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提交 b40a7ffb 编写于 作者: A Alexander Alekhin
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core: dispatch sum

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modules/core/CMakeLists.txt
浏览文件 @ b40a7ffb
......@@ -5,6 +5,7 @@ ocv_add_dispatched_file(stat SSE4_2 AVX2)
ocv_add_dispatched_file(arithm SSE2 SSE4_1 AVX2 VSX3)
ocv_add_dispatched_file(convert SSE2 AVX2)
ocv_add_dispatched_file(convert_scale SSE2 AVX2)
ocv_add_dispatched_file(sum SSE2 AVX2)
# dispatching for accuracy tests
ocv_add_dispatched_file_force_all(test_intrin128 TEST SSE2 SSE3 SSSE3 SSE4_1 SSE4_2 AVX FP16 AVX2)
......
modules/core/src/sum.dispatch.cpp
浏览文件 @ b40a7ffb
......@@ -7,440 +7,17 @@
#include "opencl_kernels_core.hpp"
#include "stat.hpp"
namespace cv
{
template <typename T, typename ST>
struct Sum_SIMD
{
int operator () (const T *, const uchar *, ST *, int, int) const
{
return 0;
}
};
#if CV_SIMD
template <>
struct Sum_SIMD<uchar, int>
{
int operator () (const uchar * src0, const uchar * mask, int * dst, int len, int cn) const
{
if (mask || (cn != 1 && cn != 2 && cn != 4))
return 0;
len *= cn;
int x = 0;
v_uint32 v_sum = vx_setzero_u32();
int len0 = len & -v_uint8::nlanes;
while (x < len0)
{
const int len_tmp = min(x + 256*v_uint16::nlanes, len0);
v_uint16 v_sum16 = vx_setzero_u16();
for (; x < len_tmp; x += v_uint8::nlanes)
{
v_uint16 v_src0, v_src1;
v_expand(vx_load(src0 + x), v_src0, v_src1);
v_sum16 += v_src0 + v_src1;
}
v_uint32 v_half0, v_half1;
v_expand(v_sum16, v_half0, v_half1);
v_sum += v_half0 + v_half1;
}
if (x <= len - v_uint16::nlanes)
{
v_uint32 v_half0, v_half1;
v_expand(vx_load_expand(src0 + x), v_half0, v_half1);
v_sum += v_half0 + v_half1;
x += v_uint16::nlanes;
}
if (x <= len - v_uint32::nlanes)
{
v_sum += vx_load_expand_q(src0 + x);
x += v_uint32::nlanes;
}
if (cn == 1)
*dst += v_reduce_sum(v_sum);
else
{
uint32_t CV_DECL_ALIGNED(CV_SIMD_WIDTH) ar[v_uint32::nlanes];
v_store_aligned(ar, v_sum);
for (int i = 0; i < v_uint32::nlanes; ++i)
dst[i % cn] += ar[i];
}
v_cleanup();
return x / cn;
}
};
template <>
struct Sum_SIMD<schar, int>
{
int operator () (const schar * src0, const uchar * mask, int * dst, int len, int cn) const
{
if (mask || (cn != 1 && cn != 2 && cn != 4))
return 0;
len *= cn;
int x = 0;
v_int32 v_sum = vx_setzero_s32();
int len0 = len & -v_int8::nlanes;
while (x < len0)
{
const int len_tmp = min(x + 256*v_int16::nlanes, len0);
v_int16 v_sum16 = vx_setzero_s16();
for (; x < len_tmp; x += v_int8::nlanes)
{
v_int16 v_src0, v_src1;
v_expand(vx_load(src0 + x), v_src0, v_src1);
v_sum16 += v_src0 + v_src1;
}
v_int32 v_half0, v_half1;
