Chiebot-Mirror
/
opencv
mirror of https://github.com/opencv/opencv.git
8
0
Fork 0
Code Issues Projects Releases Wiki Activity

Refactored Tegra related macro usage

Browse Source
pull/13383/head
Andrey Kamaev 13 years ago
parent 48ea65e69c
commit bdfd4c3062
2 changed files with 146 additions and 128 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 174
      modules/core/src/matrix.cpp
  2. 100
      modules/core/src/stat.cpp

174
modules/core/src/matrix.cpp
Unescape View File

@ -1942,7 +1942,53 @@ reduceC_( const Mat& srcmat, Mat& dstmat )
typedef void (*ReduceFunc)( const Mat& src, Mat& dst );
}
#define reduceSumR8u32s reduceR_<uchar, int, OpAdd<int> >
#define reduceSumR8u32f reduceR_<uchar, float, OpAdd<int> >
#define reduceSumR8u64f reduceR_<uchar, double,OpAdd<int> >
#define reduceSumR16u32f reduceR_<ushort,float, OpAdd<float> >
#define reduceSumR16u64f reduceR_<ushort,double,OpAdd<double> >
#define reduceSumR16s32f reduceR_<short, float, OpAdd<float> >
#define reduceSumR16s64f reduceR_<short, double,OpAdd<double> >
#define reduceSumR32f32f reduceR_<float, float, OpAdd<float> >
#define reduceSumR32f64f reduceR_<float, double,OpAdd<double> >
#define reduceSumR64f64f reduceR_<double,double,OpAdd<double> >
#define reduceMaxR8u reduceR_<uchar, uchar, OpMax<uchar> >
#define reduceMaxR16u reduceR_<ushort,ushort,OpMax<ushort> >
#define reduceMaxR16s reduceR_<short, short, OpMax<short> >
#define reduceMaxR32f reduceR_<float, float, OpMax<float> >
#define reduceMaxR64f reduceR_<double,double,OpMax<double> >
#define reduceMinR8u reduceR_<uchar, uchar, OpMin<uchar> >
#define reduceMinR16u reduceR_<ushort,ushort,OpMin<ushort> >
#define reduceMinR16s reduceR_<short, short, OpMin<short> >
#define reduceMinR32f reduceR_<float, float, OpMin<float> >
#define reduceMinR64f reduceR_<double,double,OpMin<double> >
#define reduceSumC8u32s reduceC_<uchar, int, OpAdd<int> >
#define reduceSumC8u32f reduceC_<uchar, float, OpAdd<int> >
#define reduceSumC8u64f reduceC_<uchar, double,OpAdd<int> >
#define reduceSumC16u32f reduceC_<ushort,float, OpAdd<float> >
#define reduceSumC16u64f reduceC_<ushort,double,OpAdd<double> >
#define reduceSumC16s32f reduceC_<short, float, OpAdd<float> >
#define reduceSumC16s64f reduceC_<short, double,OpAdd<double> >
#define reduceSumC32f32f reduceC_<float, float, OpAdd<float> >
#define reduceSumC32f64f reduceC_<float, double,OpAdd<double> >
#define reduceSumC64f64f reduceC_<double,double,OpAdd<double> >
#define reduceMaxC8u reduceC_<uchar, uchar, OpMax<uchar> >
#define reduceMaxC16u reduceC_<ushort,ushort,OpMax<ushort> >
#define reduceMaxC16s reduceC_<short, short, OpMax<short> >
#define reduceMaxC32f reduceC_<float, float, OpMax<float> >
#define reduceMaxC64f reduceC_<double,double,OpMax<double> >
#define reduceMinC8u reduceC_<uchar, uchar, OpMin<uchar> >
#define reduceMinC16u reduceC_<ushort,ushort,OpMin<ushort> >
#define reduceMinC16s reduceC_<short, short, OpMin<short> >
#define reduceMinC32f reduceC_<float, float, OpMin<float> >
#define reduceMinC64f reduceC_<double,double,OpMin<double> >
