mirrors/opencv
2
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2026-09-28 04:09:42 +03:00
Code Issues Packages Projects Releases Wiki Activity
Files
243f74cc8cbac3fc86bd699499af3a218ca08fc2
opencv/modules/core/src/norm.dispatch.cpp
T

874 lines
30 KiB
C++
Raw Blame History

// 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 "norm.simd.hpp"
#include "norm.simd_declarations.hpp"
/****************************************************************************************\
* norm *
\****************************************************************************************/
namespace cv { namespace hal {
extern const uchar popCountTable[256] =
{
0, 1, 1, 2, 1, 2, 2, 3, 1, 2, 2, 3, 2, 3, 3, 4, 1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5,
1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6,
1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6,
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,
1, 2, 2, 3, 2, 3, 3, 4, 2, 3, 3, 4, 3, 4, 4, 5, 2, 3, 3, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 5, 5, 6,
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,
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,
1, 2, 2, 2, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3, 1, 2, 2, 2, 2, 3, 3, 3, 2, 3, 3, 3, 2, 3, 3, 3,
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,
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,
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,
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,
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,
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,
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 cellSize)
{
int output;
CALL_HAL_RET(normHamming8u, cv_hal_normHamming8u, output, a, n, cellSize);
if( cellSize == 1 )
return normHamming(a, n);
const uchar* tab = 0;
if( cellSize == 2 )
tab = popCountTable2;
else if( cellSize == 4 )
tab = popCountTable4;
else
return -1;
int i = 0;
int result = 0;
#if (CV_SIMD || CV_SIMD_SCALABLE)
v_uint64 t = vx_setzero_u64();
if ( cellSize == 2)
{
v_uint16 mask = v_reinterpret_as_u16(vx_setall_u8(0x55));
for(; i <= n - VTraits<v_uint8>::vlanes(); i += VTraits<v_uint8>::vlanes())
{
v_uint16 a0 = v_reinterpret_as_u16(vx_load(a + i));
t = v_add(t, v_popcount(v_reinterpret_as_u64(v_and(v_or(a0, v_shr<1>(a0)), mask))));
}
}
else // cellSize == 4
{
v_uint16 mask = v_reinterpret_as_u16(vx_setall_u8(0x11));
for(; i <= n - VTraits<v_uint8>::vlanes(); i += VTraits<v_uint8>::vlanes())
{
v_uint16 a0 = v_reinterpret_as_u16(vx_load(a + i));
v_uint16 a1 = v_or(a0, v_shr<2>(a0));
t = v_add(t, v_popcount(v_reinterpret_as_u64(v_and(v_or(a1, v_shr<1>(a1)), mask))));
}
}
result += (int)v_reduce_sum(t);
vx_cleanup();
#elif 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)
{
int output;
CALL_HAL_RET(normHammingDiff8u, cv_hal_normHammingDiff8u, output, a, b, n, cellSize);
if( cellSize == 1 )
return normHamming(a, b, n);
const uchar* tab = 0;
if( cellSize == 2 )
tab = popCountTable2;
else if( cellSize == 4 )
tab = popCountTable4;
else
return -1;
int i = 0;
int result = 0;
#if (CV_SIMD || CV_SIMD_SCALABLE)
v_uint64 t = vx_setzero_u64();
if ( cellSize == 2)
{
v_uint16 mask = v_reinterpret_as_u16(vx_setall_u8(0x55));