v_expand(v_sum16, v_half0, v_half1);
v_sum += v_half0 + v_half1;
}
if (x <= len - v_int16::nlanes)
{
v_int32 v_half0, v_half1;
v_expand(vx_load_expand(src0 + x), v_half0, v_half1);
v_sum += v_half0 + v_half1;
x += v_int16::nlanes;
}
if (x <= len - v_int32::nlanes)
{
v_sum += vx_load_expand_q(src0 + x);
x += v_int32::nlanes;
}
if (cn == 1)
*dst += v_reduce_sum(v_sum);
else
{
int32_t CV_DECL_ALIGNED(CV_SIMD_WIDTH) ar[v_int32::nlanes];
v_store_aligned(ar, v_sum);
for (int i = 0; i < v_int32::nlanes; ++i)
dst[i % cn] += ar[i];
}
v_cleanup();
return x / cn;
}
};
template <>
struct Sum_SIMD<ushort, int>
{
int operator () (const ushort * src0, const uchar * mask, int * dst, int len, int cn) const
{
if (mask || (cn != 1 && cn != 2 && cn != 4))
return 0;
len *= cn;
int x = 0;
v_uint32 v_sum = vx_setzero_u32();
for (; x <= len - v_uint16::nlanes; x += v_uint16::nlanes)
{
v_uint32 v_src0, v_src1;
v_expand(vx_load(src0 + x), v_src0, v_src1);
v_sum += v_src0 + v_src1;
}
if (x <= len - v_uint32::nlanes)
{
v_sum += vx_load_expand(src0 + x);
x += v_uint32::nlanes;
}
if (cn == 1)
*dst += v_reduce_sum(v_sum);
else
{
uint32_t CV_DECL_ALIGNED(CV_SIMD_WIDTH) ar[v_uint32::nlanes];
v_store_aligned(ar, v_sum);
for (int i = 0; i < v_uint32::nlanes; ++i)
dst[i % cn] += ar[i];
}
v_cleanup();
return x / cn;
}
};
template <>
struct Sum_SIMD<short, int>
{
int operator () (const short * src0, const uchar * mask, int * dst, int len, int cn) const
{
if (mask || (cn != 1 && cn != 2 && cn != 4))
return 0;
len *= cn;
int x = 0;
v_int32 v_sum = vx_setzero_s32();
for (; x <= len - v_int16::nlanes; x += v_int16::nlanes)
{
v_int32 v_src0, v_src1;
v_expand(vx_load(src0 + x), v_src0, v_src1);
v_sum += v_src0 + v_src1;
}
if (x <= len - v_int32::nlanes)
{
v_sum += vx_load_expand(src0 + x);
x += v_int32::nlanes;
}
if (cn == 1)
*dst += v_reduce_sum(v_sum);
else
{
int32_t CV_DECL_ALIGNED(CV_SIMD_WIDTH) ar[v_int32::nlanes];
v_store_aligned(ar, v_sum);
for (int i = 0; i < v_int32::nlanes; ++i)
dst[i % cn] += ar[i];
}
v_cleanup();
return x / cn;
}
};
#include "sum.simd.hpp"
#include "sum.simd_declarations.hpp" // defines CV_CPU_DISPATCH_MODES_ALL=AVX2,...,BASELINE based on CMakeLists.txt content
#if CV_SIMD_64F
template <>
struct Sum_SIMD<int, double>
{
int operator () (const int * src0, const uchar * mask, double * dst, int len, int cn) const
{
if (mask || (cn != 1 && cn != 2 && cn != 4))
return 0;
len *= cn;
int x = 0;
v_float64 v_sum0 = vx_setzero_f64();
v_float64 v_sum1 = vx_setzero_f64();
for (; x <= len - 2 * v_int32::nlanes; x += 2 * v_int32::nlanes)
{
v_int32 v_src0 = vx_load(src0 + x);
v_int32 v_src1 = vx_load(src0 + x + v_int32::nlanes);
