void cv::gpu::Stream::enqueueConvert(const GpuMat& src, GpuMat& dst, int rtype, double alpha, double beta)
{
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 );
matrix_operations::convert_to(*psrc, sdepth, dst, ddepth, psrc->channels(), alpha, beta, impl->stream);
}
unsigned int process(const GpuMat& image, GpuMat& objectsBuf, float scaleFactor, int minNeighbors,
bool findLargestObject, bool visualizeInPlace, cv::Size minSize, cv::Size /*maxObjectSize*/)
{
CV_Assert( scaleFactor > 1 && image.depth() == CV_8U);
const int defaultObjSearchNum = 100;
if (objectsBuf.empty())
{
objectsBuf.create(1, defaultObjSearchNum, DataType<Rect>::type);
}
cv::Size ncvMinSize = this->getClassifierCvSize();
if (ncvMinSize.width < minSize.width && ncvMinSize.height < minSize.height)
{
ncvMinSize.width = minSize.width;
ncvMinSize.height = minSize.height;
}
unsigned int numDetections;
ncvSafeCall(this->process(image, objectsBuf, (float)scaleFactor, minNeighbors, findLargestObject, visualizeInPlace, ncvMinSize, numDetections));
return numDetections;
}
void cv::gpu::equalizeHist(const GpuMat& src, GpuMat& dst, GpuMat& hist, GpuMat& buf, Stream& s)
{
CV_Assert(src.type() == CV_8UC1);
dst.create(src.size(), src.type());
int intBufSize;
nppSafeCall( nppsIntegralGetBufferSize_32s(256, &intBufSize) );
ensureSizeIsEnough(1, intBufSize + 256 * sizeof(int), CV_8UC1, buf);
GpuMat intBuf(1, intBufSize, CV_8UC1, buf.ptr());
GpuMat lut(1, 256, CV_32S, buf.ptr() + intBufSize);
calcHist(src, hist, s);
cudaStream_t stream = StreamAccessor::getStream(s);
NppStreamHandler h(stream);
nppSafeCall( nppsIntegral_32s(hist.ptr<Npp32s>(), lut.ptr<Npp32s>(), 256, intBuf.ptr<Npp8u>()) );
hist::equalizeHist(src, dst, lut.ptr<int>(), stream);
}
void cv::gpu::FastNonLocalMeansDenoising::simpleMethod(const GpuMat& src, GpuMat& dst, float h, int search_window, int block_window, Stream& s)
{
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;
cv::gpu::ensureSizeIsEnough(esize, CV_8UC3, extended_src_buffer);
GpuMat extended_src(esize, src.type(), extended_src_buffer.ptr(), extended_src_buffer.step);
cv::gpu::copyMakeBorder(src, extended_src, border_size, border_size, border_size, border_size, cv::BORDER_DEFAULT, Scalar(), s);
GpuMat src_hdr = extended_src(Rect(Point2i(border_size, border_size), src.size()));
int bcols, brows;
cudev::imgproc::nln_fast_get_buffer_size(src_hdr, search_window, block_window, bcols, brows);
buffer.create(brows, bcols, CV_32S);
using namespace cv::gpu::cudev::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());
funcs[src.channels()-1](src_hdr, dst, buffer, search_window, block_window, h, StreamAccessor::getStream(s));
}
static void csbp_operator(StereoConstantSpaceBP& rthis, GpuMat u[2], GpuMat d[2], GpuMat l[2], GpuMat r[2],
GpuMat disp_selected_pyr[2], GpuMat& data_cost, GpuMat& data_cost_selected,
GpuMat& temp, GpuMat& out, const GpuMat& left, const GpuMat& right, GpuMat& disp, cudaStream_t stream)
{
CV_DbgAssert(0 < rthis.ndisp && 0 < rthis.iters && 0 < rthis.levels && 0 < rthis.nr_plane
&& left.rows == right.rows && left.cols == right.cols && left.type() == right.type());
CV_Assert(rthis.levels <= 8 && (left.type() == CV_8UC1 || left.type() == CV_8UC3));
const Scalar zero = Scalar::all(0);
////////////////////////////////////////////////////////////////////////////////////////////
// Init
int rows = left.rows;
int cols = left.cols;
rthis.levels = min(rthis.levels, int(log((double)rthis.ndisp) / log(2.0)));
int levels = rthis.levels;
AutoBuffer<int> buf(levels * 4);
int* cols_pyr = buf;
int* rows_pyr = cols_pyr + levels;
int* nr_plane_pyr = rows_pyr + levels;
int* step_pyr = nr_plane_pyr + levels;
cols_pyr[0] = cols;
rows_pyr[0] = rows;
nr_plane_pyr[0] = rthis.nr_plane;
const int n = 64;
step_pyr[0] = alignSize(cols * sizeof(T), n) / sizeof(T);
for (int i = 1; i < levels; i++)
{
cols_pyr[i] = (cols_pyr[i-1] + 1) / 2;
rows_pyr[i] = (rows_pyr[i-1] + 1) / 2;
nr_plane_pyr[i] = nr_plane_pyr[i-1] * 2;
step_pyr[i] = alignSize(cols_pyr[i] * sizeof(T), n) / sizeof(T);
}
Size msg_size(step_pyr[0], rows * nr_plane_pyr[0]);
Size data_cost_size(step_pyr[0], rows * nr_plane_pyr[0] * 2);
u[0].create(msg_size, DataType<T>::type);
d[0].create(msg_size, DataType<T>::type);
l[0].create(msg_size, DataType<T>::type);
r[0].create(msg_size, DataType<T>::type);
u[1].create(msg_size, DataType<T>::type);
d[1].create(msg_size, DataType<T>::type);
l[1].create(msg_size, DataType<T>::type);
r[1].create(msg_size, DataType<T>::type);
disp_selected_pyr[0].create(msg_size, DataType<T>::type);
disp_selected_pyr[1].create(msg_size, DataType<T>::type);
data_cost.create(data_cost_size, DataType<T>::type);
data_cost_selected.create(msg_size, DataType<T>::type);
step_pyr[0] = data_cost.step / sizeof(T);
Size temp_size = data_cost_size;
if (data_cost_size.width * data_cost_size.height < step_pyr[levels - 1] * rows_pyr[levels - 1] * rthis.ndisp)
temp_size = Size(step_pyr[levels - 1], rows_pyr[levels - 1] * rthis.ndisp);
temp.create(temp_size, DataType<T>::type);
////////////////////////////////////////////////////////////////////////////
// Compute
csbp::load_constants(rthis.ndisp, rthis.max_data_term, rthis.data_weight,
rthis.max_disc_term, rthis.disc_single_jump, rthis.min_disp_th, left, right, temp);
l[0] = zero;
d[0] = zero;
r[0] = zero;
u[0] = zero;
l[1] = zero;
d[1] = zero;
r[1] = zero;
u[1] = zero;
data_cost = zero;
data_cost_selected = zero;
int cur_idx = 0;
for (int i = levels - 1; i >= 0; i--)
{
if (i == levels - 1)
{
csbp::init_data_cost(left.rows, left.cols, disp_selected_pyr[cur_idx].ptr<T>(), data_cost_selected.ptr<T>(),
step_pyr[i], rows_pyr[i], cols_pyr[i], i, nr_plane_pyr[i], rthis.ndisp, left.channels(), rthis.use_local_init_data_cost, stream);
}
else
{
csbp::compute_data_cost(disp_selected_pyr[cur_idx].ptr<T>(), data_cost.ptr<T>(), step_pyr[i], step_pyr[i+1],
left.rows, left.cols, rows_pyr[i], cols_pyr[i], rows_pyr[i+1], i, nr_plane_pyr[i+1], left.channels(), stream);
int new_idx = (cur_idx + 1) & 1;
csbp::init_message(u[new_idx].ptr<T>(), d[new_idx].ptr<T>(), l[new_idx].ptr<T>(), r[new_idx].ptr<T>(),
u[cur_idx].ptr<T>(), d[cur_idx].ptr<T>(), l[cur_idx].ptr<T>(), r[cur_idx].ptr<T>(),
disp_selected_pyr[new_idx].ptr<T>(), disp_selected_pyr[cur_idx].ptr<T>(),
data_cost_selected.ptr<T>(), data_cost.ptr<T>(), step_pyr[i], step_pyr[i+1], rows_pyr[i],
cols_pyr[i], nr_plane_pyr[i], rows_pyr[i+1], cols_pyr[i+1], nr_plane_pyr[i+1], stream);
cur_idx = new_idx;
}
csbp::calc_all_iterations(u[cur_idx].ptr<T>(), d[cur_idx].ptr<T>(), l[cur_idx].ptr<T>(), r[cur_idx].ptr<T>(),
data_cost_selected.ptr<T>(), disp_selected_pyr[cur_idx].ptr<T>(), step_pyr[i],
rows_pyr[i], cols_pyr[i], nr_plane_pyr[i], rthis.iters, stream);
}
if (disp.empty())
disp.create(rows, cols, CV_16S);
out = ((disp.type() == CV_16S) ? disp : (out.create(rows, cols, CV_16S), out));
