text: /*M///////////////////////////////////////////////////////////////////////////////////////
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  //  If you do not agree to this license, do not download, install,
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  //
  //
  //                          License Agreement
  //                For Open Source Computer Vision Library
  //
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  //M*/
  
  #include "precomp.hpp"
  #include "opencl_kernels_stitching.hpp"
  
  namespace cv {
  namespace detail {
  
  static const float WEIGHT_EPS = 1e-5f;
  
  Ptr<Blender> Blender::createDefault(int type, bool try_gpu)
  {
      if (type == NO)
          return makePtr<Blender>();
      if (type == FEATHER)
          return makePtr<FeatherBlender>();
      if (type == MULTI_BAND)
          return makePtr<MultiBandBlender>(try_gpu);
      CV_Error(Error::StsBadArg, "unsupported blending method");
      return Ptr<Blender>();
  }
  
  
  void Blender::prepare(const std::vector<Point> &corners, const std::vector<Size> &sizes)
  {
      prepare(resultRoi(corners, sizes));
  }
  
  
  void Blender::prepare(Rect dst_roi)
  {
      dst_.create(dst_roi.size(), CV_16SC3);
      dst_.setTo(Scalar::all(0));
      dst_mask_.create(dst_roi.size(), CV_8U);
      dst_mask_.setTo(Scalar::all(0));
      dst_roi_ = dst_roi;
  }
  
  
  void Blender::feed(InputArray _img, InputArray _mask, Point tl)
  {
      Mat img = _img.getMat();
      Mat mask = _mask.getMat();
      Mat dst = dst_.getMat(ACCESS_RW);
      Mat dst_mask = dst_mask_.getMat(ACCESS_RW);
  
      CV_Assert(img.type() == CV_16SC3);
      CV_Assert(mask.type() == CV_8U);
      int dx = tl.x - dst_roi_.x;
      int dy = tl.y - dst_roi_.y;
  
      for (int y = 0; y < img.rows; ++y)
      {
          const Point3_<short> *src_row = img.ptr<Point3_<short> >(y);
          Point3_<short> *dst_row = dst.ptr<Point3_<short> >(dy + y);
          const uchar *mask_row = mask.ptr<uchar>(y);
          uchar *dst_mask_row = dst_mask.ptr<uchar>(dy + y);
  
          for (int x = 0; x < img.cols; ++x)
          {
              if (mask_row[x])
                  dst_row[dx + x] = src_row[x];
              dst_mask_row[dx + x] |= mask_row[x];
          }
      }
  }
  
  
  void Blender::blend(InputOutputArray dst, InputOutputArray dst_mask)
  {
      UMat mask;
      compare(dst_mask_, 0, mask, CMP_EQ);
      dst_.setTo(Scalar::all(0), mask);
      dst.assign(dst_);
      dst_mask.assign(dst_mask_);
      dst_.release();
      dst_mask_.release();
  }
  
  
  void FeatherBlender::prepare(Rect dst_roi)
  {
      Blender::prepare(dst_roi);
      dst_weight_map_.create(dst_roi.size(), CV_32F);
      dst_weight_map_.setTo(0);
  }
  
  
  void FeatherBlender::feed(InputArray _img, InputArray mask, Point tl)
  {
      Mat img = _img.getMat();
      Mat dst = dst_.getMat(ACCESS_RW);
  
      CV_Assert(img.type() == CV_16SC3);
      CV_Assert(mask.type() == CV_8U);
  
      createWeightMap(mask, sharpness_, weight_map_);
      Mat weight_map = weight_map_.getMat(ACCESS_READ);
      Mat dst_weight_map = dst_weight_map_.getMat(ACCESS_RW);
  
      int dx = tl.x - dst_roi_.x;
      int dy = tl.y - dst_roi_.y;
  
      for (int y = 0; y < img.rows; ++y)
      {
          const Point3_<short>* src_row = img.ptr<Point3_<short> >(y);
          Point3_<short>* dst_row = dst.ptr<Point3_<short> >(dy + y);
          const float* weight_row = weight_map.ptr<float>(y);
          float* dst_weight_row = dst_weight_map.ptr<float>(dy + y);
  
          for (int x = 0; x < img.cols; ++x)
          {
              dst_row[dx + x].x += static_cast<short>(src_row[x].x * weight_row[x]);
              dst_row[dx + x].y += static_cast<short>(src_row[x].y * weight_row[x]);
              dst_row[dx + x].z += static_cast<short>(src_row[x].z * weight_row[x]);
              dst_weight_row[dx + x] += weight_row[x];
          }
      }
  }
  
  
  void FeatherBlender::blend(InputOutputArray dst, InputOutputArray dst_mask)
  {
      normalizeUsingWeightMap(dst_weight_map_, dst_);
      compare(dst_weight_map_, WEIGHT_EPS, dst_mask_, CMP_GT);
      Blender::blend(dst, dst_mask);
  }
  