void cv::reduce(InputArray _src, OutputArray _dst, int dim, int op, int dtype)
{
Mat src = _src.getMat();
@ -1958,7 +2004,7 @@ void cv::reduce(InputArray _src, OutputArray _dst, int dim, int op, int dtype)
Mat dst = _dst.getMat(), temp = dst;
CV_Assert( op == CV_REDUCE_SUM || op == CV_REDUCE_MAX ||
op == CV_REDUCE_MIN || op == CV_REDUCE_AVG );
op == CV_REDUCE_MIN || op == CV_REDUCE_AVG );
CV_Assert( src.channels() == dst.channels() );
if( op == CV_REDUCE_AVG )
@ -1977,75 +2023,51 @@ void cv::reduce(InputArray _src, OutputArray _dst, int dim, int op, int dtype)
if( op == CV_REDUCE_SUM )
{
if(sdepth == CV_8U && ddepth == CV_32S)
#ifdef HAVE_TEGRA_OPTIMIZATION
func = tegra::getTegraOptimized_reduceR8uAdd(tegra::reduceR8uAdd);
#else
func = reduceR_<uchar,int,OpAdd<int> >;
#endif
func = GET_OPTIMIZED(reduceSumR8u32s);
else if(sdepth == CV_8U && ddepth == CV_32F)
func = reduceR_<uchar,float,OpAdd<int> >;
func = reduceSumR8u32f;
else if(sdepth == CV_8U && ddepth == CV_64F)
func = reduceR_<uchar,double,OpAdd<int> >;
func = reduceSumR8u64f;
else if(sdepth == CV_16U && ddepth == CV_32F)
func = reduceR_<ushort,float,OpAdd<float> >;
func = reduceSumR16u32f;
else if(sdepth == CV_16U && ddepth == CV_64F)
func = reduceR_<ushort,double,OpAdd<double> >;
func = reduceSumR16u64f;
else if(sdepth == CV_16S && ddepth == CV_32F)
func = reduceR_<short,float,OpAdd<float> >;
func = reduceSumR16s32f;
else if(sdepth == CV_16S && ddepth == CV_64F)
func = reduceR_<short,double,OpAdd<double> >;
else if(sdepth == CV_32F && ddepth == CV_32F)
#ifdef HAVE_TEGRA_OPTIMIZATION
func = tegra::getTegraOptimized_reduceR32fAdd(tegra::reduceR32fAdd);
#else
func = reduceR_<float,float,OpAdd<float> >;
#endif
func = reduceSumR16s64f;
else if(sdepth == CV_32F && ddepth == CV_32F)
func = GET_OPTIMIZED(reduceSumR32f32f);
else if(sdepth == CV_32F && ddepth == CV_64F)
func = reduceR_<float,double,OpAdd<double> >;
func = reduceSumR32f64f;
else if(sdepth == CV_64F && ddepth == CV_64F)
func = reduceR_<double,double,OpAdd<double> >;
func = reduceSumR64f64f;
}
else if(op == CV_REDUCE_MAX)
{
if(sdepth == CV_8U && ddepth == CV_8U)
#ifdef HAVE_TEGRA_OPTIMIZATION
func = tegra::getTegraOptimized_reduceR8uMax(tegra::reduceR8uMax);
#else
func = reduceR_<uchar, uchar, OpMax<uchar> >;
#endif
else if(sdepth == CV_16U && ddepth == CV_16U)
func = reduceR_<ushort, ushort, OpMax<ushort> >;
func = GET_OPTIMIZED(reduceMaxR8u);
else if(sdepth == CV_16U && ddepth == CV_16U)
func = reduceMaxR16u;
else if(sdepth == CV_16S && ddepth == CV_16S)
func = reduceR_<short, short, OpMax<short> >;
func = reduceMaxR16s;
else if(sdepth == CV_32F && ddepth == CV_32F)
#ifdef HAVE_TEGRA_OPTIMIZATION
func = tegra::getTegraOptimized_reduceR32fMax(tegra::reduceR32fMax);
#else
func = reduceR_<float, float, OpMax<float> >;
#endif
else if(sdepth == CV_64F && ddepth == CV_64F)
func = reduceR_<double, double, OpMax<double> >;
func = GET_OPTIMIZED(reduceMaxR32f);
else if(sdepth == CV_64F && ddepth == CV_64F)
func = reduceMaxR64f;
}
else if(op == CV_REDUCE_MIN)
{
if(sdepth == CV_8U && ddepth == CV_8U)
#ifdef HAVE_TEGRA_OPTIMIZATION
