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Commit 51d5959a authored by Vladislav Vinogradov's avatar Vladislav Vinogradov
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added gpu add, subtract, multiply, divide, absdiff with Scalar. 

added gpu exp, log, magnitude, based on NPP.
updated setTo with new NPP functions.
minor fix in tests and comments.
parent 037002d3
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Showing with 457 additions and 125 deletions
modules/gpu/include/opencv2/gpu/gpu.hpp
View file @ 51d5959a
......@@ -348,48 +348,74 @@ namespace cv
////////////////////////////// Arithmetics ///////////////////////////////////
//! adds one matrix to another (c = a + b)
//! supports CV_8UC1, CV_8UC4, CV_32SC1, CV_32FC1 types
CV_EXPORTS void add(const GpuMat& a, const GpuMat& b, GpuMat& c);
//! adds scalar to a matrix (c = a + s)
//! supports only CV_32FC1 type
CV_EXPORTS void add(const GpuMat& a, const Scalar& sc, GpuMat& c);
//! subtracts one matrix from another (c = a - b)
//! supports CV_8UC1, CV_8UC4, CV_32SC1, CV_32FC1 types
CV_EXPORTS void subtract(const GpuMat& a, const GpuMat& b, GpuMat& c);
//! subtracts scalar from a matrix (c = a - s)
//! supports only CV_32FC1 type
CV_EXPORTS void subtract(const GpuMat& a, const Scalar& sc, GpuMat& c);
//! computes element-wise product of the two arrays (c = a * b)
//! supports CV_8UC1, CV_8UC4, CV_32SC1, CV_32FC1 types
CV_EXPORTS void multiply(const GpuMat& a, const GpuMat& b, GpuMat& c);
//! multiplies matrix to a scalar (c = a * s)
//! supports only CV_32FC1 type
CV_EXPORTS void multiply(const GpuMat& a, const Scalar& sc, GpuMat& c);
//! computes element-wise quotient of the two arrays (c = a / b)
//! supports CV_8UC1, CV_8UC4, CV_32SC1, CV_32FC1 types
CV_EXPORTS void divide(const GpuMat& a, const GpuMat& b, GpuMat& c);
//! computes element-wise quotient of matrix and scalar (c = a / s)
//! supports only CV_32FC1 type
CV_EXPORTS void divide(const GpuMat& a, const Scalar& sc, GpuMat& c);
//! transposes the matrix
//! supports only CV_8UC1 type
CV_EXPORTS void transpose(const GpuMat& src1, GpuMat& dst);
//! computes element-wise absolute difference of two arrays (c = abs(a - b))
CV_EXPORTS void absdiff(const GpuMat& a, const GpuMat& b, GpuMat& c);
//! supports CV_8UC1, CV_8UC4, CV_32SC1, CV_32FC1 types
CV_EXPORTS void absdiff(const GpuMat& a, const GpuMat& b, GpuMat& c);
//! computes element-wise absolute difference of array and scalar (c = abs(a - s))
//! supports only CV_32FC1 type
CV_EXPORTS void absdiff(const GpuMat& a, const Scalar& s, GpuMat& c);
//! compares elements of two arrays (c = a <cmpop> b)
//! Now doesn't support CMP_NE.
//! supports CV_8UC4, CV_32FC1 types
CV_EXPORTS void compare(const GpuMat& a, const GpuMat& b, GpuMat& c, int cmpop);
//! computes mean value and standard deviation of all or selected array elements
//! supports only CV_8UC1 type
CV_EXPORTS void meanStdDev(const GpuMat& mtx, Scalar& mean, Scalar& stddev);
//! computes norm of array
//! Supports NORM_INF, NORM_L1, NORM_L2
//! supports NORM_INF, NORM_L1, NORM_L2
//! supports only CV_8UC1 type
CV_EXPORTS double norm(const GpuMat& src1, int normType=NORM_L2);
//! computes norm of the difference between two arrays
//! Supports NORM_INF, NORM_L1, NORM_L2
//! supports NORM_INF, NORM_L1, NORM_L2
//! supports only CV_8UC1 type
CV_EXPORTS double norm(const GpuMat& src1, const GpuMat& src2, int normType=NORM_L2);
//! reverses the order of the rows, columns or both in a matrix
//! supports CV_8UC1, CV_8UC4 types
CV_EXPORTS void flip(const GpuMat& a, GpuMat& b, int flipCode);
//! computes sum of array elements
//! supports CV_8UC1, CV_8UC4 types
CV_EXPORTS Scalar sum(const GpuMat& m);
//! finds global minimum and maximum array elements and returns their values
//! supports only CV_8UC1 type
CV_EXPORTS void minMax(const GpuMat& src, double* minVal, double* maxVal = 0);
//! transforms 8-bit unsigned integers using lookup table: dst(i)=lut(src(i))
//! supports only single channels source
//! destination array will have the same type as source
//! lut must hase CV_32S depth and the same number of channels as in the source array
//! destination array will have the depth type as lut and the same channels number as source
//! supports CV_8UC1, CV_8UC3 types
CV_EXPORTS void LUT(const GpuMat& src, const Mat& lut, GpuMat& dst);
//! makes multi-channel array out of several single-channel arrays
......@@ -416,10 +442,21 @@ namespace cv
//! copies each plane of a multi-channel array to a dedicated array (async version)
CV_EXPORTS void split(const GpuMat& src, vector<GpuMat>& dst, const Stream& stream);
//! computes exponent of each matrix element (b = e**a)
//! supports only CV_32FC1 type
CV_EXPORTS void exp(const GpuMat& a, GpuMat& b);
//! computes natural logarithm of absolute value of each matrix element: b = log(abs(a))
//! supports only CV_32FC1 type
CV_EXPORTS void log(const GpuMat& a, GpuMat& b);
//! computes magnitude (magnitude(i)) of each (x(i), y(i)) vector
CV_EXPORTS void magnitude(const GpuMat& x, const GpuMat& y, GpuMat& magnitude);
////////////////////////////// Image processing //////////////////////////////
//! DST[x,y] = SRC[xmap[x,y],ymap[x,y]] with bilinear interpolation.