  
  Rect FeatherBlender::createWeightMaps(const std::vector<UMat> &masks, const std::vector<Point> &corners,
                                        std::vector<UMat> &weight_maps)
  {
      weight_maps.resize(masks.size());
      for (size_t i = 0; i < masks.size(); ++i)
          createWeightMap(masks[i], sharpness_, weight_maps[i]);
  
      Rect dst_roi = resultRoi(corners, masks);
      Mat weights_sum(dst_roi.size(), CV_32F);
      weights_sum.setTo(0);
  
      for (size_t i = 0; i < weight_maps.size(); ++i)
      {
          Rect roi(corners[i].x - dst_roi.x, corners[i].y - dst_roi.y,
                   weight_maps[i].cols, weight_maps[i].rows);
          add(weights_sum(roi), weight_maps[i], weights_sum(roi));
      }
  
      for (size_t i = 0; i < weight_maps.size(); ++i)
      {
          Rect roi(corners[i].x - dst_roi.x, corners[i].y - dst_roi.y,
                   weight_maps[i].cols, weight_maps[i].rows);
          Mat tmp = weights_sum(roi);
          tmp.setTo(1, tmp < std::numeric_limits<float>::epsilon());
          divide(weight_maps[i], tmp, weight_maps[i]);
      }
  
      return dst_roi;
  }
  
  
  MultiBandBlender::MultiBandBlender(int try_gpu, int num_bands, int weight_type)
  {
      setNumBands(num_bands);
  
  #if defined(HAVE_OPENCV_CUDAARITHM) && defined(HAVE_OPENCV_CUDAWARPING)
      can_use_gpu_ = try_gpu && cuda::getCudaEnabledDeviceCount();
  #else
      (void) try_gpu;
      can_use_gpu_ = false;
  #endif
  
      CV_Assert(weight_type == CV_32F || weight_type == CV_16S);
      weight_type_ = weight_type;
  }
  
  
  void MultiBandBlender::prepare(Rect dst_roi)
  {
      dst_roi_final_ = dst_roi;
  
      // Crop unnecessary bands
      double max_len = static_cast<double>(std::max(dst_roi.width, dst_roi.height));
      num_bands_ = std::min(actual_num_bands_, static_cast<int>(ceil(std::log(max_len) / std::log(2.0))));
  
      // Add border to the final image, to ensure sizes are divided by (1 << num_bands_)
      dst_roi.width += ((1 << num_bands_) - dst_roi.width % (1 << num_bands_)) % (1 << num_bands_);
      dst_roi.height += ((1 << num_bands_) - dst_roi.height % (1 << num_bands_)) % (1 << num_bands_);
  
      Blender::prepare(dst_roi);
  
      dst_pyr_laplace_.resize(num_bands_ + 1);
      dst_pyr_laplace_[0] = dst_;
  
      dst_band_weights_.resize(num_bands_ + 1);
      dst_band_weights_[0].create(dst_roi.size(), weight_type_);
      dst_band_weights_[0].setTo(0);
  
      for (int i = 1; i <= num_bands_; ++i)
      {
          dst_pyr_laplace_[i].create((dst_pyr_laplace_[i - 1].rows + 1) / 2,
                                     (dst_pyr_laplace_[i - 1].cols + 1) / 2, CV_16SC3);
          dst_band_weights_[i].create((dst_band_weights_[i - 1].rows + 1) / 2,
                                      (dst_band_weights_[i - 1].cols + 1) / 2, weight_type_);
          dst_pyr_laplace_[i].setTo(Scalar::all(0));
          dst_band_weights_[i].setTo(0);
      }
  }
  
