text: /*M///////////////////////////////////////////////////////////////////////////////////////
  //
  //  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
  //
  //  By downloading, copying, installing or using the software you agree to this license.
  //  If you do not agree to this license, do not download, install,
  //  copy or use the software.
  //
  //
  //                           License Agreement
  //                For Open Source Computer Vision Library
  //
  // Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
  // Copyright (C) 2009, Willow Garage Inc., all rights reserved.
  // Third party copyrights are property of their respective owners.
  //
  // Redistribution and use in source and binary forms, with or without modification,
  // are permitted provided that the following conditions are met:
  //
  //   * Redistribution's of source code must retain the above copyright notice,
  //     this list of conditions and the following disclaimer.
  //
  //   * Redistribution's in binary form must reproduce the above copyright notice,
  //     this list of conditions and the following disclaimer in the documentation
  //     and/or other materials provided with the distribution.
  //
  //   * The name of the copyright holders may not be used to endorse or promote products
  //     derived from this software without specific prior written permission.
  //
  // This software is provided by the copyright holders and contributors "as is" and
  // any express or implied warranties, including, but not limited to, the implied
  // warranties of merchantability and fitness for a particular purpose are disclaimed.
  // In no event shall the Intel Corporation or contributors be liable for any direct,
  // indirect, incidental, special, exemplary, or consequential damages
  // (including, but not limited to, procurement of substitute goods or services;
  // loss of use, data, or profits; or business interruption) however caused
  // and on any theory of liability, whether in contract, strict liability,
  // or tort (including negligence or otherwise) arising in any way out of
  // the use of this software, even if advised of the possibility of such damage.
  //
  //M*/
  
  #include "precomp.hpp"
  #include "opencl_kernels_imgproc.hpp"
  
  /*
   * This file includes the code, contributed by Simon Perreault
   * (the function icvMedianBlur_8u_O1)
   *
   * Constant-time median filtering -- http://nomis80.org/ctmf.html
   * Copyright (C) 2006 Simon Perreault
   *
   * Contact:
   *  Laboratoire de vision et systemes numeriques
   *  Pavillon Adrien-Pouliot
   *  Universite Laval
   *  Sainte-Foy, Quebec, Canada
   *  G1K 7P4
   *
   *  perreaul@gel.ulaval.ca
   */
  
  namespace cv
  {
  
  /****************************************************************************************\
                                           Box Filter
  \****************************************************************************************/
  
  template<typename T, typename ST>
  struct RowSum :
          public BaseRowFilter
  {
      RowSum( int _ksize, int _anchor ) :
          BaseRowFilter()
      {
          ksize = _ksize;
          anchor = _anchor;
      }
  
      virtual void operator()(const uchar* src, uchar* dst, int width, int cn)
      {
          const T* S = (const T*)src;
          ST* D = (ST*)dst;
          int i = 0, k, ksz_cn = ksize*cn;
  
          width = (width - 1)*cn;
          for( k = 0; k < cn; k++, S++, D++ )
          {
              ST s = 0;
              for( i = 0; i < ksz_cn; i += cn )
                  s += S[i];
              D[0] = s;
              for( i = 0; i < width; i += cn )
              {
                  s += S[i + ksz_cn] - S[i];
                  D[i+cn] = s;
              }
          }
      }
  };
  
  
  template<typename ST, typename T>
  struct ColumnSum :
          public BaseColumnFilter
  {
      ColumnSum( int _ksize, int _anchor, double _scale ) :
          BaseColumnFilter()
      {
          ksize = _ksize;
          anchor = _anchor;
          scale = _scale;
          sumCount = 0;
      }
  
      virtual void reset() { sumCount = 0; }
  
      virtual void operator()(const uchar** src, uchar* dst, int dststep, int count, int width)
      {
          int i;
          ST* SUM;
          bool haveScale = scale != 1;
          double _scale = scale;
  
          if( width != (int)sum.size() )
          {
              sum.resize(width);
              sumCount = 0;
          }
  
          SUM = &sum[0];
          if( sumCount == 0 )
          {
              memset((void*)SUM, 0, width*sizeof(ST));
  
              for( ; sumCount < ksize - 1; sumCount++, src++ )
              {
                  const ST* Sp = (const ST*)src[0];
                  for( i = 0; i <= width - 2; i += 2 )
                  {
                      ST s0 = SUM[i] + Sp[i], s1 = SUM[i+1] + Sp[i+1];
                      SUM[i] = s0; SUM[i+1] = s1;
                  }
  
                  for( ; i < width; i++ )
                      SUM[i] += Sp[i];
              }
          }
          else
          {
              CV_Assert( sumCount == ksize-1 );
              src += ksize-1;
          }
  
          for( ; count--; src++ )
          {
              const ST* Sp = (const ST*)src[0];
              const ST* Sm = (const ST*)src[1-ksize];
              T* D = (T*)dst;
              if( haveScale )
              {
                  for( i = 0; i <= width - 2; i += 2 )
                  {
                      ST s0 = SUM[i] + Sp[i], s1 = SUM[i+1] + Sp[i+1];
                      D[i] = saturate_cast<T>(s0*_scale);
                      D[i+1] = saturate_cast<T>(s1*_scale);
                      s0 -= Sm[i]; s1 -= Sm[i+1];
                      SUM[i] = s0; SUM[i+1] = s1;
                  }
  
                  for( ; i < width; i++ )
                  {
                      ST s0 = SUM[i] + Sp[i];
                      D[i] = saturate_cast<T>(s0*_scale);
                      SUM[i] = s0 - Sm[i];
                  }
              }
              else
              {
                  for( i = 0; i <= width - 2; i += 2 )
                  {
                      ST s0 = SUM[i] + Sp[i], s1 = SUM[i+1] + Sp[i+1];
                      D[i] = saturate_cast<T>(s0);
                      D[i+1] = saturate_cast<T>(s1);
                      s0 -= Sm[i]; s1 -= Sm[i+1];
                      SUM[i] = s0; SUM[i+1] = s1;
                  }
  
                  for( ; i < width; i++ )
                  {
                      ST s0 = SUM[i] + Sp[i];
                      D[i] = saturate_cast<T>(s0);
                      SUM[i] = s0 - Sm[i];
                  }
              }
              dst += dststep;
          }
      }
  
      double scale;
      int sumCount;
      std::vector<ST> sum;
  };
  
  
  template<>
  struct ColumnSum<int, uchar> :
          public BaseColumnFilter
  {
      ColumnSum( int _ksize, int _anchor, double _scale ) :
          BaseColumnFilter()
      {
          ksize = _ksize;
          anchor = _anchor;
          scale = _scale;
          sumCount = 0;
      }
  
      virtual void reset() { sumCount = 0; }
  
      virtual void operator()(const uchar** src, uchar* dst, int dststep, int count, int width)
      {
          int i;
          int* SUM;
          bool haveScale = scale != 1;
          double _scale = scale;
  
          #if CV_SSE2
              bool haveSSE2 = checkHardwareSupport(CV_CPU_SSE2);
          #endif
  
          if( width != (int)sum.size() )
          {
              sum.resize(width);
              sumCount = 0;
          }
  
          SUM = &sum[0];
          if( sumCount == 0 )
          {
              memset((void*)SUM, 0, width*sizeof(int));
              for( ; sumCount < ksize - 1; sumCount++, src++ )
              {
                  const int* Sp = (const int*)src[0];
                  i = 0;
                  #if CV_SSE2
                  if(haveSSE2)
                  {
                      for( ; i <= width-4; i+=4 )
                      {
                          __m128i _sum = _mm_loadu_si128((const __m128i*)(SUM+i));
                          __m128i _sp = _mm_loadu_si128((const __m128i*)(Sp+i));
                          _mm_storeu_si128((__m128i*)(SUM+i),_mm_add_epi32(_sum, _sp));
                      }
                  }
                  #elif CV_NEON
                  for( ; i <= width - 4; i+=4 )
                      vst1q_s32(SUM + i, vaddq_s32(vld1q_s32(SUM + i), vld1q_s32(Sp + i)));
                  #endif
                  for( ; i < width; i++ )
                      SUM[i] += Sp[i];
              }
          }
          else
          {
              CV_Assert( sumCount == ksize-1 );
              src += ksize-1;
          }
  
          for( ; count--; src++ )
          {
              const int* Sp = (const int*)src[0];
              const int* Sm = (const int*)src[1-ksize];
              uchar* D = (uchar*)dst;
              if( haveScale )
              {
                  i = 0;
                  #if CV_SSE2
                  if(haveSSE2)
                  {
                      const __m128 scale4 = _mm_set1_ps((float)_scale);
                      for( ; i <= width-8; i+=8 )
                      {
                          __m128i _sm  = _mm_loadu_si128((const __m128i*)(Sm+i));
                          __m128i _sm1  = _mm_loadu_si128((const __m128i*)(Sm+i+4));
  
                          __m128i _s0  = _mm_add_epi32(_mm_loadu_si128((const __m128i*)(SUM+i)),
                                                       _mm_loadu_si128((const __m128i*)(Sp+i)));
                          __m128i _s01  = _mm_add_epi32(_mm_loadu_si128((const __m128i*)(SUM+i+4)),
                                                        _mm_loadu_si128((const __m128i*)(Sp+i+4)));
  
                          __m128i _s0T = _mm_cvtps_epi32(_mm_mul_ps(scale4, _mm_cvtepi32_ps(_s0)));
                          __m128i _s0T1 = _mm_cvtps_epi32(_mm_mul_ps(scale4, _mm_cvtepi32_ps(_s01)));
  
                          _s0T = _mm_packs_epi32(_s0T, _s0T1);
  
                          _mm_storel_epi64((__m128i*)(D+i), _mm_packus_epi16(_s0T, _s0T));
  
                          _mm_storeu_si128((__m128i*)(SUM+i), _mm_sub_epi32(_s0,_sm));
                          _mm_storeu_si128((__m128i*)(SUM+i+4),_mm_sub_epi32(_s01,_sm1));
                      }
                  }
                  #elif CV_NEON
                  float32x4_t v_scale = vdupq_n_f32((float)_scale);
                  for( ; i <= width-8; i+=8 )
                  {
                      int32x4_t v_s0 = vaddq_s32(vld1q_s32(SUM + i), vld1q_s32(Sp + i));
                      int32x4_t v_s01 = vaddq_s32(vld1q_s32(SUM + i + 4), vld1q_s32(Sp + i + 4));
  
                      uint32x4_t v_s0d = cv_vrndq_u32_f32(vmulq_f32(vcvtq_f32_s32(v_s0), v_scale));
                      uint32x4_t v_s01d = cv_vrndq_u32_f32(vmulq_f32(vcvtq_f32_s32(v_s01), v_scale));
  
                      uint16x8_t v_dst = vcombine_u16(vqmovn_u32(v_s0d), vqmovn_u32(v_s01d));
                      vst1_u8(D + i, vqmovn_u16(v_dst));
  
                      vst1q_s32(SUM + i, vsubq_s32(v_s0, vld1q_s32(Sm + i)));
                      vst1q_s32(SUM + i + 4, vsubq_s32(v_s01, vld1q_s32(Sm + i + 4)));
                  }
                  #endif
                  for( ; i < width; i++ )
                  {
                      int s0 = SUM[i] + Sp[i];
                      D[i] = saturate_cast<uchar>(s0*_scale);
                      SUM[i] = s0 - Sm[i];
                  }
              }
              else
              {
                  i = 0;
                  #if CV_SSE2
                  if(haveSSE2)
                  {
                      for( ; i <= width-8; i+=8 )
                      {
                          __m128i _sm  = _mm_loadu_si128((const __m128i*)(Sm+i));
                          __m128i _sm1  = _mm_loadu_si128((const __m128i*)(Sm+i+4));
  
                          __m128i _s0  = _mm_add_epi32(_mm_loadu_si128((const __m128i*)(SUM+i)),
                                                       _mm_loadu_si128((const __m128i*)(Sp+i)));
                          __m128i _s01  = _mm_add_epi32(_mm_loadu_si128((const __m128i*)(SUM+i+4)),
                                                        _mm_loadu_si128((const __m128i*)(Sp+i+4)));
  
                          __m128i _s0T = _mm_packs_epi32(_s0, _s01);
  
                          _mm_storel_epi64((__m128i*)(D+i), _mm_packus_epi16(_s0T, _s0T));
  
                          _mm_storeu_si128((__m128i*)(SUM+i), _mm_sub_epi32(_s0,_sm));
                          _mm_storeu_si128((__m128i*)(SUM+i+4),_mm_sub_epi32(_s01,_sm1));
                      }
                  }
                  #elif CV_NEON
                  for( ; i <= width-8; i+=8 )
                  {
                      int32x4_t v_s0 = vaddq_s32(vld1q_s32(SUM + i), vld1q_s32(Sp + i));
                      int32x4_t v_s01 = vaddq_s32(vld1q_s32(SUM + i + 4), vld1q_s32(Sp + i + 4));
  
                      uint16x8_t v_dst = vcombine_u16(vqmovun_s32(v_s0), vqmovun_s32(v_s01));
                      vst1_u8(D + i, vqmovn_u16(v_dst));
  
                      vst1q_s32(SUM + i, vsubq_s32(v_s0, vld1q_s32(Sm + i)));
                      vst1q_s32(SUM + i + 4, vsubq_s32(v_s01, vld1q_s32(Sm + i + 4)));
                  }
                  #endif
  
                  for( ; i < width; i++ )
                  {
                      int s0 = SUM[i] + Sp[i];
                      D[i] = saturate_cast<uchar>(s0);
                      SUM[i] = s0 - Sm[i];
                  }
              }
              dst += dststep;
          }
      }
  
      double scale;
      int sumCount;
      std::vector<int> sum;
  };
  
  template<>
  struct ColumnSum<int, short> :
          public BaseColumnFilter
  {
      ColumnSum( int _ksize, int _anchor, double _scale ) :
          BaseColumnFilter()
      {
          ksize = _ksize;
          anchor = _anchor;
          scale = _scale;
          sumCount = 0;
      }
  
      virtual void reset() { sumCount = 0; }
  
      virtual void operator()(const uchar** src, uchar* dst, int dststep, int count, int width)
      {
          int i;
          int* SUM;
          bool haveScale = scale != 1;
          double _scale = scale;
  
          #if CV_SSE2
              bool haveSSE2 = checkHardwareSupport(CV_CPU_SSE2);
          #endif
  
          if( width != (int)sum.size() )
          {
              sum.resize(width);
              sumCount = 0;
          }
          SUM = &sum[0];
          if( sumCount == 0 )
          {
              memset((void*)SUM, 0, width*sizeof(int));
              for( ; sumCount < ksize - 1; sumCount++, src++ )
              {
                  const int* Sp = (const int*)src[0];
                  i = 0;
                  #if CV_SSE2
                  if(haveSSE2)
                  {
                      for( ; i <= width-4; i+=4 )
                      {
                          __m128i _sum = _mm_loadu_si128((const __m128i*)(SUM+i));
                          __m128i _sp = _mm_loadu_si128((const __m128i*)(Sp+i));
                          _mm_storeu_si128((__m128i*)(SUM+i),_mm_add_epi32(_sum, _sp));
                      }
                  }
                  #elif CV_NEON
                  for( ; i <= width - 4; i+=4 )
                      vst1q_s32(SUM + i, vaddq_s32(vld1q_s32(SUM + i), vld1q_s32(Sp + i)));
                  #endif
                  for( ; i < width; i++ )
                      SUM[i] += Sp[i];
              }
          }
          else
          {
              CV_Assert( sumCount == ksize-1 );
              src += ksize-1;
          }
  
          for( ; count--; src++ )
          {
              const int* Sp = (const int*)src[0];
              const int* Sm = (const int*)src[1-ksize];
              short* D = (short*)dst;
              if( haveScale )
              {
                  i = 0;
                  #if CV_SSE2
                  if(haveSSE2)
                  {
                      const __m128 scale4 = _mm_set1_ps((float)_scale);
                      for( ; i <= width-8; i+=8 )
                      {
                          __m128i _sm   = _mm_loadu_si128((const __m128i*)(Sm+i));
                          __m128i _sm1  = _mm_loadu_si128((const __m128i*)(Sm+i+4));
  
                          __m128i _s0  = _mm_add_epi32(_mm_loadu_si128((const __m128i*)(SUM+i)),
                                                       _mm_loadu_si128((const __m128i*)(Sp+i)));
                          __m128i _s01  = _mm_add_epi32(_mm_loadu_si128((const __m128i*)(SUM+i+4)),
                                                        _mm_loadu_si128((const __m128i*)(Sp+i+4)));
  
                          __m128i _s0T  = _mm_cvtps_epi32(_mm_mul_ps(scale4, _mm_cvtepi32_ps(_s0)));
                          __m128i _s0T1 = _mm_cvtps_epi32(_mm_mul_ps(scale4, _mm_cvtepi32_ps(_s01)));
  
                          _mm_storeu_si128((__m128i*)(D+i), _mm_packs_epi32(_s0T, _s0T1));
  