//! xymap.type() == xymap.type() == CV_32FC1
//! supports CV_8UC1, CV_8UC3 source types and CV_32FC1 map type
CV_EXPORTS void remap(const GpuMat& src, GpuMat& dst, const GpuMat& xmap, const GpuMat& ymap);
//! Does mean shift filtering on GPU.
......@@ -452,7 +489,8 @@ namespace cv
CV_EXPORTS double threshold(const GpuMat& src, GpuMat& dst, double thresh);
//! resizes the image
//! Supports INTER_NEAREST, INTER_LINEAR, INTER_CUBIC, INTER_LANCZOS4
//! Supports INTER_NEAREST, INTER_LINEAR
//! supports CV_8UC1, CV_8UC4 types
CV_EXPORTS void resize(const GpuMat& src, GpuMat& dst, Size dsize, double fx=0, double fy=0, int interpolation = INTER_LINEAR);
//! warps the image using affine transformation
......@@ -465,16 +503,20 @@ namespace cv
//! rotate 8bit single or four channel image
//! Supports INTER_NEAREST, INTER_LINEAR, INTER_CUBIC
//! supports CV_8UC1, CV_8UC4 types
CV_EXPORTS void rotate(const GpuMat& src, GpuMat& dst, Size dsize, double angle, double xShift = 0, double yShift = 0, int interpolation = INTER_LINEAR);
//! copies 2D array to a larger destination array and pads borders with user-specifiable constant
//! supports CV_8UC1, CV_8UC4, CV_32SC1 types
CV_EXPORTS void copyMakeBorder(const GpuMat& src, GpuMat& dst, int top, int bottom, int left, int right, const Scalar& value = Scalar());
//! computes the integral image and integral for the squared image
//! sum will have CV_32S type, sqsum - CV32F type
//! supports only CV_32FC1 source type
CV_EXPORTS void integral(GpuMat& src, GpuMat& sum, GpuMat& sqsum);
//! smooths the image using the normalized box filter
//! supports CV_8UC1, CV_8UC4 types and kernel size 3, 5, 7
CV_EXPORTS void boxFilter(const GpuMat& src, GpuMat& dst, Size ksize, Point anchor = Point(-1,-1));
//! a synonym for normalized box filter
......
modules/gpu/src/arithm.cpp
View file @ 51d5959a
......@@ -49,11 +49,16 @@ using namespace std;
#if !defined (HAVE_CUDA)
void cv::gpu::add(const GpuMat&, const GpuMat&, GpuMat&) { throw_nogpu(); }
void cv::gpu::add(const GpuMat&, const Scalar&, GpuMat&) { throw_nogpu(); }
void cv::gpu::subtract(const GpuMat&, const GpuMat&, GpuMat&) { throw_nogpu(); }
void cv::gpu::subtract(const GpuMat&, const Scalar&, GpuMat&) { throw_nogpu(); }
void cv::gpu::multiply(const GpuMat&, const GpuMat&, GpuMat&) { throw_nogpu(); }
void cv::gpu::multiply(const GpuMat&, const Scalar&, GpuMat&) { throw_nogpu(); }
void cv::gpu::divide(const GpuMat&, const GpuMat&, GpuMat&) { throw_nogpu(); }
void cv::gpu::divide(const GpuMat&, const Scalar&, GpuMat&) { throw_nogpu(); }
void cv::gpu::transpose(const GpuMat&, GpuMat&) { throw_nogpu(); }
void cv::gpu::absdiff(const GpuMat&, const GpuMat&, GpuMat&) { throw_nogpu(); }
void cv::gpu::absdiff(const GpuMat&, const Scalar&, GpuMat&) { throw_nogpu(); }
void cv::gpu::compare(const GpuMat&, const GpuMat&, GpuMat&, int) { throw_nogpu(); }
void cv::gpu::meanStdDev(const GpuMat&, Scalar&, Scalar&) { throw_nogpu(); }
double cv::gpu::norm(const GpuMat&, int) { throw_nogpu(); return 0.0; }
......@@ -61,7 +66,10 @@ double cv::gpu::norm(const GpuMat&, const GpuMat&, int) { throw_nogpu(); return
void cv::gpu::flip(const GpuMat&, GpuMat&, int) { throw_nogpu(); }
Scalar cv::gpu::sum(const GpuMat&) { throw_nogpu(); return Scalar(); }
void cv::gpu::minMax(const GpuMat&, double*, double*) { throw_nogpu(); }
void cv::gpu::LUT(const GpuMat& src, const Mat& lut, GpuMat& dst) { throw_nogpu(); }
void cv::gpu::LUT(const GpuMat&, const Mat&, GpuMat&) { throw_nogpu(); }
void cv::gpu::exp(const GpuMat&, GpuMat&) { throw_nogpu(); }
void cv::gpu::log(const GpuMat&, GpuMat&) { throw_nogpu(); }
void cv::gpu::magnitude(const GpuMat&, const GpuMat&, GpuMat&) { throw_nogpu(); }
#else /* !defined (HAVE_CUDA) */
......@@ -72,15 +80,18 @@ namespace
{
typedef NppStatus (*npp_arithm_8u_t)(const Npp8u* pSrc1, int nSrc1Step, const Npp8u* pSrc2, int nSrc2Step, Npp8u* pDst, int nDstStep,
NppiSize oSizeROI, int nScaleFactor);