  #ifdef HAVE_OPENCL
  static bool ocl_MultiBandBlender_feed(InputArray _src, InputArray _weight,
          InputOutputArray _dst, InputOutputArray _dst_weight)
  {
      String buildOptions = "-D DEFINE_feed";
      ocl::buildOptionsAddMatrixDescription(buildOptions, "src", _src);
      ocl::buildOptionsAddMatrixDescription(buildOptions, "weight", _weight);
      ocl::buildOptionsAddMatrixDescription(buildOptions, "dst", _dst);
      ocl::buildOptionsAddMatrixDescription(buildOptions, "dstWeight", _dst_weight);
      ocl::Kernel k("feed", ocl::stitching::multibandblend_oclsrc, buildOptions);
      if (k.empty())
          return false;
  
      UMat src = _src.getUMat();
  
      k.args(ocl::KernelArg::ReadOnly(src),
             ocl::KernelArg::ReadOnly(_weight.getUMat()),
             ocl::KernelArg::ReadWrite(_dst.getUMat()),
             ocl::KernelArg::ReadWrite(_dst_weight.getUMat())
             );
  
      size_t globalsize[2] = {src.cols, src.rows };
      return k.run(2, globalsize, NULL, false);
  }
  #endif
  
  void MultiBandBlender::feed(InputArray _img, InputArray mask, Point tl)
  {
  #if ENABLE_LOG
      int64 t = getTickCount();
  #endif
  
      UMat img = _img.getUMat();
      CV_Assert(img.type() == CV_16SC3 || img.type() == CV_8UC3);
      CV_Assert(mask.type() == CV_8U);
  
      // Keep source image in memory with small border
      int gap = 3 * (1 << num_bands_);
      Point tl_new(std::max(dst_roi_.x, tl.x - gap),
                   std::max(dst_roi_.y, tl.y - gap));
      Point br_new(std::min(dst_roi_.br().x, tl.x + img.cols + gap),
                   std::min(dst_roi_.br().y, tl.y + img.rows + gap));
  
      // Ensure coordinates of top-left, bottom-right corners are divided by (1 << num_bands_).
      // After that scale between layers is exactly 2.
      //
      // We do it to avoid interpolation problems when keeping sub-images only. There is no such problem when
      // image is bordered to have size equal to the final image size, but this is too memory hungry approach.
      tl_new.x = dst_roi_.x + (((tl_new.x - dst_roi_.x) >> num_bands_) << num_bands_);
      tl_new.y = dst_roi_.y + (((tl_new.y - dst_roi_.y) >> num_bands_) << num_bands_);
      int width = br_new.x - tl_new.x;
      int height = br_new.y - tl_new.y;
      width += ((1 << num_bands_) - width % (1 << num_bands_)) % (1 << num_bands_);
      height += ((1 << num_bands_) - height % (1 << num_bands_)) % (1 << num_bands_);
      br_new.x = tl_new.x + width;
      br_new.y = tl_new.y + height;
      int dy = std::max(br_new.y - dst_roi_.br().y, 0);
      int dx = std::max(br_new.x - dst_roi_.br().x, 0);
      tl_new.x -= dx; br_new.x -= dx;
      tl_new.y -= dy; br_new.y -= dy;
  
      int top = tl.y - tl_new.y;
      int left = tl.x - tl_new.x;
      int bottom = br_new.y - tl.y - img.rows;
      int right = br_new.x - tl.x - img.cols;
  
      // Create the source image Laplacian pyramid
      UMat img_with_border;
      copyMakeBorder(_img, img_with_border, top, bottom, left, right,
                     BORDER_REFLECT);
      LOGLN("  Add border to the source image, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
  #if ENABLE_LOG
      t = getTickCount();
  #endif
  
      std::vector<UMat> src_pyr_laplace;
      if (can_use_gpu_ && img_with_border.depth() == CV_16S)
          createLaplacePyrGpu(img_with_border, num_bands_, src_pyr_laplace);
      else
          createLaplacePyr(img_with_border, num_bands_, src_pyr_laplace);
  
      LOGLN("  Create the source image Laplacian pyramid, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
  #if ENABLE_LOG
      t = getTickCount();
  #endif
  