for(; i <= n - VTraits<v_uint8>::vlanes(); i += VTraits<v_uint8>::vlanes())
{
v_uint16 ab0 = v_reinterpret_as_u16(v_xor(vx_load(a + i), vx_load(b + i)));
t = v_add(t, v_popcount(v_reinterpret_as_u64(v_and(v_or(ab0, v_shr<1>(ab0)), mask))));
}
}
else // cellSize == 4
{
v_uint16 mask = v_reinterpret_as_u16(vx_setall_u8(0x11));
for(; i <= n - VTraits<v_uint8>::vlanes(); i += VTraits<v_uint8>::vlanes())
{
v_uint16 ab0 = v_reinterpret_as_u16(v_xor(vx_load(a + i), vx_load(b + i)));
v_uint16 ab1 = v_or(ab0, v_shr<2>(ab0));
t = v_add(t, v_popcount(v_reinterpret_as_u64(v_and(v_or(ab1, v_shr<1>(ab1)), mask))));
}
}
result += (int)v_reduce_sum(t);
vx_cleanup();
#elif CV_ENABLE_UNROLLED
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]];
#endif
for( ; i < n; i++ )
result += tab[a[i] ^ b[i]];
return result;
}
float normL2Sqr_(const float* a, const float* b, int n)
{
int j = 0; float d = 0.f;
#if (CV_SIMD || CV_SIMD_SCALABLE)
v_float32 v_d0 = vx_setzero_f32(), v_d1 = vx_setzero_f32();
v_float32 v_d2 = vx_setzero_f32(), v_d3 = vx_setzero_f32();
for (; j <= n - 4 * VTraits<v_float32>::vlanes(); j += 4 * VTraits<v_float32>::vlanes())
{
v_float32 t0 = v_sub(vx_load(a + j), vx_load(b + j));
v_float32 t1 = v_sub(vx_load(a + j + VTraits<v_float32>::vlanes()), vx_load(b + j + VTraits<v_float32>::vlanes()));
v_d0 = v_muladd(t0, t0, v_d0);
v_float32 t2 = v_sub(vx_load(a + j + 2 * VTraits<v_float32>::vlanes()), vx_load(b + j + 2 * VTraits<v_float32>::vlanes()));
v_d1 = v_muladd(t1, t1, v_d1);
v_float32 t3 = v_sub(vx_load(a + j + 3 * VTraits<v_float32>::vlanes()), vx_load(b + j + 3 * VTraits<v_float32>::vlanes()));
v_d2 = v_muladd(t2, t2, v_d2);
v_d3 = v_muladd(t3, t3, v_d3);
}
d = v_reduce_sum(v_add(v_add(v_add(v_d0, v_d1), v_d2), v_d3));
#endif
for( ; j < n; j++ )
{
float t = a[j] - b[j];
d += t*t;
}
return d;
}
float normL1_(const float* a, const float* b, int n)
{
int j = 0; float d = 0.f;
#if (CV_SIMD || CV_SIMD_SCALABLE)
v_float32 v_d0 = vx_setzero_f32(), v_d1 = vx_setzero_f32();
v_float32 v_d2 = vx_setzero_f32(), v_d3 = vx_setzero_f32();
for (; j <= n - 4 * VTraits<v_float32>::vlanes(); j += 4 * VTraits<v_float32>::vlanes())
{
v_d0 = v_add(v_d0, v_absdiff(vx_load(a + j), vx_load(b + j)));
v_d1 = v_add(v_d1, v_absdiff(vx_load(a + j + VTraits<v_float32>::vlanes()), vx_load(b + j + VTraits<v_float32>::vlanes())));
v_d2 = v_add(v_d2, v_absdiff(vx_load(a + j + 2 * VTraits<v_float32>::vlanes()), vx_load(b + j + 2 * VTraits<v_float32>::vlanes())));
v_d3 = v_add(v_d3, v_absdiff(vx_load(a + j + 3 * VTraits<v_float32>::vlanes()), vx_load(b + j + 3 * VTraits<v_float32>::vlanes())));
}
d = v_reduce_sum(v_add(v_add(v_add(v_d0, v_d1), v_d2), v_d3));
#endif
for( ; j < n; j++ )
d += std::abs(a[j] - b[j]);
return d;
}
int normL1_(const uchar* a, const uchar* b, int n)
{
int j = 0, d = 0;
#if (CV_SIMD || CV_SIMD_SCALABLE)
for (; j <= n - 4 * VTraits<v_uint8>::vlanes(); j += 4 * VTraits<v_uint8>::vlanes())
d += v_reduce_sad(vx_load(a + j), vx_load(b + j)) +
v_reduce_sad(vx_load(a + j + VTraits<v_uint8>::vlanes()), vx_load(b + j + VTraits<v_uint8>::vlanes())) +
v_reduce_sad(vx_load(a + j + 2 * VTraits<v_uint8>::vlanes()), vx_load(b + j + 2 * VTraits<v_uint8>::vlanes())) +
v_reduce_sad(vx_load(a + j + 3 * VTraits<v_uint8>::vlanes()), vx_load(b + j + 3 * VTraits<v_uint8>::vlanes()));
