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gpu module refactoring: moved per-element operations into separated file

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This commit is contained in:
Alexey Spizhevoy 2010-12-20 09:07:19 +00:00
parent 6891a60149
commit 0465b89e7e
6 changed files with 1048 additions and 915 deletions
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148
modules/gpu/include/opencv2/gpu/gpu.hpp
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@ -362,46 +362,10 @@ 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 CV_32FC1 and CV_32FC2 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 CV_32FC1 and CV_32FC2 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 CV_32FC1 and CV_32FC2 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 CV_32FC1 and CV_32FC2 type
CV_EXPORTS void divide(const GpuMat& a, const Scalar& sc, GpuMat& c);
//! transposes the matrix
//! supports CV_8UC1, CV_8SC1, CV_8UC4, CV_8SC4, CV_16UC2, CV_16SC2, CV_32SC1, CV_32FC1 type
CV_EXPORTS void transpose(const GpuMat& src1, GpuMat& dst);
//! computes element-wise absolute difference of two arrays (c = abs(a - b))
//! 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)
//! 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);
@ -485,14 +449,6 @@ 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 of complex (x(i).re, x(i).im) vector
//! supports only CV_32FC2 type
CV_EXPORTS void magnitude(const GpuMat& x, GpuMat& magnitude);
@ -531,33 +487,6 @@ namespace cv
//! async version
CV_EXPORTS void polarToCart(const GpuMat& magnitude, const GpuMat& angle, GpuMat& x, GpuMat& y, bool angleInDegrees, const Stream& stream);
//! perfroms per-elements bit-wise inversion
CV_EXPORTS void bitwise_not(const GpuMat& src, GpuMat& dst, const GpuMat& mask=GpuMat());
//! async version
CV_EXPORTS void bitwise_not(const GpuMat& src, GpuMat& dst, const GpuMat& mask, const Stream& stream);
//! calculates per-element bit-wise disjunction of two arrays
CV_EXPORTS void bitwise_or(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask=GpuMat());
//! async version
CV_EXPORTS void bitwise_or(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, const Stream& stream);
//! calculates per-element bit-wise conjunction of two arrays
CV_EXPORTS void bitwise_and(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask=GpuMat());
//! async version
CV_EXPORTS void bitwise_and(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, const Stream& stream);
//! calculates per-element bit-wise "exclusive or" operation
CV_EXPORTS void bitwise_xor(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask=GpuMat());
//! async version
CV_EXPORTS void bitwise_xor(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, const Stream& stream);
//! Logical operators
CV_EXPORTS GpuMat operator ~ (const GpuMat& src);
CV_EXPORTS GpuMat operator | (const GpuMat& src1, const GpuMat& src2);
CV_EXPORTS GpuMat operator & (const GpuMat& src1, const GpuMat& src2);
CV_EXPORTS GpuMat operator ^ (const GpuMat& src1, const GpuMat& src2);
//! computes per-element minimum of two arrays (dst = min(src1, src2))
CV_EXPORTS void min(const GpuMat& src1, const GpuMat& src2, GpuMat& dst);
//! Async version
@ -578,6 +507,83 @@ namespace cv
//! Async version
CV_EXPORTS void max(const GpuMat& src1, double src2, GpuMat& dst, const Stream& stream);
//////////////////////////// Per-element operations ////////////////////////////////////
//! 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 CV_32FC1 and CV_32FC2 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 CV_32FC1 and CV_32FC2 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 CV_32FC1 and CV_32FC2 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 CV_32FC1 and CV_32FC2 type
CV_EXPORTS void divide(const GpuMat& a, const Scalar& sc, GpuMat& c);
//! 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 element-wise absolute difference of two arrays (c = abs(a - b))
//! 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)
//! supports CV_8UC4, CV_32FC1 types
CV_EXPORTS void compare(const GpuMat& a, const GpuMat& b, GpuMat& c, int cmpop);
//! performs per-elements bit-wise inversion
CV_EXPORTS void bitwise_not(const GpuMat& src, GpuMat& dst, const GpuMat& mask=GpuMat());
//! version without mask
CV_EXPORTS GpuMat operator ~ (const GpuMat& src);
//! async version
CV_EXPORTS void bitwise_not(const GpuMat& src, GpuMat& dst, const GpuMat& mask, const Stream& stream);
//! calculates per-element bit-wise disjunction of two arrays
CV_EXPORTS void bitwise_or(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask=GpuMat());
//! version without mask
CV_EXPORTS GpuMat operator | (const GpuMat& src1, const GpuMat& src2);
//! async version
CV_EXPORTS void bitwise_or(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, const Stream& stream);
//! calculates per-element bit-wise conjunction of two arrays
CV_EXPORTS void bitwise_and(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask=GpuMat());
//! version without mask
CV_EXPORTS GpuMat operator & (const GpuMat& src1, const GpuMat& src2);
//! async version
CV_EXPORTS void bitwise_and(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, const Stream& stream);
//! calculates per-element bit-wise "exclusive or" operation
CV_EXPORTS void bitwise_xor(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask=GpuMat());
//! version without mask
CV_EXPORTS GpuMat operator ^ (const GpuMat& src1, const GpuMat& src2);
//! async version
CV_EXPORTS void bitwise_xor(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, const Stream& stream);
////////////////////////////// Image processing //////////////////////////////

530
modules/gpu/src/arithm.cpp
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@ -48,18 +48,7 @@ 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; }
double cv::gpu::norm(const GpuMat&, const GpuMat&, int) { throw_nogpu(); return 0.0; }
@ -89,18 +78,6 @@ void cv::gpu::cartToPolar(const GpuMat&, const GpuMat&, GpuMat&, GpuMat&, bool)
void cv::gpu::cartToPolar(const GpuMat&, const GpuMat&, GpuMat&, GpuMat&, bool, const Stream&) { throw_nogpu(); }
void cv::gpu::polarToCart(const GpuMat&, const GpuMat&, GpuMat&, GpuMat&, bool) { throw_nogpu(); }
void cv::gpu::polarToCart(const GpuMat&, const GpuMat&, GpuMat&, GpuMat&, bool, const Stream&) { throw_nogpu(); }
void cv::gpu::bitwise_not(const GpuMat&, GpuMat&, const GpuMat&) { throw_nogpu(); }
void cv::gpu::bitwise_not(const GpuMat&, GpuMat&, const GpuMat&, const Stream&) { throw_nogpu(); }
void cv::gpu::bitwise_or(const GpuMat&, const GpuMat&, GpuMat&, const GpuMat&) { throw_nogpu(); }
void cv::gpu::bitwise_or(const GpuMat&, const GpuMat&, GpuMat&, const GpuMat&, const Stream&) { throw_nogpu(); }
void cv::gpu::bitwise_and(const GpuMat&, const GpuMat&, GpuMat&, const GpuMat&) { throw_nogpu(); }
void cv::gpu::bitwise_and(const GpuMat&, const GpuMat&, GpuMat&, const GpuMat&, const Stream&) { throw_nogpu(); }
