void cv::cuda::mulAndScaleSpectrums(InputArray _src1, InputArray _src2, OutputArray _dst, int flags, float scale, bool conjB, Stream& stream)
{
#ifndef HAVE_CUFFT
(void) _src1;
(void) _src2;
(void) _dst;
(void) flags;
(void) scale;
(void) conjB;
(void) stream;
throw_no_cuda();
#else
(void)flags;
typedef void (*Caller)(const PtrStep<cufftComplex>, const PtrStep<cufftComplex>, float scale, PtrStepSz<cufftComplex>, cudaStream_t stream);
static Caller callers[] = { device::mulAndScaleSpectrums, device::mulAndScaleSpectrums_CONJ };
GpuMat src1 = _src1.getGpuMat();
GpuMat src2 = _src2.getGpuMat();
CV_Assert( src1.type() == src2.type() && src1.type() == CV_32FC2);
CV_Assert( src1.size() == src2.size() );
_dst.create(src1.size(), CV_32FC2);
GpuMat dst = _dst.getGpuMat();
Caller caller = callers[(int)conjB];
caller(src1, src2, scale, dst, StreamAccessor::getStream(stream));
#endif
}
void cv::cuda::divide(InputArray _src1, InputArray _src2, OutputArray _dst, double scale, int dtype, Stream& stream)
{
if (_src1.type() == CV_8UC4 && _src2.type() == CV_32FC1)
{
GpuMat src1 = _src1.getGpuMat();
GpuMat src2 = _src2.getGpuMat();
CV_Assert( src1.size() == src2.size() );
_dst.create(src1.size(), src1.type());
GpuMat dst = _dst.getGpuMat();
divMat_8uc4_32f(src1, src2, dst, stream);
}
else if (_src1.type() == CV_16SC4 && _src2.type() == CV_32FC1)
{
GpuMat src1 = _src1.getGpuMat();
GpuMat src2 = _src2.getGpuMat();
CV_Assert( src1.size() == src2.size() );
_dst.create(src1.size(), src1.type());
GpuMat dst = _dst.getGpuMat();
divMat_16sc4_32f(src1, src2, dst, stream);
}
else
{
arithm_op(_src1, _src2, _dst, GpuMat(), scale, dtype, stream, divMat, divScalar);
}
}
/**
* Allocate memeroy for all the buffers on GPU
*/
void DepthmapDenoiseWeightedHuberImpl::allocate(int _rows, int _cols, InputArray _gxin, InputArray _gyin){
const GpuMat& gxin = _gxin.getGpuMat();
const GpuMat& gyin = _gyin.getGpuMat();
rows = _rows;
cols = _cols;
if(!(rows % 32 == 0 && cols % 32 == 0 && cols >= 64)){
CV_Assert(!"For performance reasons, DepthmapDenoiseWeightedHuber currenty only supports multiple of 32 image sizes with cols >= 64. Pad the image to achieve this.");
}
if(!_a.data){
_a.create(1, rows*cols, CV_32FC1);
_a = _a.reshape(0, rows);
}
FLATALLOC(_d, _a);
cachedG = 1;
if(gxin.empty() || gyin.empty()){
if(gxin.empty()){
FLATALLOC(_gx, _d);
cachedG = 0;
}else{
_gx = gxin;
}
if(gyin.empty()){
FLATALLOC(_gy, _d);
cachedG = 0;
}else{
_gy = gyin;
}
}else{
if(!gxin.isContinuous()){
FLATALLOC(_gx, _d);
// gxin.copyTo(_gx,cvStream);
cvStream.enqueueCopy(gxin, _gx);
}
if(!gyin.isContinuous()){
FLATALLOC(_gy, _d);
// gyin.copyTo(_gy,cvStream);
cvStream.enqueueCopy(gyin, _gy);
}
}
FLATALLOC(_qx, _d);
FLATALLOC(_qy, _d);
FLATALLOC(_g1, _d);
FLATALLOC(stableDepth, _d);
memZero(_qx, cvStream);
memZero(_qy, cvStream);
alloced = 1;
}
void cv::softcascade::SCascade::detect(InputArray _image, InputArray _rois, OutputArray _objects, cv::gpu::Stream& s) const
{
CV_Assert(fields);
// only color images and precomputed integrals are supported
int type = _image.type();
CV_Assert(type == CV_8UC3 || type == CV_32SC1 || (!_rois.empty()));
const cv::gpu::GpuMat image = _image.getGpuMat();
if (_objects.empty()) _objects.create(1, 4096 * sizeof(Detection), CV_8UC1);
cv::gpu::GpuMat rois = _rois.getGpuMat(), objects = _objects.getGpuMat();
/// roi
Fields& flds = *fields;
int shr = flds.shrinkage;
flds.mask.create( rois.cols / shr, rois.rows / shr, rois.type());
device::shrink(rois, flds.mask);
//cv::gpu::transpose(flds.genRoiTmp, flds.mask, s);
if (type == CV_8UC3)
{
flds.update(image.rows, image.cols, flds.shrinkage);
