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fixed gpu core tests (added additional check for device's feature support)

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added assertion on double types for old devices
pull/13383/head
Vladislav Vinogradov 13 years ago
parent 98d7b10c16
commit 26691e00d4
6 changed files with 1014 additions and 500 deletions
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  1. 29
      modules/gpu/src/arithm.cpp
  2. 174
      modules/gpu/src/cuda/element_operations.cu
  3. 455
      modules/gpu/src/element_operations.cpp
  4. 294
      modules/gpu/src/matrix_reductions.cpp
  5. 6
      modules/gpu/src/precomp.hpp
  6. 556
      modules/gpu/test/test_core.cpp

29
modules/gpu/src/arithm.cpp
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@ -69,16 +69,7 @@ void cv::gpu::gemm(const GpuMat& src1, const GpuMat& src2, double alpha, const G
{
#ifndef HAVE_CUBLAS
OPENCV_GPU_UNUSED(src1);
OPENCV_GPU_UNUSED(src2);
OPENCV_GPU_UNUSED(alpha);
OPENCV_GPU_UNUSED(src3);
OPENCV_GPU_UNUSED(beta);
OPENCV_GPU_UNUSED(dst);
OPENCV_GPU_UNUSED(flags);
OPENCV_GPU_UNUSED(stream);
throw_nogpu();
CV_Error(CV_StsNotImplemented, "The library was build without CUBLAS");
#else
@ -87,6 +78,12 @@ void cv::gpu::gemm(const GpuMat& src1, const GpuMat& src2, double alpha, const G
CV_Assert(src1.type() == CV_32FC1 || src1.type() == CV_32FC2 || src1.type() == CV_64FC1 || src1.type() == CV_64FC2);
CV_Assert(src2.type() == src1.type() && (src3.empty() || src3.type() == src1.type()));
if (src1.depth() == CV_64F)
{
if (!TargetArchs::builtWith(NATIVE_DOUBLE) || !DeviceInfo().supports(NATIVE_DOUBLE))
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
}
bool tr1 = (flags & GEMM_1_T) != 0;
bool tr2 = (flags & GEMM_2_T) != 0;
bool tr3 = (flags & GEMM_3_T) != 0;
@ -230,6 +227,9 @@ void cv::gpu::transpose(const GpuMat& src, GpuMat& dst, Stream& s)
}
else // if (src.elemSize() == 8)
{
if (!TargetArchs::builtWith(NATIVE_DOUBLE) || !DeviceInfo().supports(NATIVE_DOUBLE))
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
NppStStreamHandler h(stream);
NcvSize32u sz;
@ -290,7 +290,6 @@ namespace
void cv::gpu::flip(const GpuMat& src, GpuMat& dst, int flipCode, Stream& stream)
{
typedef void (*func_t)(const GpuMat& src, GpuMat& dst, int flipCode, cudaStream_t stream);
static const func_t funcs[6][4] =
{
{NppMirror<CV_8U, nppiMirror_8u_C1R>::call, 0, NppMirror<CV_8U, nppiMirror_8u_C3R>::call, NppMirror<CV_8U, nppiMirror_8u_C4R>::call},
@ -403,12 +402,12 @@ namespace
void cv::gpu::magnitude(const GpuMat& src, GpuMat& dst, Stream& stream)
{
::npp_magnitude(src, dst, nppiMagnitude_32fc32f_C1R, StreamAccessor::getStream(stream));
npp_magnitude(src, dst, nppiMagnitude_32fc32f_C1R, StreamAccessor::getStream(stream));
}
void cv::gpu::magnitudeSqr(const GpuMat& src, GpuMat& dst, Stream& stream)
{
::npp_magnitude(src, dst, nppiMagnitudeSqr_32fc32f_C1R, StreamAccessor::getStream(stream));
npp_magnitude(src, dst, nppiMagnitudeSqr_32fc32f_C1R, StreamAccessor::getStream(stream));
}
////////////////////////////////////////////////////////////////////////
@ -429,7 +428,7 @@ namespace
{
using namespace ::cv::gpu::device::mathfunc;
CV_DbgAssert(x.size() == y.size() && x.type() == y.type());
CV_Assert(x.size() == y.size() && x.type() == y.type());
CV_Assert(x.depth() == CV_32F);
if (mag)
@ -449,7 +448,7 @@ namespace
{
using namespace ::cv::gpu::device::mathfunc;
CV_DbgAssert((mag.empty() || mag.size() == angle.size()) && mag.type() == angle.type());
CV_Assert((mag.empty() || mag.size() == angle.size()) && mag.type() == angle.type());
CV_Assert(mag.depth() == CV_32F);
x.create(mag.size(), mag.type());

174
modules/gpu/src/cuda/element_operations.cu
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@ -1096,18 +1096,18 @@ namespace cv { namespace gpu { namespace device
enum { smart_shift = 4 };
};
template <typename T> void absdiff_gpu(const DevMem2Db& src1, const DevMem2Db& src2, const DevMem2Db& dst, cudaStream_t stream)
template <typename T> void absdiff_gpu(const DevMem2Db src1, const DevMem2Db src2, DevMem2Db dst, cudaStream_t stream)
{
cv::gpu::device::transform((DevMem2D_<T>)src1, (DevMem2D_<T>)src2, (DevMem2D_<T>)dst, Absdiff<T>(), WithOutMask(), stream);
}
template void absdiff_gpu<uchar >(const DevMem2Db& src1, const DevMem2Db& src2, const DevMem2Db& dst, cudaStream_t stream);
template void absdiff_gpu<schar >(const DevMem2Db& src1, const DevMem2Db& src2, const DevMem2Db& dst, cudaStream_t stream);
template void absdiff_gpu<ushort>(const DevMem2Db& src1, const DevMem2Db& src2, const DevMem2Db& dst, cudaStream_t stream);
template void absdiff_gpu<short >(const DevMem2Db& src1, const DevMem2Db& src2, const DevMem2Db& dst, cudaStream_t stream);
template void absdiff_gpu<int >(const DevMem2Db& src1, const DevMem2Db& src2, const DevMem2Db& dst, cudaStream_t stream);
template void absdiff_gpu<float >(const DevMem2Db& src1, const DevMem2Db& src2, const DevMem2Db& dst, cudaStream_t stream);
template void absdiff_gpu<double>(const DevMem2Db& src1, const DevMem2Db& src2, const DevMem2Db& dst, cudaStream_t stream);
//template void absdiff_gpu<uchar >(const DevMem2Db src1, const DevMem2Db src2, DevMem2Db dst, cudaStream_t stream);
template void absdiff_gpu<schar >(const DevMem2Db src1, const DevMem2Db src2, DevMem2Db dst, cudaStream_t stream);
//template void absdiff_gpu<ushort>(const DevMem2Db src1, const DevMem2Db src2, DevMem2Db dst, cudaStream_t stream);
template void absdiff_gpu<short >(const DevMem2Db src1, const DevMem2Db src2, DevMem2Db dst, cudaStream_t stream);
template void absdiff_gpu<int >(const DevMem2Db src1, const DevMem2Db src2, DevMem2Db dst, cudaStream_t stream);
//template void absdiff_gpu<float >(const DevMem2Db src1, const DevMem2Db src2, DevMem2Db dst, cudaStream_t stream);
template void absdiff_gpu<double>(const DevMem2Db src1, const DevMem2Db src2, DevMem2Db dst, cudaStream_t stream);
template <typename T> struct AbsdiffScalar : unary_function<T, T>
{
@ -1140,20 +1140,20 @@ namespace cv { namespace gpu { namespace device
enum { smart_shift = 4 };
};
template <typename T> void absdiff_gpu(const DevMem2Db& src1, double val, const DevMem2Db& dst, cudaStream_t stream)
template <typename T> void absdiff_gpu(const DevMem2Db src1, double val, DevMem2Db dst, cudaStream_t stream)
{
cudaSafeCall( cudaSetDoubleForDevice(&val) );
AbsdiffScalar<T> op(val);
cv::gpu::device::transform((DevMem2D_<T>)src1, (DevMem2D_<T>)dst, op, WithOutMask(), stream);
}
//template void absdiff_gpu<uchar >(const DevMem2Db& src1, double src2, const DevMem2Db& dst, cudaStream_t stream);
template void absdiff_gpu<schar >(const DevMem2Db& src1, double src2, const DevMem2Db& dst, cudaStream_t stream);
//template void absdiff_gpu<ushort>(const DevMem2Db& src1, double src2, const DevMem2Db& dst, cudaStream_t stream);
template void absdiff_gpu<short >(const DevMem2Db& src1, double src2, const DevMem2Db& dst, cudaStream_t stream);
template void absdiff_gpu<int >(const DevMem2Db& src1, double src2, const DevMem2Db& dst, cudaStream_t stream);
//template void absdiff_gpu<float >(const DevMem2Db& src1, double src2, const DevMem2Db& dst, cudaStream_t stream);
template void absdiff_gpu<double>(const DevMem2Db& src1, double src2, const DevMem2Db& dst, cudaStream_t stream);
//template void absdiff_gpu<uchar >(const DevMem2Db src1, double src2, DevMem2Db dst, cudaStream_t stream);
template void absdiff_gpu<schar >(const DevMem2Db src1, double src2, DevMem2Db dst, cudaStream_t stream);
//template void absdiff_gpu<ushort>(const DevMem2Db src1, double src2, DevMem2Db dst, cudaStream_t stream);
template void absdiff_gpu<short >(const DevMem2Db src1, double src2, DevMem2Db dst, cudaStream_t stream);
template void absdiff_gpu<int >(const DevMem2Db src1, double src2, DevMem2Db dst, cudaStream_t stream);
//template void absdiff_gpu<float >(const DevMem2Db src1, double src2, DevMem2Db dst, cudaStream_t stream);
template void absdiff_gpu<double>(const DevMem2Db src1, double src2, DevMem2Db dst, cudaStream_t stream);
//////////////////////////////////////////////////////////////////////////////////////
// Compare
@ -1587,60 +1587,60 @@ namespace cv { namespace gpu { namespace device
};
template <typename T>
void min_gpu(const DevMem2D_<T>& src1, const DevMem2D_<T>& src2, const DevMem2D_<T>& dst, cudaStream_t stream)
void min_gpu(const DevMem2Db src1, const DevMem2Db src2, DevMem2Db dst, cudaStream_t stream)
{
cv::gpu::device::transform(src1, src2, dst, minimum<T>(), WithOutMask(), stream);
cv::gpu::device::transform((DevMem2D_<T>)src1, (DevMem2D_<T>)src2, (DevMem2D_<T>)dst, minimum<T>(), WithOutMask(), stream);
}
template void min_gpu<uchar >(const DevMem2Db& src1, const DevMem2Db& src2, const DevMem2Db& dst, cudaStream_t stream);
template void min_gpu<schar >(const DevMem2D_<schar>& src1, const DevMem2D_<schar>& src2, const DevMem2D_<schar>& dst, cudaStream_t stream);
template void min_gpu<ushort>(const DevMem2D_<ushort>& src1, const DevMem2D_<ushort>& src2, const DevMem2D_<ushort>& dst, cudaStream_t stream);
template void min_gpu<short >(const DevMem2D_<short>& src1, const DevMem2D_<short>& src2, const DevMem2D_<short>& dst, cudaStream_t stream);
template void min_gpu<int >(const DevMem2D_<int>& src1, const DevMem2D_<int>& src2, const DevMem2D_<int>& dst, cudaStream_t stream);
template void min_gpu<float >(const DevMem2D_<float>& src1, const DevMem2D_<float>& src2, const DevMem2D_<float>& dst, cudaStream_t stream);
template void min_gpu<double>(const DevMem2D_<double>& src1, const DevMem2D_<double>& src2, const DevMem2D_<double>& dst, cudaStream_t stream);
template void min_gpu<uchar >(const DevMem2Db src1, const DevMem2Db src2, DevMem2Db dst, cudaStream_t stream);
template void min_gpu<schar >(const DevMem2Db src1, const DevMem2Db src2, DevMem2Db dst, cudaStream_t stream);
template void min_gpu<ushort>(const DevMem2Db src1, const DevMem2Db src2, DevMem2Db dst, cudaStream_t stream);
template void min_gpu<short >(const DevMem2Db src1, const DevMem2Db src2, DevMem2Db dst, cudaStream_t stream);
template void min_gpu<int >(const DevMem2Db src1, const DevMem2Db src2, DevMem2Db dst, cudaStream_t stream);
template void min_gpu<float >(const DevMem2Db src1, const DevMem2Db src2, DevMem2Db dst, cudaStream_t stream);
template void min_gpu<double>(const DevMem2Db src1, const DevMem2Db src2, DevMem2Db dst, cudaStream_t stream);
template <typename T>
void max_gpu(const DevMem2D_<T>& src1, const DevMem2D_<T>& src2, const DevMem2D_<T>& dst, cudaStream_t stream)
void max_gpu(const DevMem2Db src1, const DevMem2Db src2, DevMem2Db dst, cudaStream_t stream)
{
cv::gpu::device::transform(src1, src2, dst, maximum<T>(), WithOutMask(), stream);
cv::gpu::device::transform((DevMem2D_<T>)src1, (DevMem2D_<T>)src2, (DevMem2D_<T>)dst, maximum<T>(), WithOutMask(), stream);
}
template void max_gpu<uchar >(const DevMem2Db& src1, const DevMem2Db& src2, const DevMem2Db& dst, cudaStream_t stream);
template void max_gpu<schar >(const DevMem2D_<schar>& src1, const DevMem2D_<schar>& src2, const DevMem2D_<schar>& dst, cudaStream_t stream);
template void max_gpu<ushort>(const DevMem2D_<ushort>& src1, const DevMem2D_<ushort>& src2, const DevMem2D_<ushort>& dst, cudaStream_t stream);