typedef NppStatus (*npp_arithm_32s_t)(const Npp32s* pSrc1, int nSrc1Step, const Npp32s* pSrc2, int nSrc2Step, Npp32s* pDst,
int nDstStep, NppiSize oSizeROI);
typedef NppStatus (*npp_arithm_32f_t)(const Npp32f* pSrc1, int nSrc1Step, const Npp32f* pSrc2, int nSrc2Step, Npp32f* pDst,
int nDstStep, NppiSize oSizeROI);
int nDstStep, NppiSize oSizeROI);
void nppFuncCaller(const GpuMat& src1, const GpuMat& src2, GpuMat& dst,
npp_arithm_8u_t npp_func_8uc1, npp_arithm_8u_t npp_func_8uc4, npp_arithm_32f_t npp_func_32fc1)
void nppArithmCaller(const GpuMat& src1, const GpuMat& src2, GpuMat& dst,
npp_arithm_8u_t npp_func_8uc1, npp_arithm_8u_t npp_func_8uc4,
npp_arithm_32s_t npp_func_32sc1, npp_arithm_32f_t npp_func_32fc1)
{
CV_DbgAssert(src1.size() == src2.size() && src1.type() == src2.type());
CV_Assert(src1.type() == CV_8UC1 || src1.type() == CV_8UC4 || src1.type() == CV_32FC1);
CV_Assert(src1.type() == CV_8UC1 || src1.type() == CV_8UC4 || src1.type() == CV_32SC1 || src1.type() == CV_32FC1);
dst.create( src1.size(), src1.type() );
......@@ -100,6 +111,11 @@ namespace
src2.ptr<Npp8u>(), src2.step,
dst.ptr<Npp8u>(), dst.step, sz, 0) );
break;
case CV_32SC1:
nppSafeCall( npp_func_32sc1(src1.ptr<Npp32s>(), src1.step,
src2.ptr<Npp32s>(), src2.step,
dst.ptr<Npp32s>(), dst.step, sz) );
break;
case CV_32FC1:
nppSafeCall( npp_func_32fc1(src1.ptr<Npp32f>(), src1.step,
src2.ptr<Npp32f>(), src2.step,
......@@ -109,26 +125,63 @@ namespace
CV_Assert(!"Unsupported source type");
}
}
typedef NppStatus (*npp_arithm_scalar_32f_t)(const Npp32f *pSrc, int nSrcStep, Npp32f nValue, Npp32f *pDst,
int nDstStep, NppiSize oSizeROI);
void nppArithmCaller(const GpuMat& src1, const Scalar& sc, GpuMat& dst,
npp_arithm_scalar_32f_t npp_func)
{
CV_Assert(src1.type() == CV_32FC1);
dst.create(src1.size(), src1.type());
NppiSize sz;
sz.width = src1.cols;
sz.height = src1.rows;
nppSafeCall( npp_func(src1.ptr<Npp32f>(), src1.step, (Npp32f)sc[0], dst.ptr<Npp32f>(), dst.step, sz) );
}
}
void cv::gpu::add(const GpuMat& src1, const GpuMat& src2, GpuMat& dst)
{
nppFuncCaller(src1, src2, dst, nppiAdd_8u_C1RSfs, nppiAdd_8u_C4RSfs, nppiAdd_32f_C1R);
nppArithmCaller(src1, src2, dst, nppiAdd_8u_C1RSfs, nppiAdd_8u_C4RSfs, nppiAdd_32s_C1R, nppiAdd_32f_C1R);
}
void cv::gpu::subtract(const GpuMat& src1, const GpuMat& src2, GpuMat& dst)
{
nppFuncCaller(src2, src1, dst, nppiSub_8u_C1RSfs, nppiSub_8u_C4RSfs, nppiSub_32f_C1R);
nppArithmCaller(src2, src1, dst, nppiSub_8u_C1RSfs, nppiSub_8u_C4RSfs, nppiSub_32s_C1R, nppiSub_32f_C1R);
}
void cv::gpu::multiply(const GpuMat& src1, const GpuMat& src2, GpuMat& dst)
{
nppFuncCaller(src1, src2, dst, nppiMul_8u_C1RSfs, nppiMul_8u_C4RSfs, nppiMul_32f_C1R);
nppArithmCaller(src1, src2, dst, nppiMul_8u_C1RSfs, nppiMul_8u_C4RSfs, nppiMul_32s_C1R, nppiMul_32f_C1R);
}
void cv::gpu::divide(const GpuMat& src1, const GpuMat& src2, GpuMat& dst)
{
nppFuncCaller(src2, src1, dst, nppiDiv_8u_C1RSfs, nppiDiv_8u_C4RSfs, nppiDiv_32f_C1R);
nppArithmCaller(src2, src1, dst, nppiDiv_8u_C1RSfs, nppiDiv_8u_C4RSfs, nppiDiv_32s_C1R, nppiDiv_32f_C1R);
}
void cv::gpu::add(const GpuMat& src, const Scalar& sc, GpuMat& dst)
{
nppArithmCaller(src, sc, dst, nppiAddC_32f_C1R);
}
void cv::gpu::subtract(const GpuMat& src, const Scalar& sc, GpuMat& dst)
{
nppArithmCaller(src, sc, dst, nppiSubC_32f_C1R);
}
void cv::gpu::multiply(const GpuMat& src, const Scalar& sc, GpuMat& dst)
{
nppArithmCaller(src, sc, dst, nppiMulC_32f_C1R);
}
void cv::gpu::divide(const GpuMat& src, const Scalar& sc, GpuMat& dst)
{
nppArithmCaller(src, sc, dst, nppiDivC_32f_C1R);
}
////////////////////////////////////////////////////////////////////////
......@@ -154,7 +207,7 @@ void cv::gpu::absdiff(const GpuMat& src1, const GpuMat& src2, GpuMat& dst)
{
CV_DbgAssert(src1.size() == src2.size() && src1.type() == src2.type());
CV_Assert(src1.type() == CV_8UC1 || src1.type() == CV_32FC1);
CV_Assert(src1.type() == CV_8UC1 || src1.type() == CV_8UC4 || src1.type() == CV_32SC1 || src1.type() == CV_32FC1);
