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  /** Authors: Ethan Rublee, Vincent Rabaud, Gary Bradski */
  
  #include "precomp.hpp"
  #include <iterator>
  
  ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
  
  namespace cv
  {
  
  const float HARRIS_K = 0.04f;
  const int DESCRIPTOR_SIZE = 32;
  
  /**
   * Function that computes the Harris responses in a
   * blockSize x blockSize patch at given points in an image
   */
  static void
  HarrisResponses(const Mat& img, std::vector<KeyPoint>& pts, int blockSize, float harris_k)
  {
      CV_Assert( img.type() == CV_8UC1 && blockSize*blockSize <= 2048 );
  
      size_t ptidx, ptsize = pts.size();
  
      const uchar* ptr00 = img.ptr<uchar>();
      int step = (int)(img.step/img.elemSize1());
      int r = blockSize/2;
  
      float scale = (1 << 2) * blockSize * 255.0f;
      scale = 1.0f / scale;
      float scale_sq_sq = scale * scale * scale * scale;
  
      AutoBuffer<int> ofsbuf(blockSize*blockSize);
      int* ofs = ofsbuf;
      for( int i = 0; i < blockSize; i++ )
          for( int j = 0; j < blockSize; j++ )
              ofs[i*blockSize + j] = (int)(i*step + j);
  
      for( ptidx = 0; ptidx < ptsize; ptidx++ )
      {
          int x0 = cvRound(pts[ptidx].pt.x - r);
          int y0 = cvRound(pts[ptidx].pt.y - r);
  
          const uchar* ptr0 = ptr00 + y0*step + x0;
          int a = 0, b = 0, c = 0;
  
          for( int k = 0; k < blockSize*blockSize; k++ )
          {
              const uchar* ptr = ptr0 + ofs[k];
              int Ix = (ptr[1] - ptr[-1])*2 + (ptr[-step+1] - ptr[-step-1]) + (ptr[step+1] - ptr[step-1]);
              int Iy = (ptr[step] - ptr[-step])*2 + (ptr[step-1] - ptr[-step-1]) + (ptr[step+1] - ptr[-step+1]);
              a += Ix*Ix;
              b += Iy*Iy;
              c += Ix*Iy;
          }
          pts[ptidx].response = ((float)a * b - (float)c * c -
                                 harris_k * ((float)a + b) * ((float)a + b))*scale_sq_sq;
      }
  }
  
  ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
  
  static float IC_Angle(const Mat& image, const int half_k, Point2f pt,
                        const std::vector<int> & u_max)
  {
      int m_01 = 0, m_10 = 0;
  
      const uchar* center = &image.at<uchar> (cvRound(pt.y), cvRound(pt.x));
  
      // Treat the center line differently, v=0
      for (int u = -half_k; u <= half_k; ++u)
          m_10 += u * center[u];
  
      // Go line by line in the circular patch
      int step = (int)image.step1();
      for (int v = 1; v <= half_k; ++v)
      {
          // Proceed over the two lines
          int v_sum = 0;
          int d = u_max[v];
          for (int u = -d; u <= d; ++u)
          {
              int val_plus = center[u + v*step], val_minus = center[u - v*step];
              v_sum += (val_plus - val_minus);
              m_10 += u * (val_plus + val_minus);
          }
          m_01 += v * v_sum;
      }
  
      return fastAtan2((float)m_01, (float)m_10);
  }
  
  ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
  
  static void computeOrbDescriptor(const KeyPoint& kpt,
                                   const Mat& img, const Point* pattern,
                                   uchar* desc, int dsize, int WTA_K)
  {
      float angle = kpt.angle;
      //angle = cvFloor(angle/12)*12.f;
      angle *= (float)(CV_PI/180.f);
      float a = (float)cos(angle), b = (float)sin(angle);
  
      const uchar* center = &img.at<uchar>(cvRound(kpt.pt.y), cvRound(kpt.pt.x));
      int step = (int)img.step;
  
      float x, y;
      int ix, iy;
  #if 1
      #define GET_VALUE(idx) \
             (x = pattern[idx].x*a - pattern[idx].y*b, \
              y = pattern[idx].x*b + pattern[idx].y*a, \
              ix = cvRound(x), \
              iy = cvRound(y), \
              *(center + iy*step + ix) )
  #else
      #define GET_VALUE(idx) \
          (x = pattern[idx].x*a - pattern[idx].y*b, \
          y = pattern[idx].x*b + pattern[idx].y*a, \
          ix = cvFloor(x), iy = cvFloor(y), \
          x -= ix, y -= iy, \
          cvRound(center[iy*step + ix]*(1-x)*(1-y) + center[(iy+1)*step + ix]*(1-x)*y + \
                  center[iy*step + ix+1]*x*(1-y) + center[(iy+1)*step + ix+1]*x*y))
  #endif
  
      if( WTA_K == 2 )
      {
          for (int i = 0; i < dsize; ++i, pattern += 16)
          {
              int t0, t1, val;
              t0 = GET_VALUE(0); t1 = GET_VALUE(1);
              val = t0 < t1;
              t0 = GET_VALUE(2); t1 = GET_VALUE(3);
              val |= (t0 < t1) << 1;
              t0 = GET_VALUE(4); t1 = GET_VALUE(5);
              val |= (t0 < t1) << 2;
              t0 = GET_VALUE(6); t1 = GET_VALUE(7);
              val |= (t0 < t1) << 3;
              t0 = GET_VALUE(8); t1 = GET_VALUE(9);
              val |= (t0 < t1) << 4;
              t0 = GET_VALUE(10); t1 = GET_VALUE(11);
              val |= (t0 < t1) << 5;
              t0 = GET_VALUE(12); t1 = GET_VALUE(13);
              val |= (t0 < t1) << 6;
              t0 = GET_VALUE(14); t1 = GET_VALUE(15);
              val |= (t0 < t1) << 7;
  