template void max_gpu<short >(const DevMem2D_<short>& src1, const DevMem2D_<short>& src2, const DevMem2D_<short>& dst, cudaStream_t stream);
template void max_gpu<int >(const DevMem2D_<int>& src1, const DevMem2D_<int>& src2, const DevMem2D_<int>& dst, cudaStream_t stream);
template void max_gpu<float >(const DevMem2D_<float>& src1, const DevMem2D_<float>& src2, const DevMem2D_<float>& dst, cudaStream_t stream);
template void max_gpu<double>(const DevMem2D_<double>& src1, const DevMem2D_<double>& src2, const DevMem2D_<double>& dst, cudaStream_t stream);
template void max_gpu<uchar >(const DevMem2Db src1, const DevMem2Db src2, DevMem2Db dst, cudaStream_t stream);
template void max_gpu<schar >(const DevMem2Db src1, const DevMem2Db src2, DevMem2Db dst, cudaStream_t stream);
template void max_gpu<ushort>(const DevMem2Db src1, const DevMem2Db src2, DevMem2Db dst, cudaStream_t stream);
template void max_gpu<short >(const DevMem2Db src1, const DevMem2Db src2, DevMem2Db dst, cudaStream_t stream);
template void max_gpu<int >(const DevMem2Db src1, const DevMem2Db src2, DevMem2Db dst, cudaStream_t stream);
template void max_gpu<float >(const DevMem2Db src1, const DevMem2Db src2, DevMem2Db dst, cudaStream_t stream);
template void max_gpu<double>(const DevMem2Db src1, const DevMem2Db src2, DevMem2Db dst, cudaStream_t stream);
template <typename T>
void min_gpu(const DevMem2D_<T>& src1, T src2, const DevMem2D_<T>& dst, cudaStream_t stream)
void min_gpu(const DevMem2Db src, T val, DevMem2Db dst, cudaStream_t stream)
{
cv::gpu::device::transform(src1, dst, device::bind2nd(minimum<T>(), src2), WithOutMask(), stream);
cv::gpu::device::transform((DevMem2D_<T>)src, (DevMem2D_<T>)dst, device::bind2nd(minimum<T>(), val), WithOutMask(), stream);
}
template void min_gpu<uchar >(const DevMem2Db& src1, uchar src2, const DevMem2Db& dst, cudaStream_t stream);
template void min_gpu<schar >(const DevMem2D_<schar>& src1, schar src2, const DevMem2D_<schar>& dst, cudaStream_t stream);
template void min_gpu<ushort>(const DevMem2D_<ushort>& src1, ushort src2, const DevMem2D_<ushort>& dst, cudaStream_t stream);
template void min_gpu<short >(const DevMem2D_<short>& src1, short src2, const DevMem2D_<short>& dst, cudaStream_t stream);
template void min_gpu<int >(const DevMem2D_<int>& src1, int src2, const DevMem2D_<int>& dst, cudaStream_t stream);
template void min_gpu<float >(const DevMem2D_<float>& src1, float src2, const DevMem2D_<float>& dst, cudaStream_t stream);
template void min_gpu<double>(const DevMem2D_<double>& src1, double src2, const DevMem2D_<double>& dst, cudaStream_t stream);
template void min_gpu<uchar >(const DevMem2Db src, uchar val, DevMem2Db dst, cudaStream_t stream);
template void min_gpu<schar >(const DevMem2Db src, schar val, DevMem2Db dst, cudaStream_t stream);
template void min_gpu<ushort>(const DevMem2Db src, ushort val, DevMem2Db dst, cudaStream_t stream);
template void min_gpu<short >(const DevMem2Db src, short val, DevMem2Db dst, cudaStream_t stream);
template void min_gpu<int >(const DevMem2Db src, int val, DevMem2Db dst, cudaStream_t stream);
template void min_gpu<float >(const DevMem2Db src, float val, DevMem2Db dst, cudaStream_t stream);
template void min_gpu<double>(const DevMem2Db src, double val, DevMem2Db dst, cudaStream_t stream);
template <typename T>
void max_gpu(const DevMem2D_<T>& src1, T src2, const DevMem2D_<T>& dst, cudaStream_t stream)
void max_gpu(const DevMem2Db src, T val, DevMem2Db dst, cudaStream_t stream)
{
cv::gpu::device::transform(src1, dst, device::bind2nd(maximum<T>(), src2), WithOutMask(), stream);
cv::gpu::device::transform((DevMem2D_<T>)src, (DevMem2D_<T>)dst, device::bind2nd(maximum<T>(), val), WithOutMask(), stream);
}
template void max_gpu<uchar >(const DevMem2Db& src1, uchar src2, const DevMem2Db& dst, cudaStream_t stream);
template void max_gpu<schar >(const DevMem2D_<schar>& src1, schar src2, const DevMem2D_<schar>& dst, cudaStream_t stream);
template void max_gpu<ushort>(const DevMem2D_<ushort>& src1, ushort src2, const DevMem2D_<ushort>& dst, cudaStream_t stream);
template void max_gpu<short >(const DevMem2D_<short>& src1, short src2, const DevMem2D_<short>& dst, cudaStream_t stream);
template void max_gpu<int >(const DevMem2D_<int>& src1, int src2, const DevMem2D_<int>& dst, cudaStream_t stream);
template void max_gpu<float >(const DevMem2D_<float>& src1, float src2, const DevMem2D_<float>& dst, cudaStream_t stream);
template void max_gpu<double>(const DevMem2D_<double>& src1, double src2, const DevMem2D_<double>& dst, cudaStream_t stream);
template void max_gpu<uchar >(const DevMem2Db src, uchar val, DevMem2Db dst, cudaStream_t stream);
template void max_gpu<schar >(const DevMem2Db src, schar val, DevMem2Db dst, cudaStream_t stream);
template void max_gpu<ushort>(const DevMem2Db src, ushort val, DevMem2Db dst, cudaStream_t stream);
template void max_gpu<short >(const DevMem2Db src, short val, DevMem2Db dst, cudaStream_t stream);
template void max_gpu<int >(const DevMem2Db src, int val, DevMem2Db dst, cudaStream_t stream);
template void max_gpu<float >(const DevMem2Db src, float val, DevMem2Db dst, cudaStream_t stream);
template void max_gpu<double>(const DevMem2Db src, double val, DevMem2Db dst, cudaStream_t stream);
//////////////////////////////////////////////////////////////////////////
// threshold
@ -1805,18 +1805,63 @@ namespace cv { namespace gpu { namespace device
//////////////////////////////////////////////////////////////////////////
// addWeighted
template <typename T1, typename T2, typename D> struct AddWeighted : binary_function<T1, T2, D>
namespace detail
{
__host__ __device__ __forceinline__ AddWeighted(double alpha_, double beta_, double gamma_) : alpha(alpha_), beta(beta_), gamma(gamma_) {}
template <typename T> struct UseDouble
{
enum {value = 0};
};
template <> struct UseDouble<int>
{
enum {value = 1};
};
template <> struct UseDouble<float>
{
enum {value = 1};
};
template <> struct UseDouble<double>
{
enum {value = 1};
};
}
template <typename T1, typename T2, typename D> struct UseDouble
{
enum {value = (detail::UseDouble<T1>::value || detail::UseDouble<T2>::value || detail::UseDouble<D>::value)};
};
__device__ __forceinline__ D operator ()(typename TypeTraits<T1>::ParameterType a, typename TypeTraits<T2>::ParameterType b) const
namespace detail
{
template <typename T1, typename T2, typename D, bool useDouble> struct AddWeighted;
template <typename T1, typename T2, typename D> struct AddWeighted<T1, T2, D, false> : binary_function<T1, T2, D>
{
return saturate_cast<D>(alpha * a + beta * b + gamma);
}
AddWeighted(double alpha_, double beta_, double gamma_) : alpha(static_cast<float>(alpha_)), beta(static_cast<float>(beta_)), gamma(static_cast<float>(gamma_)) {}
const double alpha;
const double beta;
const double gamma;
__device__ __forceinline__ D operator ()(T1 a, T2 b) const
{
return saturate_cast<D>(a * alpha + b * beta + gamma);
}
const float alpha;
const float beta;
const float gamma;
};
template <typename T1, typename T2, typename D> struct AddWeighted<T1, T2, D, true> : binary_function<T1, T2, D>
{
AddWeighted(double alpha_, double beta_, double gamma_) : alpha(alpha_), beta(beta_), gamma(gamma_) {}
__device__ __forceinline__ D operator ()(T1 a, T2 b) const
{
return saturate_cast<D>(a * alpha + b * beta + gamma);
}
const double alpha;
const double beta;
const double gamma;
};
}
template <typename T1, typename T2, typename D> struct AddWeighted : detail::AddWeighted<T1, T2, D, UseDouble<T1, T2, D>::value>
{
AddWeighted(double alpha_, double beta_, double gamma_) : detail::AddWeighted<T1, T2, D, UseDouble<T1, T2, D>::value>(alpha_, beta_, gamma_) {}
};
template <> struct TransformFunctorTraits< AddWeighted<ushort, ushort, ushort> > : DefaultTransformFunctorTraits< AddWeighted<ushort, ushort, ushort> >
@ -1878,9 +1923,12 @@ namespace cv { namespace gpu { namespace device
template <typename T1, typename T2, typename D>
void addWeighted_gpu(const DevMem2Db& src1, double alpha, const DevMem2Db& src2, double beta, double gamma, const DevMem2Db& dst, cudaStream_t stream)
{
cudaSafeCall( cudaSetDoubleForDevice(&alpha) );
cudaSafeCall( cudaSetDoubleForDevice(&beta) );
cudaSafeCall( cudaSetDoubleForDevice(&gamma) );
if (UseDouble<T1, T2, D>::value)
{
cudaSafeCall( cudaSetDoubleForDevice(&alpha) );
cudaSafeCall( cudaSetDoubleForDevice(&beta) );
cudaSafeCall( cudaSetDoubleForDevice(&gamma) );
}
AddWeighted<T1, T2, D> op(alpha, beta, gamma);

455
modules/gpu/src/element_operations.cpp
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@ -950,90 +950,62 @@ void cv::gpu::divide(double scale, const GpuMat& src, GpuMat& dst, int dtype, St
namespace cv { namespace gpu { namespace device
{
template <typename T>
void absdiff_gpu(const DevMem2Db& src1, const DevMem2Db& src2, const DevMem2Db& dst, cudaStream_t stream);
void absdiff_gpu(const DevMem2Db src1, const DevMem2Db src2, DevMem2Db dst, cudaStream_t stream);
template <typename T>
void absdiff_gpu(const DevMem2Db& src1, double val, const DevMem2Db& dst, cudaStream_t stream);
void absdiff_gpu(const DevMem2Db src1, double val, DevMem2Db dst, cudaStream_t stream);
}}}
void cv::gpu::absdiff(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& s)
namespace
{
using namespace ::cv::gpu::device;
typedef void (*func_t)(const DevMem2Db& src1, const DevMem2Db& src2, const DevMem2Db& dst, cudaStream_t stream);
static const func_t funcs[] =
{
absdiff_gpu<unsigned char>, absdiff_gpu<signed char>, absdiff_gpu<unsigned short>, absdiff_gpu<short>, absdiff_gpu<int>, absdiff_gpu<float>, absdiff_gpu<double>
};
CV_Assert(src1.size() == src2.size() && src1.type() == src2.type());
dst.create( src1.size(), src1.type() );
cudaStream_t stream = StreamAccessor::getStream(s);
NppiSize sz;
sz.width = src1.cols * src1.channels();
sz.height = src1.rows;
if (src1.depth() == CV_8U)
template <int DEPTH> struct NppAbsDiffFunc
{
NppStreamHandler h(stream);
typedef typename NppTypeTraits<DEPTH>::npp_t npp_t;
nppSafeCall( nppiAbsDiff_8u_C1R(src1.ptr<Npp8u>(), static_cast<int>(src1.step), src2.ptr<Npp8u>(), static_cast<int>(src2.step),
dst.ptr<Npp8u>(), static_cast<int>(dst.step), sz) );
typedef NppStatus (*func_t)(const npp_t* src1, int src1_step, const npp_t* src2, int src2_step, npp_t* dst, int dst_step, NppiSize sz);
};
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
else if (src1.depth() == CV_16U)
template <int DEPTH, typename NppAbsDiffFunc<DEPTH>::func_t func> struct NppAbsDiff
{
NppStreamHandler h(stream);
typedef typename NppAbsDiffFunc<DEPTH>::npp_t npp_t;
nppSafeCall( nppiAbsDiff_16u_C1R(src1.ptr<Npp16u>(), static_cast<int>(src1.step), src2.ptr<Npp16u>(), static_cast<int>(src2.step),
dst.ptr<Npp16u>(), static_cast<int>(dst.step), sz) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
else if (src1.depth() == CV_32F)
{
NppStreamHandler h(stream);
static void call(const DevMem2Db src1, const DevMem2Db src2, DevMem2Db dst, cudaStream_t stream)
{
NppStreamHandler h(stream);
nppSafeCall( nppiAbsDiff_32f_C1R(src1.ptr<Npp32f>(), static_cast<int>(src1.step), src2.ptr<Npp32f>(), static_cast<int>(src2.step),