dst.create( src1.size(), src1.type() );
......@@ -162,20 +215,46 @@ void cv::gpu::absdiff(const GpuMat& src1, const GpuMat& src2, GpuMat& dst)
sz.width = src1.cols;
sz.height = src1.rows;
if (src1.type() == CV_8UC1)
switch (src1.type())
{
case CV_8UC1:
nppSafeCall( nppiAbsDiff_8u_C1R(src1.ptr<Npp8u>(), src1.step,
src2.ptr<Npp8u>(), src2.step,
dst.ptr<Npp8u>(), dst.step, sz) );
}
else
{
break;
case CV_8UC4:
nppSafeCall( nppiAbsDiff_8u_C4R(src1.ptr<Npp8u>(), src1.step,
src2.ptr<Npp8u>(), src2.step,
dst.ptr<Npp8u>(), dst.step, sz) );
break;
case CV_32SC1:
nppSafeCall( nppiAbsDiff_32s_C1R(src1.ptr<Npp32s>(), src1.step,
src2.ptr<Npp32s>(), src2.step,
dst.ptr<Npp32s>(), dst.step, sz) );
break;
case CV_32FC1:
nppSafeCall( nppiAbsDiff_32f_C1R(src1.ptr<Npp32f>(), src1.step,
src2.ptr<Npp32f>(), src2.step,
dst.ptr<Npp32f>(), dst.step, sz) );
break;
default:
CV_Assert(!"Unsupported source type");
}
}
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]) );
}
////////////////////////////////////////////////////////////////////////
// compare
......@@ -416,4 +495,57 @@ void cv::gpu::LUT(const GpuMat& src, const Mat& lut, GpuMat& dst)
}
}
////////////////////////////////////////////////////////////////////////
// exp
void cv::gpu::exp(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( nppiExp_32f_C1R(src.ptr<Npp32f>(), src.step, dst.ptr<Npp32f>(), dst.step, sz) );
}
////////////////////////////////////////////////////////////////////////
// log
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) );
}
////////////////////////////////////////////////////////////////////////
// magnitude
void cv::gpu::magnitude(const GpuMat& src1, const GpuMat& src2, GpuMat& dst)
{
CV_DbgAssert(src1.type() == src2.type() && src1.size() == src2.size());
CV_Assert(src1.type() == CV_32FC1);
GpuMat src(src1.size(), CV_32FC2);
GpuMat srcs[] = {src1, src2};
cv::gpu::merge(srcs, 2, src);
dst.create(src1.size(), src1.type());
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
nppSafeCall( nppiMagnitude_32fc32f_C1R(src.ptr<Npp32fc>(), src.step, dst.ptr<Npp32f>(), dst.step, sz) );
}
#endif /* !defined (HAVE_CUDA) */
\ No newline at end of file
modules/gpu/src/beliefpropagation_gpu.cpp
View file @ 51d5959a
......@@ -89,7 +89,7 @@ void cv::gpu::StereoBeliefPropagation::estimateRecommendedParams(int width, int
int mm = ::max(width, height);
iters = mm / 100 + 2;
levels = (int)(log(static_cast<double>(mm)) + 1) * 4 / 5;
levels = (int)(::log(static_cast<double>(mm)) + 1) * 4 / 5;
if (levels == 0) levels++;
}
......
modules/gpu/src/constantspacebp_gpu.cpp
View file @ 51d5959a
......@@ -116,7 +116,7 @@ void cv::gpu::StereoConstantSpaceBP::estimateRecommendedParams(int width, int he
int mm = ::max(width, height);
iters = mm / 100 + ((mm > 1200)? - 4 : 4);
levels = (int)log(static_cast<double>(mm)) * 2 / 3;
levels = (int)::log(static_cast<double>(mm)) * 2 / 3;
if (levels == 0) levels++;
nr_plane = (int) ((float) ndisp / pow(2.0, levels + 1));
......
modules/gpu/src/imgproc_gpu.cpp
View file @ 51d5959a
......@@ -592,10 +592,10 @@ double cv::gpu::threshold(const GpuMat& src, GpuMat& dst, double thresh)
void cv::gpu::resize(const GpuMat& src, GpuMat& dst, Size dsize, double fx, double fy, int interpolation)
{
static const int npp_inter[] = {NPPI_INTER_NN, NPPI_INTER_LINEAR, NPPI_INTER_CUBIC, 0, NPPI_INTER_LANCZOS};
static const int npp_inter[] = {NPPI_INTER_NN, NPPI_INTER_LINEAR/*, NPPI_INTER_CUBIC, 0, NPPI_INTER_LANCZOS*/};
CV_Assert(src.type() == CV_8UC1 || src.type() == CV_8UC4);
CV_Assert(interpolation == INTER_NEAREST || interpolation == INTER_LINEAR || interpolation == INTER_CUBIC || interpolation == INTER_LANCZOS4);
CV_Assert(interpolation == INTER_NEAREST || interpolation == INTER_LINEAR/* || interpolation == INTER_CUBIC || interpolation == INTER_LANCZOS4*/);
CV_Assert( src.size().area() > 0 );
CV_Assert( !(dsize == Size()) || (fx > 0 && fy > 0) );
......