v_sum0 += v_cvt_f64(v_src0) + v_cvt_f64(v_src1);
v_sum1 += v_cvt_f64_high(v_src0) + v_cvt_f64_high(v_src1);
}
#if CV_SIMD256 || CV_SIMD512
double CV_DECL_ALIGNED(CV_SIMD_WIDTH) ar[v_float64::nlanes];
v_store_aligned(ar, v_sum0 + v_sum1);
for (int i = 0; i < v_float64::nlanes; ++i)
dst[i % cn] += ar[i];
#else
double CV_DECL_ALIGNED(CV_SIMD_WIDTH) ar[2 * v_float64::nlanes];
v_store_aligned(ar, v_sum0);
v_store_aligned(ar + v_float64::nlanes, v_sum1);
for (int i = 0; i < 2 * v_float64::nlanes; ++i)
dst[i % cn] += ar[i];
#endif
v_cleanup();
return x / cn;
}
};
template <>
struct Sum_SIMD<float, double>
{
int operator () (const float * src0, const uchar * mask, double * dst, int len, int cn) const
{
if (mask || (cn != 1 && cn != 2 && cn != 4))
return 0;
len *= cn;
int x = 0;
v_float64 v_sum0 = vx_setzero_f64();
v_float64 v_sum1 = vx_setzero_f64();
for (; x <= len - 2 * v_float32::nlanes; x += 2 * v_float32::nlanes)
{
v_float32 v_src0 = vx_load(src0 + x);
v_float32 v_src1 = vx_load(src0 + x + v_float32::nlanes);
v_sum0 += v_cvt_f64(v_src0) + v_cvt_f64(v_src1);
v_sum1 += v_cvt_f64_high(v_src0) + v_cvt_f64_high(v_src1);
}
#if CV_SIMD256 || CV_SIMD512
double CV_DECL_ALIGNED(CV_SIMD_WIDTH) ar[v_float64::nlanes];
v_store_aligned(ar, v_sum0 + v_sum1);
for (int i = 0; i < v_float64::nlanes; ++i)
dst[i % cn] += ar[i];
#else
double CV_DECL_ALIGNED(CV_SIMD_WIDTH) ar[2 * v_float64::nlanes];
v_store_aligned(ar, v_sum0);
v_store_aligned(ar + v_float64::nlanes, v_sum1);
for (int i = 0; i < 2 * v_float64::nlanes; ++i)
dst[i % cn] += ar[i];
#endif
v_cleanup();
return x / cn;
}
};
#endif
#endif
template<typename T, typename ST>
static int sum_(const T* src0, const uchar* mask, ST* dst, int len, int cn )
namespace cv
{
const T* src = src0;
if( !mask )
{
Sum_SIMD<T, ST> vop;
int i = vop(src0, mask, dst, len, cn), k = cn % 4;
src += i * cn;
if( k == 1 )
{
ST s0 = dst[0];
#if CV_ENABLE_UNROLLED
for(; i <= len - 4; i += 4, src += cn*4 )
s0 += src[0] + src[cn] + src[cn*2] + src[cn*3];
#endif
for( ; i < len; i++, src += cn )
s0 += src[0];
dst[0] = s0;
}
else if( k == 2 )
{
ST s0 = dst[0], s1 = dst[1];
for( ; i < len; i++, src += cn )
{
s0 += src[0];
s1 += src[1];
}
dst[0] = s0;
dst[1] = s1;
}
else if( k == 3 )
{
ST s0 = dst[0], s1 = dst[1], s2 = dst[2];
for( ; i < len; i++, src += cn )
{
s0 += src[0];
s1 += src[1];
s2 += src[2];
}
dst[0] = s0;
dst[1] = s1;
dst[2] = s2;
}
for( ; k < cn; k += 4 )
{
src = src0 + i*cn + k;
ST s0 = dst[k], s1 = dst[k+1], s2 = dst[k+2], s3 = dst[k+3];
for( ; i < len; i++, src += cn )
{
s0 += src[0]; s1 += src[1];
s2 += src[2]; s3 += src[3];
}
dst[k] = s0;
dst[k+1] = s1;
dst[k+2] = s2;
dst[k+3] = s3;
}
return len;
}