out = zero;
csbp::compute_disp(u[cur_idx].ptr<T>(), d[cur_idx].ptr<T>(), l[cur_idx].ptr<T>(), r[cur_idx].ptr<T>(),
data_cost_selected.ptr<T>(), disp_selected_pyr[cur_idx].ptr<T>(), step_pyr[0], out, nr_plane_pyr[0], stream);
if (disp.type() != CV_16S)
out.convertTo(disp, disp.type());
}
void cv::gpu::gemm(const GpuMat& src1, const GpuMat& src2, double alpha, const GpuMat& src3, double beta, GpuMat& dst, int flags, Stream& stream)
{
#ifndef HAVE_CUBLAS
(void)src1;
(void)src2;
(void)alpha;
(void)src3;
(void)beta;
(void)dst;
(void)flags;
(void)stream;
CV_Error(CV_StsNotImplemented, "The library was build without CUBLAS");
#else
// CUBLAS works with column-major matrices
CV_Assert(src1.type() == CV_32FC1 || src1.type() == CV_32FC2 || src1.type() == CV_64FC1 || src1.type() == CV_64FC2);
CV_Assert(src2.type() == src1.type() && (src3.empty() || src3.type() == src1.type()));
if (src1.depth() == CV_64F)
{
if (!deviceSupports(NATIVE_DOUBLE))
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
}
bool tr1 = (flags & GEMM_1_T) != 0;
bool tr2 = (flags & GEMM_2_T) != 0;
bool tr3 = (flags & GEMM_3_T) != 0;
if (src1.type() == CV_64FC2)
{
if (tr1 || tr2 || tr3)
CV_Error(CV_StsNotImplemented, "transpose operation doesn't implemented for CV_64FC2 type");
}
Size src1Size = tr1 ? Size(src1.rows, src1.cols) : src1.size();
Size src2Size = tr2 ? Size(src2.rows, src2.cols) : src2.size();
Size src3Size = tr3 ? Size(src3.rows, src3.cols) : src3.size();
Size dstSize(src2Size.width, src1Size.height);
CV_Assert(src1Size.width == src2Size.height);
CV_Assert(src3.empty() || src3Size == dstSize);
dst.create(dstSize, src1.type());
if (beta != 0)
{
if (src3.empty())
{
if (stream)
stream.enqueueMemSet(dst, Scalar::all(0));
else
dst.setTo(Scalar::all(0));
}
else
{
if (tr3)
{
transpose(src3, dst, stream);
}
else
{
if (stream)
stream.enqueueCopy(src3, dst);
else
src3.copyTo(dst);
}
}
}
cublasHandle_t handle;
cublasSafeCall( cublasCreate_v2(&handle) );
cublasSafeCall( cublasSetStream_v2(handle, StreamAccessor::getStream(stream)) );
cublasSafeCall( cublasSetPointerMode_v2(handle, CUBLAS_POINTER_MODE_HOST) );
const float alphaf = static_cast<float>(alpha);
const float betaf = static_cast<float>(beta);
const cuComplex alphacf = make_cuComplex(alphaf, 0);
const cuComplex betacf = make_cuComplex(betaf, 0);
const cuDoubleComplex alphac = make_cuDoubleComplex(alpha, 0);
const cuDoubleComplex betac = make_cuDoubleComplex(beta, 0);
cublasOperation_t transa = tr2 ? CUBLAS_OP_T : CUBLAS_OP_N;
cublasOperation_t transb = tr1 ? CUBLAS_OP_T : CUBLAS_OP_N;
switch (src1.type())
{
case CV_32FC1:
cublasSafeCall( cublasSgemm_v2(handle, transa, transb, tr2 ? src2.rows : src2.cols, tr1 ? src1.cols : src1.rows, tr2 ? src2.cols : src2.rows,
&alphaf,
src2.ptr<float>(), static_cast<int>(src2.step / sizeof(float)),
src1.ptr<float>(), static_cast<int>(src1.step / sizeof(float)),
&betaf,
dst.ptr<float>(), static_cast<int>(dst.step / sizeof(float))) );
break;
case CV_64FC1:
cublasSafeCall( cublasDgemm_v2(handle, transa, transb, tr2 ? src2.rows : src2.cols, tr1 ? src1.cols : src1.rows, tr2 ? src2.cols : src2.rows,
&alpha,
src2.ptr<double>(), static_cast<int>(src2.step / sizeof(double)),
src1.ptr<double>(), static_cast<int>(src1.step / sizeof(double)),
&beta,
dst.ptr<double>(), static_cast<int>(dst.step / sizeof(double))) );
break;
case CV_32FC2:
cublasSafeCall( cublasCgemm_v2(handle, transa, transb, tr2 ? src2.rows : src2.cols, tr1 ? src1.cols : src1.rows, tr2 ? src2.cols : src2.rows,
&alphacf,
src2.ptr<cuComplex>(), static_cast<int>(src2.step / sizeof(cuComplex)),
src1.ptr<cuComplex>(), static_cast<int>(src1.step / sizeof(cuComplex)),
&betacf,
dst.ptr<cuComplex>(), static_cast<int>(dst.step / sizeof(cuComplex))) );
break;
case CV_64FC2:
cublasSafeCall( cublasZgemm_v2(handle, transa, transb, tr2 ? src2.rows : src2.cols, tr1 ? src1.cols : src1.rows, tr2 ? src2.cols : src2.rows,
&alphac,
src2.ptr<cuDoubleComplex>(), static_cast<int>(src2.step / sizeof(cuDoubleComplex)),
src1.ptr<cuDoubleComplex>(), static_cast<int>(src1.step / sizeof(cuDoubleComplex)),
&betac,
dst.ptr<cuDoubleComplex>(), static_cast<int>(dst.step / sizeof(cuDoubleComplex))) );
break;
}
cublasSafeCall( cublasDestroy_v2(handle) );
#endif
}
void cv::gpu::LUT(const GpuMat& src, const Mat& lut, GpuMat& dst, Stream& s)
{
class LevelsInit
{
public:
Npp32s pLevels[256];
const Npp32s* pLevels3[3];
int nValues3[3];
#if (CUDA_VERSION > 4020)
GpuMat d_pLevels;
#endif
LevelsInit()
{
nValues3[0] = nValues3[1] = nValues3[2] = 256;
for (int i = 0; i < 256; ++i)
pLevels[i] = i;
#if (CUDA_VERSION <= 4020)
pLevels3[0] = pLevels3[1] = pLevels3[2] = pLevels;
#else
d_pLevels.upload(Mat(1, 256, CV_32S, pLevels));
pLevels3[0] = pLevels3[1] = pLevels3[2] = d_pLevels.ptr<Npp32s>();
#endif
}
};
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);
cudaStream_t stream = StreamAccessor::getStream(s);
NppStreamHandler h(stream);
if (src.type() == CV_8UC1)
{
#if (CUDA_VERSION <= 4020)
nppSafeCall( nppiLUT_Linear_8u_C1R(src.ptr<Npp8u>(), static_cast<int>(src.step),
dst.ptr<Npp8u>(), static_cast<int>(dst.step), sz, nppLut.ptr<Npp32s>(), lvls.pLevels, 256) );
#else
GpuMat d_nppLut(Mat(1, 256, CV_32S, nppLut.data));
nppSafeCall( nppiLUT_Linear_8u_C1R(src.ptr<Npp8u>(), static_cast<int>(src.step),
dst.ptr<Npp8u>(), static_cast<int>(dst.step), sz, d_nppLut.ptr<Npp32s>(), lvls.d_pLevels.ptr<Npp32s>(), 256) );
#endif
}
else
{
const Npp32s* pValues3[3];
Mat nppLut3[3];
if (nppLut.channels() == 1)
{
#if (CUDA_VERSION <= 4020)
pValues3[0] = pValues3[1] = pValues3[2] = nppLut.ptr<Npp32s>();
#else
GpuMat d_nppLut(Mat(1, 256, CV_32S, nppLut.data));
pValues3[0] = pValues3[1] = pValues3[2] = d_nppLut.ptr<Npp32s>();
#endif
}
else
{
cv::split(nppLut, nppLut3);
#if (CUDA_VERSION <= 4020)
pValues3[0] = nppLut3[0].ptr<Npp32s>();
pValues3[1] = nppLut3[1].ptr<Npp32s>();
pValues3[2] = nppLut3[2].ptr<Npp32s>();
#else
GpuMat d_nppLut0(Mat(1, 256, CV_32S, nppLut3[0].data));
GpuMat d_nppLut1(Mat(1, 256, CV_32S, nppLut3[1].data));
GpuMat d_nppLut2(Mat(1, 256, CV_32S, nppLut3[2].data));
pValues3[0] = d_nppLut0.ptr<Npp32s>();
pValues3[1] = d_nppLut1.ptr<Npp32s>();
pValues3[2] = d_nppLut2.ptr<Npp32s>();
#endif
}
nppSafeCall( nppiLUT_Linear_8u_C3R(src.ptr<Npp8u>(), static_cast<int>(src.step),
dst.ptr<Npp8u>(), static_cast<int>(dst.step), sz, pValues3, lvls.pLevels3, lvls.nValues3) );
}
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
void cv::gpu::resize(const GpuMat& src, GpuMat& dst, Size dsize, double fx, double fy, int interpolation, Stream& stream)
{
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);
#ifdef OPENCV_TINY_GPU_MODULE
static const func_t funcs[6][4] =
{
{device::resize<uchar> , 0 /*device::resize<uchar2>*/ , device::resize<uchar3> , device::resize<uchar4> },
{0, 0, 0, 0},
{0, 0, 0, 0},
{0, 0, 0, 0},
{0, 0, 0, 0},