                          _mm_storeu_si128((__m128i*)(SUM+i),_mm_sub_epi32(_s0,_sm));
                          _mm_storeu_si128((__m128i*)(SUM+i+4), _mm_sub_epi32(_s01,_sm1));
                      }
                  }
                  #elif CV_NEON
                  float32x4_t v_scale = vdupq_n_f32((float)_scale);
                  for( ; i <= width-8; i+=8 )
                  {
                      int32x4_t v_s0 = vaddq_s32(vld1q_s32(SUM + i), vld1q_s32(Sp + i));
                      int32x4_t v_s01 = vaddq_s32(vld1q_s32(SUM + i + 4), vld1q_s32(Sp + i + 4));
  
                      int32x4_t v_s0d = cv_vrndq_s32_f32(vmulq_f32(vcvtq_f32_s32(v_s0), v_scale));
                      int32x4_t v_s01d = cv_vrndq_s32_f32(vmulq_f32(vcvtq_f32_s32(v_s01), v_scale));
                      vst1q_s16(D + i, vcombine_s16(vqmovn_s32(v_s0d), vqmovn_s32(v_s01d)));
  
                      vst1q_s32(SUM + i, vsubq_s32(v_s0, vld1q_s32(Sm + i)));
                      vst1q_s32(SUM + i + 4, vsubq_s32(v_s01, vld1q_s32(Sm + i + 4)));
                  }
                  #endif
                  for( ; i < width; i++ )
                  {
                      int s0 = SUM[i] + Sp[i];
                      D[i] = saturate_cast<short>(s0*_scale);
                      SUM[i] = s0 - Sm[i];
                  }
              }
              else
              {
                  i = 0;
                  #if CV_SSE2
                  if(haveSSE2)
                  {
                      for( ; i <= width-8; i+=8 )
                      {
  
                          __m128i _sm  = _mm_loadu_si128((const __m128i*)(Sm+i));
                          __m128i _sm1  = _mm_loadu_si128((const __m128i*)(Sm+i+4));
  
                          __m128i _s0  = _mm_add_epi32(_mm_loadu_si128((const __m128i*)(SUM+i)),
                                                       _mm_loadu_si128((const __m128i*)(Sp+i)));
                          __m128i _s01  = _mm_add_epi32(_mm_loadu_si128((const __m128i*)(SUM+i+4)),
                                                        _mm_loadu_si128((const __m128i*)(Sp+i+4)));
  
                          _mm_storeu_si128((__m128i*)(D+i), _mm_packs_epi32(_s0, _s01));
  
                          _mm_storeu_si128((__m128i*)(SUM+i), _mm_sub_epi32(_s0,_sm));
                          _mm_storeu_si128((__m128i*)(SUM+i+4),_mm_sub_epi32(_s01,_sm1));
                      }
                  }
                  #elif CV_NEON
                  for( ; i <= width-8; i+=8 )
                  {
                      int32x4_t v_s0 = vaddq_s32(vld1q_s32(SUM + i), vld1q_s32(Sp + i));
                      int32x4_t v_s01 = vaddq_s32(vld1q_s32(SUM + i + 4), vld1q_s32(Sp + i + 4));
  
                      vst1q_s16(D + i, vcombine_s16(vqmovn_s32(v_s0), vqmovn_s32(v_s01)));
  
                      vst1q_s32(SUM + i, vsubq_s32(v_s0, vld1q_s32(Sm + i)));
                      vst1q_s32(SUM + i + 4, vsubq_s32(v_s01, vld1q_s32(Sm + i + 4)));
                  }
                  #endif
  
                  for( ; i < width; i++ )
                  {
                      int s0 = SUM[i] + Sp[i];
                      D[i] = saturate_cast<short>(s0);
                      SUM[i] = s0 - Sm[i];
                  }
              }
              dst += dststep;
          }
      }
  
      double scale;
      int sumCount;
      std::vector<int> sum;
  };
  
  
  template<>
  struct ColumnSum<int, ushort> :
          public BaseColumnFilter
  {
      ColumnSum( int _ksize, int _anchor, double _scale ) :
          BaseColumnFilter()
      {
          ksize = _ksize;
          anchor = _anchor;
          scale = _scale;
          sumCount = 0;
      }
  
      virtual void reset() { sumCount = 0; }
  
      virtual void operator()(const uchar** src, uchar* dst, int dststep, int count, int width)
      {
          int i;
          int* SUM;
          bool haveScale = scale != 1;
          double _scale = scale;
          #if CV_SSE2
                  bool haveSSE2 =  checkHardwareSupport(CV_CPU_SSE2);
          #endif
  
          if( width != (int)sum.size() )
          {
              sum.resize(width);
              sumCount = 0;
          }
          SUM = &sum[0];
          if( sumCount == 0 )
          {
              memset((void*)SUM, 0, width*sizeof(int));
              for( ; sumCount < ksize - 1; sumCount++, src++ )
              {
                  const int* Sp = (const int*)src[0];
                  i = 0;
                  #if CV_SSE2
                  if(haveSSE2)
                  {
                      for( ; i < width-4; i+=4 )
                      {
                          __m128i _sum = _mm_loadu_si128((const __m128i*)(SUM+i));
                          __m128i _sp = _mm_loadu_si128((const __m128i*)(Sp+i));
                          _mm_storeu_si128((__m128i*)(SUM+i), _mm_add_epi32(_sum, _sp));
                      }
                  }
                  #elif CV_NEON
                  for( ; i <= width - 4; i+=4 )
                      vst1q_s32(SUM + i, vaddq_s32(vld1q_s32(SUM + i), vld1q_s32(Sp + i)));
                  #endif
                  for( ; i < width; i++ )
                      SUM[i] += Sp[i];
              }
          }
          else
          {
              CV_Assert( sumCount == ksize-1 );
              src += ksize-1;
          }
  
          for( ; count--; src++ )
          {
              const int* Sp = (const int*)src[0];
              const int* Sm = (const int*)src[1-ksize];
              ushort* D = (ushort*)dst;
              if( haveScale )
              {
                  i = 0;
                  #if CV_SSE2
                  if(haveSSE2)
                  {
                      const __m128 scale4 = _mm_set1_ps((float)_scale);
                      const __m128i delta0 = _mm_set1_epi32(0x8000);
                      const __m128i delta1 = _mm_set1_epi32(0x80008000);
  
                      for( ; i < width-4; i+=4)
                      {
                          __m128i _sm   = _mm_loadu_si128((const __m128i*)(Sm+i));
                          __m128i _s0   = _mm_add_epi32(_mm_loadu_si128((const __m128i*)(SUM+i)),
                                                        _mm_loadu_si128((const __m128i*)(Sp+i)));
  
                          __m128i _res = _mm_cvtps_epi32(_mm_mul_ps(scale4, _mm_cvtepi32_ps(_s0)));
  
                          _res = _mm_sub_epi32(_res, delta0);
                          _res = _mm_add_epi16(_mm_packs_epi32(_res, _res), delta1);
  
                          _mm_storel_epi64((__m128i*)(D+i), _res);
                          _mm_storeu_si128((__m128i*)(SUM+i), _mm_sub_epi32(_s0,_sm));
                      }
                  }
                  #elif CV_NEON
                  float32x4_t v_scale = vdupq_n_f32((float)_scale);
                  for( ; i <= width-8; i+=8 )
                  {
                      int32x4_t v_s0 = vaddq_s32(vld1q_s32(SUM + i), vld1q_s32(Sp + i));
                      int32x4_t v_s01 = vaddq_s32(vld1q_s32(SUM + i + 4), vld1q_s32(Sp + i + 4));
  
                      uint32x4_t v_s0d = cv_vrndq_u32_f32(vmulq_f32(vcvtq_f32_s32(v_s0), v_scale));
                      uint32x4_t v_s01d = cv_vrndq_u32_f32(vmulq_f32(vcvtq_f32_s32(v_s01), v_scale));
                      vst1q_u16(D + i, vcombine_u16(vqmovn_u32(v_s0d), vqmovn_u32(v_s01d)));
  
                      vst1q_s32(SUM + i, vsubq_s32(v_s0, vld1q_s32(Sm + i)));
                      vst1q_s32(SUM + i + 4, vsubq_s32(v_s01, vld1q_s32(Sm + i + 4)));
                  }
                  #endif
                  for( ; i < width; i++ )
                  {
                      int s0 = SUM[i] + Sp[i];
                      D[i] = saturate_cast<ushort>(s0*_scale);
                      SUM[i] = s0 - Sm[i];
                  }
              }
              else
              {
                  i = 0;
                  #if  CV_SSE2
                  if(haveSSE2)
                  {
                      const __m128i delta0 = _mm_set1_epi32(0x8000);
                      const __m128i delta1 = _mm_set1_epi32(0x80008000);
  
                      for( ; i < width-4; i+=4 )
                      {
                          __m128i _sm   = _mm_loadu_si128((const __m128i*)(Sm+i));
                          __m128i _s0   = _mm_add_epi32(_mm_loadu_si128((const __m128i*)(SUM+i)),
                                                        _mm_loadu_si128((const __m128i*)(Sp+i)));
  
                          __m128i _res = _mm_sub_epi32(_s0, delta0);
                          _res = _mm_add_epi16(_mm_packs_epi32(_res, _res), delta1);
  
                          _mm_storel_epi64((__m128i*)(D+i), _res);
                          _mm_storeu_si128((__m128i*)(SUM+i), _mm_sub_epi32(_s0,_sm));
                      }
                  }
                  #elif CV_NEON
                  for( ; i <= width-8; i+=8 )
                  {
                      int32x4_t v_s0 = vaddq_s32(vld1q_s32(SUM + i), vld1q_s32(Sp + i));
                      int32x4_t v_s01 = vaddq_s32(vld1q_s32(SUM + i + 4), vld1q_s32(Sp + i + 4));
  
                      vst1q_u16(D + i, vcombine_u16(vqmovun_s32(v_s0), vqmovun_s32(v_s01)));
  
                      vst1q_s32(SUM + i, vsubq_s32(v_s0, vld1q_s32(Sm + i)));
                      vst1q_s32(SUM + i + 4, vsubq_s32(v_s01, vld1q_s32(Sm + i + 4)));
                  }
                  #endif
  
                  for( ; i < width; i++ )
                  {
                      int s0 = SUM[i] + Sp[i];
                      D[i] = saturate_cast<ushort>(s0);
                      SUM[i] = s0 - Sm[i];
                  }
              }
              dst += dststep;
          }
      }
  
      double scale;
      int sumCount;
      std::vector<int> sum;
  };
  
  template<>
  struct ColumnSum<int, float> :
          public BaseColumnFilter
  {
      ColumnSum( int _ksize, int _anchor, double _scale ) :
          BaseColumnFilter()
      {
          ksize = _ksize;
          anchor = _anchor;
          scale = _scale;
          sumCount = 0;
      }
  
      virtual void reset() { sumCount = 0; }
  
      virtual void operator()(const uchar** src, uchar* dst, int dststep, int count, int width)
      {
          int i;
          int* SUM;
          bool haveScale = scale != 1;
          double _scale = scale;
  
          #if CV_SSE2
          bool haveSSE2 =  checkHardwareSupport(CV_CPU_SSE2);
          #endif
  
          if( width != (int)sum.size() )
          {
              sum.resize(width);
              sumCount = 0;
          }
  
          SUM = &sum[0];
          if( sumCount == 0 )
          {
              memset((void *)SUM, 0, sizeof(int) * width);
  
              for( ; sumCount < ksize - 1; sumCount++, src++ )
              {
                  const int* Sp = (const int*)src[0];
                  i = 0;
  
                  #if CV_SSE2
                  if(haveSSE2)
                  {
                      for( ; i < width-4; i+=4 )
                      {
                          __m128i _sum = _mm_loadu_si128((const __m128i*)(SUM+i));
                          __m128i _sp = _mm_loadu_si128((const __m128i*)(Sp+i));
                          _mm_storeu_si128((__m128i*)(SUM+i), _mm_add_epi32(_sum, _sp));
                      }
                  }
                  #elif CV_NEON
                  for( ; i <= width - 4; i+=4 )
                      vst1q_s32(SUM + i, vaddq_s32(vld1q_s32(SUM + i), vld1q_s32(Sp + i)));
                  #endif
  
                  for( ; i < width; i++ )
                      SUM[i] += Sp[i];
              }
          }
          else
          {
              CV_Assert( sumCount == ksize-1 );
              src += ksize-1;
          }
  
          for( ; count--; src++ )
          {
              const int * Sp = (const int*)src[0];
              const int * Sm = (const int*)src[1-ksize];
              float* D = (float*)dst;
              if( haveScale )
              {
                  i = 0;
  
                  #if CV_SSE2
                  if(haveSSE2)
                  {
                      const __m128 scale4 = _mm_set1_ps((float)_scale);
  
                      for( ; i < width-4; i+=4)
                      {
                          __m128i _sm   = _mm_loadu_si128((const __m128i*)(Sm+i));
                          __m128i _s0   = _mm_add_epi32(_mm_loadu_si128((const __m128i*)(SUM+i)),
                                                        _mm_loadu_si128((const __m128i*)(Sp+i)));
  
                          _mm_storeu_ps(D+i, _mm_mul_ps(scale4, _mm_cvtepi32_ps(_s0)));
                          _mm_storeu_si128((__m128i*)(SUM+i), _mm_sub_epi32(_s0,_sm));
                      }
                  }
                  #elif CV_NEON
                  float32x4_t v_scale = vdupq_n_f32((float)_scale);
                  for( ; i <= width-8; i+=8 )
                  {
                      int32x4_t v_s0 = vaddq_s32(vld1q_s32(SUM + i), vld1q_s32(Sp + i));
                      int32x4_t v_s01 = vaddq_s32(vld1q_s32(SUM + i + 4), vld1q_s32(Sp + i + 4));
  
                      vst1q_f32(D + i, vmulq_f32(vcvtq_f32_s32(v_s0), v_scale));
                      vst1q_f32(D + i + 4, vmulq_f32(vcvtq_f32_s32(v_s01), v_scale));
  
                      vst1q_s32(SUM + i, vsubq_s32(v_s0, vld1q_s32(Sm + i)));
                      vst1q_s32(SUM + i + 4, vsubq_s32(v_s01, vld1q_s32(Sm + i + 4)));
                  }
                  #endif
  
                  for( ; i < width; i++ )
                  {
                      int s0 = SUM[i] + Sp[i];
                      D[i] = (float)(s0*_scale);
                      SUM[i] = s0 - Sm[i];
                  }
              }
              else
              {
                  i = 0;
  
                  #if CV_SSE2
                  if(haveSSE2)
                  {
                      for( ; i < width-4; i+=4)
                      {
                          __m128i _sm   = _mm_loadu_si128((const __m128i*)(Sm+i));
                          __m128i _s0   = _mm_add_epi32(_mm_loadu_si128((const __m128i*)(SUM+i)),
                                                        _mm_loadu_si128((const __m128i*)(Sp+i)));
  
                          _mm_storeu_ps(D+i, _mm_cvtepi32_ps(_s0));
                          _mm_storeu_si128((__m128i*)(SUM+i), _mm_sub_epi32(_s0,_sm));
                      }
                  }
                  #elif CV_NEON
                  for( ; i <= width-8; i+=8 )
                  {
                      int32x4_t v_s0 = vaddq_s32(vld1q_s32(SUM + i), vld1q_s32(Sp + i));
                      int32x4_t v_s01 = vaddq_s32(vld1q_s32(SUM + i + 4), vld1q_s32(Sp + i + 4));
  
                      vst1q_f32(D + i, vcvtq_f32_s32(v_s0));
                      vst1q_f32(D + i + 4, vcvtq_f32_s32(v_s01));
  
                      vst1q_s32(SUM + i, vsubq_s32(v_s0, vld1q_s32(Sm + i)));
                      vst1q_s32(SUM + i + 4, vsubq_s32(v_s01, vld1q_s32(Sm + i + 4)));
                  }
                  #endif
  
                  for( ; i < width; i++ )
                  {
                      int s0 = SUM[i] + Sp[i];
                      D[i] = (float)(s0);
                      SUM[i] = s0 - Sm[i];
                  }
              }
              dst += dststep;
          }
      }
  
      double scale;
      int sumCount;
      std::vector<int> sum;
  };
  
  #ifdef HAVE_OPENCL
  
  #define DIVUP(total, grain) ((total + grain - 1) / (grain))
  #define ROUNDUP(sz, n)      ((sz) + (n) - 1 - (((sz) + (n) - 1) % (n)))
  
  static bool ocl_boxFilter( InputArray _src, OutputArray _dst, int ddepth,
                             Size ksize, Point anchor, int borderType, bool normalize, bool sqr = false )
  {
      const ocl::Device & dev = ocl::Device::getDefault();
      int type = _src.type(), sdepth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type), esz = CV_ELEM_SIZE(type);
      bool doubleSupport = dev.doubleFPConfig() > 0;
  
      if (ddepth < 0)
          ddepth = sdepth;
  
      if (cn > 4 || (!doubleSupport && (sdepth == CV_64F || ddepth == CV_64F)) ||
          _src.offset() % esz != 0 || _src.step() % esz != 0)
          return false;
  
      if (anchor.x < 0)
          anchor.x = ksize.width / 2;
      if (anchor.y < 0)
          anchor.y = ksize.height / 2;
  
      int computeUnits = ocl::Device::getDefault().maxComputeUnits();
      float alpha = 1.0f / (ksize.height * ksize.width);
      Size size = _src.size(), wholeSize;
      bool isolated = (borderType & BORDER_ISOLATED) != 0;
      borderType &= ~BORDER_ISOLATED;
      int wdepth = std::max(CV_32F, std::max(ddepth, sdepth)),
          wtype = CV_MAKE_TYPE(wdepth, cn), dtype = CV_MAKE_TYPE(ddepth, cn);
  
      const char * const borderMap[] = { "BORDER_CONSTANT", "BORDER_REPLICATE", "BORDER_REFLECT", 0, "BORDER_REFLECT_101" };
      size_t globalsize[2] = { size.width, size.height };
      size_t localsize_general[2] = { 0, 1 }, * localsize = NULL;
  