#endif
for( ; j < n; j++ )
d += std::abs(a[j] - b[j]);
return d;
}
} //cv::hal
//==================================================================================================
typedef int (*NormFunc)(const uchar*, const uchar*, uchar*, int, int);
typedef int (*NormDiffFunc)(const uchar*, const uchar*, const uchar*, uchar*, int, int);
#ifdef HAVE_OPENCL
static bool ocl_norm( InputArray _src, int normType, InputArray _mask, double & result )
{
const ocl::Device & d = ocl::Device::getDefault();
#ifdef __ANDROID__
if (d.isNVidia())
return false;
#endif
const int cn = _src.channels();
if (cn > 4)
return false;
int type = _src.type(), depth = CV_MAT_DEPTH(type);
bool doubleSupport = d.doubleFPConfig() > 0,
haveMask = _mask.kind() != _InputArray::NONE;
if (depth >= CV_16F)
return false; // TODO: support FP16
if ( !(normType == NORM_INF || normType == NORM_L1 || normType == NORM_L2 || normType == NORM_L2SQR) ||
(!doubleSupport && depth == CV_64F))
return false;
UMat src = _src.getUMat();
if (normType == NORM_INF)
{
if (!ocl_minMaxIdx(_src, NULL, &result, NULL, NULL, _mask,
std::max(depth, CV_32S), depth != CV_8U && depth != CV_16U))
return false;
}
else if (normType == NORM_L1 || normType == NORM_L2 || normType == NORM_L2SQR)
{
Scalar sc;
bool unstype = depth == CV_8U || depth == CV_16U;
if ( !ocl_sum(haveMask ? src : src.reshape(1), sc, normType == NORM_L2 || normType == NORM_L2SQR ?
OCL_OP_SUM_SQR : (unstype ? OCL_OP_SUM : OCL_OP_SUM_ABS), _mask) )
return false;
double s = 0.0;
for (int i = 0; i < (haveMask ? cn : 1); ++i)
s += sc[i];
result = normType == NORM_L1 || normType == NORM_L2SQR ? s : std::sqrt(s);
}
return true;
}
#endif
static NormFunc getNormFunc(int normType, int depth) {
CV_INSTRUMENT_REGION();
CV_CPU_DISPATCH(getNormFunc, (normType, depth), CV_CPU_DISPATCH_MODES_ALL);
}
static NormDiffFunc getNormDiffFunc(int normType, int depth) {
CV_INSTRUMENT_REGION();
CV_CPU_DISPATCH(getNormDiffFunc, (normType, depth), CV_CPU_DISPATCH_MODES_ALL);
}
double norm( InputArray _src, int normType, InputArray _mask )
{
CV_INSTRUMENT_REGION();
normType &= NORM_TYPE_MASK;
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 defined HAVE_OPENCL
double _result = 0;
#endif
#ifdef HAVE_OPENCL
CV_OCL_RUN_(OCL_PERFORMANCE_CHECK(_src.isUMat()) && _src.dims() <= 2,
ocl_norm(_src, normType, _mask, _result),
_result)
#endif
Mat src = _src.getMat(), mask = _mask.getMat();
int depth = src.depth(), cn = src.channels();
if( src.dims <= 2 )
{
double result;
CALL_HAL_RET(norm, cv_hal_norm, result, src.data, src.step, mask.data, mask.step, src.cols, src.rows, src.type(), normType);
}
else if( src.isContinuous() && mask.isContinuous() )
{
double result;
CALL_HAL_RET(norm, cv_hal_norm, result, src.data, 0, mask.data, 0, (int)src.total(), 1, src.type(), normType);
}
NormFunc func = getNormFunc(normType >> 1, depth == CV_16F ? CV_32F : depth);
CV_Assert( (normType >> 1) >= 3 || func != 0 );
if( src.isContinuous() && mask.empty() )
{
size_t len = src.total()*cn;
if( len == (size_t)(int)len )
{
if( depth == CV_32F )
{
const uchar* data = src.ptr<const uchar>();
if( normType == NORM_L2 || normType == NORM_L2SQR || normType == NORM_L1 )
{
double result = 0;