              desc[i] = (uchar)val;
          }
      }
      else if( WTA_K == 3 )
      {
          for (int i = 0; i < dsize; ++i, pattern += 12)
          {
              int t0, t1, t2, val;
              t0 = GET_VALUE(0); t1 = GET_VALUE(1); t2 = GET_VALUE(2);
              val = t2 > t1 ? (t2 > t0 ? 2 : 0) : (t1 > t0);
  
              t0 = GET_VALUE(3); t1 = GET_VALUE(4); t2 = GET_VALUE(5);
              val |= (t2 > t1 ? (t2 > t0 ? 2 : 0) : (t1 > t0)) << 2;
  
              t0 = GET_VALUE(6); t1 = GET_VALUE(7); t2 = GET_VALUE(8);
              val |= (t2 > t1 ? (t2 > t0 ? 2 : 0) : (t1 > t0)) << 4;
  
              t0 = GET_VALUE(9); t1 = GET_VALUE(10); t2 = GET_VALUE(11);
              val |= (t2 > t1 ? (t2 > t0 ? 2 : 0) : (t1 > t0)) << 6;
  
              desc[i] = (uchar)val;
          }
      }
      else if( WTA_K == 4 )
      {
          for (int i = 0; i < dsize; ++i, pattern += 16)
          {
              int t0, t1, t2, t3, u, v, k, val;
              t0 = GET_VALUE(0); t1 = GET_VALUE(1);
              t2 = GET_VALUE(2); t3 = GET_VALUE(3);
              u = 0, v = 2;
              if( t1 > t0 ) t0 = t1, u = 1;
              if( t3 > t2 ) t2 = t3, v = 3;
              k = t0 > t2 ? u : v;
              val = k;
  
              t0 = GET_VALUE(4); t1 = GET_VALUE(5);
              t2 = GET_VALUE(6); t3 = GET_VALUE(7);
              u = 0, v = 2;
              if( t1 > t0 ) t0 = t1, u = 1;
              if( t3 > t2 ) t2 = t3, v = 3;
              k = t0 > t2 ? u : v;
              val |= k << 2;
  
              t0 = GET_VALUE(8); t1 = GET_VALUE(9);
              t2 = GET_VALUE(10); t3 = GET_VALUE(11);
              u = 0, v = 2;
              if( t1 > t0 ) t0 = t1, u = 1;
              if( t3 > t2 ) t2 = t3, v = 3;
              k = t0 > t2 ? u : v;
              val |= k << 4;
  
              t0 = GET_VALUE(12); t1 = GET_VALUE(13);
              t2 = GET_VALUE(14); t3 = GET_VALUE(15);
              u = 0, v = 2;
              if( t1 > t0 ) t0 = t1, u = 1;
              if( t3 > t2 ) t2 = t3, v = 3;
              k = t0 > t2 ? u : v;
              val |= k << 6;
  
              desc[i] = (uchar)val;
          }
      }
      else
          CV_Error( Error::StsBadSize, "Wrong WTA_K. It can be only 2, 3 or 4." );
  
      #undef GET_VALUE
  }
  
  
  static void initializeOrbPattern( const Point* pattern0, std::vector<Point>& pattern, int ntuples, int tupleSize, int poolSize )
  {
      RNG rng(0x12345678);
      int i, k, k1;
      pattern.resize(ntuples*tupleSize);
  
      for( i = 0; i < ntuples; i++ )
      {
          for( k = 0; k < tupleSize; k++ )
          {
              for(;;)
              {
                  int idx = rng.uniform(0, poolSize);
                  Point pt = pattern0[idx];
                  for( k1 = 0; k1 < k; k1++ )
                      if( pattern[tupleSize*i + k1] == pt )
                          break;
                  if( k1 == k )
                  {
                      pattern[tupleSize*i + k] = pt;
                      break;
                  }
              }
          }
      }
  }
  
