1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html
#include "precomp.hpp"
#include "opencl_kernels_core.hpp"
#include "stat.hpp"
#include "sum.simd.hpp"
#include "sum.simd_declarations.hpp" // defines CV_CPU_DISPATCH_MODES_ALL=AVX2,...,BASELINE based on CMakeLists.txt content
#undef HAVE_IPP
#undef CV_IPP_RUN_FAST
#define CV_IPP_RUN_FAST(f, ...)
#undef CV_IPP_RUN
#define CV_IPP_RUN(c, f, ...)
namespace cv
{
SumFunc getSumFunc(int depth)
{
CV_INSTRUMENT_REGION();
CV_CPU_DISPATCH(getSumFunc, (depth),
CV_CPU_DISPATCH_MODES_ALL);
}
#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, true))
{
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
Scalar 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;
}
} // namespace