OpenCV源码阅读(3)---base.hpp
base.h处于core模块中，是OpenCV的核心类。其作用是定义了OpenCV的基本错误类型，在程序运行出现错误是抛出错误，防止数据溢出。总而言之，其功能主要是考虑程序的健壮性。
头文件
#ifndef __OPENCV_CORE_BASE_HPP__
#define __OPENCV_CORE_BASE_HPP__
#ifndef __cplusplus
# error base.hpp header must be compiled as C++
#endif
#include <climits>
#include "opencv2/core/cvdef.h"
#include "opencv2/core/cvstd.hpp"
namespace cv
{
和其他程序一样，base.h中包含头文件cvdef.h,和cvstd.hpp。从名字中就可以读出,base.hpp，是一个.hpp类型的文件，它既包含了声明，又包含了定义(h+cpp)。
namespace Error {
enum {
StsOk= 0, /* everithing is ok */
StsBackTrace= -1, /* pseudo error for back trace */
StsError= -2, /* unknown /unspecified error */
StsInternal= -3, /* internal error (bad state) */
StsNoMem= -4, /* insufficient memory */
StsBadArg= -5, /* function arg/param is bad */
StsBadFunc= -6, /* unsupported function */
StsNoConv= -7, /* iter. didn't converge */
StsAutoTrace= -8, /* tracing */
HeaderIsNull= -9, /* image header is NULL */
BadImageSize= -10, /* image size is invalid */
BadOffset= -11, /* offset is invalid */
BadDataPtr= -12, /**/
BadStep= -13, /**/
BadModelOrChSeq= -14, /**/
BadNumChannels= -15, /**/
BadNumChannel1U= -16, /**/
BadDepth= -17, /**/
BadAlphaChannel= -18, /**/
BadOrder= -19, /**/
BadOrigin= -20, /**/
BadAlign= -21, /**/
BadCallBack= -22, /**/
BadTileSize= -23, /**/
BadCOI= -24, /**/
BadROISize= -25, /**/
MaskIsTiled= -26, /**/
StsNullPtr= -27, /* null pointer */
StsVecLengthErr= -28, /* incorrect vector length */
StsFilterStructContentErr= -29, /* incorr. filter structure content */
StsKernelStructContentErr= -30, /* incorr. transform kernel content */
StsFilterOffsetErr= -31, /* incorrect filter ofset value */
StsBadSize= -201, /* the input/output structure size is incorrect */
StsDivByZero= -202, /* division by zero */
StsInplaceNotSupported= -203, /* in-place operation is not supported */
StsObjectNotFound= -204, /* request can't be completed */
StsUnmatchedFormats= -205, /* formats of input/output arrays differ */
StsBadFlag= -206, /* flag is wrong or not supported */
StsBadPoint= -207, /* bad CvPoint */
StsBadMask= -208, /* bad format of mask (neither 8uC1 nor 8sC1)*/
StsUnmatchedSizes= -209, /* sizes of input/output structures do not match */
StsUnsupportedFormat= -210, /* the data format/type is not supported by the function*/
StsOutOfRange= -211, /* some of parameters are out of range */
StsParseError= -212, /* invalid syntax/structure of the parsed file */
StsNotImplemented= -213, /* the requested function/feature is not implemented */
StsBadMemBlock= -214, /* an allocated block has been corrupted */
StsAssert= -215, /* assertion failed */
GpuNotSupported= -216,
GpuApiCallError= -217,
OpenGlNotSupported= -218,
OpenGlApiCallError= -219,
OpenCLApiCallError= -220,
OpenCLDoubleNotSupported= -221,
OpenCLInitError= -222,
OpenCLNoAMDBlasFft= -223
};
} //Error首先为error专门开了一个namespace，这里用枚举类型的方式定义了错误的类型，这样就把ID和名称联系在了一起，之后只要使用错误的名称就可以调用错误的ID了，增强程序的可读性。
enum { DECOMP_LU = 0,
DECOMP_SVD = 1,
DECOMP_EIG = 2,
DECOMP_CHOLESKY = 3,
DECOMP_QR = 4,
DECOMP_NORMAL = 16
};矩阵的分解方式
enum { NORM_INF = 1,
NORM_L1 = 2,
NORM_L2 = 4,
NORM_L2SQR = 5,
NORM_HAMMING = 6,
NORM_HAMMING2 = 7,
NORM_TYPE_MASK = 7,
NORM_RELATIVE = 8,
