void cv::gpu::rectStdDev(const GpuMat& src, const GpuMat& sqr, GpuMat& dst, const Rect& rect, Stream& s)
{
CV_Assert(src.type() == CV_32SC1 && sqr.type() == CV_64FC1);
dst.create(src.size(), CV_32FC1);
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
NppiRect nppRect;
nppRect.height = rect.height;
nppRect.width = rect.width;
nppRect.x = rect.x;
nppRect.y = rect.y;
cudaStream_t stream = StreamAccessor::getStream(s);
NppStreamHandler h(stream);
nppSafeCall( nppiRectStdDev_32s32f_C1R(src.ptr<Npp32s>(), static_cast<int>(src.step), sqr.ptr<Npp64f>(), static_cast<int>(sqr.step),
dst.ptr<Npp32f>(), static_cast<int>(dst.step), sz, nppRect) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
void cv::gpu::bilateralFilter(const GpuMat& src, GpuMat& dst, int kernel_size, float sigma_color, float sigma_spatial, int borderMode, Stream& s)
{
using cv::gpu::cudev::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;
CV_Assert(src.depth() <= CV_32F && src.channels() <= 4);
const func_t func = funcs[src.depth()][src.channels() - 1];
CV_Assert(func != 0);
CV_Assert(borderMode == BORDER_REFLECT101 || borderMode == BORDER_REPLICATE || borderMode == BORDER_CONSTANT || borderMode == BORDER_REFLECT || borderMode == BORDER_WRAP);
int gpuBorderType;
CV_Assert(tryConvertToGpuBorderType(borderMode, gpuBorderType));
dst.create(src.size(), src.type());
func(src, dst, kernel_size, sigma_spatial, sigma_color, gpuBorderType, StreamAccessor::getStream(s));
}
void cv::gpu::VIBE_GPU::initialize(const GpuMat& firstFrame, Stream& s)
{
using namespace cv::gpu::device::vibe;
CV_Assert(firstFrame.type() == CV_8UC1 || firstFrame.type() == CV_8UC3 || firstFrame.type() == CV_8UC4);
cudaStream_t stream = StreamAccessor::getStream(s);
loadConstants(nbSamples, reqMatches, radius, subsamplingFactor);
frameSize_ = firstFrame.size();
if (randStates_.size() != frameSize_)
{
cv::RNG rng(rngSeed_);
cv::Mat h_randStates(frameSize_, CV_8UC4);
rng.fill(h_randStates, cv::RNG::UNIFORM, 0, 255);
randStates_.upload(h_randStates);
}
int ch = firstFrame.channels();
int sample_ch = ch == 1 ? 1 : 4;
samples_.create(nbSamples * frameSize_.height, frameSize_.width, CV_8UC(sample_ch));
init_gpu(firstFrame, ch, samples_, randStates_, stream);
}
void cv::gpu::Stream::enqueueConvert(const GpuMat& src, GpuMat& dst, int rtype, double alpha, double beta)
{
CV_Assert((src.depth() != CV_64F && CV_MAT_DEPTH(rtype) != CV_64F) ||
(TargetArchs::builtWith(NATIVE_DOUBLE) && DeviceInfo().supports(NATIVE_DOUBLE)));
bool noScale = fabs(alpha-1) < std::numeric_limits<double>::epsilon() && fabs(beta) < std::numeric_limits<double>::epsilon();
if( rtype < 0 )
rtype = src.type();
else
rtype = CV_MAKETYPE(CV_MAT_DEPTH(rtype), src.channels());
int sdepth = src.depth(), ddepth = CV_MAT_DEPTH(rtype);
if( sdepth == ddepth && noScale )
{
src.copyTo(dst);
return;
}
GpuMat temp;
const GpuMat* psrc = &src;
if( sdepth != ddepth && psrc == &dst )
psrc = &(temp = src);
dst.create( src.size(), rtype );
convertTo(src, dst, alpha, beta, Impl::getStream(impl));
}
void cv::gpu::minMax(const GpuMat& src, double* minVal, double* maxVal, const GpuMat& mask, GpuMat& buf)
{