int i, nzm = 0;
if( cn == 1 )
{
ST s = dst[0];
for( i = 0; i < len; i++ )
if( mask[i] )
{
s += src[i];
nzm++;
}
dst[0] = s;
}
else if( cn == 3 )
{
ST s0 = dst[0], s1 = dst[1], s2 = dst[2];
for( i = 0; i < len; i++, src += 3 )
if( mask[i] )
{
s0 += src[0];
s1 += src[1];
s2 += src[2];
nzm++;
}
dst[0] = s0;
dst[1] = s1;
dst[2] = s2;
}
else
{
for( i = 0; i < len; i++, src += cn )
if( mask[i] )
{
int k = 0;
#if CV_ENABLE_UNROLLED
for( ; k <= cn - 4; k += 4 )
{
ST s0, s1;
s0 = dst[k] + src[k];
s1 = dst[k+1] + src[k+1];
dst[k] = s0; dst[k+1] = s1;
s0 = dst[k+2] + src[k+2];
s1 = dst[k+3] + src[k+3];
dst[k+2] = s0; dst[k+3] = s1;
}
#endif
for( ; k < cn; k++ )
dst[k] += src[k];
nzm++;
}
}
return nzm;
}
static int sum8u( const uchar* src, const uchar* mask, int* dst, int len, int cn )
{ return sum_(src, mask, dst, len, cn); }
static int sum8s( const schar* src, const uchar* mask, int* dst, int len, int cn )
{ return sum_(src, mask, dst, len, cn); }
static int sum16u( const ushort* src, const uchar* mask, int* dst, int len, int cn )
{ return sum_(src, mask, dst, len, cn); }
static int sum16s( const short* src, const uchar* mask, int* dst, int len, int cn )
{ return sum_(src, mask, dst, len, cn); }
static int sum32s( const int* src, const uchar* mask, double* dst, int len, int cn )
{ return sum_(src, mask, dst, len, cn); }
static int sum32f( const float* src, const uchar* mask, double* dst, int len, int cn )
{ return sum_(src, mask, dst, len, cn); }
static int sum64f( const double* src, const uchar* mask, double* dst, int len, int cn )
{ return sum_(src, mask, dst, len, cn); }
SumFunc getSumFunc(int depth)
{
static SumFunc sumTab[] =
{
(SumFunc)GET_OPTIMIZED(sum8u), (SumFunc)sum8s,
(SumFunc)sum16u, (SumFunc)sum16s,
(SumFunc)sum32s,
(SumFunc)GET_OPTIMIZED(sum32f), (SumFunc)sum64f,
0
};
return sumTab[depth];
CV_INSTRUMENT_REGION();
CV_CPU_DISPATCH(getSumFunc, (depth),
CV_CPU_DISPATCH_MODES_ALL);
}
#ifdef HAVE_OPENCL
......@@ -593,9 +170,7 @@ static bool ipp_sum(Mat &src, Scalar &_res)
}
#endif
} // cv::
cv::Scalar cv::sum( InputArray _src )
Scalar sum(InputArray _src)
{
CV_INSTRUMENT_REGION();
......@@ -660,3 +235,5 @@ cv::Scalar cv::sum( InputArray _src )
}
return s;
}
} // namespace
modules/core/src/sum.simd.hpp
浏览文件 @ b40a7ffb
......@@ -4,11 +4,14 @@
#include "precomp.hpp"
#include "opencl_kernels_core.hpp"
#include "stat.hpp"
namespace cv
{
namespace cv {
CV_CPU_OPTIMIZATION_NAMESPACE_BEGIN
SumFunc getSumFunc(int depth);
#ifndef CV_CPU_OPTIMIZATION_DECLARATIONS_ONLY
template <typename T, typename ST>
struct Sum_SIMD
......@@ -409,25 +412,25 @@ static int sum_(const T* src0, const uchar* mask, ST* dst, int len, int cn )