void cv::gpu::bitwise_xor(const GpuMat&, const GpuMat&, GpuMat&, const GpuMat&) { throw_nogpu(); }
void cv::gpu::bitwise_xor(const GpuMat&, const GpuMat&, GpuMat&, const GpuMat&, const Stream&) { throw_nogpu(); }
cv::gpu::GpuMat cv::gpu::operator ~ (const GpuMat&) { throw_nogpu(); return GpuMat(); }
cv::gpu::GpuMat cv::gpu::operator | (const GpuMat&, const GpuMat&) { throw_nogpu(); return GpuMat(); }
cv::gpu::GpuMat cv::gpu::operator & (const GpuMat&, const GpuMat&) { throw_nogpu(); return GpuMat(); }
cv::gpu::GpuMat cv::gpu::operator ^ (const GpuMat&, const GpuMat&) { throw_nogpu(); return GpuMat(); }
void cv::gpu::min(const GpuMat&, const GpuMat&, GpuMat&) { throw_nogpu(); }
void cv::gpu::min(const GpuMat&, const GpuMat&, GpuMat&, const Stream&) { throw_nogpu(); }
void cv::gpu::min(const GpuMat&, double, GpuMat&) { throw_nogpu(); }
@ -112,164 +89,6 @@ void cv::gpu::max(const GpuMat&, double, GpuMat&, const Stream&) { throw_nogpu()
#else /* !defined (HAVE_CUDA) */
////////////////////////////////////////////////////////////////////////
// add subtract multiply divide
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);
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_32SC1 || src1.type() == CV_32FC1);
dst.create( src1.size(), src1.type() );
NppiSize sz;
sz.width = src1.cols;
sz.height = src1.rows;
switch (src1.type())
{
case CV_8UC1:
nppSafeCall( npp_func_8uc1(src1.ptr<Npp8u>(), src1.step,
src2.ptr<Npp8u>(), src2.step,
dst.ptr<Npp8u>(), dst.step, sz, 0) );
break;
case CV_8UC4:
nppSafeCall( npp_func_8uc4(src1.ptr<Npp8u>(), src1.step,
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,
dst.ptr<Npp32f>(), dst.step, sz) );
break;
default:
CV_Assert(!"Unsupported source type");
}
}
template<int SCN> struct NppArithmScalarFunc;
template<> struct NppArithmScalarFunc<1>
{
typedef NppStatus (*func_ptr)(const Npp32f *pSrc, int nSrcStep, Npp32f nValue, Npp32f *pDst,
int nDstStep, NppiSize oSizeROI);
};
template<> struct NppArithmScalarFunc<2>
{
typedef NppStatus (*func_ptr)(const Npp32fc *pSrc, int nSrcStep, Npp32fc nValue, Npp32fc *pDst,
int nDstStep, NppiSize oSizeROI);
};
template<int SCN, typename NppArithmScalarFunc<SCN>::func_ptr func> struct NppArithmScalar;
template<typename NppArithmScalarFunc<1>::func_ptr func> struct NppArithmScalar<1, func>
{
static void calc(const GpuMat& src, const Scalar& sc, GpuMat& dst)
{
dst.create(src.size(), src.type());
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
nppSafeCall( func(src.ptr<Npp32f>(), src.step, (Npp32f)sc[0], dst.ptr<Npp32f>(), dst.step, sz) );
}
};
template<typename NppArithmScalarFunc<2>::func_ptr func> struct NppArithmScalar<2, func>
{
static void calc(const GpuMat& src, const Scalar& sc, GpuMat& dst)
{
dst.create(src.size(), src.type());
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
Npp32fc nValue;
nValue.re = (Npp32f)sc[0];
nValue.im = (Npp32f)sc[1];
nppSafeCall( func(src.ptr<Npp32fc>(), src.step, nValue, dst.ptr<Npp32fc>(), dst.step, sz) );
}
};
}
void cv::gpu::add(const GpuMat& src1, const GpuMat& src2, GpuMat& dst)
{
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)
{
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)
{
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)
{
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)
{
typedef void (*caller_t)(const GpuMat& src, const Scalar& sc, GpuMat& dst);
static const caller_t callers[] = {0, NppArithmScalar<1, nppiAddC_32f_C1R>::calc, NppArithmScalar<2, nppiAddC_32fc_C1R>::calc};
CV_Assert(src.type() == CV_32FC1 || src.type() == CV_32FC2);
callers[src.channels()](src, sc, dst);
}
void cv::gpu::subtract(const GpuMat& src, const Scalar& sc, GpuMat& dst)
{
typedef void (*caller_t)(const GpuMat& src, const Scalar& sc, GpuMat& dst);
static const caller_t callers[] = {0, NppArithmScalar<1, nppiSubC_32f_C1R>::calc, NppArithmScalar<2, nppiSubC_32fc_C1R>::calc};
CV_Assert(src.type() == CV_32FC1 || src.type() == CV_32FC2);
callers[src.channels()](src, sc, dst);
}
void cv::gpu::multiply(const GpuMat& src, const Scalar& sc, GpuMat& dst)
{
typedef void (*caller_t)(const GpuMat& src, const Scalar& sc, GpuMat& dst);
static const caller_t callers[] = {0, NppArithmScalar<1, nppiMulC_32f_C1R>::calc, NppArithmScalar<2, nppiMulC_32fc_C1R>::calc};
CV_Assert(src.type() == CV_32FC1 || src.type() == CV_32FC2);
callers[src.channels()](src, sc, dst);
}
void cv::gpu::divide(const GpuMat& src, const Scalar& sc, GpuMat& dst)
{
typedef void (*caller_t)(const GpuMat& src, const Scalar& sc, GpuMat& dst);
static const caller_t callers[] = {0, NppArithmScalar<1, nppiDivC_32f_C1R>::calc, NppArithmScalar<2, nppiDivC_32fc_C1R>::calc};
CV_Assert(src.type() == CV_32FC1 || src.type() == CV_32FC2);
callers[src.channels()](src, sc, dst);
}
////////////////////////////////////////////////////////////////////////
// transpose
@ -299,112 +118,6 @@ void cv::gpu::transpose(const GpuMat& src, GpuMat& dst)
}
}
////////////////////////////////////////////////////////////////////////
// absdiff
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_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;
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) );
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
namespace cv { namespace gpu { namespace mathfunc
{
void compare_ne_8uc4(const DevMem2D& src1, const DevMem2D& src2, const DevMem2D& dst);
void compare_ne_32f(const DevMem2D& src1, const DevMem2D& src2, const DevMem2D& dst);
}}}
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);
}
}
}
////////////////////////////////////////////////////////////////////////
// meanStdDev
@ -997,249 +710,6 @@ void cv::gpu::polarToCart(const GpuMat& magnitude, const GpuMat& angle, GpuMat&
::polarToCart_caller(magnitude, angle, x, y, angleInDegrees, StreamAccessor::getStream(stream));
}
//////////////////////////////////////////////////////////////////////////////
// Per-element bit-wise logical matrix operations
namespace cv { namespace gpu { namespace mathfunc
{
void bitwise_not_caller(int rows, int cols, int elem_size1, int cn, const PtrStep src, PtrStep dst, cudaStream_t stream);
template <typename T>
void bitwise_mask_not_caller(int rows, int cols, int cn, const PtrStep src, const PtrStep mask, PtrStep dst, cudaStream_t stream);
void bitwise_or_caller(int rows, int cols, int elem_size1, int cn, const PtrStep src1, const PtrStep src2, PtrStep dst, cudaStream_t stream);
template <typename T>
void bitwise_mask_or_caller(int rows, int cols, int cn, const PtrStep src1, const PtrStep src2, const PtrStep mask, PtrStep dst, cudaStream_t stream);
void bitwise_and_caller(int rows, int cols, int elem_size1, int cn, const PtrStep src1, const PtrStep src2, PtrStep dst, cudaStream_t stream);
template <typename T>
void bitwise_mask_and_caller(int rows, int cols, int cn, const PtrStep src1, const PtrStep src2, const PtrStep mask, PtrStep dst, cudaStream_t stream);
void bitwise_xor_caller(int rows, int cols, int elem_size1, int cn, const PtrStep src1, const PtrStep src2, PtrStep dst, cudaStream_t stream);