NORM_MINMAX = 32
};正规化的类型
enum { CMP_EQ = 0,
CMP_GT = 1,
CMP_GE = 2,
CMP_LT = 3,
CMP_LE = 4,
CMP_NE = 5
};比较的类型
enum { GEMM_1_T = 1,
GEMM_2_T = 2,
GEMM_3_T = 4
};
enum { DFT_INVERSE = 1,
DFT_SCALE = 2,
DFT_ROWS = 4,
DFT_COMPLEX_OUTPUT = 16,
DFT_REAL_OUTPUT = 32,
DCT_INVERSE = DFT_INVERSE,
DCT_ROWS = DFT_ROWS
};
//! Various border types, image boundaries are denoted with '|'
enum {
BORDER_CONSTANT = 0, // iiiiii|abcdefgh|iiiiiii with some specified 'i'
BORDER_REPLICATE = 1, // aaaaaa|abcdefgh|hhhhhhh
BORDER_REFLECT = 2, // fedcba|abcdefgh|hgfedcb
BORDER_WRAP = 3, // cdefgh|abcdefgh|abcdefg
BORDER_REFLECT_101 = 4, // gfedcb|abcdefgh|gfedcba
BORDER_TRANSPARENT = 5, // uvwxyz|absdefgh|ijklmno
BORDER_REFLECT101 = BORDER_REFLECT_101,
BORDER_DEFAULT = BORDER_REFLECT_101,
BORDER_ISOLATED = 16 // do not look outside of ROI
};
这些都大同小异，通过枚举类的方式实现变量名和ID的替换。
//////////////// static assert /////////////////
#define CVAUX_CONCAT_EXP(a, b) a##b
#define CVAUX_CONCAT(a, b) CVAUX_CONCAT_EXP(a,b)
#if defined(__clang__)
# ifndef __has_extension
# define __has_extension __has_feature /* compatibility, for older versions of clang */
# endif
# if __has_extension(cxx_static_assert)
# define CV_StaticAssert(condition, reason) static_assert((condition), reason " " #condition)
# endif
#elif defined(__GNUC__)
# if (defined(__GXX_EXPERIMENTAL_CXX0X__) || __cplusplus >= 201103L)
# define CV_StaticAssert(condition, reason) static_assert((condition), reason " " #condition)
# endif
#elif defined(_MSC_VER)
# if _MSC_VER >= 1600 /* MSVC 10 */
# define CV_StaticAssert(condition, reason) static_assert((condition), reason " " #condition)
# endif
#endif
#ifndef CV_StaticAssert
# if defined(__GNUC__) && (__GNUC__ > 3) && (__GNUC_MINOR__ > 2)
# define CV_StaticAssert(condition, reason) ({ extern int __attribute__((error("CV_StaticAssert: " reason " " #condition))) CV_StaticAssert(); ((condition) ? 0 : CV_StaticAssert()); })
# else
template <bool x> struct CV_StaticAssert_failed;
template <> struct CV_StaticAssert_failed<true> { enum { val = 1 }; };
template<int x> struct CV_StaticAssert_test {};
# define CV_StaticAssert(condition, reason)\
typedef cv::CV_StaticAssert_test< sizeof(cv::CV_StaticAssert_failed< static_cast<bool>(condition) >) > CVAUX_CONCAT(CV_StaticAssert_failed_at_, __LINE__)
# endif
#endif
//! Suppress warning "-Wdeprecated-declarations" / C4996
#if defined(_MSC_VER)
#define CV_DO_PRAGMA(x) __pragma(x)
#elif defined(__GNUC__)
#define CV_DO_PRAGMA(x) _Pragma (#x)
#else
#define CV_DO_PRAGMA(x)
#endif
#ifdef _MSC_VER
#define CV_SUPPRESS_DEPRECATED_START \
CV_DO_PRAGMA(warning(push)) \
CV_DO_PRAGMA(warning(disable: 4996))
#define CV_SUPPRESS_DEPRECATED_END CV_DO_PRAGMA(warning(pop))
#elif defined (__clang__) || ((__GNUC__) && ((__GNUC__ > 4) || ((__GNUC__ == 4) && (__GNUC_MINOR__ > 5))))
#define CV_SUPPRESS_DEPRECATED_START \
CV_DO_PRAGMA(GCC diagnostic push) \
CV_DO_PRAGMA(GCC diagnostic ignored "-Wdeprecated-declarations")
#define CV_SUPPRESS_DEPRECATED_END CV_DO_PRAGMA(GCC diagnostic pop)
#else
#define CV_SUPPRESS_DEPRECATED_START
#define CV_SUPPRESS_DEPRECATED_END
#endif
/*! @brief Signals an error and raises the exception.