      UMat src = _src.getUMat();
      if (!isolated)
      {
          Point ofs;
          src.locateROI(wholeSize, ofs);
      }
  
      int h = isolated ? size.height : wholeSize.height;
      int w = isolated ? size.width : wholeSize.width;
  
      size_t maxWorkItemSizes[32];
      ocl::Device::getDefault().maxWorkItemSizes(maxWorkItemSizes);
      int tryWorkItems = (int)maxWorkItemSizes[0];
  
      ocl::Kernel kernel;
  
      if (dev.isIntel() && !(dev.type() & ocl::Device::TYPE_CPU) &&
          ((ksize.width < 5 && ksize.height < 5 && esz <= 4) ||
           (ksize.width == 5 && ksize.height == 5 && cn == 1)))
      {
          if (w < ksize.width || h < ksize.height)
              return false;
  
          // Figure out what vector size to use for loading the pixels.
          int pxLoadNumPixels = cn != 1 || size.width % 4 ? 1 : 4;
          int pxLoadVecSize = cn * pxLoadNumPixels;
  
          // Figure out how many pixels per work item to compute in X and Y
          // directions.  Too many and we run out of registers.
          int pxPerWorkItemX = 1, pxPerWorkItemY = 1;
          if (cn <= 2 && ksize.width <= 4 && ksize.height <= 4)
          {
              pxPerWorkItemX = size.width % 8 ? size.width % 4 ? size.width % 2 ? 1 : 2 : 4 : 8;
              pxPerWorkItemY = size.height % 2 ? 1 : 2;
          }
          else if (cn < 4 || (ksize.width <= 4 && ksize.height <= 4))
          {
              pxPerWorkItemX = size.width % 2 ? 1 : 2;
              pxPerWorkItemY = size.height % 2 ? 1 : 2;
          }
          globalsize[0] = size.width / pxPerWorkItemX;
          globalsize[1] = size.height / pxPerWorkItemY;
  
          // Need some padding in the private array for pixels
          int privDataWidth = ROUNDUP(pxPerWorkItemX + ksize.width - 1, pxLoadNumPixels);
  
          // Make the global size a nice round number so the runtime can pick
          // from reasonable choices for the workgroup size
          const int wgRound = 256;
          globalsize[0] = ROUNDUP(globalsize[0], wgRound);
  
          char build_options[1024], cvt[2][40];
          sprintf(build_options, "-D cn=%d "
                  "-D ANCHOR_X=%d -D ANCHOR_Y=%d -D KERNEL_SIZE_X=%d -D KERNEL_SIZE_Y=%d "
                  "-D PX_LOAD_VEC_SIZE=%d -D PX_LOAD_NUM_PX=%d "
                  "-D PX_PER_WI_X=%d -D PX_PER_WI_Y=%d -D PRIV_DATA_WIDTH=%d -D %s -D %s "
                  "-D PX_LOAD_X_ITERATIONS=%d -D PX_LOAD_Y_ITERATIONS=%d "
                  "-D srcT=%s -D srcT1=%s -D dstT=%s -D dstT1=%s -D WT=%s -D WT1=%s "
                  "-D convertToWT=%s -D convertToDstT=%s%s%s -D PX_LOAD_FLOAT_VEC_CONV=convert_%s -D OP_BOX_FILTER",
                  cn, anchor.x, anchor.y, ksize.width, ksize.height,
                  pxLoadVecSize, pxLoadNumPixels,
                  pxPerWorkItemX, pxPerWorkItemY, privDataWidth, borderMap[borderType],
                  isolated ? "BORDER_ISOLATED" : "NO_BORDER_ISOLATED",
                  privDataWidth / pxLoadNumPixels, pxPerWorkItemY + ksize.height - 1,
                  ocl::typeToStr(type), ocl::typeToStr(sdepth), ocl::typeToStr(dtype),
                  ocl::typeToStr(ddepth), ocl::typeToStr(wtype), ocl::typeToStr(wdepth),
                  ocl::convertTypeStr(sdepth, wdepth, cn, cvt[0]),
                  ocl::convertTypeStr(wdepth, ddepth, cn, cvt[1]),
                  normalize ? " -D NORMALIZE" : "", sqr ? " -D SQR" : "",
                  ocl::typeToStr(CV_MAKE_TYPE(wdepth, pxLoadVecSize)) //PX_LOAD_FLOAT_VEC_CONV
                  );
  
  
          if (!kernel.create("filterSmall", cv::ocl::imgproc::filterSmall_oclsrc, build_options))
              return false;
      }
      else
      {
          localsize = localsize_general;
          for ( ; ; )
          {
              int BLOCK_SIZE_X = tryWorkItems, BLOCK_SIZE_Y = std::min(ksize.height * 10, size.height);
  
              while (BLOCK_SIZE_X > 32 && BLOCK_SIZE_X >= ksize.width * 2 && BLOCK_SIZE_X > size.width * 2)
                  BLOCK_SIZE_X /= 2;
              while (BLOCK_SIZE_Y < BLOCK_SIZE_X / 8 && BLOCK_SIZE_Y * computeUnits * 32 < size.height)
                  BLOCK_SIZE_Y *= 2;
  
              if (ksize.width > BLOCK_SIZE_X || w < ksize.width || h < ksize.height)
                  return false;
  
              char cvt[2][50];
              String opts = format("-D LOCAL_SIZE_X=%d -D BLOCK_SIZE_Y=%d -D ST=%s -D DT=%s -D WT=%s -D convertToDT=%s -D convertToWT=%s"
                                   " -D ANCHOR_X=%d -D ANCHOR_Y=%d -D KERNEL_SIZE_X=%d -D KERNEL_SIZE_Y=%d -D %s%s%s%s%s"
                                   " -D ST1=%s -D DT1=%s -D cn=%d",
                                   BLOCK_SIZE_X, BLOCK_SIZE_Y, ocl::typeToStr(type), ocl::typeToStr(CV_MAKE_TYPE(ddepth, cn)),
                                   ocl::typeToStr(CV_MAKE_TYPE(wdepth, cn)),
                                   ocl::convertTypeStr(wdepth, ddepth, cn, cvt[0]),
                                   ocl::convertTypeStr(sdepth, wdepth, cn, cvt[1]),
                                   anchor.x, anchor.y, ksize.width, ksize.height, borderMap[borderType],
                                   isolated ? " -D BORDER_ISOLATED" : "", doubleSupport ? " -D DOUBLE_SUPPORT" : "",
                                   normalize ? " -D NORMALIZE" : "", sqr ? " -D SQR" : "",
                                   ocl::typeToStr(sdepth), ocl::typeToStr(ddepth), cn);
  
              localsize[0] = BLOCK_SIZE_X;
              globalsize[0] = DIVUP(size.width, BLOCK_SIZE_X - (ksize.width - 1)) * BLOCK_SIZE_X;
              globalsize[1] = DIVUP(size.height, BLOCK_SIZE_Y);
  
              kernel.create("boxFilter", cv::ocl::imgproc::boxFilter_oclsrc, opts);
              if (kernel.empty())
                  return false;
  
              size_t kernelWorkGroupSize = kernel.workGroupSize();
              if (localsize[0] <= kernelWorkGroupSize)
                  break;
              if (BLOCK_SIZE_X < (int)kernelWorkGroupSize)
                  return false;
  
              tryWorkItems = (int)kernelWorkGroupSize;
          }
      }
  
      _dst.create(size, CV_MAKETYPE(ddepth, cn));
      UMat dst = _dst.getUMat();
  
      int idxArg = kernel.set(0, ocl::KernelArg::PtrReadOnly(src));
      idxArg = kernel.set(idxArg, (int)src.step);
      int srcOffsetX = (int)((src.offset % src.step) / src.elemSize());
      int srcOffsetY = (int)(src.offset / src.step);
      int srcEndX = isolated ? srcOffsetX + size.width : wholeSize.width;
      int srcEndY = isolated ? srcOffsetY + size.height : wholeSize.height;
      idxArg = kernel.set(idxArg, srcOffsetX);
      idxArg = kernel.set(idxArg, srcOffsetY);
      idxArg = kernel.set(idxArg, srcEndX);
      idxArg = kernel.set(idxArg, srcEndY);
      idxArg = kernel.set(idxArg, ocl::KernelArg::WriteOnly(dst));
      if (normalize)
          idxArg = kernel.set(idxArg, (float)alpha);
  
      return kernel.run(2, globalsize, localsize, false);
  }
  
  #undef ROUNDUP
  
  #endif
  
  }
  
  
  cv::Ptr<cv::BaseRowFilter> cv::getRowSumFilter(int srcType, int sumType, int ksize, int anchor)
  {
      int sdepth = CV_MAT_DEPTH(srcType), ddepth = CV_MAT_DEPTH(sumType);
      CV_Assert( CV_MAT_CN(sumType) == CV_MAT_CN(srcType) );
  
      if( anchor < 0 )
          anchor = ksize/2;
  
      if( sdepth == CV_8U && ddepth == CV_32S )
          return makePtr<RowSum<uchar, int> >(ksize, anchor);
      if( sdepth == CV_8U && ddepth == CV_64F )
          return makePtr<RowSum<uchar, double> >(ksize, anchor);
      if( sdepth == CV_16U && ddepth == CV_32S )
          return makePtr<RowSum<ushort, int> >(ksize, anchor);
      if( sdepth == CV_16U && ddepth == CV_64F )
          return makePtr<RowSum<ushort, double> >(ksize, anchor);
      if( sdepth == CV_16S && ddepth == CV_32S )
          return makePtr<RowSum<short, int> >(ksize, anchor);
      if( sdepth == CV_32S && ddepth == CV_32S )
          return makePtr<RowSum<int, int> >(ksize, anchor);
      if( sdepth == CV_16S && ddepth == CV_64F )
          return makePtr<RowSum<short, double> >(ksize, anchor);
      if( sdepth == CV_32F && ddepth == CV_64F )
          return makePtr<RowSum<float, double> >(ksize, anchor);
      if( sdepth == CV_64F && ddepth == CV_64F )
          return makePtr<RowSum<double, double> >(ksize, anchor);
  
      CV_Error_( CV_StsNotImplemented,
          ("Unsupported combination of source format (=%d), and buffer format (=%d)",
          srcType, sumType));
  
      return Ptr<BaseRowFilter>();
  }
  
  
  cv::Ptr<cv::BaseColumnFilter> cv::getColumnSumFilter(int sumType, int dstType, int ksize,
                                                       int anchor, double scale)
  {
      int sdepth = CV_MAT_DEPTH(sumType), ddepth = CV_MAT_DEPTH(dstType);
      CV_Assert( CV_MAT_CN(sumType) == CV_MAT_CN(dstType) );
  
      if( anchor < 0 )
          anchor = ksize/2;
  
      if( ddepth == CV_8U && sdepth == CV_32S )
          return makePtr<ColumnSum<int, uchar> >(ksize, anchor, scale);
      if( ddepth == CV_8U && sdepth == CV_64F )
          return makePtr<ColumnSum<double, uchar> >(ksize, anchor, scale);
      if( ddepth == CV_16U && sdepth == CV_32S )
          return makePtr<ColumnSum<int, ushort> >(ksize, anchor, scale);
      if( ddepth == CV_16U && sdepth == CV_64F )
          return makePtr<ColumnSum<double, ushort> >(ksize, anchor, scale);
      if( ddepth == CV_16S && sdepth == CV_32S )
          return makePtr<ColumnSum<int, short> >(ksize, anchor, scale);
      if( ddepth == CV_16S && sdepth == CV_64F )
          return makePtr<ColumnSum<double, short> >(ksize, anchor, scale);
      if( ddepth == CV_32S && sdepth == CV_32S )
          return makePtr<ColumnSum<int, int> >(ksize, anchor, scale);
      if( ddepth == CV_32F && sdepth == CV_32S )
          return makePtr<ColumnSum<int, float> >(ksize, anchor, scale);
      if( ddepth == CV_32F && sdepth == CV_64F )
          return makePtr<ColumnSum<double, float> >(ksize, anchor, scale);
      if( ddepth == CV_64F && sdepth == CV_32S )
          return makePtr<ColumnSum<int, double> >(ksize, anchor, scale);
      if( ddepth == CV_64F && sdepth == CV_64F )
          return makePtr<ColumnSum<double, double> >(ksize, anchor, scale);
  
      CV_Error_( CV_StsNotImplemented,
          ("Unsupported combination of sum format (=%d), and destination format (=%d)",
          sumType, dstType));
  
      return Ptr<BaseColumnFilter>();
  }
  
  
  cv::Ptr<cv::FilterEngine> cv::createBoxFilter( int srcType, int dstType, Size ksize,
                      Point anchor, bool normalize, int borderType )
  {
      int sdepth = CV_MAT_DEPTH(srcType);
      int cn = CV_MAT_CN(srcType), sumType = CV_64F;
      if( sdepth <= CV_32S && (!normalize ||
          ksize.width*ksize.height <= (sdepth == CV_8U ? (1<<23) :
              sdepth == CV_16U ? (1 << 15) : (1 << 16))) )
          sumType = CV_32S;
      sumType = CV_MAKETYPE( sumType, cn );
  
      Ptr<BaseRowFilter> rowFilter = getRowSumFilter(srcType, sumType, ksize.width, anchor.x );
      Ptr<BaseColumnFilter> columnFilter = getColumnSumFilter(sumType,
          dstType, ksize.height, anchor.y, normalize ? 1./(ksize.width*ksize.height) : 1);
  
      return makePtr<FilterEngine>(Ptr<BaseFilter>(), rowFilter, columnFilter,
             srcType, dstType, sumType, borderType );
  }
  
  
  void cv::boxFilter( InputArray _src, OutputArray _dst, int ddepth,
                  Size ksize, Point anchor,
                  bool normalize, int borderType )
  {
      CV_OCL_RUN(_dst.isUMat(), ocl_boxFilter(_src, _dst, ddepth, ksize, anchor, borderType, normalize))
  
      Mat src = _src.getMat();
      int stype = src.type(), sdepth = CV_MAT_DEPTH(stype), cn = CV_MAT_CN(stype);
      if( ddepth < 0 )
          ddepth = sdepth;
      _dst.create( src.size(), CV_MAKETYPE(ddepth, cn) );
      Mat dst = _dst.getMat();
      if( borderType != BORDER_CONSTANT && normalize && (borderType & BORDER_ISOLATED) != 0 )
      {
          if( src.rows == 1 )
              ksize.height = 1;
          if( src.cols == 1 )
              ksize.width = 1;
      }
  #ifdef HAVE_TEGRA_OPTIMIZATION
      if ( tegra::box(src, dst, ksize, anchor, normalize, borderType) )
          return;
  #endif
  
  #if defined(HAVE_IPP)
      CV_IPP_CHECK()
      {
          int ippBorderType = borderType & ~BORDER_ISOLATED;
          Point ocvAnchor, ippAnchor;
          ocvAnchor.x = anchor.x < 0 ? ksize.width / 2 : anchor.x;
          ocvAnchor.y = anchor.y < 0 ? ksize.height / 2 : anchor.y;
          ippAnchor.x = ksize.width / 2 - (ksize.width % 2 == 0 ? 1 : 0);
          ippAnchor.y = ksize.height / 2 - (ksize.height % 2 == 0 ? 1 : 0);
  
          if (normalize && !src.isSubmatrix() && ddepth == sdepth &&
              (/*ippBorderType == BORDER_REPLICATE ||*/ /* returns ippStsStepErr: Step value is not valid */
               ippBorderType == BORDER_CONSTANT) && ocvAnchor == ippAnchor &&
               dst.cols != ksize.width && dst.rows != ksize.height) // returns ippStsMaskSizeErr: mask has an illegal value
          {
              Ipp32s bufSize = 0;
              IppiSize roiSize = { dst.cols, dst.rows }, maskSize = { ksize.width, ksize.height };
  
