Chiebot-Mirror
/
opencv
8
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

switched to Input/Output Array in bitwise logical operations

Browse Source
pull/978/head
Vladislav Vinogradov 12 years ago
parent 04a1a6dd8f
commit d81f54db0b
2 changed files with 91 additions and 197 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 33
      modules/gpuarithm/include/opencv2/gpuarithm.hpp
  2. 255
      modules/gpuarithm/src/element_operations.cpp

33
modules/gpuarithm/include/opencv2/gpuarithm.hpp
Unescape View File

@ -95,6 +95,18 @@ CV_EXPORTS void pow(InputArray src, double power, OutputArray dst, Stream& strea
//! compares elements of two arrays (dst = src1 <cmpop> src2)
CV_EXPORTS void compare(InputArray src1, InputArray src2, OutputArray dst, int cmpop, Stream& stream = Stream::Null());
//! performs per-elements bit-wise inversion
CV_EXPORTS void bitwise_not(InputArray src, OutputArray dst, InputArray mask = noArray(), Stream& stream = Stream::Null());
//! calculates per-element bit-wise disjunction of two arrays
CV_EXPORTS void bitwise_or(InputArray src1, InputArray src2, OutputArray dst, InputArray mask = noArray(), Stream& stream = Stream::Null());
//! calculates per-element bit-wise conjunction of two arrays
CV_EXPORTS void bitwise_and(InputArray src1, InputArray src2, OutputArray dst, InputArray mask = noArray(), Stream& stream = Stream::Null());
//! calculates per-element bit-wise "exclusive or" operation
CV_EXPORTS void bitwise_xor(InputArray src1, InputArray src2, OutputArray dst, InputArray mask = noArray(), Stream& stream = Stream::Null());
//! computes the weighted sum of two arrays (dst = alpha*src1 + beta*src2 + gamma)
CV_EXPORTS void addWeighted(const GpuMat& src1, double alpha, const GpuMat& src2, double beta, double gamma, GpuMat& dst,
int dtype = -1, Stream& stream = Stream::Null());
@ -105,27 +117,6 @@ static inline void scaleAdd(const GpuMat& src1, double alpha, const GpuMat& src2
addWeighted(src1, alpha, src2, 1.0, 0.0, dst, -1, stream);
}
//! performs per-elements bit-wise inversion
CV_EXPORTS void bitwise_not(const GpuMat& src, GpuMat& dst, const GpuMat& mask=GpuMat(), Stream& stream = Stream::Null());
//! 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(), Stream& stream = Stream::Null());
//! calculates per-element bit-wise disjunction of array and scalar
//! supports 1, 3 and 4 channels images with CV_8U, CV_16U or CV_32S depth
CV_EXPORTS void bitwise_or(const GpuMat& src1, const Scalar& sc, GpuMat& dst, Stream& stream = Stream::Null());
//! 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(), Stream& stream = Stream::Null());
//! calculates per-element bit-wise conjunction of array and scalar
//! supports 1, 3 and 4 channels images with CV_8U, CV_16U or CV_32S depth
CV_EXPORTS void bitwise_and(const GpuMat& src1, const Scalar& sc, GpuMat& dst, Stream& stream = Stream::Null());
//! 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(), Stream& stream = Stream::Null());
//! calculates per-element bit-wise "exclusive or" of array and scalar
//! supports 1, 3 and 4 channels images with CV_8U, CV_16U or CV_32S depth
CV_EXPORTS void bitwise_xor(const GpuMat& src1, const Scalar& sc, GpuMat& dst, Stream& stream = Stream::Null());
//! pixel by pixel right shift of an image by a constant value
//! supports 1, 3 and 4 channels images with integers elements
CV_EXPORTS void rshift(const GpuMat& src, Scalar_<int> sc, GpuMat& dst, Stream& stream = Stream::Null());