By default the function prints information about the error to stderr,
then it either stops if setBreakOnError() had been called before or raises the exception.
It is possible to alternate error processing by using redirectError().
@param _code - error code @see CVStatus
@param _err - error description
@param _func - function name. Available only when the compiler supports getting it
@param _file - source file name where the error has occured
@param _line - line number in the source file where the error has occured
*/
CV_EXPORTS void error(int _code, const String& _err, const char* _func, const char* _file, int _line);
#ifdef __GNUC__
# if defined __clang__ || defined __APPLE__
# pragma GCC diagnostic push
# pragma GCC diagnostic ignored "-Winvalid-noreturn"
# endif
#endif
CV_INLINE CV_NORETURN void errorNoReturn(int _code, const String& _err, const char* _func, const char* _file, int _line)
{
error(_code, _err, _func, _file, _line);
#ifdef __GNUC__
# if !defined __clang__ && !defined __APPLE__
// this suppresses this warning: "noreturn" function does return [enabled by default]
__builtin_trap();
// or use infinite loop: for (;;) {}
# endif
#endif
}
#ifdef __GNUC__
# if defined __clang__ || defined __APPLE__
# pragma GCC diagnostic pop
# endif
#endif
#if defined __GNUC__
#define CV_Func __func__
#elif defined _MSC_VER
#define CV_Func __FUNCTION__
#else
#define CV_Func ""
#endif
#define CV_Error( code, msg ) cv::error( code, msg, CV_Func, __FILE__, __LINE__ )
#define CV_Error_( code, args ) cv::error( code, cv::format args, CV_Func, __FILE__, __LINE__ )
#define CV_Assert( expr ) if(!!(expr)) ; else cv::error( cv::Error::StsAssert, #expr, CV_Func, __FILE__, __LINE__ )
#define CV_ErrorNoReturn( code, msg ) cv::errorNoReturn( code, msg, CV_Func, __FILE__, __LINE__ )
#define CV_ErrorNoReturn_( code, args ) cv::errorNoReturn( code, cv::format args, CV_Func, __FILE__, __LINE__ )
#ifdef _DEBUG
# define CV_DbgAssert(expr) CV_Assert(expr)
#else
# define CV_DbgAssert(expr)
#endif
这段带n个#号的程序就是opencv里的断言机制了，在后面的matx.h经常用到CV_StaticAssert(abc==def,”XXXXXX”）就是通过这个实现的，但是其实最基础的还是c++自带的staticAssert。
这里使用了宏的方式来控制编译的进行，if define 后面接了编译器的标志，程序可以判断使用何种编译器来决定编译哪段程序。
/////////////// saturate_cast (used in image & signal processing) ///////////////////
template<typename _Tp> static inline _Tp saturate_cast(uchar v) { return _Tp(v); }
template<typename _Tp> static inline _Tp saturate_cast(schar v) { return _Tp(v); }
template<typename _Tp> static inline _Tp saturate_cast(ushort v) { return _Tp(v); }
template<typename _Tp> static inline _Tp saturate_cast(short v) { return _Tp(v); }
template<typename _Tp> static inline _Tp saturate_cast(unsigned v) { return _Tp(v); }
template<typename _Tp> static inline _Tp saturate_cast(int v) { return _Tp(v); }
template<typename _Tp> static inline _Tp saturate_cast(float v) { return _Tp(v); }
template<typename _Tp> static inline _Tp saturate_cast(double v) { return _Tp(v); }
template<> inline uchar saturate_cast<uchar>(schar v) { return (uchar)std::max((int)v, 0); }