if (flds.check((float)minScale, (float)maxScale, scales))
flds.createLevels(image.rows, image.cols);
flds.preprocessor->apply(image, flds.shrunk);
integral(flds.shrunk, flds.hogluv, flds.integralBuffer, s);
}
else
{
if (s)
s.enqueueCopy(image, flds.hogluv);
else
image.copyTo(flds.hogluv);
}
flds.detect(objects, s);
if ( (flags && NMS_MASK) != NO_REJECT)
{
cv::gpu::GpuMat spr(objects, cv::Rect(0, 0, flds.suppressed.cols, flds.suppressed.rows));
flds.suppress(objects, s);
flds.suppressed.copyTo(spr);
}
}
void cv::cuda::bilateralFilter(InputArray _src, OutputArray _dst, int kernel_size, float sigma_color, float sigma_spatial, int borderMode, Stream& stream)
{
using cv::cuda::device::imgproc::bilateral_filter_gpu;
typedef void (*func_t)(const PtrStepSzb& src, PtrStepSzb dst, int kernel_size, float sigma_spatial, float sigma_color, int borderMode, cudaStream_t s);
static const func_t funcs[6][4] =
{
{bilateral_filter_gpu<uchar> , 0 /*bilateral_filter_gpu<uchar2>*/ , bilateral_filter_gpu<uchar3> , bilateral_filter_gpu<uchar4> },
{0 /*bilateral_filter_gpu<schar>*/, 0 /*bilateral_filter_gpu<schar2>*/ , 0 /*bilateral_filter_gpu<schar3>*/, 0 /*bilateral_filter_gpu<schar4>*/},
{bilateral_filter_gpu<ushort> , 0 /*bilateral_filter_gpu<ushort2>*/, bilateral_filter_gpu<ushort3> , bilateral_filter_gpu<ushort4> },
{bilateral_filter_gpu<short> , 0 /*bilateral_filter_gpu<short2>*/ , bilateral_filter_gpu<short3> , bilateral_filter_gpu<short4> },
{0 /*bilateral_filter_gpu<int>*/ , 0 /*bilateral_filter_gpu<int2>*/ , 0 /*bilateral_filter_gpu<int3>*/ , 0 /*bilateral_filter_gpu<int4>*/ },
{bilateral_filter_gpu<float> , 0 /*bilateral_filter_gpu<float2>*/ , bilateral_filter_gpu<float3> , bilateral_filter_gpu<float4> }
};
sigma_color = (sigma_color <= 0 ) ? 1 : sigma_color;
sigma_spatial = (sigma_spatial <= 0 ) ? 1 : sigma_spatial;
int radius = (kernel_size <= 0) ? cvRound(sigma_spatial*1.5) : kernel_size/2;
kernel_size = std::max(radius, 1)*2 + 1;
GpuMat src = _src.getGpuMat();
CV_Assert( src.depth() <= CV_32F && src.channels() <= 4 );
CV_Assert( borderMode == BORDER_REFLECT101 || borderMode == BORDER_REPLICATE || borderMode == BORDER_CONSTANT || borderMode == BORDER_REFLECT || borderMode == BORDER_WRAP );
const func_t func = funcs[src.depth()][src.channels() - 1];
CV_Assert( func != 0 );
_dst.create(src.size(), src.type());
GpuMat dst = _dst.getGpuMat();
func(src, dst, kernel_size, sigma_spatial, sigma_color, borderMode, StreamAccessor::getStream(stream));
}
void cv::cuda::rotate(InputArray _src, OutputArray _dst, Size dsize, double angle, double xShift, double yShift, int interpolation, Stream& stream)
{
typedef void (*func_t)(const GpuMat& src, GpuMat& dst, Size dsize, double angle, double xShift, double yShift, int interpolation, cudaStream_t stream);
static const func_t funcs[6][4] =
{
{NppRotate<CV_8U, nppiRotate_8u_C1R>::call, 0, NppRotate<CV_8U, nppiRotate_8u_C3R>::call, NppRotate<CV_8U, nppiRotate_8u_C4R>::call},
{0,0,0,0},
{NppRotate<CV_16U, nppiRotate_16u_C1R>::call, 0, NppRotate<CV_16U, nppiRotate_16u_C3R>::call, NppRotate<CV_16U, nppiRotate_16u_C4R>::call},
{0,0,0,0},
{0,0,0,0},
{NppRotate<CV_32F, nppiRotate_32f_C1R>::call, 0, NppRotate<CV_32F, nppiRotate_32f_C3R>::call, NppRotate<CV_32F, nppiRotate_32f_C4R>::call}
};
GpuMat src = _src.getGpuMat();
CV_Assert( src.depth() == CV_8U || src.depth() == CV_16U || src.depth() == CV_32F );
CV_Assert( src.channels() == 1 || src.channels() == 3 || src.channels() == 4 );
CV_Assert( interpolation == INTER_NEAREST || interpolation == INTER_LINEAR || interpolation == INTER_CUBIC );
_dst.create(dsize, src.type());
GpuMat dst = _dst.getGpuMat();