template <typename T>
void bitwise_mask_xor_caller(int rows, int cols, int cn, const PtrStep src1, const PtrStep src2, const PtrStep mask, PtrStep dst, cudaStream_t stream);
}}}
namespace
{
void bitwise_not_caller(const GpuMat& src, GpuMat& dst, cudaStream_t stream)
{
dst.create(src.size(), src.type());
cv::gpu::mathfunc::bitwise_not_caller(src.rows, src.cols, src.elemSize1(),
dst.channels(), src, dst, stream);
}
void bitwise_not_caller(const GpuMat& src, GpuMat& dst, const GpuMat& mask, cudaStream_t stream)
{
using namespace cv::gpu;
typedef void (*Caller)(int, int, int, const PtrStep, const PtrStep, PtrStep, cudaStream_t);
static Caller callers[] = {mathfunc::bitwise_mask_not_caller<unsigned char>, mathfunc::bitwise_mask_not_caller<unsigned char>,
mathfunc::bitwise_mask_not_caller<unsigned short>, mathfunc::bitwise_mask_not_caller<unsigned short>,
mathfunc::bitwise_mask_not_caller<unsigned int>, mathfunc::bitwise_mask_not_caller<unsigned int>,
mathfunc::bitwise_mask_not_caller<unsigned int>};
CV_Assert(mask.type() == CV_8U && mask.size() == src.size());
dst.create(src.size(), src.type());
Caller caller = callers[src.depth()];
CV_Assert(caller);
int cn = src.depth() != CV_64F ? src.channels() : src.channels() * (sizeof(double) / sizeof(unsigned int));
caller(src.rows, src.cols, cn, src, mask, dst, stream);
}
void bitwise_or_caller(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, cudaStream_t stream)
{
CV_Assert(src1.size() == src2.size() && src1.type() == src2.type());
dst.create(src1.size(), src1.type());
cv::gpu::mathfunc::bitwise_or_caller(dst.rows, dst.cols, dst.elemSize1(),
dst.channels(), src1, src2, dst, stream);
}
void bitwise_or_caller(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, cudaStream_t stream)
{
using namespace cv::gpu;
typedef void (*Caller)(int, int, int, const PtrStep, const PtrStep, const PtrStep, PtrStep, cudaStream_t);
static Caller callers[] = {mathfunc::bitwise_mask_or_caller<unsigned char>, mathfunc::bitwise_mask_or_caller<unsigned char>,
mathfunc::bitwise_mask_or_caller<unsigned short>, mathfunc::bitwise_mask_or_caller<unsigned short>,
mathfunc::bitwise_mask_or_caller<unsigned int>, mathfunc::bitwise_mask_or_caller<unsigned int>,
mathfunc::bitwise_mask_or_caller<unsigned int>};
CV_Assert(src1.size() == src2.size() && src1.type() == src2.type());
dst.create(src1.size(), src1.type());
Caller caller = callers[src1.depth()];
CV_Assert(caller);
int cn = dst.depth() != CV_64F ? dst.channels() : dst.channels() * (sizeof(double) / sizeof(unsigned int));
caller(dst.rows, dst.cols, cn, src1, src2, mask, dst, stream);
}
void bitwise_and_caller(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, cudaStream_t stream)
{
CV_Assert(src1.size() == src2.size() && src1.type() == src2.type());
dst.create(src1.size(), src1.type());
cv::gpu::mathfunc::bitwise_and_caller(dst.rows, dst.cols, dst.elemSize1(),
dst.channels(), src1, src2, dst, stream);
}
void bitwise_and_caller(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, cudaStream_t stream)
{
using namespace cv::gpu;
typedef void (*Caller)(int, int, int, const PtrStep, const PtrStep, const PtrStep, PtrStep, cudaStream_t);
static Caller callers[] = {mathfunc::bitwise_mask_and_caller<unsigned char>, mathfunc::bitwise_mask_and_caller<unsigned char>,
mathfunc::bitwise_mask_and_caller<unsigned short>, mathfunc::bitwise_mask_and_caller<unsigned short>,
mathfunc::bitwise_mask_and_caller<unsigned int>, mathfunc::bitwise_mask_and_caller<unsigned int>,
mathfunc::bitwise_mask_and_caller<unsigned int>};
CV_Assert(src1.size() == src2.size() && src1.type() == src2.type());
dst.create(src1.size(), src1.type());
Caller caller = callers[src1.depth()];
CV_Assert(caller);
int cn = dst.depth() != CV_64F ? dst.channels() : dst.channels() * (sizeof(double) / sizeof(unsigned int));
caller(dst.rows, dst.cols, cn, src1, src2, mask, dst, stream);
}
void bitwise_xor_caller(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, cudaStream_t stream)
{
CV_Assert(src1.size() == src2.size() && src1.type() == src2.type());
dst.create(src1.size(), src1.type());
cv::gpu::mathfunc::bitwise_xor_caller(dst.rows, dst.cols, dst.elemSize1(),
dst.channels(), src1, src2, dst, stream);
}
void bitwise_xor_caller(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, cudaStream_t stream)
{
using namespace cv::gpu;
typedef void (*Caller)(int, int, int, const PtrStep, const PtrStep, const PtrStep, PtrStep, cudaStream_t);
static Caller callers[] = {mathfunc::bitwise_mask_xor_caller<unsigned char>, mathfunc::bitwise_mask_xor_caller<unsigned char>,
mathfunc::bitwise_mask_xor_caller<unsigned short>, mathfunc::bitwise_mask_xor_caller<unsigned short>,
mathfunc::bitwise_mask_xor_caller<unsigned int>, mathfunc::bitwise_mask_xor_caller<unsigned int>,
mathfunc::bitwise_mask_xor_caller<unsigned int>};
CV_Assert(src1.size() == src2.size() && src1.type() == src2.type());
dst.create(src1.size(), src1.type());
Caller caller = callers[src1.depth()];
CV_Assert(caller);
int cn = dst.depth() != CV_64F ? dst.channels() : dst.channels() * (sizeof(double) / sizeof(unsigned int));
caller(dst.rows, dst.cols, cn, src1, src2, mask, dst, stream);
}
}
void cv::gpu::bitwise_not(const GpuMat& src, GpuMat& dst, const GpuMat& mask)
{
if (mask.empty())
::bitwise_not_caller(src, dst, 0);
else
::bitwise_not_caller(src, dst, mask, 0);
}
void cv::gpu::bitwise_not(const GpuMat& src, GpuMat& dst, const GpuMat& mask, const Stream& stream)
{
if (mask.empty())
::bitwise_not_caller(src, dst, StreamAccessor::getStream(stream));
else
::bitwise_not_caller(src, dst, mask, StreamAccessor::getStream(stream));
}
void cv::gpu::bitwise_or(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask)
{
if (mask.empty())
::bitwise_or_caller(src1, src2, dst, 0);
else
::bitwise_or_caller(src1, src2, dst, mask, 0);
}
void cv::gpu::bitwise_or(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, const Stream& stream)
{
if (mask.empty())
::bitwise_or_caller(src1, src2, dst, StreamAccessor::getStream(stream));
else
::bitwise_or_caller(src1, src2, dst, mask, StreamAccessor::getStream(stream));
}
void cv::gpu::bitwise_and(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask)
{
if (mask.empty())
::bitwise_and_caller(src1, src2, dst, 0);
else
::bitwise_and_caller(src1, src2, dst, mask, 0);
}
void cv::gpu::bitwise_and(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, const Stream& stream)
{
if (mask.empty())
::bitwise_and_caller(src1, src2, dst, StreamAccessor::getStream(stream));
else
::bitwise_and_caller(src1, src2, dst, mask, StreamAccessor::getStream(stream));
}
void cv::gpu::bitwise_xor(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask)
{
if (mask.empty())
::bitwise_xor_caller(src1, src2, dst, 0);
else
::bitwise_xor_caller(src1, src2, dst, mask, 0);
}
void cv::gpu::bitwise_xor(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, const Stream& stream)
{
if (mask.empty())
::bitwise_xor_caller(src1, src2, dst, StreamAccessor::getStream(stream));
else