static int sum8u( const uchar* src, const uchar* mask, int* dst, int len, int cn )
{ return sum_(src, mask, dst, len, cn); }
{ CV_INSTRUMENT_REGION(); return sum_(src, mask, dst, len, cn); }
static int sum8s( const schar* src, const uchar* mask, int* dst, int len, int cn )
{ return sum_(src, mask, dst, len, cn); }
{ CV_INSTRUMENT_REGION(); return sum_(src, mask, dst, len, cn); }
static int sum16u( const ushort* src, const uchar* mask, int* dst, int len, int cn )
{ return sum_(src, mask, dst, len, cn); }
{ CV_INSTRUMENT_REGION(); return sum_(src, mask, dst, len, cn); }
static int sum16s( const short* src, const uchar* mask, int* dst, int len, int cn )
{ return sum_(src, mask, dst, len, cn); }
{ CV_INSTRUMENT_REGION(); return sum_(src, mask, dst, len, cn); }
static int sum32s( const int* src, const uchar* mask, double* dst, int len, int cn )
{ return sum_(src, mask, dst, len, cn); }
{ CV_INSTRUMENT_REGION(); return sum_(src, mask, dst, len, cn); }
static int sum32f( const float* src, const uchar* mask, double* dst, int len, int cn )
{ return sum_(src, mask, dst, len, cn); }
{ CV_INSTRUMENT_REGION(); return sum_(src, mask, dst, len, cn); }
static int sum64f( const double* src, const uchar* mask, double* dst, int len, int cn )
{ return sum_(src, mask, dst, len, cn); }
{ CV_INSTRUMENT_REGION(); return sum_(src, mask, dst, len, cn); }
SumFunc getSumFunc(int depth)
{
......@@ -443,220 +446,7 @@ SumFunc getSumFunc(int depth)
return sumTab[depth];
}
#ifdef HAVE_OPENCL
bool ocl_sum( InputArray _src, Scalar & res, int sum_op, InputArray _mask,
InputArray _src2, bool calc2, const Scalar & res2 )
{
CV_Assert(sum_op == OCL_OP_SUM || sum_op == OCL_OP_SUM_ABS || sum_op == OCL_OP_SUM_SQR);
const ocl::Device & dev = ocl::Device::getDefault();
bool doubleSupport = dev.doubleFPConfig() > 0,
haveMask = _mask.kind() != _InputArray::NONE,
haveSrc2 = _src2.kind() != _InputArray::NONE;
int type = _src.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type),
kercn = cn == 1 && !haveMask ? ocl::predictOptimalVectorWidth(_src, _src2) : 1,
mcn = std::max(cn, kercn);
CV_Assert(!haveSrc2 || _src2.type() == type);
int convert_cn = haveSrc2 ? mcn : cn;
if ( (!doubleSupport && depth == CV_64F) || cn > 4 )
return false;
int ngroups = dev.maxComputeUnits(), dbsize = ngroups * (calc2 ? 2 : 1);
size_t wgs = dev.maxWorkGroupSize();
int ddepth = std::max(sum_op == OCL_OP_SUM_SQR ? CV_32F : CV_32S, depth),
dtype = CV_MAKE_TYPE(ddepth, cn);
CV_Assert(!haveMask || _mask.type() == CV_8UC1);
int wgs2_aligned = 1;
while (wgs2_aligned < (int)wgs)
wgs2_aligned <<= 1;
wgs2_aligned >>= 1;
static const char * const opMap[3] = { "OP_SUM", "OP_SUM_ABS", "OP_SUM_SQR" };