255
modules/gpuarithm/src/element_operations.cpp
Unescape View File

@ -71,16 +71,13 @@ void cv::gpu::pow(InputArray, double, OutputArray, Stream&) { throw_no_cuda(); }
void cv::gpu::compare(InputArray, InputArray, OutputArray, int, Stream&) { throw_no_cuda(); }
void cv::gpu::bitwise_not(const GpuMat&, GpuMat&, const GpuMat&, Stream&) { throw_no_cuda(); }
void cv::gpu::bitwise_not(InputArray, OutputArray, InputArray, Stream&) { throw_no_cuda(); }
void cv::gpu::bitwise_or(const GpuMat&, const GpuMat&, GpuMat&, const GpuMat&, Stream&) { throw_no_cuda(); }
void cv::gpu::bitwise_or(const GpuMat&, const Scalar&, GpuMat&, Stream&) { throw_no_cuda(); }
void cv::gpu::bitwise_or(InputArray, InputArray, OutputArray, InputArray, Stream&) { throw_no_cuda(); }
void cv::gpu::bitwise_and(const GpuMat&, const GpuMat&, GpuMat&, const GpuMat&, Stream&) { throw_no_cuda(); }
void cv::gpu::bitwise_and(const GpuMat&, const Scalar&, GpuMat&, Stream&) { throw_no_cuda(); }
void cv::gpu::bitwise_and(InputArray, InputArray, OutputArray, InputArray, Stream&) { throw_no_cuda(); }
void cv::gpu::bitwise_xor(const GpuMat&, const GpuMat&, GpuMat&, const GpuMat&, Stream&) { throw_no_cuda(); }
void cv::gpu::bitwise_xor(const GpuMat&, const Scalar&, GpuMat&, Stream&) { throw_no_cuda(); }
void cv::gpu::bitwise_xor(InputArray, InputArray, OutputArray, InputArray, Stream&) { throw_no_cuda(); }
void cv::gpu::rshift(const GpuMat&, Scalar_<int>, GpuMat&, Stream&) { throw_no_cuda(); }
@ -1889,25 +1886,29 @@ void cv::gpu::compare(InputArray src1, InputArray src2, OutputArray dst, int cmp
}
//////////////////////////////////////////////////////////////////////////////
// Unary bitwise logical operations
// bitwise_not
namespace arithm
{
template <typename T> void bitMatNot(PtrStepSzb src, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
}
void cv::gpu::bitwise_not(const GpuMat& src, GpuMat& dst, const GpuMat& mask, Stream& s)
void cv::gpu::bitwise_not(InputArray _src, OutputArray _dst, InputArray _mask, Stream& _stream)
{
using namespace arithm;
GpuMat src = _src.getGpuMat();
GpuMat mask = _mask.getGpuMat();
const int depth = src.depth();
CV_Assert( depth <= CV_64F );
CV_Assert( mask.empty() || (mask.type() == CV_8UC1 && mask.size() == src.size()) );
dst.create(src.size(), src.type());
_dst.create(src.size(), src.type());
GpuMat dst = _dst.getGpuMat();
cudaStream_t stream = StreamAccessor::getStream(s);
cudaStream_t stream = StreamAccessor::getStream(_stream);
const int bcols = (int) (src.cols * src.elemSize());
@ -1941,6 +1942,16 @@ void cv::gpu::bitwise_not(const GpuMat& src, GpuMat& dst, const GpuMat& mask, St
//////////////////////////////////////////////////////////////////////////////
// Binary bitwise logical operations
namespace
{
enum
{
BIT_OP_AND,
BIT_OP_OR,
BIT_OP_XOR
};
}
namespace arithm
{
template <typename T> void bitMatAnd(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
@ -1948,113 +1959,31 @@ namespace arithm
template <typename T> void bitMatXor(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
}
void cv::gpu::bitwise_and(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, Stream& s)
{
using namespace arithm;
const int depth = src1.depth();
CV_Assert( depth <= CV_64F );
CV_Assert( src2.size() == src1.size() && src2.type() == src1.type() );
CV_Assert( mask.empty() || (mask.type() == CV_8UC1 && mask.size() == src1.size()) );