dst.ptr<Npp32f>(), static_cast<int>(dst.step), sz) );
NppiSize sz;
sz.width = src1.cols;
sz.height = src1.rows;
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
else
{
const func_t func = funcs[src1.depth()];
CV_Assert(func != 0);
nppSafeCall( func((const npp_t*)src1.data, static_cast<int>(src1.step), (const npp_t*)src2.data, static_cast<int>(src2.step),
(npp_t*)dst.data, static_cast<int>(dst.step), sz) );
func(src1.reshape(1), src2.reshape(1), dst.reshape(1), stream);
}
}
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
}
};
namespace
{
template <int DEPTH> struct NppAbsDiffCFunc
{
typedef typename NppTypeTraits<DEPTH>::npp_t npp_t;
typedef npp_t scalar_t;
typedef NppStatus (*func_t)(const npp_t* pSrc1, int nSrc1Step, npp_t* pDst, int nDstStep, NppiSize oSizeROI, npp_t nConstant);
};
template <> struct NppAbsDiffCFunc<CV_16U>
{
typedef NppTypeTraits<CV_16U>::npp_t npp_t;
typedef Npp32u scalar_t;
typedef NppStatus (*func_t)(const Npp16u* pSrc1, int nSrc1Step, Npp16u* pDst, int nDstStep, NppiSize oSizeROI, Npp32u nConstant);
};
template <int DEPTH, typename NppAbsDiffCFunc<DEPTH>::func_t func> struct NppAbsDiffC
{
typedef typename NppTypeTraits<DEPTH>::npp_t npp_t;
typedef typename NppAbsDiffCFunc<DEPTH>::npp_t npp_t;
typedef typename NppAbsDiffCFunc<DEPTH>::scalar_t scalar_t;
static void call(const DevMem2Db& src1, double val, const DevMem2Db& dst, cudaStream_t stream)
static void call(const DevMem2Db src1, double val, DevMem2Db dst, cudaStream_t stream)
{
NppStreamHandler h(stream);
@ -1041,8 +1013,8 @@ namespace
sz.width = src1.cols;
sz.height = src1.rows;
nppSafeCall( func((const npp_t*)src1.data, static_cast<int>(src1.step), (npp_t*)dst.data, static_cast<int>(dst.step),
sz, static_cast<npp_t>(val)) );
nppSafeCall( func((const npp_t*)src1.data, static_cast<int>(src1.step),
(npp_t*)dst.data, static_cast<int>(dst.step), sz, static_cast<scalar_t>(val)) );
if (stream == 0)
cudaSafeCall( cudaDeviceSynchronize() );
@ -1050,12 +1022,41 @@ namespace
};
}
void cv::gpu::absdiff(const GpuMat& src1, const Scalar& src2, GpuMat& dst, Stream& s)
void cv::gpu::absdiff(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream)
{
using namespace cv::gpu::device;
typedef void (*func_t)(const DevMem2Db& src1, double val, const DevMem2Db& dst, cudaStream_t stream);
typedef void (*func_t)(const DevMem2Db src1, const DevMem2Db src2, DevMem2Db dst, cudaStream_t stream);
static const func_t funcs[] =
{
NppAbsDiff<CV_8U, nppiAbsDiff_8u_C1R>::call,
absdiff_gpu<signed char>,
NppAbsDiff<CV_16U, nppiAbsDiff_16u_C1R>::call,
absdiff_gpu<short>,
absdiff_gpu<int>,
NppAbsDiff<CV_32F, nppiAbsDiff_32f_C1R>::call,
absdiff_gpu<double>
};
CV_Assert(src1.depth() <= CV_64F);
CV_Assert(src1.size() == src2.size() && src1.type() == src2.type());
if (src1.depth() == CV_64F)
{
if (!TargetArchs::builtWith(NATIVE_DOUBLE) || !DeviceInfo().supports(NATIVE_DOUBLE))
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
}
dst.create(src1.size(), src1.type());
funcs[src1.depth()](src1.reshape(1), src2.reshape(1), dst.reshape(1), StreamAccessor::getStream(stream));
}
void cv::gpu::absdiff(const GpuMat& src1, const Scalar& src2, GpuMat& dst, Stream& stream)
{
using namespace cv::gpu::device;
typedef void (*func_t)(const DevMem2Db src1, double val, DevMem2Db dst, cudaStream_t stream);
static const func_t funcs[] =
{
NppAbsDiffC<CV_8U, nppiAbsDiffC_8u_C1R>::call,
@ -1067,13 +1068,18 @@ void cv::gpu::absdiff(const GpuMat& src1, const Scalar& src2, GpuMat& dst, Strea
absdiff_gpu<double>
};
CV_Assert(src1.depth() <= CV_64F);
CV_Assert(src1.channels() == 1);
dst.create(src1.size(), src1.type());
if (src1.depth() == CV_64F)
{
if (!TargetArchs::builtWith(NATIVE_DOUBLE) || !DeviceInfo().supports(NATIVE_DOUBLE))
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
}
cudaStream_t stream = StreamAccessor::getStream(s);
dst.create(src1.size(), src1.type());
funcs[src1.depth()](src1, src2.val[0], dst, stream);
funcs[src1.depth()](src1, src2.val[0], dst, StreamAccessor::getStream(stream));
}
//////////////////////////////////////////////////////////////////////////////
@ -1359,34 +1365,38 @@ namespace cv { namespace gpu { namespace device
void cv::gpu::compare(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, int cmpop, Stream& stream)
{
using namespace ::cv::gpu::device;
using namespace cv::gpu::device;
typedef void (*func_t)(const DevMem2Db& src1, const DevMem2Db& src2, const DevMem2Db& dst, cudaStream_t stream);
static const func_t funcs[7][4] =
{
{compare_eq<unsigned char>, compare_ne<unsigned char>, compare_lt<unsigned char>, compare_le<unsigned char>},
{compare_eq<signed char>, compare_ne<signed char>, compare_lt<signed char>, compare_le<signed char>},
{compare_eq<unsigned char> , compare_ne<unsigned char> , compare_lt<unsigned char> , compare_le<unsigned char> },
{compare_eq<signed char> , compare_ne<signed char> , compare_lt<signed char> , compare_le<signed char> },
{compare_eq<unsigned short>, compare_ne<unsigned short>, compare_lt<unsigned short>, compare_le<unsigned short>},
{compare_eq<short>, compare_ne<short>, compare_lt<short>, compare_le<short>},
{compare_eq<int>, compare_ne<int>, compare_lt<int>, compare_le<int>},
{compare_eq<float>, compare_ne<float>, compare_lt<float>, compare_le<float>},
{compare_eq<double>, compare_ne<double>, compare_lt<double>, compare_le<double>}
{compare_eq<short> , compare_ne<short> , compare_lt<short> , compare_le<short> },
{compare_eq<int> , compare_ne<int> , compare_lt<int> , compare_le<int> },
{compare_eq<float> , compare_ne<float> , compare_lt<float> , compare_le<float> },
{compare_eq<double> , compare_ne<double> , compare_lt<double> , compare_le<double> }
};
CV_Assert(src1.depth() <= CV_64F);
CV_Assert(src1.size() == src2.size() && src1.type() == src2.type());
CV_Assert(cmpop >= CMP_EQ && cmpop <= CMP_NE);
if (src1.depth() == CV_64F)
{
if (!TargetArchs::builtWith(NATIVE_DOUBLE) || !DeviceInfo().supports(NATIVE_DOUBLE))
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
}
static const int codes[] =
{
0, 2, 3, 2, 3, 1
};
const GpuMat* psrc1[] =
{
&src1, &src2, &src2, &src1, &src1, &src1
};
const GpuMat* psrc2[] =
{
&src2, &src1, &src1, &src2, &src2, &src2
@ -1415,17 +1425,15 @@ namespace
{
dst.create(src.size(), src.type());
::cv::gpu::device::bitwiseNotCaller(src.rows, src.cols, src.elemSize1(), dst.channels(), src, dst, stream);
cv::gpu::device::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::device;
using namespace cv::gpu::device;
typedef void (*Caller)(int, int, int, const PtrStepb, const PtrStepb, PtrStepb, cudaStream_t);
static Caller callers[] =
typedef void (*func_t)(int, int, int, const PtrStepb, const PtrStepb, PtrStepb, cudaStream_t);
static func_t funcs[] =
{
bitwiseMaskNotCaller<unsigned char>, bitwiseMaskNotCaller<unsigned char>,
bitwiseMaskNotCaller<unsigned short>, bitwiseMaskNotCaller<unsigned short>,
@ -1433,19 +1441,19 @@ namespace
bitwiseMaskNotCaller<unsigned int>
};
CV_Assert(src.depth() <= CV_64F);
CV_Assert(mask.type() == CV_8U && mask.size() == src.size());
dst.create(src.size(), src.type());
Caller caller = callers[src.depth()];
CV_Assert(caller);
const func_t func = funcs[src.depth()];
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);
}
func(src.rows, src.cols, cn, src, mask, dst, stream);
}
}
void cv::gpu::bitwise_not(const GpuMat& src, GpuMat& dst, const GpuMat& mask, Stream& stream)
{
if (mask.empty())
@ -1454,7 +1462,6 @@ void cv::gpu::bitwise_not(const GpuMat& src, GpuMat& dst, const GpuMat& mask, St
bitwiseNotCaller(src, dst, mask, StreamAccessor::getStream(stream));
}
//////////////////////////////////////////////////////////////////////////////
// Binary bitwise logical operations
@ -1481,18 +1488,18 @@ 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::device::bitwiseOrCaller(dst.rows, dst.cols, dst.elemSize1(), dst.channels(), src1, src2, dst, stream);
cv::gpu::device::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::device;
typedef void (*Caller)(int, int, int, const PtrStepb, const PtrStepb, const PtrStepb, PtrStepb, cudaStream_t);
using namespace cv::gpu::device;
static Caller callers[] =
typedef void (*func_t)(int, int, int, const PtrStepb, const PtrStepb, const PtrStepb, PtrStepb, cudaStream_t);
static func_t funcs[] =
{
bitwiseMaskOrCaller<unsigned char>, bitwiseMaskOrCaller<unsigned char>,
bitwiseMaskOrCaller<unsigned short>, bitwiseMaskOrCaller<unsigned short>,
@ -1500,33 +1507,35 @@ namespace
bitwiseMaskOrCaller<unsigned int>
};
CV_Assert(src1.depth() <= CV_64F);
CV_Assert(src1.size() == src2.size() && src1.type() == src2.type());
CV_Assert(mask.type() == CV_8U && mask.size() == src1.size());
dst.create(src1.size(), src1.type());
Caller caller = callers[src1.depth()];
CV_Assert(caller);
const func_t func = funcs[src1.depth()];
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);
func(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::device::bitwiseAndCaller(dst.rows, dst.cols, dst.elemSize1(), dst.channels(), src1, src2, dst, stream);
cv::gpu::device::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::device;
using namespace cv::gpu::device;
typedef void (*Caller)(int, int, int, const PtrStepb, const PtrStepb, const PtrStepb, PtrStepb, cudaStream_t);
static Caller callers[] =
typedef void (*func_t)(int, int, int, const PtrStepb, const PtrStepb, const PtrStepb, PtrStepb, cudaStream_t);
static func_t funcs[] =
{
bitwiseMaskAndCaller<unsigned char>, bitwiseMaskAndCaller<unsigned char>,
bitwiseMaskAndCaller<unsigned short>, bitwiseMaskAndCaller<unsigned short>,
@ -1534,33 +1543,35 @@ namespace
bitwiseMaskAndCaller<unsigned int>
};
CV_Assert(src1.depth() <= CV_64F);
CV_Assert(src1.size() == src2.size() && src1.type() == src2.type());
CV_Assert(mask.type() == CV_8U && mask.size() == src1.size());
dst.create(src1.size(), src1.type());
Caller caller = callers[src1.depth()];
CV_Assert(caller);
const func_t func = funcs[src1.depth()];
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);
func(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::device::bitwiseXorCaller(dst.rows, dst.cols, dst.elemSize1(), dst.channels(), src1, src2, dst, stream);
cv::gpu::device::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::device;
using namespace cv::gpu::device;
typedef void (*Caller)(int, int, int, const PtrStepb, const PtrStepb, const PtrStepb, PtrStepb, cudaStream_t);
static Caller callers[] =
typedef void (*func_t)(int, int, int, const PtrStepb, const PtrStepb, const PtrStepb, PtrStepb, cudaStream_t);
static func_t funcs[] =
{
bitwiseMaskXorCaller<unsigned char>, bitwiseMaskXorCaller<unsigned char>,
bitwiseMaskXorCaller<unsigned short>, bitwiseMaskXorCaller<unsigned short>,
@ -1568,14 +1579,17 @@ namespace
bitwiseMaskXorCaller<unsigned int>
};
CV_Assert(src1.depth() <= CV_64F);
CV_Assert(src1.size() == src2.size() && src1.type() == src2.type());
CV_Assert(mask.type() == CV_8U && mask.size() == src1.size());
dst.create(src1.size(), src1.type());
Caller caller = callers[src1.depth()];
CV_Assert(caller);
const func_t func = funcs[src1.depth()];
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);
func(dst.rows, dst.cols, cn, src1, src2, mask, dst, stream);
}
}
@ -1661,10 +1675,9 @@ namespace