typedef void (*func_t)(const PtrStepSzb src, const PtrStepb mask, double* minval, double* maxval, PtrStepb buf);
#ifdef OPENCV_TINY_GPU_MODULE
static const func_t funcs[] =
{
::minMax::run<uchar>,
0/*::minMax::run<schar>*/,
0/*::minMax::run<ushort>*/,
0/*::minMax::run<short>*/,
0/*::minMax::run<int>*/,
::minMax::run<float>,
0/*::minMax::run<double>*/,
};
#else
static const func_t funcs[] =
{
::minMax::run<uchar>,
::minMax::run<schar>,
::minMax::run<ushort>,
::minMax::run<short>,
::minMax::run<int>,
::minMax::run<float>,
::minMax::run<double>,
};
#endif
CV_Assert( src.channels() == 1 );
CV_Assert( mask.empty() || (mask.size() == src.size() && mask.type() == CV_8U) );
if (src.depth() == CV_64F)
{
if (!deviceSupports(NATIVE_DOUBLE))
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
}
Size buf_size;
::minMax::getBufSize(src.cols, src.rows, buf_size.width, buf_size.height);
ensureSizeIsEnough(buf_size, CV_8U, buf);
const func_t func = funcs[src.depth()];
if (!func)
CV_Error(CV_StsUnsupportedFormat, "Unsupported combination of source and destination types");
double temp1, temp2;
func(src, mask, minVal ? minVal : &temp1, maxVal ? maxVal : &temp2, buf);
}
void cv::gpu::FastNonLocalMeansDenoising::labMethod( const GpuMat& src, GpuMat& dst, float h_luminance, float h_color, int search_window, int block_window, Stream& s)
{
CV_Assert(src.type() == CV_8UC3);
lab.create(src.size(), src.type());
cv::gpu::cvtColor(src, lab, cv::COLOR_BGR2Lab, 0, s);
l.create(src.size(), CV_8U);
ab.create(src.size(), CV_8UC2);
cudev::imgproc::fnlm_split_channels(lab, l, ab, StreamAccessor::getStream(s));
simpleMethod(l, l, h_luminance, search_window, block_window, s);
simpleMethod(ab, ab, h_color, search_window, block_window, s);
cudev::imgproc::fnlm_merge_channels(l, ab, lab, StreamAccessor::getStream(s));
cv::gpu::cvtColor(lab, dst, cv::COLOR_Lab2BGR, 0, s);
}
void cv::gpu::blendLinear(const GpuMat& img1, const GpuMat& img2, const GpuMat& weights1, const GpuMat& weights2,
GpuMat& result, Stream& stream)
{
CV_Assert(img1.size() == img2.size());
CV_Assert(img1.type() == img2.type());
CV_Assert(weights1.size() == img1.size());
CV_Assert(weights2.size() == img2.size());
CV_Assert(weights1.type() == CV_32F);
CV_Assert(weights2.type() == CV_32F);
const Size size = img1.size();
const int depth = img1.depth();
const int cn = img1.channels();
result.create(size, CV_MAKE_TYPE(depth, cn));
switch (depth)
{
case CV_8U:
if (cn != 4)
blendLinearCaller<uchar>(size.height, size.width, cn, img1, img2, weights1, weights2, result, StreamAccessor::getStream(stream));
else
blendLinearCaller8UC4(size.height, size.width, img1, img2, weights1, weights2, result, StreamAccessor::getStream(stream));
break;
case CV_32F:
blendLinearCaller<float>(size.height, size.width, cn, img1, img2, weights1, weights2, result, StreamAccessor::getStream(stream));
break;
default:
CV_Error(CV_StsUnsupportedFormat, "bad image depth in linear blending function");
}
}
double cv::gpu::norm(const GpuMat& src, int normType, const GpuMat& mask, GpuMat& buf)
{
CV_Assert(normType == NORM_INF || normType == NORM_L1 || normType == NORM_L2);
CV_Assert(mask.empty() || (mask.type() == CV_8UC1 && mask.size() == src.size() && src.channels() == 1));