func(data, 0, (uchar*)&result, (int)len, 1);
return normType == NORM_L2 ? std::sqrt(result) : result;
}
if( normType == NORM_INF )
{
float result = 0;
func(data, 0, (uchar*)&result, (int)len, 1);
return result;
}
}
if( depth == CV_8U )
{
const uchar* data = src.ptr<uchar>();
if( normType == NORM_HAMMING )
{
return hal::normHamming(data, (int)len, 1);
}
if( normType == NORM_HAMMING2 )
{
return hal::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 += hal::normHamming(ptrs[0], total, cellSize);
}
return result;
}
const Mat* arrays[] = {&src, &mask, 0};
uchar* ptrs[2] = {};
union
{
double d;
int i;
float f;
}
result;
result.d = 0;
NAryMatIterator it(arrays, ptrs);
CV_CheckLT((size_t)it.size, (size_t)INT_MAX, "");
if ((normType == NORM_L1 && depth <= CV_16S) ||
((normType == NORM_L2 || normType == NORM_L2SQR) && depth <= CV_8S))
{
// special case to handle "integer" overflow in accumulator
const size_t esz = src.elemSize();
const int total = (int)it.size;
const int intSumBlockSize = (normType == NORM_L1 && depth <= CV_8S ? (1 << 23) : (1 << 15))/cn;
const int blockSize = std::min(total, intSumBlockSize);
int isum = 0;
int count = 0;
for (size_t i = 0; i < it.nplanes; i++, ++it)
{
for (int j = 0; j < total; j += blockSize)
{
int bsz = std::min(total - j, blockSize);
func(ptrs[0], ptrs[1], (uchar*)&isum, bsz, cn);
count += bsz;
if (count + blockSize >= intSumBlockSize || (i+1 >= it.nplanes && j+bsz >= total))
{
result.d += isum;
isum = 0;
count = 0;
}
ptrs[0] += bsz*esz;
if (ptrs[1])
ptrs[1] += bsz;
}
}
}
else if (depth == CV_16F)
{
const size_t esz = src.elemSize();
const int total = (int)it.size;
const int blockSize = std::min(total, divUp(1024, cn));
AutoBuffer<float, 1026/*divUp(1024,3)*3*/> fltbuf(blockSize * cn);
float* data0 = fltbuf.data();
for (size_t i = 0; i < it.nplanes; i++, ++it)
{
for (int j = 0; j < total; j += blockSize)
{
int bsz = std::min(total - j, blockSize);
hal::cvt16f32f((const hfloat*)ptrs[0], data0, bsz * cn);
func((uchar*)data0, ptrs[1], (uchar*)&result.f, bsz, cn);
ptrs[0] += bsz*esz;
if (ptrs[1])
ptrs[1] += bsz;
}
}
}
else
{
// generic implementation
for (size_t i = 0; i < it.nplanes; i++, ++it)
{
func(ptrs[0], ptrs[1], (uchar*)&result, (int)it.size, cn);
}
}
if( normType == NORM_INF )
{
if(depth == CV_64F)
return result.d;
else if (depth == CV_32F || depth == CV_16F)
return result.f;
else
return result.i;
}
else if( normType == NORM_L2 )
return std::sqrt(result.d);
return result.d;
}
//==================================================================================================
#ifdef HAVE_OPENCL
static bool ocl_norm( InputArray _src1, InputArray _src2, int normType, InputArray _mask, double & result )
{
#ifdef __ANDROID__
if (ocl::Device::getDefault().isNVidia())
return false;
#endif
Scalar sc1, sc2;
int cn = _src1.channels();
if (cn > 4)
return false;
int type = _src1.type(), depth = CV_MAT_DEPTH(type);
bool relative = (normType & NORM_RELATIVE) != 0;
normType &= ~NORM_RELATIVE;
bool normsum = normType == NORM_L1 || normType == NORM_L2 || normType == NORM_L2SQR;
#ifdef __APPLE__
if(normType == NORM_L1 && type == CV_16UC3 && !_mask.empty())
return false;
#endif
if (normsum)
{
if (!ocl_sum(_src1, sc1, normType == NORM_L2 || normType == NORM_L2SQR ?