      // Create the weight map Gaussian pyramid
      UMat weight_map;
      std::vector<UMat> weight_pyr_gauss(num_bands_ + 1);
  
      if(weight_type_ == CV_32F)
      {
          mask.getUMat().convertTo(weight_map, CV_32F, 1./255.);
      }
      else // weight_type_ == CV_16S
      {
          mask.getUMat().convertTo(weight_map, CV_16S);
          UMat add_mask;
          compare(mask, 0, add_mask, CMP_NE);
          add(weight_map, Scalar::all(1), weight_map, add_mask);
      }
  
      copyMakeBorder(weight_map, weight_pyr_gauss[0], top, bottom, left, right, BORDER_CONSTANT);
  
      for (int i = 0; i < num_bands_; ++i)
          pyrDown(weight_pyr_gauss[i], weight_pyr_gauss[i + 1]);
  
      LOGLN("  Create the weight map Gaussian pyramid, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
  #if ENABLE_LOG
      t = getTickCount();
  #endif
  
      int y_tl = tl_new.y - dst_roi_.y;
      int y_br = br_new.y - dst_roi_.y;
      int x_tl = tl_new.x - dst_roi_.x;
      int x_br = br_new.x - dst_roi_.x;
  
      // Add weighted layer of the source image to the final Laplacian pyramid layer
      for (int i = 0; i <= num_bands_; ++i)
      {
          Rect rc(x_tl, y_tl, x_br - x_tl, y_br - y_tl);
  #ifdef HAVE_OPENCL
          if ( !cv::ocl::useOpenCL() ||
               !ocl_MultiBandBlender_feed(src_pyr_laplace[i], weight_pyr_gauss[i],
                      dst_pyr_laplace_[i](rc), dst_band_weights_[i](rc)) )
  #endif
          {
              Mat _src_pyr_laplace = src_pyr_laplace[i].getMat(ACCESS_READ);
              Mat _dst_pyr_laplace = dst_pyr_laplace_[i](rc).getMat(ACCESS_RW);
              Mat _weight_pyr_gauss = weight_pyr_gauss[i].getMat(ACCESS_READ);
              Mat _dst_band_weights = dst_band_weights_[i](rc).getMat(ACCESS_RW);
              if(weight_type_ == CV_32F)
              {
                  for (int y = 0; y < rc.height; ++y)
                  {
                      const Point3_<short>* src_row = _src_pyr_laplace.ptr<Point3_<short> >(y);
                      Point3_<short>* dst_row = _dst_pyr_laplace.ptr<Point3_<short> >(y);
                      const float* weight_row = _weight_pyr_gauss.ptr<float>(y);
                      float* dst_weight_row = _dst_band_weights.ptr<float>(y);
  
                      for (int x = 0; x < rc.width; ++x)
                      {
                          dst_row[x].x += static_cast<short>(src_row[x].x * weight_row[x]);
                          dst_row[x].y += static_cast<short>(src_row[x].y * weight_row[x]);
                          dst_row[x].z += static_cast<short>(src_row[x].z * weight_row[x]);
                          dst_weight_row[x] += weight_row[x];
                      }
                  }
              }
              else // weight_type_ == CV_16S
              {
                  for (int y = 0; y < y_br - y_tl; ++y)
                  {
                      const Point3_<short>* src_row = _src_pyr_laplace.ptr<Point3_<short> >(y);
                      Point3_<short>* dst_row = _dst_pyr_laplace.ptr<Point3_<short> >(y);
                      const short* weight_row = _weight_pyr_gauss.ptr<short>(y);
                      short* dst_weight_row = _dst_band_weights.ptr<short>(y);
  
                      for (int x = 0; x < x_br - x_tl; ++x)
                      {
                          dst_row[x].x += short((src_row[x].x * weight_row[x]) >> 8);
                          dst_row[x].y += short((src_row[x].y * weight_row[x]) >> 8);
                          dst_row[x].z += short((src_row[x].z * weight_row[x]) >> 8);
                          dst_weight_row[x] += weight_row[x];
                      }
                  }
              }
          }
  #ifdef HAVE_OPENCL
          else
          {
              CV_IMPL_ADD(CV_IMPL_OCL);
          }
  #endif
  
          x_tl /= 2; y_tl /= 2;
          x_br /= 2; y_br /= 2;
      }
  
      LOGLN("  Add weighted layer of the source image to the final Laplacian pyramid layer, time: " << ((getTickCount() - t) / getTickFrequency()) << " sec");
  }
  
  
  void MultiBandBlender::blend(InputOutputArray dst, InputOutputArray dst_mask)
  {
      for (int i = 0; i <= num_bands_; ++i)
          normalizeUsingWeightMap(dst_band_weights_[i], dst_pyr_laplace_[i]);
  
      if (can_use_gpu_)
          restoreImageFromLaplacePyrGpu(dst_pyr_laplace_);
      else
          restoreImageFromLaplacePyr(dst_pyr_laplace_);
  