void cv::gpu::bitwise_or(const GpuMat& src, const Scalar& sc, GpuMat& dst, Stream& stream)
{
typedef void (*func_t)(const GpuMat& src, Scalar sc, GpuMat& dst, cudaStream_t stream);
static const func_t funcs[5][4] =
{
{NppBitwiseC<CV_8U, 1, nppiOrC_8u_C1R>::call, 0, NppBitwiseC<CV_8U, 3, nppiOrC_8u_C3R>::call, NppBitwiseC<CV_8U, 4, nppiOrC_8u_C4R>::call},
{NppBitwiseC<CV_8U , 1, nppiOrC_8u_C1R >::call, 0, NppBitwiseC<CV_8U , 3, nppiOrC_8u_C3R >::call, NppBitwiseC<CV_8U , 4, nppiOrC_8u_C4R >::call},
{0,0,0,0},
{NppBitwiseC<CV_16U, 1, nppiOrC_16u_C1R>::call, 0, NppBitwiseC<CV_16U, 3, nppiOrC_16u_C3R>::call, NppBitwiseC<CV_16U, 4, nppiOrC_16u_C4R>::call},
{0,0,0,0},
@ -1682,10 +1695,9 @@ void cv::gpu::bitwise_or(const GpuMat& src, const Scalar& sc, GpuMat& dst, Strea
void cv::gpu::bitwise_and(const GpuMat& src, const Scalar& sc, GpuMat& dst, Stream& stream)
{
typedef void (*func_t)(const GpuMat& src, Scalar sc, GpuMat& dst, cudaStream_t stream);
static const func_t funcs[5][4] =
{
{NppBitwiseC<CV_8U, 1, nppiAndC_8u_C1R>::call, 0, NppBitwiseC<CV_8U, 3, nppiAndC_8u_C3R>::call, NppBitwiseC<CV_8U, 4, nppiAndC_8u_C4R>::call},
{NppBitwiseC<CV_8U , 1, nppiAndC_8u_C1R >::call, 0, NppBitwiseC<CV_8U , 3, nppiAndC_8u_C3R >::call, NppBitwiseC<CV_8U , 4, nppiAndC_8u_C4R >::call},
{0,0,0,0},
{NppBitwiseC<CV_16U, 1, nppiAndC_16u_C1R>::call, 0, NppBitwiseC<CV_16U, 3, nppiAndC_16u_C3R>::call, NppBitwiseC<CV_16U, 4, nppiAndC_16u_C4R>::call},
{0,0,0,0},
@ -1703,10 +1715,9 @@ void cv::gpu::bitwise_and(const GpuMat& src, const Scalar& sc, GpuMat& dst, Stre
void cv::gpu::bitwise_xor(const GpuMat& src, const Scalar& sc, GpuMat& dst, Stream& stream)
{
typedef void (*func_t)(const GpuMat& src, Scalar sc, GpuMat& dst, cudaStream_t stream);
static const func_t funcs[5][4] =
{
{NppBitwiseC<CV_8U, 1, nppiXorC_8u_C1R>::call, 0, NppBitwiseC<CV_8U, 3, nppiXorC_8u_C3R>::call, NppBitwiseC<CV_8U, 4, nppiXorC_8u_C4R>::call},
{NppBitwiseC<CV_8U , 1, nppiXorC_8u_C1R >::call, 0, NppBitwiseC<CV_8U , 3, nppiXorC_8u_C3R >::call, NppBitwiseC<CV_8U , 4, nppiXorC_8u_C4R >::call},
{0,0,0,0},
{NppBitwiseC<CV_16U, 1, nppiXorC_16u_C1R>::call, 0, NppBitwiseC<CV_16U, 3, nppiXorC_16u_C3R>::call, NppBitwiseC<CV_16U, 4, nppiXorC_16u_C4R>::call},
{0,0,0,0},
@ -1822,107 +1833,140 @@ void cv::gpu::lshift(const GpuMat& src, Scalar_<int> sc, GpuMat& dst, Stream& st
namespace cv { namespace gpu { namespace device
{
template <typename T>
void min_gpu(const DevMem2D_<T>& src1, const DevMem2D_<T>& src2, const DevMem2D_<T>& dst, cudaStream_t stream);
template <typename T>
void max_gpu(const DevMem2D_<T>& src1, const DevMem2D_<T>& src2, const DevMem2D_<T>& dst, cudaStream_t stream);
template <typename T> void min_gpu(const DevMem2Db src1, const DevMem2Db src2, DevMem2Db dst, cudaStream_t stream);
template <typename T> void max_gpu(const DevMem2Db src1, const DevMem2Db src2, DevMem2Db dst, cudaStream_t stream);
template <typename T>
void min_gpu(const DevMem2D_<T>& src1, T src2, const DevMem2D_<T>& dst, cudaStream_t stream);
template <typename T>
void max_gpu(const DevMem2D_<T>& src1, T src2, const DevMem2D_<T>& dst, cudaStream_t stream);
template <typename T> void min_gpu(const DevMem2Db src, T val, DevMem2Db dst, cudaStream_t stream);
template <typename T> void max_gpu(const DevMem2Db src, T val, DevMem2Db dst, cudaStream_t stream);
}}}
namespace
void cv::gpu::min(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream)
{
template <typename T>
void min_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::device::min_gpu<T>(src1.reshape(1), src2.reshape(1), dst.reshape(1), stream);
}
using namespace cv::gpu::device;
template <typename T>
void min_caller(const GpuMat& src1, double src2, GpuMat& dst, cudaStream_t stream)
typedef void (*func_t)(const DevMem2Db src1, const DevMem2Db src2, DevMem2Db dst, cudaStream_t stream);
static const func_t funcs[] =
{
dst.create(src1.size(), src1.type());
::cv::gpu::device::min_gpu<T>(src1.reshape(1), saturate_cast<T>(src2), dst.reshape(1), stream);
}
min_gpu<unsigned char>,
min_gpu<signed char>,
min_gpu<unsigned short>,
min_gpu<short>,
min_gpu<int>,
min_gpu<float>,
min_gpu<double>
};
template <typename T>
void max_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::device::max_gpu<T>(src1.reshape(1), src2.reshape(1), dst.reshape(1), stream);
}
CV_Assert(src1.depth() <= CV_64F);
CV_Assert(src1.size() == src2.size() && src1.type() == src2.type());
template <typename T>
void max_caller(const GpuMat& src1, double src2, GpuMat& dst, cudaStream_t stream)
if (src1.depth() == CV_64F)
{
dst.create(src1.size(), src1.type());
::cv::gpu::device::max_gpu<T>(src1.reshape(1), saturate_cast<T>(src2), dst.reshape(1), stream);
if (!TargetArchs::builtWith(NATIVE_DOUBLE) || !DeviceInfo().supports(NATIVE_DOUBLE))
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
}
dst.create(src1.size(), src1.type());
funcs[src1.depth()](src1.reshape(1), src2.reshape(1), dst.reshape(1), StreamAccessor::getStream(stream));
}
void cv::gpu::min(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream)
void cv::gpu::max(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream)
{
CV_Assert(src1.size() == src2.size() && src1.type() == src2.type());
CV_Assert((src1.depth() != CV_64F) ||
(TargetArchs::builtWith(NATIVE_DOUBLE) && DeviceInfo().supports(NATIVE_DOUBLE)));
using namespace cv::gpu::device;
typedef void (*func_t)(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, cudaStream_t stream);
typedef void (*func_t)(const DevMem2Db src1, const DevMem2Db src2, DevMem2Db dst, cudaStream_t stream);
static const func_t funcs[] =
{
min_caller<unsigned char>, min_caller<signed char>, min_caller<unsigned short>, min_caller<short>, min_caller<int>,
min_caller<float>, min_caller<double>
max_gpu<unsigned char>,
max_gpu<signed char>,
max_gpu<unsigned short>,
max_gpu<short>,
max_gpu<int>,
max_gpu<float>,
max_gpu<double>
};
funcs[src1.depth()](src1, src2, dst, StreamAccessor::getStream(stream));
CV_Assert(src1.depth() <= CV_64F);
CV_Assert(src1.size() == src2.size() && src1.type() == src2.type());
if (src1.depth() == CV_64F)
{
if (!TargetArchs::builtWith(NATIVE_DOUBLE) || !DeviceInfo().supports(NATIVE_DOUBLE))
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
}
dst.create(src1.size(), src1.type());
funcs[src1.depth()](src1.reshape(1), src2.reshape(1), dst.reshape(1), StreamAccessor::getStream(stream));
}
void cv::gpu::min(const GpuMat& src1, double src2, GpuMat& dst, Stream& stream)
namespace
{
CV_Assert((src1.depth() != CV_64F) ||
(TargetArchs::builtWith(NATIVE_DOUBLE) && DeviceInfo().supports(NATIVE_DOUBLE)));
template <typename T> void minScalar(const DevMem2Db src, double val, DevMem2Db dst, cudaStream_t stream)
{
cv::gpu::device::min_gpu(src, saturate_cast<T>(val), dst, stream);
}
typedef void (*func_t)(const GpuMat& src1, double src2, GpuMat& dst, cudaStream_t stream);
static const func_t funcs[] =
template <typename T> void maxScalar(const DevMem2Db src, double val, DevMem2Db dst, cudaStream_t stream)
{
min_caller<unsigned char>, min_caller<signed char>, min_caller<unsigned short>, min_caller<short>, min_caller<int>,
min_caller<float>, min_caller<double>
};
funcs[src1.depth()](src1, src2, dst, StreamAccessor::getStream(stream));
cv::gpu::device::max_gpu(src, saturate_cast<T>(val), dst, stream);
}
}
void cv::gpu::max(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, Stream& stream)
void cv::gpu::min(const GpuMat& src, double val, GpuMat& dst, Stream& stream)
{
CV_Assert(src1.size() == src2.size() && src1.type() == src2.type());
CV_Assert((src1.depth() != CV_64F) ||
(TargetArchs::builtWith(NATIVE_DOUBLE) && DeviceInfo().supports(NATIVE_DOUBLE)));
typedef void (*func_t)(const GpuMat& src1, const GpuMat& src2, GpuMat& dst, cudaStream_t stream);
typedef void (*func_t)(const DevMem2Db src1, double src2, DevMem2Db dst, cudaStream_t stream);
static const func_t funcs[] =
{
max_caller<unsigned char>, max_caller<signed char>, max_caller<unsigned short>, max_caller<short>, max_caller<int>,
max_caller<float>, max_caller<double>
minScalar<unsigned char>,
minScalar<signed char>,
minScalar<unsigned short>,
minScalar<short>,
minScalar<int>,
minScalar<float>,
minScalar<double>
};
funcs[src1.depth()](src1, src2, dst, StreamAccessor::getStream(stream));
CV_Assert(src.depth() <= CV_64F);
CV_Assert(src.channels() == 1);
if (src.depth() == CV_64F)
{
if (!TargetArchs::builtWith(NATIVE_DOUBLE) || !DeviceInfo().supports(NATIVE_DOUBLE))
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
}
dst.create(src.size(), src.type());
funcs[src.depth()](src, val, dst, StreamAccessor::getStream(stream));
}
void cv::gpu::max(const GpuMat& src1, double src2, GpuMat& dst, Stream& stream)
void cv::gpu::max(const GpuMat& src, double val, GpuMat& dst, Stream& stream)
{
CV_Assert((src1.depth() != CV_64F) ||
(TargetArchs::builtWith(NATIVE_DOUBLE) && DeviceInfo().supports(NATIVE_DOUBLE)));
typedef void (*func_t)(const GpuMat& src1, double src2, GpuMat& dst, cudaStream_t stream);
typedef void (*func_t)(const DevMem2Db src1, double src2, DevMem2Db dst, cudaStream_t stream);
static const func_t funcs[] =
{
max_caller<unsigned char>, max_caller<signed char>, max_caller<unsigned short>, max_caller<short>, max_caller<int>,
max_caller<float>, max_caller<double>
maxScalar<unsigned char>,
maxScalar<signed char>,
maxScalar<unsigned short>,
maxScalar<short>,
maxScalar<int>,
maxScalar<float>,
maxScalar<double>
};
funcs[src1.depth()](src1, src2, dst, StreamAccessor::getStream(stream));
CV_Assert(src.depth() <= CV_64F);
CV_Assert(src.channels() == 1);
if (src.depth() == CV_64F)
{
if (!TargetArchs::builtWith(NATIVE_DOUBLE) || !DeviceInfo().supports(NATIVE_DOUBLE))
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
}
dst.create(src.size(), src.type());
funcs[src.depth()](src, val, dst, StreamAccessor::getStream(stream));
}
////////////////////////////////////////////////////////////////////////
@ -1947,6 +1991,12 @@ double cv::gpu::threshold(const GpuMat& src, GpuMat& dst, double thresh, double
CV_Assert(src.channels() == 1 && src.depth() <= CV_64F);
CV_Assert(type <= THRESH_TOZERO_INV);
if (src.depth() == CV_64F)
{
if (!TargetArchs::builtWith(NATIVE_DOUBLE) || !DeviceInfo().supports(NATIVE_DOUBLE))
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
}
dst.create(src.size(), src.type());
cudaStream_t stream = StreamAccessor::getStream(s);
@ -1967,9 +2017,8 @@ double cv::gpu::threshold(const GpuMat& src, GpuMat& dst, double thresh, double
}
else
{
typedef void (*caller_t)(const GpuMat& src, GpuMat& dst, double thresh, double maxVal, int type, cudaStream_t stream);
static const caller_t callers[] =
typedef void (*func_t)(const GpuMat& src, GpuMat& dst, double thresh, double maxVal, int type, cudaStream_t stream);
static const func_t funcs[] =
{
threshold_caller<unsigned char>, threshold_caller<signed char>,
threshold_caller<unsigned short>, threshold_caller<short>,
@ -1982,7 +2031,7 @@ double cv::gpu::threshold(const GpuMat& src, GpuMat& dst, double thresh, double
maxVal = cvRound(maxVal);
}
callers[src.depth()](src, dst, thresh, maxVal, type, stream);
funcs[src.depth()](src, dst, thresh, maxVal, type, stream);
}
return thresh;