template<> inline uchar saturate_cast<uchar>(ushort v) { return (uchar)std::min((unsigned)v, (unsigned)UCHAR_MAX); }
template<> inline uchar saturate_cast<uchar>(int v) { return (uchar)((unsigned)v <= UCHAR_MAX ? v : v > 0 ? UCHAR_MAX : 0); }
template<> inline uchar saturate_cast<uchar>(short v) { return saturate_cast<uchar>((int)v); }
template<> inline uchar saturate_cast<uchar>(unsigned v) { return (uchar)std::min(v, (unsigned)UCHAR_MAX); }
template<> inline uchar saturate_cast<uchar>(float v) { int iv = cvRound(v); return saturate_cast<uchar>(iv); }
template<> inline uchar saturate_cast<uchar>(double v) { int iv = cvRound(v); return saturate_cast<uchar>(iv); }
template<> inline schar saturate_cast<schar>(uchar v) { return (schar)std::min((int)v, SCHAR_MAX); }
template<> inline schar saturate_cast<schar>(ushort v) { return (schar)std::min((unsigned)v, (unsigned)SCHAR_MAX); }
template<> inline schar saturate_cast<schar>(int v) { return (schar)((unsigned)(v-SCHAR_MIN) <= (unsigned)UCHAR_MAX ? v : v > 0 ? SCHAR_MAX : SCHAR_MIN); }
template<> inline schar saturate_cast<schar>(short v) { return saturate_cast<schar>((int)v); }
template<> inline schar saturate_cast<schar>(unsigned v) { return (schar)std::min(v, (unsigned)SCHAR_MAX); }
template<> inline schar saturate_cast<schar>(float v) { int iv = cvRound(v); return saturate_cast<schar>(iv); }
template<> inline schar saturate_cast<schar>(double v) { int iv = cvRound(v); return saturate_cast<schar>(iv); }
template<> inline ushort saturate_cast<ushort>(schar v) { return (ushort)std::max((int)v, 0); }
template<> inline ushort saturate_cast<ushort>(short v) { return (ushort)std::max((int)v, 0); }
template<> inline ushort saturate_cast<ushort>(int v) { return (ushort)((unsigned)v <= (unsigned)USHRT_MAX ? v : v > 0 ? USHRT_MAX : 0); }
template<> inline ushort saturate_cast<ushort>(unsigned v) { return (ushort)std::min(v, (unsigned)USHRT_MAX); }
template<> inline ushort saturate_cast<ushort>(float v) { int iv = cvRound(v); return saturate_cast<ushort>(iv); }
template<> inline ushort saturate_cast<ushort>(double v) { int iv = cvRound(v); return saturate_cast<ushort>(iv); }
template<> inline short saturate_cast<short>(ushort v) { return (short)std::min((int)v, SHRT_MAX); }
template<> inline short saturate_cast<short>(int v) { return (short)((unsigned)(v - SHRT_MIN) <= (unsigned)USHRT_MAX ? v : v > 0 ? SHRT_MAX : SHRT_MIN); }
template<> inline short saturate_cast<short>(unsigned v) { return (short)std::min(v, (unsigned)SHRT_MAX); }
template<> inline short saturate_cast<short>(float v) { int iv = cvRound(v); return saturate_cast<short>(iv); }
template<> inline short saturate_cast<short>(double v) { int iv = cvRound(v); return saturate_cast<short>(iv); }
template<> inline int saturate_cast<int>(float v) { return cvRound(v); }
template<> inline int saturate_cast<int>(double v) { return cvRound(v); }
// we intentionally do not clip negative numbers, to make -1 become 0xffffffff etc.
