{

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>();

{

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;

{

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];

}

}

{

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();

{

Blender::prepare(dst_roi);

dst_weight_map_.create(dst_roi.size(), CV_32F);

dst_weight_map_.setTo(0);

{

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];

}

}

{

normalizeUsingWeightMap(dst_weight_map_, dst_);

compare(dst_weight_map_, WEIGHT_EPS, dst_mask_, CMP_GT);

Blender::blend(dst, dst_mask);

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;

{

num_bands_ = 0;

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;

{

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);

#if defined(HAVE_OPENCV_CUDAARITHM) && defined(HAVE_OPENCV_CUDAWARPING)

if (can_use_gpu_)

{

gpu_dst_pyr_laplace_.resize(num_bands_ + 1);

gpu_dst_pyr_laplace_[0].create(dst_roi.size(), CV_16SC3);

gpu_dst_pyr_laplace_[0].setTo(Scalar::all(0));

gpu_dst_band_weights_.resize(num_bands_ + 1);

gpu_dst_band_weights_[0].create(dst_roi.size(), weight_type_);

gpu_dst_band_weights_[0].setTo(0);

for (int i = 1; i <= num_bands_; ++i)

{

gpu_dst_pyr_laplace_[i].create((gpu_dst_pyr_laplace_[i - 1].rows + 1) / 2,

(gpu_dst_pyr_laplace_[i - 1].cols + 1) / 2, CV_16SC3);

gpu_dst_band_weights_[i].create((gpu_dst_band_weights_[i - 1].rows + 1) / 2,

(gpu_dst_band_weights_[i - 1].cols + 1) / 2, weight_type_);

gpu_dst_pyr_laplace_[i].setTo(Scalar::all(0));

gpu_dst_band_weights_[i].setTo(0);

}

}

else

#endif

{

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);

}

}

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] = {(size_t)src.cols, (size_t)src.rows };

return k.run(2, globalsize, NULL, false);

{

#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;

#if defined(HAVE_OPENCV_CUDAARITHM) && defined(HAVE_OPENCV_CUDAWARPING)

if (can_use_gpu_)

{

// Create the source image Laplacian pyramid

cuda::GpuMat gpu_img;

gpu_img.upload(img);

cuda::GpuMat img_with_border;

cuda::copyMakeBorder(gpu_img, img_with_border, top, bottom, left, right, BORDER_REFLECT);

std::vector<cuda::GpuMat> gpu_src_pyr_laplace(num_bands_ + 1);

img_with_border.convertTo(gpu_src_pyr_laplace[0], CV_16S);

for (int i = 0; i < num_bands_; ++i)

cuda::pyrDown(gpu_src_pyr_laplace[i], gpu_src_pyr_laplace[i + 1]);

for (int i = 0; i < num_bands_; ++i)

{

cuda::GpuMat up;

cuda::pyrUp(gpu_src_pyr_laplace[i + 1], up);

cuda::subtract(gpu_src_pyr_laplace[i], up, gpu_src_pyr_laplace[i]);

}

// Create the weight map Gaussian pyramid

cuda::GpuMat gpu_mask;

gpu_mask.upload(mask);

cuda::GpuMat weight_map;

std::vector<cuda::GpuMat> gpu_weight_pyr_gauss(num_bands_ + 1);

if (weight_type_ == CV_32F)

{

gpu_mask.convertTo(weight_map, CV_32F, 1. / 255.);

}

else // weight_type_ == CV_16S

{

gpu_mask.convertTo(weight_map, CV_16S);

cuda::GpuMat add_mask;

cuda::compare(gpu_mask, 0, add_mask, CMP_NE);

cuda::add(weight_map, Scalar::all(1), weight_map, add_mask);

}

cuda::copyMakeBorder(weight_map, gpu_weight_pyr_gauss[0], top, bottom, left, right, BORDER_CONSTANT);

for (int i = 0; i < num_bands_; ++i)

cuda::pyrDown(gpu_weight_pyr_gauss[i], gpu_weight_pyr_gauss[i + 1]);

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);

cuda::GpuMat &_src_pyr_laplace = gpu_src_pyr_laplace[i];

cuda::GpuMat _dst_pyr_laplace = gpu_dst_pyr_laplace_[i](rc);

cuda::GpuMat &_weight_pyr_gauss = gpu_weight_pyr_gauss[i];

cuda::GpuMat _dst_band_weights = gpu_dst_band_weights_[i](rc);

using namespace cv::cuda::device::blend;

if (weight_type_ == CV_32F)

{

addSrcWeightGpu32F(_src_pyr_laplace, _weight_pyr_gauss, _dst_pyr_laplace, _dst_band_weights, rc);

}

else

{

addSrcWeightGpu16S(_src_pyr_laplace, _weight_pyr_gauss, _dst_pyr_laplace, _dst_band_weights, rc);

}

x_tl /= 2; y_tl /= 2;

x_br /= 2; y_br /= 2;

}

return;

}

#endif

// 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;

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::isOpenCLActivated() ||

!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");

{

cv::UMat dst_band_weights_0;

Rect dst_rc(0, 0, dst_roi_final_.width, dst_roi_final_.height);

#if defined(HAVE_OPENCV_CUDAARITHM) && defined(HAVE_OPENCV_CUDAWARPING)

if (can_use_gpu_)

{

for (int i = 0; i <= num_bands_; ++i)

{

cuda::GpuMat dst_i = gpu_dst_pyr_laplace_[i];

cuda::GpuMat weight_i = gpu_dst_band_weights_[i];

using namespace ::cv::cuda::device::blend;

if (weight_type_ == CV_32F)

{

normalizeUsingWeightMapGpu32F(weight_i, dst_i, weight_i.cols, weight_i.rows);

}

else

{

normalizeUsingWeightMapGpu16S(weight_i, dst_i, weight_i.cols, weight_i.rows);

}

}

// Restore image from Laplacian pyramid

for (size_t i = num_bands_; i > 0; --i)

{

cuda::GpuMat up;

cuda::pyrUp(gpu_dst_pyr_laplace_[i], up);

cuda::add(up, gpu_dst_pyr_laplace_[i - 1], gpu_dst_pyr_laplace_[i - 1]);

}

gpu_dst_pyr_laplace_[0](dst_rc).download(dst_);

gpu_dst_band_weights_[0].download(dst_band_weights_0);

gpu_dst_pyr_laplace_.clear();

gpu_dst_band_weights_.clear();

}

else

#endif

{

for (int i = 0; i <= num_bands_; ++i)

normalizeUsingWeightMap(dst_band_weights_[i], dst_pyr_laplace_[i]);

restoreImageFromLaplacePyr(dst_pyr_laplace_);

dst_ = dst_pyr_laplace_[0](dst_rc);

dst_band_weights_0 = dst_band_weights_[0];

dst_pyr_laplace_.clear();

dst_band_weights_.clear();

}

compare(dst_band_weights_0(dst_rc), WEIGHT_EPS, dst_mask_, CMP_GT);

Blender::blend(dst, dst_mask);

{

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] = {(size_t)mat.cols, (size_t)mat.rows };

return k.run(2, globalsize, NULL, false);

{

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::isOpenCLActivated() ||

!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

}