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merged 2.4 into trunk

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This commit is contained in:
Vadim Pisarevsky
2012-04-30 14:33:52 +00:00
parent 3f1c6d7357
commit d5a0088bbe
194 changed files with 10158 additions and 8225 deletions
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208
modules/core/src/stat.cpp
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@@ -834,7 +834,6 @@ float normL2Sqr_(const float* a, const float* b, int n)
}
else
#endif
//vz why do we need unroll here? no sse = no need to unroll
{
for( ; j <= n - 4; j += 4 )
{
@@ -875,7 +874,6 @@ float normL1_(const float* a, const float* b, int n)
}
else
#endif
//vz no need to unroll here - if no sse
{
for( ; j <= n - 4; j += 4 )
{
@@ -916,7 +914,6 @@ int normL1_(const uchar* a, const uchar* b, int n)
}
else
#endif
//vz why do we need unroll here? no sse = no unroll
{
for( ; j <= n - 4; j += 4 )
{
@@ -965,6 +962,34 @@ 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
};
int normHamming(const uchar* a, int n)
{
int i = 0, result = 0;
#if CV_NEON
if (CPU_HAS_NEON_FEATURE)
{
uint32x4_t bits = vmovq_n_u32(0);
for (; i <= n - 16; i += 16) {
uint8x16_t A_vec = vld1q_u8 (a + i);
uint8x16_t bitsSet = vcntq_u8 (A_vec);
uint16x8_t bitSet8 = vpaddlq_u8 (bitsSet);
uint32x4_t bitSet4 = vpaddlq_u16 (bitSet8);
bits = vaddq_u32(bits, bitSet4);
}
uint64x2_t bitSet2 = vpaddlq_u32 (bits);
result = vgetq_lane_s32 (vreinterpretq_s32_u64(bitSet2),0);
result += vgetq_lane_s32 (vreinterpretq_s32_u64(bitSet2),2);
}
else
#endif
for( ; i <= n - 4; i += 4 )
result += popCountTable[a[i]] + popCountTable[a[i+1]] +
popCountTable[a[i+2]] + popCountTable[a[i+3]];
for( ; i < n; i++ )
result += popCountTable[a[i]];
return result;
}
int normHamming(const uchar* a, const uchar* b, int n)
{
int i = 0, result = 0;
@@ -995,6 +1020,27 @@ int normHamming(const uchar* a, const uchar* b, int n)
return result;
}
int normHamming(const uchar* a, int n, int cellSize)
{
if( cellSize == 1 )
return normHamming(a, n);
const uchar* tab = 0;
if( cellSize == 2 )
tab = popCountTable2;
else if( cellSize == 4 )
tab = popCountTable4;
else
CV_Error( CV_StsBadSize, "bad cell size (not 1, 2 or 4) in normHamming" );
int i = 0, result = 0;
#if CV_ENABLE_UNROLLED
for( ; i <= n - 4; i += 4 )
result += tab[a[i]] + tab[a[i+1]] + tab[a[i+2]] + tab[a[i+3]];
#endif
for( ; i < n; i++ )
result += tab[a[i]];
return result;
}
int normHamming(const uchar* a, const uchar* b, int n, int cellSize)
{
if( cellSize == 1 )
@@ -1221,38 +1267,80 @@ double cv::norm( InputArray _src, int normType, InputArray _mask )
int depth = src.depth(), cn = src.channels();
normType &= 7;
CV_Assert( normType == NORM_INF || normType == NORM_L1 || normType == NORM_L2 );
CV_Assert( normType == NORM_INF || normType == NORM_L1 || normType == NORM_L2 || normType == NORM_L2SQR ||
((normType == NORM_HAMMING || normType == NORM_HAMMING2) && src.type() == CV_8U) );
if( depth == CV_32F && src.isContinuous() && mask.empty() )
if( src.isContinuous() && mask.empty() )
{
size_t len = src.total()*cn;
if( len == (size_t)(int)len )
{
const float* data = src.ptr<float>();
if( normType == NORM_L2 )
if( depth == CV_32F )
{
double result = 0;
GET_OPTIMIZED(normL2_32f)(data, 0, &result, (int)len, 1);
return std::sqrt(result);
const float* data = src.ptr<float>();
if( normType == NORM_L2 )
{
double result = 0;
GET_OPTIMIZED(normL2_32f)(data, 0, &result, (int)len, 1);
return std::sqrt(result);
}
if( normType == NORM_L2SQR )
{
double result = 0;
GET_OPTIMIZED(normL2_32f)(data, 0, &result, (int)len, 1);
return result;
}
if( normType == NORM_L1 )
{
double result = 0;
GET_OPTIMIZED(normL1_32f)(data, 0, &result, (int)len, 1);