dst.setTo(Scalar::all(0), stream);
funcs[src.depth()][src.channels() - 1](src, dst, dsize, angle, xShift, yShift, interpolation, StreamAccessor::getStream(stream));
}
void cv::gpu::split(InputArray _src, std::vector<GpuMat>& dst, Stream& stream)
{
GpuMat src = _src.getGpuMat();
dst.resize(src.channels());
if(src.channels() > 0)
split_caller(src, &dst[0], stream);
}
void cv::gpu::pyrDown(InputArray _src, OutputArray _dst, Stream& stream)
{
using namespace cv::gpu::cudev::imgproc;
typedef void (*func_t)(PtrStepSzb src, PtrStepSzb dst, cudaStream_t stream);
static const func_t funcs[6][4] =
{
{pyrDown_gpu<uchar> , 0 /*pyrDown_gpu<uchar2>*/ , pyrDown_gpu<uchar3> , pyrDown_gpu<uchar4> },
{0 /*pyrDown_gpu<schar>*/, 0 /*pyrDown_gpu<schar2>*/ , 0 /*pyrDown_gpu<schar3>*/, 0 /*pyrDown_gpu<schar4>*/},
{pyrDown_gpu<ushort> , 0 /*pyrDown_gpu<ushort2>*/, pyrDown_gpu<ushort3> , pyrDown_gpu<ushort4> },
{pyrDown_gpu<short> , 0 /*pyrDown_gpu<short2>*/ , pyrDown_gpu<short3> , pyrDown_gpu<short4> },
{0 /*pyrDown_gpu<int>*/ , 0 /*pyrDown_gpu<int2>*/ , 0 /*pyrDown_gpu<int3>*/ , 0 /*pyrDown_gpu<int4>*/ },
{pyrDown_gpu<float> , 0 /*pyrDown_gpu<float2>*/ , pyrDown_gpu<float3> , pyrDown_gpu<float4> }
};
GpuMat src = _src.getGpuMat();
CV_Assert( src.depth() <= CV_32F && src.channels() <= 4 );
const func_t func = funcs[src.depth()][src.channels() - 1];
CV_Assert( func != 0 );
_dst.create((src.rows + 1) / 2, (src.cols + 1) / 2, src.type());
GpuMat dst = _dst.getGpuMat();
func(src, dst, StreamAccessor::getStream(stream));
}
void cv::gpu::equalizeHist(InputArray _src, OutputArray _dst, InputOutputArray _buf, Stream& _stream)
{
GpuMat src = _src.getGpuMat();
CV_Assert( src.type() == CV_8UC1 );
_dst.create(src.size(), src.type());
GpuMat dst = _dst.getGpuMat();
int intBufSize;
nppSafeCall( nppsIntegralGetBufferSize_32s(256, &intBufSize) );
size_t bufSize = intBufSize + 2 * 256 * sizeof(int);
ensureSizeIsEnough(1, static_cast<int>(bufSize), CV_8UC1, _buf);
GpuMat buf = _buf.getGpuMat();
GpuMat hist(1, 256, CV_32SC1, buf.data);
GpuMat lut(1, 256, CV_32SC1, buf.data + 256 * sizeof(int));
GpuMat intBuf(1, intBufSize, CV_8UC1, buf.data + 2 * 256 * sizeof(int));
gpu::calcHist(src, hist, _stream);
cudaStream_t stream = StreamAccessor::getStream(_stream);
NppStreamHandler h(stream);
nppSafeCall( nppsIntegral_32s(hist.ptr<Npp32s>(), lut.ptr<Npp32s>(), 256, intBuf.ptr<Npp8u>()) );
hist::equalizeHist(src, dst, lut.ptr<int>(), stream);
}
cv::GlBuffer::GlBuffer(InputArray mat_, Usage _usage) : rows_(0), cols_(0), type_(0), usage_(_usage)
{
#ifndef HAVE_OPENGL
(void)mat_;
(void)_usage;
throw_nogl;
#else
int kind = mat_.kind();
Size _size = mat_.size();
int _type = mat_.type();
if (kind == _InputArray::GPU_MAT)
{
#if !defined HAVE_CUDA || defined(CUDA_DISABLER)
throw_nocuda;
#else
GpuMat d_mat = mat_.getGpuMat();
impl_ = new Impl(d_mat.rows, d_mat.cols, d_mat.type(), _usage);
impl_->copyFrom(d_mat);
#endif
}
else
{
Mat mat = mat_.getMat();
impl_ = new Impl(mat, _usage);
}
rows_ = _size.height;
cols_ = _size.width;
type_ = _type;
#endif
}
void DepthmapDenoiseWeightedHuberImpl::cacheGValues(InputArray _visibleLightImage){
using namespace cv::cuda::device::dtam_denoise;
localStream = cv::cuda::StreamAccessor::getStream(cvStream);
if (!_visibleLightImage.empty()){
visibleLightImage=_visibleLightImage.getGpuMat();
cachedG=0;
}
if(cachedG)
return;//already cached
if(!alloced)
allocate(rows,cols);
// Call the gpu function for caching g's
loadConstants(rows, cols, 0, 0, 0, 0, 0, 0,
0, 0);
CV_Assert(_g1.isContinuous());
float* pp = (float*) visibleLightImage.data;//TODO: write a color version.