  static int bit_pattern_31_[256*4] =
  {
      8,-3, 9,5/*mean (0), correlation (0)*/,
      4,2, 7,-12/*mean (1.12461e-05), correlation (0.0437584)*/,
      -11,9, -8,2/*mean (3.37382e-05), correlation (0.0617409)*/,
      7,-12, 12,-13/*mean (5.62303e-05), correlation (0.0636977)*/,
      2,-13, 2,12/*mean (0.000134953), correlation (0.085099)*/,
      1,-7, 1,6/*mean (0.000528565), correlation (0.0857175)*/,
      -2,-10, -2,-4/*mean (0.0188821), correlation (0.0985774)*/,
      -13,-13, -11,-8/*mean (0.0363135), correlation (0.0899616)*/,
      -13,-3, -12,-9/*mean (0.121806), correlation (0.099849)*/,
      10,4, 11,9/*mean (0.122065), correlation (0.093285)*/,
      -13,-8, -8,-9/*mean (0.162787), correlation (0.0942748)*/,
      -11,7, -9,12/*mean (0.21561), correlation (0.0974438)*/,
      7,7, 12,6/*mean (0.160583), correlation (0.130064)*/,
      -4,-5, -3,0/*mean (0.228171), correlation (0.132998)*/,
      -13,2, -12,-3/*mean (0.00997526), correlation (0.145926)*/,
      -9,0, -7,5/*mean (0.198234), correlation (0.143636)*/,
      12,-6, 12,-1/*mean (0.0676226), correlation (0.16689)*/,
      -3,6, -2,12/*mean (0.166847), correlation (0.171682)*/,
      -6,-13, -4,-8/*mean (0.101215), correlation (0.179716)*/,
      11,-13, 12,-8/*mean (0.200641), correlation (0.192279)*/,
      4,7, 5,1/*mean (0.205106), correlation (0.186848)*/,
      5,-3, 10,-3/*mean (0.234908), correlation (0.192319)*/,
      3,-7, 6,12/*mean (0.0709964), correlation (0.210872)*/,
      -8,-7, -6,-2/*mean (0.0939834), correlation (0.212589)*/,
      -2,11, -1,-10/*mean (0.127778), correlation (0.20866)*/,
      -13,12, -8,10/*mean (0.14783), correlation (0.206356)*/,
      -7,3, -5,-3/*mean (0.182141), correlation (0.198942)*/,
      -4,2, -3,7/*mean (0.188237), correlation (0.21384)*/,
      -10,-12, -6,11/*mean (0.14865), correlation (0.23571)*/,
      5,-12, 6,-7/*mean (0.222312), correlation (0.23324)*/,
      5,-6, 7,-1/*mean (0.229082), correlation (0.23389)*/,
      1,0, 4,-5/*mean (0.241577), correlation (0.215286)*/,
      9,11, 11,-13/*mean (0.00338507), correlation (0.251373)*/,
      4,7, 4,12/*mean (0.131005), correlation (0.257622)*/,
      2,-1, 4,4/*mean (0.152755), correlation (0.255205)*/,
      -4,-12, -2,7/*mean (0.182771), correlation (0.244867)*/,
      -8,-5, -7,-10/*mean (0.186898), correlation (0.23901)*/,
      4,11, 9,12/*mean (0.226226), correlation (0.258255)*/,
      0,-8, 1,-13/*mean (0.0897886), correlation (0.274827)*/,
      -13,-2, -8,2/*mean (0.148774), correlation (0.28065)*/,
      -3,-2, -2,3/*mean (0.153048), correlation (0.283063)*/,
      -6,9, -4,-9/*mean (0.169523), correlation (0.278248)*/,
      8,12, 10,7/*mean (0.225337), correlation (0.282851)*/,
      0,9, 1,3/*mean (0.226687), correlation (0.278734)*/,
      7,-5, 11,-10/*mean (0.00693882), correlation (0.305161)*/,
      -13,-6, -11,0/*mean (0.0227283), correlation (0.300181)*/,
      10,7, 12,1/*mean (0.125517), correlation (0.31089)*/,
      -6,-3, -6,12/*mean (0.131748), correlation (0.312779)*/,
      10,-9, 12,-4/*mean (0.144827), correlation (0.292797)*/,
      -13,8, -8,-12/*mean (0.149202), correlation (0.308918)*/,
      -13,0, -8,-4/*mean (0.160909), correlation (0.310013)*/,
      3,3, 7,8/*mean (0.177755), correlation (0.309394)*/,
      5,7, 10,-7/*mean (0.212337), correlation (0.310315)*/,
      -1,7, 1,-12/*mean (0.214429), correlation (0.311933)*/,
      3,-10, 5,6/*mean (0.235807), correlation (0.313104)*/,
      2,-4, 3,-10/*mean (0.00494827), correlation (0.344948)*/,
      -13,0, -13,5/*mean (0.0549145), correlation (0.344675)*/,
      -13,-7, -12,12/*mean (0.103385), correlation (0.342715)*/,
      -13,3, -11,8/*mean (0.134222), correlation (0.322922)*/,
      -7,12, -4,7/*mean (0.153284), correlation (0.337061)*/,
      6,-10, 12,8/*mean (0.154881), correlation (0.329257)*/,
      -9,-1, -7,-6/*mean (0.200967), correlation (0.33312)*/,
      -2,-5, 0,12/*mean (0.201518), correlation (0.340635)*/,
      -12,5, -7,5/*mean (0.207805), correlation (0.335631)*/,
      3,-10, 8,-13/*mean (0.224438), correlation (0.34504)*/,
      -7,-7, -4,5/*mean (0.239361), correlation (0.338053)*/,
      -3,-2, -1,-7/*mean (0.240744), correlation (0.344322)*/,
      2,9, 5,-11/*mean (0.242949), correlation (0.34145)*/,
      -11,-13, -5,-13/*mean (0.244028), correlation (0.336861)*/,
      -1,6, 0,-1/*mean (0.247571), correlation (0.343684)*/,
      5,-3, 5,2/*mean (0.000697256), correlation (0.357265)*/,
      -4,-13, -4,12/*mean (0.00213675), correlation (0.373827)*/,
      -9,-6, -9,6/*mean (0.0126856), correlation (0.373938)*/,
      -12,-10, -8,-4/*mean (0.0152497), correlation (0.364237)*/,
      10,2, 12,-3/*mean (0.0299933), correlation (0.345292)*/,
      7,12, 12,12/*mean (0.0307242), correlation (0.366299)*/,
      -7,-13, -6,5/*mean (0.0534975), correlation (0.368357)*/,
      -4,9, -3,4/*mean (0.099865), correlation (0.372276)*/,
      7,-1, 12,2/*mean (0.117083), correlation (0.364529)*/,
      -7,6, -5,1/*mean (0.126125), correlation (0.369606)*/,
      -13,11, -12,5/*mean (0.130364), correlation (0.358502)*/,
      -3,7, -2,-6/*mean (0.131691), correlation (0.375531)*/,
      7,-8, 12,-7/*mean (0.160166), correlation (0.379508)*/,
      -13,-7, -11,-12/*mean (0.167848), correlation (0.353343)*/,
      1,-3, 12,12/*mean (0.183378), correlation (0.371916)*/,