  #define IPP_FILTER_BOX_BORDER(ippType, ippDataType, flavor) \
              do \
              { \
                  if (ippiFilterBoxBorderGetBufferSize(roiSize, maskSize, ippDataType, cn, &bufSize) >= 0) \
                  { \
                      Ipp8u * buffer = ippsMalloc_8u(bufSize); \
                      ippType borderValue[4] = { 0, 0, 0, 0 }; \
                      ippBorderType = ippBorderType == BORDER_CONSTANT ? ippBorderConst : ippBorderRepl; \
                      IppStatus status = ippiFilterBoxBorder_##flavor(src.ptr<ippType>(), (int)src.step, dst.ptr<ippType>(), \
                                                                      (int)dst.step, roiSize, maskSize, \
                                                                      (IppiBorderType)ippBorderType, borderValue, buffer); \
                      ippsFree(buffer); \
                      if (status >= 0) \
                      { \
                          CV_IMPL_ADD(CV_IMPL_IPP); \
                          return; \
                      } \
                  } \
                  setIppErrorStatus(); \
              } while ((void)0, 0)
  
              if (stype == CV_8UC1)
                  IPP_FILTER_BOX_BORDER(Ipp8u, ipp8u, 8u_C1R);
              else if (stype == CV_8UC3)
                  IPP_FILTER_BOX_BORDER(Ipp8u, ipp8u, 8u_C3R);
              else if (stype == CV_8UC4)
                  IPP_FILTER_BOX_BORDER(Ipp8u, ipp8u, 8u_C4R);
  
              else if (stype == CV_16UC1)
                  IPP_FILTER_BOX_BORDER(Ipp16u, ipp16u, 16u_C1R);
              else if (stype == CV_16UC3)
                  IPP_FILTER_BOX_BORDER(Ipp16u, ipp16u, 16u_C3R);
              else if (stype == CV_16UC4)
                  IPP_FILTER_BOX_BORDER(Ipp16u, ipp16u, 16u_C4R);
  
              else if (stype == CV_16SC1)
                  IPP_FILTER_BOX_BORDER(Ipp16s, ipp16s, 16s_C1R);
              else if (stype == CV_16SC3)
                  IPP_FILTER_BOX_BORDER(Ipp16s, ipp16s, 16s_C3R);
              else if (stype == CV_16SC4)
                  IPP_FILTER_BOX_BORDER(Ipp16s, ipp16s, 16s_C4R);
  
              else if (stype == CV_32FC1)
                  IPP_FILTER_BOX_BORDER(Ipp32f, ipp32f, 32f_C1R);
              else if (stype == CV_32FC3)
                  IPP_FILTER_BOX_BORDER(Ipp32f, ipp32f, 32f_C3R);
              else if (stype == CV_32FC4)
                  IPP_FILTER_BOX_BORDER(Ipp32f, ipp32f, 32f_C4R);
          }
  #undef IPP_FILTER_BOX_BORDER
      }
  #endif
  
      Ptr<FilterEngine> f = createBoxFilter( src.type(), dst.type(),
                          ksize, anchor, normalize, borderType );
      f->apply( src, dst );
  }
  
  void cv::blur( InputArray src, OutputArray dst,
             Size ksize, Point anchor, int borderType )
  {
      boxFilter( src, dst, -1, ksize, anchor, true, borderType );
  }
  
  
  /****************************************************************************************\
                                      Squared Box Filter
  \****************************************************************************************/
  
  namespace cv
  {
  
  template<typename T, typename ST>
  struct SqrRowSum :
          public BaseRowFilter
  {
      SqrRowSum( int _ksize, int _anchor ) :
          BaseRowFilter()
      {
          ksize = _ksize;
          anchor = _anchor;
      }
  
      virtual void operator()(const uchar* src, uchar* dst, int width, int cn)
      {
          const T* S = (const T*)src;
          ST* D = (ST*)dst;
          int i = 0, k, ksz_cn = ksize*cn;
  
          width = (width - 1)*cn;
          for( k = 0; k < cn; k++, S++, D++ )
          {
              ST s = 0;
              for( i = 0; i < ksz_cn; i += cn )
              {
                  ST val = (ST)S[i];
                  s += val*val;
              }
              D[0] = s;
              for( i = 0; i < width; i += cn )
              {
                  ST val0 = (ST)S[i], val1 = (ST)S[i + ksz_cn];
                  s += val1*val1 - val0*val0;
                  D[i+cn] = s;
              }
          }
      }
  };
  
  static Ptr<BaseRowFilter> getSqrRowSumFilter(int srcType, int sumType, int ksize, int anchor)
  {
      int sdepth = CV_MAT_DEPTH(srcType), ddepth = CV_MAT_DEPTH(sumType);
      CV_Assert( CV_MAT_CN(sumType) == CV_MAT_CN(srcType) );
  
      if( anchor < 0 )
          anchor = ksize/2;
  
      if( sdepth == CV_8U && ddepth == CV_32S )
          return makePtr<SqrRowSum<uchar, int> >(ksize, anchor);
      if( sdepth == CV_8U && ddepth == CV_64F )
          return makePtr<SqrRowSum<uchar, double> >(ksize, anchor);
      if( sdepth == CV_16U && ddepth == CV_64F )
          return makePtr<SqrRowSum<ushort, double> >(ksize, anchor);
      if( sdepth == CV_16S && ddepth == CV_64F )
          return makePtr<SqrRowSum<short, double> >(ksize, anchor);
      if( sdepth == CV_32F && ddepth == CV_64F )
          return makePtr<SqrRowSum<float, double> >(ksize, anchor);
      if( sdepth == CV_64F && ddepth == CV_64F )
          return makePtr<SqrRowSum<double, double> >(ksize, anchor);
  
      CV_Error_( CV_StsNotImplemented,
                ("Unsupported combination of source format (=%d), and buffer format (=%d)",
                 srcType, sumType));
  
      return Ptr<BaseRowFilter>();
  }
  
  }
  
  void cv::sqrBoxFilter( InputArray _src, OutputArray _dst, int ddepth,
                         Size ksize, Point anchor,
                         bool normalize, int borderType )
  {
      int srcType = _src.type(), sdepth = CV_MAT_DEPTH(srcType), cn = CV_MAT_CN(srcType);
      Size size = _src.size();
  
      if( ddepth < 0 )
          ddepth = sdepth < CV_32F ? CV_32F : CV_64F;
  
      if( borderType != BORDER_CONSTANT && normalize )
      {
          if( size.height == 1 )
              ksize.height = 1;
          if( size.width == 1 )
              ksize.width = 1;
      }
  
      CV_OCL_RUN(_dst.isUMat() && _src.dims() <= 2,
                 ocl_boxFilter(_src, _dst, ddepth, ksize, anchor, borderType, normalize, true))
  
      int sumDepth = CV_64F;
      if( sdepth == CV_8U )
          sumDepth = CV_32S;
      int sumType = CV_MAKETYPE( sumDepth, cn ), dstType = CV_MAKETYPE(ddepth, cn);
  
      Mat src = _src.getMat();
      _dst.create( size, dstType );
      Mat dst = _dst.getMat();
  
      Ptr<BaseRowFilter> rowFilter = getSqrRowSumFilter(srcType, sumType, ksize.width, anchor.x );
      Ptr<BaseColumnFilter> columnFilter = getColumnSumFilter(sumType,
                                                              dstType, ksize.height, anchor.y,
                                                              normalize ? 1./(ksize.width*ksize.height) : 1);
  
      Ptr<FilterEngine> f = makePtr<FilterEngine>(Ptr<BaseFilter>(), rowFilter, columnFilter,
                                                  srcType, dstType, sumType, borderType );
      f->apply( src, dst );
  }
  
  
  /****************************************************************************************\
                                       Gaussian Blur
  \****************************************************************************************/
  
  cv::Mat cv::getGaussianKernel( int n, double sigma, int ktype )
  {
      const int SMALL_GAUSSIAN_SIZE = 7;
      static const float small_gaussian_tab[][SMALL_GAUSSIAN_SIZE] =
      {
          {1.f},
          {0.25f, 0.5f, 0.25f},
          {0.0625f, 0.25f, 0.375f, 0.25f, 0.0625f},
          {0.03125f, 0.109375f, 0.21875f, 0.28125f, 0.21875f, 0.109375f, 0.03125f}
      };
  
      const float* fixed_kernel = n % 2 == 1 && n <= SMALL_GAUSSIAN_SIZE && sigma <= 0 ?
          small_gaussian_tab[n>>1] : 0;
  
      CV_Assert( ktype == CV_32F || ktype == CV_64F );
      Mat kernel(n, 1, ktype);
      float* cf = kernel.ptr<float>();
      double* cd = kernel.ptr<double>();
  
      double sigmaX = sigma > 0 ? sigma : ((n-1)*0.5 - 1)*0.3 + 0.8;
      double scale2X = -0.5/(sigmaX*sigmaX);
      double sum = 0;
  
      int i;
      for( i = 0; i < n; i++ )
      {
          double x = i - (n-1)*0.5;
          double t = fixed_kernel ? (double)fixed_kernel[i] : std::exp(scale2X*x*x);
          if( ktype == CV_32F )
          {
              cf[i] = (float)t;
              sum += cf[i];
          }
          else
          {
              cd[i] = t;
              sum += cd[i];
          }
      }
  
      sum = 1./sum;
      for( i = 0; i < n; i++ )
      {
          if( ktype == CV_32F )
              cf[i] = (float)(cf[i]*sum);
          else
              cd[i] *= sum;
      }
  
      return kernel;
  }
  
  namespace cv {
  
  static void createGaussianKernels( Mat & kx, Mat & ky, int type, Size ksize,
                                     double sigma1, double sigma2 )
  {
      int depth = CV_MAT_DEPTH(type);
      if( sigma2 <= 0 )
          sigma2 = sigma1;
  
      // automatic detection of kernel size from sigma
      if( ksize.width <= 0 && sigma1 > 0 )
          ksize.width = cvRound(sigma1*(depth == CV_8U ? 3 : 4)*2 + 1)|1;
      if( ksize.height <= 0 && sigma2 > 0 )
          ksize.height = cvRound(sigma2*(depth == CV_8U ? 3 : 4)*2 + 1)|1;
  
      CV_Assert( ksize.width > 0 && ksize.width % 2 == 1 &&
          ksize.height > 0 && ksize.height % 2 == 1 );
  
      sigma1 = std::max( sigma1, 0. );
      sigma2 = std::max( sigma2, 0. );
  
      kx = getGaussianKernel( ksize.width, sigma1, std::max(depth, CV_32F) );
      if( ksize.height == ksize.width && std::abs(sigma1 - sigma2) < DBL_EPSILON )
          ky = kx;
      else
          ky = getGaussianKernel( ksize.height, sigma2, std::max(depth, CV_32F) );
  }
  
  }
  
  cv::Ptr<cv::FilterEngine> cv::createGaussianFilter( int type, Size ksize,
                                          double sigma1, double sigma2,
                                          int borderType )
  {
      Mat kx, ky;
      createGaussianKernels(kx, ky, type, ksize, sigma1, sigma2);
  
      return createSeparableLinearFilter( type, type, kx, ky, Point(-1,-1), 0, borderType );
  }
  
  
  void cv::GaussianBlur( InputArray _src, OutputArray _dst, Size ksize,
                     double sigma1, double sigma2,
                     int borderType )
  {
      int type = _src.type();
      Size size = _src.size();
      _dst.create( size, type );
  
      if( borderType != BORDER_CONSTANT && (borderType & BORDER_ISOLATED) != 0 )
      {
          if( size.height == 1 )
              ksize.height = 1;
          if( size.width == 1 )
              ksize.width = 1;
      }
  
      if( ksize.width == 1 && ksize.height == 1 )
      {
          _src.copyTo(_dst);
          return;
      }
  
  #ifdef HAVE_TEGRA_OPTIMIZATION
      if(sigma1 == 0 && sigma2 == 0 && tegra::gaussian(_src.getMat(), _dst.getMat(), ksize, borderType))
          return;
  #endif
  
  #if IPP_VERSION_X100 >= 801 && 0 // these functions are slower in IPP 8.1
      CV_IPP_CHECK()
      {
          int depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type);
  
          if ((depth == CV_8U || depth == CV_16U || depth == CV_16S || depth == CV_32F) && (cn == 1 || cn == 3) &&
                  sigma1 == sigma2 && ksize.width == ksize.height && sigma1 != 0.0 )
          {
              IppiBorderType ippBorder = ippiGetBorderType(borderType);
              if (ippBorderConst == ippBorder || ippBorderRepl == ippBorder)
              {
                  Mat src = _src.getMat(), dst = _dst.getMat();
                  IppiSize roiSize = { src.cols, src.rows };
                  IppDataType dataType = ippiGetDataType(depth);
                  Ipp32s specSize = 0, bufferSize = 0;
  
                  if (ippiFilterGaussianGetBufferSize(roiSize, (Ipp32u)ksize.width, dataType, cn, &specSize, &bufferSize) >= 0)
                  {
                      IppFilterGaussianSpec * pSpec = (IppFilterGaussianSpec *)ippMalloc(specSize);
                      Ipp8u * pBuffer = (Ipp8u*)ippMalloc(bufferSize);
  
                      if (ippiFilterGaussianInit(roiSize, (Ipp32u)ksize.width, (Ipp32f)sigma1, ippBorder, dataType, 1, pSpec, pBuffer) >= 0)
                      {
  #define IPP_FILTER_GAUSS(ippfavor, ippcn) \
          do \
          { \
              typedef Ipp##ippfavor ippType; \
              ippType borderValues[] = { 0, 0, 0 }; \
              IppStatus status = ippcn == 1 ? \
                  ippiFilterGaussianBorder_##ippfavor##_C1R(src.ptr<ippType>(), (int)src.step, \
                      dst.ptr<ippType>(), (int)dst.step, roiSize, borderValues[0], pSpec, pBuffer) : \
                  ippiFilterGaussianBorder_##ippfavor##_C3R(src.ptr<ippType>(), (int)src.step, \
                      dst.ptr<ippType>(), (int)dst.step, roiSize, borderValues, pSpec, pBuffer); \
              ippFree(pBuffer); \
              ippFree(pSpec); \
              if (status >= 0) \
              { \
                  CV_IMPL_ADD(CV_IMPL_IPP); \
                  return; \
              } \
          } while ((void)0, 0)
  
                          if (type == CV_8UC1)
                              IPP_FILTER_GAUSS(8u, 1);
                          else if (type == CV_8UC3)
                              IPP_FILTER_GAUSS(8u, 3);
                          else if (type == CV_16UC1)
                              IPP_FILTER_GAUSS(16u, 1);
                          else if (type == CV_16UC3)
                              IPP_FILTER_GAUSS(16u, 3);
                          else if (type == CV_16SC1)
                              IPP_FILTER_GAUSS(16s, 1);
                          else if (type == CV_16SC3)
                              IPP_FILTER_GAUSS(16s, 3);
                          else if (type == CV_32FC1)
                              IPP_FILTER_GAUSS(32f, 1);
                          else if (type == CV_32FC3)
                              IPP_FILTER_GAUSS(32f, 3);
  #undef IPP_FILTER_GAUSS
                      }
                  }
                  setIppErrorStatus();
              }
          }
      }
  #endif
  
      Mat kx, ky;
      createGaussianKernels(kx, ky, type, ksize, sigma1, sigma2);
      sepFilter2D(_src, _dst, CV_MAT_DEPTH(type), kx, ky, Point(-1,-1), 0, borderType );
  }
  
  /****************************************************************************************\
                                        Median Filter
  \****************************************************************************************/
  
  namespace cv
  {
  typedef ushort HT;
  
  /**
   * This structure represents a two-tier histogram. The first tier (known as the
   * "coarse" level) is 4 bit wide and the second tier (known as the "fine" level)
   * is 8 bit wide. Pixels inserted in the fine level also get inserted into the
   * coarse bucket designated by the 4 MSBs of the fine bucket value.
   *
   * The structure is aligned on 16 bits, which is a prerequisite for SIMD
   * instructions. Each bucket is 16 bit wide, which means that extra care must be
   * taken to prevent overflow.
   */
  typedef struct
  {
      HT coarse[16];
      HT fine[16][16];
  } Histogram;
  
  
  #if CV_SSE2
  #define MEDIAN_HAVE_SIMD 1
  
  static inline void histogram_add_simd( const HT x[16], HT y[16] )
  {
      const __m128i* rx = (const __m128i*)x;
      __m128i* ry = (__m128i*)y;
      __m128i r0 = _mm_add_epi16(_mm_load_si128(ry+0),_mm_load_si128(rx+0));
      __m128i r1 = _mm_add_epi16(_mm_load_si128(ry+1),_mm_load_si128(rx+1));
      _mm_store_si128(ry+0, r0);
      _mm_store_si128(ry+1, r1);
  }
  
  static inline void histogram_sub_simd( const HT x[16], HT y[16] )
  {
      const __m128i* rx = (const __m128i*)x;
      __m128i* ry = (__m128i*)y;
      __m128i r0 = _mm_sub_epi16(_mm_load_si128(ry+0),_mm_load_si128(rx+0));
      __m128i r1 = _mm_sub_epi16(_mm_load_si128(ry+1),_mm_load_si128(rx+1));
      _mm_store_si128(ry+0, r0);
      _mm_store_si128(ry+1, r1);
  }
  
  #elif CV_NEON
  #define MEDIAN_HAVE_SIMD 1
  
  static inline void histogram_add_simd( const HT x[16], HT y[16] )
  {
      vst1q_u16(y, vaddq_u16(vld1q_u16(x), vld1q_u16(y)));
      vst1q_u16(y + 8, vaddq_u16(vld1q_u16(x + 8), vld1q_u16(y + 8)));
  }
  
  static inline void histogram_sub_simd( const HT x[16], HT y[16] )
  {
      vst1q_u16(y, vsubq_u16(vld1q_u16(x), vld1q_u16(y)));
      vst1q_u16(y + 8, vsubq_u16(vld1q_u16(x + 8), vld1q_u16(y + 8)));
  }
  
  #else
  #define MEDIAN_HAVE_SIMD 0
  #endif
  
  
  static inline void histogram_add( const HT x[16], HT y[16] )
  {
      int i;
      for( i = 0; i < 16; ++i )
          y[i] = (HT)(y[i] + x[i]);
  }
  
  static inline void histogram_sub( const HT x[16], HT y[16] )
  {
      int i;
      for( i = 0; i < 16; ++i )
          y[i] = (HT)(y[i] - x[i]);
  }
  
  static inline void histogram_muladd( int a, const HT x[16],
          HT y[16] )
  {
      for( int i = 0; i < 16; ++i )
          y[i] = (HT)(y[i] + a * x[i]);
  }
  
  static void
  medianBlur_8u_O1( const Mat& _src, Mat& _dst, int ksize )
  {
  /**
   * HOP is short for Histogram OPeration. This macro makes an operation \a op on
   * histogram \a h for pixel value \a x. It takes care of handling both levels.
   */
  #define HOP(h,x,op) \
      h.coarse[x>>4] op, \
      *((HT*)h.fine + x) op
  