dst.create(src1.size(), src1.type());
cudaStream_t stream = StreamAccessor::getStream(s);
const int bcols = (int) (src1.cols * src1.elemSize());
if ((bcols & 3) == 0)
{
const int vcols = bcols >> 2;
bitMatAnd<unsigned int>(
PtrStepSzb(src1.rows, vcols, src1.data, src1.step),
PtrStepSzb(src1.rows, vcols, src2.data, src2.step),
PtrStepSzb(src1.rows, vcols, dst.data, dst.step),
mask, stream);
}
else if ((bcols & 1) == 0)
{
const int vcols = bcols >> 1;
bitMatAnd<unsigned int>(
PtrStepSzb(src1.rows, vcols, src1.data, src1.step),
PtrStepSzb(src1.rows, vcols, src2.data, src2.step),
PtrStepSzb(src1.rows, vcols, dst.data, dst.step),
mask, stream);
}
else
{
bitMatAnd<unsigned int>(
PtrStepSzb(src1.rows, bcols, src1.data, src1.step),
PtrStepSzb(src1.rows, bcols, src2.data, src2.step),
PtrStepSzb(src1.rows, bcols, dst.data, dst.step),
mask, stream);
}
}
void cv::gpu::bitwise_or(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, Stream& s)
static void bitMat(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, double, Stream& _stream, int op)
{
using namespace arithm;
const int depth = src1.depth();
CV_Assert( depth <= CV_64F );
CV_Assert( src2.size() == src1.size() && src2.type() == src1.type() );
CV_Assert( mask.empty() || (mask.type() == CV_8UC1 && mask.size() == src1.size()) );
dst.create(src1.size(), src1.type());
cudaStream_t stream = StreamAccessor::getStream(s);
const int bcols = (int) (src1.cols * src1.elemSize());
if ((bcols & 3) == 0)
typedef void (*func_t)(PtrStepSzb src1, PtrStepSzb src2, PtrStepSzb dst, PtrStepb mask, cudaStream_t stream);
static const func_t funcs32[] =
{
const int vcols = bcols >> 2;
bitMatOr<unsigned int>(
PtrStepSzb(src1.rows, vcols, src1.data, src1.step),
PtrStepSzb(src1.rows, vcols, src2.data, src2.step),
PtrStepSzb(src1.rows, vcols, dst.data, dst.step),
mask, stream);
}
else if ((bcols & 1) == 0)
bitMatAnd<uint>,
bitMatOr<uint>,
bitMatXor<uint>
};
static const func_t funcs16[] =
{
const int vcols = bcols >> 1;
bitMatOr<unsigned int>(
PtrStepSzb(src1.rows, vcols, src1.data, src1.step),
PtrStepSzb(src1.rows, vcols, src2.data, src2.step),
PtrStepSzb(src1.rows, vcols, dst.data, dst.step),
mask, stream);
}
else
bitMatAnd<ushort>,
bitMatOr<ushort>,
bitMatXor<ushort>
};
static const func_t funcs8[] =
{
bitMatAnd<uchar>,
bitMatOr<uchar>,
bitMatXor<uchar>
};
bitMatOr<unsigned int>(
PtrStepSzb(src1.rows, bcols, src1.data, src1.step),
PtrStepSzb(src1.rows, bcols, src2.data, src2.step),
PtrStepSzb(src1.rows, bcols, dst.data, dst.step),
mask, stream);
}
}
void cv::gpu::bitwise_xor(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, const GpuMat& mask, Stream& s)
{
using namespace arithm;
const int depth = src1.depth();
CV_Assert( depth <= CV_64F );
CV_Assert( src2.size() == src1.size() && src2.type() == src1.type() );
CV_Assert( mask.empty() || (mask.type() == CV_8UC1 && mask.size() == src1.size()) );
dst.create(src1.size(), src1.type());
cudaStream_t stream = StreamAccessor::getStream(s);
cudaStream_t stream = StreamAccessor::getStream(_stream);
const int bcols = (int) (src1.cols * src1.elemSize());
@ -2062,8 +1991,7 @@ void cv::gpu::bitwise_xor(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, c
{
const int vcols = bcols >> 2;
bitMatXor<unsigned int>(
PtrStepSzb(src1.rows, vcols, src1.data, src1.step),
funcs32[op](PtrStepSzb(src1.rows, vcols, src1.data, src1.step),
PtrStepSzb(src1.rows, vcols, src2.data, src2.step),