return result;
}
if( normType == NORM_INF )
{
float result = 0;
GET_OPTIMIZED(normInf_32f)(data, 0, &result, (int)len, 1);
return result;
}
}
if( normType == NORM_L1 )
if( depth == CV_8U )
{
double result = 0;
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);
return result;
const uchar* data = src.ptr<uchar>();
if( normType == NORM_HAMMING )
return normHamming(data, (int)len);
if( normType == NORM_HAMMING2 )
return normHamming(data, (int)len, 2);
}
}
}
CV_Assert( mask.empty() || mask.type() == CV_8U );
if( normType == NORM_HAMMING || normType == NORM_HAMMING2 )
{
if( !mask.empty() )
{
Mat temp;
bitwise_and(src, mask, temp);
return norm(temp, normType);
}
int cellSize = normType == NORM_HAMMING ? 1 : 2;
const Mat* arrays[] = {&src, 0};
uchar* ptrs[1];
NAryMatIterator it(arrays, ptrs);
int total = (int)it.size;
int result = 0;
for( size_t i = 0; i < it.nplanes; i++, ++it )
result += normHamming(ptrs[0], total, cellSize);
return result;
}
NormFunc func = normTab[normType >> 1][depth];
CV_Assert( func != 0 );
@@ -1269,7 +1357,7 @@ 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 || normType == NORM_L2SQR) && depth <= CV_8S);
int isum = 0;
int *ibuf = &result.i;
size_t esz = 0;
@@ -1328,38 +1416,72 @@ double cv::norm( InputArray _src1, InputArray _src2, int normType, InputArray _m
CV_Assert( src1.size == src2.size && src1.type() == src2.type() );
normType &= 7;
CV_Assert( normType == NORM_INF || normType == NORM_L1 || normType == NORM_L2 );
CV_Assert( normType == NORM_INF || normType == NORM_L1 || normType == NORM_L2 || normType == NORM_L2SQR ||
((normType == NORM_HAMMING || normType == NORM_HAMMING2) && src1.type() == CV_8U) );
if( src1.depth() == CV_32F && src1.isContinuous() && src2.isContinuous() && mask.empty() )
if( src1.isContinuous() && src2.isContinuous() && mask.empty() )
{
size_t len = src1.total()*src1.channels();
if( len == (size_t)(int)len )
{
const float* data1 = src1.ptr<float>();
const float* data2 = src2.ptr<float>();
if( normType == NORM_L2 )
if( src1.depth() == CV_32F )
{
double result = 0;
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);
return result;
}
{
float result = 0;
GET_OPTIMIZED(normDiffInf_32f)(data1, data2, 0, &result, (int)len, 1);
return result;
const float* data1 = src1.ptr<float>();
const float* data2 = src2.ptr<float>();
if( normType == NORM_L2 )
{
double result = 0;
GET_OPTIMIZED(normDiffL2_32f)(data1, data2, 0, &result, (int)len, 1);
return std::sqrt(result);
}
if( normType == NORM_L2SQR )
{
double result = 0;
GET_OPTIMIZED(normDiffL2_32f)(data1, data2, 0, &result, (int)len, 1);
return result;
}
if( normType == NORM_L1 )
{
double result = 0;
GET_OPTIMIZED(normDiffL1_32f)(data1, data2, 0, &result, (int)len, 1);
return result;
}
if( normType == NORM_INF )
{
float result = 0;
GET_OPTIMIZED(normDiffInf_32f)(data1, data2, 0, &result, (int)len, 1);
return result;
}
}
}
}
CV_Assert( mask.empty() || mask.type() == CV_8U );
if( normType == NORM_HAMMING || normType == NORM_HAMMING2 )
{
if( !mask.empty() )
{
Mat temp;
bitwise_xor(src1, src2, temp);
bitwise_and(temp, mask, temp);
return norm(temp, normType);
}
int cellSize = normType == NORM_HAMMING ? 1 : 2;
const Mat* arrays[] = {&src1, &src2, 0};
uchar* ptrs[2];
NAryMatIterator it(arrays, ptrs);
int total = (int)it.size;
int result = 0;
for( size_t i = 0; i < it.nplanes; i++, ++it )
result += normHamming(ptrs[0], ptrs[1], total, cellSize);
return result;
}
NormDiffFunc func = normDiffTab[normType >> 1][depth];
CV_Assert( func != 0 );
@@ -1377,7 +1499,7 @@ 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 || normType == NORM_L2SQR) && depth <= CV_8S);
unsigned isum = 0;
unsigned *ibuf = &result.u;
size_t esz = 0;