func = tegra::getTegraOptimized_reduceR8uMin(tegra::reduceR8uMin);
#else
func = reduceR_<uchar, uchar, OpMin<uchar> >;
#endif
func = GET_OPTIMIZED(reduceMinR8u);
else if(sdepth == CV_16U && ddepth == CV_16U)
func = reduceR_<ushort, ushort, OpMin<ushort> >;
func = reduceMinR16u;
else if(sdepth == CV_16S && ddepth == CV_16S)
func = reduceR_<short, short, OpMin<short> >;
func = reduceMinR16s;
else if(sdepth == CV_32F && ddepth == CV_32F)
#ifdef HAVE_TEGRA_OPTIMIZATION
func = tegra::getTegraOptimized_reduceR32fMin(tegra::reduceR32fMin);
#else
func = reduceR_<float, float, OpMin<float> >;
#endif
func = GET_OPTIMIZED(reduceMinR32f);
else if(sdepth == CV_64F && ddepth == CV_64F)
func = reduceR_<double, double, OpMin<double> >;
func = reduceMinR64f;
}
}
else
@ -2053,67 +2075,63 @@ void cv::reduce(InputArray _src, OutputArray _dst, int dim, int op, int dtype)
if(op == CV_REDUCE_SUM)
{
if(sdepth == CV_8U && ddepth == CV_32S)
#ifdef HAVE_TEGRA_OPTIMIZATION
func = tegra::getTegraOptimized_reduceC8uAdd(tegra::reduceC8uAdd);
#else
func = reduceC_<uchar,int,OpAdd<int> >;
#endif
func = GET_OPTIMIZED(reduceSumC8u32s);
else if(sdepth == CV_8U && ddepth == CV_32F)
func = reduceC_<uchar,float,OpAdd<int> >;
func = reduceSumC8u32f;
else if(sdepth == CV_8U && ddepth == CV_64F)
func = reduceC_<uchar,double,OpAdd<int> >;
func = reduceSumC8u64f;
else if(sdepth == CV_16U && ddepth == CV_32F)
func = reduceC_<ushort,float,OpAdd<float> >;
func = reduceSumC16u32f;
else if(sdepth == CV_16U && ddepth == CV_64F)
func = reduceC_<ushort,double,OpAdd<double> >;
func = reduceSumC16u64f;
else if(sdepth == CV_16S && ddepth == CV_32F)
func = reduceC_<short,float,OpAdd<float> >;
func = reduceSumC16s32f;
else if(sdepth == CV_16S && ddepth == CV_64F)
func = reduceC_<short,double,OpAdd<double> >;
else if(sdepth == CV_32F && ddepth == CV_32F)
func = reduceC_<float,float,OpAdd<float> >;
func = reduceSumC16s64f;
else if(sdepth == CV_32F && ddepth == CV_32F)
func = GET_OPTIMIZED(reduceSumC32f32f);
else if(sdepth == CV_32F && ddepth == CV_64F)
func = reduceC_<float,double,OpAdd<double> >;
func = reduceSumC32f64f;
else if(sdepth == CV_64F && ddepth == CV_64F)
func = reduceC_<double,double,OpAdd<double> >;
func = reduceSumC64f64f;
}
else if(op == CV_REDUCE_MAX)
{
if(sdepth == CV_8U && ddepth == CV_8U)
func = reduceC_<uchar, uchar, OpMax<uchar> >;
else if(sdepth == CV_16U && ddepth == CV_16U)
func = reduceC_<ushort, ushort, OpMax<ushort> >;
func = GET_OPTIMIZED(reduceMaxC8u);
else if(sdepth == CV_16U && ddepth == CV_16U)
func = reduceMaxC16u;
else if(sdepth == CV_16S && ddepth == CV_16S)
func = reduceC_<short, short, OpMax<short> >;
func = reduceMaxC16s;
else if(sdepth == CV_32F && ddepth == CV_32F)
func = reduceC_<float, float, OpMax<float> >;
func = GET_OPTIMIZED(reduceMaxC32f);
else if(sdepth == CV_64F && ddepth == CV_64F)
func = reduceC_<double, double, OpMax<double> >;
func = reduceMaxC64f;
}
else if(op == CV_REDUCE_MIN)
{
if(sdepth == CV_8U && ddepth == CV_8U)
func = reduceC_<uchar, uchar, OpMin<uchar> >;
func = GET_OPTIMIZED(reduceMinC8u);
else if(sdepth == CV_16U && ddepth == CV_16U)