{device::resize<float> , 0 /*device::resize<float2>*/ , device::resize<float3> , device::resize<float4> }
};
#else
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> }
};
#endif
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());
if (dsize == src.size())
{
if (stream)
stream.enqueueCopy(src, dst);
else
src.copyTo(dst);
return;
}
const func_t func = funcs[src.depth()][src.channels() - 1];
if (!func)
CV_Error(CV_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));
}
void cv::gpu::copyMakeBorder(const GpuMat& src, GpuMat& dst, int top, int bottom, int left, int right, int borderType, const Scalar& value, Stream& s)
{
CV_Assert(src.depth() <= CV_32F && src.channels() <= 4);
CV_Assert(borderType == BORDER_REFLECT_101 || borderType == BORDER_REPLICATE || borderType == BORDER_CONSTANT || borderType == BORDER_REFLECT || borderType == BORDER_WRAP);
dst.create(src.rows + top + bottom, src.cols + left + right, src.type());
cudaStream_t stream = StreamAccessor::getStream(s);
if (borderType == BORDER_CONSTANT && (src.type() == CV_8UC1 || src.type() == CV_8UC4 || src.type() == CV_32SC1 || src.type() == CV_32FC1))
{
NppiSize srcsz;
srcsz.width = src.cols;
srcsz.height = src.rows;
NppiSize dstsz;
dstsz.width = dst.cols;
dstsz.height = dst.rows;
NppStreamHandler h(stream);
switch (src.type())
{
case CV_8UC1:
{
Npp8u nVal = saturate_cast<Npp8u>(value[0]);
nppSafeCall( nppiCopyConstBorder_8u_C1R(src.ptr<Npp8u>(), static_cast<int>(src.step), srcsz,
dst.ptr<Npp8u>(), static_cast<int>(dst.step), dstsz, top, left, nVal) );
break;
}
case CV_8UC4:
{
Npp8u nVal[] = {saturate_cast<Npp8u>(value[0]), saturate_cast<Npp8u>(value[1]), saturate_cast<Npp8u>(value[2]), saturate_cast<Npp8u>(value[3])};
nppSafeCall( nppiCopyConstBorder_8u_C4R(src.ptr<Npp8u>(), static_cast<int>(src.step), srcsz,
dst.ptr<Npp8u>(), static_cast<int>(dst.step), dstsz, top, left, nVal) );
break;
}
case CV_32SC1:
{
Npp32s nVal = saturate_cast<Npp32s>(value[0]);
nppSafeCall( nppiCopyConstBorder_32s_C1R(src.ptr<Npp32s>(), static_cast<int>(src.step), srcsz,
dst.ptr<Npp32s>(), static_cast<int>(dst.step), dstsz, top, left, nVal) );
break;
}
case CV_32FC1:
{
Npp32f val = saturate_cast<Npp32f>(value[0]);
Npp32s nVal = *(reinterpret_cast<Npp32s_a*>(&val));
nppSafeCall( nppiCopyConstBorder_32s_C1R(src.ptr<Npp32s>(), static_cast<int>(src.step), srcsz,
dst.ptr<Npp32s>(), static_cast<int>(dst.step), dstsz, top, left, nVal) );
break;
}
}
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
else
{
typedef void (*caller_t)(const PtrStepSzb& src, const PtrStepSzb& dst, int top, int left, int borderType, const Scalar& value, cudaStream_t stream);
static const caller_t callers[6][4] =
{
{ copyMakeBorder_caller<uchar, 1> , copyMakeBorder_caller<uchar, 2> , copyMakeBorder_caller<uchar, 3> , copyMakeBorder_caller<uchar, 4>},
{0/*copyMakeBorder_caller<schar, 1>*/, 0/*copyMakeBorder_caller<schar, 2>*/ , 0/*copyMakeBorder_caller<schar, 3>*/, 0/*copyMakeBorder_caller<schar, 4>*/},
{ copyMakeBorder_caller<ushort, 1> , 0/*copyMakeBorder_caller<ushort, 2>*/, copyMakeBorder_caller<ushort, 3> , copyMakeBorder_caller<ushort, 4>},
{ copyMakeBorder_caller<short, 1> , 0/*copyMakeBorder_caller<short, 2>*/ , copyMakeBorder_caller<short, 3> , copyMakeBorder_caller<short, 4>},
{0/*copyMakeBorder_caller<int, 1>*/, 0/*copyMakeBorder_caller<int, 2>*/ , 0/*copyMakeBorder_caller<int, 3>*/, 0/*copyMakeBorder_caller<int , 4>*/},
{ copyMakeBorder_caller<float, 1> , 0/*copyMakeBorder_caller<float, 2>*/ , copyMakeBorder_caller<float, 3> , copyMakeBorder_caller<float ,4>}
};
caller_t func = callers[src.depth()][src.channels() - 1];
CV_Assert(func != 0);
func(src, dst, top, left, borderType, value, stream);
}
}
void cv::gpu::reduce(const GpuMat& src, GpuMat& dst, int dim, int reduceOp, int dtype, Stream& stream)
{
using namespace ::cv::gpu::device::matrix_reductions;
CV_Assert(src.depth() <= CV_32F && src.channels() <= 4 && dtype <= CV_32F);
CV_Assert(dim == 0 || dim == 1);
CV_Assert(reduceOp == CV_REDUCE_SUM || reduceOp == CV_REDUCE_AVG || reduceOp == CV_REDUCE_MAX || reduceOp == CV_REDUCE_MIN);
if (dtype < 0)
dtype = src.depth();
dst.create(1, dim == 0 ? src.cols : src.rows, CV_MAKETYPE(dtype, src.channels()));
if (dim == 0)
{
typedef void (*caller_t)(const PtrStepSzb& src, const PtrStepSzb& dst, int reduceOp, cudaStream_t stream);
static const caller_t callers[6][6] =
{
{
reduceRows_gpu<unsigned char, int, unsigned char>,
0/*reduceRows_gpu<unsigned char, int, signed char>*/,
0/*reduceRows_gpu<unsigned char, int, unsigned short>*/,
0/*reduceRows_gpu<unsigned char, int, short>*/,
reduceRows_gpu<unsigned char, int, int>,
reduceRows_gpu<unsigned char, int, float>
},
{
0/*reduceRows_gpu<signed char, int, unsigned char>*/,
0/*reduceRows_gpu<signed char, int, signed char>*/,
0/*reduceRows_gpu<signed char, int, unsigned short>*/,
0/*reduceRows_gpu<signed char, int, short>*/,
0/*reduceRows_gpu<signed char, int, int>*/,
0/*reduceRows_gpu<signed char, int, float>*/
},
{
0/*reduceRows_gpu<unsigned short, int, unsigned char>*/,
0/*reduceRows_gpu<unsigned short, int, signed char>*/,
reduceRows_gpu<unsigned short, int, unsigned short>,
0/*reduceRows_gpu<unsigned short, int, short>*/,
reduceRows_gpu<unsigned short, int, int>,
reduceRows_gpu<unsigned short, int, float>
},
{
0/*reduceRows_gpu<short, int, unsigned char>*/,
0/*reduceRows_gpu<short, int, signed char>*/,
0/*reduceRows_gpu<short, int, unsigned short>*/,
reduceRows_gpu<short, int, short>,
reduceRows_gpu<short, int, int>,
reduceRows_gpu<short, int, float>
},
{
0/*reduceRows_gpu<int, int, unsigned char>*/,
0/*reduceRows_gpu<int, int, signed char>*/,
0/*reduceRows_gpu<int, int, unsigned short>*/,
0/*reduceRows_gpu<int, int, short>*/,
reduceRows_gpu<int, int, int>,
reduceRows_gpu<int, int, float>
},
{
0/*reduceRows_gpu<float, float, unsigned char>*/,
0/*reduceRows_gpu<float, float, signed char>*/,
0/*reduceRows_gpu<float, float, unsigned short>*/,
0/*reduceRows_gpu<float, float, short>*/,
0/*reduceRows_gpu<float, float, int>*/,
reduceRows_gpu<float, float, float>
}
};
const caller_t func = callers[src.depth()][dst.depth()];
if (!func)
CV_Error(CV_StsUnsupportedFormat, "Unsupported combination of input and output array formats");
func(src.reshape(1), dst.reshape(1), reduceOp, StreamAccessor::getStream(stream));
}
else
{
typedef void (*caller_t)(const PtrStepSzb& src, int cn, const PtrStepSzb& dst, int reduceOp, cudaStream_t stream);
static const caller_t callers[6][6] =
{
{
reduceCols_gpu<unsigned char, int, unsigned char>,
0/*reduceCols_gpu<unsigned char, int, signed char>*/,
0/*reduceCols_gpu<unsigned char, int, unsigned short>*/,
0/*reduceCols_gpu<unsigned char, int, short>*/,
reduceCols_gpu<unsigned char, int, int>,
reduceCols_gpu<unsigned char, int, float>
},
{
0/*reduceCols_gpu<signed char, int, unsigned char>*/,