char cvt[2][40];
String opts = format("-D srcT=%s -D srcT1=%s -D dstT=%s -D dstTK=%s -D dstT1=%s -D ddepth=%d -D cn=%d"
" -D convertToDT=%s -D %s -D WGS=%d -D WGS2_ALIGNED=%d%s%s%s%s -D kercn=%d%s%s%s -D convertFromU=%s",
ocl::typeToStr(CV_MAKE_TYPE(depth, mcn)), ocl::typeToStr(depth),
ocl::typeToStr(dtype), ocl::typeToStr(CV_MAKE_TYPE(ddepth, mcn)),
ocl::typeToStr(ddepth), ddepth, cn,
ocl::convertTypeStr(depth, ddepth, mcn, cvt[0]),
opMap[sum_op], (int)wgs, wgs2_aligned,
doubleSupport ? " -D DOUBLE_SUPPORT" : "",
haveMask ? " -D HAVE_MASK" : "",
_src.isContinuous() ? " -D HAVE_SRC_CONT" : "",
haveMask && _mask.isContinuous() ? " -D HAVE_MASK_CONT" : "", kercn,
haveSrc2 ? " -D HAVE_SRC2" : "", calc2 ? " -D OP_CALC2" : "",
haveSrc2 && _src2.isContinuous() ? " -D HAVE_SRC2_CONT" : "",
depth <= CV_32S && ddepth == CV_32S ? ocl::convertTypeStr(CV_8U, ddepth, convert_cn, cvt[1]) : "noconvert");
ocl::Kernel k("reduce", ocl::core::reduce_oclsrc, opts);
if (k.empty())
return false;
UMat src = _src.getUMat(), src2 = _src2.getUMat(),
db(1, dbsize, dtype), mask = _mask.getUMat();
ocl::KernelArg srcarg = ocl::KernelArg::ReadOnlyNoSize(src),
dbarg = ocl::KernelArg::PtrWriteOnly(db),
maskarg = ocl::KernelArg::ReadOnlyNoSize(mask),
src2arg = ocl::KernelArg::ReadOnlyNoSize(src2);
if (haveMask)
{
if (haveSrc2)
k.args(srcarg, src.cols, (int)src.total(), ngroups, dbarg, maskarg, src2arg);
else
k.args(srcarg, src.cols, (int)src.total(), ngroups, dbarg, maskarg);
}
else
{
if (haveSrc2)
k.args(srcarg, src.cols, (int)src.total(), ngroups, dbarg, src2arg);
else
k.args(srcarg, src.cols, (int)src.total(), ngroups, dbarg);
}
size_t globalsize = ngroups * wgs;
if (k.run(1, &globalsize, &wgs, false))
{
typedef Scalar (*part_sum)(Mat m);
part_sum funcs[3] = { ocl_part_sum<int>, ocl_part_sum<float>, ocl_part_sum<double> },
func = funcs[ddepth - CV_32S];
Mat mres = db.getMat(ACCESS_READ);
if (calc2)
const_cast<Scalar &>(res2) = func(mres.colRange(ngroups, dbsize));
res = func(mres.colRange(0, ngroups));
return true;
}
return false;
}
#endif
#ifdef HAVE_IPP
static bool ipp_sum(Mat &src, Scalar &_res)
{
CV_INSTRUMENT_REGION_IPP();
#if IPP_VERSION_X100 >= 700
int cn = src.channels();
if (cn > 4)
return false;
size_t total_size = src.total();
int rows = src.size[0], cols = rows ? (int)(total_size/rows) : 0;
if( src.dims == 2 || (src.isContinuous() && cols > 0 && (size_t)rows*cols == total_size) )
{
IppiSize sz = { cols, rows };
int type = src.type();
typedef IppStatus (CV_STDCALL* ippiSumFuncHint)(const void*, int, IppiSize, double *, IppHintAlgorithm);
typedef IppStatus (CV_STDCALL* ippiSumFuncNoHint)(const void*, int, IppiSize, double *);