float* g1p = (float*)_g1.data;
float* gxp = (float*)_gx.data;
float* gyp = (float*)_gy.data;
computeGCaller(pp, g1p, gxp, gyp, cols);
cachedG=1;
}
void cv::cuda::fastNlMeansDenoising(InputArray _src, OutputArray _dst, float h, int search_window, int block_window, Stream& stream)
{
const GpuMat src = _src.getGpuMat();
CV_Assert(src.depth() == CV_8U && src.channels() < 4);
int border_size = search_window/2 + block_window/2;
Size esize = src.size() + Size(border_size, border_size) * 2;
BufferPool pool(stream);
GpuMat extended_src = pool.getBuffer(esize, src.type());
cv::cuda::copyMakeBorder(src, extended_src, border_size, border_size, border_size, border_size, cv::BORDER_DEFAULT, Scalar(), stream);
GpuMat src_hdr = extended_src(Rect(Point2i(border_size, border_size), src.size()));
int bcols, brows;
device::imgproc::nln_fast_get_buffer_size(src_hdr, search_window, block_window, bcols, brows);
GpuMat buffer = pool.getBuffer(brows, bcols, CV_32S);
using namespace cv::cuda::device::imgproc;
typedef void (*nlm_fast_t)(const PtrStepSzb&, PtrStepSzb, PtrStepi, int, int, float, cudaStream_t);
static const nlm_fast_t funcs[] = { nlm_fast_gpu<uchar>, nlm_fast_gpu<uchar2>, nlm_fast_gpu<uchar3>, 0};
_dst.create(src.size(), src.type());
GpuMat dst = _dst.getGpuMat();
funcs[src.channels()-1](src_hdr, dst, buffer, search_window, block_window, h, StreamAccessor::getStream(stream));
}
void cv::cuda::magnitudeSqr(InputArray _src, OutputArray _dst, Stream& stream)
{
GpuMat src = _src.getGpuMat();
_dst.create(src.size(), CV_32FC1);
GpuMat dst = _dst.getGpuMat();
npp_magnitude(src, dst, nppiMagnitudeSqr_32fc32f_C1R, StreamAccessor::getStream(stream));
}
GpuMat DepthmapDenoiseWeightedHuberImpl::operator()(InputArray _ain, float epsilon,float theta){
const GpuMat& ain=_ain.getGpuMat();
using namespace cv::cuda::device::dtam_denoise;
localStream = cv::cuda::StreamAccessor::getStream(cvStream);
rows=ain.rows;
cols=ain.cols;
CV_Assert(ain.cols>0);
if(!(ain.rows % 32 == 0 && ain.cols % 32 == 0 && ain.cols >= 64)){
CV_Assert(!"For performance reasons, DepthmapDenoiseWeightedHuber currenty only supports multiple of 32 image sizes with cols >= 64. Pad the image to achieve this.");
}
rows=ain.rows;
cols=ain.cols;
if(!ain.isContinuous()){
_a.create(1,rows*cols, CV_32FC1);
_a=_a.reshape(0,rows);
ain.copyTo(_a,cvStream);
}else{
_a=ain;
}
if(!alloced){
allocate(rows,cols);
}
if(!visibleLightImage.empty())
cacheGValues();
if(!cachedG){
_gx.setTo(1,cvStream);
_gy.setTo(1,cvStream);
}
if(!dInited){
_a.copyTo(_d,cvStream);
dInited=1;
}
computeSigmas(epsilon,theta);
float* d = (float*) _d.data;
float* a = (float*) _a.data;
float* gxpt = (float*)_gx.data;
float* gypt = (float*)_gy.data;
float* gqxpt = (float*)_qx.data;
float* gqypt = (float*)_qy.data;
loadConstants(rows, cols, 0, 0, 0, 0, 0, 0,
0, 0);
updateQDCaller ( gqxpt, gqypt, d, a,
gxpt, gypt, cols, sigma_q, sigma_d, epsilon, theta);
cudaSafeCall(cudaGetLastError());
return _d;
}
void cv::cuda::remap(InputArray _src, OutputArray _dst, InputArray _xmap, InputArray _ymap, int interpolation, int borderMode, Scalar borderValue, Stream& stream)
{
using namespace cv::cuda::device::imgproc;
typedef void (*func_t)(PtrStepSzb src, PtrStepSzb srcWhole, int xoff, int yoff, PtrStepSzf xmap, PtrStepSzf ymap, PtrStepSzb dst, int interpolation,
int borderMode, const float* borderValue, cudaStream_t stream, bool cc20);