PtrStepSzb(src1.rows, vcols, dst.data, dst.step),
mask, stream);
@ -2072,8 +2000,7 @@ void cv::gpu::bitwise_xor(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, c
{
const int vcols = bcols >> 1;
bitMatXor<unsigned int>(
PtrStepSzb(src1.rows, vcols, src1.data, src1.step),
funcs16[op](PtrStepSzb(src1.rows, vcols, src1.data, src1.step),
PtrStepSzb(src1.rows, vcols, src2.data, src2.step),
PtrStepSzb(src1.rows, vcols, dst.data, dst.step),
mask, stream);
@ -2081,17 +2008,13 @@ void cv::gpu::bitwise_xor(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, c
else
{
bitMatXor<unsigned int>(
PtrStepSzb(src1.rows, bcols, src1.data, src1.step),
PtrStepSzb(src1.rows, bcols, src2.data, src2.step),
PtrStepSzb(src1.rows, bcols, dst.data, dst.step),
mask, stream);
funcs8[op](PtrStepSzb(src1.rows, bcols, src1.data, src1.step),
PtrStepSzb(src1.rows, bcols, src2.data, src2.step),
PtrStepSzb(src1.rows, bcols, dst.data, dst.step),
mask, stream);
}
}
//////////////////////////////////////////////////////////////////////////////
// Binary bitwise logical operations with scalars
namespace arithm
{
template <typename T> void bitScalarAnd(PtrStepSzb src1, unsigned int src2, PtrStepSzb dst, cudaStream_t stream);
@ -2179,18 +2102,34 @@ namespace
};
}
void cv::gpu::bitwise_and(const GpuMat& src, const Scalar& sc, GpuMat& dst, Stream& stream)
static void bitScalar(const GpuMat& src, Scalar val, bool, GpuMat& dst, const GpuMat& mask, double, Stream& stream, int op)
{
using namespace arithm;
typedef void (*func_t)(const GpuMat& src, Scalar sc, GpuMat& dst, cudaStream_t stream);
static const func_t funcs[5][4] =
static const func_t funcs[3][5][4] =
{
{BitScalar<unsigned char, bitScalarAnd<unsigned char> >::call , 0, NppBitwiseC<CV_8U , 3, nppiAndC_8u_C3R >::call, BitScalar4< bitScalarAnd<unsigned int> >::call},
{0,0,0,0},
{BitScalar<unsigned short, bitScalarAnd<unsigned short> >::call, 0, NppBitwiseC<CV_16U, 3, nppiAndC_16u_C3R>::call, NppBitwiseC<CV_16U, 4, nppiAndC_16u_C4R>::call},
{0,0,0,0},
{BitScalar<int, bitScalarAnd<int> >::call , 0, NppBitwiseC<CV_32S, 3, nppiAndC_32s_C3R>::call, NppBitwiseC<CV_32S, 4, nppiAndC_32s_C4R>::call}
{
{BitScalar<unsigned char, bitScalarAnd<unsigned char> >::call , 0, NppBitwiseC<CV_8U , 3, nppiAndC_8u_C3R >::call, BitScalar4< bitScalarAnd<unsigned int> >::call},
{0,0,0,0},
{BitScalar<unsigned short, bitScalarAnd<unsigned short> >::call, 0, NppBitwiseC<CV_16U, 3, nppiAndC_16u_C3R>::call, NppBitwiseC<CV_16U, 4, nppiAndC_16u_C4R>::call},
{0,0,0,0},
{BitScalar<int, bitScalarAnd<int> >::call , 0, NppBitwiseC<CV_32S, 3, nppiAndC_32s_C3R>::call, NppBitwiseC<CV_32S, 4, nppiAndC_32s_C4R>::call}
},
{
{BitScalar<unsigned char, bitScalarOr<unsigned char> >::call , 0, NppBitwiseC<CV_8U , 3, nppiOrC_8u_C3R >::call, BitScalar4< bitScalarOr<unsigned int> >::call},
{0,0,0,0},
{BitScalar<unsigned short, bitScalarOr<unsigned short> >::call, 0, NppBitwiseC<CV_16U, 3, nppiOrC_16u_C3R>::call, NppBitwiseC<CV_16U, 4, nppiOrC_16u_C4R>::call},
{0,0,0,0},
{BitScalar<int, bitScalarOr<int> >::call , 0, NppBitwiseC<CV_32S, 3, nppiOrC_32s_C3R>::call, NppBitwiseC<CV_32S, 4, nppiOrC_32s_C4R>::call}
},
{
{BitScalar<unsigned char, bitScalarXor<unsigned char> >::call , 0, NppBitwiseC<CV_8U , 3, nppiXorC_8u_C3R >::call, BitScalar4< bitScalarXor<unsigned int> >::call},
{0,0,0,0},
{BitScalar<unsigned short, bitScalarXor<unsigned short> >::call, 0, NppBitwiseC<CV_16U, 3, nppiXorC_16u_C3R>::call, NppBitwiseC<CV_16U, 4, nppiXorC_16u_C4R>::call},