      2,-6, 3,0/*mean (0.228711), correlation (0.371761)*/,
      -4,3, -2,-13/*mean (0.247211), correlation (0.364063)*/,
      -1,-13, 1,9/*mean (0.249325), correlation (0.378139)*/,
      7,1, 8,-6/*mean (0.000652272), correlation (0.411682)*/,
      1,-1, 3,12/*mean (0.00248538), correlation (0.392988)*/,
      9,1, 12,6/*mean (0.0206815), correlation (0.386106)*/,
      -1,-9, -1,3/*mean (0.0364485), correlation (0.410752)*/,
      -13,-13, -10,5/*mean (0.0376068), correlation (0.398374)*/,
      7,7, 10,12/*mean (0.0424202), correlation (0.405663)*/,
      12,-5, 12,9/*mean (0.0942645), correlation (0.410422)*/,
      6,3, 7,11/*mean (0.1074), correlation (0.413224)*/,
      5,-13, 6,10/*mean (0.109256), correlation (0.408646)*/,
      2,-12, 2,3/*mean (0.131691), correlation (0.416076)*/,
      3,8, 4,-6/*mean (0.165081), correlation (0.417569)*/,
      2,6, 12,-13/*mean (0.171874), correlation (0.408471)*/,
      9,-12, 10,3/*mean (0.175146), correlation (0.41296)*/,
      -8,4, -7,9/*mean (0.183682), correlation (0.402956)*/,
      -11,12, -4,-6/*mean (0.184672), correlation (0.416125)*/,
      1,12, 2,-8/*mean (0.191487), correlation (0.386696)*/,
      6,-9, 7,-4/*mean (0.192668), correlation (0.394771)*/,
      2,3, 3,-2/*mean (0.200157), correlation (0.408303)*/,
      6,3, 11,0/*mean (0.204588), correlation (0.411762)*/,
      3,-3, 8,-8/*mean (0.205904), correlation (0.416294)*/,
      7,8, 9,3/*mean (0.213237), correlation (0.409306)*/,
      -11,-5, -6,-4/*mean (0.243444), correlation (0.395069)*/,
      -10,11, -5,10/*mean (0.247672), correlation (0.413392)*/,
      -5,-8, -3,12/*mean (0.24774), correlation (0.411416)*/,
      -10,5, -9,0/*mean (0.00213675), correlation (0.454003)*/,
      8,-1, 12,-6/*mean (0.0293635), correlation (0.455368)*/,
      4,-6, 6,-11/*mean (0.0404971), correlation (0.457393)*/,
      -10,12, -8,7/*mean (0.0481107), correlation (0.448364)*/,
      4,-2, 6,7/*mean (0.050641), correlation (0.455019)*/,
      -2,0, -2,12/*mean (0.0525978), correlation (0.44338)*/,
      -5,-8, -5,2/*mean (0.0629667), correlation (0.457096)*/,
      7,-6, 10,12/*mean (0.0653846), correlation (0.445623)*/,
      -9,-13, -8,-8/*mean (0.0858749), correlation (0.449789)*/,
      -5,-13, -5,-2/*mean (0.122402), correlation (0.450201)*/,
      8,-8, 9,-13/*mean (0.125416), correlation (0.453224)*/,
      -9,-11, -9,0/*mean (0.130128), correlation (0.458724)*/,
      1,-8, 1,-2/*mean (0.132467), correlation (0.440133)*/,
      7,-4, 9,1/*mean (0.132692), correlation (0.454)*/,
      -2,1, -1,-4/*mean (0.135695), correlation (0.455739)*/,
      11,-6, 12,-11/*mean (0.142904), correlation (0.446114)*/,
      -12,-9, -6,4/*mean (0.146165), correlation (0.451473)*/,
      3,7, 7,12/*mean (0.147627), correlation (0.456643)*/,
      5,5, 10,8/*mean (0.152901), correlation (0.455036)*/,
      0,-4, 2,8/*mean (0.167083), correlation (0.459315)*/,
      -9,12, -5,-13/*mean (0.173234), correlation (0.454706)*/,
      0,7, 2,12/*mean (0.18312), correlation (0.433855)*/,
      -1,2, 1,7/*mean (0.185504), correlation (0.443838)*/,
      5,11, 7,-9/*mean (0.185706), correlation (0.451123)*/,
      3,5, 6,-8/*mean (0.188968), correlation (0.455808)*/,
      -13,-4, -8,9/*mean (0.191667), correlation (0.459128)*/,
      -5,9, -3,-3/*mean (0.193196), correlation (0.458364)*/,
      -4,-7, -3,-12/*mean (0.196536), correlation (0.455782)*/,
      6,5, 8,0/*mean (0.1972), correlation (0.450481)*/,
      -7,6, -6,12/*mean (0.199438), correlation (0.458156)*/,
      -13,6, -5,-2/*mean (0.211224), correlation (0.449548)*/,
      1,-10, 3,10/*mean (0.211718), correlation (0.440606)*/,
      4,1, 8,-4/*mean (0.213034), correlation (0.443177)*/,
      -2,-2, 2,-13/*mean (0.234334), correlation (0.455304)*/,
      2,-12, 12,12/*mean (0.235684), correlation (0.443436)*/,
      -2,-13, 0,-6/*mean (0.237674), correlation (0.452525)*/,
      4,1, 9,3/*mean (0.23962), correlation (0.444824)*/,
      -6,-10, -3,-5/*mean (0.248459), correlation (0.439621)*/,
      -3,-13, -1,1/*mean (0.249505), correlation (0.456666)*/,
      7,5, 12,-11/*mean (0.00119208), correlation (0.495466)*/,
      4,-2, 5,-7/*mean (0.00372245), correlation (0.484214)*/,
      -13,9, -9,-5/*mean (0.00741116), correlation (0.499854)*/,
      7,1, 8,6/*mean (0.0208952), correlation (0.499773)*/,
      7,-8, 7,6/*mean (0.0220085), correlation (0.501609)*/,
      -7,-4, -7,1/*mean (0.0233806), correlation (0.496568)*/,
      -8,11, -7,-8/*mean (0.0236505), correlation (0.489719)*/,
      -13,6, -12,-8/*mean (0.0268781), correlation (0.503487)*/,
      2,4, 3,9/*mean (0.0323324), correlation (0.501938)*/,
      10,-5, 12,3/*mean (0.0399235), correlation (0.494029)*/,
      -6,-5, -6,7/*mean (0.0420153), correlation (0.486579)*/,
      8,-3, 9,-8/*mean (0.0548021), correlation (0.484237)*/,
      2,-12, 2,8/*mean (0.0616622), correlation (0.496642)*/,
      -11,-2, -10,3/*mean (0.0627755), correlation (0.498563)*/,
      -12,-13, -7,-9/*mean (0.0829622), correlation (0.495491)*/,
      -11,0, -10,-5/*mean (0.0843342), correlation (0.487146)*/,
      5,-3, 11,8/*mean (0.0929937), correlation (0.502315)*/,
      -2,-13, -1,12/*mean (0.113327), correlation (0.48941)*/,
      -1,-8, 0,9/*mean (0.132119), correlation (0.467268)*/,