@ -1993,8 +2042,7 @@ double cv::gpu::threshold(const GpuMat& src, GpuMat& dst, double thresh, double
namespace cv { namespace gpu { namespace device
{
template<typename T>
void pow_caller(DevMem2Db src, double power, DevMem2Db dst, cudaStream_t stream);
template<typename T> void pow_caller(DevMem2Db src, double power, DevMem2Db dst, cudaStream_t stream);
}}}
void cv::gpu::pow(const GpuMat& src, double power, GpuMat& dst, Stream& stream)
@ -2002,7 +2050,6 @@ void cv::gpu::pow(const GpuMat& src, double power, GpuMat& dst, Stream& stream)
using namespace cv::gpu::device;
typedef void (*func_t)(DevMem2Db src, double power, DevMem2Db dst, cudaStream_t stream);
static const func_t funcs[] =
{
pow_caller<unsigned char>, pow_caller<signed char>,
@ -2010,6 +2057,14 @@ void cv::gpu::pow(const GpuMat& src, double power, GpuMat& dst, Stream& stream)
pow_caller<int>, pow_caller<float>, pow_caller<double>
};
CV_Assert(src.depth() <= CV_64F);
if (src.depth() == CV_64F)
{
if (!TargetArchs::builtWith(NATIVE_DOUBLE) || !DeviceInfo().supports(NATIVE_DOUBLE))
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
}
dst.create(src.size(), src.type());
funcs[src.depth()](src.reshape(1), power, dst.reshape(1), StreamAccessor::getStream(stream));
@ -2075,8 +2130,7 @@ void cv::gpu::alphaComp(const GpuMat& img1, const GpuMat& img2, GpuMat& dst, int
NppAlphaComp<CV_16U, nppiAlphaComp_16u_AC4R>::call,
0,
NppAlphaComp<CV_32S, nppiAlphaComp_32s_AC4R>::call,
NppAlphaComp<CV_32F, nppiAlphaComp_32f_AC4R>::call,
0
NppAlphaComp<CV_32F, nppiAlphaComp_32f_AC4R>::call
};
CV_Assert(img1.type() == CV_8UC4 || img1.type() == CV_16UC4 || img1.type() == CV_32SC4 || img1.type() == CV_32FC4);
@ -2085,7 +2139,6 @@ void cv::gpu::alphaComp(const GpuMat& img1, const GpuMat& img2, GpuMat& dst, int
dst.create(img1.size(), img1.type());
const func_t func = funcs[img1.depth()];
CV_Assert(func != 0);
func(img1, img2, dst, npp_alpha_ops[alpha_op], StreamAccessor::getStream(stream));
}
@ -2569,6 +2622,14 @@ void cv::gpu::addWeighted(const GpuMat& src1, double alpha, const GpuMat& src2,
dtype = dtype >= 0 ? CV_MAKETYPE(dtype, src1.channels()) : src1.type();
CV_Assert(src1.depth() <= CV_64F && src2.depth() <= CV_64F && CV_MAT_DEPTH(dtype) <= CV_64F);
if (src1.depth() == CV_64F || src2.depth() == CV_64F || CV_MAT_DEPTH(dtype) == CV_64F)
{
if (!TargetArchs::builtWith(NATIVE_DOUBLE) || !DeviceInfo().supports(NATIVE_DOUBLE))
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
}
dst.create(src1.size(), dtype);
const GpuMat* psrc1 = &src1;
@ -2581,7 +2642,9 @@ void cv::gpu::addWeighted(const GpuMat& src1, double alpha, const GpuMat& src2,
}
const func_t func = funcs[psrc1->depth()][psrc2->depth()][dst.depth()];
CV_Assert(func != 0);
if (!func)
CV_Error(CV_StsUnsupportedFormat, "Unsupported combination of source and destination types");
func(psrc1->reshape(1), alpha, psrc2->reshape(1), beta, gamma, dst.reshape(1), StreamAccessor::getStream(stream));
}

294
modules/gpu/src/matrix_reductions.cpp
Unescape View File

@ -132,7 +132,7 @@ void cv::gpu::meanStdDev(const GpuMat& src, Scalar& mean, Scalar& stddev, GpuMat
nppSafeCall( nppiMean_StdDev_8u_C1R(src.ptr<Npp8u>(), static_cast<int>(src.step), sz, buf.ptr<Npp8u>(), dbuf, (double*)dbuf + 1) );
cudaSafeCall( cudaDeviceSynchronize() );
double* ptrs[2] = {mean.val, stddev.val};
dbuf.download(ptrs);
}
@ -148,6 +148,8 @@ double cv::gpu::norm(const GpuMat& src, int normType)
double cv::gpu::norm(const GpuMat& src, int normType, GpuMat& buf)
{
CV_Assert(normType == NORM_INF || normType == NORM_L1 || normType == NORM_L2);
GpuMat src_single_channel = src.reshape(1);
if (normType == NORM_L1)
@ -156,22 +158,16 @@ double cv::gpu::norm(const GpuMat& src, int normType, GpuMat& buf)
if (normType == NORM_L2)
return std::sqrt(sqrSum(src_single_channel, buf)[0]);
if (normType == NORM_INF)
{
double min_val, max_val;
minMax(src_single_channel, &min_val, &max_val, GpuMat(), buf);
return std::max(std::abs(min_val), std::abs(max_val));
}
CV_Error(CV_StsBadArg, "norm: unsupported norm type");
return 0;
// NORM_INF
double min_val, max_val;
minMax(src_single_channel, &min_val, &max_val, GpuMat(), buf);
return std::max(std::abs(min_val), std::abs(max_val));
}
double cv::gpu::norm(const GpuMat& src1, const GpuMat& src2, int normType)
{
CV_DbgAssert(src1.size() == src2.size() && src1.type() == src2.type());
CV_Assert(src1.type() == CV_8UC1);
CV_Assert(src1.size() == src2.size() && src1.type() == src2.type());
CV_Assert(normType == NORM_INF || normType == NORM_L1 || normType == NORM_L2);
typedef NppStatus (*npp_norm_diff_func_t)(const Npp8u* pSrc1, int nSrcStep1, const Npp8u* pSrc2, int nSrcStep2,
@ -184,7 +180,7 @@ double cv::gpu::norm(const GpuMat& src1, const GpuMat& src2, int normType)
sz.height = src1.rows;
int funcIdx = normType >> 1;
double retVal;
DeviceBuffer dbuf;
@ -192,7 +188,7 @@ double cv::gpu::norm(const GpuMat& src1, const GpuMat& src2, int normType)
nppSafeCall( npp_norm_diff_func[funcIdx](src1.ptr<Npp8u>(), static_cast<int>(src1.step), src2.ptr<Npp8u>(), static_cast<int>(src2.step), sz, dbuf) );
cudaSafeCall( cudaDeviceSynchronize() );
dbuf.download(&retVal);
return retVal;
@ -201,9 +197,9 @@ double cv::gpu::norm(const GpuMat& src1, const GpuMat& src2, int normType)
////////////////////////////////////////////////////////////////////////
// Sum
namespace cv { namespace gpu { namespace device
namespace cv { namespace gpu { namespace device
{
namespace matrix_reductions
namespace matrix_reductions
{
namespace sum
{
@ -230,34 +226,36 @@ namespace cv { namespace gpu { namespace device
}
}}}
Scalar cv::gpu::sum(const GpuMat& src)
Scalar cv::gpu::sum(const GpuMat& src)
{
GpuMat buf;
return sum(src, buf);
}
Scalar cv::gpu::sum(const GpuMat& src, GpuMat& buf)
Scalar cv::gpu::sum(const GpuMat& src, GpuMat& buf)
{
using namespace ::cv::gpu::device::matrix_reductions::sum;
using namespace cv::gpu::device::matrix_reductions::sum;
typedef void (*Caller)(const DevMem2Db, PtrStepb, double*, int);
static Caller multipass_callers[7] =
{
sumMultipassCaller<unsigned char>, sumMultipassCaller<char>,
sumMultipassCaller<unsigned short>, sumMultipassCaller<short>,
sumMultipassCaller<int>, sumMultipassCaller<float>, 0
static Caller multipass_callers[] =
{
sumMultipassCaller<unsigned char>, sumMultipassCaller<char>,
sumMultipassCaller<unsigned short>, sumMultipassCaller<short>,
sumMultipassCaller<int>, sumMultipassCaller<float>
};
static Caller singlepass_callers[7] = {
sumCaller<unsigned char>, sumCaller<char>,
sumCaller<unsigned short>, sumCaller<short>,
sumCaller<int>, sumCaller<float>, 0
static Caller singlepass_callers[] = {
sumCaller<unsigned char>, sumCaller<char>,
sumCaller<unsigned short>, sumCaller<short>,
sumCaller<int>, sumCaller<float>
};
CV_Assert(src.depth() <= CV_32F);
Size buf_size;
getBufSizeRequired(src.cols, src.rows, src.channels(), buf_size.width, buf_size.height);
getBufSizeRequired(src.cols, src.rows, src.channels(), buf_size.width, buf_size.height);
ensureSizeIsEnough(buf_size, CV_8U, buf);
Caller* callers = multipass_callers;
@ -265,7 +263,6 @@ Scalar cv::gpu::sum(const GpuMat& src, GpuMat& buf)
callers = singlepass_callers;
Caller caller = callers[src.depth()];
if (!caller) CV_Error(CV_StsBadArg, "sum: unsupported type");
double result[4];
caller(src, buf, result, src.channels());
@ -273,35 +270,37 @@ Scalar cv::gpu::sum(const GpuMat& src, GpuMat& buf)
}
Scalar cv::gpu::absSum(const GpuMat& src)
Scalar cv::gpu::absSum(const GpuMat& src)
{
GpuMat buf;
return absSum(src, buf);
}
Scalar cv::gpu::absSum(const GpuMat& src, GpuMat& buf)
Scalar cv::gpu::absSum(const GpuMat& src, GpuMat& buf)
{
using namespace ::cv::gpu::device::matrix_reductions::sum;
using namespace cv::gpu::device::matrix_reductions::sum;
typedef void (*Caller)(const DevMem2Db, PtrStepb, double*, int);
static Caller multipass_callers[7] =
{
absSumMultipassCaller<unsigned char>, absSumMultipassCaller<char>,
absSumMultipassCaller<unsigned short>, absSumMultipassCaller<short>,
absSumMultipassCaller<int>, absSumMultipassCaller<float>, 0
static Caller multipass_callers[] =
{
absSumMultipassCaller<unsigned char>, absSumMultipassCaller<char>,
absSumMultipassCaller<unsigned short>, absSumMultipassCaller<short>,
absSumMultipassCaller<int>, absSumMultipassCaller<float>
};
static Caller singlepass_callers[7] =
{
absSumCaller<unsigned char>, absSumCaller<char>,
absSumCaller<unsigned short>, absSumCaller<short>,
absSumCaller<int>, absSumCaller<float>, 0
static Caller singlepass_callers[] =
{
absSumCaller<unsigned char>, absSumCaller<char>,
absSumCaller<unsigned short>, absSumCaller<short>,
absSumCaller<int>, absSumCaller<float>
};
CV_Assert(src.depth() <= CV_32F);
Size buf_size;
getBufSizeRequired(src.cols, src.rows, src.channels(), buf_size.width, buf_size.height);
getBufSizeRequired(src.cols, src.rows, src.channels(), buf_size.width, buf_size.height);
ensureSizeIsEnough(buf_size, CV_8U, buf);
Caller* callers = multipass_callers;
@ -309,7 +308,6 @@ Scalar cv::gpu::absSum(const GpuMat& src, GpuMat& buf)
callers = singlepass_callers;
Caller caller = callers[src.depth()];
if (!caller) CV_Error(CV_StsBadArg, "absSum: unsupported type");
double result[4];
caller(src, buf, result, src.channels());
@ -317,43 +315,44 @@ Scalar cv::gpu::absSum(const GpuMat& src, GpuMat& buf)
}
Scalar cv::gpu::sqrSum(const GpuMat& src)
Scalar cv::gpu::sqrSum(const GpuMat& src)
{
GpuMat buf;
return sqrSum(src, buf);
}
Scalar cv::gpu::sqrSum(const GpuMat& src, GpuMat& buf)
Scalar cv::gpu::sqrSum(const GpuMat& src, GpuMat& buf)
{
using namespace ::cv::gpu::device::matrix_reductions::sum;
using namespace cv::gpu::device::matrix_reductions::sum;
typedef void (*Caller)(const DevMem2Db, PtrStepb, double*, int);
static Caller multipass_callers[7] =
{
sqrSumMultipassCaller<unsigned char>, sqrSumMultipassCaller<char>,
sqrSumMultipassCaller<unsigned short>, sqrSumMultipassCaller<short>,
sqrSumMultipassCaller<int>, sqrSumMultipassCaller<float>, 0
static Caller multipass_callers[] =
{
sqrSumMultipassCaller<unsigned char>, sqrSumMultipassCaller<char>,
sqrSumMultipassCaller<unsigned short>, sqrSumMultipassCaller<short>,
sqrSumMultipassCaller<int>, sqrSumMultipassCaller<float>
};
static Caller singlepass_callers[7] =
{
sqrSumCaller<unsigned char>, sqrSumCaller<char>,
sqrSumCaller<unsigned short>, sqrSumCaller<short>,
sqrSumCaller<int>, sqrSumCaller<float>, 0
static Caller singlepass_callers[7] =
{
sqrSumCaller<unsigned char>, sqrSumCaller<char>,
sqrSumCaller<unsigned short>, sqrSumCaller<short>,
sqrSumCaller<int>, sqrSumCaller<float>
};
CV_Assert(src.depth() <= CV_32F);
Caller* callers = multipass_callers;
if (TargetArchs::builtWith(GLOBAL_ATOMICS) && DeviceInfo().supports(GLOBAL_ATOMICS))
callers = singlepass_callers;
Size buf_size;
getBufSizeRequired(src.cols, src.rows, src.channels(), buf_size.width, buf_size.height);
getBufSizeRequired(src.cols, src.rows, src.channels(), buf_size.width, buf_size.height);
ensureSizeIsEnough(buf_size, CV_8U, buf);
Caller caller = callers[src.depth()];
if (!caller) CV_Error(CV_StsBadArg, "sqrSum: unsupported type");
double result[4];
caller(src, buf, result, src.channels());
@ -363,24 +362,24 @@ Scalar cv::gpu::sqrSum(const GpuMat& src, GpuMat& buf)
