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Refactored Tegra related macro usage

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This commit is contained in:
Andrey Kamaev
2011-12-22 12:51:01 +00:00
parent 48ea65e69c
commit bdfd4c3062
2 changed files with 146 additions and 128 deletions
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100
modules/core/src/stat.cpp
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@@ -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 );
}