static const func_t funcs[6][4] =
{
{remap_gpu<uchar> , 0 /*remap_gpu<uchar2>*/ , remap_gpu<uchar3> , remap_gpu<uchar4> },
{0 /*remap_gpu<schar>*/, 0 /*remap_gpu<char2>*/ , 0 /*remap_gpu<char3>*/, 0 /*remap_gpu<char4>*/},
{remap_gpu<ushort> , 0 /*remap_gpu<ushort2>*/, remap_gpu<ushort3> , remap_gpu<ushort4> },
{remap_gpu<short> , 0 /*remap_gpu<short2>*/ , remap_gpu<short3> , remap_gpu<short4> },
{0 /*remap_gpu<int>*/ , 0 /*remap_gpu<int2>*/ , 0 /*remap_gpu<int3>*/ , 0 /*remap_gpu<int4>*/ },
{remap_gpu<float> , 0 /*remap_gpu<float2>*/ , remap_gpu<float3> , remap_gpu<float4> }
};
GpuMat src = _src.getGpuMat();
GpuMat xmap = _xmap.getGpuMat();
GpuMat ymap = _ymap.getGpuMat();
CV_Assert( src.depth() <= CV_32F && src.channels() <= 4 );
CV_Assert( xmap.type() == CV_32F && ymap.type() == CV_32F && xmap.size() == ymap.size() );
CV_Assert( interpolation == INTER_NEAREST || interpolation == INTER_LINEAR || interpolation == INTER_CUBIC );
CV_Assert( borderMode == BORDER_REFLECT101 || borderMode == BORDER_REPLICATE || borderMode == BORDER_CONSTANT || borderMode == BORDER_REFLECT || borderMode == BORDER_WRAP );
const func_t func = funcs[src.depth()][src.channels() - 1];
if (!func)
CV_Error(Error::StsUnsupportedFormat, "Unsupported input type");
_dst.create(xmap.size(), src.type());
GpuMat dst = _dst.getGpuMat();
Scalar_<float> borderValueFloat;
borderValueFloat = borderValue;
Size wholeSize;
Point ofs;
src.locateROI(wholeSize, ofs);
func(src, PtrStepSzb(wholeSize.height, wholeSize.width, src.datastart, src.step), ofs.x, ofs.y, xmap, ymap,
dst, interpolation, borderMode, borderValueFloat.val, StreamAccessor::getStream(stream), deviceSupports(FEATURE_SET_COMPUTE_20));
}
void cv::gpu::histRange(InputArray _src, OutputArray hist, InputArray _levels, InputOutputArray buf, Stream& stream)
{
typedef void (*hist_t)(const GpuMat& src, OutputArray hist, const GpuMat& levels, InputOutputArray buf, cudaStream_t stream);
static const hist_t hist_callers[] =
{
NppHistogramRangeC1<CV_8U , nppiHistogramRange_8u_C1R , nppiHistogramRangeGetBufferSize_8u_C1R >::hist,
0,
NppHistogramRangeC1<CV_16U, nppiHistogramRange_16u_C1R, nppiHistogramRangeGetBufferSize_16u_C1R>::hist,
NppHistogramRangeC1<CV_16S, nppiHistogramRange_16s_C1R, nppiHistogramRangeGetBufferSize_16s_C1R>::hist,
0,
NppHistogramRangeC1<CV_32F, nppiHistogramRange_32f_C1R, nppiHistogramRangeGetBufferSize_32f_C1R>::hist
};
GpuMat src = _src.getGpuMat();
GpuMat levels = _levels.getGpuMat();
CV_Assert( src.type() == CV_8UC1 || src.type() == CV_16UC1 || src.type() == CV_16SC1 || src.type() == CV_32FC1 );
hist_callers[src.depth()](src, hist, levels, buf, StreamAccessor::getStream(stream));
}
Mat getMat(InputArray arr)
{
if (arr.kind() == _InputArray::GPU_MAT)
{
Mat m;
arr.getGpuMat().download(m);
return m;
}
return arr.getMat();
}
void cv::ogl::Buffer::copyFrom(InputArray arr, Target target, bool autoRelease)
{
#ifndef HAVE_OPENGL
(void) arr;
(void) target;
(void) autoRelease;
throw_nogl();
#else
const int kind = arr.kind();
if (kind == _InputArray::OPENGL_TEXTURE)
{
ogl::Texture2D tex = arr.getOGlTexture2D();
tex.copyTo(*this);
setAutoRelease(autoRelease);
return;
}
const Size asize = arr.size();
const int atype = arr.type();
create(asize, atype, target, autoRelease);
switch (kind)
{
case _InputArray::OPENGL_BUFFER:
{
ogl::Buffer buf = arr.getOGlBuffer();