OCL_OP_SUM_SQR : OCL_OP_SUM, _mask, _src2, relative, sc2))
return false;
}
else
{
if (!ocl_minMaxIdx(_src1, NULL, &sc1[0], NULL, NULL, _mask, std::max(CV_32S, depth),
false, _src2, relative ? &sc2[0] : NULL))
return false;
cn = 1;
}
double s2 = 0;
for (int i = 0; i < cn; ++i)
{
result += sc1[i];
if (relative)
s2 += sc2[i];
}
if (normType == NORM_L2)
{
result = std::sqrt(result);
if (relative)
s2 = std::sqrt(s2);
}
if (relative)
result /= (s2 + DBL_EPSILON);
return true;
} // ocl_norm()
#endif // HAVE_OPENCL
double norm( InputArray _src1, InputArray _src2, int normType, InputArray _mask )
{
CV_INSTRUMENT_REGION();
CV_CheckTypeEQ(_src1.type(), _src2.type(), "Input type mismatch");
CV_Assert(_src1.sameSize(_src2));
#if defined HAVE_OPENCL
double _result = 0;
#endif
#ifdef HAVE_OPENCL
CV_OCL_RUN_(OCL_PERFORMANCE_CHECK(_src1.isUMat()),
ocl_norm(_src1, _src2, normType, _mask, _result),
_result)
#endif
Mat src1 = _src1.getMat(), src2 = _src2.getMat(), mask = _mask.getMat();
int depth = src1.depth(), cn = src1.channels();
if( src1.dims <= 2 )
{
double result;
CALL_HAL_RET(normDiff, cv_hal_normDiff, result, src1.data, src1.step, src2.data, src2.step, mask.data, mask.step, src1.cols, src1.rows, src1.type(), normType);
}
else if( src1.isContinuous() && src2.isContinuous() && mask.isContinuous() )
{
double result;
CALL_HAL_RET(normDiff, cv_hal_normDiff, result, src1.data, 0, src2.data, 0, mask.data, 0, (int)src1.total(), 1, src1.type(), normType);
}
if( normType & CV_RELATIVE )
{
return norm(_src1, _src2, normType & ~CV_RELATIVE, _mask)/(norm(_src2, normType, _mask) + DBL_EPSILON);
}
normType &= 7;
CV_Assert( normType == NORM_INF || normType == NORM_L1 ||
normType == NORM_L2 || normType == NORM_L2SQR ||
((normType == NORM_HAMMING || normType == NORM_HAMMING2) && src1.type() == CV_8U) );
NormDiffFunc func = getNormDiffFunc(normType >> 1, depth == CV_16F ? CV_32F : depth);
CV_Assert( (normType >> 1) >= 3 || func != 0 );
if( src1.isContinuous() && src2.isContinuous() && mask.empty() )
{
size_t len = src1.total()*src1.channels();
if( len == (size_t)(int)len )
{
if( src1.depth() == CV_32F )
{
const uchar* data1 = src1.ptr<const uchar>();
const uchar* data2 = src2.ptr<const uchar>();
if( normType == NORM_L2 || normType == NORM_L2SQR || normType == NORM_L1 )
{
double result = 0;
func(data1, data2, 0, (uchar*)&result, (int)len, 1);
return normType == NORM_L2 ? std::sqrt(result) : result;
}
if( normType == NORM_INF )
{
float result = 0;
func(data1, data2, 0, (uchar*)&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 += hal::normHamming(ptrs[0], ptrs[1], total, cellSize);
}
return result;
}
const Mat* arrays[] = {&src1, &src2, &mask, 0};
uchar* ptrs[3] = {};
union
{
double d;
float f;
int i;
unsigned u;
}
result;
result.d = 0;
NAryMatIterator it(arrays, ptrs);
CV_CheckLT((size_t)it.size, (size_t)INT_MAX, "");
if ((normType == NORM_L1 && depth <= CV_16S) ||
((normType == NORM_L2 || normType == NORM_L2SQR) && depth <= CV_8S))