::bitwise_xor_caller(src1, src2, dst, mask, StreamAccessor::getStream(stream));
}
cv::gpu::GpuMat cv::gpu::operator ~ (const GpuMat& src)
{
GpuMat dst;
bitwise_not(src, dst);
return dst;
}
cv::gpu::GpuMat cv::gpu::operator | (const GpuMat& src1, const GpuMat& src2)
{
GpuMat dst;
bitwise_or(src1, src2, dst);
return dst;
}
cv::gpu::GpuMat cv::gpu::operator & (const GpuMat& src1, const GpuMat& src2)
{
GpuMat dst;
bitwise_and(src1, src2, dst);
return dst;
}
cv::gpu::GpuMat cv::gpu::operator ^ (const GpuMat& src1, const GpuMat& src2)
{
GpuMat dst;
bitwise_xor(src1, src2, dst);
return dst;
}
//////////////////////////////////////////////////////////////////////////////
// min/max

348
modules/gpu/src/cuda/element_operations.cu
View File

@ -0,0 +1,348 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "opencv2/gpu/device/vecmath.hpp"
#include "transform.hpp"
#include "internal_shared.hpp"
using namespace cv::gpu;
using namespace cv::gpu::device;
namespace cv { namespace gpu { namespace mathfunc
{
//////////////////////////////////////////////////////////////////////////////////////
// Compare
template <typename T1, typename T2>
struct NotEqual
{
__device__ uchar operator()(const T1& src1, const T2& src2)
{
return static_cast<uchar>(static_cast<int>(src1 != src2) * 255);
}
};
template <typename T1, typename T2>
inline void compare_ne(const DevMem2D& src1, const DevMem2D& src2, const DevMem2D& dst)
{
NotEqual<T1, T2> op;
transform(static_cast< DevMem2D_<T1> >(src1), static_cast< DevMem2D_<T2> >(src2), dst, op, 0);
}
void compare_ne_8uc4(const DevMem2D& src1, const DevMem2D& src2, const DevMem2D& dst)
{
compare_ne<uint, uint>(src1, src2, dst);
}
void compare_ne_32f(const DevMem2D& src1, const DevMem2D& src2, const DevMem2D& dst)
{
compare_ne<float, float>(src1, src2, dst);
}
//////////////////////////////////////////////////////////////////////////
// Unary bitwise logical matrix operations
enum { UN_OP_NOT };
template <typename T, int opid>
struct UnOp;
template <typename T>
struct UnOp<T, UN_OP_NOT>
{
static __device__ T call(T v) { return ~v; }
};
template <int opid>
__global__ void bitwiseUnOpKernel(int rows, int width, const PtrStep src, PtrStep dst)
{
const int x = (blockDim.x * blockIdx.x + threadIdx.x) * 4;
const int y = blockDim.y * blockIdx.y + threadIdx.y;
if (y < rows)
{
uchar* dst_ptr = dst.ptr(y) + x;
const uchar* src_ptr = src.ptr(y) + x;
if (x + sizeof(uint) - 1 < width)
{
*(uint*)dst_ptr = UnOp<uint, opid>::call(*(uint*)src_ptr);
}
else
{
const uchar* src_end = src.ptr(y) + width;
while (src_ptr < src_end)
{
*dst_ptr++ = UnOp<uchar, opid>::call(*src_ptr++);
}
}
}
}
template <int opid>
void bitwiseUnOp(int rows, int width, const PtrStep src, PtrStep dst,
cudaStream_t stream)
{
dim3 threads(16, 16);
dim3 grid(divUp(width, threads.x * sizeof(uint)),
divUp(rows, threads.y));
bitwiseUnOpKernel<opid><<<grid, threads>>>(rows, width, src, dst);
if (stream == 0)
cudaSafeCall(cudaThreadSynchronize());
}
template <typename T, int opid>
__global__ void bitwiseUnOpKernel(int rows, int cols, int cn, const PtrStep src,
const PtrStep mask, PtrStep dst)
{
const int x = blockDim.x * blockIdx.x + threadIdx.x;
const int y = blockDim.y * blockIdx.y + threadIdx.y;
if (x < cols && y < rows && mask.ptr(y)[x / cn])
{
T* dst_row = (T*)dst.ptr(y);
const T* src_row = (const T*)src.ptr(y);
dst_row[x] = UnOp<T, opid>::call(src_row[x]);
}
}
template <typename T, int opid>
void bitwiseUnOp(int rows, int cols, int cn, const PtrStep src,
const PtrStep mask, PtrStep dst, cudaStream_t stream)
{
dim3 threads(16, 16);
dim3 grid(divUp(cols, threads.x), divUp(rows, threads.y));
bitwiseUnOpKernel<T, opid><<<grid, threads>>>(rows, cols, cn, src, mask, dst);
if (stream == 0)
cudaSafeCall(cudaThreadSynchronize());
}
void bitwiseNotCaller(int rows, int cols, int elem_size1, int cn,
const PtrStep src, PtrStep dst, cudaStream_t stream)
{
bitwiseUnOp<UN_OP_NOT>(rows, cols * elem_size1 * cn, src, dst, stream);
}
template <typename T>
void bitwiseMaskNotCaller(int rows, int cols, int cn, const PtrStep src,
const PtrStep mask, PtrStep dst, cudaStream_t stream)
{
bitwiseUnOp<T, UN_OP_NOT>(rows, cols * cn, cn, src, mask, dst, stream);
}
template void bitwiseMaskNotCaller<uchar>(int, int, int, const PtrStep, const PtrStep, PtrStep, cudaStream_t);
template void bitwiseMaskNotCaller<ushort>(int, int, int, const PtrStep, const PtrStep, PtrStep, cudaStream_t);
template void bitwiseMaskNotCaller<uint>(int, int, int, const PtrStep, const PtrStep, PtrStep, cudaStream_t);
//////////////////////////////////////////////////////////////////////////
// Binary bitwise logical matrix operations
enum { BIN_OP_OR, BIN_OP_AND, BIN_OP_XOR };
template <typename T, int opid>
struct BinOp;
template <typename T>
struct BinOp<T, BIN_OP_OR>
{
static __device__ T call(T a, T b) { return a | b; }
};
template <typename T>
struct BinOp<T, BIN_OP_AND>
{
static __device__ T call(T a, T b) { return a & b; }
};
template <typename T>
struct BinOp<T, BIN_OP_XOR>
{
static __device__ T call(T a, T b) { return a ^ b; }
};
template <int opid>
__global__ void bitwiseBinOpKernel(int rows, int width, const PtrStep src1,
const PtrStep src2, PtrStep dst)
{
const int x = (blockDim.x * blockIdx.x + threadIdx.x) * 4;
const int y = blockDim.y * blockIdx.y + threadIdx.y;
if (y < rows)
{
uchar* dst_ptr = dst.ptr(y) + x;
const uchar* src1_ptr = src1.ptr(y) + x;
const uchar* src2_ptr = src2.ptr(y) + x;
if (x + sizeof(uint) - 1 < width)
{
*(uint*)dst_ptr = BinOp<uint, opid>::call(*(uint*)src1_ptr, *(uint*)src2_ptr);
}
else
{
const uchar* src1_end = src1.ptr(y) + width;
while (src1_ptr < src1_end)
{
*dst_ptr++ = BinOp<uchar, opid>::call(*src1_ptr++, *src2_ptr++);
}
}
}
}
template <int opid>
void bitwiseBinOp(int rows, int width, const PtrStep src1, const PtrStep src2,
PtrStep dst, cudaStream_t stream)
{
dim3 threads(16, 16);
dim3 grid(divUp(width, threads.x * sizeof(uint)), divUp(rows, threads.y));
bitwiseBinOpKernel<opid><<<grid, threads>>>(rows, width, src1, src2, dst);
if (stream == 0)
cudaSafeCall(cudaThreadSynchronize());
}
template <typename T, int opid>
__global__ void bitwiseBinOpKernel(
int rows, int cols, int cn, const PtrStep src1, const PtrStep src2,
const PtrStep mask, PtrStep dst)
{
const int x = blockDim.x * blockIdx.x + threadIdx.x;
const int y = blockDim.y * blockIdx.y + threadIdx.y;
if (x < cols && y < rows && mask.ptr(y)[x / cn])
{
T* dst_row = (T*)dst.ptr(y);
const T* src1_row = (const T*)src1.ptr(y);
const T* src2_row = (const T*)src2.ptr(y);
dst_row[x] = BinOp<T, opid>::call(src1_row[x], src2_row[x]);
}
}
template <typename T, int opid>
void bitwiseBinOp(int rows, int cols, int cn, const PtrStep src1, const PtrStep src2,
const PtrStep mask, PtrStep dst, cudaStream_t stream)
{
dim3 threads(16, 16);
dim3 grid(divUp(cols, threads.x), divUp(rows, threads.y));
bitwiseBinOpKernel<T, opid><<<grid, threads>>>(rows, cols, cn, src1, src2, mask, dst);
if (stream == 0)
cudaSafeCall(cudaThreadSynchronize());
}
