API_EXTRA_HPP_

#include "opencv2/core/core.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/imgproc/imgproc_c.h"
#include "opencv2/calib3d/calib3d.hpp"

namespace cv
{

template<typename _Tp>
static inline void mv2vv(const vector<Mat>& src, vector<vector<_Tp> >& dst)
{
    size_t i, n = src.size();
    dst.resize(src.size());
    for( i = 0; i < n; i++ )
        src[i].copyTo(dst[i]);
}

///////////////////////////// core /////////////////////////////

CV_WRAP_AS(getTickCount) static inline double getTickCount_()
{
    return (double)getTickCount();
}

CV_WRAP_AS(getCPUTickCount) static inline double getCPUTickCount_()
{
    return (double)getCPUTickCount();
}

CV_WRAP void randShuffle(const Mat& src, CV_OUT Mat& dst, double iterFactor=1.)
{
    src.copyTo(dst);
    randShuffle(dst, iterFactor, 0);
}

CV_WRAP static inline void SVDecomp(const Mat& src, CV_OUT Mat& w, CV_OUT Mat& u, CV_OUT Mat& vt, int flags=0 )
{
    SVD::compute(src, w, u, vt, flags);
}

CV_WRAP static inline void SVBackSubst( const Mat& w, const Mat& u, const Mat& vt,
                                        const Mat& rhs, CV_OUT Mat& dst )
{
    SVD::backSubst(w, u, vt, rhs, dst);
}

CV_WRAP static inline void mixChannels(const vector<Mat>& src, vector<Mat>& dst,
                                       const vector<int>& fromTo)
{
    if(fromTo.empty())
        return;
    CV_Assert(fromTo.size()%2 == 0);
    mixChannels(&src[0], (int)src.size(), &dst[0], (int)dst.size(), &fromTo[0], (int)(fromTo.size()/2));
}

CV_WRAP static inline bool eigen(const Mat& src, bool computeEigenvectors,
                                 CV_OUT Mat& eigenvalues, CV_OUT Mat& eigenvectors,
                                 int lowindex=-1, int highindex=-1)
{
    return computeEigenvectors ? eigen(src, eigenvalues, eigenvectors, lowindex, highindex) :
                                eigen(src, eigenvalues, lowindex, highindex);
}

CV_WRAP static inline void fillConvexPoly(Mat& img, const Mat& points,
                               const Scalar& color, int lineType=8,
                               int shift=0)
{
    CV_Assert(points.checkVector(2, CV_32S) >= 0);
    fillConvexPoly(img, (const Point*)points.data, points.rows*points.cols*points.channels()/2, color, lineType, shift);
}

CV_WRAP static inline void fillPoly(Mat& img, const vector<Mat>& pts,
                                   const Scalar& color, int lineType=8, int shift=0,
                                   Point offset=Point() )
{
    if( pts.empty() )
        return;
    AutoBuffer<Point*> _ptsptr(pts.size());
    AutoBuffer<int> _npts(pts.size());
    Point** ptsptr = _ptsptr;
    int* npts = _npts;
    
    for( size_t i = 0; i < pts.size(); i++ )
    {
        const Mat& p = pts[i];
        CV_Assert(p.checkVector(2, CV_32S) >= 0);
        ptsptr[i] = (Point*)p.data;
        npts[i] = p.rows*p.cols*p.channels()/2;
    }
    fillPoly(img, (const Point**)ptsptr, npts, (int)pts.size(), color, lineType, shift, offset);
}

CV_WRAP static inline void polylines(Mat& img, const vector<Mat>& pts,
                          bool isClosed, const Scalar& color,
                          int thickness=1, int lineType=8, int shift=0 )
{
    if( pts.empty() )
        return;
    AutoBuffer<Point*> _ptsptr(pts.size());
    AutoBuffer<int> _npts(pts.size());
    Point** ptsptr = _ptsptr;
    int* npts = _npts;
    
    for( size_t i = 0; i < pts.size(); i++ )
    {
        const Mat& p = pts[i];
        CV_Assert(p.checkVector(2, CV_32S) >= 0);
        ptsptr[i] = (Point*)p.data;
        npts[i] = p.rows*p.cols*p.channels()/2;
    }
    polylines(img, (const Point**)ptsptr, npts, (int)pts.size(), isClosed, color, thickness, lineType, shift);
}