      Rect dst_rc(0, 0, dst_roi_final_.width, dst_roi_final_.height);
      dst_ = dst_pyr_laplace_[0](dst_rc);
      UMat _dst_mask;
      compare(dst_band_weights_[0](dst_rc), WEIGHT_EPS, dst_mask_, CMP_GT);
      dst_pyr_laplace_.clear();
      dst_band_weights_.clear();
  
      Blender::blend(dst, dst_mask);
  }
  
  
  //////////////////////////////////////////////////////////////////////////////
  // Auxiliary functions
  
  #ifdef HAVE_OPENCL
  static bool ocl_normalizeUsingWeightMap(InputArray _weight, InputOutputArray _mat)
  {
      String buildOptions = "-D DEFINE_normalizeUsingWeightMap";
      ocl::buildOptionsAddMatrixDescription(buildOptions, "mat", _mat);
      ocl::buildOptionsAddMatrixDescription(buildOptions, "weight", _weight);
      ocl::Kernel k("normalizeUsingWeightMap", ocl::stitching::multibandblend_oclsrc, buildOptions);
      if (k.empty())
          return false;
  
      UMat mat = _mat.getUMat();
  
      k.args(ocl::KernelArg::ReadWrite(mat),
             ocl::KernelArg::ReadOnly(_weight.getUMat())
             );
  
      size_t globalsize[2] = {mat.cols, mat.rows };
      return k.run(2, globalsize, NULL, false);
  }
  #endif
  
  void normalizeUsingWeightMap(InputArray _weight, InputOutputArray _src)
  {
      Mat src;
      Mat weight;
  #ifdef HAVE_TEGRA_OPTIMIZATION
      src = _src.getMat();
      weight = _weight.getMat();
      if(tegra::useTegra() && tegra::normalizeUsingWeightMap(weight, src))
          return;
  #endif
  
  #ifdef HAVE_OPENCL
      if ( !cv::ocl::useOpenCL() ||
              !ocl_normalizeUsingWeightMap(_weight, _src) )
  #endif
      {
          src = _src.getMat();
          weight = _weight.getMat();
  
          CV_Assert(src.type() == CV_16SC3);
  
          if (weight.type() == CV_32FC1)
          {
              for (int y = 0; y < src.rows; ++y)
              {
                  Point3_<short> *row = src.ptr<Point3_<short> >(y);
                  const float *weight_row = weight.ptr<float>(y);
  
                  for (int x = 0; x < src.cols; ++x)
                  {
                      row[x].x = static_cast<short>(row[x].x / (weight_row[x] + WEIGHT_EPS));
                      row[x].y = static_cast<short>(row[x].y / (weight_row[x] + WEIGHT_EPS));
                      row[x].z = static_cast<short>(row[x].z / (weight_row[x] + WEIGHT_EPS));
                  }
              }
          }
          else
          {
              CV_Assert(weight.type() == CV_16SC1);
  
              for (int y = 0; y < src.rows; ++y)
              {
                  const short *weight_row = weight.ptr<short>(y);
                  Point3_<short> *row = src.ptr<Point3_<short> >(y);
  
                  for (int x = 0; x < src.cols; ++x)
                  {
                      int w = weight_row[x] + 1;
                      row[x].x = static_cast<short>((row[x].x << 8) / w);
                      row[x].y = static_cast<short>((row[x].y << 8) / w);
                      row[x].z = static_cast<short>((row[x].z << 8) / w);
                  }
              }
          }
      }
  #ifdef HAVE_OPENCL
      else
      {
          CV_IMPL_ADD(CV_IMPL_OCL);
      }
  #endif
  }
  
  
  void createWeightMap(InputArray mask, float sharpness, InputOutputArray weight)
  {
      CV_Assert(mask.type() == CV_8U);
      distanceTransform(mask, weight, DIST_L1, 3);
      UMat tmp;
      multiply(weight, sharpness, tmp);
      threshold(tmp, weight, 1.f, 1.f, THRESH_TRUNC);
  }
  
  
  void createLaplacePyr(InputArray img, int num_levels, std::vector<UMat> &pyr)
  {
  #ifdef HAVE_TEGRA_OPTIMIZATION
      cv::Mat imgMat = img.getMat();
      if(tegra::useTegra() && tegra::createLaplacePyr(imgMat, num_levels, pyr))
          return;
  #endif
  
      pyr.resize(num_levels + 1);
  
      if(img.depth() == CV_8U)
      {
          if(num_levels == 0)
          {
              img.getUMat().convertTo(pyr[0], CV_16S);
              return;
          }
  