void bitwiseOrCaller(int rows, int cols, int elem_size1, int cn, const PtrStep src1,
const PtrStep src2, PtrStep dst, cudaStream_t stream)
{
bitwiseBinOp<BIN_OP_OR>(rows, cols * elem_size1 * cn, src1, src2, dst, stream);
}
template <typename T>
void bitwiseMaskOrCaller(int rows, int cols, int cn, const PtrStep src1, const PtrStep src2,
const PtrStep mask, PtrStep dst, cudaStream_t stream)
{
bitwiseBinOp<T, BIN_OP_OR>(rows, cols * cn, cn, src1, src2, mask, dst, stream);
}
template void bitwiseMaskOrCaller<uchar>(int, int, int, const PtrStep, const PtrStep, const PtrStep, PtrStep, cudaStream_t);
template void bitwiseMaskOrCaller<ushort>(int, int, int, const PtrStep, const PtrStep, const PtrStep, PtrStep, cudaStream_t);
template void bitwiseMaskOrCaller<uint>(int, int, int, const PtrStep, const PtrStep, const PtrStep, PtrStep, cudaStream_t);
void bitwiseAndCaller(int rows, int cols, int elem_size1, int cn, const PtrStep src1,
const PtrStep src2, PtrStep dst, cudaStream_t stream)
{
bitwiseBinOp<BIN_OP_AND>(rows, cols * elem_size1 * cn, src1, src2, dst, stream);
}
template <typename T>
void bitwiseMaskAndCaller(int rows, int cols, int cn, const PtrStep src1, const PtrStep src2,
const PtrStep mask, PtrStep dst, cudaStream_t stream)
{
bitwiseBinOp<T, BIN_OP_AND>(rows, cols * cn, cn, src1, src2, mask, dst, stream);
}
template void bitwiseMaskAndCaller<uchar>(int, int, int, const PtrStep, const PtrStep, const PtrStep, PtrStep, cudaStream_t);
template void bitwiseMaskAndCaller<ushort>(int, int, int, const PtrStep, const PtrStep, const PtrStep, PtrStep, cudaStream_t);
template void bitwiseMaskAndCaller<uint>(int, int, int, const PtrStep, const PtrStep, const PtrStep, PtrStep, cudaStream_t);
void bitwiseXorCaller(int rows, int cols, int elem_size1, int cn, const PtrStep src1,
const PtrStep src2, PtrStep dst, cudaStream_t stream)
{
bitwiseBinOp<BIN_OP_XOR>(rows, cols * elem_size1 * cn, src1, src2, dst, stream);
}
template <typename T>
void bitwiseMaskXorCaller(int rows, int cols, int cn, const PtrStep src1, const PtrStep src2,
const PtrStep mask, PtrStep dst, cudaStream_t stream)
{
bitwiseBinOp<T, BIN_OP_XOR>(rows, cols * cn, cn, src1, src2, mask, dst, stream);
}
template void bitwiseMaskXorCaller<uchar>(int, int, int, const PtrStep, const PtrStep, const PtrStep, PtrStep, cudaStream_t);
template void bitwiseMaskXorCaller<ushort>(int, int, int, const PtrStep, const PtrStep, const PtrStep, PtrStep, cudaStream_t);
template void bitwiseMaskXorCaller<uint>(int, int, int, const PtrStep, const PtrStep, const PtrStep, PtrStep, cudaStream_t);
}}}

330
modules/gpu/src/cuda/mathfunc.cu
View File

@ -78,6 +78,29 @@ namespace cv { namespace gpu { namespace mathfunc
}
}
struct Mask8U
{
explicit Mask8U(PtrStep mask): mask(mask) {}
__device__ bool operator()(int y, int x) const
{
return mask.ptr(y)[x];
}
PtrStep mask;
};
struct MaskTrue
{
__device__ bool operator()(int y, int x) const
{
return true;
}
};
struct Nothing
{
static __device__ void calc(int, int, float, float, float*, size_t, float)
@ -235,313 +258,6 @@ namespace cv { namespace gpu { namespace mathfunc
callers[mag.data == 0](mag, angle, x, y, angleInDegrees, stream);
}
//////////////////////////////////////////////////////////////////////////////////////
// Compare
template <typename T1, typename T2>
struct NotEqual
{
__device__ uchar operator()(const T1& src1, const T2& src2)
{
return static_cast<uchar>(static_cast<int>(src1 != src2) * 255);
}
};
template <typename T1, typename T2>
inline void compare_ne(const DevMem2D& src1, const DevMem2D& src2, const DevMem2D& dst)
{
NotEqual<T1, T2> op;
transform(static_cast< DevMem2D_<T1> >(src1), static_cast< DevMem2D_<T2> >(src2), dst, op, 0);
}
void compare_ne_8uc4(const DevMem2D& src1, const DevMem2D& src2, const DevMem2D& dst)
{
compare_ne<uint, uint>(src1, src2, dst);
}
void compare_ne_32f(const DevMem2D& src1, const DevMem2D& src2, const DevMem2D& dst)
{
compare_ne<float, float>(src1, src2, dst);
}
//////////////////////////////////////////////////////////////////////////////
// Per-element bit-wise logical matrix operations
struct Mask8U
{
explicit Mask8U(PtrStep mask): mask(mask) {}
__device__ bool operator()(int y, int x) const
{
return mask.ptr(y)[x];
}
PtrStep mask;
};
struct MaskTrue
{
__device__ bool operator()(int y, int x) const
{
return true;
}
};
//------------------------------------------------------------------------
// Unary operations
enum { UN_OP_NOT };
template <typename T, int opid>
struct UnOp;
template <typename T>
struct UnOp<T, UN_OP_NOT>
{
static __device__ T call(T v) { return ~v; }
};
template <int opid>
__global__ void bitwise_un_op_kernel(int rows, int width, const PtrStep src, PtrStep dst)
{
const int x = (blockDim.x * blockIdx.x + threadIdx.x) * 4;
const int y = blockDim.y * blockIdx.y + threadIdx.y;
if (y < rows)
{
uchar* dst_ptr = dst.ptr(y) + x;
const uchar* src_ptr = src.ptr(y) + x;
if (x + sizeof(uint) - 1 < width)
{
*(uint*)dst_ptr = UnOp<uint, opid>::call(*(uint*)src_ptr);
}
else
{
const uchar* src_end = src.ptr(y) + width;
while (src_ptr < src_end)
{
*dst_ptr++ = UnOp<uchar, opid>::call(*src_ptr++);
}
}
}
}
template <int opid>
void bitwise_un_op(int rows, int width, const PtrStep src, PtrStep dst, cudaStream_t stream)
{
dim3 threads(16, 16);
dim3 grid(divUp(width, threads.x * sizeof(uint)),
divUp(rows, threads.y));
bitwise_un_op_kernel<opid><<<grid, threads>>>(rows, width, src, dst);
if (stream == 0)
cudaSafeCall(cudaThreadSynchronize());
}
template <typename T, int opid>
__global__ void bitwise_un_op_kernel(int rows, int cols, int cn, const PtrStep src, const PtrStep mask, PtrStep dst)
{
const int x = blockDim.x * blockIdx.x + threadIdx.x;
const int y = blockDim.y * blockIdx.y + threadIdx.y;
if (x < cols && y < rows && mask.ptr(y)[x / cn])
{
T* dst_row = (T*)dst.ptr(y);
const T* src_row = (const T*)src.ptr(y);
dst_row[x] = UnOp<T, opid>::call(src_row[x]);
}
}
template <typename T, int opid>
void bitwise_un_op(int rows, int cols, int cn, const PtrStep src, const PtrStep mask, PtrStep dst, cudaStream_t stream)
{
dim3 threads(16, 16);
dim3 grid(divUp(cols, threads.x), divUp(rows, threads.y));
bitwise_un_op_kernel<T, opid><<<grid, threads>>>(rows, cols, cn, src, mask, dst);
if (stream == 0)
cudaSafeCall(cudaThreadSynchronize());
}
void bitwise_not_caller(int rows, int cols, int elem_size1, int cn, const PtrStep src, PtrStep dst, cudaStream_t stream)
{
bitwise_un_op<UN_OP_NOT>(rows, cols * elem_size1 * cn, src, dst, stream);
}
template <typename T>
void bitwise_mask_not_caller(int rows, int cols, int cn, const PtrStep src, const PtrStep mask, PtrStep dst, cudaStream_t stream)
{
bitwise_un_op<T, UN_OP_NOT>(rows, cols * cn, cn, src, mask, dst, stream);
}
template void bitwise_mask_not_caller<uchar>(int, int, int, const PtrStep, const PtrStep, PtrStep, cudaStream_t);
template void bitwise_mask_not_caller<ushort>(int, int, int, const PtrStep, const PtrStep, PtrStep, cudaStream_t);
template void bitwise_mask_not_caller<uint>(int, int, int, const PtrStep, const PtrStep, PtrStep, cudaStream_t);