modules/gpu/src/matrix_operations.cpp
View file @ 51d5959a
......@@ -151,15 +151,19 @@ void cv::gpu::GpuMat::convertTo( GpuMat& dst, int rtype, double alpha, double be
GpuMat& GpuMat::operator = (const Scalar& s)
{
matrix_operations::set_to_without_mask( *this, depth(), s.val, channels());
setTo(s);
return *this;
}
GpuMat& GpuMat::setTo(const Scalar& s, const GpuMat& mask)
{
//CV_Assert(mask.type() == CV_8U);
CV_Assert(mask.type() == CV_8UC1);
CV_DbgAssert(!this->empty());
NppiSize sz;
sz.width = cols;
sz.height = rows;
if (mask.empty())
{
......@@ -167,38 +171,74 @@ GpuMat& GpuMat::setTo(const Scalar& s, const GpuMat& mask)
{
case CV_8UC1:
{
NppiSize sz;
sz.width = cols;
sz.height = rows;
Npp8u nVal = (Npp8u)s[0];
nppSafeCall( nppiSet_8u_C1R(nVal, (Npp8u*)ptr<char>(), step, sz) );
nppSafeCall( nppiSet_8u_C1R(nVal, ptr<Npp8u>(), step, sz) );
break;
}
case CV_8UC4:
{
NppiSize sz;
sz.width = cols;
sz.height = rows;
Npp8u nVal[] = {(Npp8u)s[0], (Npp8u)s[1], (Npp8u)s[2], (Npp8u)s[3]};
nppSafeCall( nppiSet_8u_C4R(nVal, (Npp8u*)ptr<char>(), step, sz) );
Scalar_<Npp8u> nVal = s;
nppSafeCall( nppiSet_8u_C4R(nVal.val, ptr<Npp8u>(), step, sz) );
break;
}
case CV_16UC1:
{
Npp16u nVal = (Npp16u)s[0];
nppSafeCall( nppiSet_16u_C1R(nVal, ptr<Npp16u>(), step, sz) );
break;
}
/*case CV_16UC2:
{
Scalar_<Npp16u> nVal = s;
nppSafeCall( nppiSet_16u_C2R(nVal.val, ptr<Npp16u>(), step, sz) );
break;
}*/
case CV_16UC4:
{
Scalar_<Npp16u> nVal = s;
nppSafeCall( nppiSet_16u_C4R(nVal.val, ptr<Npp16u>(), step, sz) );
break;
}
case CV_16SC1:
{
Npp16s nVal = (Npp16s)s[0];
nppSafeCall( nppiSet_16s_C1R(nVal, ptr<Npp16s>(), step, sz) );
break;
}
/*case CV_16SC2:
{
Scalar_<Npp16s> nVal = s;
nppSafeCall( nppiSet_16s_C2R(nVal.val, ptr<Npp16s>(), step, sz) );
break;
}*/
case CV_16SC4:
{
Scalar_<Npp16s> nVal = s;
nppSafeCall( nppiSet_16s_C4R(nVal.val, ptr<Npp16s>(), step, sz) );
break;
}
case CV_32SC1:
{
NppiSize sz;
sz.width = cols;
sz.height = rows;
Npp32s nVal = (Npp32s)s[0];
nppSafeCall( nppiSet_32s_C1R(nVal, (Npp32s*)ptr<char>(), step, sz) );
nppSafeCall( nppiSet_32s_C1R(nVal, ptr<Npp32s>(), step, sz) );
break;
}
case CV_32SC4:
{
Scalar_<Npp32s> nVal = s;
nppSafeCall( nppiSet_32s_C4R(nVal.val, ptr<Npp32s>(), step, sz) );
break;
}
case CV_32FC1:
{
NppiSize sz;
sz.width = cols;
sz.height = rows;
Npp32f nVal = (Npp32f)s[0];
nppSafeCall( nppiSet_32f_C1R(nVal, (Npp32f*)ptr<char>(), step, sz) );
nppSafeCall( nppiSet_32f_C1R(nVal, ptr<Npp32f>(), step, sz) );
break;
}
case CV_32FC4:
{
Scalar_<Npp32f> nVal = s;
nppSafeCall( nppiSet_32f_C4R(nVal.val, ptr<Npp32f>(), step, sz) );
break;
}
default:
......@@ -206,7 +246,73 @@ GpuMat& GpuMat::setTo(const Scalar& s, const GpuMat& mask)
}
}
else
matrix_operations::set_to_with_mask( *this, depth(), s.val, mask, channels());
{
switch (type())
{
case CV_8UC1:
{
Npp8u nVal = (Npp8u)s[0];
nppSafeCall( nppiSet_8u_C1MR(nVal, ptr<Npp8u>(), step, sz, mask.ptr<Npp8u>(), mask.step) );
break;
}
case CV_8UC4:
{
Scalar_<Npp8u> nVal = s;
nppSafeCall( nppiSet_8u_C4MR(nVal.val, ptr<Npp8u>(), step, sz, mask.ptr<Npp8u>(), mask.step) );
break;
}
case CV_16UC1:
{
Npp16u nVal = (Npp16u)s[0];
nppSafeCall( nppiSet_16u_C1MR(nVal, ptr<Npp16u>(), step, sz, mask.ptr<Npp8u>(), mask.step) );
break;
}
case CV_16UC4:
{
Scalar_<Npp16u> nVal = s;
nppSafeCall( nppiSet_16u_C4MR(nVal.val, ptr<Npp16u>(), step, sz, mask.ptr<Npp8u>(), mask.step) );
break;
}
case CV_16SC1:
{
Npp16s nVal = (Npp16s)s[0];
nppSafeCall( nppiSet_16s_C1MR(nVal, ptr<Npp16s>(), step, sz, mask.ptr<Npp8u>(), mask.step) );
break;
}
case CV_16SC4:
{
Scalar_<Npp16s> nVal = s;
nppSafeCall( nppiSet_16s_C4MR(nVal.val, ptr<Npp16s>(), step, sz, mask.ptr<Npp8u>(), mask.step) );
break;
}