{
// special case to handle "integer" overflow in accumulator
const size_t esz = src1.elemSize();
const int total = (int)it.size;
const int intSumBlockSize = (normType == NORM_L1 && depth <= CV_8S ? (1 << 23) : (1 << 15))/cn;
const int blockSize = std::min(total, intSumBlockSize);
int isum = 0;
int count = 0;
for (size_t i = 0; i < it.nplanes; i++, ++it)
{
for (int j = 0; j < total; j += blockSize)
{
int bsz = std::min(total - j, blockSize);
func(ptrs[0], ptrs[1], ptrs[2], (uchar*)&isum, bsz, cn);
count += bsz;
if (count + blockSize >= intSumBlockSize || (i+1 >= it.nplanes && j+bsz >= total))
{
result.d += isum;
isum = 0;
count = 0;
}
ptrs[0] += bsz*esz;
ptrs[1] += bsz*esz;
if (ptrs[2])
ptrs[2] += bsz;
}
}
}
else if (depth == CV_16F)
{
const size_t esz = src1.elemSize();
const int total = (int)it.size;
const int blockSize = std::min(total, divUp(512, cn));
AutoBuffer<float, 1026/*divUp(512,3)*3*2*/> fltbuf(blockSize * cn * 2);
float* data0 = fltbuf.data();
float* data1 = fltbuf.data() + blockSize * cn;
for (size_t i = 0; i < it.nplanes; i++, ++it)
{
for (int j = 0; j < total; j += blockSize)
{
int bsz = std::min(total - j, blockSize);
hal::cvt16f32f((const hfloat*)ptrs[0], data0, bsz * cn);
hal::cvt16f32f((const hfloat*)ptrs[1], data1, bsz * cn);
func((uchar*)data0, (uchar*)data1, ptrs[2], (uchar*)&result.f, bsz, cn);
ptrs[0] += bsz*esz;
ptrs[1] += bsz*esz;
if (ptrs[2])
ptrs[2] += bsz;
}
}
}
else
{
// generic implementation
for (size_t i = 0; i < it.nplanes; i++, ++it)
{
func(ptrs[0], ptrs[1], ptrs[2], (uchar*)&result, (int)it.size, cn);
}
}
if( normType == NORM_INF )
{
if (depth == CV_64F)
return result.d;
else if (depth == CV_32F || depth == CV_16F)
return result.f;
else
return result.u;
}
else if( normType == NORM_L2 )
return std::sqrt(result.d);
return result.d;
}
cv::Hamming::ResultType Hamming::operator()( const unsigned char* a, const unsigned char* b, int size ) const
{
return cv::hal::normHamming(a, b, size);
}
double PSNR(InputArray _src1, InputArray _src2, double R)
{
CV_INSTRUMENT_REGION();
//Input arrays must have depth CV_8U
CV_Assert( _src1.type() == _src2.type() );
double diff = std::sqrt(norm(_src1, _src2, NORM_L2SQR)/(_src1.total()*_src1.channels()));
return 20*log10(R/(diff+DBL_EPSILON));
}
#ifdef HAVE_OPENCL
static bool ocl_normalize( InputArray _src, InputOutputArray _dst, InputArray _mask, int dtype,
double scale, double delta )
{
UMat src = _src.getUMat();
if( _mask.empty() )
src.convertTo( _dst, dtype, scale, delta );
else if (src.channels() <= 4)
{
const ocl::Device & dev = ocl::Device::getDefault();
int stype = _src.type(), sdepth = CV_MAT_DEPTH(stype), cn = CV_MAT_CN(stype),
ddepth = CV_MAT_DEPTH(dtype), wdepth = std::max(CV_32F, std::max(sdepth, ddepth)),
rowsPerWI = dev.isIntel() ? 4 : 1;
float fscale = static_cast<float>(scale), fdelta = static_cast<float>(delta);
bool haveScale = std::fabs(scale - 1) > DBL_EPSILON,