//------------------------------------------------------------------------
// Binary operations
enum { BIN_OP_OR, BIN_OP_AND, BIN_OP_XOR };
template <typename T, int opid>
struct BinOp;
template <typename T>
struct BinOp<T, BIN_OP_OR>
{
static __device__ T call(T a, T b) { return a | b; }
};
template <typename T>
struct BinOp<T, BIN_OP_AND>
{
static __device__ T call(T a, T b) { return a & b; }
};
template <typename T>
struct BinOp<T, BIN_OP_XOR>
{
static __device__ T call(T a, T b) { return a ^ b; }
};
template <int opid>
__global__ void bitwise_bin_op_kernel(int rows, int width, const PtrStep src1, const PtrStep src2, PtrStep dst)
{
const int x = (blockDim.x * blockIdx.x + threadIdx.x) * 4;
const int y = blockDim.y * blockIdx.y + threadIdx.y;
if (y < rows)
{
uchar* dst_ptr = dst.ptr(y) + x;
const uchar* src1_ptr = src1.ptr(y) + x;
const uchar* src2_ptr = src2.ptr(y) + x;
if (x + sizeof(uint) - 1 < width)
{
*(uint*)dst_ptr = BinOp<uint, opid>::call(*(uint*)src1_ptr, *(uint*)src2_ptr);
}
else
{
const uchar* src1_end = src1.ptr(y) + width;
while (src1_ptr < src1_end)
{
*dst_ptr++ = BinOp<uchar, opid>::call(*src1_ptr++, *src2_ptr++);
}
}
}
}
template <int opid>
void bitwise_bin_op(int rows, int width, const PtrStep src1, const PtrStep src2, PtrStep dst,
cudaStream_t stream)
{
dim3 threads(16, 16);
dim3 grid(divUp(width, threads.x * sizeof(uint)), divUp(rows, threads.y));
bitwise_bin_op_kernel<opid><<<grid, threads>>>(rows, width, src1, src2, dst);
if (stream == 0)
cudaSafeCall(cudaThreadSynchronize());
}
template <typename T, int opid>
__global__ void bitwise_bin_op_kernel(
int rows, int cols, int cn, const PtrStep src1, const PtrStep src2,
const PtrStep mask, PtrStep dst)
{
const int x = blockDim.x * blockIdx.x + threadIdx.x;
const int y = blockDim.y * blockIdx.y + threadIdx.y;
if (x < cols && y < rows && mask.ptr(y)[x / cn])
{
T* dst_row = (T*)dst.ptr(y);
const T* src1_row = (const T*)src1.ptr(y);
const T* src2_row = (const T*)src2.ptr(y);
dst_row[x] = BinOp<T, opid>::call(src1_row[x], src2_row[x]);
}
}
template <typename T, int opid>
void bitwise_bin_op(int rows, int cols, int cn, const PtrStep src1, const PtrStep src2,
const PtrStep mask, PtrStep dst, cudaStream_t stream)
{
dim3 threads(16, 16);
dim3 grid(divUp(cols, threads.x), divUp(rows, threads.y));
bitwise_bin_op_kernel<T, opid><<<grid, threads>>>(rows, cols, cn, src1, src2, mask, dst);
if (stream == 0)
cudaSafeCall(cudaThreadSynchronize());
}
void bitwise_or_caller(int rows, int cols, int elem_size1, int cn, const PtrStep src1, const PtrStep src2, PtrStep dst, cudaStream_t stream)
{
bitwise_bin_op<BIN_OP_OR>(rows, cols * elem_size1 * cn, src1, src2, dst, stream);
}
template <typename T>
void bitwise_mask_or_caller(int rows, int cols, int cn, const PtrStep src1, const PtrStep src2, const PtrStep mask, PtrStep dst, cudaStream_t stream)
{
bitwise_bin_op<T, BIN_OP_OR>(rows, cols * cn, cn, src1, src2, mask, dst, stream);
}
template void bitwise_mask_or_caller<uchar>(int, int, int, const PtrStep, const PtrStep, const PtrStep, PtrStep, cudaStream_t);
template void bitwise_mask_or_caller<ushort>(int, int, int, const PtrStep, const PtrStep, const PtrStep, PtrStep, cudaStream_t);
template void bitwise_mask_or_caller<uint>(int, int, int, const PtrStep, const PtrStep, const PtrStep, PtrStep, cudaStream_t);
void bitwise_and_caller(int rows, int cols, int elem_size1, int cn, const PtrStep src1, const PtrStep src2, PtrStep dst, cudaStream_t stream)
{
bitwise_bin_op<BIN_OP_AND>(rows, cols * elem_size1 * cn, src1, src2, dst, stream);
}
template <typename T>
void bitwise_mask_and_caller(int rows, int cols, int cn, const PtrStep src1, const PtrStep src2, const PtrStep mask, PtrStep dst, cudaStream_t stream)
{
bitwise_bin_op<T, BIN_OP_AND>(rows, cols * cn, cn, src1, src2, mask, dst, stream);
}
template void bitwise_mask_and_caller<uchar>(int, int, int, const PtrStep, const PtrStep, const PtrStep, PtrStep, cudaStream_t);
template void bitwise_mask_and_caller<ushort>(int, int, int, const PtrStep, const PtrStep, const PtrStep, PtrStep, cudaStream_t);
template void bitwise_mask_and_caller<uint>(int, int, int, const PtrStep, const PtrStep, const PtrStep, PtrStep, cudaStream_t);
void bitwise_xor_caller(int rows, int cols, int elem_size1, int cn, const PtrStep src1, const PtrStep src2, PtrStep dst, cudaStream_t stream)
{
bitwise_bin_op<BIN_OP_XOR>(rows, cols * elem_size1 * cn, src1, src2, dst, stream);
}
template <typename T>
void bitwise_mask_xor_caller(int rows, int cols, int cn, const PtrStep src1, const PtrStep src2, const PtrStep mask, PtrStep dst, cudaStream_t stream)
{
bitwise_bin_op<T, BIN_OP_XOR>(rows, cols * cn, cn, src1, src2, mask, dst, stream);
}
template void bitwise_mask_xor_caller<uchar>(int, int, int, const PtrStep, const PtrStep, const PtrStep, PtrStep, cudaStream_t);
template void bitwise_mask_xor_caller<ushort>(int, int, int, const PtrStep, const PtrStep, const PtrStep, PtrStep, cudaStream_t);
template void bitwise_mask_xor_caller<uint>(int, int, int, const PtrStep, const PtrStep, const PtrStep, PtrStep, cudaStream_t);
//////////////////////////////////////////////////////////////////////////////
// Min max

605
modules/gpu/src/element_operations.cpp
View File

@ -1,16 +1,609 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other GpuMaterials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or bpied warranties, including, but not limited to, the bpied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
using namespace cv;
using namespace cv::gpu;
#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::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::bitwise_not(const GpuMat&, GpuMat&, const GpuMat&) { throw_nogpu(); }
void cv::gpu::bitwise_not(const GpuMat&, GpuMat&, const GpuMat&, const Stream&) { throw_nogpu(); }
void cv::gpu::bitwise_or(const GpuMat&, const GpuMat&, GpuMat&, const GpuMat&) { throw_nogpu(); }
void cv::gpu::bitwise_or(const GpuMat&, const GpuMat&, GpuMat&, const GpuMat&, const Stream&) { throw_nogpu(); }
void cv::gpu::bitwise_and(const GpuMat&, const GpuMat&, GpuMat&, const GpuMat&) { throw_nogpu(); }
void cv::gpu::bitwise_and(const GpuMat&, const GpuMat&, GpuMat&, const GpuMat&, const Stream&) { throw_nogpu(); }
void cv::gpu::bitwise_xor(const GpuMat&, const GpuMat&, GpuMat&, const GpuMat&) { throw_nogpu(); }
void cv::gpu::bitwise_xor(const GpuMat&, const GpuMat&, GpuMat&, const GpuMat&, const Stream&) { throw_nogpu(); }
cv::gpu::GpuMat cv::gpu::operator ~ (const GpuMat&) { throw_nogpu(); return GpuMat(); }
cv::gpu::GpuMat cv::gpu::operator | (const GpuMat&, const GpuMat&) { throw_nogpu(); return GpuMat(); }
cv::gpu::GpuMat cv::gpu::operator & (const GpuMat&, const GpuMat&) { throw_nogpu(); return GpuMat(); }
cv::gpu::GpuMat cv::gpu::operator ^ (const GpuMat&, const GpuMat&) { throw_nogpu(); return GpuMat(); }
#else
////////////////////////////////////////////////////////////////////////
// Basic arithmetical operations (add subtract multiply divide)