0/*reduceCols_gpu<signed char, int, signed char>*/,
0/*reduceCols_gpu<signed char, int, unsigned short>*/,
0/*reduceCols_gpu<signed char, int, short>*/,
0/*reduceCols_gpu<signed char, int, int>*/,
0/*reduceCols_gpu<signed char, int, float>*/
},
{
0/*reduceCols_gpu<unsigned short, int, unsigned char>*/,
0/*reduceCols_gpu<unsigned short, int, signed char>*/,
reduceCols_gpu<unsigned short, int, unsigned short>,
0/*reduceCols_gpu<unsigned short, int, short>*/,
reduceCols_gpu<unsigned short, int, int>,
reduceCols_gpu<unsigned short, int, float>
},
{
0/*reduceCols_gpu<short, int, unsigned char>*/,
0/*reduceCols_gpu<short, int, signed char>*/,
0/*reduceCols_gpu<short, int, unsigned short>*/,
reduceCols_gpu<short, int, short>,
reduceCols_gpu<short, int, int>,
reduceCols_gpu<short, int, float>
},
{
0/*reduceCols_gpu<int, int, unsigned char>*/,
0/*reduceCols_gpu<int, int, signed char>*/,
0/*reduceCols_gpu<int, int, unsigned short>*/,
0/*reduceCols_gpu<int, int, short>*/,
reduceCols_gpu<int, int, int>,
reduceCols_gpu<int, int, float>
},
{
0/*reduceCols_gpu<float, unsigned char>*/,
0/*reduceCols_gpu<float, signed char>*/,
0/*reduceCols_gpu<float, unsigned short>*/,
0/*reduceCols_gpu<float, short>*/,
0/*reduceCols_gpu<float, int>*/,
reduceCols_gpu<float, float, float>
}
};
const caller_t func = callers[src.depth()][dst.depth()];
if (!func)
CV_Error(CV_StsUnsupportedFormat, "Unsupported combination of input and output array formats");
func(src, src.channels(), dst, reduceOp, StreamAccessor::getStream(stream));
}
}
void cv::gpu::FarnebackOpticalFlow::operator ()(
const GpuMat &frame0, const GpuMat &frame1, GpuMat &flowx, GpuMat &flowy, Stream &s)
{
CV_Assert(frame0.channels() == 1 && frame1.channels() == 1);
CV_Assert(frame0.size() == frame1.size());
CV_Assert(polyN == 5 || polyN == 7);
CV_Assert(!fastPyramids || std::abs(pyrScale - 0.5) < 1e-6);
Stream streams[5];
if (S(s))
streams[0] = s;
Size size = frame0.size();
GpuMat prevFlowX, prevFlowY, curFlowX, curFlowY;
flowx.create(size, CV_32F);
flowy.create(size, CV_32F);
GpuMat flowx0 = flowx;
GpuMat flowy0 = flowy;
// Crop unnecessary levels
double scale = 1;
int numLevelsCropped = 0;
for (; numLevelsCropped < numLevels; numLevelsCropped++)
{
scale *= pyrScale;
if (size.width*scale < MIN_SIZE || size.height*scale < MIN_SIZE)
break;
}
streams[0].enqueueConvert(frame0, frames_[0], CV_32F);
streams[1].enqueueConvert(frame1, frames_[1], CV_32F);
if (fastPyramids)
{
// Build Gaussian pyramids using pyrDown()
pyramid0_.resize(numLevelsCropped + 1);
pyramid1_.resize(numLevelsCropped + 1);
pyramid0_[0] = frames_[0];
pyramid1_[0] = frames_[1];
for (int i = 1; i <= numLevelsCropped; ++i)
{
pyrDown(pyramid0_[i - 1], pyramid0_[i], streams[0]);
pyrDown(pyramid1_[i - 1], pyramid1_[i], streams[1]);
}
}
setPolynomialExpansionConsts(polyN, polySigma);
device::optflow_farneback::setUpdateMatricesConsts();
for (int k = numLevelsCropped; k >= 0; k--)
{
streams[0].waitForCompletion();
scale = 1;
for (int i = 0; i < k; i++)
scale *= pyrScale;
double sigma = (1./scale - 1) * 0.5;
int smoothSize = cvRound(sigma*5) | 1;
smoothSize = std::max(smoothSize, 3);
int width = cvRound(size.width*scale);
int height = cvRound(size.height*scale);
if (fastPyramids)
{
width = pyramid0_[k].cols;
height = pyramid0_[k].rows;
}
if (k > 0)
{
curFlowX.create(height, width, CV_32F);
curFlowY.create(height, width, CV_32F);
}
else
{
curFlowX = flowx0;
curFlowY = flowy0;
}
if (!prevFlowX.data)
{
if (flags & OPTFLOW_USE_INITIAL_FLOW)
{
#if ENABLE_GPU_RESIZE
resize(flowx0, curFlowX, Size(width, height), 0, 0, INTER_LINEAR, streams[0]);
resize(flowy0, curFlowY, Size(width, height), 0, 0, INTER_LINEAR, streams[1]);
streams[0].enqueueConvert(curFlowX, curFlowX, curFlowX.depth(), scale);
streams[1].enqueueConvert(curFlowY, curFlowY, curFlowY.depth(), scale);
#else
Mat tmp1, tmp2;
flowx0.download(tmp1);
resize(tmp1, tmp2, Size(width, height), 0, 0, INTER_AREA);
tmp2 *= scale;
curFlowX.upload(tmp2);
flowy0.download(tmp1);
resize(tmp1, tmp2, Size(width, height), 0, 0, INTER_AREA);
tmp2 *= scale;
curFlowY.upload(tmp2);
#endif
}
else
{
streams[0].enqueueMemSet(curFlowX, 0);
streams[1].enqueueMemSet(curFlowY, 0);
}
}
else
{
#if ENABLE_GPU_RESIZE
resize(prevFlowX, curFlowX, Size(width, height), 0, 0, INTER_LINEAR, streams[0]);
resize(prevFlowY, curFlowY, Size(width, height), 0, 0, INTER_LINEAR, streams[1]);
streams[0].enqueueConvert(curFlowX, curFlowX, curFlowX.depth(), 1./pyrScale);
streams[1].enqueueConvert(curFlowY, curFlowY, curFlowY.depth(), 1./pyrScale);
#else
Mat tmp1, tmp2;
prevFlowX.download(tmp1);
resize(tmp1, tmp2, Size(width, height), 0, 0, INTER_LINEAR);
tmp2 *= 1./pyrScale;
curFlowX.upload(tmp2);
prevFlowY.download(tmp1);
resize(tmp1, tmp2, Size(width, height), 0, 0, INTER_LINEAR);
tmp2 *= 1./pyrScale;
curFlowY.upload(tmp2);
#endif
}
GpuMat M = allocMatFromBuf(5*height, width, CV_32F, M_);
GpuMat bufM = allocMatFromBuf(5*height, width, CV_32F, bufM_);
GpuMat R[2] =
{
allocMatFromBuf(5*height, width, CV_32F, R_[0]),
allocMatFromBuf(5*height, width, CV_32F, R_[1])
};
if (fastPyramids)
{
device::optflow_farneback::polynomialExpansionGpu(pyramid0_[k], polyN, R[0], S(streams[0]));
device::optflow_farneback::polynomialExpansionGpu(pyramid1_[k], polyN, R[1], S(streams[1]));
}
else
{
GpuMat blurredFrame[2] =
{
allocMatFromBuf(size.height, size.width, CV_32F, blurredFrame_[0]),
allocMatFromBuf(size.height, size.width, CV_32F, blurredFrame_[1])
};
GpuMat pyrLevel[2] =
{
allocMatFromBuf(height, width, CV_32F, pyrLevel_[0]),
allocMatFromBuf(height, width, CV_32F, pyrLevel_[1])
};
Mat g = getGaussianKernel(smoothSize, sigma, CV_32F);
device::optflow_farneback::setGaussianBlurKernel(g.ptr<float>(smoothSize/2), smoothSize/2);
for (int i = 0; i < 2; i++)
{
device::optflow_farneback::gaussianBlurGpu(
frames_[i], smoothSize/2, blurredFrame[i], BORDER_REFLECT101_GPU, S(streams[i]));
#if ENABLE_GPU_RESIZE
resize(blurredFrame[i], pyrLevel[i], Size(width, height), INTER_LINEAR, streams[i]);
#else
Mat tmp1, tmp2;
tmp[i].download(tmp1);
resize(tmp1, tmp2, Size(width, height), INTER_LINEAR);
I[i].upload(tmp2);
#endif
device::optflow_farneback::polynomialExpansionGpu(pyrLevel[i], polyN, R[i], S(streams[i]));
}
}
streams[1].waitForCompletion();
device::optflow_farneback::updateMatricesGpu(curFlowX, curFlowY, R[0], R[1], M, S(streams[0]));
if (flags & OPTFLOW_FARNEBACK_GAUSSIAN)
{
Mat g = getGaussianKernel(winSize, winSize/2*0.3f, CV_32F);
device::optflow_farneback::setGaussianBlurKernel(g.ptr<float>(winSize/2), winSize/2);
}
for (int i = 0; i < numIters; i++)
{
if (flags & OPTFLOW_FARNEBACK_GAUSSIAN)
updateFlow_gaussianBlur(R[0], R[1], curFlowX, curFlowY, M, bufM, winSize, i < numIters-1, streams);
else
updateFlow_boxFilter(R[0], R[1], curFlowX, curFlowY, M, bufM, winSize, i < numIters-1, streams);
}
prevFlowX = curFlowX;