template<> inline unsigned saturate_cast<unsigned>(float v) { return cvRound(v); }
template<> inline unsigned saturate_cast<unsigned>(double v) { return cvRound(v); }
这就是防止数据溢出的程序的具体实现全部使用的是内联实现的方式,很符合.hpp的风格嘛~~函数会判断输入的数据和输出数据目标数据类型。将数据进行合适的截断
这回答了之前在matx.hpp中提出的问题，为什么要使用两个模板。例如：
Matx<_Tp,m,n>::Matx(const Matx<_Tp, m, n>& a, const Matx<_Tp, m, n>& b, Matx_SubOp)
{
for( int i = 0; i < channels; i++ )
val[i] = saturate_cast<_Tp>(a.val[i] - b.val[i]);
}两个矩阵的减法，如果是两个usigned数据类型的矩阵，做减法很可能就会减出负值，这时候就需要防止数据溢出的函数，来确保这个负值不会变成一个很大的正值。
//////////////////////////////// low-level functions ////////////////////////////////
CV_EXPORTS int LU(float* A, size_t astep, int m, float* b, size_t bstep, int n);
CV_EXPORTS int LU(double* A, size_t astep, int m, double* b, size_t bstep, int n);
CV_EXPORTS bool Cholesky(float* A, size_t astep, int m, float* b, size_t bstep, int n);
CV_EXPORTS bool Cholesky(double* A, size_t astep, int m, double* b, size_t bstep, int n);
CV_EXPORTS int normL1_(const uchar* a, const uchar* b, int n);
CV_EXPORTS int normHamming(const uchar* a, const uchar* b, int n);
CV_EXPORTS int normHamming(const uchar* a, const uchar* b, int n, int cellSize);
CV_EXPORTS float normL1_(const float* a, const float* b, int n);
CV_EXPORTS float normL2Sqr_(const float* a, const float* b, int n);
CV_EXPORTS void exp(const float* src, float* dst, int n);
CV_EXPORTS void log(const float* src, float* dst, int n);
CV_EXPORTS void fastAtan2(const float* y, const float* x, float* dst, int n, bool angleInDegrees);
CV_EXPORTS void magnitude(const float* x, const float* y, float* dst, int n);
//! computes cube root of the argument
CV_EXPORTS_W float cubeRoot(float val);
//! computes the angle in degrees (0..360) of the vector (x,y)
CV_EXPORTS_W float fastAtan2(float y, float x);定义了很多基本运算的操作，cv_exports是宏定义，和编译器以及环境有关。
/////////////////////////////////// inline norms ////////////////////////////////////
template<typename _Tp, typename _AccTp> static inline
_AccTp normL2Sqr(const _Tp* a, int n)
{
_AccTp s = 0;
int i=0;
#if CV_ENABLE_UNROLLED
for( ; i <= n - 4; i += 4 )
{
_AccTp v0 = a[i], v1 = a[i+1], v2 = a[i+2], v3 = a[i+3];
s += v0*v0 + v1*v1 + v2*v2 + v3*v3;
}
#endif
for( ; i < n; i++ )
{
_AccTp v = a[i];
s += v*v;
}
return s;
}
template<typename _Tp, typename _AccTp> static inline
_AccTp normL1(const _Tp* a, int n)
{
_AccTp s = 0;
int i = 0;
#if CV_ENABLE_UNROLLED
for(; i <= n - 4; i += 4 )
{
s += (_AccTp)std::abs(a[i]) + (_AccTp)std::abs(a[i+1]) +
(_AccTp)std::abs(a[i+2]) + (_AccTp)std::abs(a[i+3]);
}
#endif
for( ; i < n; i++ )
s += std::abs(a[i]);
return s;
}
template<typename _Tp, typename _AccTp> static inline
_AccTp normInf(const _Tp* a, int n)
{
_AccTp s = 0;
for( int i = 0; i < n; i++ )