GpuMat src_single_channel = src.reshape(1);
if (normType == NORM_L1)
return absSum(src_single_channel, mask, buf)[0];
if (normType == NORM_L2)
return std::sqrt(sqrSum(src_single_channel, mask, buf)[0]);
// NORM_INF
double min_val, max_val;
minMax(src_single_channel, &min_val, &max_val, mask, buf);
return std::max(std::abs(min_val), std::abs(max_val));
}
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));
}
void cv::gpu::remap(const GpuMat& src, GpuMat& dst, const GpuMat& xmap, const GpuMat& ymap, int interpolation, int borderMode, Scalar borderValue, Stream& stream)
{
using namespace cv::gpu::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, int cc);
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> }
};
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];
CV_Assert(func != 0);
int gpuBorderType;
CV_Assert(tryConvertToGpuBorderType(borderMode, gpuBorderType));
dst.create(xmap.size(), src.type());
Scalar_<float> borderValueFloat;
borderValueFloat = borderValue;
DeviceInfo info;
int cc = info.majorVersion() * 10 + info.minorVersion();
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, gpuBorderType, borderValueFloat.val, StreamAccessor::getStream(stream), cc);
}
void cv::gpu::filter2D(const GpuMat& src, GpuMat& dst, int ddepth, const Mat& kernel, Point anchor, Stream& stream)
{
if( ddepth < 0 )
ddepth = src.depth();
dst.create(src.size(), CV_MAKETYPE(ddepth, src.channels()));
Ptr<FilterEngine_GPU> f = createLinearFilter_GPU(src.type(), dst.type(), kernel, anchor);
f->apply(src, dst, Rect(0, 0, -1, -1), stream);
}
void cv::gpu::GeneralizedHough_GPU::detect(const GpuMat& image, GpuMat& positions, int cannyThreshold)
{
CV_Assert(image.type() == CV_8UC1);
CV_Assert(cannyThreshold > 0);
ensureSizeIsEnough(image.size(), CV_8UC1, edges_);
Canny(image, cannyBuf_, edges_, cannyThreshold / 2, cannyThreshold);
detectImpl(edges_, cannyBuf_.dx, cannyBuf_.dy, positions);
}
void cv::gpu::sepFilter2D(const GpuMat& src, GpuMat& dst, int ddepth, const Mat& kernelX, const Mat& kernelY, Point anchor)
{
if( ddepth < 0 )
ddepth = src.depth();
dst.create(src.size(), CV_MAKETYPE(ddepth, src.channels()));
Ptr<FilterEngine_GPU> f = createSeparableLinearFilter_GPU(src.type(), dst.type(), kernelX, kernelY, anchor);
f->apply(src, dst);
}
void cv::gpu::MOG2_GPU::operator()(const GpuMat& frame, GpuMat& fgmask, float learningRate, Stream& stream)
{
using namespace cv::gpu::cudev::mog;
int ch = frame.channels();
int work_ch = ch;
if (nframes_ == 0 || learningRate >= 1.0f || frame.size() != frameSize_ || work_ch != mean_.channels())
initialize(frame.size(), frame.type());
fgmask.create(frameSize_, CV_8UC1);
fgmask.setTo(cv::Scalar::all(0));
++nframes_;
learningRate = learningRate >= 0.0f && nframes_ > 1 ? learningRate : 1.0f / std::min(2 * nframes_, history);
CV_Assert(learningRate >= 0.0f);
mog2_gpu(frame, frame.channels(), fgmask, bgmodelUsedModes_, weight_, variance_, mean_, learningRate, -learningRate * fCT, bShadowDetection, StreamAccessor::getStream(stream));
}
void cv::gpu::LUT(const GpuMat& src, const Mat& lut, GpuMat& dst)
{
class LevelsInit
{
public:
Npp32s pLevels[256];
const Npp32s* pLevels3[3];