case CV_32SC1:
{
Npp32s nVal = (Npp32s)s[0];
nppSafeCall( nppiSet_32s_C1MR(nVal, ptr<Npp32s>(), step, sz, mask.ptr<Npp8u>(), mask.step) );
break;
}
case CV_32SC4:
{
Scalar_<Npp32s> nVal = s;
nppSafeCall( nppiSet_32s_C4MR(nVal.val, ptr<Npp32s>(), step, sz, mask.ptr<Npp8u>(), mask.step) );
break;
}
case CV_32FC1:
{
Npp32f nVal = (Npp32f)s[0];
nppSafeCall( nppiSet_32f_C1MR(nVal, ptr<Npp32f>(), step, sz, mask.ptr<Npp8u>(), mask.step) );
break;
}
case CV_32FC4:
{
Scalar_<Npp32f> nVal = s;
nppSafeCall( nppiSet_32f_C4MR(nVal.val, ptr<Npp32f>(), step, sz, mask.ptr<Npp8u>(), mask.step) );
break;
}
default:
matrix_operations::set_to_with_mask( *this, depth(), s.val, mask, channels());
}
}
return *this;
}
......
tests/gpu/src/arithm.cpp
View file @ 51d5959a
......@@ -74,8 +74,8 @@ int CV_GpuArithmTest::test(int type)
cv::Size sz(200, 200);
cv::Mat mat1(sz, type), mat2(sz, type);
cv::RNG rng(*ts->get_rng());
rng.fill(mat1, cv::RNG::UNIFORM, cv::Scalar::all(10), cv::Scalar::all(100));
rng.fill(mat2, cv::RNG::UNIFORM, cv::Scalar::all(10), cv::Scalar::all(100));
rng.fill(mat1, cv::RNG::UNIFORM, cv::Scalar::all(1), cv::Scalar::all(20));
rng.fill(mat2, cv::RNG::UNIFORM, cv::Scalar::all(1), cv::Scalar::all(20));
return test(mat1, mat2);
}
......@@ -114,8 +114,8 @@ void CV_GpuArithmTest::run( int )
int testResult = CvTS::OK;
try
{
const int types[] = {CV_8UC1, CV_8UC3, CV_8UC4, CV_32FC1};
const char* type_names[] = {"CV_8UC1", "CV_8UC3", "CV_8UC4", "CV_32FC1"};
const int types[] = {CV_8UC1, CV_8UC3, CV_8UC4, CV_32SC1, CV_32FC1};
const char* type_names[] = {"CV_8UC1", "CV_8UC3", "CV_8UC4", "CV_32SC1", "CV_32FC1"};
const int type_count = sizeof(types)/sizeof(types[0]);
//run tests
......@@ -151,7 +151,7 @@ struct CV_GpuNppImageAddTest : public CV_GpuArithmTest
virtual int test(const Mat& mat1, const Mat& mat2)
{
if (mat1.type() != CV_8UC1 && mat1.type() != CV_8UC4 && mat1.type() != CV_32FC1)
if (mat1.type() != CV_8UC1 && mat1.type() != CV_8UC4 && mat1.type() != CV_32SC1 && mat1.type() != CV_32FC1)
{
ts->printf(CvTS::LOG, "\nUnsupported type\n");
return CvTS::OK;
......@@ -177,7 +177,7 @@ struct CV_GpuNppImageSubtractTest : public CV_GpuArithmTest
int test( const Mat& mat1, const Mat& mat2 )
{
if (mat1.type() != CV_8UC1 && mat1.type() != CV_8UC4 && mat1.type() != CV_32FC1)
if (mat1.type() != CV_8UC1 && mat1.type() != CV_8UC4 && mat1.type() != CV_32SC1 && mat1.type() != CV_32FC1)
{
ts->printf(CvTS::LOG, "\nUnsupported type\n");
return CvTS::OK;
......@@ -203,7 +203,7 @@ struct CV_GpuNppImageMultiplyTest : public CV_GpuArithmTest
int test( const Mat& mat1, const Mat& mat2 )
{
if (mat1.type() != CV_8UC1 && mat1.type() != CV_8UC4 && mat1.type() != CV_32FC1)
if (mat1.type() != CV_8UC1 && mat1.type() != CV_8UC4 && mat1.type() != CV_32SC1 && mat1.type() != CV_32FC1)
{
ts->printf(CvTS::LOG, "\nUnsupported type\n");
return CvTS::OK;
......@@ -229,7 +229,7 @@ struct CV_GpuNppImageDivideTest : public CV_GpuArithmTest
int test( const Mat& mat1, const Mat& mat2 )
{
if (mat1.type() != CV_8UC1 && mat1.type() != CV_8UC4 && mat1.type() != CV_32FC1)
if (mat1.type() != CV_8UC1 && mat1.type() != CV_8UC4 && mat1.type() != CV_32SC1 && mat1.type() != CV_32FC1)
{
ts->printf(CvTS::LOG, "\nUnsupported type\n");
return CvTS::OK;
......@@ -280,7 +280,7 @@ struct CV_GpuNppImageAbsdiffTest : public CV_GpuArithmTest
int test( const Mat& mat1, const Mat& mat2 )
{
if (mat1.type() != CV_8UC1 && mat1.type() != CV_32FC1)
if (mat1.type() != CV_8UC1 && mat1.type() != CV_8UC4 && mat1.type() != CV_32SC1 && mat1.type() != CV_32FC1)
{
ts->printf(CvTS::LOG, "\nUnsupported type\n");
return CvTS::OK;
......@@ -532,6 +532,82 @@ struct CV_GpuNppImageLUTTest : public CV_GpuArithmTest
}
};