      -13,-11, -12,-5/*mean (0.136269), correlation (0.498771)*/,
      -10,-2, -10,11/*mean (0.142173), correlation (0.498714)*/,
      -3,9, -2,-13/*mean (0.144141), correlation (0.491973)*/,
      2,-3, 3,2/*mean (0.14892), correlation (0.500782)*/,
      -9,-13, -4,0/*mean (0.150371), correlation (0.498211)*/,
      -4,6, -3,-10/*mean (0.152159), correlation (0.495547)*/,
      -4,12, -2,-7/*mean (0.156152), correlation (0.496925)*/,
      -6,-11, -4,9/*mean (0.15749), correlation (0.499222)*/,
      6,-3, 6,11/*mean (0.159211), correlation (0.503821)*/,
      -13,11, -5,5/*mean (0.162427), correlation (0.501907)*/,
      11,11, 12,6/*mean (0.16652), correlation (0.497632)*/,
      7,-5, 12,-2/*mean (0.169141), correlation (0.484474)*/,
      -1,12, 0,7/*mean (0.169456), correlation (0.495339)*/,
      -4,-8, -3,-2/*mean (0.171457), correlation (0.487251)*/,
      -7,1, -6,7/*mean (0.175), correlation (0.500024)*/,
      -13,-12, -8,-13/*mean (0.175866), correlation (0.497523)*/,
      -7,-2, -6,-8/*mean (0.178273), correlation (0.501854)*/,
      -8,5, -6,-9/*mean (0.181107), correlation (0.494888)*/,
      -5,-1, -4,5/*mean (0.190227), correlation (0.482557)*/,
      -13,7, -8,10/*mean (0.196739), correlation (0.496503)*/,
      1,5, 5,-13/*mean (0.19973), correlation (0.499759)*/,
      1,0, 10,-13/*mean (0.204465), correlation (0.49873)*/,
      9,12, 10,-1/*mean (0.209334), correlation (0.49063)*/,
      5,-8, 10,-9/*mean (0.211134), correlation (0.503011)*/,
      -1,11, 1,-13/*mean (0.212), correlation (0.499414)*/,
      -9,-3, -6,2/*mean (0.212168), correlation (0.480739)*/,
      -1,-10, 1,12/*mean (0.212731), correlation (0.502523)*/,
      -13,1, -8,-10/*mean (0.21327), correlation (0.489786)*/,
      8,-11, 10,-6/*mean (0.214159), correlation (0.488246)*/,
      2,-13, 3,-6/*mean (0.216993), correlation (0.50287)*/,
      7,-13, 12,-9/*mean (0.223639), correlation (0.470502)*/,
      -10,-10, -5,-7/*mean (0.224089), correlation (0.500852)*/,
      -10,-8, -8,-13/*mean (0.228666), correlation (0.502629)*/,
      4,-6, 8,5/*mean (0.22906), correlation (0.498305)*/,
      3,12, 8,-13/*mean (0.233378), correlation (0.503825)*/,
      -4,2, -3,-3/*mean (0.234323), correlation (0.476692)*/,
      5,-13, 10,-12/*mean (0.236392), correlation (0.475462)*/,
      4,-13, 5,-1/*mean (0.236842), correlation (0.504132)*/,
      -9,9, -4,3/*mean (0.236977), correlation (0.497739)*/,
      0,3, 3,-9/*mean (0.24314), correlation (0.499398)*/,
      -12,1, -6,1/*mean (0.243297), correlation (0.489447)*/,
      3,2, 4,-8/*mean (0.00155196), correlation (0.553496)*/,
      -10,-10, -10,9/*mean (0.00239541), correlation (0.54297)*/,
      8,-13, 12,12/*mean (0.0034413), correlation (0.544361)*/,
      -8,-12, -6,-5/*mean (0.003565), correlation (0.551225)*/,
      2,2, 3,7/*mean (0.00835583), correlation (0.55285)*/,
      10,6, 11,-8/*mean (0.00885065), correlation (0.540913)*/,
      6,8, 8,-12/*mean (0.0101552), correlation (0.551085)*/,
      -7,10, -6,5/*mean (0.0102227), correlation (0.533635)*/,
      -3,-9, -3,9/*mean (0.0110211), correlation (0.543121)*/,
      -1,-13, -1,5/*mean (0.0113473), correlation (0.550173)*/,
      -3,-7, -3,4/*mean (0.0140913), correlation (0.554774)*/,
      -8,-2, -8,3/*mean (0.017049), correlation (0.55461)*/,
      4,2, 12,12/*mean (0.01778), correlation (0.546921)*/,
      2,-5, 3,11/*mean (0.0224022), correlation (0.549667)*/,
      6,-9, 11,-13/*mean (0.029161), correlation (0.546295)*/,
      3,-1, 7,12/*mean (0.0303081), correlation (0.548599)*/,
      11,-1, 12,4/*mean (0.0355151), correlation (0.523943)*/,
      -3,0, -3,6/*mean (0.0417904), correlation (0.543395)*/,
      4,-11, 4,12/*mean (0.0487292), correlation (0.542818)*/,
      2,-4, 2,1/*mean (0.0575124), correlation (0.554888)*/,
      -10,-6, -8,1/*mean (0.0594242), correlation (0.544026)*/,
      -13,7, -11,1/*mean (0.0597391), correlation (0.550524)*/,
      -13,12, -11,-13/*mean (0.0608974), correlation (0.55383)*/,
      6,0, 11,-13/*mean (0.065126), correlation (0.552006)*/,
      0,-1, 1,4/*mean (0.074224), correlation (0.546372)*/,
      -13,3, -9,-2/*mean (0.0808592), correlation (0.554875)*/,
      -9,8, -6,-3/*mean (0.0883378), correlation (0.551178)*/,
      -13,-6, -8,-2/*mean (0.0901035), correlation (0.548446)*/,
      5,-9, 8,10/*mean (0.0949843), correlation (0.554694)*/,
      2,7, 3,-9/*mean (0.0994152), correlation (0.550979)*/,
      -1,-6, -1,-1/*mean (0.10045), correlation (0.552714)*/,
      9,5, 11,-2/*mean (0.100686), correlation (0.552594)*/,
      11,-3, 12,-8/*mean (0.101091), correlation (0.532394)*/,
      3,0, 3,5/*mean (0.101147), correlation (0.525576)*/,
      -1,4, 0,10/*mean (0.105263), correlation (0.531498)*/,
      3,-6, 4,5/*mean (0.110785), correlation (0.540491)*/,
      -13,0, -10,5/*mean (0.112798), correlation (0.536582)*/,
      5,8, 12,11/*mean (0.114181), correlation (0.555793)*/,
      8,9, 9,-6/*mean (0.117431), correlation (0.553763)*/,
      7,-4, 8,-12/*mean (0.118522), correlation (0.553452)*/,
      -10,4, -10,9/*mean (0.12094), correlation (0.554785)*/,
      7,3, 12,4/*mean (0.122582), correlation (0.555825)*/,
      9,-7, 10,-2/*mean (0.124978), correlation (0.549846)*/,
      7,0, 12,-2/*mean (0.127002), correlation (0.537452)*/,
      -1,-6, 0,-11/*mean (0.127148), correlation (0.547401)*/
  };
  