func = reduceC_<ushort, ushort, OpMin<ushort> >;
func = reduceMinC16u;
else if(sdepth == CV_16S && ddepth == CV_16S)
func = reduceC_<short, short, OpMin<short> >;
func = reduceMinC16s;
else if(sdepth == CV_32F && ddepth == CV_32F)
func = reduceC_<float, float, OpMin<float> >;
func = GET_OPTIMIZED(reduceMinC32f);
else if(sdepth == CV_64F && ddepth == CV_64F)
func = reduceC_<double, double, OpMin<double> >;
func = reduceMinC64f;
}
}
if( !func )
CV_Error( CV_StsUnsupportedFormat,
"Unsupported combination of input and output array formats" );
"Unsupported combination of input and output array formats" );
func( src, temp );
if( op0 == CV_REDUCE_AVG )
if( op0 == CV_REDUCE_AVG )
temp.convertTo(dst, dst.type(), 1./(dim == 0 ? src.rows : src.cols));
}
}
//////////////////////////////////////// sort ///////////////////////////////////////////

100
modules/core/src/stat.cpp
Unescape View File

@ -169,7 +169,7 @@ static int sum_(const T* src0, const uchar* mask, ST* dst, int len, int cn )
return nzm;
}
static int sum8u( const uchar* src, const uchar* mask, int* dst, int len, int cn )
{ return sum_(src, mask, dst, len, cn); }
@ -201,7 +201,7 @@ static SumFunc sumTab[] =
(SumFunc)GET_OPTIMIZED(sum32f), (SumFunc)sum64f,
0
};
template<typename T>
static int countNonZero_(const T* src, int len )
{
@ -229,7 +229,7 @@ static int countNonZero64f( const double* src, int len )
{ return countNonZero_(src, len); }
typedef int (*CountNonZeroFunc)(const uchar*, int);
static CountNonZeroFunc countNonZeroTab[] =
{
(CountNonZeroFunc)GET_OPTIMIZED(countNonZero8u), (CountNonZeroFunc)(countNonZero8u),
@ -238,7 +238,7 @@ static CountNonZeroFunc countNonZeroTab[] =
(CountNonZeroFunc)countNonZero64f, 0
};
template<typename T, typename ST, typename SQT>
static int sumsqr_(const T* src0, const uchar* mask, ST* sum, SQT* sqsum, int len, int cn )
{
@ -393,7 +393,7 @@ static SumSqrFunc sumSqrTab[] =
};
}
cv::Scalar cv::sum( InputArray _src )
{
Mat src = _src.getMat();
@ -419,12 +419,12 @@ cv::Scalar cv::sum( InputArray _src )
blockSize = std::min(blockSize, intSumBlockSize);
_buf.allocate(cn);
buf = _buf;
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 )
@ -464,7 +464,7 @@ int cv::countNonZero( InputArray _src )
return nz;
}
cv::Scalar cv::mean( InputArray _src, InputArray _mask )
{
Mat src = _src.getMat(), mask = _mask.getMat();
@ -523,7 +523,7 @@ cv::Scalar cv::mean( InputArray _src, InputArray _mask )
return s*(nz0 ? 1./nz0 : 0);
}
void cv::meanStdDev( InputArray _src, OutputArray _mean, OutputArray _sdv, InputArray _mask )
{
Mat src = _src.getMat(), mask = _mask.getMat();
@ -610,7 +610,7 @@ void cv::meanStdDev( InputArray _src, OutputArray _mean, OutputArray _sdv, Input
Mat dst = _dst.getMat();
int dcn = (int)dst.total();
CV_Assert( dst.type() == CV_64F && dst.isContinuous() &&
(dst.cols == 1 || dst.rows == 1) && dcn >= cn );
(dst.cols == 1 || dst.rows == 1) && dcn >= cn );
double* dptr = dst.ptr<double>();
for( k = 0; k < cn; k++ )
dptr[k] = sptr[k];
@ -701,7 +701,7 @@ static void minMaxIdx_32f(const float* src, const uchar* mask, float* minval, fl