CV_WRAP static inline void PCACompute(const Mat& data, CV_OUT Mat& mean,
                                      CV_OUT Mat& eigenvectors, int maxComponents=0)
{
    PCA pca;
    pca.mean = mean;
    pca.eigenvectors = eigenvectors;
    pca(data, Mat(), 0, maxComponents);
    pca.mean.copyTo(mean);
    pca.eigenvectors.copyTo(eigenvectors);
}
    
CV_WRAP static inline void PCAProject(const Mat& data, const Mat& mean,
                                      const Mat& eigenvectors, CV_OUT Mat& result)
{
    PCA pca;
    pca.mean = mean;
    pca.eigenvectors = eigenvectors;
    pca.project(data, result);
}

CV_WRAP static inline void PCABackProject(const Mat& data, const Mat& mean,
                                          const Mat& eigenvectors, CV_OUT Mat& result)
{
    PCA pca;
    pca.mean = mean;
    pca.eigenvectors = eigenvectors;
    pca.backProject(data, result);
}    

/////////////////////////// imgproc /////////////////////////////////

CV_WRAP static inline void HuMoments(const Moments& m, CV_OUT vector<double>& hu)
{
    hu.resize(7);
    HuMoments(m, &hu[0]);
}

CV_WRAP static inline Mat getPerspectiveTransform(const vector<Point2f>& src, const vector<Point2f>& dst)
{
    CV_Assert(src.size() == 4 && dst.size() == 4);
    return getPerspectiveTransform(&src[0], &dst[0]);
}

CV_WRAP static inline Mat getAffineTransform(const vector<Point2f>& src, const vector<Point2f>& dst)
{
    CV_Assert(src.size() == 3 && dst.size() == 3);
    return getAffineTransform(&src[0], &dst[0]);
}

CV_WRAP static inline void calcHist( const vector<Mat>& images, const vector<int>& channels,
                                     const Mat& mask, CV_OUT Mat& hist,
                                     const vector<int>& histSize,
                                     const vector<float>& ranges,
                                     bool accumulate=false)
{
    int i, dims = (int)histSize.size(), rsz = (int)ranges.size(), csz = (int)channels.size();
    CV_Assert(images.size() > 0 && dims > 0);
    CV_Assert(rsz == dims*2 || (rsz == 0 && images[0].depth() == CV_8U));
    CV_Assert(csz == 0 || csz == dims);
    float* _ranges[CV_MAX_DIM];
    if( rsz > 0 )
    {
        for( i = 0; i < rsz/2; i++ )
            _ranges[i] = (float*)&ranges[i*2];
    }
    calcHist(&images[0], (int)images.size(), csz ? &channels[0] : 0,
            mask, hist, dims, &histSize[0], rsz ? (const float**)_ranges : 0,
            true, accumulate);
}

                                     
CV_WRAP void calcBackProject( const vector<Mat>& images, const vector<int>& channels,
                              const Mat& hist, CV_OUT Mat& dst,
                              const vector<float>& ranges,
                              double scale=1 )
{
    int i, dims = hist.dims, rsz = (int)ranges.size(), csz = (int)channels.size();
    CV_Assert(images.size() > 0);
    CV_Assert(rsz == dims*2 || (rsz == 0 && images[0].depth() == CV_8U));
    CV_Assert(csz == 0 || csz == dims);
    float* _ranges[CV_MAX_DIM];
    if( rsz > 0 )
    {
        for( i = 0; i < rsz/2; i++ )
            _ranges[i] = (float*)&ranges[i*2];
    }
    calcBackProject(&images[0], (int)images.size(), csz ? &channels[0] : 0,
        hist, dst, rsz ? (const float**)_ranges : 0, scale, true);
}


/////////////////////////////// calib3d ///////////////////////////////////////////

//! finds circles' grid pattern of the specified size in the image
CV_WRAP static inline void findCirclesGridDefault( InputArray image, Size patternSize,
                                                   OutputArray centers, int flags=CALIB_CB_SYMMETRIC_GRID )
{
    findCirclesGrid(image, patternSize, centers, flags);
}
 
}

#endif