{0,0,0,0},
{BitScalar<int, bitScalarXor<int> >::call , 0, NppBitwiseC<CV_32S, 3, nppiXorC_32s_C3R>::call, NppBitwiseC<CV_32S, 4, nppiXorC_32s_C4R>::call}
}
};
const int depth = src.depth();
@ -2198,60 +2137,24 @@ void cv::gpu::bitwise_and(const GpuMat& src, const Scalar& sc, GpuMat& dst, Stre
CV_Assert( depth == CV_8U || depth == CV_16U || depth == CV_32S );
CV_Assert( cn == 1 || cn == 3 || cn == 4 );
CV_Assert( mask.empty() );
dst.create(src.size(), src.type());
funcs[depth][cn - 1](src, sc, dst, StreamAccessor::getStream(stream));
funcs[op][depth][cn - 1](src, val, dst, StreamAccessor::getStream(stream));
}
void cv::gpu::bitwise_or(const GpuMat& src, const Scalar& sc, GpuMat& dst, Stream& stream)
void cv::gpu::bitwise_or(InputArray src1, InputArray src2, OutputArray dst, InputArray mask, Stream& stream)
{
using namespace arithm;
typedef void (*func_t)(const GpuMat& src, Scalar sc, GpuMat& dst, cudaStream_t stream);
static const func_t funcs[5][4] =
{
{BitScalar<unsigned char, bitScalarOr<unsigned char> >::call , 0, NppBitwiseC<CV_8U , 3, nppiOrC_8u_C3R >::call, BitScalar4< bitScalarOr<unsigned int> >::call},
{0,0,0,0},
{BitScalar<unsigned short, bitScalarOr<unsigned short> >::call, 0, NppBitwiseC<CV_16U, 3, nppiOrC_16u_C3R>::call, NppBitwiseC<CV_16U, 4, nppiOrC_16u_C4R>::call},
{0,0,0,0},
{BitScalar<int, bitScalarOr<int> >::call , 0, NppBitwiseC<CV_32S, 3, nppiOrC_32s_C3R>::call, NppBitwiseC<CV_32S, 4, nppiOrC_32s_C4R>::call}
};
const int depth = src.depth();
const int cn = src.channels();
CV_Assert( depth == CV_8U || depth == CV_16U || depth == CV_32S );
CV_Assert( cn == 1 || cn == 3 || cn == 4 );
dst.create(src.size(), src.type());
funcs[depth][cn - 1](src, sc, dst, StreamAccessor::getStream(stream));
arithm_op(src1, src2, dst, mask, 1.0, -1, stream, bitMat, bitScalar, BIT_OP_OR);
}
void cv::gpu::bitwise_xor(const GpuMat& src, const Scalar& sc, GpuMat& dst, Stream& stream)
void cv::gpu::bitwise_and(InputArray src1, InputArray src2, OutputArray dst, InputArray mask, Stream& stream)
{
using namespace arithm;
typedef void (*func_t)(const GpuMat& src, Scalar sc, GpuMat& dst, cudaStream_t stream);
static const func_t funcs[5][4] =
{
{BitScalar<unsigned char, bitScalarXor<unsigned char> >::call , 0, NppBitwiseC<CV_8U , 3, nppiXorC_8u_C3R >::call, BitScalar4< bitScalarXor<unsigned int> >::call},
{0,0,0,0},
{BitScalar<unsigned short, bitScalarXor<unsigned short> >::call, 0, NppBitwiseC<CV_16U, 3, nppiXorC_16u_C3R>::call, NppBitwiseC<CV_16U, 4, nppiXorC_16u_C4R>::call},
{0,0,0,0},
{BitScalar<int, bitScalarXor<int> >::call , 0, NppBitwiseC<CV_32S, 3, nppiXorC_32s_C3R>::call, NppBitwiseC<CV_32S, 4, nppiXorC_32s_C4R>::call}
};
const int depth = src.depth();
const int cn = src.channels();
CV_Assert( depth == CV_8U || depth == CV_16U || depth == CV_32S );
CV_Assert( cn == 1 || cn == 3 || cn == 4 );
dst.create(src.size(), src.type());
arithm_op(src1, src2, dst, mask, 1.0, -1, stream, bitMat, bitScalar, BIT_OP_AND);
}
funcs[depth][cn - 1](src, sc, dst, StreamAccessor::getStream(stream));
void cv::gpu::bitwise_xor(InputArray src1, InputArray src2, OutputArray dst, InputArray mask, Stream& stream)
{
arithm_op(src1, src2, dst, mask, 1.0, -1, stream, bitMat, bitScalar, BIT_OP_XOR);
}
//////////////////////////////////////////////////////////////////////////////

Write Preview
Loading…
Cancel
Save