static void minMaxIdx_64f(const double* src, const uchar* mask, double* minval, double* maxval,
size_t* minidx, size_t* maxidx, int len, size_t startidx )
{ minMaxIdx_(src, mask, minval, maxval, minidx, maxidx, len, startidx ); }
typedef void (*MinMaxIdxFunc)(const uchar*, const uchar*, int*, int*, size_t*, size_t*, int, size_t);
static MinMaxIdxFunc minmaxTab[] =
@ -712,7 +712,7 @@ static MinMaxIdxFunc minmaxTab[] =
(MinMaxIdxFunc)GET_OPTIMIZED(minMaxIdx_32f), (MinMaxIdxFunc)GET_OPTIMIZED(minMaxIdx_64f),
0
};
static void ofs2idx(const Mat& a, size_t ofs, int* idx)
{
int i, d = a.dims;
@ -732,7 +732,7 @@ static void ofs2idx(const Mat& a, size_t ofs, int* idx)
idx[i] = -1;
}
}
}
void cv::minMaxIdx(InputArray _src, double* minVal,
@ -784,9 +784,9 @@ void cv::minMaxIdx(InputArray _src, double* minVal,
if( maxIdx )
ofs2idx(src, maxidx, maxIdx);
}
void cv::minMaxLoc( InputArray _img, double* minVal, double* maxVal,
Point* minLoc, Point* maxLoc, InputArray mask )
Point* minLoc, Point* maxLoc, InputArray mask )
{
Mat img = _img.getMat();
CV_Assert(img.dims <= 2);
@ -797,7 +797,7 @@ void cv::minMaxLoc( InputArray _img, double* minVal, double* maxVal,
if( maxLoc )
std::swap(maxLoc->x, maxLoc->y);
}
/****************************************************************************************\
* norm *
\****************************************************************************************/
@ -842,7 +842,7 @@ float normL2Sqr_(const float* a, const float* b, int n)
return d;
}
float normL1_(const float* a, const float* b, int n)
{
int j = 0; float d = 0.f;
@ -870,7 +870,7 @@ float normL1_(const float* a, const float* b, int n)
for( ; j <= n - 4; j += 4 )
{
d += std::abs(a[j] - b[j]) + std::abs(a[j+1] - b[j+1]) +
std::abs(a[j+2] - b[j+2]) + std::abs(a[j+3] - b[j+3]);
std::abs(a[j+2] - b[j+2]) + std::abs(a[j+3] - b[j+3]);
}
}
@ -910,7 +910,7 @@ int normL1_(const uchar* a, const uchar* b, int n)
for( ; j <= n - 4; j += 4 )
{
d += std::abs(a[j] - b[j]) + std::abs(a[j+1] - b[j+1]) +
std::abs(a[j+2] - b[j+2]) + std::abs(a[j+3] - b[j+3]);
std::abs(a[j+2] - b[j+2]) + std::abs(a[j+3] - b[j+3]);
}
}
@ -930,7 +930,7 @@ static const uchar popCountTable[] =
2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6, 3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7,
3, 4, 4, 5, 4, 5, 5, 6, 4, 5, 5, 6, 5, 6, 6, 7, 4, 5, 5, 6, 5, 6, 6, 7, 5, 6, 6, 7, 6, 7, 7, 8
};
static const uchar popCountTable2[] =
{
0, 1, 1, 1, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3,
@ -942,7 +942,7 @@ static const uchar popCountTable2[] =
1, 2, 2, 2, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 3, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4,
2, 3, 3, 3, 3, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4, 2, 3, 3, 3, 3, 4, 4, 4, 3, 4, 4, 4, 3, 4, 4, 4
};
static const uchar popCountTable4[] =
{
0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
@ -954,7 +954,7 @@ static const uchar popCountTable4[] =
1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2
};
int normHamming(const uchar* a, const uchar* b, int n)
{