prevFlowY = curFlowY;
}
flowx = curFlowX;
flowy = curFlowY;
if (!S(s))
streams[0].waitForCompletion();
}
void cv::gpu::graphcut(GpuMat& terminals, GpuMat& leftTransp, GpuMat& rightTransp, GpuMat& top, GpuMat& topLeft, GpuMat& topRight,
GpuMat& bottom, GpuMat& bottomLeft, GpuMat& bottomRight, GpuMat& labels, GpuMat& buf, Stream& s)
{
#if (CUDA_VERSION < 5000)
CV_Assert(terminals.type() == CV_32S);
#else
CV_Assert(terminals.type() == CV_32S || terminals.type() == CV_32F);
#endif
Size src_size = terminals.size();
CV_Assert(leftTransp.size() == Size(src_size.height, src_size.width));
CV_Assert(leftTransp.type() == terminals.type());
CV_Assert(rightTransp.size() == Size(src_size.height, src_size.width));
CV_Assert(rightTransp.type() == terminals.type());
CV_Assert(top.size() == src_size);
CV_Assert(top.type() == terminals.type());
CV_Assert(topLeft.size() == src_size);
CV_Assert(topLeft.type() == terminals.type());
CV_Assert(topRight.size() == src_size);
CV_Assert(topRight.type() == terminals.type());
CV_Assert(bottom.size() == src_size);
CV_Assert(bottom.type() == terminals.type());
CV_Assert(bottomLeft.size() == src_size);
CV_Assert(bottomLeft.type() == terminals.type());
CV_Assert(bottomRight.size() == src_size);
CV_Assert(bottomRight.type() == terminals.type());
labels.create(src_size, CV_8U);
NppiSize sznpp;
sznpp.width = src_size.width;
sznpp.height = src_size.height;
int bufsz;
nppSafeCall( nppiGraphcut8GetSize(sznpp, &bufsz) );
ensureSizeIsEnough(1, bufsz, CV_8U, buf);
cudaStream_t stream = StreamAccessor::getStream(s);
NppStreamHandler h(stream);
NppiGraphcutStateHandler state(sznpp, buf.ptr<Npp8u>(), nppiGraphcut8InitAlloc);
#if (CUDA_VERSION < 5000)
nppSafeCall( nppiGraphcut8_32s8u(terminals.ptr<Npp32s>(), leftTransp.ptr<Npp32s>(), rightTransp.ptr<Npp32s>(),
top.ptr<Npp32s>(), topLeft.ptr<Npp32s>(), topRight.ptr<Npp32s>(),
bottom.ptr<Npp32s>(), bottomLeft.ptr<Npp32s>(), bottomRight.ptr<Npp32s>(),
static_cast<int>(terminals.step), static_cast<int>(leftTransp.step), sznpp, labels.ptr<Npp8u>(), static_cast<int>(labels.step), state) );
#else
if (terminals.type() == CV_32S)
{
nppSafeCall( nppiGraphcut8_32s8u(terminals.ptr<Npp32s>(), leftTransp.ptr<Npp32s>(), rightTransp.ptr<Npp32s>(),
top.ptr<Npp32s>(), topLeft.ptr<Npp32s>(), topRight.ptr<Npp32s>(),
bottom.ptr<Npp32s>(), bottomLeft.ptr<Npp32s>(), bottomRight.ptr<Npp32s>(),
static_cast<int>(terminals.step), static_cast<int>(leftTransp.step), sznpp, labels.ptr<Npp8u>(), static_cast<int>(labels.step), state) );
}
else
{
nppSafeCall( nppiGraphcut8_32f8u(terminals.ptr<Npp32f>(), leftTransp.ptr<Npp32f>(), rightTransp.ptr<Npp32f>(),
top.ptr<Npp32f>(), topLeft.ptr<Npp32f>(), topRight.ptr<Npp32f>(),
bottom.ptr<Npp32f>(), bottomLeft.ptr<Npp32f>(), bottomRight.ptr<Npp32f>(),
static_cast<int>(terminals.step), static_cast<int>(leftTransp.step), sznpp, labels.ptr<Npp8u>(), static_cast<int>(labels.step), state) );
}
#endif
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
void cv::cuda::calcOpticalFlowBM(const GpuMat& prev, const GpuMat& curr, Size blockSize, Size shiftSize, Size maxRange, bool usePrevious, GpuMat& velx, GpuMat& vely, GpuMat& buf, Stream& st)
{
CV_Assert( prev.type() == CV_8UC1 );
CV_Assert( curr.size() == prev.size() && curr.type() == prev.type() );
const Size velSize((prev.cols - blockSize.width + shiftSize.width) / shiftSize.width,
(prev.rows - blockSize.height + shiftSize.height) / shiftSize.height);
velx.create(velSize, CV_32FC1);
vely.create(velSize, CV_32FC1);
// scanning scheme coordinates
std::vector<short2> ss((2 * maxRange.width + 1) * (2 * maxRange.height + 1));
int ssCount = 0;
// Calculate scanning scheme
const int minCount = std::min(maxRange.width, maxRange.height);
// use spiral search pattern
//
// 9 10 11 12
// 8 1 2 13
// 7 * 3 14
// 6 5 4 15
//... 20 19 18 17
//
for (int i = 0; i < minCount; ++i)
{
// four cycles along sides
int x = -i - 1, y = x;
// upper side
for (int j = -i; j <= i + 1; ++j, ++ssCount)
{
ss[ssCount].x = (short) ++x;
ss[ssCount].y = (short) y;
}
// right side
for (int j = -i; j <= i + 1; ++j, ++ssCount)
{
ss[ssCount].x = (short) x;
ss[ssCount].y = (short) ++y;
}
// bottom side
for (int j = -i; j <= i + 1; ++j, ++ssCount)
{
ss[ssCount].x = (short) --x;
ss[ssCount].y = (short) y;
}
// left side
for (int j = -i; j <= i + 1; ++j, ++ssCount)
{
ss[ssCount].x = (short) x;
ss[ssCount].y = (short) --y;
}
}
// the rest part
if (maxRange.width < maxRange.height)
{
const int xleft = -minCount;
// cycle by neighbor rings
for (int i = minCount; i < maxRange.height; ++i)
{
// two cycles by x
int y = -(i + 1);
int x = xleft;
// upper side
for (int j = -maxRange.width; j <= maxRange.width; ++j, ++ssCount, ++x)
{
ss[ssCount].x = (short) x;
ss[ssCount].y = (short) y;
}
x = xleft;
y = -y;
// bottom side
for (int j = -maxRange.width; j <= maxRange.width; ++j, ++ssCount, ++x)
{
ss[ssCount].x = (short) x;
ss[ssCount].y = (short) y;
}
}
}
else if (maxRange.width > maxRange.height)
{
const int yupper = -minCount;
// cycle by neighbor rings
for (int i = minCount; i < maxRange.width; ++i)
{
// two cycles by y
int x = -(i + 1);
int y = yupper;
// left side
for (int j = -maxRange.height; j <= maxRange.height; ++j, ++ssCount, ++y)
{
ss[ssCount].x = (short) x;
ss[ssCount].y = (short) y;
}
y = yupper;
x = -x;
// right side
for (int j = -maxRange.height; j <= maxRange.height; ++j, ++ssCount, ++y)
{
ss[ssCount].x = (short) x;
ss[ssCount].y = (short) y;
}
}
}
const cudaStream_t stream = StreamAccessor::getStream(st);
ensureSizeIsEnough(1, ssCount, CV_16SC2, buf);
if (stream == 0)
cudaSafeCall( cudaMemcpy(buf.data, &ss[0], ssCount * sizeof(short2), cudaMemcpyHostToDevice) );
else
cudaSafeCall( cudaMemcpyAsync(buf.data, &ss[0], ssCount * sizeof(short2), cudaMemcpyHostToDevice, stream) );
const int maxX = prev.cols - blockSize.width;
const int maxY = prev.rows - blockSize.height;
const int SMALL_DIFF = 2;
const int BIG_DIFF = 128;
const int blSize = blockSize.area();
const int acceptLevel = blSize * SMALL_DIFF;
const int escapeLevel = blSize * BIG_DIFF;
optflowbm::calc(prev, curr, velx, vely,
make_int2(blockSize.width, blockSize.height), make_int2(shiftSize.width, shiftSize.height), usePrevious,
maxX, maxY, acceptLevel, escapeLevel, buf.ptr<short2>(), ssCount, stream);
}
void cv::gpu::warpPerspective(const GpuMat& src, GpuMat& dst, const Mat& M, Size dsize, int flags, int borderMode, Scalar borderValue, Stream& s)
{
CV_Assert(M.rows == 3 && M.cols == 3);
int interpolation = flags & INTER_MAX;
CV_Assert(src.depth() <= CV_32F && src.channels() <= 4);
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);
Size wholeSize;
Point ofs;
src.locateROI(wholeSize, ofs);
static const bool useNppTab[6][4][3] =
{
{
{false, false, true},
{false, false, false},
{false, true, true},
{false, false, false}
},
{
{false, false, false},
{false, false, false},