////////////////////////////////////////////////////////////////////////
// Find min or max
namespace cv { namespace gpu { namespace device
namespace cv { namespace gpu { namespace device
{
namespace matrix_reductions
namespace matrix_reductions
{
namespace minmax
namespace minmax
{
void getBufSizeRequired(int cols, int rows, int elem_size, int& bufcols, int& bufrows);
template <typename T>
template <typename T>
void minMaxCaller(const DevMem2Db src, double* minval, double* maxval, PtrStepb buf);
template <typename T>
template <typename T>
void minMaxMaskCaller(const DevMem2Db src, const PtrStepb mask, double* minval, double* maxval, PtrStepb buf);
template <typename T>
template <typename T>
void minMaxMultipassCaller(const DevMem2Db src, double* minval, double* maxval, PtrStepb buf);
template <typename T>
template <typename T>
void minMaxMaskMultipassCaller(const DevMem2Db src, const PtrStepb mask, double* minval, double* maxval, PtrStepb buf);
}
}
@ -401,41 +400,47 @@ void cv::gpu::minMax(const GpuMat& src, double* minVal, double* maxVal, const Gp
typedef void (*Caller)(const DevMem2Db, double*, double*, PtrStepb);
typedef void (*MaskedCaller)(const DevMem2Db, const PtrStepb, double*, double*, PtrStepb);
static Caller multipass_callers[7] =
{
minMaxMultipassCaller<unsigned char>, minMaxMultipassCaller<char>,
minMaxMultipassCaller<unsigned short>, minMaxMultipassCaller<short>,
minMaxMultipassCaller<int>, minMaxMultipassCaller<float>, 0
static Caller multipass_callers[] =
{
minMaxMultipassCaller<unsigned char>, minMaxMultipassCaller<char>,
minMaxMultipassCaller<unsigned short>, minMaxMultipassCaller<short>,
minMaxMultipassCaller<int>, minMaxMultipassCaller<float>, 0
};
static Caller singlepass_callers[7] =
{
minMaxCaller<unsigned char>, minMaxCaller<char>,
minMaxCaller<unsigned short>, minMaxCaller<short>,
minMaxCaller<int>, minMaxCaller<float>, minMaxCaller<double>
static Caller singlepass_callers[] =
{
minMaxCaller<unsigned char>, minMaxCaller<char>,
minMaxCaller<unsigned short>, minMaxCaller<short>,
minMaxCaller<int>, minMaxCaller<float>, minMaxCaller<double>
};
static MaskedCaller masked_multipass_callers[7] =
{
minMaxMaskMultipassCaller<unsigned char>, minMaxMaskMultipassCaller<char>,
static MaskedCaller masked_multipass_callers[] =
{
minMaxMaskMultipassCaller<unsigned char>, minMaxMaskMultipassCaller<char>,
minMaxMaskMultipassCaller<unsigned short>, minMaxMaskMultipassCaller<short>,
minMaxMaskMultipassCaller<int>, minMaxMaskMultipassCaller<float>, 0
};
static MaskedCaller masked_singlepass_callers[7] =
{
minMaxMaskCaller<unsigned char>, minMaxMaskCaller<char>,
minMaxMaskCaller<unsigned short>, minMaxMaskCaller<short>,
minMaxMaskCaller<int>, minMaxMaskCaller<float>, minMaxMaskCaller<double>
static MaskedCaller masked_singlepass_callers[] =
{
minMaxMaskCaller<unsigned char>, minMaxMaskCaller<char>,
minMaxMaskCaller<unsigned short>, minMaxMaskCaller<short>,
minMaxMaskCaller<int>, minMaxMaskCaller<float>, minMaxMaskCaller<double>
};
CV_Assert(src.depth() <= CV_64F);
CV_Assert(src.channels() == 1);
CV_Assert(mask.empty() || (mask.type() == CV_8U && src.size() == mask.size()));
if (src.depth() == CV_64F)
{
if (!TargetArchs::builtWith(NATIVE_DOUBLE) || !DeviceInfo().supports(NATIVE_DOUBLE))
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
}
double minVal_; if (!minVal) minVal = &minVal_;
double maxVal_; if (!maxVal) maxVal = &maxVal_;
Size buf_size;
getBufSizeRequired(src.cols, src.rows, static_cast<int>(src.elemSize()), buf_size.width, buf_size.height);
ensureSizeIsEnough(buf_size, CV_8U, buf);
@ -447,7 +452,7 @@ void cv::gpu::minMax(const GpuMat& src, double* minVal, double* maxVal, const Gp
callers = singlepass_callers;
Caller caller = callers[src.type()];
if (!caller) CV_Error(CV_StsBadArg, "minMax: unsupported type");
CV_Assert(caller != 0);
caller(src, minVal, maxVal, buf);
}
else
@ -457,7 +462,7 @@ void cv::gpu::minMax(const GpuMat& src, double* minVal, double* maxVal, const Gp
callers = masked_singlepass_callers;
MaskedCaller caller = callers[src.type()];
if (!caller) CV_Error(CV_StsBadArg, "minMax: unsupported type");
CV_Assert(caller != 0);
caller(src, mask, minVal, maxVal, buf);
}
}
@ -466,36 +471,36 @@ void cv::gpu::minMax(const GpuMat& src, double* minVal, double* maxVal, const Gp
////////////////////////////////////////////////////////////////////////
// Locate min and max
namespace cv { namespace gpu { namespace device
namespace cv { namespace gpu { namespace device
{
namespace matrix_reductions
namespace matrix_reductions
{
namespace minmaxloc
namespace minmaxloc
{
void getBufSizeRequired(int cols, int rows, int elem_size, int& b1cols,
void getBufSizeRequired(int cols, int rows, int elem_size, int& b1cols,
int& b1rows, int& b2cols, int& b2rows);
template <typename T>
void minMaxLocCaller(const DevMem2Db src, double* minval, double* maxval,
template <typename T>
void minMaxLocCaller(const DevMem2Db src, double* minval, double* maxval,
int minloc[2], int maxloc[2], PtrStepb valBuf, PtrStepb locBuf);
template <typename T>
void minMaxLocMaskCaller(const DevMem2Db src, const PtrStepb mask, double* minval, double* maxval,
template <typename T>
void minMaxLocMaskCaller(const DevMem2Db src, const PtrStepb mask, double* minval, double* maxval,
int minloc[2], int maxloc[2], PtrStepb valBuf, PtrStepb locBuf);
template <typename T>
void minMaxLocMultipassCaller(const DevMem2Db src, double* minval, double* maxval,
template <typename T>
void minMaxLocMultipassCaller(const DevMem2Db src, double* minval, double* maxval,
int minloc[2], int maxloc[2], PtrStepb valBuf, PtrStepb locBuf);
template <typename T>
void minMaxLocMaskMultipassCaller(const DevMem2Db src, const PtrStepb mask, double* minval, double* maxval,
template <typename T>
void minMaxLocMaskMultipassCaller(const DevMem2Db src, const PtrStepb mask, double* minval, double* maxval,
int minloc[2], int maxloc[2], PtrStepb valBuf, PtrStepb locBuf);
}
}
}}}
void cv::gpu::minMaxLoc(const GpuMat& src, double* minVal, double* maxVal, Point* minLoc, Point* maxLoc, const GpuMat& mask)
{
{
GpuMat valBuf, locBuf;
minMaxLoc(src, minVal, maxVal, minLoc, maxLoc, mask, valBuf, locBuf);
}
@ -508,45 +513,51 @@ void cv::gpu::minMaxLoc(const GpuMat& src, double* minVal, double* maxVal, Point
typedef void (*Caller)(const DevMem2Db, double*, double*, int[2], int[2], PtrStepb, PtrStepb);
typedef void (*MaskedCaller)(const DevMem2Db, const PtrStepb, double*, double*, int[2], int[2], PtrStepb, PtrStepb);
static Caller multipass_callers[7] =
static Caller multipass_callers[] =
{
minMaxLocMultipassCaller<unsigned char>, minMaxLocMultipassCaller<char>,
minMaxLocMultipassCaller<unsigned short>, minMaxLocMultipassCaller<short>,
minMaxLocMultipassCaller<int>, minMaxLocMultipassCaller<float>, 0
minMaxLocMultipassCaller<unsigned char>, minMaxLocMultipassCaller<char>,
minMaxLocMultipassCaller<unsigned short>, minMaxLocMultipassCaller<short>,
minMaxLocMultipassCaller<int>, minMaxLocMultipassCaller<float>, 0
};
static Caller singlepass_callers[7] =
static Caller singlepass_callers[] =
{
minMaxLocCaller<unsigned char>, minMaxLocCaller<char>,
minMaxLocCaller<unsigned short>, minMaxLocCaller<short>,
minMaxLocCaller<int>, minMaxLocCaller<float>, minMaxLocCaller<double>
minMaxLocCaller<unsigned char>, minMaxLocCaller<char>,
minMaxLocCaller<unsigned short>, minMaxLocCaller<short>,
minMaxLocCaller<int>, minMaxLocCaller<float>, minMaxLocCaller<double>
};
static MaskedCaller masked_multipass_callers[7] =
static MaskedCaller masked_multipass_callers[] =
{
minMaxLocMaskMultipassCaller<unsigned char>, minMaxLocMaskMultipassCaller<char>,
minMaxLocMaskMultipassCaller<unsigned short>, minMaxLocMaskMultipassCaller<short>,
minMaxLocMaskMultipassCaller<int>, minMaxLocMaskMultipassCaller<float>, 0
minMaxLocMaskMultipassCaller<unsigned short>, minMaxLocMaskMultipassCaller<short>,
minMaxLocMaskMultipassCaller<int>, minMaxLocMaskMultipassCaller<float>, 0
};
static MaskedCaller masked_singlepass_callers[7] =
{
minMaxLocMaskCaller<unsigned char>, minMaxLocMaskCaller<char>,
minMaxLocMaskCaller<unsigned short>, minMaxLocMaskCaller<short>,
minMaxLocMaskCaller<int>, minMaxLocMaskCaller<float>, minMaxLocMaskCaller<double>
static MaskedCaller masked_singlepass_callers[] =
{
minMaxLocMaskCaller<unsigned char>, minMaxLocMaskCaller<char>,
minMaxLocMaskCaller<unsigned short>, minMaxLocMaskCaller<short>,
minMaxLocMaskCaller<int>, minMaxLocMaskCaller<float>, minMaxLocMaskCaller<double>
};
CV_Assert(src.depth() <= CV_64F);
CV_Assert(src.channels() == 1);
CV_Assert(mask.empty() || (mask.type() == CV_8U && src.size() == mask.size()));
if (src.depth() == CV_64F)
{
if (!TargetArchs::builtWith(NATIVE_DOUBLE) || !DeviceInfo().supports(NATIVE_DOUBLE))
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
}
double minVal_; if (!minVal) minVal = &minVal_;
double maxVal_; if (!maxVal) maxVal = &maxVal_;
int minLoc_[2];
int maxLoc_[2];
Size valbuf_size, locbuf_size;
getBufSizeRequired(src.cols, src.rows, static_cast<int>(src.elemSize()), valbuf_size.width,
getBufSizeRequired(src.cols, src.rows, static_cast<int>(src.elemSize()), valbuf_size.width,
valbuf_size.height, locbuf_size.width, locbuf_size.height);
ensureSizeIsEnough(valbuf_size, CV_8U, valBuf);
ensureSizeIsEnough(locbuf_size, CV_8U, locBuf);
@ -558,7 +569,7 @@ void cv::gpu::minMaxLoc(const GpuMat& src, double* minVal, double* maxVal, Point
callers = singlepass_callers;
Caller caller = callers[src.type()];
if (!caller) CV_Error(CV_StsBadArg, "minMaxLoc: unsupported type");
CV_Assert(caller != 0);
caller(src, minVal, maxVal, minLoc_, maxLoc_, valBuf, locBuf);
}
else
@ -568,7 +579,7 @@ void cv::gpu::minMaxLoc(const GpuMat& src, double* minVal, double* maxVal, Point
callers = masked_singlepass_callers;
MaskedCaller caller = callers[src.type()];
if (!caller) CV_Error(CV_StsBadArg, "minMaxLoc: unsupported type");
CV_Assert(caller != 0);
caller(src, mask, minVal, maxVal, minLoc_, maxLoc_, valBuf, locBuf);
}
@ -579,18 +590,18 @@ void cv::gpu::minMaxLoc(const GpuMat& src, double* minVal, double* maxVal, Point
//////////////////////////////////////////////////////////////////////////////
// Count non-zero elements
namespace cv { namespace gpu { namespace device
namespace cv { namespace gpu { namespace device
{
namespace matrix_reductions
namespace matrix_reductions
{
namespace countnonzero
namespace countnonzero
{
void getBufSizeRequired(int cols, int rows, int& bufcols, int& bufrows);
template <typename T>
template <typename T>
int countNonZeroCaller(const DevMem2Db src, PtrStepb buf);
template <typename T>
template <typename T>
int countNonZeroMultipassCaller(const DevMem2Db src, PtrStepb buf);
}
}
@ -609,21 +620,28 @@ int cv::gpu::countNonZero(const GpuMat& src, GpuMat& buf)
typedef int (*Caller)(const DevMem2Db src, PtrStepb buf);
static Caller multipass_callers[7] =
static Caller multipass_callers[7] =
{
countNonZeroMultipassCaller<unsigned char>, countNonZeroMultipassCaller<char>,
countNonZeroMultipassCaller<unsigned short>, countNonZeroMultipassCaller<short>,
countNonZeroMultipassCaller<int>, countNonZeroMultipassCaller<float>, 0
countNonZeroMultipassCaller<int>, countNonZeroMultipassCaller<float>, 0