  #define COP(c,j,x,op) \
      h_coarse[ 16*(n*c+j) + (x>>4) ] op, \
      h_fine[ 16 * (n*(16*c+(x>>4)) + j) + (x & 0xF) ] op
  
      int cn = _dst.channels(), m = _dst.rows, r = (ksize-1)/2;
      size_t sstep = _src.step, dstep = _dst.step;
      Histogram CV_DECL_ALIGNED(16) H[4];
      HT CV_DECL_ALIGNED(16) luc[4][16];
  
      int STRIPE_SIZE = std::min( _dst.cols, 512/cn );
  
      std::vector<HT> _h_coarse(1 * 16 * (STRIPE_SIZE + 2*r) * cn + 16);
      std::vector<HT> _h_fine(16 * 16 * (STRIPE_SIZE + 2*r) * cn + 16);
      HT* h_coarse = alignPtr(&_h_coarse[0], 16);
      HT* h_fine = alignPtr(&_h_fine[0], 16);
  #if MEDIAN_HAVE_SIMD
      volatile bool useSIMD = checkHardwareSupport(CV_CPU_SSE2) || checkHardwareSupport(CV_CPU_NEON);
  #endif
  
      for( int x = 0; x < _dst.cols; x += STRIPE_SIZE )
      {
          int i, j, k, c, n = std::min(_dst.cols - x, STRIPE_SIZE) + r*2;
          const uchar* src = _src.ptr() + x*cn;
          uchar* dst = _dst.ptr() + (x - r)*cn;
  
          memset( h_coarse, 0, 16*n*cn*sizeof(h_coarse[0]) );
          memset( h_fine, 0, 16*16*n*cn*sizeof(h_fine[0]) );
  
          // First row initialization
          for( c = 0; c < cn; c++ )
          {
              for( j = 0; j < n; j++ )
                  COP( c, j, src[cn*j+c], += (cv::HT)(r+2) );
  
              for( i = 1; i < r; i++ )
              {
                  const uchar* p = src + sstep*std::min(i, m-1);
                  for ( j = 0; j < n; j++ )
                      COP( c, j, p[cn*j+c], ++ );
              }
          }
  
          for( i = 0; i < m; i++ )
          {
              const uchar* p0 = src + sstep * std::max( 0, i-r-1 );
              const uchar* p1 = src + sstep * std::min( m-1, i+r );
  
              memset( H, 0, cn*sizeof(H[0]) );
              memset( luc, 0, cn*sizeof(luc[0]) );
              for( c = 0; c < cn; c++ )
              {
                  // Update column histograms for the entire row.
                  for( j = 0; j < n; j++ )
                  {
                      COP( c, j, p0[j*cn + c], -- );
                      COP( c, j, p1[j*cn + c], ++ );
                  }
  
                  // First column initialization
                  for( k = 0; k < 16; ++k )
                      histogram_muladd( 2*r+1, &h_fine[16*n*(16*c+k)], &H[c].fine[k][0] );
  
              #if MEDIAN_HAVE_SIMD
                  if( useSIMD )
                  {
                      for( j = 0; j < 2*r; ++j )
                          histogram_add_simd( &h_coarse[16*(n*c+j)], H[c].coarse );
  
                      for( j = r; j < n-r; j++ )
                      {
                          int t = 2*r*r + 2*r, b, sum = 0;
                          HT* segment;
  
                          histogram_add_simd( &h_coarse[16*(n*c + std::min(j+r,n-1))], H[c].coarse );
  
                          // Find median at coarse level
                          for ( k = 0; k < 16 ; ++k )
                          {
                              sum += H[c].coarse[k];
                              if ( sum > t )
                              {
                                  sum -= H[c].coarse[k];
                                  break;
                              }
                          }
                          assert( k < 16 );
  
                          /* Update corresponding histogram segment */
                          if ( luc[c][k] <= j-r )
                          {
                              memset( &H[c].fine[k], 0, 16 * sizeof(HT) );
                              for ( luc[c][k] = cv::HT(j-r); luc[c][k] < MIN(j+r+1,n); ++luc[c][k] )
                                  histogram_add_simd( &h_fine[16*(n*(16*c+k)+luc[c][k])], H[c].fine[k] );
  
                              if ( luc[c][k] < j+r+1 )
                              {
                                  histogram_muladd( j+r+1 - n, &h_fine[16*(n*(16*c+k)+(n-1))], &H[c].fine[k][0] );
                                  luc[c][k] = (HT)(j+r+1);
                              }
                          }
                          else
                          {
                              for ( ; luc[c][k] < j+r+1; ++luc[c][k] )
                              {
                                  histogram_sub_simd( &h_fine[16*(n*(16*c+k)+MAX(luc[c][k]-2*r-1,0))], H[c].fine[k] );
                                  histogram_add_simd( &h_fine[16*(n*(16*c+k)+MIN(luc[c][k],n-1))], H[c].fine[k] );
                              }
                          }
  
                          histogram_sub_simd( &h_coarse[16*(n*c+MAX(j-r,0))], H[c].coarse );
  
                          /* Find median in segment */
                          segment = H[c].fine[k];
                          for ( b = 0; b < 16 ; b++ )
                          {
                              sum += segment[b];
                              if ( sum > t )
                              {
                                  dst[dstep*i+cn*j+c] = (uchar)(16*k + b);
                                  break;
                              }
                          }
                          assert( b < 16 );
                      }
                  }
                  else
              #endif
                  {
                      for( j = 0; j < 2*r; ++j )
                          histogram_add( &h_coarse[16*(n*c+j)], H[c].coarse );
  
                      for( j = r; j < n-r; j++ )
                      {
                          int t = 2*r*r + 2*r, b, sum = 0;
                          HT* segment;
  
                          histogram_add( &h_coarse[16*(n*c + std::min(j+r,n-1))], H[c].coarse );
  
                          // Find median at coarse level
                          for ( k = 0; k < 16 ; ++k )
                          {
                              sum += H[c].coarse[k];
                              if ( sum > t )
                              {
                                  sum -= H[c].coarse[k];
                                  break;
                              }
                          }
                          assert( k < 16 );
  
                          /* Update corresponding histogram segment */
                          if ( luc[c][k] <= j-r )
                          {
                              memset( &H[c].fine[k], 0, 16 * sizeof(HT) );
                              for ( luc[c][k] = cv::HT(j-r); luc[c][k] < MIN(j+r+1,n); ++luc[c][k] )
                                  histogram_add( &h_fine[16*(n*(16*c+k)+luc[c][k])], H[c].fine[k] );
  
                              if ( luc[c][k] < j+r+1 )
                              {
                                  histogram_muladd( j+r+1 - n, &h_fine[16*(n*(16*c+k)+(n-1))], &H[c].fine[k][0] );
                                  luc[c][k] = (HT)(j+r+1);
                              }
                          }
                          else
                          {
                              for ( ; luc[c][k] < j+r+1; ++luc[c][k] )
                              {
                                  histogram_sub( &h_fine[16*(n*(16*c+k)+MAX(luc[c][k]-2*r-1,0))], H[c].fine[k] );
                                  histogram_add( &h_fine[16*(n*(16*c+k)+MIN(luc[c][k],n-1))], H[c].fine[k] );
                              }
                          }
  
                          histogram_sub( &h_coarse[16*(n*c+MAX(j-r,0))], H[c].coarse );
  
                          /* Find median in segment */
                          segment = H[c].fine[k];
                          for ( b = 0; b < 16 ; b++ )
                          {
                              sum += segment[b];
                              if ( sum > t )
                              {
                                  dst[dstep*i+cn*j+c] = (uchar)(16*k + b);
                                  break;
                              }
                          }
                          assert( b < 16 );
                      }
                  }
              }
          }
      }
  
  #undef HOP
  #undef COP
  }
  
  static void
  medianBlur_8u_Om( const Mat& _src, Mat& _dst, int m )
  {
      #define N  16
      int     zone0[4][N];
      int     zone1[4][N*N];
      int     x, y;
      int     n2 = m*m/2;
      Size    size = _dst.size();
      const uchar* src = _src.ptr();
      uchar*  dst = _dst.ptr();
      int     src_step = (int)_src.step, dst_step = (int)_dst.step;
      int     cn = _src.channels();
      const uchar*  src_max = src + size.height*src_step;
  
      #define UPDATE_ACC01( pix, cn, op ) \
      {                                   \
          int p = (pix);                  \
          zone1[cn][p] op;                \
          zone0[cn][p >> 4] op;           \
      }
  
      //CV_Assert( size.height >= nx && size.width >= nx );
      for( x = 0; x < size.width; x++, src += cn, dst += cn )
      {
          uchar* dst_cur = dst;
          const uchar* src_top = src;
          const uchar* src_bottom = src;
          int k, c;
          int src_step1 = src_step, dst_step1 = dst_step;
  
          if( x % 2 != 0 )
          {
              src_bottom = src_top += src_step*(size.height-1);
              dst_cur += dst_step*(size.height-1);
              src_step1 = -src_step1;
              dst_step1 = -dst_step1;
          }
  
          // init accumulator
          memset( zone0, 0, sizeof(zone0[0])*cn );
          memset( zone1, 0, sizeof(zone1[0])*cn );
  
          for( y = 0; y <= m/2; y++ )
          {
              for( c = 0; c < cn; c++ )
              {
                  if( y > 0 )
                  {
                      for( k = 0; k < m*cn; k += cn )
                          UPDATE_ACC01( src_bottom[k+c], c, ++ );
                  }
                  else
                  {
                      for( k = 0; k < m*cn; k += cn )
                          UPDATE_ACC01( src_bottom[k+c], c, += m/2+1 );
                  }
              }
  
              if( (src_step1 > 0 && y < size.height-1) ||
                  (src_step1 < 0 && size.height-y-1 > 0) )
                  src_bottom += src_step1;
          }
  
          for( y = 0; y < size.height; y++, dst_cur += dst_step1 )
          {
              // find median
              for( c = 0; c < cn; c++ )
              {
                  int s = 0;
                  for( k = 0; ; k++ )
                  {
                      int t = s + zone0[c][k];
                      if( t > n2 ) break;
                      s = t;
                  }
  
                  for( k *= N; ;k++ )
                  {
                      s += zone1[c][k];
                      if( s > n2 ) break;
                  }
  
                  dst_cur[c] = (uchar)k;
              }
  
              if( y+1 == size.height )
                  break;
  
              if( cn == 1 )
              {
                  for( k = 0; k < m; k++ )
                  {
                      int p = src_top[k];
                      int q = src_bottom[k];
                      zone1[0][p]--;
                      zone0[0][p>>4]--;
                      zone1[0][q]++;
                      zone0[0][q>>4]++;
                  }
              }
              else if( cn == 3 )
              {
                  for( k = 0; k < m*3; k += 3 )
                  {
                      UPDATE_ACC01( src_top[k], 0, -- );
                      UPDATE_ACC01( src_top[k+1], 1, -- );
                      UPDATE_ACC01( src_top[k+2], 2, -- );
  
                      UPDATE_ACC01( src_bottom[k], 0, ++ );
                      UPDATE_ACC01( src_bottom[k+1], 1, ++ );
                      UPDATE_ACC01( src_bottom[k+2], 2, ++ );
                  }
              }
              else
              {
                  assert( cn == 4 );
                  for( k = 0; k < m*4; k += 4 )
                  {
                      UPDATE_ACC01( src_top[k], 0, -- );
                      UPDATE_ACC01( src_top[k+1], 1, -- );
                      UPDATE_ACC01( src_top[k+2], 2, -- );
                      UPDATE_ACC01( src_top[k+3], 3, -- );
  
                      UPDATE_ACC01( src_bottom[k], 0, ++ );
                      UPDATE_ACC01( src_bottom[k+1], 1, ++ );
                      UPDATE_ACC01( src_bottom[k+2], 2, ++ );
                      UPDATE_ACC01( src_bottom[k+3], 3, ++ );
                  }
              }
  
              if( (src_step1 > 0 && src_bottom + src_step1 < src_max) ||
                  (src_step1 < 0 && src_bottom + src_step1 >= src) )
                  src_bottom += src_step1;
  
              if( y >= m/2 )
                  src_top += src_step1;
          }
      }
  #undef N
  #undef UPDATE_ACC
  }
  
  
  struct MinMax8u
  {
      typedef uchar value_type;
      typedef int arg_type;
      enum { SIZE = 1 };
      arg_type load(const uchar* ptr) { return *ptr; }
      void store(uchar* ptr, arg_type val) { *ptr = (uchar)val; }
      void operator()(arg_type& a, arg_type& b) const
      {
          int t = CV_FAST_CAST_8U(a - b);
          b += t; a -= t;
      }
  };
  
  struct MinMax16u
  {
      typedef ushort value_type;
      typedef int arg_type;
      enum { SIZE = 1 };
      arg_type load(const ushort* ptr) { return *ptr; }
      void store(ushort* ptr, arg_type val) { *ptr = (ushort)val; }
      void operator()(arg_type& a, arg_type& b) const
      {
          arg_type t = a;
          a = std::min(a, b);
          b = std::max(b, t);
      }
  };
  
  struct MinMax16s
  {
      typedef short value_type;
      typedef int arg_type;
      enum { SIZE = 1 };
      arg_type load(const short* ptr) { return *ptr; }
      void store(short* ptr, arg_type val) { *ptr = (short)val; }
      void operator()(arg_type& a, arg_type& b) const
      {
          arg_type t = a;
          a = std::min(a, b);
          b = std::max(b, t);
      }
  };
  
  struct MinMax32f
  {
      typedef float value_type;
      typedef float arg_type;
      enum { SIZE = 1 };
      arg_type load(const float* ptr) { return *ptr; }
      void store(float* ptr, arg_type val) { *ptr = val; }
      void operator()(arg_type& a, arg_type& b) const
      {
          arg_type t = a;
          a = std::min(a, b);
          b = std::max(b, t);
      }
  };
  
  #if CV_SSE2
  
  struct MinMaxVec8u
  {
      typedef uchar value_type;
      typedef __m128i arg_type;
      enum { SIZE = 16 };
      arg_type load(const uchar* ptr) { return _mm_loadu_si128((const __m128i*)ptr); }
      void store(uchar* ptr, arg_type val) { _mm_storeu_si128((__m128i*)ptr, val); }
      void operator()(arg_type& a, arg_type& b) const
      {
          arg_type t = a;
          a = _mm_min_epu8(a, b);
          b = _mm_max_epu8(b, t);
      }
  };
  
  
  struct MinMaxVec16u
  {
      typedef ushort value_type;
      typedef __m128i arg_type;
      enum { SIZE = 8 };
      arg_type load(const ushort* ptr) { return _mm_loadu_si128((const __m128i*)ptr); }
      void store(ushort* ptr, arg_type val) { _mm_storeu_si128((__m128i*)ptr, val); }
      void operator()(arg_type& a, arg_type& b) const
      {
          arg_type t = _mm_subs_epu16(a, b);
          a = _mm_subs_epu16(a, t);
          b = _mm_adds_epu16(b, t);
      }
  };
  
  
  struct MinMaxVec16s
  {
      typedef short value_type;
      typedef __m128i arg_type;
      enum { SIZE = 8 };
      arg_type load(const short* ptr) { return _mm_loadu_si128((const __m128i*)ptr); }
      void store(short* ptr, arg_type val) { _mm_storeu_si128((__m128i*)ptr, val); }
      void operator()(arg_type& a, arg_type& b) const
      {
          arg_type t = a;
          a = _mm_min_epi16(a, b);
          b = _mm_max_epi16(b, t);
      }
  };
  
  
  struct MinMaxVec32f
  {
      typedef float value_type;
      typedef __m128 arg_type;
      enum { SIZE = 4 };
      arg_type load(const float* ptr) { return _mm_loadu_ps(ptr); }
      void store(float* ptr, arg_type val) { _mm_storeu_ps(ptr, val); }
      void operator()(arg_type& a, arg_type& b) const
      {
          arg_type t = a;
          a = _mm_min_ps(a, b);
          b = _mm_max_ps(b, t);
      }
  };
  