int i = 0, result = 0;
@ -979,12 +979,12 @@ int normHamming(const uchar* a, const uchar* b, int n)
#endif
for( ; i <= n - 4; i += 4 )
result += popCountTable[a[i] ^ b[i]] + popCountTable[a[i+1] ^ b[i+1]] +
popCountTable[a[i+2] ^ b[i+2]] + popCountTable[a[i+3] ^ b[i+3]];
popCountTable[a[i+2] ^ b[i+2]] + popCountTable[a[i+3] ^ b[i+3]];
for( ; i < n; i++ )
result += popCountTable[a[i] ^ b[i]];
return result;
}
int normHamming(const uchar* a, const uchar* b, int n, int cellSize)
{
if( cellSize == 1 )
@ -999,13 +999,13 @@ int normHamming(const uchar* a, const uchar* b, int n, int cellSize)
int i = 0, result = 0;
for( ; i <= n - 4; i += 4 )
result += tab[a[i] ^ b[i]] + tab[a[i+1] ^ b[i+1]] +
tab[a[i+2] ^ b[i+2]] + tab[a[i+3] ^ b[i+3]];
tab[a[i+2] ^ b[i+2]] + tab[a[i+3] ^ b[i+3]];
for( ; i < n; i++ )
result += tab[a[i] ^ b[i]];
return result;
}
template<typename T, typename ST> int
normInf_(const T* src, const uchar* mask, ST* _result, int len, int cn)
{
@ -1026,7 +1026,7 @@ normInf_(const T* src, const uchar* mask, ST* _result, int len, int cn)
*_result = result;
return 0;
}
template<typename T, typename ST> int
normL1_(const T* src, const uchar* mask, ST* _result, int len, int cn)
{
@ -1071,7 +1071,7 @@ normL2_(const T* src, const uchar* mask, ST* _result, int len, int cn)
*_result = result;
return 0;
}
template<typename T, typename ST> int
normDiffInf_(const T* src1, const T* src2, const uchar* mask, ST* _result, int len, int cn)
{
@ -1140,16 +1140,16 @@ normDiffL2_(const T* src1, const T* src2, const uchar* mask, ST* _result, int le
#define CV_DEF_NORM_FUNC(L, suffix, type, ntype) \
static int norm##L##_##suffix(const type* src, const uchar* mask, ntype* r, int len, int cn) \
static int norm##L##_##suffix(const type* src, const uchar* mask, ntype* r, int len, int cn) \
{ return norm##L##_(src, mask, r, len, cn); } \
static int normDiff##L##_##suffix(const type* src1, const type* src2, \
const uchar* mask, ntype* r, int len, int cn) \
static int normDiff##L##_##suffix(const type* src1, const type* src2, \
const uchar* mask, ntype* r, int len, int cn) \
{ return normDiff##L##_(src1, src2, mask, r, (int)len, cn); }
#define CV_DEF_NORM_ALL(suffix, type, inftype, l1type, l2type) \
CV_DEF_NORM_FUNC(Inf, suffix, type, inftype) \
CV_DEF_NORM_FUNC(L1, suffix, type, l1type) \
CV_DEF_NORM_FUNC(L2, suffix, type, l2type)
CV_DEF_NORM_FUNC(Inf, suffix, type, inftype) \
CV_DEF_NORM_FUNC(L1, suffix, type, l1type) \
CV_DEF_NORM_FUNC(L2, suffix, type, l2type)
CV_DEF_NORM_ALL(8u, uchar, int, int, int)
CV_DEF_NORM_ALL(8s, schar, int, int, int)
@ -1159,7 +1159,7 @@ CV_DEF_NORM_ALL(32s, int, int, double, double)
CV_DEF_NORM_ALL(32f, float, float, double, double)
CV_DEF_NORM_ALL(64f, double, double, double, double)
typedef int (*NormFunc)(const uchar*, const uchar*, uchar*, int, int);
typedef int (*NormDiffFunc)(const uchar*, const uchar*, const uchar*, uchar*, int, int);
@ -1202,7 +1202,7 @@ static NormDiffFunc normDiffTab[3][8] =
};
}
double cv::norm( InputArray _src, int normType, InputArray _mask )
{
Mat src = _src.getMat(), mask = _mask.getMat();