};
static Caller singlepass_callers[7] =
static Caller singlepass_callers[7] =
{
countNonZeroCaller<unsigned char>, countNonZeroCaller<char>,
countNonZeroCaller<unsigned short>, countNonZeroCaller<short>,
countNonZeroCaller<int>, countNonZeroCaller<float>, countNonZeroCaller<double> };
CV_Assert(src.depth() <= CV_64F);
CV_Assert(src.channels() == 1);
if (src.depth() == CV_64F)
{
if (!TargetArchs::builtWith(NATIVE_DOUBLE) || !DeviceInfo().supports(NATIVE_DOUBLE))
CV_Error(CV_StsUnsupportedFormat, "The device doesn't support double");
}
Size buf_size;
getBufSizeRequired(src.cols, src.rows, buf_size.width, buf_size.height);
ensureSizeIsEnough(buf_size, CV_8U, buf);
@ -633,16 +651,16 @@ int cv::gpu::countNonZero(const GpuMat& src, GpuMat& buf)
callers = singlepass_callers;
Caller caller = callers[src.type()];
if (!caller) CV_Error(CV_StsBadArg, "countNonZero: unsupported type");
CV_Assert(caller != 0);
return caller(src, buf);
}
//////////////////////////////////////////////////////////////////////////////
// reduce
namespace cv { namespace gpu { namespace device
namespace cv { namespace gpu { namespace device
{
namespace matrix_reductions
namespace matrix_reductions
{
template <typename T, typename S, typename D> void reduceRows_gpu(const DevMem2Db& src, const DevMem2Db& dst, int reduceOp, cudaStream_t stream);
template <typename T, typename S, typename D> void reduceCols_gpu(const DevMem2Db& src, int cn, const DevMem2Db& dst, int reduceOp, cudaStream_t stream);
@ -666,7 +684,7 @@ void cv::gpu::reduce(const GpuMat& src, GpuMat& dst, int dim, int reduceOp, int
{
typedef void (*caller_t)(const DevMem2Db& src, const DevMem2Db& dst, int reduceOp, cudaStream_t stream);
static const caller_t callers[6][6] =
static const caller_t callers[6][6] =
{
{
reduceRows_gpu<unsigned char, int, unsigned char>,
@ -719,6 +737,7 @@ void cv::gpu::reduce(const GpuMat& src, GpuMat& dst, int dim, int reduceOp, int
};
const caller_t func = callers[src.depth()][dst.depth()];
if (!func)
CV_Error(CV_StsUnsupportedFormat, "Unsupported combination of input and output array formats");
@ -728,7 +747,7 @@ void cv::gpu::reduce(const GpuMat& src, GpuMat& dst, int dim, int reduceOp, int
{
typedef void (*caller_t)(const DevMem2Db& src, int cn, const DevMem2Db& dst, int reduceOp, cudaStream_t stream);
static const caller_t callers[6][6] =
static const caller_t callers[6][6] =
{
{
reduceCols_gpu<unsigned char, int, unsigned char>,
@ -781,10 +800,11 @@ void cv::gpu::reduce(const GpuMat& src, GpuMat& dst, int dim, int reduceOp, int
};
const caller_t func = callers[src.depth()][dst.depth()];
if (!func)
CV_Error(CV_StsUnsupportedFormat, "Unsupported combination of input and output array formats");
func(src, src.channels(), dst, reduceOp, StreamAccessor::getStream(stream));
func(src, src.channels(), dst, reduceOp, StreamAccessor::getStream(stream));
}
}

6
modules/gpu/src/precomp.hpp
Unescape View File

@ -74,7 +74,7 @@
#include "cuda.h"
#include "cuda_runtime_api.h"
#include "npp.h"
#ifdef HAVE_CUFFT
#include "cufft.h"
#endif
@ -85,7 +85,7 @@
#include "internal_shared.hpp"
#include "opencv2/gpu/stream_accessor.hpp"
#include "nvidia/core/NCV.hpp"
#include "nvidia/NPP_staging/NPP_staging.hpp"
#include "nvidia/NCVHaarObjectDetection.hpp"
@ -106,7 +106,7 @@
#error "OpenCV GPU module doesn't support NVIDIA compute capability 1.0"
#endif
static inline void throw_nogpu() { CV_Error(CV_GpuNotSupported, "The called functionality is disabled for current build or platform"); }
static inline void throw_nogpu() { CV_Error(CV_StsNotImplemented, "The called functionality is disabled for current build or platform"); }
#else /* defined(HAVE_CUDA) */

556
modules/gpu/test/test_core.cpp
Unescape View File

@ -995,13 +995,28 @@ TEST_P(AbsDiff, Array)
cv::Mat src1 = randomMat(size, depth);
cv::Mat src2 = randomMat(size, depth);
cv::gpu::GpuMat dst = createMat(size, depth, useRoi);
cv::gpu::absdiff(loadMat(src1, useRoi), loadMat(src2, useRoi), dst);
if (depth == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
{
try
{
cv::gpu::GpuMat dst;
cv::gpu::absdiff(loadMat(src1), loadMat(src2), dst);
}
catch (const cv::Exception& e)
{
ASSERT_EQ(CV_StsUnsupportedFormat, e.code);
}
}
else
{
cv::gpu::GpuMat dst = createMat(size, depth, useRoi);
cv::gpu::absdiff(loadMat(src1, useRoi), loadMat(src2, useRoi), dst);
cv::Mat dst_gold;
cv::absdiff(src1, src2, dst_gold);
cv::Mat dst_gold;
cv::absdiff(src1, src2, dst_gold);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
}
TEST_P(AbsDiff, Scalar)
@ -1009,13 +1024,28 @@ TEST_P(AbsDiff, Scalar)
cv::Mat src = randomMat(size, depth);
cv::Scalar val = randomScalar(0.0, 255.0);
cv::gpu::GpuMat dst = createMat(size, depth, useRoi);
cv::gpu::absdiff(loadMat(src, useRoi), val, dst);
if (depth == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
{
try
{
cv::gpu::GpuMat dst;
cv::gpu::absdiff(loadMat(src), val, dst);
}
catch (const cv::Exception& e)
{
ASSERT_EQ(CV_StsUnsupportedFormat, e.code);
}
}
else
{
cv::gpu::GpuMat dst = createMat(size, depth, useRoi);
cv::gpu::absdiff(loadMat(src, useRoi), val, dst);
cv::Mat dst_gold;
cv::absdiff(src, val, dst_gold);
cv::Mat dst_gold;
cv::absdiff(src, val, dst_gold);
EXPECT_MAT_NEAR(dst_gold, dst, depth <= CV_32F ? 1.0 : 1e-5);
EXPECT_MAT_NEAR(dst_gold, dst, depth <= CV_32F ? 1.0 : 1e-5);
}
}
INSTANTIATE_TEST_CASE_P(GPU_Core, AbsDiff, testing::Combine(
@ -1243,6 +1273,40 @@ INSTANTIATE_TEST_CASE_P(GPU_Core, Log, testing::Combine(
////////////////////////////////////////////////////////////////////////////////
// Exp
template <typename T> void expImpl(const cv::Mat& src, cv::Mat& dst)
{
dst.create(src.size(), src.type());
for (int y = 0; y < src.rows; ++y)
{
for (int x = 0; x < src.cols; ++x)
dst.at<T>(y, x) = cv::saturate_cast<T>(static_cast<int>(std::exp(static_cast<float>(src.at<T>(y, x)))));
}
}
void expImpl_float(const cv::Mat& src, cv::Mat& dst)
{
dst.create(src.size(), src.type());
for (int y = 0; y < src.rows; ++y)
{
for (int x = 0; x < src.cols; ++x)
dst.at<float>(y, x) = std::exp(static_cast<float>(src.at<float>(y, x)));
}
}
void expGold(const cv::Mat& src, cv::Mat& dst)
{
typedef void (*func_t)(const cv::Mat& src, cv::Mat& dst);
const func_t funcs[] =
{
expImpl<uchar>, expImpl<schar>, expImpl<ushort>, expImpl<short>,
expImpl<int>, expImpl_float
};
funcs[src.depth()](src, dst);
}
PARAM_TEST_CASE(Exp, cv::gpu::DeviceInfo, cv::Size, MatType, UseRoi)
{
cv::gpu::DeviceInfo devInfo;
@ -1269,7 +1333,7 @@ TEST_P(Exp, Accuracy)
cv::gpu::exp(loadMat(src, useRoi), dst);
cv::Mat dst_gold;
cv::exp(src, dst_gold);
expGold(src, dst_gold);
EXPECT_MAT_NEAR(dst_gold, dst, 1e-2);
}
@ -1277,7 +1341,10 @@ TEST_P(Exp, Accuracy)
INSTANTIATE_TEST_CASE_P(GPU_Core, Exp, testing::Combine(
ALL_DEVICES,
DIFFERENT_SIZES,
testing::Values(MatType(CV_32FC1)),
testing::Values(MatType(CV_8UC1),
MatType(CV_16UC1),
MatType(CV_16SC1),
MatType(CV_32FC1)),
WHOLE_SUBMAT));
////////////////////////////////////////////////////////////////////////////////
@ -1311,13 +1378,28 @@ TEST_P(Compare, Accuracy)
cv::Mat src1 = randomMat(size, depth);
cv::Mat src2 = randomMat(size, depth);
cv::gpu::GpuMat dst = createMat(size, CV_8UC1, useRoi);
cv::gpu::compare(loadMat(src1, useRoi), loadMat(src2, useRoi), dst, cmp_code);
if (depth == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
{
try
{
cv::gpu::GpuMat dst;
cv::gpu::compare(loadMat(src1), loadMat(src2), dst, cmp_code);
}
catch (const cv::Exception& e)
{
ASSERT_EQ(CV_StsUnsupportedFormat, e.code);
}
}
else
{
cv::gpu::GpuMat dst = createMat(size, CV_8UC1, useRoi);
cv::gpu::compare(loadMat(src1, useRoi), loadMat(src2, useRoi), dst, cmp_code);
cv::Mat dst_gold;
cv::compare(src1, src2, dst_gold, cmp_code);
cv::Mat dst_gold;
cv::compare(src1, src2, dst_gold, cmp_code);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
}
INSTANTIATE_TEST_CASE_P(GPU_Core, Compare, testing::Combine(
@ -1635,17 +1717,60 @@ PARAM_TEST_CASE(Min, cv::gpu::DeviceInfo, cv::Size, MatDepth, UseRoi)
}
};
TEST_P(Min, Accuracy)
TEST_P(Min, Array)
{
cv::Mat src1 = randomMat(size, depth);
cv::Mat src2 = randomMat(size, depth);
cv::gpu::GpuMat dst = createMat(size, depth, useRoi);
cv::gpu::min(loadMat(src1, useRoi), loadMat(src2, useRoi), dst);
if (depth == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
{
try
{
cv::gpu::GpuMat dst;
cv::gpu::min(loadMat(src1), loadMat(src2), dst);
}
catch (const cv::Exception& e)
{
ASSERT_EQ(CV_StsUnsupportedFormat, e.code);
}
}
else
{
cv::gpu::GpuMat dst = createMat(size, depth, useRoi);
cv::gpu::min(loadMat(src1, useRoi), loadMat(src2, useRoi), dst);
cv::Mat dst_gold = cv::min(src1, src2);
cv::Mat dst_gold = cv::min(src1, src2);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
}
TEST_P(Min, Scalar)
{
cv::Mat src = randomMat(size, depth);
double val = randomDouble(0.0, 255.0);
if (depth == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
{
try
{
cv::gpu::GpuMat dst;
cv::gpu::min(loadMat(src), val, dst);
}
catch (const cv::Exception& e)
{
ASSERT_EQ(CV_StsUnsupportedFormat, e.code);
}
}
else
{
cv::gpu::GpuMat dst = createMat(size, depth, useRoi);
cv::gpu::min(loadMat(src, useRoi), val, dst);
cv::Mat dst_gold = cv::min(src, val);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
}
INSTANTIATE_TEST_CASE_P(GPU_Core, Min, testing::Combine(
@ -1675,17 +1800,60 @@ PARAM_TEST_CASE(Max, cv::gpu::DeviceInfo, cv::Size, MatDepth, UseRoi)
}
};
TEST_P(Max, Accuracy)
TEST_P(Max, Array)
{
cv::Mat src1 = randomMat(size, depth);
cv::Mat src2 = randomMat(size, depth);
cv::gpu::GpuMat dst = createMat(size, depth, useRoi);
cv::gpu::max(loadMat(src1, useRoi), loadMat(src2, useRoi), dst);
if (depth == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
{
try
{
cv::gpu::GpuMat dst;
cv::gpu::max(loadMat(src1), loadMat(src2), dst);
}
catch (const cv::Exception& e)
{
ASSERT_EQ(CV_StsUnsupportedFormat, e.code);
}
}
else
{
cv::gpu::GpuMat dst = createMat(size, depth, useRoi);
cv::gpu::max(loadMat(src1, useRoi), loadMat(src2, useRoi), dst);
cv::Mat dst_gold = cv::max(src1, src2);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
}
cv::Mat dst_gold = cv::max(src1, src2);
TEST_P(Max, Scalar)
{
cv::Mat src = randomMat(size, depth);
double val = randomDouble(0.0, 255.0);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
if (depth == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
{
try
{
cv::gpu::GpuMat dst;
cv::gpu::max(loadMat(src), val, dst);
}
catch (const cv::Exception& e)
{
ASSERT_EQ(CV_StsUnsupportedFormat, e.code);
}
}
else
{
cv::gpu::GpuMat dst = createMat(size, depth, useRoi);
cv::gpu::max(loadMat(src, useRoi), val, dst);
cv::Mat dst_gold = cv::max(src, val);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
}
INSTANTIATE_TEST_CASE_P(GPU_Core, Max, testing::Combine(
@ -1723,13 +1891,28 @@ TEST_P(Pow, Accuracy)
if (src.depth() < CV_32F)
power = static_cast<int>(power);
cv::gpu::GpuMat dst = createMat(size, depth, useRoi);
cv::gpu::pow(loadMat(src, useRoi), power, dst);
if (depth == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
{
try
{
cv::gpu::GpuMat dst;
cv::gpu::pow(loadMat(src), power, dst);
}
catch (const cv::Exception& e)
{
ASSERT_EQ(CV_StsUnsupportedFormat, e.code);
}
}
else
{