  #elif CV_NEON
  
  struct MinMaxVec8u
  {
      typedef uchar value_type;
      typedef uint8x16_t arg_type;
      enum { SIZE = 16 };
      arg_type load(const uchar* ptr) { return vld1q_u8(ptr); }
      void store(uchar* ptr, arg_type val) { vst1q_u8(ptr, val); }
      void operator()(arg_type& a, arg_type& b) const
      {
          arg_type t = a;
          a = vminq_u8(a, b);
          b = vmaxq_u8(b, t);
      }
  };
  
  
  struct MinMaxVec16u
  {
      typedef ushort value_type;
      typedef uint16x8_t arg_type;
      enum { SIZE = 8 };
      arg_type load(const ushort* ptr) { return vld1q_u16(ptr); }
      void store(ushort* ptr, arg_type val) { vst1q_u16(ptr, val); }
      void operator()(arg_type& a, arg_type& b) const
      {
          arg_type t = a;
          a = vminq_u16(a, b);
          b = vmaxq_u16(b, t);
      }
  };
  
  
  struct MinMaxVec16s
  {
      typedef short value_type;
      typedef int16x8_t arg_type;
      enum { SIZE = 8 };
      arg_type load(const short* ptr) { return vld1q_s16(ptr); }
      void store(short* ptr, arg_type val) { vst1q_s16(ptr, val); }
      void operator()(arg_type& a, arg_type& b) const
      {
          arg_type t = a;
          a = vminq_s16(a, b);
          b = vmaxq_s16(b, t);
      }
  };
  
  
  struct MinMaxVec32f
  {
      typedef float value_type;
      typedef float32x4_t arg_type;
      enum { SIZE = 4 };
      arg_type load(const float* ptr) { return vld1q_f32(ptr); }
      void store(float* ptr, arg_type val) { vst1q_f32(ptr, val); }
      void operator()(arg_type& a, arg_type& b) const
      {
          arg_type t = a;
          a = vminq_f32(a, b);
          b = vmaxq_f32(b, t);
      }
  };
  
  
  #else
  
  typedef MinMax8u MinMaxVec8u;
  typedef MinMax16u MinMaxVec16u;
  typedef MinMax16s MinMaxVec16s;
  typedef MinMax32f MinMaxVec32f;
  
  #endif
  
  template<class Op, class VecOp>
  static void
  medianBlur_SortNet( const Mat& _src, Mat& _dst, int m )
  {
      typedef typename Op::value_type T;
      typedef typename Op::arg_type WT;
      typedef typename VecOp::arg_type VT;
  
      const T* src = _src.ptr<T>();
      T* dst = _dst.ptr<T>();
      int sstep = (int)(_src.step/sizeof(T));
      int dstep = (int)(_dst.step/sizeof(T));
      Size size = _dst.size();
      int i, j, k, cn = _src.channels();
      Op op;
      VecOp vop;
      volatile bool useSIMD = checkHardwareSupport(CV_CPU_SSE2) || checkHardwareSupport(CV_CPU_NEON);
  
      if( m == 3 )
      {
          if( size.width == 1 || size.height == 1 )
          {
              int len = size.width + size.height - 1;
              int sdelta = size.height == 1 ? cn : sstep;
              int sdelta0 = size.height == 1 ? 0 : sstep - cn;
              int ddelta = size.height == 1 ? cn : dstep;
  
              for( i = 0; i < len; i++, src += sdelta0, dst += ddelta )
                  for( j = 0; j < cn; j++, src++ )
                  {
                      WT p0 = src[i > 0 ? -sdelta : 0];
                      WT p1 = src[0];
                      WT p2 = src[i < len - 1 ? sdelta : 0];
  
                      op(p0, p1); op(p1, p2); op(p0, p1);
                      dst[j] = (T)p1;
                  }
              return;
          }
  
          size.width *= cn;
          for( i = 0; i < size.height; i++, dst += dstep )
          {
              const T* row0 = src + std::max(i - 1, 0)*sstep;
              const T* row1 = src + i*sstep;
              const T* row2 = src + std::min(i + 1, size.height-1)*sstep;
              int limit = useSIMD ? cn : size.width;
  
              for(j = 0;; )
              {
                  for( ; j < limit; j++ )
                  {
                      int j0 = j >= cn ? j - cn : j;
                      int j2 = j < size.width - cn ? j + cn : j;
                      WT p0 = row0[j0], p1 = row0[j], p2 = row0[j2];
                      WT p3 = row1[j0], p4 = row1[j], p5 = row1[j2];
                      WT p6 = row2[j0], p7 = row2[j], p8 = row2[j2];
  
                      op(p1, p2); op(p4, p5); op(p7, p8); op(p0, p1);
                      op(p3, p4); op(p6, p7); op(p1, p2); op(p4, p5);
                      op(p7, p8); op(p0, p3); op(p5, p8); op(p4, p7);
                      op(p3, p6); op(p1, p4); op(p2, p5); op(p4, p7);
                      op(p4, p2); op(p6, p4); op(p4, p2);
                      dst[j] = (T)p4;
                  }
  
                  if( limit == size.width )
                      break;
  
                  for( ; j <= size.width - VecOp::SIZE - cn; j += VecOp::SIZE )
                  {
                      VT p0 = vop.load(row0+j-cn), p1 = vop.load(row0+j), p2 = vop.load(row0+j+cn);
                      VT p3 = vop.load(row1+j-cn), p4 = vop.load(row1+j), p5 = vop.load(row1+j+cn);
                      VT p6 = vop.load(row2+j-cn), p7 = vop.load(row2+j), p8 = vop.load(row2+j+cn);
  
                      vop(p1, p2); vop(p4, p5); vop(p7, p8); vop(p0, p1);
                      vop(p3, p4); vop(p6, p7); vop(p1, p2); vop(p4, p5);
                      vop(p7, p8); vop(p0, p3); vop(p5, p8); vop(p4, p7);
                      vop(p3, p6); vop(p1, p4); vop(p2, p5); vop(p4, p7);
                      vop(p4, p2); vop(p6, p4); vop(p4, p2);
                      vop.store(dst+j, p4);
                  }
  
                  limit = size.width;
              }
          }
      }
      else if( m == 5 )
      {
          if( size.width == 1 || size.height == 1 )
          {
              int len = size.width + size.height - 1;
              int sdelta = size.height == 1 ? cn : sstep;
              int sdelta0 = size.height == 1 ? 0 : sstep - cn;
              int ddelta = size.height == 1 ? cn : dstep;
  
              for( i = 0; i < len; i++, src += sdelta0, dst += ddelta )
                  for( j = 0; j < cn; j++, src++ )
                  {
                      int i1 = i > 0 ? -sdelta : 0;
                      int i0 = i > 1 ? -sdelta*2 : i1;
                      int i3 = i < len-1 ? sdelta : 0;
                      int i4 = i < len-2 ? sdelta*2 : i3;
                      WT p0 = src[i0], p1 = src[i1], p2 = src[0], p3 = src[i3], p4 = src[i4];
  
                      op(p0, p1); op(p3, p4); op(p2, p3); op(p3, p4); op(p0, p2);
                      op(p2, p4); op(p1, p3); op(p1, p2);
                      dst[j] = (T)p2;
                  }
              return;
          }
  
          size.width *= cn;
          for( i = 0; i < size.height; i++, dst += dstep )
          {
              const T* row[5];
              row[0] = src + std::max(i - 2, 0)*sstep;
              row[1] = src + std::max(i - 1, 0)*sstep;
              row[2] = src + i*sstep;
              row[3] = src + std::min(i + 1, size.height-1)*sstep;
              row[4] = src + std::min(i + 2, size.height-1)*sstep;
              int limit = useSIMD ? cn*2 : size.width;
  
              for(j = 0;; )
              {
                  for( ; j < limit; j++ )
                  {
                      WT p[25];
                      int j1 = j >= cn ? j - cn : j;
                      int j0 = j >= cn*2 ? j - cn*2 : j1;
                      int j3 = j < size.width - cn ? j + cn : j;
                      int j4 = j < size.width - cn*2 ? j + cn*2 : j3;
                      for( k = 0; k < 5; k++ )
                      {
                          const T* rowk = row[k];
                          p[k*5] = rowk[j0]; p[k*5+1] = rowk[j1];
                          p[k*5+2] = rowk[j]; p[k*5+3] = rowk[j3];
                          p[k*5+4] = rowk[j4];
                      }
  
                      op(p[1], p[2]); op(p[0], p[1]); op(p[1], p[2]); op(p[4], p[5]); op(p[3], p[4]);
                      op(p[4], p[5]); op(p[0], p[3]); op(p[2], p[5]); op(p[2], p[3]); op(p[1], p[4]);
                      op(p[1], p[2]); op(p[3], p[4]); op(p[7], p[8]); op(p[6], p[7]); op(p[7], p[8]);
                      op(p[10], p[11]); op(p[9], p[10]); op(p[10], p[11]); op(p[6], p[9]); op(p[8], p[11]);
                      op(p[8], p[9]); op(p[7], p[10]); op(p[7], p[8]); op(p[9], p[10]); op(p[0], p[6]);
                      op(p[4], p[10]); op(p[4], p[6]); op(p[2], p[8]); op(p[2], p[4]); op(p[6], p[8]);
                      op(p[1], p[7]); op(p[5], p[11]); op(p[5], p[7]); op(p[3], p[9]); op(p[3], p[5]);
                      op(p[7], p[9]); op(p[1], p[2]); op(p[3], p[4]); op(p[5], p[6]); op(p[7], p[8]);
                      op(p[9], p[10]); op(p[13], p[14]); op(p[12], p[13]); op(p[13], p[14]); op(p[16], p[17]);
                      op(p[15], p[16]); op(p[16], p[17]); op(p[12], p[15]); op(p[14], p[17]); op(p[14], p[15]);
                      op(p[13], p[16]); op(p[13], p[14]); op(p[15], p[16]); op(p[19], p[20]); op(p[18], p[19]);
                      op(p[19], p[20]); op(p[21], p[22]); op(p[23], p[24]); op(p[21], p[23]); op(p[22], p[24]);
                      op(p[22], p[23]); op(p[18], p[21]); op(p[20], p[23]); op(p[20], p[21]); op(p[19], p[22]);
                      op(p[22], p[24]); op(p[19], p[20]); op(p[21], p[22]); op(p[23], p[24]); op(p[12], p[18]);
                      op(p[16], p[22]); op(p[16], p[18]); op(p[14], p[20]); op(p[20], p[24]); op(p[14], p[16]);
                      op(p[18], p[20]); op(p[22], p[24]); op(p[13], p[19]); op(p[17], p[23]); op(p[17], p[19]);
                      op(p[15], p[21]); op(p[15], p[17]); op(p[19], p[21]); op(p[13], p[14]); op(p[15], p[16]);
                      op(p[17], p[18]); op(p[19], p[20]); op(p[21], p[22]); op(p[23], p[24]); op(p[0], p[12]);
                      op(p[8], p[20]); op(p[8], p[12]); op(p[4], p[16]); op(p[16], p[24]); op(p[12], p[16]);
                      op(p[2], p[14]); op(p[10], p[22]); op(p[10], p[14]); op(p[6], p[18]); op(p[6], p[10]);
                      op(p[10], p[12]); op(p[1], p[13]); op(p[9], p[21]); op(p[9], p[13]); op(p[5], p[17]);
                      op(p[13], p[17]); op(p[3], p[15]); op(p[11], p[23]); op(p[11], p[15]); op(p[7], p[19]);
                      op(p[7], p[11]); op(p[11], p[13]); op(p[11], p[12]);
                      dst[j] = (T)p[12];
                  }
  
                  if( limit == size.width )
                      break;
  
                  for( ; j <= size.width - VecOp::SIZE - cn*2; j += VecOp::SIZE )
                  {
                      VT p[25];
                      for( k = 0; k < 5; k++ )
                      {
                          const T* rowk = row[k];
                          p[k*5] = vop.load(rowk+j-cn*2); p[k*5+1] = vop.load(rowk+j-cn);
                          p[k*5+2] = vop.load(rowk+j); p[k*5+3] = vop.load(rowk+j+cn);
                          p[k*5+4] = vop.load(rowk+j+cn*2);
                      }
  
                      vop(p[1], p[2]); vop(p[0], p[1]); vop(p[1], p[2]); vop(p[4], p[5]); vop(p[3], p[4]);
                      vop(p[4], p[5]); vop(p[0], p[3]); vop(p[2], p[5]); vop(p[2], p[3]); vop(p[1], p[4]);
                      vop(p[1], p[2]); vop(p[3], p[4]); vop(p[7], p[8]); vop(p[6], p[7]); vop(p[7], p[8]);
                      vop(p[10], p[11]); vop(p[9], p[10]); vop(p[10], p[11]); vop(p[6], p[9]); vop(p[8], p[11]);
                      vop(p[8], p[9]); vop(p[7], p[10]); vop(p[7], p[8]); vop(p[9], p[10]); vop(p[0], p[6]);
                      vop(p[4], p[10]); vop(p[4], p[6]); vop(p[2], p[8]); vop(p[2], p[4]); vop(p[6], p[8]);
                      vop(p[1], p[7]); vop(p[5], p[11]); vop(p[5], p[7]); vop(p[3], p[9]); vop(p[3], p[5]);
                      vop(p[7], p[9]); vop(p[1], p[2]); vop(p[3], p[4]); vop(p[5], p[6]); vop(p[7], p[8]);
                      vop(p[9], p[10]); vop(p[13], p[14]); vop(p[12], p[13]); vop(p[13], p[14]); vop(p[16], p[17]);
                      vop(p[15], p[16]); vop(p[16], p[17]); vop(p[12], p[15]); vop(p[14], p[17]); vop(p[14], p[15]);
                      vop(p[13], p[16]); vop(p[13], p[14]); vop(p[15], p[16]); vop(p[19], p[20]); vop(p[18], p[19]);
                      vop(p[19], p[20]); vop(p[21], p[22]); vop(p[23], p[24]); vop(p[21], p[23]); vop(p[22], p[24]);
                      vop(p[22], p[23]); vop(p[18], p[21]); vop(p[20], p[23]); vop(p[20], p[21]); vop(p[19], p[22]);
                      vop(p[22], p[24]); vop(p[19], p[20]); vop(p[21], p[22]); vop(p[23], p[24]); vop(p[12], p[18]);
                      vop(p[16], p[22]); vop(p[16], p[18]); vop(p[14], p[20]); vop(p[20], p[24]); vop(p[14], p[16]);
                      vop(p[18], p[20]); vop(p[22], p[24]); vop(p[13], p[19]); vop(p[17], p[23]); vop(p[17], p[19]);
                      vop(p[15], p[21]); vop(p[15], p[17]); vop(p[19], p[21]); vop(p[13], p[14]); vop(p[15], p[16]);
                      vop(p[17], p[18]); vop(p[19], p[20]); vop(p[21], p[22]); vop(p[23], p[24]); vop(p[0], p[12]);
                      vop(p[8], p[20]); vop(p[8], p[12]); vop(p[4], p[16]); vop(p[16], p[24]); vop(p[12], p[16]);
                      vop(p[2], p[14]); vop(p[10], p[22]); vop(p[10], p[14]); vop(p[6], p[18]); vop(p[6], p[10]);
                      vop(p[10], p[12]); vop(p[1], p[13]); vop(p[9], p[21]); vop(p[9], p[13]); vop(p[5], p[17]);
                      vop(p[13], p[17]); vop(p[3], p[15]); vop(p[11], p[23]); vop(p[11], p[15]); vop(p[7], p[19]);
                      vop(p[7], p[11]); vop(p[11], p[13]); vop(p[11], p[12]);
                      vop.store(dst+j, p[12]);
                  }
  
                  limit = size.width;
              }
          }
      }
  }
  
  #ifdef HAVE_OPENCL
  
  static bool ocl_medianFilter(InputArray _src, OutputArray _dst, int m)
  {
      size_t localsize[2] = { 16, 16 };
      size_t globalsize[2];
      int type = _src.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type);
  
      if ( !((depth == CV_8U || depth == CV_16U || depth == CV_16S || depth == CV_32F) && cn <= 4 && (m == 3 || m == 5)) )
          return false;
  
      Size imgSize = _src.size();
      bool useOptimized = (1 == cn) &&
                          (size_t)imgSize.width >= localsize[0] * 8  &&
                          (size_t)imgSize.height >= localsize[1] * 8 &&
                          imgSize.width % 4 == 0 &&
                          imgSize.height % 4 == 0 &&
                          (ocl::Device::getDefault().isIntel());
  
      cv::String kname = format( useOptimized ? "medianFilter%d_u" : "medianFilter%d", m) ;
      cv::String kdefs = useOptimized ?
                           format("-D T=%s -D T1=%s -D T4=%s%d -D cn=%d -D USE_4OPT", ocl::typeToStr(type),
                           ocl::typeToStr(depth), ocl::typeToStr(depth), cn*4, cn)
                           :
                           format("-D T=%s -D T1=%s -D cn=%d", ocl::typeToStr(type), ocl::typeToStr(depth), cn) ;
  
      ocl::Kernel k(kname.c_str(), ocl::imgproc::medianFilter_oclsrc, kdefs.c_str() );
  
      if (k.empty())
          return false;
  
      UMat src = _src.getUMat();
      _dst.create(src.size(), type);
      UMat dst = _dst.getUMat();
  
      k.args(ocl::KernelArg::ReadOnlyNoSize(src), ocl::KernelArg::WriteOnly(dst));
  
      if( useOptimized )
      {
          globalsize[0] = DIVUP(src.cols / 4, localsize[0]) * localsize[0];
          globalsize[1] = DIVUP(src.rows / 4, localsize[1]) * localsize[1];
      }
      else
      {
          globalsize[0] = (src.cols + localsize[0] + 2) / localsize[0] * localsize[0];
          globalsize[1] = (src.rows + localsize[1] - 1) / localsize[1] * localsize[1];
      }
  
      return k.run(2, globalsize, localsize, false);
  }
  
  #endif
  
  }
  
  void cv::medianBlur( InputArray _src0, OutputArray _dst, int ksize )
  {
      CV_Assert( (ksize % 2 == 1) && (_src0.dims() <= 2 ));
  
      if( ksize <= 1 )
      {
          _src0.copyTo(_dst);
          return;
      }
  
      CV_OCL_RUN(_dst.isUMat(),
                 ocl_medianFilter(_src0,_dst, ksize))
  