s = std::max(s, (_AccTp)std::abs(a[i]));
return s;
}
template<typename _Tp, typename _AccTp> static inline
_AccTp normL2Sqr(const _Tp* a, const _Tp* b, int n)
{
_AccTp s = 0;
int i= 0;
#if CV_ENABLE_UNROLLED
for(; i <= n - 4; i += 4 )
{
_AccTp v0 = _AccTp(a[i] - b[i]), v1 = _AccTp(a[i+1] - b[i+1]), v2 = _AccTp(a[i+2] - b[i+2]), v3 = _AccTp(a[i+3] - b[i+3]);
s += v0*v0 + v1*v1 + v2*v2 + v3*v3;
}
#endif
for( ; i < n; i++ )
{
_AccTp v = _AccTp(a[i] - b[i]);
s += v*v;
}
return s;
}
template<> inline
float normL2Sqr(const float* a, const float* b, int n)
{
if( n >= 8 )
return normL2Sqr_(a, b, n);
float s = 0;
for( int i = 0; i < n; i++ )
{
float v = a[i] - b[i];
s += v*v;
}
return s;
}
template<typename _Tp, typename _AccTp> static inline
_AccTp normL1(const _Tp* a, const _Tp* b, int n)
{
_AccTp s = 0;
int i= 0;
#if CV_ENABLE_UNROLLED
for(; i <= n - 4; i += 4 )
{
_AccTp v0 = _AccTp(a[i] - b[i]), v1 = _AccTp(a[i+1] - b[i+1]), v2 = _AccTp(a[i+2] - b[i+2]), v3 = _AccTp(a[i+3] - b[i+3]);
s += std::abs(v0) + std::abs(v1) + std::abs(v2) + std::abs(v3);
}
#endif
for( ; i < n; i++ )
{
_AccTp v = _AccTp(a[i] - b[i]);
s += std::abs(v);
}
return s;
}
template<> inline
float normL1(const float* a, const float* b, int n)
{
if( n >= 8 )
return normL1_(a, b, n);
float s = 0;
for( int i = 0; i < n; i++ )
{
float v = a[i] - b[i];
s += std::abs(v);
}
return s;
}
template<> inline
int normL1(const uchar* a, const uchar* b, int n)
{
return normL1_(a, b, n);
}
template<typename _Tp, typename _AccTp> static inline
_AccTp normInf(const _Tp* a, const _Tp* b, int n)
{
_AccTp s = 0;
for( int i = 0; i < n; i++ )
{
_AccTp v0 = a[i] - b[i];
s = std::max(s, std::abs(v0));
}
return s;
}这里使用一些内联函数实现了一些基本运算方法，例如最小二乘法等。
////////////////// forward declarations for important OpenCV types //////////////////
template<typename _Tp, int cn> class Vec;
template<typename _Tp, int m, int n> class Matx;
template<typename _Tp> class Complex;
template<typename _Tp> class Point_;
template<typename _Tp> class Point3_;
template<typename _Tp> class Size_;
template<typename _Tp> class Rect_;
template<typename _Tp> class Scalar_;
class CV_EXPORTS RotatedRect;
class CV_EXPORTS Range;
class CV_EXPORTS TermCriteria;
class CV_EXPORTS KeyPoint;
class CV_EXPORTS DMatch;
class CV_EXPORTS RNG;
class CV_EXPORTS Mat;
class CV_EXPORTS MatExpr;
class CV_EXPORTS UMat;
class CV_EXPORTS SparseMat;
typedef Mat MatND;
template<typename _Tp> class Mat_;
template<typename _Tp> class SparseMat_;
class CV_EXPORTS MatConstIterator;
class CV_EXPORTS SparseMatIterator;
class CV_EXPORTS SparseMatConstIterator;
template<typename _Tp> class MatIterator_;
template<typename _Tp> class MatConstIterator_;
template<typename _Tp> class SparseMatIterator_;