////////////////////////////////////////////////////////////////////////////////
// exp
struct CV_GpuNppImageExpTest : public CV_GpuArithmTest
{
CV_GpuNppImageExpTest() : CV_GpuArithmTest( "GPU-NppImageExp", "exp" ) {}
int test( const Mat& mat1, const Mat& )
{
if (mat1.type() != CV_32FC1)
{
ts->printf(CvTS::LOG, "\nUnsupported type\n");
return CvTS::OK;
}
cv::Mat cpuRes;
cv::exp(mat1, cpuRes);
GpuMat gpu1(mat1);
GpuMat gpuRes;
cv::gpu::exp(gpu1, gpuRes);
return CheckNorm(cpuRes, gpuRes);
}
};
////////////////////////////////////////////////////////////////////////////////
// log
struct CV_GpuNppImageLogTest : public CV_GpuArithmTest
{
CV_GpuNppImageLogTest() : CV_GpuArithmTest( "GPU-NppImageLog", "log" ) {}
int test( const Mat& mat1, const Mat& )
{
if (mat1.type() != CV_32FC1)
{
ts->printf(CvTS::LOG, "\nUnsupported type\n");
return CvTS::OK;
}
cv::Mat cpuRes;
cv::log(mat1, cpuRes);
GpuMat gpu1(mat1);
GpuMat gpuRes;
cv::gpu::log(gpu1, gpuRes);
return CheckNorm(cpuRes, gpuRes);
}
};
////////////////////////////////////////////////////////////////////////////////
// magnitude
struct CV_GpuNppImageMagnitudeTest : public CV_GpuArithmTest
{
CV_GpuNppImageMagnitudeTest() : CV_GpuArithmTest( "GPU-NppImageMagnitude", "magnitude" ) {}
int test( const Mat& mat1, const Mat& mat2 )
{
if (mat1.type() != CV_32FC1)
{
ts->printf(CvTS::LOG, "\nUnsupported type\n");
return CvTS::OK;
}
cv::Mat cpuRes;
cv::magnitude(mat1, mat2, cpuRes);
GpuMat gpu1(mat1);
GpuMat gpu2(mat2);
GpuMat gpuRes;
cv::gpu::magnitude(gpu1, gpu2, gpuRes);
return CheckNorm(cpuRes, gpuRes);
}
};
/////////////////////////////////////////////////////////////////////////////
/////////////////// tests registration /////////////////////////////////////
/////////////////////////////////////////////////////////////////////////////
......@@ -552,4 +628,7 @@ CV_GpuNppImageNormTest CV_GpuNppImageNorm_test;
CV_GpuNppImageFlipTest CV_GpuNppImageFlip_test;
CV_GpuNppImageSumTest CV_GpuNppImageSum_test;
CV_GpuNppImageMinNaxTest CV_GpuNppImageMinNax_test;
CV_GpuNppImageLUTTest CV_GpuNppImageLUT_test;
\ No newline at end of file
CV_GpuNppImageLUTTest CV_GpuNppImageLUT_test;
CV_GpuNppImageExpTest CV_GpuNppImageExp_test;
CV_GpuNppImageLogTest CV_GpuNppImageLog_test;
CV_GpuNppImageMagnitudeTest CV_GpuNppImageMagnitude_test;
tests/gpu/src/gputest_main.cpp
View file @ 51d5959a
......@@ -45,19 +45,22 @@ CvTS test_system;
const char* blacklist[] =
{
"GPU-NppImageSum",
"GPU-MatOperatorAsyncCall",
//"GPU-NppErode",
//"GPU-NppDilate",
//"GPU-NppMorphologyEx",
//"GPU-NppImageDivide",
//"GPU-NppImageMeanStdDev",
//"GPU-NppImageMinNax",
//"GPU-NppImageResize",
//"GPU-NppImageWarpAffine",
//"GPU-NppImageWarpPerspective",
//"GPU-NppImageIntegral",
//"GPU-NppImageBlur",
"GPU-NppImageSum", // crash
"GPU-MatOperatorAsyncCall", // crash
//"GPU-NppErode", // npp func returns error code (CUDA_KERNEL_LAUNCH_ERROR or TEXTURE_BIND_ERROR)
//"GPU-NppDilate", // npp func returns error code (CUDA_KERNEL_LAUNCH_ERROR or TEXTURE_BIND_ERROR)
//"GPU-NppMorphologyEx", // npp func returns error code (CUDA_KERNEL_LAUNCH_ERROR or TEXTURE_BIND_ERROR)
//"GPU-NppImageDivide", // different round mode
//"GPU-NppImageMeanStdDev", // different precision
//"GPU-NppImageMinNax", // npp bug
//"GPU-NppImageResize", // different precision in interpolation
//"GPU-NppImageWarpAffine", // different precision in interpolation
//"GPU-NppImageWarpPerspective", // different precision in interpolation
//"GPU-NppImageIntegral", // different precision
//"GPU-NppImageBlur", // different precision
//"GPU-NppImageExp", // different precision
//"GPU-NppImageLog", // different precision
//"GPU-NppImageMagnitude", // different precision
0
};
......