      Mat src0 = _src0.getMat();
      _dst.create( src0.size(), src0.type() );
      Mat dst = _dst.getMat();
  
  #if IPP_VERSION_X100 >= 801
      CV_IPP_CHECK()
      {
  #define IPP_FILTER_MEDIAN_BORDER(ippType, ippDataType, flavor) \
          do \
          { \
              if (ippiFilterMedianBorderGetBufferSize(dstRoiSize, maskSize, \
                  ippDataType, CV_MAT_CN(type), &bufSize) >= 0) \
              { \
                  Ipp8u * buffer = ippsMalloc_8u(bufSize); \
                  IppStatus status = ippiFilterMedianBorder_##flavor(src.ptr<ippType>(), (int)src.step, \
                      dst.ptr<ippType>(), (int)dst.step, dstRoiSize, maskSize, \
                      ippBorderRepl, (ippType)0, buffer); \
                  ippsFree(buffer); \
                  if (status >= 0) \
                  { \
                      CV_IMPL_ADD(CV_IMPL_IPP); \
                      return; \
                  } \
              } \
              setIppErrorStatus(); \
          } \
          while ((void)0, 0)
  
          if( ksize <= 5 )
          {
              Ipp32s bufSize;
              IppiSize dstRoiSize = ippiSize(dst.cols, dst.rows), maskSize = ippiSize(ksize, ksize);
              Mat src;
              if( dst.data != src0.data )
                  src = src0;
              else
                  src0.copyTo(src);
  
              int type = src0.type();
              if (type == CV_8UC1)
                  IPP_FILTER_MEDIAN_BORDER(Ipp8u, ipp8u, 8u_C1R);
              else if (type == CV_16UC1)
                  IPP_FILTER_MEDIAN_BORDER(Ipp16u, ipp16u, 16u_C1R);
              else if (type == CV_16SC1)
                  IPP_FILTER_MEDIAN_BORDER(Ipp16s, ipp16s, 16s_C1R);
              else if (type == CV_32FC1)
                  IPP_FILTER_MEDIAN_BORDER(Ipp32f, ipp32f, 32f_C1R);
          }
  #undef IPP_FILTER_MEDIAN_BORDER
      }
  #endif
  
  #ifdef HAVE_TEGRA_OPTIMIZATION
      if (tegra::medianBlur(src0, dst, ksize))
          return;
  #endif
  
      bool useSortNet = ksize == 3 || (ksize == 5
  #if !(CV_SSE2 || CV_NEON)
              && src0.depth() > CV_8U
  #endif
          );
  
      Mat src;
      if( useSortNet )
      {
          if( dst.data != src0.data )
              src = src0;
          else
              src0.copyTo(src);
  
          if( src.depth() == CV_8U )
              medianBlur_SortNet<MinMax8u, MinMaxVec8u>( src, dst, ksize );
          else if( src.depth() == CV_16U )
              medianBlur_SortNet<MinMax16u, MinMaxVec16u>( src, dst, ksize );
          else if( src.depth() == CV_16S )
              medianBlur_SortNet<MinMax16s, MinMaxVec16s>( src, dst, ksize );
          else if( src.depth() == CV_32F )
              medianBlur_SortNet<MinMax32f, MinMaxVec32f>( src, dst, ksize );
          else
              CV_Error(CV_StsUnsupportedFormat, "");
  
          return;
      }
      else
      {
          cv::copyMakeBorder( src0, src, 0, 0, ksize/2, ksize/2, BORDER_REPLICATE );
  
          int cn = src0.channels();
          CV_Assert( src.depth() == CV_8U && (cn == 1 || cn == 3 || cn == 4) );
  
          double img_size_mp = (double)(src0.total())/(1 << 20);
          if( ksize <= 3 + (img_size_mp < 1 ? 12 : img_size_mp < 4 ? 6 : 2)*
              (MEDIAN_HAVE_SIMD && (checkHardwareSupport(CV_CPU_SSE2) || checkHardwareSupport(CV_CPU_NEON)) ? 1 : 3))
              medianBlur_8u_Om( src, dst, ksize );
          else
              medianBlur_8u_O1( src, dst, ksize );
      }
  }
  
  /****************************************************************************************\
                                     Bilateral Filtering
  \****************************************************************************************/
  
  namespace cv
  {
  
  class BilateralFilter_8u_Invoker :
      public ParallelLoopBody
  {
  public:
      BilateralFilter_8u_Invoker(Mat& _dest, const Mat& _temp, int _radius, int _maxk,
          int* _space_ofs, float *_space_weight, float *_color_weight) :
          temp(&_temp), dest(&_dest), radius(_radius),
          maxk(_maxk), space_ofs(_space_ofs), space_weight(_space_weight), color_weight(_color_weight)
      {
      }
  
      virtual void operator() (const Range& range) const
      {
          int i, j, cn = dest->channels(), k;
          Size size = dest->size();
          #if CV_SSE3
          int CV_DECL_ALIGNED(16) buf[4];
          float CV_DECL_ALIGNED(16) bufSum[4];
          static const int CV_DECL_ALIGNED(16) bufSignMask[] = { 0x80000000, 0x80000000, 0x80000000, 0x80000000 };
          bool haveSSE3 = checkHardwareSupport(CV_CPU_SSE3);
          #endif
  
          for( i = range.start; i < range.end; i++ )
          {
              const uchar* sptr = temp->ptr(i+radius) + radius*cn;
              uchar* dptr = dest->ptr(i);
  
              if( cn == 1 )
              {
                  for( j = 0; j < size.width; j++ )
                  {
                      float sum = 0, wsum = 0;
                      int val0 = sptr[j];
                      k = 0;
                      #if CV_SSE3
                      if( haveSSE3 )
                      {
                          __m128 _val0 = _mm_set1_ps(static_cast<float>(val0));
                          const __m128 _signMask = _mm_load_ps((const float*)bufSignMask);
  
                          for( ; k <= maxk - 4; k += 4 )
                          {
                              __m128 _valF = _mm_set_ps(sptr[j + space_ofs[k+3]], sptr[j + space_ofs[k+2]],
                                                        sptr[j + space_ofs[k+1]], sptr[j + space_ofs[k]]);
  
                              __m128 _val = _mm_andnot_ps(_signMask, _mm_sub_ps(_valF, _val0));
                              _mm_store_si128((__m128i*)buf, _mm_cvtps_epi32(_val));
  
                              __m128 _cw = _mm_set_ps(color_weight[buf[3]],color_weight[buf[2]],
                                                      color_weight[buf[1]],color_weight[buf[0]]);
                              __m128 _sw = _mm_loadu_ps(space_weight+k);
                              __m128 _w = _mm_mul_ps(_cw, _sw);
                               _cw = _mm_mul_ps(_w, _valF);
  
                               _sw = _mm_hadd_ps(_w, _cw);
                               _sw = _mm_hadd_ps(_sw, _sw);
                               _mm_storel_pi((__m64*)bufSum, _sw);
  
                               sum += bufSum[1];
                               wsum += bufSum[0];
                          }
                      }
                      #endif
                      for( ; k < maxk; k++ )
                      {
                          int val = sptr[j + space_ofs[k]];
                          float w = space_weight[k]*color_weight[std::abs(val - val0)];
                          sum += val*w;
                          wsum += w;
                      }
                      // overflow is not possible here => there is no need to use cv::saturate_cast
                      dptr[j] = (uchar)cvRound(sum/wsum);
                  }
              }
              else
              {
                  assert( cn == 3 );
                  for( j = 0; j < size.width*3; j += 3 )
                  {
                      float sum_b = 0, sum_g = 0, sum_r = 0, wsum = 0;
                      int b0 = sptr[j], g0 = sptr[j+1], r0 = sptr[j+2];
                      k = 0;
                      #if CV_SSE3
                      if( haveSSE3 )
                      {
                          const __m128i izero = _mm_setzero_si128();
                          const __m128 _b0 = _mm_set1_ps(static_cast<float>(b0));
                          const __m128 _g0 = _mm_set1_ps(static_cast<float>(g0));
                          const __m128 _r0 = _mm_set1_ps(static_cast<float>(r0));
                          const __m128 _signMask = _mm_load_ps((const float*)bufSignMask);
  
                          for( ; k <= maxk - 4; k += 4 )
                          {
                              const int* const sptr_k0  = reinterpret_cast<const int*>(sptr + j + space_ofs[k]);
                              const int* const sptr_k1  = reinterpret_cast<const int*>(sptr + j + space_ofs[k+1]);
                              const int* const sptr_k2  = reinterpret_cast<const int*>(sptr + j + space_ofs[k+2]);
                              const int* const sptr_k3  = reinterpret_cast<const int*>(sptr + j + space_ofs[k+3]);
  
                              __m128 _b = _mm_cvtepi32_ps(_mm_unpacklo_epi16(_mm_unpacklo_epi8(_mm_cvtsi32_si128(sptr_k0[0]), izero), izero));
                              __m128 _g = _mm_cvtepi32_ps(_mm_unpacklo_epi16(_mm_unpacklo_epi8(_mm_cvtsi32_si128(sptr_k1[0]), izero), izero));
                              __m128 _r = _mm_cvtepi32_ps(_mm_unpacklo_epi16(_mm_unpacklo_epi8(_mm_cvtsi32_si128(sptr_k2[0]), izero), izero));
                              __m128 _z = _mm_cvtepi32_ps(_mm_unpacklo_epi16(_mm_unpacklo_epi8(_mm_cvtsi32_si128(sptr_k3[0]), izero), izero));
  
                              _MM_TRANSPOSE4_PS(_b, _g, _r, _z);
  
                              __m128 bt = _mm_andnot_ps(_signMask, _mm_sub_ps(_b,_b0));
                              __m128 gt = _mm_andnot_ps(_signMask, _mm_sub_ps(_g,_g0));
                              __m128 rt = _mm_andnot_ps(_signMask, _mm_sub_ps(_r,_r0));
  
                              bt =_mm_add_ps(rt, _mm_add_ps(bt, gt));
                              _mm_store_si128((__m128i*)buf, _mm_cvtps_epi32(bt));
  
                              __m128 _w  = _mm_set_ps(color_weight[buf[3]],color_weight[buf[2]],
                                                      color_weight[buf[1]],color_weight[buf[0]]);
                              __m128 _sw = _mm_loadu_ps(space_weight+k);
  
                              _w = _mm_mul_ps(_w,_sw);
                              _b = _mm_mul_ps(_b, _w);
                              _g = _mm_mul_ps(_g, _w);
                              _r = _mm_mul_ps(_r, _w);
  
                               _w = _mm_hadd_ps(_w, _b);
                               _g = _mm_hadd_ps(_g, _r);
  
                               _w = _mm_hadd_ps(_w, _g);
                               _mm_store_ps(bufSum, _w);
  
                               wsum  += bufSum[0];
                               sum_b += bufSum[1];
                               sum_g += bufSum[2];
                               sum_r += bufSum[3];
                           }
                      }
                      #endif
  
                      for( ; k < maxk; k++ )
                      {
                          const uchar* sptr_k = sptr + j + space_ofs[k];
                          int b = sptr_k[0], g = sptr_k[1], r = sptr_k[2];
                          float w = space_weight[k]*color_weight[std::abs(b - b0) +
                                                                 std::abs(g - g0) + std::abs(r - r0)];
                          sum_b += b*w; sum_g += g*w; sum_r += r*w;
                          wsum += w;
                      }
                      wsum = 1.f/wsum;
                      b0 = cvRound(sum_b*wsum);
                      g0 = cvRound(sum_g*wsum);
                      r0 = cvRound(sum_r*wsum);
                      dptr[j] = (uchar)b0; dptr[j+1] = (uchar)g0; dptr[j+2] = (uchar)r0;
                  }
              }
          }
      }
  
  private:
      const Mat *temp;
      Mat *dest;
      int radius, maxk, *space_ofs;
      float *space_weight, *color_weight;
  };
  
  #if defined (HAVE_IPP) && !defined(HAVE_IPP_ICV_ONLY) && 0
  class IPPBilateralFilter_8u_Invoker :
      public ParallelLoopBody
  {
  public:
      IPPBilateralFilter_8u_Invoker(Mat &_src, Mat &_dst, double _sigma_color, double _sigma_space, int _radius, bool *_ok) :
        ParallelLoopBody(), src(_src), dst(_dst), sigma_color(_sigma_color), sigma_space(_sigma_space), radius(_radius), ok(_ok)
        {
            *ok = true;
        }
  
        virtual void operator() (const Range& range) const
        {
            int d = radius * 2 + 1;
            IppiSize kernel = {d, d};
            IppiSize roi={dst.cols, range.end - range.start};
            int bufsize=0;
            if (0 > ippiFilterBilateralGetBufSize_8u_C1R( ippiFilterBilateralGauss, roi, kernel, &bufsize))
            {
                *ok = false;
                return;
            }
            AutoBuffer<uchar> buf(bufsize);
            IppiFilterBilateralSpec *pSpec = (IppiFilterBilateralSpec *)alignPtr(&buf[0], 32);
            if (0 > ippiFilterBilateralInit_8u_C1R( ippiFilterBilateralGauss, kernel, (Ipp32f)sigma_color, (Ipp32f)sigma_space, 1, pSpec ))
            {
                *ok = false;
                return;
            }
            if (0 > ippiFilterBilateral_8u_C1R( src.ptr<uchar>(range.start) + radius * ((int)src.step[0] + 1), (int)src.step[0], dst.ptr<uchar>(range.start), (int)dst.step[0], roi, kernel, pSpec ))
                *ok = false;
            else
            {
              CV_IMPL_ADD(CV_IMPL_IPP|CV_IMPL_MT);
            }
        }
  private:
      Mat &src;
      Mat &dst;
      double sigma_color;
      double sigma_space;
      int radius;
      bool *ok;
      const IPPBilateralFilter_8u_Invoker& operator= (const IPPBilateralFilter_8u_Invoker&);
  };
  #endif
  
  #ifdef HAVE_OPENCL
  
  static bool ocl_bilateralFilter_8u(InputArray _src, OutputArray _dst, int d,
                                     double sigma_color, double sigma_space,
                                     int borderType)
  {
      int type = _src.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type);
      int i, j, maxk, radius;
  
      if (depth != CV_8U || cn > 4)
          return false;
  
      if (sigma_color <= 0)
          sigma_color = 1;
      if (sigma_space <= 0)
          sigma_space = 1;
  
      double gauss_color_coeff = -0.5 / (sigma_color * sigma_color);
      double gauss_space_coeff = -0.5 / (sigma_space * sigma_space);
  
      if ( d <= 0 )
          radius = cvRound(sigma_space * 1.5);
      else
          radius = d / 2;
      radius = MAX(radius, 1);
      d = radius * 2 + 1;
  
      UMat src = _src.getUMat(), dst = _dst.getUMat(), temp;
      if (src.u == dst.u)
          return false;
  
      copyMakeBorder(src, temp, radius, radius, radius, radius, borderType);
      std::vector<float> _space_weight(d * d);
      std::vector<int> _space_ofs(d * d);
      float * const space_weight = &_space_weight[0];
      int * const space_ofs = &_space_ofs[0];
  
      // initialize space-related bilateral filter coefficients
      for( i = -radius, maxk = 0; i <= radius; i++ )
          for( j = -radius; j <= radius; j++ )
          {
              double r = std::sqrt((double)i * i + (double)j * j);
              if ( r > radius )
                  continue;
              space_weight[maxk] = (float)std::exp(r * r * gauss_space_coeff);
              space_ofs[maxk++] = (int)(i * temp.step + j * cn);
          }
  
      char cvt[3][40];
      String cnstr = cn > 1 ? format("%d", cn) : "";
      String kernelName("bilateral");
      size_t sizeDiv = 1;
      if ((ocl::Device::getDefault().isIntel()) &&
          (ocl::Device::getDefault().type() == ocl::Device::TYPE_GPU))
      {
              //Intel GPU
              if (dst.cols % 4 == 0 && cn == 1) // For single channel x4 sized images.
              {
                  kernelName = "bilateral_float4";
                  sizeDiv = 4;
              }
       }
       ocl::Kernel k(kernelName.c_str(), ocl::imgproc::bilateral_oclsrc,
              format("-D radius=%d -D maxk=%d -D cn=%d -D int_t=%s -D uint_t=uint%s -D convert_int_t=%s"
              " -D uchar_t=%s -D float_t=%s -D convert_float_t=%s -D convert_uchar_t=%s -D gauss_color_coeff=%f",
              radius, maxk, cn, ocl::typeToStr(CV_32SC(cn)), cnstr.c_str(),
              ocl::convertTypeStr(CV_8U, CV_32S, cn, cvt[0]),
              ocl::typeToStr(type), ocl::typeToStr(CV_32FC(cn)),
              ocl::convertTypeStr(CV_32S, CV_32F, cn, cvt[1]),
              ocl::convertTypeStr(CV_32F, CV_8U, cn, cvt[2]), gauss_color_coeff));
      if (k.empty())
          return false;
  
      Mat mspace_weight(1, d * d, CV_32FC1, space_weight);
      Mat mspace_ofs(1, d * d, CV_32SC1, space_ofs);
      UMat ucolor_weight, uspace_weight, uspace_ofs;
  
      mspace_weight.copyTo(uspace_weight);
      mspace_ofs.copyTo(uspace_ofs);
  
      k.args(ocl::KernelArg::ReadOnlyNoSize(temp), ocl::KernelArg::WriteOnly(dst),
             ocl::KernelArg::PtrReadOnly(uspace_weight),
             ocl::KernelArg::PtrReadOnly(uspace_ofs));
  
      size_t globalsize[2] = { dst.cols / sizeDiv, dst.rows };
      return k.run(2, globalsize, NULL, false);
  }
  