template<typename _Tp> class SparseMatConstIterator_;
namespace ogl
{
class CV_EXPORTS Buffer;
class CV_EXPORTS Texture2D;
class CV_EXPORTS Arrays;
}
namespace cuda
{
class CV_EXPORTS GpuMat;
class CV_EXPORTS CudaMem;
class CV_EXPORTS Stream;
class CV_EXPORTS Event;
}
namespace cudev
{
template <typename _Tp> class GpuMat_;
}
namespace ipp
{
CV_EXPORTS void setIppStatus(int status, const char * const funcname = NULL, const char * const filename = NULL,
int line = 0);
CV_EXPORTS int getIppStatus();
CV_EXPORTS String getIppErrorLocation();
CV_EXPORTS bool useIPP();
CV_EXPORTS void setUseIPP(bool flag);
} // ipp
对一些类进行前向声明，但是没有使用，具体如何实现要看其他文件
#if CV_NEON
inline int32x2_t cv_vrnd_s32_f32(float32x2_t v)
{
static int32x2_t v_sign = vdup_n_s32(1 << 31),
v_05 = vreinterpret_s32_f32(vdup_n_f32(0.5f));
int32x2_t v_addition = vorr_s32(v_05, vand_s32(v_sign, vreinterpret_s32_f32(v)));
return vcvt_s32_f32(vadd_f32(v, vreinterpret_f32_s32(v_addition)));
}
inline int32x4_t cv_vrndq_s32_f32(float32x4_t v)
{
static int32x4_t v_sign = vdupq_n_s32(1 << 31),
v_05 = vreinterpretq_s32_f32(vdupq_n_f32(0.5f));
int32x4_t v_addition = vorrq_s32(v_05, vandq_s32(v_sign, vreinterpretq_s32_f32(v)));
return vcvtq_s32_f32(vaddq_f32(v, vreinterpretq_f32_s32(v_addition)));
}
inline uint32x2_t cv_vrnd_u32_f32(float32x2_t v)
{
static float32x2_t v_05 = vdup_n_f32(0.5f);
return vcvt_u32_f32(vadd_f32(v, v_05));
}
inline uint32x4_t cv_vrndq_u32_f32(float32x4_t v)
{
static float32x4_t v_05 = vdupq_n_f32(0.5f);
return vcvtq_u32_f32(vaddq_f32(v, v_05));
}
inline float32x4_t cv_vrecpq_f32(float32x4_t val)
{
float32x4_t reciprocal = vrecpeq_f32(val);
reciprocal = vmulq_f32(vrecpsq_f32(val, reciprocal), reciprocal);
reciprocal = vmulq_f32(vrecpsq_f32(val, reciprocal), reciprocal);
return reciprocal;
}
inline float32x2_t cv_vrecp_f32(float32x2_t val)
{
float32x2_t reciprocal = vrecpe_f32(val);
reciprocal = vmul_f32(vrecps_f32(val, reciprocal), reciprocal);
reciprocal = vmul_f32(vrecps_f32(val, reciprocal), reciprocal);
return reciprocal;
}
inline float32x4_t cv_vrsqrtq_f32(float32x4_t val)
{
float32x4_t e = vrsqrteq_f32(val);
e = vmulq_f32(vrsqrtsq_f32(vmulq_f32(e, e), val), e);
e = vmulq_f32(vrsqrtsq_f32(vmulq_f32(e, e), val), e);
return e;
}
inline float32x2_t cv_vrsqrt_f32(float32x2_t val)
{
float32x2_t e = vrsqrte_f32(val);
e = vmul_f32(vrsqrts_f32(vmul_f32(e, e), val), e);
e = vmul_f32(vrsqrts_f32(vmul_f32(e, e), val), e);
return e;
}
inline float32x4_t cv_vsqrtq_f32(float32x4_t val)
{
return cv_vrecpq_f32(cv_vrsqrtq_f32(val));
}
inline float32x2_t cv_vsqrt_f32(float32x2_t val)
{
return cv_vrecp_f32(cv_vrsqrt_f32(val));
}
#endif
这段没有注释，具体实现后续再议
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