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);
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_32SC1 || src1.type() == CV_32FC1);
dst.create( src1.size(), src1.type() );
NppiSize sz;
sz.width = src1.cols;
sz.height = src1.rows;
switch (src1.type())
{
case CV_8UC1:
nppSafeCall( npp_func_8uc1(src1.ptr<Npp8u>(), src1.step,
src2.ptr<Npp8u>(), src2.step,
dst.ptr<Npp8u>(), dst.step, sz, 0) );
break;
case CV_8UC4:
nppSafeCall( npp_func_8uc4(src1.ptr<Npp8u>(), src1.step,
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,
dst.ptr<Npp32f>(), dst.step, sz) );
break;
default:
CV_Assert(!"Unsupported source type");
}
}
template<int SCN> struct NppArithmScalarFunc;
template<> struct NppArithmScalarFunc<1>
{
typedef NppStatus (*func_ptr)(const Npp32f *pSrc, int nSrcStep, Npp32f nValue, Npp32f *pDst,
int nDstStep, NppiSize oSizeROI);
};
template<> struct NppArithmScalarFunc<2>
{
typedef NppStatus (*func_ptr)(const Npp32fc *pSrc, int nSrcStep, Npp32fc nValue, Npp32fc *pDst,
int nDstStep, NppiSize oSizeROI);
};
template<int SCN, typename NppArithmScalarFunc<SCN>::func_ptr func> struct NppArithmScalar;
template<typename NppArithmScalarFunc<1>::func_ptr func> struct NppArithmScalar<1, func>
{
static void calc(const GpuMat& src, const Scalar& sc, GpuMat& dst)
{
dst.create(src.size(), src.type());
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
nppSafeCall( func(src.ptr<Npp32f>(), src.step, (Npp32f)sc[0], dst.ptr<Npp32f>(), dst.step, sz) );
}
};
template<typename NppArithmScalarFunc<2>::func_ptr func> struct NppArithmScalar<2, func>
{
static void calc(const GpuMat& src, const Scalar& sc, GpuMat& dst)
{
dst.create(src.size(), src.type());
NppiSize sz;
sz.width = src.cols;
sz.height = src.rows;
Npp32fc nValue;
nValue.re = (Npp32f)sc[0];
nValue.im = (Npp32f)sc[1];
nppSafeCall( func(src.ptr<Npp32fc>(), src.step, nValue, dst.ptr<Npp32fc>(), dst.step, sz) );
}
};
}
void cv::gpu::add(const GpuMat& src1, const GpuMat& src2, GpuMat& dst)
{
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)
{
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)
{
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)
{
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)
{
typedef void (*caller_t)(const GpuMat& src, const Scalar& sc, GpuMat& dst);
static const caller_t callers[] = {0, NppArithmScalar<1, nppiAddC_32f_C1R>::calc, NppArithmScalar<2, nppiAddC_32fc_C1R>::calc};
CV_Assert(src.type() == CV_32FC1 || src.type() == CV_32FC2);
callers[src.channels()](src, sc, dst);
}
void cv::gpu::subtract(const GpuMat& src, const Scalar& sc, GpuMat& dst)
{
typedef void (*caller_t)(const GpuMat& src, const Scalar& sc, GpuMat& dst);
static const caller_t callers[] = {0, NppArithmScalar<1, nppiSubC_32f_C1R>::calc, NppArithmScalar<2, nppiSubC_32fc_C1R>::calc};
CV_Assert(src.type() == CV_32FC1 || src.type() == CV_32FC2);
callers[src.channels()](src, sc, dst);
}
void cv::gpu::multiply(const GpuMat& src, const Scalar& sc, GpuMat& dst)
{
typedef void (*caller_t)(const GpuMat& src, const Scalar& sc, GpuMat& dst);
static const caller_t callers[] = {0, NppArithmScalar<1, nppiMulC_32f_C1R>::calc, NppArithmScalar<2, nppiMulC_32fc_C1R>::calc};
CV_Assert(src.type() == CV_32FC1 || src.type() == CV_32FC2);
callers[src.channels()](src, sc, dst);
}
void cv::gpu::divide(const GpuMat& src, const Scalar& sc, GpuMat& dst)
{
typedef void (*caller_t)(const GpuMat& src, const Scalar& sc, GpuMat& dst);
static const caller_t callers[] = {0, NppArithmScalar<1, nppiDivC_32f_C1R>::calc, NppArithmScalar<2, nppiDivC_32fc_C1R>::calc};
CV_Assert(src.type() == CV_32FC1 || src.type() == CV_32FC2);
callers[src.channels()](src, sc, dst);
}
//////////////////////////////////////////////////////////////////////////////
// Absolute difference
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_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;
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) );
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]) );
}
//////////////////////////////////////////////////////////////////////////////
// Comparison of two matrixes
namespace cv { namespace gpu { namespace mathfunc
{
void compare_ne_8uc4(const DevMem2D& src1, const DevMem2D& src2, const DevMem2D& dst);
void compare_ne_32f(const DevMem2D& src1, const DevMem2D& src2, const DevMem2D& dst);
}}}
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);
}
}
}
//////////////////////////////////////////////////////////////////////////////////////////////////
////////////////////////// Unary per-element operations /////////////////////////////////////////
// operation(GpuMat src, GpuMat dst)
//////////////////////////////////////////////////////////////////////////////
// Unary bitwise logical operations
namespace cv { namespace gpu { namespace mathfunc
{
void bitwiseNotCaller(int rows, int cols, int elem_size1, int cn, const PtrStep src, PtrStep dst, cudaStream_t stream);
template <typename T>
void bitwiseMaskNotCaller(int rows, int cols, int cn, const PtrStep src, const PtrStep mask, PtrStep dst, cudaStream_t stream);
}}}
namespace
{
void bitwiseNotCaller(const GpuMat& src, GpuMat& dst, cudaStream_t stream)
{
dst.create(src.size(), src.type());
cv::gpu::mathfunc::bitwiseNotCaller(src.rows, src.cols, src.elemSize1(),
dst.channels(), src, dst, stream);
}
void bitwiseNotCaller(const GpuMat& src, GpuMat& dst, const GpuMat& mask, cudaStream_t stream)
{
using namespace cv::gpu;
typedef void (*Caller)(int, int, int, const PtrStep, const PtrStep, PtrStep, cudaStream_t);
static Caller callers[] = {mathfunc::bitwiseMaskNotCaller<unsigned char>, mathfunc::bitwiseMaskNotCaller<unsigned char>,
mathfunc::bitwiseMaskNotCaller<unsigned short>, mathfunc::bitwiseMaskNotCaller<unsigned short>,
mathfunc::bitwiseMaskNotCaller<unsigned int>, mathfunc::bitwiseMaskNotCaller<unsigned int>,
mathfunc::bitwiseMaskNotCaller<unsigned int>};
CV_Assert(mask.type() == CV_8U && mask.size() == src.size());
dst.create(src.size(), src.type());
Caller caller = callers[src.depth()];
CV_Assert(caller);
int cn = src.depth() != CV_64F ? src.channels() : src.channels() * (sizeof(double) / sizeof(unsigned int));
caller(src.rows, src.cols, cn, src, mask, dst, stream);
}
}
//////////////////////////////////////////////////////////////////////////////////////////////////
////////////////////////// Binary per-element operations ////////////////////////////////////////
// operation(GpuMat src1, GpuMat src2, GpuMat dst)
void cv::gpu::bitwise_not(const GpuMat& src, GpuMat& dst, const GpuMat& mask)
{
if (mask.empty())
::bitwiseNotCaller(src, dst, 0);
else
::bitwiseNotCaller(src, dst, mask, 0);
}
void cv::gpu::bitwise_not(const GpuMat& src, GpuMat& dst, const GpuMat& mask, const Stream& stream)
{
if (mask.empty())
::bitwiseNotCaller(src, dst, StreamAccessor::getStream(stream));
else
::bitwiseNotCaller(src, dst, mask, StreamAccessor::getStream(stream));
}
cv::gpu::GpuMat cv::gpu::operator ~ (const GpuMat& src)
{
GpuMat dst;
bitwise_not(src, dst);
return dst;
}