haveZeroScale = !(std::fabs(scale) > DBL_EPSILON),
haveDelta = std::fabs(delta) > DBL_EPSILON,
doubleSupport = dev.doubleFPConfig() > 0;
if (!haveScale && !haveDelta && stype == dtype)
{
_src.copyTo(_dst, _mask);
return true;
}
if (haveZeroScale)
{
_dst.setTo(Scalar(delta), _mask);
return true;
}
if ((sdepth == CV_64F || ddepth == CV_64F) && !doubleSupport)
return false;
char cvt[2][50];
String opts = format("-D srcT=%s -D dstT=%s -D convertToWT=%s -D cn=%d -D rowsPerWI=%d"
" -D convertToDT=%s -D workT=%s%s%s%s -D srcT1=%s -D dstT1=%s",
ocl::typeToStr(stype), ocl::typeToStr(dtype),
ocl::convertTypeStr(sdepth, wdepth, cn, cvt[0], sizeof(cvt[0])), cn,
rowsPerWI, ocl::convertTypeStr(wdepth, ddepth, cn, cvt[1], sizeof(cvt[1])),
ocl::typeToStr(CV_MAKE_TYPE(wdepth, cn)),
doubleSupport ? " -D DOUBLE_SUPPORT" : "",
haveScale ? " -D HAVE_SCALE" : "",
haveDelta ? " -D HAVE_DELTA" : "",
ocl::typeToStr(sdepth), ocl::typeToStr(ddepth));
ocl::Kernel k("normalizek", ocl::core::normalize_oclsrc, opts);
if (k.empty())
return false;
UMat mask = _mask.getUMat(), dst = _dst.getUMat();
ocl::KernelArg srcarg = ocl::KernelArg::ReadOnlyNoSize(src),
maskarg = ocl::KernelArg::ReadOnlyNoSize(mask),
dstarg = ocl::KernelArg::ReadWrite(dst);
if (haveScale)
{
if (haveDelta)
k.args(srcarg, maskarg, dstarg, fscale, fdelta);
else
k.args(srcarg, maskarg, dstarg, fscale);
}
else
{
if (haveDelta)
k.args(srcarg, maskarg, dstarg, fdelta);
else
k.args(srcarg, maskarg, dstarg);
}
size_t globalsize[2] = { (size_t)src.cols, ((size_t)src.rows + rowsPerWI - 1) / rowsPerWI };
return k.run(2, globalsize, NULL, false);
}
else
{
UMat temp;
src.convertTo( temp, dtype, scale, delta );
temp.copyTo( _dst, _mask );
}
return true;
} // ocl_normalize
#endif // HAVE_OPENCL
void normalize(InputArray _src, InputOutputArray _dst, double a, double b,
int norm_type, int rtype, InputArray _mask)
{
CV_INSTRUMENT_REGION();
double scale = 1, shift = 0;
int type = _src.type(), depth = CV_MAT_DEPTH(type);
if( rtype < 0 )
rtype = _dst.fixedType() ? _dst.depth() : depth;
if( norm_type == CV_MINMAX )
{
double smin = 0, smax = 0;
double dmin = MIN( a, b ), dmax = MAX( a, b );
minMaxIdx( _src, &smin, &smax, 0, 0, _mask );
scale = (dmax - dmin)*(smax - smin > DBL_EPSILON ? 1./(smax - smin) : 0);
if( rtype == CV_32F )
{
scale = (float)scale;
shift = (float)dmin - (float)(smin*scale);
}
else
shift = dmin - smin*scale;
}
else if( norm_type == CV_L2 || norm_type == CV_L1 || norm_type == CV_C )
{
scale = norm( _src, norm_type, _mask );
scale = scale > DBL_EPSILON ? a/scale : 0.;
shift = 0;
}
else
CV_Error( cv::Error::StsBadArg, "Unknown/unsupported norm type" );
CV_OCL_RUN(_dst.isUMat(),
ocl_normalize(_src, _dst, _mask, rtype, scale, shift))
Mat src = _src.getMat();
if( _mask.empty() )
src.convertTo( _dst, rtype, scale, shift );
else
{
Mat temp;
src.convertTo( temp, rtype, scale, shift );
temp.copyTo( _dst, _mask );
}
}
} // namespace
Reference in New Issue View Git Blame Copy Permalink