impl_->copyFrom(buf.bufId(), asize.area() * CV_ELEM_SIZE(atype));
break;
}
case _InputArray::GPU_MAT:
{
#if !defined HAVE_CUDA || defined(CUDA_DISABLER)
throw_nocuda();
#else
GpuMat dmat = arr.getGpuMat();
impl_->copyFrom(dmat.data, dmat.step, dmat.cols * dmat.elemSize(), dmat.rows);
#endif
break;
}
default:
{
Mat mat = arr.getMat();
CV_Assert( mat.isContinuous() );
impl_->copyFrom(asize.area() * CV_ELEM_SIZE(atype), mat.data);
}
}
#endif
}
void cv::gpu::calcHist(InputArray _src, OutputArray _hist, Stream& stream)
{
GpuMat src = _src.getGpuMat();
CV_Assert( src.type() == CV_8UC1 );
_hist.create(1, 256, CV_32SC1);
GpuMat hist = _hist.getGpuMat();
hist.setTo(Scalar::all(0), stream);
hist::histogram256(src, hist.ptr<int>(), StreamAccessor::getStream(stream));
}
void cv::cuda::resize(InputArray _src, OutputArray _dst, Size dsize, double fx, double fy, int interpolation, Stream& stream)
{
GpuMat src = _src.getGpuMat();
typedef void (*func_t)(const PtrStepSzb& src, const PtrStepSzb& srcWhole, int yoff, int xoff, const PtrStepSzb& dst, float fy, float fx, int interpolation, cudaStream_t stream);
static const func_t funcs[6][4] =
{
{device::resize<uchar> , 0 /*device::resize<uchar2>*/ , device::resize<uchar3> , device::resize<uchar4> },
{0 /*device::resize<schar>*/, 0 /*device::resize<char2>*/ , 0 /*device::resize<char3>*/, 0 /*device::resize<char4>*/},
{device::resize<ushort> , 0 /*device::resize<ushort2>*/, device::resize<ushort3> , device::resize<ushort4> },
{device::resize<short> , 0 /*device::resize<short2>*/ , device::resize<short3> , device::resize<short4> },
{0 /*device::resize<int>*/ , 0 /*device::resize<int2>*/ , 0 /*device::resize<int3>*/ , 0 /*device::resize<int4>*/ },
{device::resize<float> , 0 /*device::resize<float2>*/ , device::resize<float3> , device::resize<float4> }
};
CV_Assert( src.depth() <= CV_32F && src.channels() <= 4 );
CV_Assert( interpolation == INTER_NEAREST || interpolation == INTER_LINEAR || interpolation == INTER_CUBIC || interpolation == INTER_AREA );
CV_Assert( !(dsize == Size()) || (fx > 0 && fy > 0) );
if (dsize == Size())
{
dsize = Size(saturate_cast<int>(src.cols * fx), saturate_cast<int>(src.rows * fy));
}
else
{
fx = static_cast<double>(dsize.width) / src.cols;
fy = static_cast<double>(dsize.height) / src.rows;
}
_dst.create(dsize, src.type());
GpuMat dst = _dst.getGpuMat();
if (dsize == src.size())
{
src.copyTo(dst, stream);
return;
}
const func_t func = funcs[src.depth()][src.channels() - 1];
if (!func)
CV_Error(Error::StsUnsupportedFormat, "Unsupported combination of source and destination types");
Size wholeSize;
Point ofs;
src.locateROI(wholeSize, ofs);
PtrStepSzb wholeSrc(wholeSize.height, wholeSize.width, src.datastart, src.step);
func(src, wholeSrc, ofs.y, ofs.x, dst, static_cast<float>(1.0 / fy), static_cast<float>(1.0 / fx), interpolation, StreamAccessor::getStream(stream));
}
GpuMat cv::superres::arrGetGpuMat(InputArray arr, GpuMat& buf)
{
switch (arr.kind())
{
case _InputArray::GPU_MAT:
return arr.getGpuMat();
case _InputArray::OPENGL_BUFFER:
arr.getOGlBuffer().copyTo(buf);
return buf;
default:
buf.upload(arr.getMat());
return buf;
}
}
UMat cv::superres::arrGetUMat(InputArray arr, UMat& buf)
{
switch (arr.kind())
{
case _InputArray::CUDA_GPU_MAT:
arr.getGpuMat().download(buf);
return buf;
case _InputArray::OPENGL_BUFFER:
arr.getOGlBuffer().copyTo(buf);
return buf;
default:
return arr.getUMat();
}
}
void cv::GlTexture::copyFrom(InputArray mat_, bool bgra)
{
#ifndef HAVE_OPENGL
(void)mat_;
(void)bgra;
throw_nogl;
#else
int kind = mat_.kind();
Size _size = mat_.size();
int _type = mat_.type();
create(_size, _type);
switch(kind)
{
case _InputArray::OPENGL_TEXTURE:
{
GlTexture tex = mat_.getGlTexture();
*this = tex;
break;
}
case _InputArray::OPENGL_BUFFER:
{
GlBuffer buf = mat_.getGlBuffer();
impl_->copyFrom(buf, bgra);
break;
}
case _InputArray::GPU_MAT:
{
#if !defined HAVE_CUDA || defined(CUDA_DISABLER)
throw_nocuda;
#else
GpuMat d_mat = mat_.getGpuMat();
buf_.copyFrom(d_mat);
impl_->copyFrom(buf_, bgra);
#endif
break;
}
default:
{
Mat mat = mat_.getMat();
impl_->copyFrom(mat, bgra);
}
}
#endif
}
void cv::gpu::histEven(InputArray _src, GpuMat hist[4], InputOutputArray buf, int histSize[4], int lowerLevel[4], int upperLevel[4], Stream& stream)
{
typedef void (*hist_t)(const GpuMat& src, GpuMat hist[4], InputOutputArray buf, int levels[4], int lowerLevel[4], int upperLevel[4], cudaStream_t stream);
static const hist_t hist_callers[] =
{
NppHistogramEvenC4<CV_8U , nppiHistogramEven_8u_C4R , nppiHistogramEvenGetBufferSize_8u_C4R >::hist,
0,
NppHistogramEvenC4<CV_16U, nppiHistogramEven_16u_C4R, nppiHistogramEvenGetBufferSize_16u_C4R>::hist,
NppHistogramEvenC4<CV_16S, nppiHistogramEven_16s_C4R, nppiHistogramEvenGetBufferSize_16s_C4R>::hist
};
GpuMat src = _src.getGpuMat();
CV_Assert( src.type() == CV_8UC4 || src.type() == CV_16UC4 || src.type() == CV_16SC4 );
hist_callers[src.depth()](src, hist, buf, histSize, lowerLevel, upperLevel, StreamAccessor::getStream(stream));
}
cv::GlTexture::GlTexture(InputArray mat_, bool bgra) : rows_(0), cols_(0), type_(0), buf_(GlBuffer::TEXTURE_BUFFER)
{
#ifndef HAVE_OPENGL
(void)mat_;
(void)bgra;
throw_nogl;
#else
int kind = mat_.kind();
Size _size = mat_.size();
int _type = mat_.type();
switch (kind)
{
case _InputArray::OPENGL_BUFFER:
{
GlBuffer buf = mat_.getGlBuffer();
impl_ = new Impl(buf, bgra);
break;
}
case _InputArray::GPU_MAT:
{
#if !defined HAVE_CUDA || defined(CUDA_DISABLER)
throw_nocuda;
#else
GpuMat d_mat = mat_.getGpuMat();
GlBuffer buf(d_mat, GlBuffer::TEXTURE_BUFFER);
impl_ = new Impl(buf, bgra);
#endif
break;
}
default:
{
Mat mat = mat_.getMat();
impl_ = new Impl(mat, bgra);
break;
}
}
rows_ = _size.height;
cols_ = _size.width;
type_ = _type;
#endif
}
void cv::ogl::Buffer::copyFrom(InputArray arr, Target target, bool autoRelease)
{
#ifndef HAVE_OPENGL
(void) arr;
(void) target;
(void) autoRelease;
throw_no_ogl();
#else
const int kind = arr.kind();
const Size asize = arr.size();
const int atype = arr.type();
create(asize, atype, target, autoRelease);
switch (kind)
{
case _InputArray::OPENGL_BUFFER:
{
ogl::Buffer buf = arr.getOGlBuffer();
impl_->copyFrom(buf.bufId(), asize.area() * CV_ELEM_SIZE(atype));
break;
}
case _InputArray::CUDA_GPU_MAT:
{
#ifndef HAVE_CUDA
throw_no_cuda();
#else
GpuMat dmat = arr.getGpuMat();
impl_->copyFrom(dmat.data, dmat.step, dmat.cols * dmat.elemSize(), dmat.rows);
#endif
break;
}
default:
{
Mat mat = arr.getMat();
CV_Assert( mat.isContinuous() );
impl_->copyFrom(asize.area() * CV_ELEM_SIZE(atype), mat.data);
}
}
#endif
}
void cv::cuda::fastNlMeansDenoisingColored(InputArray _src, OutputArray _dst, float h_luminance, float h_color, int search_window, int block_window, Stream& stream)