{false, false, false},
{false, false, false}
},
{
{false, true, true},
{false, false, false},
{false, true, true},
{false, false, false}
},
{
{false, false, false},
{false, false, false},
{false, false, false},
{false, false, false}
},
{
{false, true, true},
{false, false, false},
{false, true, true},
{false, false, true}
},
{
{false, true, true},
{false, false, false},
{false, true, true},
{false, false, true}
}
};
bool useNpp = borderMode == BORDER_CONSTANT;
useNpp = useNpp && useNppTab[src.depth()][src.channels() - 1][interpolation];
#ifdef linux
// NPP bug on float data
useNpp = useNpp && src.depth() != CV_32F;
#endif
if (useNpp)
{
typedef void (*func_t)(const cv::gpu::GpuMat& src, cv::Size wholeSize, cv::Point ofs, cv::gpu::GpuMat& dst, double coeffs[][3], cv::Size dsize, int flags, cudaStream_t stream);
static const func_t funcs[2][6][4] =
{
{
{NppWarp<CV_8U, nppiWarpPerspective_8u_C1R>::call, 0, NppWarp<CV_8U, nppiWarpPerspective_8u_C3R>::call, NppWarp<CV_8U, nppiWarpPerspective_8u_C4R>::call},
{0, 0, 0, 0},
{NppWarp<CV_16U, nppiWarpPerspective_16u_C1R>::call, 0, NppWarp<CV_16U, nppiWarpPerspective_16u_C3R>::call, NppWarp<CV_16U, nppiWarpPerspective_16u_C4R>::call},
{0, 0, 0, 0},
{NppWarp<CV_32S, nppiWarpPerspective_32s_C1R>::call, 0, NppWarp<CV_32S, nppiWarpPerspective_32s_C3R>::call, NppWarp<CV_32S, nppiWarpPerspective_32s_C4R>::call},
{NppWarp<CV_32F, nppiWarpPerspective_32f_C1R>::call, 0, NppWarp<CV_32F, nppiWarpPerspective_32f_C3R>::call, NppWarp<CV_32F, nppiWarpPerspective_32f_C4R>::call}
},
{
{NppWarp<CV_8U, nppiWarpPerspectiveBack_8u_C1R>::call, 0, NppWarp<CV_8U, nppiWarpPerspectiveBack_8u_C3R>::call, NppWarp<CV_8U, nppiWarpPerspectiveBack_8u_C4R>::call},
{0, 0, 0, 0},
{NppWarp<CV_16U, nppiWarpPerspectiveBack_16u_C1R>::call, 0, NppWarp<CV_16U, nppiWarpPerspectiveBack_16u_C3R>::call, NppWarp<CV_16U, nppiWarpPerspectiveBack_16u_C4R>::call},
{0, 0, 0, 0},
{NppWarp<CV_32S, nppiWarpPerspectiveBack_32s_C1R>::call, 0, NppWarp<CV_32S, nppiWarpPerspectiveBack_32s_C3R>::call, NppWarp<CV_32S, nppiWarpPerspectiveBack_32s_C4R>::call},
{NppWarp<CV_32F, nppiWarpPerspectiveBack_32f_C1R>::call, 0, NppWarp<CV_32F, nppiWarpPerspectiveBack_32f_C3R>::call, NppWarp<CV_32F, nppiWarpPerspectiveBack_32f_C4R>::call}
}
};
double coeffs[3][3];
Mat coeffsMat(3, 3, CV_64F, (void*)coeffs);
M.convertTo(coeffsMat, coeffsMat.type());
const func_t func = funcs[(flags & WARP_INVERSE_MAP) != 0][src.depth()][src.channels() - 1];
CV_Assert(func != 0);
func(src, wholeSize, ofs, dst, coeffs, dsize, interpolation, StreamAccessor::getStream(s));
}
else
{
using namespace cv::gpu::device::imgproc;
typedef void (*func_t)(DevMem2Db src, DevMem2Db srcWhole, int xoff, int yoff, float coeffs[2 * 3], DevMem2Db dst, int interpolation,
int borderMode, const float* borderValue, cudaStream_t stream, int cc);
static const func_t funcs[6][4] =
{
{warpPerspective_gpu<uchar> , 0 /*warpPerspective_gpu<uchar2>*/ , warpPerspective_gpu<uchar3> , warpPerspective_gpu<uchar4> },
{0 /*warpPerspective_gpu<schar>*/, 0 /*warpPerspective_gpu<char2>*/ , 0 /*warpPerspective_gpu<char3>*/, 0 /*warpPerspective_gpu<char4>*/},
{warpPerspective_gpu<ushort> , 0 /*warpPerspective_gpu<ushort2>*/, warpPerspective_gpu<ushort3> , warpPerspective_gpu<ushort4> },
{warpPerspective_gpu<short> , 0 /*warpPerspective_gpu<short2>*/ , warpPerspective_gpu<short3> , warpPerspective_gpu<short4> },
{0 /*warpPerspective_gpu<int>*/ , 0 /*warpPerspective_gpu<int2>*/ , 0 /*warpPerspective_gpu<int3>*/ , 0 /*warpPerspective_gpu<int4>*/ },
{warpPerspective_gpu<float> , 0 /*warpPerspective_gpu<float2>*/ , warpPerspective_gpu<float3> , warpPerspective_gpu<float4> }
};
const func_t func = funcs[src.depth()][src.channels() - 1];
CV_Assert(func != 0);
int gpuBorderType;
CV_Assert(tryConvertToGpuBorderType(borderMode, gpuBorderType));
dst.create(dsize, src.type());
float coeffs[3 * 3];
Mat coeffsMat(3, 3, CV_32F, (void*)coeffs);
if (flags & WARP_INVERSE_MAP)
M.convertTo(coeffsMat, coeffsMat.type());
else
{
cv::Mat iM;
invert(M, iM);
iM.convertTo(coeffsMat, coeffsMat.type());
}
Scalar_<float> borderValueFloat;
borderValueFloat = borderValue;
DeviceInfo info;
int cc = info.majorVersion() * 10 + info.minorVersion();
func(src, DevMem2Db(wholeSize.height, wholeSize.width, src.datastart, src.step), ofs.x, ofs.y, coeffs,
dst, interpolation, gpuBorderType, borderValueFloat.val, StreamAccessor::getStream(s), cc);
}
}
void cv::gpu::resize(const GpuMat& src, GpuMat& dst, Size dsize, double fx, double fy, int interpolation, Stream& s)
{
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;
}
if (dsize != dst.size())
dst.create(dsize, src.type());
if (dsize == src.size())
{
if (s)
s.enqueueCopy(src, dst);
else
src.copyTo(dst);
return;
}
cudaStream_t stream = StreamAccessor::getStream(s);
Size wholeSize;
Point ofs;
src.locateROI(wholeSize, ofs);
bool useNpp = (src.type() == CV_8UC1 || src.type() == CV_8UC4);
useNpp = useNpp && (interpolation == INTER_NEAREST || interpolation == INTER_LINEAR || (src.type() == CV_8UC4 && interpolation != INTER_AREA));
if (useNpp)
{
typedef NppStatus (*func_t)(const Npp8u * pSrc, NppiSize oSrcSize, int nSrcStep, NppiRect oSrcROI, Npp8u * pDst, int nDstStep, NppiSize dstROISize,
double xFactor, double yFactor, int eInterpolation);
const func_t funcs[4] = { nppiResize_8u_C1R, 0, 0, nppiResize_8u_C4R };
static const int npp_inter[] = {NPPI_INTER_NN, NPPI_INTER_LINEAR, NPPI_INTER_CUBIC, 0, NPPI_INTER_LANCZOS};
NppiSize srcsz;
srcsz.width = wholeSize.width;
srcsz.height = wholeSize.height;
NppiRect srcrect;
srcrect.x = ofs.x;
srcrect.y = ofs.y;
srcrect.width = src.cols;
srcrect.height = src.rows;
NppiSize dstsz;
dstsz.width = dst.cols;
dstsz.height = dst.rows;
NppStreamHandler h(stream);
nppSafeCall( funcs[src.channels() - 1](src.datastart, srcsz, static_cast<int>(src.step), srcrect,
dst.ptr<Npp8u>(), static_cast<int>(dst.step), dstsz, fx, fy, npp_inter[interpolation]) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
else
{
using namespace ::cv::gpu::device::imgproc;
typedef void (*func_t)(DevMem2Db src, DevMem2Db srcWhole, int xoff, int yoff, float fx, float fy, DevMem2Db dst, int interpolation, cudaStream_t stream);
static const func_t funcs[6][4] =
{
{resize_gpu<uchar> , 0 /*resize_gpu<uchar2>*/ , resize_gpu<uchar3> , resize_gpu<uchar4> },
{0 /*resize_gpu<schar>*/, 0 /*resize_gpu<char2>*/ , 0 /*resize_gpu<char3>*/, 0 /*resize_gpu<char4>*/},
{resize_gpu<ushort> , 0 /*resize_gpu<ushort2>*/, resize_gpu<ushort3> , resize_gpu<ushort4> },
{resize_gpu<short> , 0 /*resize_gpu<short2>*/ , resize_gpu<short3> , resize_gpu<short4> },
{0 /*resize_gpu<int>*/ , 0 /*resize_gpu<int2>*/ , 0 /*resize_gpu<int3>*/ , 0 /*resize_gpu<int4>*/ },
{resize_gpu<float> , 0 /*resize_gpu<float2>*/ , resize_gpu<float3> , resize_gpu<float4> }
};
const func_t func = funcs[src.depth()][src.channels() - 1];