  
  static void makeRandomPattern(int patchSize, Point* pattern, int npoints)
  {
      RNG rng(0x34985739); // we always start with a fixed seed,
                           // to make patterns the same on each run
      for( int i = 0; i < npoints; i++ )
      {
          pattern[i].x = rng.uniform(-patchSize/2, patchSize/2+1);
          pattern[i].y = rng.uniform(-patchSize/2, patchSize/2+1);
      }
  }
  
  
  static inline float getScale(int level, int firstLevel, double scaleFactor)
  {
      return (float)std::pow(scaleFactor, (double)(level - firstLevel));
  }
  
  /** Constructor
   * @param detector_params parameters to use
   */
  ORB::ORB(int _nfeatures, float _scaleFactor, int _nlevels, int _edgeThreshold,
           int _firstLevel, int _WTA_K, int _scoreType, int _patchSize) :
      nfeatures(_nfeatures), scaleFactor(_scaleFactor), nlevels(_nlevels),
      edgeThreshold(_edgeThreshold), firstLevel(_firstLevel), WTA_K(_WTA_K),
      scoreType(_scoreType), patchSize(_patchSize)
  {}
  
  
  int ORB::descriptorSize() const
  {
      return kBytes;
  }
  
  int ORB::descriptorType() const
  {
      return CV_8U;
  }
  
  int ORB::defaultNorm() const
  {
      return NORM_HAMMING;
  }
  
  /** Compute the ORB features and descriptors on an image
   * @param img the image to compute the features and descriptors on
   * @param mask the mask to apply
   * @param keypoints the resulting keypoints
   */
  void ORB::operator()(InputArray image, InputArray mask, std::vector<KeyPoint>& keypoints) const
  {
      (*this)(image, mask, keypoints, noArray(), false);
  }
  
  
  /** Compute the ORB keypoint orientations
   * @param image the image to compute the features and descriptors on
   * @param integral_image the integral image of the iamge (can be empty, but the computation will be slower)
   * @param scale the scale at which we compute the orientation
   * @param keypoints the resulting keypoints
   */
  static void computeOrientation(const Mat& image, std::vector<KeyPoint>& keypoints,
                                 int halfPatchSize, const std::vector<int>& umax)
  {
      // Process each keypoint
      for (std::vector<KeyPoint>::iterator keypoint = keypoints.begin(),
           keypointEnd = keypoints.end(); keypoint != keypointEnd; ++keypoint)
      {
          keypoint->angle = IC_Angle(image, halfPatchSize, keypoint->pt, umax);
      }
  }
  
  
  /** Compute the ORB keypoints on an image
   * @param image_pyramid the image pyramid to compute the features and descriptors on
   * @param mask_pyramid the masks to apply at every level
   * @param keypoints the resulting keypoints, clustered per level
   */
  static void computeKeyPoints(const std::vector<Mat>& imagePyramid,
                               const std::vector<Mat>& maskPyramid,
                               std::vector<std::vector<KeyPoint> >& allKeypoints,
                               int nfeatures, int firstLevel, double scaleFactor,
                               int edgeThreshold, int patchSize, int scoreType )
  {
      int nlevels = (int)imagePyramid.size();
      std::vector<int> nfeaturesPerLevel(nlevels);
  
      // fill the extractors and descriptors for the corresponding scales
      float factor = (float)(1.0 / scaleFactor);
      float ndesiredFeaturesPerScale = nfeatures*(1 - factor)/(1 - (float)std::pow((double)factor, (double)nlevels));
  
      int sumFeatures = 0;
      for( int level = 0; level < nlevels-1; level++ )
      {
          nfeaturesPerLevel[level] = cvRound(ndesiredFeaturesPerScale);
          sumFeatures += nfeaturesPerLevel[level];
          ndesiredFeaturesPerScale *= factor;
      }
      nfeaturesPerLevel[nlevels-1] = std::max(nfeatures - sumFeatures, 0);
  
      // Make sure we forget about what is too close to the boundary
      //edge_threshold_ = std::max(edge_threshold_, patch_size_/2 + kKernelWidth / 2 + 2);
  
      // pre-compute the end of a row in a circular patch
      int halfPatchSize = patchSize / 2;
      std::vector<int> umax(halfPatchSize + 2);
  
      int v, v0, vmax = cvFloor(halfPatchSize * std::sqrt(2.f) / 2 + 1);
      int vmin = cvCeil(halfPatchSize * std::sqrt(2.f) / 2);
      for (v = 0; v <= vmax; ++v)
          umax[v] = cvRound(std::sqrt((double)halfPatchSize * halfPatchSize - v * v));
  