  #endif
  static void
  bilateralFilter_8u( const Mat& src, Mat& dst, int d,
      double sigma_color, double sigma_space,
      int borderType )
  {
      int cn = src.channels();
      int i, j, maxk, radius;
      Size size = src.size();
  
      CV_Assert( (src.type() == CV_8UC1 || src.type() == CV_8UC3) && src.data != dst.data );
  
      if( sigma_color <= 0 )
          sigma_color = 1;
      if( sigma_space <= 0 )
          sigma_space = 1;
  
      double gauss_color_coeff = -0.5/(sigma_color*sigma_color);
      double gauss_space_coeff = -0.5/(sigma_space*sigma_space);
  
      if( d <= 0 )
          radius = cvRound(sigma_space*1.5);
      else
          radius = d/2;
      radius = MAX(radius, 1);
      d = radius*2 + 1;
  
      Mat temp;
      copyMakeBorder( src, temp, radius, radius, radius, radius, borderType );
  
  #if defined HAVE_IPP && (IPP_VERSION_MAJOR >= 7) && 0
      CV_IPP_CHECK()
      {
          if( cn == 1 )
          {
              bool ok;
              IPPBilateralFilter_8u_Invoker body(temp, dst, sigma_color * sigma_color, sigma_space * sigma_space, radius, &ok );
              parallel_for_(Range(0, dst.rows), body, dst.total()/(double)(1<<16));
              if( ok )
              {
                  CV_IMPL_ADD(CV_IMPL_IPP|CV_IMPL_MT);
                  return;
              }
              setIppErrorStatus();
          }
      }
  #endif
  
      std::vector<float> _color_weight(cn*256);
      std::vector<float> _space_weight(d*d);
      std::vector<int> _space_ofs(d*d);
      float* color_weight = &_color_weight[0];
      float* space_weight = &_space_weight[0];
      int* space_ofs = &_space_ofs[0];
  
      // initialize color-related bilateral filter coefficients
  
      for( i = 0; i < 256*cn; i++ )
          color_weight[i] = (float)std::exp(i*i*gauss_color_coeff);
  
      // initialize space-related bilateral filter coefficients
      for( i = -radius, maxk = 0; i <= radius; i++ )
      {
          j = -radius;
  
          for( ; j <= radius; j++ )
          {
              double r = std::sqrt((double)i*i + (double)j*j);
              if( r > radius )
                  continue;
              space_weight[maxk] = (float)std::exp(r*r*gauss_space_coeff);
              space_ofs[maxk++] = (int)(i*temp.step + j*cn);
          }
      }
  
      BilateralFilter_8u_Invoker body(dst, temp, radius, maxk, space_ofs, space_weight, color_weight);
      parallel_for_(Range(0, size.height), body, dst.total()/(double)(1<<16));
  }
  
  
  class BilateralFilter_32f_Invoker :
      public ParallelLoopBody
  {
  public:
  
      BilateralFilter_32f_Invoker(int _cn, int _radius, int _maxk, int *_space_ofs,
          const Mat& _temp, Mat& _dest, float _scale_index, float *_space_weight, float *_expLUT) :
          cn(_cn), radius(_radius), maxk(_maxk), space_ofs(_space_ofs),
          temp(&_temp), dest(&_dest), scale_index(_scale_index), space_weight(_space_weight), expLUT(_expLUT)
      {
      }
  
      virtual void operator() (const Range& range) const
      {
          int i, j, k;
          Size size = dest->size();
          #if CV_SSE3
          int CV_DECL_ALIGNED(16) idxBuf[4];
          float CV_DECL_ALIGNED(16) bufSum32[4];
          static const int CV_DECL_ALIGNED(16) bufSignMask[] = { 0x80000000, 0x80000000, 0x80000000, 0x80000000 };
          bool haveSSE3 = checkHardwareSupport(CV_CPU_SSE3);
          #endif
  
          for( i = range.start; i < range.end; i++ )
          {
              const float* sptr = temp->ptr<float>(i+radius) + radius*cn;
              float* dptr = dest->ptr<float>(i);
  
              if( cn == 1 )
              {
                  for( j = 0; j < size.width; j++ )
                  {
                      float sum = 0, wsum = 0;
                      float val0 = sptr[j];
                      k = 0;
                      #if CV_SSE3
                      if( haveSSE3 )
                      {
                          __m128 psum = _mm_setzero_ps();
                          const __m128 _val0 = _mm_set1_ps(sptr[j]);
                          const __m128 _scale_index = _mm_set1_ps(scale_index);
                          const __m128 _signMask = _mm_load_ps((const float*)bufSignMask);
  
                          for( ; k <= maxk - 4 ; k += 4 )
                          {
                              __m128 _sw    = _mm_loadu_ps(space_weight + k);
                              __m128 _val   = _mm_set_ps(sptr[j + space_ofs[k+3]], sptr[j + space_ofs[k+2]],
                                                         sptr[j + space_ofs[k+1]], sptr[j + space_ofs[k]]);
                              __m128 _alpha = _mm_mul_ps(_mm_andnot_ps( _signMask, _mm_sub_ps(_val,_val0)), _scale_index);
  
                              __m128i _idx = _mm_cvtps_epi32(_alpha);
                              _mm_store_si128((__m128i*)idxBuf, _idx);
                              _alpha = _mm_sub_ps(_alpha, _mm_cvtepi32_ps(_idx));
  
                              __m128 _explut  = _mm_set_ps(expLUT[idxBuf[3]], expLUT[idxBuf[2]],
                                                           expLUT[idxBuf[1]], expLUT[idxBuf[0]]);
                              __m128 _explut1 = _mm_set_ps(expLUT[idxBuf[3]+1], expLUT[idxBuf[2]+1],
                                                           expLUT[idxBuf[1]+1], expLUT[idxBuf[0]+1]);
  
                              __m128 _w = _mm_mul_ps(_sw, _mm_add_ps(_explut, _mm_mul_ps(_alpha, _mm_sub_ps(_explut1, _explut))));
                              _val = _mm_mul_ps(_w, _val);
  
                               _sw = _mm_hadd_ps(_w, _val);
                               _sw = _mm_hadd_ps(_sw, _sw);
                               psum = _mm_add_ps(_sw, psum);
                          }
                          _mm_storel_pi((__m64*)bufSum32, psum);
  
                          sum = bufSum32[1];
                          wsum = bufSum32[0];
                      }
                      #endif
  
                      for( ; k < maxk; k++ )
                      {
                          float val = sptr[j + space_ofs[k]];
                          float alpha = (float)(std::abs(val - val0)*scale_index);
                          int idx = cvFloor(alpha);
                          alpha -= idx;
                          float w = space_weight[k]*(expLUT[idx] + alpha*(expLUT[idx+1] - expLUT[idx]));
                          sum += val*w;
                          wsum += w;
                      }
                      dptr[j] = (float)(sum/wsum);
                  }
              }
              else
              {
                  CV_Assert( cn == 3 );
                  for( j = 0; j < size.width*3; j += 3 )
                  {
                      float sum_b = 0, sum_g = 0, sum_r = 0, wsum = 0;
                      float b0 = sptr[j], g0 = sptr[j+1], r0 = sptr[j+2];
                      k = 0;
                      #if  CV_SSE3
                      if( haveSSE3 )
                      {
                          __m128 sum = _mm_setzero_ps();
                          const __m128 _b0 = _mm_set1_ps(b0);
                          const __m128 _g0 = _mm_set1_ps(g0);
                          const __m128 _r0 = _mm_set1_ps(r0);
                          const __m128 _scale_index = _mm_set1_ps(scale_index);
                          const __m128 _signMask = _mm_load_ps((const float*)bufSignMask);
  
                          for( ; k <= maxk-4; k += 4 )
                          {
                              __m128 _sw = _mm_loadu_ps(space_weight + k);
  
                              const float* const sptr_k0 = sptr + j + space_ofs[k];
                              const float* const sptr_k1 = sptr + j + space_ofs[k+1];
                              const float* const sptr_k2 = sptr + j + space_ofs[k+2];
                              const float* const sptr_k3 = sptr + j + space_ofs[k+3];
  
                              __m128 _b = _mm_loadu_ps(sptr_k0);
                              __m128 _g = _mm_loadu_ps(sptr_k1);
                              __m128 _r = _mm_loadu_ps(sptr_k2);
                              __m128 _z = _mm_loadu_ps(sptr_k3);
                              _MM_TRANSPOSE4_PS(_b, _g, _r, _z);
  
                              __m128 _bt = _mm_andnot_ps(_signMask,_mm_sub_ps(_b,_b0));
                              __m128 _gt = _mm_andnot_ps(_signMask,_mm_sub_ps(_g,_g0));
                              __m128 _rt = _mm_andnot_ps(_signMask,_mm_sub_ps(_r,_r0));
  
                              __m128 _alpha = _mm_mul_ps(_scale_index, _mm_add_ps(_rt,_mm_add_ps(_bt, _gt)));
  
                              __m128i _idx  = _mm_cvtps_epi32(_alpha);
                              _mm_store_si128((__m128i*)idxBuf, _idx);
                              _alpha = _mm_sub_ps(_alpha, _mm_cvtepi32_ps(_idx));
  
                              __m128 _explut  = _mm_set_ps(expLUT[idxBuf[3]], expLUT[idxBuf[2]], expLUT[idxBuf[1]], expLUT[idxBuf[0]]);
                              __m128 _explut1 = _mm_set_ps(expLUT[idxBuf[3]+1], expLUT[idxBuf[2]+1], expLUT[idxBuf[1]+1], expLUT[idxBuf[0]+1]);
  
                              __m128 _w = _mm_mul_ps(_sw, _mm_add_ps(_explut, _mm_mul_ps(_alpha, _mm_sub_ps(_explut1, _explut))));
  
                              _b = _mm_mul_ps(_b, _w);
                              _g = _mm_mul_ps(_g, _w);
                              _r = _mm_mul_ps(_r, _w);
  
                               _w = _mm_hadd_ps(_w, _b);
                               _g = _mm_hadd_ps(_g, _r);
  
                               _w = _mm_hadd_ps(_w, _g);
                               sum = _mm_add_ps(sum, _w);
                          }
                          _mm_store_ps(bufSum32, sum);
                          wsum  = bufSum32[0];
                          sum_b = bufSum32[1];
                          sum_g = bufSum32[2];
                          sum_r = bufSum32[3];
                      }
                      #endif
  
                      for(; k < maxk; k++ )
                      {
                          const float* sptr_k = sptr + j + space_ofs[k];
                          float b = sptr_k[0], g = sptr_k[1], r = sptr_k[2];
                          float alpha = (float)((std::abs(b - b0) +
                              std::abs(g - g0) + std::abs(r - r0))*scale_index);
                          int idx = cvFloor(alpha);
                          alpha -= idx;
                          float w = space_weight[k]*(expLUT[idx] + alpha*(expLUT[idx+1] - expLUT[idx]));
                          sum_b += b*w; sum_g += g*w; sum_r += r*w;
                          wsum += w;
                      }
                      wsum = 1.f/wsum;
                      b0 = sum_b*wsum;
                      g0 = sum_g*wsum;
                      r0 = sum_r*wsum;
                      dptr[j] = b0; dptr[j+1] = g0; dptr[j+2] = r0;
                  }
              }
          }
      }
  
  private:
      int cn, radius, maxk, *space_ofs;
      const Mat* temp;
      Mat *dest;
      float scale_index, *space_weight, *expLUT;
  };
  
  
  static void
  bilateralFilter_32f( const Mat& src, Mat& dst, int d,
                       double sigma_color, double sigma_space,
                       int borderType )
  {
      int cn = src.channels();
      int i, j, maxk, radius;
      double minValSrc=-1, maxValSrc=1;
      const int kExpNumBinsPerChannel = 1 << 12;
      int kExpNumBins = 0;
      float lastExpVal = 1.f;
      float len, scale_index;
      Size size = src.size();
  
      CV_Assert( (src.type() == CV_32FC1 || src.type() == CV_32FC3) && src.data != dst.data );
  
      if( sigma_color <= 0 )
          sigma_color = 1;
      if( sigma_space <= 0 )
          sigma_space = 1;
  
      double gauss_color_coeff = -0.5/(sigma_color*sigma_color);
      double gauss_space_coeff = -0.5/(sigma_space*sigma_space);
  
      if( d <= 0 )
          radius = cvRound(sigma_space*1.5);
      else
          radius = d/2;
      radius = MAX(radius, 1);
      d = radius*2 + 1;
      // compute the min/max range for the input image (even if multichannel)
  
      minMaxLoc( src.reshape(1), &minValSrc, &maxValSrc );
      if(std::abs(minValSrc - maxValSrc) < FLT_EPSILON)
      {
          src.copyTo(dst);
          return;
      }
  
      // temporary copy of the image with borders for easy processing
      Mat temp;
      copyMakeBorder( src, temp, radius, radius, radius, radius, borderType );
      const double insteadNaNValue = -5. * sigma_color;
      patchNaNs( temp, insteadNaNValue ); // this replacement of NaNs makes the assumption that depth values are nonnegative
                                          // TODO: make insteadNaNValue avalible in the outside function interface to control the cases breaking the assumption
      // allocate lookup tables
      std::vector<float> _space_weight(d*d);
      std::vector<int> _space_ofs(d*d);
      float* space_weight = &_space_weight[0];
      int* space_ofs = &_space_ofs[0];
  
      // assign a length which is slightly more than needed
      len = (float)(maxValSrc - minValSrc) * cn;
      kExpNumBins = kExpNumBinsPerChannel * cn;
      std::vector<float> _expLUT(kExpNumBins+2);
      float* expLUT = &_expLUT[0];
  
      scale_index = kExpNumBins/len;
  
      // initialize the exp LUT
      for( i = 0; i < kExpNumBins+2; i++ )
      {
          if( lastExpVal > 0.f )
          {
              double val =  i / scale_index;
              expLUT[i] = (float)std::exp(val * val * gauss_color_coeff);
              lastExpVal = expLUT[i];
          }
          else
              expLUT[i] = 0.f;
      }
  
      // initialize space-related bilateral filter coefficients
      for( i = -radius, maxk = 0; i <= radius; i++ )
          for( j = -radius; j <= radius; j++ )
          {
              double r = std::sqrt((double)i*i + (double)j*j);
              if( r > radius )
                  continue;
              space_weight[maxk] = (float)std::exp(r*r*gauss_space_coeff);
              space_ofs[maxk++] = (int)(i*(temp.step/sizeof(float)) + j*cn);
          }
  
      // parallel_for usage
  
      BilateralFilter_32f_Invoker body(cn, radius, maxk, space_ofs, temp, dst, scale_index, space_weight, expLUT);
      parallel_for_(Range(0, size.height), body, dst.total()/(double)(1<<16));
  }
  
  }
  
  void cv::bilateralFilter( InputArray _src, OutputArray _dst, int d,
                        double sigmaColor, double sigmaSpace,
                        int borderType )
  {
      _dst.create( _src.size(), _src.type() );
  
      CV_OCL_RUN(_src.dims() <= 2 && _dst.isUMat(),
                 ocl_bilateralFilter_8u(_src, _dst, d, sigmaColor, sigmaSpace, borderType))
  
      Mat src = _src.getMat(), dst = _dst.getMat();
  
      if( src.depth() == CV_8U )
          bilateralFilter_8u( src, dst, d, sigmaColor, sigmaSpace, borderType );
      else if( src.depth() == CV_32F )
          bilateralFilter_32f( src, dst, d, sigmaColor, sigmaSpace, borderType );
      else
          CV_Error( CV_StsUnsupportedFormat,
          "Bilateral filtering is only implemented for 8u and 32f images" );
  }
  
  //////////////////////////////////////////////////////////////////////////////////////////
  
  CV_IMPL void
  cvSmooth( const void* srcarr, void* dstarr, int smooth_type,
            int param1, int param2, double param3, double param4 )
  {
      cv::Mat src = cv::cvarrToMat(srcarr), dst0 = cv::cvarrToMat(dstarr), dst = dst0;
  
      CV_Assert( dst.size() == src.size() &&
          (smooth_type == CV_BLUR_NO_SCALE || dst.type() == src.type()) );
  
      if( param2 <= 0 )
          param2 = param1;
  
      if( smooth_type == CV_BLUR || smooth_type == CV_BLUR_NO_SCALE )
          cv::boxFilter( src, dst, dst.depth(), cv::Size(param1, param2), cv::Point(-1,-1),
              smooth_type == CV_BLUR, cv::BORDER_REPLICATE );
      else if( smooth_type == CV_GAUSSIAN )
          cv::GaussianBlur( src, dst, cv::Size(param1, param2), param3, param4, cv::BORDER_REPLICATE );
      else if( smooth_type == CV_MEDIAN )
          cv::medianBlur( src, dst, param1 );
      else
          cv::bilateralFilter( src, dst, param1, param3, param4, cv::BORDER_REPLICATE );
  
      if( dst.data != dst0.data )
          CV_Error( CV_StsUnmatchedFormats, "The destination image does not have the proper type" );
  }
  
  /* End of file. */
  