int nValues3[3];
LevelsInit()
{
nValues3[0] = nValues3[1] = nValues3[2] = 256;
for (int i = 0; i < 256; ++i)
pLevels[i] = i;
pLevels3[0] = pLevels3[1] = pLevels3[2] = pLevels;
}
};
static LevelsInit lvls;
int cn = src.channels();
CV_Assert(src.type() == CV_8UC1 || src.type() == CV_8UC3);
CV_Assert(lut.depth() == CV_8U && (lut.channels() == 1 || lut.channels() == cn) && lut.rows * lut.cols == 256 && lut.isContinuous());
dst.create(src.size(), CV_MAKETYPE(lut.depth(), cn));
NppiSize sz;
sz.height = src.rows;
sz.width = src.cols;
Mat nppLut;
lut.convertTo(nppLut, CV_32S);
if (src.type() == CV_8UC1)
{
nppSafeCall( nppiLUT_Linear_8u_C1R(src.ptr<Npp8u>(), src.step, dst.ptr<Npp8u>(), dst.step, sz,
nppLut.ptr<Npp32s>(), lvls.pLevels, 256) );
}
else
{
Mat nppLut3[3];
const Npp32s* pValues3[3];
if (nppLut.channels() == 1)
pValues3[0] = pValues3[1] = pValues3[2] = nppLut.ptr<Npp32s>();
else
{
cv::split(nppLut, nppLut3);
pValues3[0] = nppLut3[0].ptr<Npp32s>();
pValues3[1] = nppLut3[1].ptr<Npp32s>();
pValues3[2] = nppLut3[2].ptr<Npp32s>();
}
nppSafeCall( nppiLUT_Linear_8u_C3R(src.ptr<Npp8u>(), src.step, dst.ptr<Npp8u>(), dst.step, sz,
pValues3, lvls.pLevels3, lvls.nValues3) );
}
}
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::boxFilter(const GpuMat& src, GpuMat& dst, int ddepth, Size ksize, Point anchor)
{
int sdepth = src.depth(), cn = src.channels();
if( ddepth < 0 )
ddepth = sdepth;
dst.create(src.size(), CV_MAKETYPE(ddepth, cn));
Ptr<FilterEngine_GPU> f = createBoxFilter_GPU(src.type(), dst.type(), ksize, anchor);
f->apply(src, dst);
}
void cv::gpu::sepFilter2D(const GpuMat& src, GpuMat& dst, int ddepth, const Mat& kernelX, const Mat& kernelY, Point anchor, int rowBorderType, int columnBorderType,
Stream& stream)
{
if( ddepth < 0 )
ddepth = src.depth();
dst.create(src.size(), CV_MAKETYPE(ddepth, src.channels()));
Ptr<FilterEngine_GPU> f = createSeparableLinearFilter_GPU(src.type(), dst.type(), kernelX, kernelY, anchor, rowBorderType, columnBorderType);
f->apply(src, dst, Rect(0, 0, src.cols, src.rows), stream);
}
void cv::gpu::compare(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, int cmpop)
{
CV_DbgAssert(src1.size() == src2.size() && src1.type() == src2.type());
CV_Assert(src1.type() == CV_8UC4 || src1.type() == CV_32FC1);
dst.create( src1.size(), CV_8UC1 );
static const NppCmpOp nppCmpOp[] = { NPP_CMP_EQ, NPP_CMP_GREATER, NPP_CMP_GREATER_EQ, NPP_CMP_LESS, NPP_CMP_LESS_EQ };
NppiSize sz;
sz.width = src1.cols;
sz.height = src1.rows;
if (src1.type() == CV_8UC4)
{
if (cmpop != CMP_NE)
{
nppSafeCall( nppiCompare_8u_C4R(src1.ptr<Npp8u>(), src1.step,
src2.ptr<Npp8u>(), src2.step,
dst.ptr<Npp8u>(), dst.step, sz, nppCmpOp[cmpop]) );
}
else
{
mathfunc::compare_ne_8uc4(src1, src2, dst);
}
}
else
{
if (cmpop != CMP_NE)
{
nppSafeCall( nppiCompare_32f_C1R(src1.ptr<Npp32f>(), src1.step,
src2.ptr<Npp32f>(), src2.step,
dst.ptr<Npp8u>(), dst.step, sz, nppCmpOp[cmpop]) );
}
else
{
mathfunc::compare_ne_32f(src1, src2, dst);
}
}
}
void cv::gpu::absdiff(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& s)
{
CV_DbgAssert(src1.size() == src2.size() && src1.type() == src2.type());
