opencv/modules/core/src/rand.cpp
2012-10-17 15:32:23 +04:00

879 lines
27 KiB
C++

/*M///////////////////////////////////////////////////////////////////////////////////////
//
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//
// License Agreement
// For Open Source Computer Vision Library
//
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/* ////////////////////////////////////////////////////////////////////
//
// Filling CvMat/IplImage instances with random numbers
//
// */
#include "precomp.hpp"
#if defined __SSE2__ || (defined _M_IX86_FP && 2 == _M_IX86_FP)
#include "emmintrin.h"
#endif
namespace cv
{
///////////////////////////// Functions Declaration //////////////////////////////////////
/*
Multiply-with-carry generator is used here:
temp = ( A*X(n) + carry )
X(n+1) = temp mod (2^32)
carry = temp / (2^32)
*/
#define RNG_NEXT(x) ((uint64)(unsigned)(x)*CV_RNG_COEFF + ((x) >> 32))
/***************************************************************************************\
* Pseudo-Random Number Generators (PRNGs) *
\***************************************************************************************/
template<typename T> static void
randBits_( T* arr, int len, uint64* state, const Vec2i* p, bool small_flag )
{
uint64 temp = *state;
int i;
if( !small_flag )
{
for( i = 0; i <= len - 4; i += 4 )
{
int t0, t1;
temp = RNG_NEXT(temp);
t0 = ((int)temp & p[i][0]) + p[i][1];
temp = RNG_NEXT(temp);
t1 = ((int)temp & p[i+1][0]) + p[i+1][1];
arr[i] = saturate_cast<T>(t0);
arr[i+1] = saturate_cast<T>(t1);
temp = RNG_NEXT(temp);
t0 = ((int)temp & p[i+2][0]) + p[i+2][1];
temp = RNG_NEXT(temp);
t1 = ((int)temp & p[i+3][0]) + p[i+3][1];
arr[i+2] = saturate_cast<T>(t0);
arr[i+3] = saturate_cast<T>(t1);
}
}
else
{
for( i = 0; i <= len - 4; i += 4 )
{
int t0, t1, t;
temp = RNG_NEXT(temp);
t = (int)temp;
t0 = (t & p[i][0]) + p[i][1];
t1 = ((t >> 8) & p[i+1][0]) + p[i+1][1];
arr[i] = saturate_cast<T>(t0);
arr[i+1] = saturate_cast<T>(t1);
t0 = ((t >> 16) & p[i+2][0]) + p[i+2][1];
t1 = ((t >> 24) & p[i+3][0]) + p[i+3][1];
arr[i+2] = saturate_cast<T>(t0);
arr[i+3] = saturate_cast<T>(t1);
}
}
for( ; i < len; i++ )
{
int t0;
temp = RNG_NEXT(temp);
t0 = ((int)temp & p[i][0]) + p[i][1];
arr[i] = saturate_cast<T>(t0);
}
*state = temp;
}
struct DivStruct
{
unsigned d;
unsigned M;
int sh1, sh2;
int delta;
};
template<typename T> static void
randi_( T* arr, int len, uint64* state, const DivStruct* p )
{
uint64 temp = *state;
int i = 0;
unsigned t0, t1, v0, v1;
for( i = 0; i <= len - 4; i += 4 )
{
temp = RNG_NEXT(temp);
t0 = (unsigned)temp;
temp = RNG_NEXT(temp);
t1 = (unsigned)temp;
v0 = (unsigned)(((uint64)t0 * p[i].M) >> 32);
v1 = (unsigned)(((uint64)t1 * p[i+1].M) >> 32);
v0 = (v0 + ((t0 - v0) >> p[i].sh1)) >> p[i].sh2;
v1 = (v1 + ((t1 - v1) >> p[i+1].sh1)) >> p[i+1].sh2;
v0 = t0 - v0*p[i].d + p[i].delta;
v1 = t1 - v1*p[i+1].d + p[i+1].delta;
arr[i] = saturate_cast<T>((int)v0);
arr[i+1] = saturate_cast<T>((int)v1);
temp = RNG_NEXT(temp);
t0 = (unsigned)temp;
temp = RNG_NEXT(temp);