      // Make sure we are symmetric
      for (v = halfPatchSize, v0 = 0; v >= vmin; --v)
      {
          while (umax[v0] == umax[v0 + 1])
              ++v0;
          umax[v] = v0;
          ++v0;
      }
  
      allKeypoints.resize(nlevels);
  
      for (int level = 0; level < nlevels; ++level)
      {
          int featuresNum = nfeaturesPerLevel[level];
          allKeypoints[level].reserve(featuresNum*2);
  
          std::vector<KeyPoint> & keypoints = allKeypoints[level];
  
          // Detect FAST features, 20 is a good threshold
          FastFeatureDetector fd(20, true);
          fd.detect(imagePyramid[level], keypoints, maskPyramid[level]);
  
          // Remove keypoints very close to the border
          KeyPointsFilter::runByImageBorder(keypoints, imagePyramid[level].size(), edgeThreshold);
  
          if( scoreType == ORB::HARRIS_SCORE )
          {
              // Keep more points than necessary as FAST does not give amazing corners
              KeyPointsFilter::retainBest(keypoints, 2 * featuresNum);
  
              // Compute the Harris cornerness (better scoring than FAST)
              HarrisResponses(imagePyramid[level], keypoints, 7, HARRIS_K);
          }
  
          //cull to the final desired level, using the new Harris scores or the original FAST scores.
          KeyPointsFilter::retainBest(keypoints, featuresNum);
  
          float sf = getScale(level, firstLevel, scaleFactor);
  
          // Set the level of the coordinates
          for (std::vector<KeyPoint>::iterator keypoint = keypoints.begin(),
               keypointEnd = keypoints.end(); keypoint != keypointEnd; ++keypoint)
          {
              keypoint->octave = level;
              keypoint->size = patchSize*sf;
          }
  
          computeOrientation(imagePyramid[level], keypoints, halfPatchSize, umax);
      }
  }
  
  
  /** Compute the ORB decriptors
   * @param image the image to compute the features and descriptors on
   * @param integral_image the integral image of the image (can be empty, but the computation will be slower)
   * @param level the scale at which we compute the orientation
   * @param keypoints the keypoints to use
   * @param descriptors the resulting descriptors
   */
  static void computeDescriptors(const Mat& image, std::vector<KeyPoint>& keypoints, Mat& descriptors,
                                 const std::vector<Point>& pattern, int dsize, int WTA_K)
  {
      //convert to grayscale if more than one color
      CV_Assert(image.type() == CV_8UC1);
      //create the descriptor mat, keypoints.size() rows, BYTES cols
      descriptors = Mat::zeros((int)keypoints.size(), dsize, CV_8UC1);
  
      for (size_t i = 0; i < keypoints.size(); i++)
          computeOrbDescriptor(keypoints[i], image, &pattern[0], descriptors.ptr((int)i), dsize, WTA_K);
  }
  
  
  /** Compute the ORB features and descriptors on an image
   * @param img the image to compute the features and descriptors on
   * @param mask the mask to apply
   * @param keypoints the resulting keypoints
   * @param descriptors the resulting descriptors
   * @param do_keypoints if true, the keypoints are computed, otherwise used as an input
   * @param do_descriptors if true, also computes the descriptors
   */
  void ORB::operator()( InputArray _image, InputArray _mask, std::vector<KeyPoint>& _keypoints,
                        OutputArray _descriptors, bool useProvidedKeypoints) const
  {
      CV_Assert(patchSize >= 2);
  
      bool do_keypoints = !useProvidedKeypoints;
      bool do_descriptors = _descriptors.needed();
  
      if( (!do_keypoints && !do_descriptors) || _image.empty() )
          return;
  
      //ROI handling
      const int HARRIS_BLOCK_SIZE = 9;
      int halfPatchSize = patchSize / 2;
      int border = std::max(edgeThreshold, std::max(halfPatchSize, HARRIS_BLOCK_SIZE/2))+1;
  
      Mat image = _image.getMat(), mask = _mask.getMat();
      if( image.type() != CV_8UC1 )
          cvtColor(_image, image, COLOR_BGR2GRAY);
  
      int levelsNum = this->nlevels;
  
      if( !do_keypoints )
      {
          // if we have pre-computed keypoints, they may use more levels than it is set in parameters
          // !!!TODO!!! implement more correct method, independent from the used keypoint detector.
          // Namely, the detector should provide correct size of each keypoint. Based on the keypoint size
          // and the algorithm used (i.e. BRIEF, running on 31x31 patches) we should compute the approximate
          // scale-factor that we need to apply. Then we should cluster all the computed scale-factors and
          // for each cluster compute the corresponding image.
          //
          // In short, ultimately the descriptor should
          // ignore octave parameter and deal only with the keypoint size.
          levelsNum = 0;
          for( size_t i = 0; i < _keypoints.size(); i++ )
              levelsNum = std::max(levelsNum, std::max(_keypoints[i].octave, 0));
          levelsNum++;
      }
  
      // Pre-compute the scale pyramids
      std::vector<Mat> imagePyramid(levelsNum), maskPyramid(levelsNum);
      for (int level = 0; level < levelsNum; ++level)
      {
          float scale = 1/getScale(level, firstLevel, scaleFactor);
          Size sz(cvRound(image.cols*scale), cvRound(image.rows*scale));
          Size wholeSize(sz.width + border*2, sz.height + border*2);
          Mat temp(wholeSize, image.type()), masktemp;
          imagePyramid[level] = temp(Rect(border, border, sz.width, sz.height));
  
          if( !mask.empty() )
          {
              masktemp = Mat(wholeSize, mask.type());
              maskPyramid[level] = masktemp(Rect(border, border, sz.width, sz.height));
          }
  