cv::gpu::GpuMat dst = createMat(size, depth, useRoi);
cv::gpu::pow(loadMat(src, useRoi), power, dst);
cv::Mat dst_gold;
cv::pow(src, power, dst_gold);
cv::Mat dst_gold;
cv::pow(src, power, dst_gold);
EXPECT_MAT_NEAR(dst_gold, dst, depth < CV_32F ? 0.0 : 1e-1);
EXPECT_MAT_NEAR(dst_gold, dst, depth < CV_32F ? 0.0 : 1e-1);
}
}
INSTANTIATE_TEST_CASE_P(GPU_Core, Pow, testing::Combine(
@ -1750,7 +1933,6 @@ PARAM_TEST_CASE(AddWeighted, cv::gpu::DeviceInfo, cv::Size, MatDepth, MatDepth,
int dst_depth;
bool useRoi;
virtual void SetUp()
{
devInfo = GET_PARAM(0);
@ -1772,13 +1954,28 @@ TEST_P(AddWeighted, Accuracy)
double beta = randomDouble(-10.0, 10.0);
double gamma = randomDouble(-10.0, 10.0);
cv::gpu::GpuMat dst = createMat(size, dst_depth, useRoi);
cv::gpu::addWeighted(loadMat(src1, useRoi), alpha, loadMat(src2, useRoi), beta, gamma, dst, dst_depth);
if ((depth1 == CV_64F || depth2 == CV_64F || dst_depth == CV_64F) && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
{
try
{
cv::gpu::GpuMat dst;
cv::gpu::addWeighted(loadMat(src1), alpha, loadMat(src2), beta, gamma, dst, dst_depth);
}
catch (const cv::Exception& e)
{
ASSERT_EQ(CV_StsUnsupportedFormat, e.code);
}
}
else
{
cv::gpu::GpuMat dst = createMat(size, dst_depth, useRoi);
cv::gpu::addWeighted(loadMat(src1, useRoi), alpha, loadMat(src2, useRoi), beta, gamma, dst, dst_depth);
cv::Mat dst_gold;
cv::addWeighted(src1, alpha, src2, beta, gamma, dst_gold, dst_depth);
cv::Mat dst_gold;
cv::addWeighted(src1, alpha, src2, beta, gamma, dst_gold, dst_depth);
EXPECT_MAT_NEAR(dst_gold, dst, dst_depth < CV_32F ? 1.0 : 1e-12);
EXPECT_MAT_NEAR(dst_gold, dst, dst_depth < CV_32F ? 1.0 : 1e-12);
}
}
INSTANTIATE_TEST_CASE_P(GPU_Core, AddWeighted, testing::Combine(
@ -1823,13 +2020,52 @@ TEST_P(GEMM, Accuracy)
double alpha = randomDouble(-10.0, 10.0);
double beta = randomDouble(-10.0, 10.0);
cv::gpu::GpuMat dst = createMat(size, type, useRoi);
cv::gpu::gemm(loadMat(src1, useRoi), loadMat(src2, useRoi), alpha, loadMat(src3, useRoi), beta, dst, flags);
#ifndef HAVE_CUBLAS
try
{
cv::gpu::GpuMat dst;
cv::gpu::gemm(loadMat(src1), loadMat(src2), alpha, loadMat(src3), beta, dst, flags);
}
catch (const cv::Exception& e)
{
ASSERT_EQ(CV_StsNotImplemented, e.code);
}
#else
if (CV_MAT_DEPTH(type) == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
{
try
{
cv::gpu::GpuMat dst;
cv::gpu::gemm(loadMat(src1), loadMat(src2), alpha, loadMat(src3), beta, dst, flags);
}
catch (const cv::Exception& e)
{
ASSERT_EQ(CV_StsUnsupportedFormat, e.code);
}
}
else if (type == CV_64FC2 && flags != 0)
{
try
{
cv::gpu::GpuMat dst;
cv::gpu::gemm(loadMat(src1), loadMat(src2), alpha, loadMat(src3), beta, dst, flags);
}
catch (const cv::Exception& e)
{
ASSERT_EQ(CV_StsNotImplemented, e.code);
}
}
else
{
cv::gpu::GpuMat dst = createMat(size, type, useRoi);
cv::gpu::gemm(loadMat(src1, useRoi), loadMat(src2, useRoi), alpha, loadMat(src3, useRoi), beta, dst, flags);
cv::Mat dst_gold;
cv::gemm(src1, src2, alpha, src3, beta, dst_gold, flags);
cv::Mat dst_gold;
cv::gemm(src1, src2, alpha, src3, beta, dst_gold, flags);
EXPECT_MAT_NEAR(dst_gold, dst, CV_MAT_DEPTH(type) == CV_32F ? 1e-1 : 1e-10);
EXPECT_MAT_NEAR(dst_gold, dst, CV_MAT_DEPTH(type) == CV_32F ? 1e-1 : 1e-10);
}
#endif
}
INSTANTIATE_TEST_CASE_P(GPU_Core, GEMM, testing::Combine(
@ -1864,13 +2100,28 @@ TEST_P(Transpose, Accuracy)
{
cv::Mat src = randomMat(size, type);
cv::gpu::GpuMat dst = createMat(cv::Size(size.height, size.width), type, useRoi);
cv::gpu::transpose(loadMat(src, useRoi), dst);
if (CV_MAT_DEPTH(type) == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
{
try
{
cv::gpu::GpuMat dst;
cv::gpu::transpose(loadMat(src), dst);
}
catch (const cv::Exception& e)
{
ASSERT_EQ(CV_StsUnsupportedFormat, e.code);
}
}
else
{
cv::gpu::GpuMat dst = createMat(cv::Size(size.height, size.width), type, useRoi);
cv::gpu::transpose(loadMat(src, useRoi), dst);
cv::Mat dst_gold;
cv::transpose(src, dst_gold);
cv::Mat dst_gold;
cv::transpose(src, dst_gold);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
EXPECT_MAT_NEAR(dst_gold, dst, 0.0);
}
}
INSTANTIATE_TEST_CASE_P(GPU_Core, Transpose, testing::Combine(
@ -2498,14 +2749,29 @@ TEST_P(MinMax, WithoutMask)
{
cv::Mat src = randomMat(size, depth);
double minVal, maxVal;
cv::gpu::minMax(loadMat(src, useRoi), &minVal, &maxVal);
if (depth == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
{
try
{
double minVal, maxVal;
cv::gpu::minMax(loadMat(src), &minVal, &maxVal);
}
catch (const cv::Exception& e)
{
ASSERT_EQ(CV_StsUnsupportedFormat, e.code);
}
}
else
{
double minVal, maxVal;
cv::gpu::minMax(loadMat(src, useRoi), &minVal, &maxVal);
double minVal_gold, maxVal_gold;
minMaxLocGold(src, &minVal_gold, &maxVal_gold);
double minVal_gold, maxVal_gold;
minMaxLocGold(src, &minVal_gold, &maxVal_gold);
EXPECT_DOUBLE_EQ(minVal_gold, minVal);
EXPECT_DOUBLE_EQ(maxVal_gold, maxVal);
EXPECT_DOUBLE_EQ(minVal_gold, minVal);
EXPECT_DOUBLE_EQ(maxVal_gold, maxVal);
}
}
TEST_P(MinMax, WithMask)
@ -2513,21 +2779,60 @@ TEST_P(MinMax, WithMask)
cv::Mat src = randomMat(size, depth);
cv::Mat mask = randomMat(size, CV_8UC1, 0.0, 2.0);
double minVal, maxVal;
cv::gpu::minMax(loadMat(src, useRoi), &minVal, &maxVal, loadMat(mask, useRoi));
if (depth == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
{
try
{
double minVal, maxVal;
cv::gpu::minMax(loadMat(src), &minVal, &maxVal, loadMat(mask));
}
catch (const cv::Exception& e)
{
ASSERT_EQ(CV_StsUnsupportedFormat, e.code);
}
}
else
{
double minVal, maxVal;
cv::gpu::minMax(loadMat(src, useRoi), &minVal, &maxVal, loadMat(mask, useRoi));
double minVal_gold, maxVal_gold;
minMaxLocGold(src, &minVal_gold, &maxVal_gold, 0, 0, mask);
double minVal_gold, maxVal_gold;
minMaxLocGold(src, &minVal_gold, &maxVal_gold, 0, 0, mask);
EXPECT_DOUBLE_EQ(minVal_gold, minVal);
EXPECT_DOUBLE_EQ(maxVal_gold, maxVal);
EXPECT_DOUBLE_EQ(minVal_gold, minVal);
EXPECT_DOUBLE_EQ(maxVal_gold, maxVal);
}
}
TEST_P(MinMax, NullPtr)
{
cv::Mat src = randomMat(size, depth);
cv::gpu::minMax(loadMat(src, useRoi), 0, 0);
if (depth == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
{
try
{
double minVal, maxVal;
cv::gpu::minMax(loadMat(src), &minVal, 0);
cv::gpu::minMax(loadMat(src), 0, &maxVal);
}
catch (const cv::Exception& e)
{
ASSERT_EQ(CV_StsUnsupportedFormat, e.code);
}
}
else
{
double minVal, maxVal;
cv::gpu::minMax(loadMat(src, useRoi), &minVal, 0);
cv::gpu::minMax(loadMat(src, useRoi), 0, &maxVal);
double minVal_gold, maxVal_gold;
minMaxLocGold(src, &minVal_gold, &maxVal_gold, 0, 0);
EXPECT_DOUBLE_EQ(minVal_gold, minVal);
EXPECT_DOUBLE_EQ(maxVal_gold, maxVal);
}
}
INSTANTIATE_TEST_CASE_P(GPU_Core, MinMax, testing::Combine(
@ -2585,19 +2890,35 @@ TEST_P(MinMaxLoc, WithoutMask)
{
cv::Mat src = randomMat(size, depth);
double minVal, maxVal;
cv::Point minLoc, maxLoc;
cv::gpu::minMaxLoc(loadMat(src, useRoi), &minVal, &maxVal, &minLoc, &maxLoc);
if (depth == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
{
try
{
double minVal, maxVal;
cv::Point minLoc, maxLoc;
cv::gpu::minMaxLoc(loadMat(src), &minVal, &maxVal, &minLoc, &maxLoc);
}
catch (const cv::Exception& e)
{
ASSERT_EQ(CV_StsUnsupportedFormat, e.code);
}
}
else
{
double minVal, maxVal;
cv::Point minLoc, maxLoc;
cv::gpu::minMaxLoc(loadMat(src, useRoi), &minVal, &maxVal, &minLoc, &maxLoc);
double minVal_gold, maxVal_gold;
cv::Point minLoc_gold, maxLoc_gold;
minMaxLocGold(src, &minVal_gold, &maxVal_gold, &minLoc_gold, &maxLoc_gold);
double minVal_gold, maxVal_gold;
cv::Point minLoc_gold, maxLoc_gold;
minMaxLocGold(src, &minVal_gold, &maxVal_gold, &minLoc_gold, &maxLoc_gold);
EXPECT_DOUBLE_EQ(minVal_gold, minVal);
EXPECT_DOUBLE_EQ(maxVal_gold, maxVal);
EXPECT_DOUBLE_EQ(minVal_gold, minVal);
EXPECT_DOUBLE_EQ(maxVal_gold, maxVal);
expectEqual(src, minLoc_gold, minLoc);
expectEqual(src, maxLoc_gold, maxLoc);
expectEqual(src, minLoc_gold, minLoc);
expectEqual(src, maxLoc_gold, maxLoc);
}
}
TEST_P(MinMaxLoc, WithMask)
@ -2605,26 +2926,76 @@ TEST_P(MinMaxLoc, WithMask)
cv::Mat src = randomMat(size, depth);
cv::Mat mask = randomMat(size, CV_8UC1, 0.0, 2.0);
double minVal, maxVal;
cv::Point minLoc, maxLoc;
cv::gpu::minMaxLoc(loadMat(src, useRoi), &minVal, &maxVal, &minLoc, &maxLoc, loadMat(mask, useRoi));
if (depth == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
{
try
{
double minVal, maxVal;
cv::Point minLoc, maxLoc;
cv::gpu::minMaxLoc(loadMat(src), &minVal, &maxVal, &minLoc, &maxLoc, loadMat(mask));
}
catch (const cv::Exception& e)
{
ASSERT_EQ(CV_StsUnsupportedFormat, e.code);
}
}
else
{
double minVal, maxVal;
cv::Point minLoc, maxLoc;
cv::gpu::minMaxLoc(loadMat(src, useRoi), &minVal, &maxVal, &minLoc, &maxLoc, loadMat(mask, useRoi));
double minVal_gold, maxVal_gold;
cv::Point minLoc_gold, maxLoc_gold;
minMaxLocGold(src, &minVal_gold, &maxVal_gold, &minLoc_gold, &maxLoc_gold, mask);
double minVal_gold, maxVal_gold;
cv::Point minLoc_gold, maxLoc_gold;
minMaxLocGold(src, &minVal_gold, &maxVal_gold, &minLoc_gold, &maxLoc_gold, mask);
EXPECT_DOUBLE_EQ(minVal_gold, minVal);
EXPECT_DOUBLE_EQ(maxVal_gold, maxVal);
EXPECT_DOUBLE_EQ(minVal_gold, minVal);
EXPECT_DOUBLE_EQ(maxVal_gold, maxVal);
expectEqual(src, minLoc_gold, minLoc);
expectEqual(src, maxLoc_gold, maxLoc);
expectEqual(src, minLoc_gold, minLoc);
expectEqual(src, maxLoc_gold, maxLoc);
}
}
TEST_P(MinMaxLoc, NullPtr)
{
cv::Mat src = randomMat(size, depth);
cv::gpu::minMaxLoc(loadMat(src, useRoi), 0, 0, 0, 0);
if (depth == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
{
try
{
double minVal, maxVal;
cv::Point minLoc, maxLoc;
cv::gpu::minMaxLoc(loadMat(src, useRoi), &minVal, 0, 0, 0);
cv::gpu::minMaxLoc(loadMat(src, useRoi), 0, &maxVal, 0, 0);
cv::gpu::minMaxLoc(loadMat(src, useRoi), 0, 0, &minLoc, 0);
cv::gpu::minMaxLoc(loadMat(src, useRoi), 0, 0, 0, &maxLoc);
}
catch (const cv::Exception& e)
{
ASSERT_EQ(CV_StsUnsupportedFormat, e.code);
}
}
else
{
double minVal, maxVal;
cv::Point minLoc, maxLoc;
cv::gpu::minMaxLoc(loadMat(src, useRoi), &minVal, 0, 0, 0);
cv::gpu::minMaxLoc(loadMat(src, useRoi), 0, &maxVal, 0, 0);
cv::gpu::minMaxLoc(loadMat(src, useRoi), 0, 0, &minLoc, 0);
cv::gpu::minMaxLoc(loadMat(src, useRoi), 0, 0, 0, &maxLoc);
double minVal_gold, maxVal_gold;
cv::Point minLoc_gold, maxLoc_gold;
minMaxLocGold(src, &minVal_gold, &maxVal_gold, &minLoc_gold, &maxLoc_gold);
EXPECT_DOUBLE_EQ(minVal_gold, minVal);
EXPECT_DOUBLE_EQ(maxVal_gold, maxVal);
expectEqual(src, minLoc_gold, minLoc);
expectEqual(src, maxLoc_gold, maxLoc);
}
}
INSTANTIATE_TEST_CASE_P(GPU_Core, MinMaxLoc, testing::Combine(
@ -2661,12 +3032,25 @@ TEST_P(CountNonZero, Accuracy)
cv::Mat src;
srcBase.convertTo(src, depth);
int val = cv::gpu::countNonZero(loadMat(src, useRoi));
int val_gold = cv::countNonZero(src);
if (depth == CV_64F && !supportFeature(devInfo, cv::gpu::NATIVE_DOUBLE))
{
try
{
cv::gpu::countNonZero(loadMat(src));
}
catch (const cv::Exception& e)
{
ASSERT_EQ(CV_StsUnsupportedFormat, e.code);
}
}
else
{
int val = cv::gpu::countNonZero(loadMat(src, useRoi));
int val_gold = cv::countNonZero(src);
ASSERT_EQ(val_gold, val);
ASSERT_EQ(val_gold, val);
}
}
INSTANTIATE_TEST_CASE_P(GPU_Core, CountNonZero, testing::Combine(

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