tests/gpu/src/operator_convert_to.cpp
View file @ 51d5959a
......@@ -68,7 +68,6 @@ void CV_GpuMatOpConvertToTest::run(int /* start_from */)
const int types[] = {CV_8U, CV_8S, CV_16U, CV_16S, CV_32S, CV_32F, CV_64F};
const int types_num = sizeof(types) / sizeof(int);
const char* types_str[] = {"CV_8U", "CV_8S", "CV_16U", "CV_16S", "CV_32S", "CV_32F", "CV_64F"};
bool passed = true;
......@@ -78,17 +77,16 @@ void CV_GpuMatOpConvertToTest::run(int /* start_from */)
{
for (int j = 0; j < types_num && passed; ++j)
{
for (int c = 1; c < 2 && passed; ++c)
for (int c = 1; c < 5 && passed; ++c)
{
const int src_type = CV_MAKETYPE(types[i], c);
const int dst_type = types[j];
const double alpha = (double)rand() / RAND_MAX * 2.0;
const double beta = (double)rand() / RAND_MAX * 150.0 - 75;
cv::RNG rng(*ts->get_rng());
const double alpha = rng.uniform(0.0, 2.0);
const double beta = rng.uniform(-75.0, 75.0);
Mat cpumatsrc(img_size, src_type);
rng.fill(cpumatsrc, RNG::UNIFORM, Scalar::all(0), Scalar::all(300));
GpuMat gpumatsrc(cpumatsrc);
......
tests/gpu/src/operator_set_to.cpp
View file @ 51d5959a
......@@ -40,15 +40,7 @@
//M*/
#include "gputest.hpp"
#include "highgui.h"
#include <string>
#include <iostream>
#include <fstream>
#include <iterator>
#include <limits>
#include <numeric>
#include <iomanip> // for cout << setw()
using namespace cv;
using namespace std;
......@@ -62,9 +54,8 @@ public:
protected:
void run(int);
void print_mat(cv::Mat & mat, std::string name = "cpu mat");
void print_mat(gpu::GpuMat & mat, std::string name = "gpu mat");
bool compare_matrix(cv::Mat & cpumat, gpu::GpuMat & gpumat);
bool testSetTo(cv::Mat& cpumat, gpu::GpuMat& gpumat, const cv::Mat& cpumask = cv::Mat(), const cv::gpu::GpuMat& gpumask = cv::gpu::GpuMat());
private:
int rows;
......@@ -74,51 +65,23 @@ private:
CV_GpuMatOpSetToTest::CV_GpuMatOpSetToTest(): CvTest( "GPU-MatOperatorSetTo", "setTo" )
{
rows = 256;
cols = 124;
rows = 35;
cols = 67;
s.val[0] = 127.0;
s.val[1] = 127.0;
s.val[2] = 127.0;
s.val[3] = 127.0;
//#define PRINT_MATRIX
}
void CV_GpuMatOpSetToTest::print_mat(cv::Mat & mat, std::string name )
bool CV_GpuMatOpSetToTest::testSetTo(cv::Mat& cpumat, gpu::GpuMat& gpumat, const cv::Mat& cpumask, const cv::gpu::GpuMat& gpumask)
{
cv::imshow(name, mat);
}
cpumat.setTo(s, cpumask);
gpumat.setTo(s, gpumask);
void CV_GpuMatOpSetToTest::print_mat(gpu::GpuMat & mat, std::string name)
{
cv::Mat newmat;
mat.download(newmat);
print_mat(newmat, name);
}
double ret = norm(cpumat, gpumat, NORM_INF);
bool CV_GpuMatOpSetToTest::compare_matrix(cv::Mat & cpumat, gpu::GpuMat & gpumat)
{
//int64 time = getTickCount();
cpumat.setTo(s);
//int64 time1 = getTickCount();
gpumat.setTo(s);
//int64 time2 = getTickCount();
//std::cout << "\ntime cpu: " << std::fixed << std::setprecision(12) << double((time1 - time) / (double)getTickFrequency());
//std::cout << "\ntime gpu: " << std::fixed << std::setprecision(12) << double((time2 - time1) / (double)getTickFrequency());
//std::cout << "\n";
#ifdef PRINT_MATRIX
print_mat(cpumat);
print_mat(gpumat);
cv::waitKey(0);
#endif
double ret = norm(cpumat, gpumat);
if (ret < 1.0)
if (ret < std::numeric_limits<double>::epsilon())
return true;
else
{
......@@ -133,11 +96,20 @@ void CV_GpuMatOpSetToTest::run( int /* start_from */)
try
{
cv::Mat cpumask(rows, cols, CV_8UC1);
cv::RNG rng(*ts->get_rng());
rng.fill(cpumask, RNG::UNIFORM, cv::Scalar::all(0.0), cv::Scalar(1.5));
cv::gpu::GpuMat gpumask(cpumask);
for (int i = 0; i < 7; i++)
{
Mat cpumat(rows, cols, i, Scalar::all(0));
GpuMat gpumat(cpumat);
is_test_good &= compare_matrix(cpumat, gpumat);
for (int cn = 1; cn <= 4; ++cn)
{
int mat_type = CV_MAKETYPE(i, cn);
Mat cpumat(rows, cols, mat_type, Scalar::all(0));
GpuMat gpumat(cpumat);
is_test_good &= testSetTo(cpumat, gpumat, cpumask, gpumask);
}
}
}
catch(const cv::Exception& e)
......
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