CV_Assert(func != 0);
func(src, DevMem2Db(wholeSize.height, wholeSize.width, src.datastart, src.step), ofs.x, ofs.y,
static_cast<float>(1.0 / fx), static_cast<float>(1.0 / fy), dst, interpolation, stream);
}
}
void cv::gpu::reduce(const GpuMat& src, GpuMat& dst, int dim, int reduceOp, int dtype, Stream& stream)
{
CV_Assert( src.channels() <= 4 );
CV_Assert( dim == 0 || dim == 1 );
CV_Assert( reduceOp == REDUCE_SUM || reduceOp == REDUCE_AVG || reduceOp == REDUCE_MAX || reduceOp == REDUCE_MIN );
if (dtype < 0)
dtype = src.depth();
dst.create(1, dim == 0 ? src.cols : src.rows, CV_MAKE_TYPE(CV_MAT_DEPTH(dtype), src.channels()));
if (dim == 0)
{
typedef void (*func_t)(PtrStepSzb src, void* dst, int op, cudaStream_t stream);
static const func_t funcs[7][7] =
{
{
::reduce::rows<unsigned char, int, unsigned char>,
0/*::reduce::rows<unsigned char, int, signed char>*/,
0/*::reduce::rows<unsigned char, int, unsigned short>*/,
0/*::reduce::rows<unsigned char, int, short>*/,
::reduce::rows<unsigned char, int, int>,
::reduce::rows<unsigned char, float, float>,
::reduce::rows<unsigned char, double, double>
},
{
0/*::reduce::rows<signed char, int, unsigned char>*/,
0/*::reduce::rows<signed char, int, signed char>*/,
0/*::reduce::rows<signed char, int, unsigned short>*/,
0/*::reduce::rows<signed char, int, short>*/,
0/*::reduce::rows<signed char, int, int>*/,
0/*::reduce::rows<signed char, float, float>*/,
0/*::reduce::rows<signed char, double, double>*/
},
{
0/*::reduce::rows<unsigned short, int, unsigned char>*/,
0/*::reduce::rows<unsigned short, int, signed char>*/,
::reduce::rows<unsigned short, int, unsigned short>,
0/*::reduce::rows<unsigned short, int, short>*/,
::reduce::rows<unsigned short, int, int>,
::reduce::rows<unsigned short, float, float>,
::reduce::rows<unsigned short, double, double>
},
{
0/*::reduce::rows<short, int, unsigned char>*/,
0/*::reduce::rows<short, int, signed char>*/,
0/*::reduce::rows<short, int, unsigned short>*/,
::reduce::rows<short, int, short>,
::reduce::rows<short, int, int>,
::reduce::rows<short, float, float>,
::reduce::rows<short, double, double>
},
{
0/*::reduce::rows<int, int, unsigned char>*/,
0/*::reduce::rows<int, int, signed char>*/,
0/*::reduce::rows<int, int, unsigned short>*/,
0/*::reduce::rows<int, int, short>*/,
::reduce::rows<int, int, int>,
::reduce::rows<int, float, float>,
::reduce::rows<int, double, double>
},
{
0/*::reduce::rows<float, float, unsigned char>*/,
0/*::reduce::rows<float, float, signed char>*/,
0/*::reduce::rows<float, float, unsigned short>*/,
0/*::reduce::rows<float, float, short>*/,
0/*::reduce::rows<float, float, int>*/,
::reduce::rows<float, float, float>,
::reduce::rows<float, double, double>
},
{
0/*::reduce::rows<double, double, unsigned char>*/,
0/*::reduce::rows<double, double, signed char>*/,
0/*::reduce::rows<double, double, unsigned short>*/,
0/*::reduce::rows<double, double, short>*/,
0/*::reduce::rows<double, double, int>*/,
0/*::reduce::rows<double, double, float>*/,
::reduce::rows<double, double, double>
}
};
const func_t func = funcs[src.depth()][dst.depth()];
if (!func)
CV_Error(cv::Error::StsUnsupportedFormat, "Unsupported combination of input and output array formats");
func(src.reshape(1), dst.data, reduceOp, StreamAccessor::getStream(stream));
}
else
{
typedef void (*func_t)(PtrStepSzb src, void* dst, int cn, int op, cudaStream_t stream);
static const func_t funcs[7][7] =
{
{
::reduce::cols<unsigned char, int, unsigned char>,
0/*::reduce::cols<unsigned char, int, signed char>*/,
0/*::reduce::cols<unsigned char, int, unsigned short>*/,
0/*::reduce::cols<unsigned char, int, short>*/,
::reduce::cols<unsigned char, int, int>,
::reduce::cols<unsigned char, float, float>,
::reduce::cols<unsigned char, double, double>
},
{
0/*::reduce::cols<signed char, int, unsigned char>*/,
0/*::reduce::cols<signed char, int, signed char>*/,
0/*::reduce::cols<signed char, int, unsigned short>*/,
0/*::reduce::cols<signed char, int, short>*/,
0/*::reduce::cols<signed char, int, int>*/,
0/*::reduce::cols<signed char, float, float>*/,
0/*::reduce::cols<signed char, double, double>*/
},
{
0/*::reduce::cols<unsigned short, int, unsigned char>*/,
0/*::reduce::cols<unsigned short, int, signed char>*/,
::reduce::cols<unsigned short, int, unsigned short>,
0/*::reduce::cols<unsigned short, int, short>*/,
::reduce::cols<unsigned short, int, int>,
::reduce::cols<unsigned short, float, float>,
::reduce::cols<unsigned short, double, double>
},
{
0/*::reduce::cols<short, int, unsigned char>*/,
0/*::reduce::cols<short, int, signed char>*/,
0/*::reduce::cols<short, int, unsigned short>*/,
::reduce::cols<short, int, short>,
::reduce::cols<short, int, int>,
::reduce::cols<short, float, float>,
::reduce::cols<short, double, double>
},
{
0/*::reduce::cols<int, int, unsigned char>*/,
0/*::reduce::cols<int, int, signed char>*/,
0/*::reduce::cols<int, int, unsigned short>*/,
0/*::reduce::cols<int, int, short>*/,
::reduce::cols<int, int, int>,
::reduce::cols<int, float, float>,
::reduce::cols<int, double, double>
},
{
0/*::reduce::cols<float, float, unsigned char>*/,
0/*::reduce::cols<float, float, signed char>*/,
0/*::reduce::cols<float, float, unsigned short>*/,
0/*::reduce::cols<float, float, short>*/,
0/*::reduce::cols<float, float, int>*/,
::reduce::cols<float, float, float>,
::reduce::cols<float, double, double>
},
{
0/*::reduce::cols<double, double, unsigned char>*/,
0/*::reduce::cols<double, double, signed char>*/,
0/*::reduce::cols<double, double, unsigned short>*/,
0/*::reduce::cols<double, double, short>*/,
0/*::reduce::cols<double, double, int>*/,
0/*::reduce::cols<double, double, float>*/,
::reduce::cols<double, double, double>
}
};
const func_t func = funcs[src.depth()][dst.depth()];
if (!func)
CV_Error(cv::Error::StsUnsupportedFormat, "Unsupported combination of input and output array formats");
func(src, dst.data, src.channels(), reduceOp, StreamAccessor::getStream(stream));
}
}
static void csbp_operator(StereoConstantSpaceBP& rthis, GpuMat& mbuf, GpuMat& temp, GpuMat& out, const GpuMat& left, const GpuMat& right, GpuMat& disp, Stream& stream)
{
CV_DbgAssert(0 < rthis.ndisp && 0 < rthis.iters && 0 < rthis.levels && 0 < rthis.nr_plane
&& left.rows == right.rows && left.cols == right.cols && left.type() == right.type());
CV_Assert(rthis.levels <= 8 && (left.type() == CV_8UC1 || left.type() == CV_8UC3 || left.type() == CV_8UC4));
const Scalar zero = Scalar::all(0);
cudaStream_t cudaStream = StreamAccessor::getStream(stream);
////////////////////////////////////////////////////////////////////////////////////////////
// Init
int rows = left.rows;
int cols = left.cols;
rthis.levels = min(rthis.levels, int(log((double)rthis.ndisp) / log(2.0)));
int levels = rthis.levels;
// compute sizes
AutoBuffer<int> buf(levels * 3);