          // Compute the resized image
          if( level != firstLevel )
          {
              if( level < firstLevel )
              {
                  resize(image, imagePyramid[level], sz, 0, 0, INTER_LINEAR);
                  if (!mask.empty())
                      resize(mask, maskPyramid[level], sz, 0, 0, INTER_LINEAR);
              }
              else
              {
                  resize(imagePyramid[level-1], imagePyramid[level], sz, 0, 0, INTER_LINEAR);
                  if (!mask.empty())
                  {
                      resize(maskPyramid[level-1], maskPyramid[level], sz, 0, 0, INTER_LINEAR);
                      threshold(maskPyramid[level], maskPyramid[level], 254, 0, THRESH_TOZERO);
                  }
              }
  
              copyMakeBorder(imagePyramid[level], temp, border, border, border, border,
                             BORDER_REFLECT_101+BORDER_ISOLATED);
              if (!mask.empty())
                  copyMakeBorder(maskPyramid[level], masktemp, border, border, border, border,
                                 BORDER_CONSTANT+BORDER_ISOLATED);
          }
          else
          {
              copyMakeBorder(image, temp, border, border, border, border,
                             BORDER_REFLECT_101);
              if( !mask.empty() )
                  copyMakeBorder(mask, masktemp, border, border, border, border,
                                 BORDER_CONSTANT+BORDER_ISOLATED);
          }
      }
  
      // Pre-compute the keypoints (we keep the best over all scales, so this has to be done beforehand
      std::vector < std::vector<KeyPoint> > allKeypoints;
      if( do_keypoints )
      {
          // Get keypoints, those will be far enough from the border that no check will be required for the descriptor
          computeKeyPoints(imagePyramid, maskPyramid, allKeypoints,
                           nfeatures, firstLevel, scaleFactor,
                           edgeThreshold, patchSize, scoreType);
  
          // make sure we have the right number of keypoints keypoints
          /*std::vector<KeyPoint> temp;
  
          for (int level = 0; level < n_levels; ++level)
          {
              std::vector<KeyPoint>& keypoints = all_keypoints[level];
              temp.insert(temp.end(), keypoints.begin(), keypoints.end());
              keypoints.clear();
          }
  
          KeyPoint::retainBest(temp, n_features_);
  
          for (std::vector<KeyPoint>::iterator keypoint = temp.begin(),
               keypoint_end = temp.end(); keypoint != keypoint_end; ++keypoint)
              all_keypoints[keypoint->octave].push_back(*keypoint);*/
      }
      else
      {
          // Remove keypoints very close to the border
          KeyPointsFilter::runByImageBorder(_keypoints, image.size(), edgeThreshold);
  
          // Cluster the input keypoints depending on the level they were computed at
          allKeypoints.resize(levelsNum);
          for (std::vector<KeyPoint>::iterator keypoint = _keypoints.begin(),
               keypointEnd = _keypoints.end(); keypoint != keypointEnd; ++keypoint)
              allKeypoints[keypoint->octave].push_back(*keypoint);
  
          // Make sure we rescale the coordinates
          for (int level = 0; level < levelsNum; ++level)
          {
              if (level == firstLevel)
                  continue;
  
              std::vector<KeyPoint> & keypoints = allKeypoints[level];
              float scale = 1/getScale(level, firstLevel, scaleFactor);
              for (std::vector<KeyPoint>::iterator keypoint = keypoints.begin(),
                   keypointEnd = keypoints.end(); keypoint != keypointEnd; ++keypoint)
                  keypoint->pt *= scale;
          }
      }
  
      Mat descriptors;
      std::vector<Point> pattern;
  
      if( do_descriptors )
      {
          int nkeypoints = 0;
          for (int level = 0; level < levelsNum; ++level)
              nkeypoints += (int)allKeypoints[level].size();
          if( nkeypoints == 0 )
              _descriptors.release();
          else
          {
              _descriptors.create(nkeypoints, descriptorSize(), CV_8U);
              descriptors = _descriptors.getMat();
          }
  
          const int npoints = 512;
          Point patternbuf[npoints];
          const Point* pattern0 = (const Point*)bit_pattern_31_;
  
          if( patchSize != 31 )
          {
              pattern0 = patternbuf;
              makeRandomPattern(patchSize, patternbuf, npoints);
          }
  
          CV_Assert( WTA_K == 2 || WTA_K == 3 || WTA_K == 4 );
  
          if( WTA_K == 2 )
              std::copy(pattern0, pattern0 + npoints, std::back_inserter(pattern));
          else
          {
              int ntuples = descriptorSize()*4;
              initializeOrbPattern(pattern0, pattern, ntuples, WTA_K, npoints);
          }
      }
  
      _keypoints.clear();
      int offset = 0;
      for (int level = 0; level < levelsNum; ++level)
      {
          // Get the features and compute their orientation
          std::vector<KeyPoint>& keypoints = allKeypoints[level];
          int nkeypoints = (int)keypoints.size();
  
          // Compute the descriptors
          if (do_descriptors)
          {
              Mat desc;
              if (!descriptors.empty())
              {
                  desc = descriptors.rowRange(offset, offset + nkeypoints);
              }
  
              offset += nkeypoints;
              // preprocess the resized image
              Mat& workingMat = imagePyramid[level];
              //boxFilter(working_mat, working_mat, working_mat.depth(), Size(5,5), Point(-1,-1), true, BORDER_REFLECT_101);
              GaussianBlur(workingMat, workingMat, Size(7, 7), 2, 2, BORDER_REFLECT_101);
              computeDescriptors(workingMat, keypoints, desc, pattern, descriptorSize(), WTA_K);
          }
  
          // Copy to the output data
          if (level != firstLevel)
          {
              float scale = getScale(level, firstLevel, scaleFactor);
              for (std::vector<KeyPoint>::iterator keypoint = keypoints.begin(),
                   keypointEnd = keypoints.end(); keypoint != keypointEnd; ++keypoint)
                  keypoint->pt *= scale;
          }
          // And add the keypoints to the output
          _keypoints.insert(_keypoints.end(), keypoints.begin(), keypoints.end());
      }
  }
  
  void ORB::detectImpl( const Mat& image, std::vector<KeyPoint>& keypoints, const Mat& mask) const
  {
      (*this)(image, mask, keypoints, noArray(), false);
  }
  
  void ORB::computeImpl( const Mat& image, std::vector<KeyPoint>& keypoints, Mat& descriptors) const
  {
      (*this)(image, Mat(), keypoints, descriptors, true);
  }
  
  }
  