CV_Assert(src1.type() == CV_8UC1 || src1.type() == CV_8UC4 || src1.type() == CV_32SC1 || src1.type() == CV_32FC1);
dst.create( src1.size(), src1.type() );
NppiSize sz;
sz.width = src1.cols;
sz.height = src1.rows;
cudaStream_t stream = StreamAccessor::getStream(s);
NppStreamHandler h(stream);
switch (src1.type())
{
case CV_8UC1:
nppSafeCall( nppiAbsDiff_8u_C1R(src1.ptr<Npp8u>(), static_cast<int>(src1.step), src2.ptr<Npp8u>(), static_cast<int>(src2.step),
dst.ptr<Npp8u>(), static_cast<int>(dst.step), sz) );
break;
case CV_8UC4:
nppSafeCall( nppiAbsDiff_8u_C4R(src1.ptr<Npp8u>(), static_cast<int>(src1.step), src2.ptr<Npp8u>(), static_cast<int>(src2.step),
dst.ptr<Npp8u>(), static_cast<int>(dst.step), sz) );
break;
case CV_32SC1:
nppSafeCall( nppiAbsDiff_32s_C1R(src1.ptr<Npp32s>(), static_cast<int>(src1.step), src2.ptr<Npp32s>(), static_cast<int>(src2.step),
dst.ptr<Npp32s>(), static_cast<int>(dst.step), sz) );
break;
case CV_32FC1:
nppSafeCall( nppiAbsDiff_32f_C1R(src1.ptr<Npp32f>(), static_cast<int>(src1.step), src2.ptr<Npp32f>(), static_cast<int>(src2.step),
dst.ptr<Npp32f>(), static_cast<int>(dst.step), sz) );
break;
default:
CV_Assert(!"Unsupported source type");
}
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
void cv::gpu::absdiff(const GpuMat& src, const Scalar& s, GpuMat& dst)
{
CV_Assert(src.type() == CV_32FC1);
dst.create( src.size(), src.type() );
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
nppSafeCall( nppiAbsDiffC_32f_C1R(src.ptr<Npp32f>(), src.step, dst.ptr<Npp32f>(), dst.step, sz, (Npp32f)s[0]) );
}
void cv::gpu::cornerMinEigenVal(const GpuMat& src, GpuMat& dst, GpuMat& Dx, GpuMat& Dy, GpuMat& buf, int blockSize, int ksize, int borderType, Stream& stream)
{
using namespace ::cv::gpu::cudev::imgproc;
CV_Assert(borderType == cv::BORDER_REFLECT101 || borderType == cv::BORDER_REPLICATE || borderType == cv::BORDER_REFLECT);
extractCovData(src, Dx, Dy, buf, blockSize, ksize, borderType, stream);
dst.create(src.size(), CV_32F);
cornerMinEigenVal_gpu(blockSize, Dx, Dy, dst, borderType, StreamAccessor::getStream(stream));
}
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::gpu::log(const GpuMat& src, GpuMat& dst)
{
CV_Assert(src.type() == CV_32FC1);
dst.create(src.size(), src.type());
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
nppSafeCall( nppiLn_32f_C1R(src.ptr<Npp32f>(), src.step, dst.ptr<Npp32f>(), dst.step, sz) );
}
void cv::gpu::labelComponents(const GpuMat& mask, GpuMat& components, int flags, Stream& s)
{
CV_Assert(!mask.empty() && mask.type() == CV_8U);
if (!deviceSupports(SHARED_ATOMICS))
CV_Error(CV_StsNotImplemented, "The device doesn't support shared atomics and communicative synchronization!");
components.create(mask.size(), CV_32SC1);
cudaStream_t stream = StreamAccessor::getStream(s);
device::ccl::labelComponents(mask, components, flags, stream);
}
NCVStatus process(const GpuMat& src, GpuMat& objects, float scaleStep, int minNeighbors,
bool findLargestObject, bool visualizeInPlace, cv::Size ncvMinSize,
/*out*/unsigned int& numDetections)
{
calculateMemReqsAndAllocate(src.size());
NCVMemPtr src_beg;
src_beg.ptr = (void*)src.ptr<Ncv8u>();
src_beg.memtype = NCVMemoryTypeDevice;
NCVMemSegment src_seg;