{
const GpuMat src = _src.getGpuMat();
CV_Assert(src.type() == CV_8UC3);
BufferPool pool(stream);
GpuMat lab = pool.getBuffer(src.size(), src.type());
cv::cuda::cvtColor(src, lab, cv::COLOR_BGR2Lab, 0, stream);
GpuMat l = pool.getBuffer(src.size(), CV_8U);
GpuMat ab = pool.getBuffer(src.size(), CV_8UC2);
device::imgproc::fnlm_split_channels(lab, l, ab, StreamAccessor::getStream(stream));
fastNlMeansDenoising(l, l, h_luminance, search_window, block_window, stream);
fastNlMeansDenoising(ab, ab, h_color, search_window, block_window, stream);
device::imgproc::fnlm_merge_channels(l, ab, lab, StreamAccessor::getStream(stream));
cv::cuda::cvtColor(lab, _dst, cv::COLOR_Lab2BGR, 0, stream);
}
void cv::cuda::nonLocalMeans(InputArray _src, OutputArray _dst, float h, int search_window, int block_window, int borderMode, Stream& stream)
{
using cv::cuda::device::imgproc::nlm_bruteforce_gpu;
typedef void (*func_t)(const PtrStepSzb& src, PtrStepSzb dst, int search_radius, int block_radius, float h, int borderMode, cudaStream_t stream);
static const func_t funcs[4] = { nlm_bruteforce_gpu<uchar>, nlm_bruteforce_gpu<uchar2>, nlm_bruteforce_gpu<uchar3>, 0/*nlm_bruteforce_gpu<uchar4>,*/ };
const GpuMat src = _src.getGpuMat();
CV_Assert(src.type() == CV_8U || src.type() == CV_8UC2 || src.type() == CV_8UC3);
const func_t func = funcs[src.channels() - 1];
CV_Assert(func != 0);
int b = borderMode;
CV_Assert(b == BORDER_REFLECT101 || b == BORDER_REPLICATE || b == BORDER_CONSTANT || b == BORDER_REFLECT || b == BORDER_WRAP);
_dst.create(src.size(), src.type());
GpuMat dst = _dst.getGpuMat();
func(src, dst, search_window/2, block_window/2, h, borderMode, StreamAccessor::getStream(stream));
}
void cv::cuda::lshift(InputArray _src, Scalar_<int> val, OutputArray _dst, Stream& stream)
{
typedef void (*func_t)(const GpuMat& src, Scalar_<Npp32u> sc, GpuMat& dst, cudaStream_t stream);
static const func_t funcs[5][4] =
{
{NppShift<CV_8U , 1, nppiLShiftC_8u_C1R>::call , 0, NppShift<CV_8U , 3, nppiLShiftC_8u_C3R>::call , NppShift<CV_8U , 4, nppiLShiftC_8u_C4R>::call },
{0 , 0, 0 , 0 },
{NppShift<CV_16U, 1, nppiLShiftC_16u_C1R>::call, 0, NppShift<CV_16U, 3, nppiLShiftC_16u_C3R>::call, NppShift<CV_16U, 4, nppiLShiftC_16u_C4R>::call},
{0 , 0, 0 , 0 },
{NppShift<CV_32S, 1, nppiLShiftC_32s_C1R>::call, 0, NppShift<CV_32S, 3, nppiLShiftC_32s_C3R>::call, NppShift<CV_32S, 4, nppiLShiftC_32s_C4R>::call},
};
GpuMat src = _src.getGpuMat();
CV_Assert( src.depth() == CV_8U || src.depth() == CV_16U || src.depth() == CV_32S );
CV_Assert( src.channels() == 1 || src.channels() == 3 || src.channels() == 4 );
_dst.create(src.size(), src.type());
GpuMat dst = _dst.getGpuMat();
funcs[src.depth()][src.channels() - 1](src, val, dst, StreamAccessor::getStream(stream));
}
void cv::GlBuffer::copyFrom(InputArray mat_)
{
#ifndef HAVE_OPENGL
(void)mat_;
throw_nogl;
#else
int kind = mat_.kind();
Size _size = mat_.size();
int _type = mat_.type();
create(_size, _type);
switch (kind)
{
case _InputArray::OPENGL_BUFFER:
{
GlBuffer buf = mat_.getGlBuffer();
*this = buf;
break;
}
case _InputArray::GPU_MAT:
{
#if !defined HAVE_CUDA || defined(CUDA_DISABLER)
throw_nocuda;
#else
GpuMat d_mat = mat_.getGpuMat();
impl_->copyFrom(d_mat);
#endif
break;
}
default:
{
Mat mat = mat_.getMat();
impl_->copyFrom(mat, usage_);
}
}
#endif
}