int* cols_pyr = buf;
int* rows_pyr = cols_pyr + levels;
int* nr_plane_pyr = rows_pyr + levels;
cols_pyr[0] = cols;
rows_pyr[0] = rows;
nr_plane_pyr[0] = rthis.nr_plane;
for (int i = 1; i < levels; i++)
{
cols_pyr[i] = cols_pyr[i-1] / 2;
rows_pyr[i] = rows_pyr[i-1] / 2;
nr_plane_pyr[i] = nr_plane_pyr[i-1] * 2;
}
GpuMat u[2], d[2], l[2], r[2], disp_selected_pyr[2], data_cost, data_cost_selected;
//allocate buffers
int buffers_count = 10; // (up + down + left + right + disp_selected_pyr) * 2
buffers_count += 2; // data_cost has twice more rows than other buffers, what's why +2, not +1;
buffers_count += 1; // data_cost_selected
mbuf.create(rows * rthis.nr_plane * buffers_count, cols, DataType<T>::type);
data_cost = mbuf.rowRange(0, rows * rthis.nr_plane * 2);
data_cost_selected = mbuf.rowRange(data_cost.rows, data_cost.rows + rows * rthis.nr_plane);
for(int k = 0; k < 2; ++k) // in/out
{
GpuMat sub1 = mbuf.rowRange(data_cost.rows + data_cost_selected.rows, mbuf.rows);
GpuMat sub2 = sub1.rowRange((k+0)*sub1.rows/2, (k+1)*sub1.rows/2);
GpuMat *buf_ptrs[] = { &u[k], &d[k], &l[k], &r[k], &disp_selected_pyr[k] };
for(int _r = 0; _r < 5; ++_r)
{
*buf_ptrs[_r] = sub2.rowRange(_r * sub2.rows/5, (_r+1) * sub2.rows/5);
assert(buf_ptrs[_r]->cols == cols && buf_ptrs[_r]->rows == rows * rthis.nr_plane);
}
};
size_t elem_step = mbuf.step / sizeof(T);
Size temp_size = data_cost.size();
if ((size_t)temp_size.area() < elem_step * rows_pyr[levels - 1] * rthis.ndisp)
temp_size = Size(static_cast<int>(elem_step), rows_pyr[levels - 1] * rthis.ndisp);
temp.create(temp_size, DataType<T>::type);
////////////////////////////////////////////////////////////////////////////
// Compute
load_constants(rthis.ndisp, rthis.max_data_term, rthis.data_weight, rthis.max_disc_term, rthis.disc_single_jump, rthis.min_disp_th, left, right, temp);
if (stream)
{
stream.enqueueMemSet(l[0], zero);
stream.enqueueMemSet(d[0], zero);
stream.enqueueMemSet(r[0], zero);
stream.enqueueMemSet(u[0], zero);
stream.enqueueMemSet(l[1], zero);
stream.enqueueMemSet(d[1], zero);
stream.enqueueMemSet(r[1], zero);
stream.enqueueMemSet(u[1], zero);
stream.enqueueMemSet(data_cost, zero);
stream.enqueueMemSet(data_cost_selected, zero);
}
else
{
l[0].setTo(zero);
d[0].setTo(zero);
r[0].setTo(zero);
u[0].setTo(zero);
l[1].setTo(zero);
d[1].setTo(zero);
r[1].setTo(zero);
u[1].setTo(zero);
data_cost.setTo(zero);
data_cost_selected.setTo(zero);
}
int cur_idx = 0;
for (int i = levels - 1; i >= 0; i--)
{
if (i == levels - 1)
{
init_data_cost(left.rows, left.cols, disp_selected_pyr[cur_idx].ptr<T>(), data_cost_selected.ptr<T>(),
elem_step, rows_pyr[i], cols_pyr[i], i, nr_plane_pyr[i], rthis.ndisp, left.channels(), rthis.use_local_init_data_cost, cudaStream);
}
else
{
compute_data_cost(disp_selected_pyr[cur_idx].ptr<T>(), data_cost.ptr<T>(), elem_step,
left.rows, left.cols, rows_pyr[i], cols_pyr[i], rows_pyr[i+1], i, nr_plane_pyr[i+1], left.channels(), cudaStream);
int new_idx = (cur_idx + 1) & 1;
init_message(u[new_idx].ptr<T>(), d[new_idx].ptr<T>(), l[new_idx].ptr<T>(), r[new_idx].ptr<T>(),
u[cur_idx].ptr<T>(), d[cur_idx].ptr<T>(), l[cur_idx].ptr<T>(), r[cur_idx].ptr<T>(),
disp_selected_pyr[new_idx].ptr<T>(), disp_selected_pyr[cur_idx].ptr<T>(),
data_cost_selected.ptr<T>(), data_cost.ptr<T>(), elem_step, rows_pyr[i],
cols_pyr[i], nr_plane_pyr[i], rows_pyr[i+1], cols_pyr[i+1], nr_plane_pyr[i+1], cudaStream);
cur_idx = new_idx;
}
calc_all_iterations(u[cur_idx].ptr<T>(), d[cur_idx].ptr<T>(), l[cur_idx].ptr<T>(), r[cur_idx].ptr<T>(),
data_cost_selected.ptr<T>(), disp_selected_pyr[cur_idx].ptr<T>(), elem_step,
rows_pyr[i], cols_pyr[i], nr_plane_pyr[i], rthis.iters, cudaStream);
}
if (disp.empty())
disp.create(rows, cols, CV_16S);
out = ((disp.type() == CV_16S) ? disp : (out.create(rows, cols, CV_16S), out));
if (stream)
stream.enqueueMemSet(out, zero);
else
out.setTo(zero);
compute_disp(u[cur_idx].ptr<T>(), d[cur_idx].ptr<T>(), l[cur_idx].ptr<T>(), r[cur_idx].ptr<T>(),
data_cost_selected.ptr<T>(), disp_selected_pyr[cur_idx].ptr<T>(), elem_step, out, nr_plane_pyr[0], cudaStream);
if (disp.type() != CV_16S)
{
if (stream)
stream.enqueueConvert(out, disp, disp.type());
else
out.convertTo(disp, disp.type());
}
}
void cv::gpu::OpticalFlowDual_TVL1_GPU::operator ()(const GpuMat& I0, const GpuMat& I1, GpuMat& flowx, GpuMat& flowy)
{
CV_Assert( I0.type() == CV_8UC1 || I0.type() == CV_32FC1 );
CV_Assert( I0.size() == I1.size() );
CV_Assert( I0.type() == I1.type() );
CV_Assert( !useInitialFlow || (flowx.size() == I0.size() && flowx.type() == CV_32FC1 && flowy.size() == flowx.size() && flowy.type() == flowx.type()) );
CV_Assert( nscales > 0 );
// allocate memory for the pyramid structure
I0s.resize(nscales);
I1s.resize(nscales);
u1s.resize(nscales);
u2s.resize(nscales);
I0.convertTo(I0s[0], CV_32F, I0.depth() == CV_8U ? 1.0 : 255.0);
I1.convertTo(I1s[0], CV_32F, I1.depth() == CV_8U ? 1.0 : 255.0);
if (!useInitialFlow)
{
flowx.create(I0.size(), CV_32FC1);
flowy.create(I0.size(), CV_32FC1);
}
u1s[0] = flowx;
u2s[0] = flowy;
I1x_buf.create(I0.size(), CV_32FC1);
I1y_buf.create(I0.size(), CV_32FC1);
I1w_buf.create(I0.size(), CV_32FC1);
I1wx_buf.create(I0.size(), CV_32FC1);
I1wy_buf.create(I0.size(), CV_32FC1);
grad_buf.create(I0.size(), CV_32FC1);
rho_c_buf.create(I0.size(), CV_32FC1);
p11_buf.create(I0.size(), CV_32FC1);
p12_buf.create(I0.size(), CV_32FC1);
p21_buf.create(I0.size(), CV_32FC1);
p22_buf.create(I0.size(), CV_32FC1);
diff_buf.create(I0.size(), CV_32FC1);
// create the scales
for (int s = 1; s < nscales; ++s)
{
gpu::resize(I0s[s-1], I0s[s], Size(), scaleStep, scaleStep);
gpu::resize(I1s[s-1], I1s[s], Size(), scaleStep, scaleStep);
if (I0s[s].cols < 16 || I0s[s].rows < 16)
{
nscales = s;
break;
}
if (useInitialFlow)
{
gpu::resize(u1s[s-1], u1s[s], Size(), scaleStep, scaleStep);
gpu::resize(u2s[s-1], u2s[s], Size(), scaleStep, scaleStep);
gpu::multiply(u1s[s], Scalar::all(scaleStep), u1s[s]);
gpu::multiply(u2s[s], Scalar::all(scaleStep), u2s[s]);
}
else
{
u1s[s].create(I0s[s].size(), CV_32FC1);
u2s[s].create(I0s[s].size(), CV_32FC1);
}
}
if (!useInitialFlow)
{
u1s[nscales-1].setTo(Scalar::all(0));
u2s[nscales-1].setTo(Scalar::all(0));
}
// pyramidal structure for computing the optical flow
for (int s = nscales - 1; s >= 0; --s)
{
// compute the optical flow at the current scale
procOneScale(I0s[s], I1s[s], u1s[s], u2s[s]);
// if this was the last scale, finish now
if (s == 0)
break;
// otherwise, upsample the optical flow
// zoom the optical flow for the next finer scale
gpu::resize(u1s[s], u1s[s - 1], I0s[s - 1].size());
gpu::resize(u2s[s], u2s[s - 1], I0s[s - 1].size());
// scale the optical flow with the appropriate zoom factor
gpu::multiply(u1s[s - 1], Scalar::all(1/scaleStep), u1s[s - 1]);
gpu::multiply(u2s[s - 1], Scalar::all(1/scaleStep), u2s[s - 1]);
}
}