ippiSumFuncHint ippiSumHint =
type == CV_32FC1 ? (ippiSumFuncHint)ippiSum_32f_C1R :
type == CV_32FC3 ? (ippiSumFuncHint)ippiSum_32f_C3R :
type == CV_32FC4 ? (ippiSumFuncHint)ippiSum_32f_C4R :
0;
ippiSumFuncNoHint ippiSum =
type == CV_8UC1 ? (ippiSumFuncNoHint)ippiSum_8u_C1R :
type == CV_8UC3 ? (ippiSumFuncNoHint)ippiSum_8u_C3R :
type == CV_8UC4 ? (ippiSumFuncNoHint)ippiSum_8u_C4R :
type == CV_16UC1 ? (ippiSumFuncNoHint)ippiSum_16u_C1R :
type == CV_16UC3 ? (ippiSumFuncNoHint)ippiSum_16u_C3R :
type == CV_16UC4 ? (ippiSumFuncNoHint)ippiSum_16u_C4R :
type == CV_16SC1 ? (ippiSumFuncNoHint)ippiSum_16s_C1R :
type == CV_16SC3 ? (ippiSumFuncNoHint)ippiSum_16s_C3R :
type == CV_16SC4 ? (ippiSumFuncNoHint)ippiSum_16s_C4R :
0;
CV_Assert(!ippiSumHint || !ippiSum);
if( ippiSumHint || ippiSum )
{
Ipp64f res[4];
IppStatus ret = ippiSumHint ?
CV_INSTRUMENT_FUN_IPP(ippiSumHint, src.ptr(), (int)src.step[0], sz, res, ippAlgHintAccurate) :
CV_INSTRUMENT_FUN_IPP(ippiSum, src.ptr(), (int)src.step[0], sz, res);
if( ret >= 0 )
{
for( int i = 0; i < cn; i++ )
_res[i] = res[i];
return true;
}
}
}
#else
CV_UNUSED(src); CV_UNUSED(_res);
#endif
return false;
}
#endif
} // cv::
cv::Scalar cv::sum( InputArray _src )
{
CV_INSTRUMENT_REGION();
#if defined HAVE_OPENCL || defined HAVE_IPP
Scalar _res;
#endif
#ifdef HAVE_OPENCL
CV_OCL_RUN_(OCL_PERFORMANCE_CHECK(_src.isUMat()) && _src.dims() <= 2,
ocl_sum(_src, _res, OCL_OP_SUM),
_res)
#endif
Mat src = _src.getMat();
CV_IPP_RUN(IPP_VERSION_X100 >= 700, ipp_sum(src, _res), _res);
int k, cn = src.channels(), depth = src.depth();
SumFunc func = getSumFunc(depth);
CV_Assert( cn <= 4 && func != 0 );
const Mat* arrays[] = {&src, 0};
uchar* ptrs[1] = {};
NAryMatIterator it(arrays, ptrs);
Scalar s;
int total = (int)it.size, blockSize = total, intSumBlockSize = 0;
int j, count = 0;
AutoBuffer<int> _buf;
int* buf = (int*)&s[0];
size_t esz = 0;
bool blockSum = depth < CV_32S;
if( blockSum )
{
intSumBlockSize = depth <= CV_8S ? (1 << 23) : (1 << 15);
blockSize = std::min(blockSize, intSumBlockSize);
_buf.allocate(cn);
buf = _buf.data();
for( k = 0; k < cn; k++ )
buf[k] = 0;
esz = src.elemSize();
}
for( size_t i = 0; i < it.nplanes; i++, ++it )
{
for( j = 0; j < total; j += blockSize )
{
int bsz = std::min(total - j, blockSize);
func( ptrs[0], 0, (uchar*)buf, bsz, cn );
count += bsz;
if( blockSum && (count + blockSize >= intSumBlockSize || (i+1 >= it.nplanes && j+bsz >= total)) )
{
for( k = 0; k < cn; k++ )
{
s[k] += buf[k];
buf[k] = 0;
}
count = 0;
}
ptrs[0] += bsz*esz;
}
}
return s;
}
CV_CPU_OPTIMIZATION_NAMESPACE_END
} // namespace
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