src_seg.begin = src_beg;
src_seg.size = src.step * src.rows;
NCVMatrixReuse<Ncv8u> d_src(src_seg, static_cast<int>(devProp.textureAlignment), src.cols, src.rows, static_cast<int>(src.step), true);
ncvAssertReturn(d_src.isMemReused(), NCV_ALLOCATOR_BAD_REUSE);
CV_Assert(objects.rows == 1);
NCVMemPtr objects_beg;
objects_beg.ptr = (void*)objects.ptr<NcvRect32u>();
objects_beg.memtype = NCVMemoryTypeDevice;
NCVMemSegment objects_seg;
objects_seg.begin = objects_beg;
objects_seg.size = objects.step * objects.rows;
NCVVectorReuse<NcvRect32u> d_rects(objects_seg, objects.cols);
ncvAssertReturn(d_rects.isMemReused(), NCV_ALLOCATOR_BAD_REUSE);
NcvSize32u roi;
roi.width = d_src.width();
roi.height = d_src.height();
NcvSize32u winMinSize(ncvMinSize.width, ncvMinSize.height);
Ncv32u flags = 0;
flags |= findLargestObject? NCVPipeObjDet_FindLargestObject : 0;
flags |= visualizeInPlace ? NCVPipeObjDet_VisualizeInPlace : 0;
ncvStat = ncvDetectObjectsMultiScale_device(
d_src, roi, d_rects, numDetections, haar, *h_haarStages,
*d_haarStages, *d_haarNodes, *d_haarFeatures,
winMinSize,
minNeighbors,
scaleStep, 1,
flags,
*gpuAllocator, *cpuAllocator, devProp, 0);
ncvAssertReturnNcvStat(ncvStat);
ncvAssertCUDAReturn(cudaStreamSynchronize(0), NCV_CUDA_ERROR);
return NCV_SUCCESS;
}
int cv::gpu::FAST_GPU::calcKeyPointsLocation(const GpuMat& img, const GpuMat& mask)
{
using namespace cv::gpu::cudev::fast;
CV_Assert(img.type() == CV_8UC1);
CV_Assert(mask.empty() || (mask.type() == CV_8UC1 && mask.size() == img.size()));
int maxKeypoints = static_cast<int>(keypointsRatio * img.size().area());
ensureSizeIsEnough(1, maxKeypoints, CV_16SC2, kpLoc_);
if (nonmaxSupression)
{
ensureSizeIsEnough(img.size(), CV_32SC1, score_);
score_.setTo(Scalar::all(0));
}
count_ = calcKeypoints_gpu(img, mask, kpLoc_.ptr<short2>(), maxKeypoints, nonmaxSupression ? score_ : PtrStepSzi(), threshold);
count_ = std::min(count_, maxKeypoints);
return count_;
}
void cv::gpu::GaussianBlur(const GpuMat& src, GpuMat& dst, Size ksize, double sigma1, double sigma2)
{
if (ksize.width == 1 && ksize.height == 1)
{
src.copyTo(dst);
return;
}
dst.create(src.size(), src.type());
Ptr<FilterEngine_GPU> f = createGaussianFilter_GPU(src.type(), ksize, sigma1, sigma2);
f->apply(src, dst);
}
void cv::gpu::GaussianBlur(const GpuMat& src, GpuMat& dst, Size ksize, double sigma1, double sigma2, int rowBorderType, int columnBorderType, Stream& stream)
{
if (ksize.width == 1 && ksize.height == 1)
{
src.copyTo(dst);
return;
}
dst.create(src.size(), src.type());
Ptr<FilterEngine_GPU> f = createGaussianFilter_GPU(src.type(), ksize, sigma1, sigma2, rowBorderType, columnBorderType);
f->apply(src, dst, Rect(0, 0, src.cols, src.rows), stream);
}
void cv::gpu::GeneralizedHough_GPU::setTemplate(const GpuMat& templ, int cannyThreshold, Point templCenter)
{
CV_Assert(templ.type() == CV_8UC1);
CV_Assert(cannyThreshold > 0);
ensureSizeIsEnough(templ.size(), CV_8UC1, edges_);
Canny(templ, cannyBuf_, edges_, cannyThreshold / 2, cannyThreshold);
if (templCenter == Point(-1, -1))
templCenter = Point(templ.cols / 2, templ.rows / 2);
setTemplateImpl(edges_, cannyBuf_.dx, cannyBuf_.dy, templCenter);
}