@ -1221,18 +1221,18 @@ double cv::norm( InputArray _src, int normType, InputArray _mask )
if( normType == NORM_L2 )
{
double result = 0;
GET_OPTIMIZED(normL2_32f)(data, 0, &result, (int)len, 1);
GET_OPTIMIZED(normL2_32f)(data, 0, &result, (int)len, 1);
return std::sqrt(result);
}
if( normType == NORM_L1 )
{
double result = 0;
GET_OPTIMIZED(normL1_32f)(data, 0, &result, (int)len, 1);
GET_OPTIMIZED(normL1_32f)(data, 0, &result, (int)len, 1);
return result;
}
{
float result = 0;
GET_OPTIMIZED(normInf_32f)(data, 0, &result, (int)len, 1);
GET_OPTIMIZED(normInf_32f)(data, 0, &result, (int)len, 1);
return result;
}
@ -1257,7 +1257,7 @@ double cv::norm( InputArray _src, int normType, InputArray _mask )
NAryMatIterator it(arrays, ptrs);
int j, total = (int)it.size, blockSize = total, intSumBlockSize = 0, count = 0;
bool blockSum = (normType == NORM_L1 && depth <= CV_16S) ||
(normType == NORM_L2 && depth <= CV_8S);
(normType == NORM_L2 && depth <= CV_8S);
int isum = 0;
int *ibuf = &result.i;
size_t esz = 0;
@ -1275,8 +1275,8 @@ double cv::norm( InputArray _src, int normType, InputArray _mask )
for( j = 0; j < total; j += blockSize )
{
int bsz = std::min(total - j, blockSize);
func( ptrs[0], ptrs[1], (uchar*)ibuf, bsz, cn );
count += bsz;
func( ptrs[0], ptrs[1], (uchar*)ibuf, bsz, cn );
count += bsz;
if( blockSum && (count + blockSize >= intSumBlockSize || (i+1 >= it.nplanes && j+bsz >= total)) )
{
result.d += isum;
@ -1304,7 +1304,7 @@ double cv::norm( InputArray _src, int normType, InputArray _mask )
return result.d;
}
double cv::norm( InputArray _src1, InputArray _src2, int normType, InputArray _mask )
{
if( normType & CV_RELATIVE )
@ -1329,18 +1329,18 @@ double cv::norm( InputArray _src1, InputArray _src2, int normType, InputArray _m
if( normType == NORM_L2 )
{
double result = 0;
GET_OPTIMIZED(normDiffL2_32f)(data1, data2, 0, &result, (int)len, 1);
GET_OPTIMIZED(normDiffL2_32f)(data1, data2, 0, &result, (int)len, 1);
return std::sqrt(result);
}
if( normType == NORM_L1 )
{
double result = 0;
GET_OPTIMIZED(normDiffL1_32f)(data1, data2, 0, &result, (int)len, 1);
GET_OPTIMIZED(normDiffL1_32f)(data1, data2, 0, &result, (int)len, 1);
return result;
}
{
float result = 0;
GET_OPTIMIZED(normDiffInf_32f)(data1, data2, 0, &result, (int)len, 1);
GET_OPTIMIZED(normDiffInf_32f)(data1, data2, 0, &result, (int)len, 1);
return result;
}
}
@ -1365,7 +1365,7 @@ double cv::norm( InputArray _src1, InputArray _src2, int normType, InputArray _m
NAryMatIterator it(arrays, ptrs);
int j, total = (int)it.size, blockSize = total, intSumBlockSize = 0, count = 0;
bool blockSum = (normType == NORM_L1 && depth <= CV_16S) ||
(normType == NORM_L2 && depth <= CV_8S);
(normType == NORM_L2 && depth <= CV_8S);
unsigned isum = 0;
unsigned *ibuf = &result.u;
size_t esz = 0;
@ -1496,7 +1496,7 @@ cvMinMaxLoc( const void* imgarr, double* _minVal, double* _maxVal,
cv::extractImageCOI(imgarr, img);
cv::minMaxLoc( img, _minVal, _maxVal,
(cv::Point*)_minLoc, (cv::Point*)_maxLoc, mask );
(cv::Point*)_minLoc, (cv::Point*)_maxLoc, mask );
}

Write Preview
Loading…
Cancel
Save