          UMat downNext;
          UMat current = img.getUMat();
          pyrDown(img, downNext);
  
          for(int i = 1; i < num_levels; ++i)
          {
              UMat lvl_up;
              UMat lvl_down;
  
              pyrDown(downNext, lvl_down);
              pyrUp(downNext, lvl_up, current.size());
              subtract(current, lvl_up, pyr[i-1], noArray(), CV_16S);
  
              current = downNext;
              downNext = lvl_down;
          }
  
          {
              UMat lvl_up;
              pyrUp(downNext, lvl_up, current.size());
              subtract(current, lvl_up, pyr[num_levels-1], noArray(), CV_16S);
  
              downNext.convertTo(pyr[num_levels], CV_16S);
          }
      }
      else
      {
          pyr[0] = img.getUMat();
          for (int i = 0; i < num_levels; ++i)
              pyrDown(pyr[i], pyr[i + 1]);
          UMat tmp;
          for (int i = 0; i < num_levels; ++i)
          {
              pyrUp(pyr[i + 1], tmp, pyr[i].size());
              subtract(pyr[i], tmp, pyr[i]);
          }
      }
  }
  
  
  void createLaplacePyrGpu(InputArray img, int num_levels, std::vector<UMat> &pyr)
  {
  #if defined(HAVE_OPENCV_CUDAARITHM) && defined(HAVE_OPENCV_CUDAWARPING)
      pyr.resize(num_levels + 1);
  
      std::vector<cuda::GpuMat> gpu_pyr(num_levels + 1);
      gpu_pyr[0].upload(img);
      for (int i = 0; i < num_levels; ++i)
          cuda::pyrDown(gpu_pyr[i], gpu_pyr[i + 1]);
  
      cuda::GpuMat tmp;
      for (int i = 0; i < num_levels; ++i)
      {
          cuda::pyrUp(gpu_pyr[i + 1], tmp);
          cuda::subtract(gpu_pyr[i], tmp, gpu_pyr[i]);
          gpu_pyr[i].download(pyr[i]);
      }
  
      gpu_pyr[num_levels].download(pyr[num_levels]);
  #else
      (void)img;
      (void)num_levels;
      (void)pyr;
      CV_Error(Error::StsNotImplemented, "CUDA optimization is unavailable");
  #endif
  }
  
  
  void restoreImageFromLaplacePyr(std::vector<UMat> &pyr)
  {
      if (pyr.empty())
          return;
      UMat tmp;
      for (size_t i = pyr.size() - 1; i > 0; --i)
      {
          pyrUp(pyr[i], tmp, pyr[i - 1].size());
          add(tmp, pyr[i - 1], pyr[i - 1]);
      }
  }
  
  
  void restoreImageFromLaplacePyrGpu(std::vector<UMat> &pyr)
  {
  #if defined(HAVE_OPENCV_CUDAARITHM) && defined(HAVE_OPENCV_CUDAWARPING)
      if (pyr.empty())
          return;
  
      std::vector<cuda::GpuMat> gpu_pyr(pyr.size());
      for (size_t i = 0; i < pyr.size(); ++i)
          gpu_pyr[i].upload(pyr[i]);
  
      cuda::GpuMat tmp;
      for (size_t i = pyr.size() - 1; i > 0; --i)
      {
          cuda::pyrUp(gpu_pyr[i], tmp);
          cuda::add(tmp, gpu_pyr[i - 1], gpu_pyr[i - 1]);
      }
  
      gpu_pyr[0].download(pyr[0]);
  #else
      (void)pyr;
      CV_Error(Error::StsNotImplemented, "CUDA optimization is unavailable");
  #endif
  }
  
  } // namespace detail
  } // namespace cv
  