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added NORM_HAMMING* support to cv::norm (ticket #1840)

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This commit is contained in:
Vadim Pisarevsky 2012-04-23 12:19:02 +00:00
parent 8dae3431c7
commit 40f2c716af
3 changed files with 225 additions and 40 deletions
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189
modules/core/src/stat.cpp
View File

@ -965,6 +965,34 @@ 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 +1023,27 @@ 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 +1270,74 @@ 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_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_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 );
@ -1328,38 +1413,66 @@ 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_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_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 );

10
modules/core/test/test_arithm.cpp
View File

@ -1226,7 +1226,14 @@ struct NormOp : public BaseElemWiseOp
};
int getRandomType(RNG& rng)
{
return cvtest::randomType(rng, DEPTH_MASK_ALL_BUT_8S, 1, 4);
int type = cvtest::randomType(rng, DEPTH_MASK_ALL_BUT_8S, 1, 4);
normType = 1 << rng.uniform(0, 3);
if( CV_MAT_DEPTH(type) == CV_8U && (rng.next() & 8) != 0 )
{
normType = cv::NORM_HAMMING + rng.uniform(0, 2);
type = CV_MAT_DEPTH(type);
}
return type;
}
void op(const vector<Mat>& src, Mat& dst, const Mat& mask)
{
@ -1242,7 +1249,6 @@ struct NormOp : public BaseElemWiseOp
}
void generateScalars(int, RNG& rng)
{
normType = 1 << rng.uniform(0, 3);
}
double getMaxErr(int)
{

66
modules/ts/src/ts_func.cpp
View File

@ -1100,6 +1100,23 @@ void minMaxLoc(const Mat& src, double* _minval, double* _maxval,
}
static int
normHamming(const uchar* src, size_t total, int cellSize)
{
int result = 0;
int mask = cellSize == 1 ? 1 : cellSize == 2 ? 3 : cellSize == 4 ? 15 : -1;
CV_Assert( mask >= 0 );
for( size_t i = 0; i < total; i++ )
{
unsigned a = src[i];
for( ; a != 0; a >>= cellSize )
result += (a & mask) != 0;
}
return result;
}
template<typename _Tp> static double
norm_(const _Tp* src, size_t total, int cn, int normType, double startval, const uchar* mask)
{
@ -1216,8 +1233,34 @@ norm_(const _Tp* src1, const _Tp* src2, size_t total, int cn, int normType, doub
double norm(const Mat& src, int normType, const Mat& mask)
{
if( normType == NORM_HAMMING || normType == NORM_HAMMING2 )
{
if( !mask.empty() )
{
Mat temp;
bitwise_and(src, mask, temp);
return norm(temp, normType, Mat());
}
CV_Assert( src.depth() == CV_8U );
const Mat *arrays[]={&src, 0};
Mat planes[1];
NAryMatIterator it(arrays, planes);
size_t total = planes[0].total();
size_t i, nplanes = it.nplanes;
double result = 0;
int cellSize = normType == NORM_HAMMING ? 1 : 2;
for( i = 0; i < nplanes; i++, ++it )
result += normHamming(planes[0].data, total, cellSize);
return result;
}
CV_Assert( mask.empty() || (src.size == mask.size && mask.type() == CV_8U) );
CV_Assert( normType == NORM_INF || normType == NORM_L1 || normType == NORM_L2 );
const Mat *arrays[]={&src, &mask, 0};
Mat planes[2];
@ -1267,6 +1310,29 @@ double norm(const Mat& src, int normType, const Mat& mask)
double norm(const Mat& src1, const Mat& src2, int normType, const Mat& mask)
{
if( normType == NORM_HAMMING || normType == NORM_HAMMING2 )
{
Mat temp;
bitwise_xor(src1, src2, temp);
if( !mask.empty() )
bitwise_and(temp, mask, temp);
CV_Assert( temp.depth() == CV_8U );
const Mat *arrays[]={&temp, 0};
Mat planes[1];
NAryMatIterator it(arrays, planes);
size_t total = planes[0].total();
size_t i, nplanes = it.nplanes;
double result = 0;
int cellSize = normType == NORM_HAMMING ? 1 : 2;
for( i = 0; i < nplanes; i++, ++it )
result += normHamming(planes[0].data, total, cellSize);
return result;
}
CV_Assert( src1.type() == src2.type() && src1.size == src2.size );
CV_Assert( mask.empty() || (src1.size == mask.size && mask.type() == CV_8U) );
CV_Assert( normType == NORM_INF || normType == NORM_L1 || normType == NORM_L2 );