//////////////////////////////////////////////////////////////////////////////
// Binary bitwise logical operations
namespace cv { namespace gpu { namespace mathfunc
{
void bitwiseOrCaller(int rows, int cols, int elem_size1, int cn, const PtrStep src1, const PtrStep src2, PtrStep dst, cudaStream_t stream);
template <typename T>
void bitwiseMaskOrCaller(int rows, int cols, int cn, const PtrStep src1, const PtrStep src2, const PtrStep mask, PtrStep dst, cudaStream_t stream);
void bitwiseAndCaller(int rows, int cols, int elem_size1, int cn, const PtrStep src1, const PtrStep src2, PtrStep dst, cudaStream_t stream);
template <typename T>
void bitwiseMaskAndCaller(int rows, int cols, int cn, const PtrStep src1, const PtrStep src2, const PtrStep mask, PtrStep dst, cudaStream_t stream);
void bitwiseXorCaller(int rows, int cols, int elem_size1, int cn, const PtrStep src1, const PtrStep src2, PtrStep dst, cudaStream_t stream);
template <typename T>
void bitwiseMaskXorCaller(int rows, int cols, int cn, const PtrStep src1, const PtrStep src2, const PtrStep mask, PtrStep dst, cudaStream_t stream);
}}}
namespace
{
void bitwiseOrCaller(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, cudaStream_t stream)
{
CV_Assert(src1.size() == src2.size() && src1.type() == src2.type());
dst.create(src1.size(), src1.type());
cv::gpu::mathfunc::bitwiseOrCaller(dst.rows, dst.cols, dst.elemSize1(),
dst.channels(), src1, src2, dst, stream);
}
void bitwiseOrCaller(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, cudaStream_t stream)
{
using namespace cv::gpu;
typedef void (*Caller)(int, int, int, const PtrStep, const PtrStep, const PtrStep, PtrStep, cudaStream_t);
static Caller callers[] = {mathfunc::bitwiseMaskOrCaller<unsigned char>, mathfunc::bitwiseMaskOrCaller<unsigned char>,
mathfunc::bitwiseMaskOrCaller<unsigned short>, mathfunc::bitwiseMaskOrCaller<unsigned short>,
mathfunc::bitwiseMaskOrCaller<unsigned int>, mathfunc::bitwiseMaskOrCaller<unsigned int>,
mathfunc::bitwiseMaskOrCaller<unsigned int>};
CV_Assert(src1.size() == src2.size() && src1.type() == src2.type());
dst.create(src1.size(), src1.type());
Caller caller = callers[src1.depth()];
CV_Assert(caller);
int cn = dst.depth() != CV_64F ? dst.channels() : dst.channels() * (sizeof(double) / sizeof(unsigned int));
caller(dst.rows, dst.cols, cn, src1, src2, mask, dst, stream);
}
void bitwiseAndCaller(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, cudaStream_t stream)
{
CV_Assert(src1.size() == src2.size() && src1.type() == src2.type());
dst.create(src1.size(), src1.type());
cv::gpu::mathfunc::bitwiseAndCaller(dst.rows, dst.cols, dst.elemSize1(),
dst.channels(), src1, src2, dst, stream);
}
void bitwiseAndCaller(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, cudaStream_t stream)
{
using namespace cv::gpu;
typedef void (*Caller)(int, int, int, const PtrStep, const PtrStep, const PtrStep, PtrStep, cudaStream_t);
static Caller callers[] = {mathfunc::bitwiseMaskAndCaller<unsigned char>, mathfunc::bitwiseMaskAndCaller<unsigned char>,
mathfunc::bitwiseMaskAndCaller<unsigned short>, mathfunc::bitwiseMaskAndCaller<unsigned short>,
mathfunc::bitwiseMaskAndCaller<unsigned int>, mathfunc::bitwiseMaskAndCaller<unsigned int>,
mathfunc::bitwiseMaskAndCaller<unsigned int>};
CV_Assert(src1.size() == src2.size() && src1.type() == src2.type());
dst.create(src1.size(), src1.type());
Caller caller = callers[src1.depth()];
CV_Assert(caller);
int cn = dst.depth() != CV_64F ? dst.channels() : dst.channels() * (sizeof(double) / sizeof(unsigned int));
caller(dst.rows, dst.cols, cn, src1, src2, mask, dst, stream);
}
void bitwiseXorCaller(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, cudaStream_t stream)
{
CV_Assert(src1.size() == src2.size() && src1.type() == src2.type());
dst.create(src1.size(), src1.type());
cv::gpu::mathfunc::bitwiseXorCaller(dst.rows, dst.cols, dst.elemSize1(),
dst.channels(), src1, src2, dst, stream);
}
void bitwiseXorCaller(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, cudaStream_t stream)
{
using namespace cv::gpu;
typedef void (*Caller)(int, int, int, const PtrStep, const PtrStep, const PtrStep, PtrStep, cudaStream_t);
static Caller callers[] = {mathfunc::bitwiseMaskXorCaller<unsigned char>, mathfunc::bitwiseMaskXorCaller<unsigned char>,
mathfunc::bitwiseMaskXorCaller<unsigned short>, mathfunc::bitwiseMaskXorCaller<unsigned short>,
mathfunc::bitwiseMaskXorCaller<unsigned int>, mathfunc::bitwiseMaskXorCaller<unsigned int>,
mathfunc::bitwiseMaskXorCaller<unsigned int>};
CV_Assert(src1.size() == src2.size() && src1.type() == src2.type());
dst.create(src1.size(), src1.type());
Caller caller = callers[src1.depth()];
CV_Assert(caller);
int cn = dst.depth() != CV_64F ? dst.channels() : dst.channels() * (sizeof(double) / sizeof(unsigned int));
caller(dst.rows, dst.cols, cn, src1, src2, mask, dst, stream);
}
}
void cv::gpu::bitwise_or(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask)
{
if (mask.empty())
::bitwiseOrCaller(src1, src2, dst, 0);
else
::bitwiseOrCaller(src1, src2, dst, mask, 0);
}
void cv::gpu::bitwise_or(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, const Stream& stream)
{
if (mask.empty())
::bitwiseOrCaller(src1, src2, dst, StreamAccessor::getStream(stream));
else
::bitwiseOrCaller(src1, src2, dst, mask, StreamAccessor::getStream(stream));
}
void cv::gpu::bitwise_and(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask)
{
if (mask.empty())
::bitwiseAndCaller(src1, src2, dst, 0);
else
::bitwiseAndCaller(src1, src2, dst, mask, 0);
}
void cv::gpu::bitwise_and(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, const Stream& stream)
{
if (mask.empty())
::bitwiseAndCaller(src1, src2, dst, StreamAccessor::getStream(stream));
else
::bitwiseAndCaller(src1, src2, dst, mask, StreamAccessor::getStream(stream));
}
void cv::gpu::bitwise_xor(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask)
{
if (mask.empty())
::bitwiseXorCaller(src1, src2, dst, 0);
else
::bitwiseXorCaller(src1, src2, dst, mask, 0);
}
void cv::gpu::bitwise_xor(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, const Stream& stream)
{
if (mask.empty())
::bitwiseXorCaller(src1, src2, dst, StreamAccessor::getStream(stream));
else
::bitwiseXorCaller(src1, src2, dst, mask, StreamAccessor::getStream(stream));
}
cv::gpu::GpuMat cv::gpu::operator | (const GpuMat& src1, const GpuMat& src2)
{
GpuMat dst;
bitwise_or(src1, src2, dst);
return dst;
}
cv::gpu::GpuMat cv::gpu::operator & (const GpuMat& src1, const GpuMat& src2)
{
GpuMat dst;
bitwise_and(src1, src2, dst);
return dst;
}
cv::gpu::GpuMat cv::gpu::operator ^ (const GpuMat& src1, const GpuMat& src2)
{
GpuMat dst;
bitwise_xor(src1, src2, dst);
return dst;
}
#endif

2
tests/gpu/src/bitwise_oper.cpp
View File

@ -53,7 +53,7 @@ using namespace std;
struct CV_GpuBitwiseTest: public CvTest
{
CV_GpuBitwiseTest(): CvTest("GPU-BitwiseOpers", "bitwiseMatOperators") {}
CV_GpuBitwiseTest(): CvTest("GPU-BitwiseOpersTest", "bitwiseMatOperators") {}
void run(int)
{