t1 = (unsigned)temp;
v0 = (unsigned)(((uint64)t0 * p[i+2].M) >> 32);
v1 = (unsigned)(((uint64)t1 * p[i+3].M) >> 32);
v0 = (v0 + ((t0 - v0) >> p[i+2].sh1)) >> p[i+2].sh2;
v1 = (v1 + ((t1 - v1) >> p[i+3].sh1)) >> p[i+3].sh2;
v0 = t0 - v0*p[i+2].d + p[i+2].delta;
v1 = t1 - v1*p[i+3].d + p[i+3].delta;
arr[i+2] = saturate_cast<T>((int)v0);
arr[i+3] = saturate_cast<T>((int)v1);
}
for( ; i < len; i++ )
{
temp = RNG_NEXT(temp);
t0 = (unsigned)temp;
v0 = (unsigned)(((uint64)t0 * p[i].M) >> 32);
v0 = (v0 + ((t0 - v0) >> p[i].sh1)) >> p[i].sh2;
v0 = t0 - v0*p[i].d + p[i].delta;
arr[i] = saturate_cast<T>((int)v0);
}
*state = temp;
}
#define DEF_RANDI_FUNC(suffix, type) \
static void randBits_##suffix(type* arr, int len, uint64* state, \
const Vec2i* p, bool small_flag) \
{ randBits_(arr, len, state, p, small_flag); } \
\
static void randi_##suffix(type* arr, int len, uint64* state, \
const DivStruct* p, bool ) \
{ randi_(arr, len, state, p); }
DEF_RANDI_FUNC(8u, uchar)
DEF_RANDI_FUNC(8s, schar)
DEF_RANDI_FUNC(16u, ushort)
DEF_RANDI_FUNC(16s, short)
DEF_RANDI_FUNC(32s, int)
static void randf_32f( float* arr, int len, uint64* state, const Vec2f* p, bool )
{
uint64 temp = *state;
int i = 0;
for( ; i <= len - 4; i += 4 )
{
float f[4];
f[0] = (float)(int)(temp = RNG_NEXT(temp));
f[1] = (float)(int)(temp = RNG_NEXT(temp));
f[2] = (float)(int)(temp = RNG_NEXT(temp));
f[3] = (float)(int)(temp = RNG_NEXT(temp));
// handwritten SSE is required not for performance but for numerical stability!
// both 32-bit gcc and MSVC compilers trend to generate double precision SSE
// while 64-bit compilers generate single precision SIMD instructions
// so manual vectorisation forces all compilers to the single precision
#if defined __SSE2__ || (defined _M_IX86_FP && 2 == _M_IX86_FP)
__m128 q0 = _mm_loadu_ps((const float*)(p + i));
__m128 q1 = _mm_loadu_ps((const float*)(p + i + 2));
__m128 q01l = _mm_unpacklo_ps(q0, q1);
__m128 q01h = _mm_unpackhi_ps(q0, q1);
__m128 p0 = _mm_unpacklo_ps(q01l, q01h);
__m128 p1 = _mm_unpackhi_ps(q01l, q01h);
_mm_storeu_ps(arr + i, _mm_add_ps(_mm_mul_ps(_mm_loadu_ps(f), p0), p1));
#else
arr[i+0] = f[0]*p[i+0][0] + p[i+0][1];
arr[i+1] = f[1]*p[i+1][0] + p[i+1][1];
arr[i+2] = f[2]*p[i+2][0] + p[i+2][1];
arr[i+3] = f[3]*p[i+3][0] + p[i+3][1];
#endif
}
for( ; i < len; i++ )
{
temp = RNG_NEXT(temp);
#if defined __SSE2__ || (defined _M_IX86_FP && 2 == _M_IX86_FP)
_mm_store_ss(arr + i, _mm_add_ss(
_mm_mul_ss(_mm_set_ss((float)(int)temp), _mm_set_ss(p[i][0])),
_mm_set_ss(p[i][1]))
);
#else
arr[i] = (int)temp*p[i][0] + p[i][1];
#endif
}
*state = temp;
}
static void
randf_64f( double* arr, int len, uint64* state, const Vec2d* p, bool )
{
uint64 temp = *state;
int64 v = 0;
int i;
for( i = 0; i <= len - 4; i += 4 )
{
double f0, f1;
temp = RNG_NEXT(temp);
v = (temp >> 32)|(temp << 32);
f0 = v*p[i][0] + p[i][1];
temp = RNG_NEXT(temp);
v = (temp >> 32)|(temp << 32);
f1 = v*p[i+1][0] + p[i+1][1];
arr[i] = f0; arr[i+1] = f1;
temp = RNG_NEXT(temp);
v = (temp >> 32)|(temp << 32);
f0 = v*p[i+2][0] + p[i+2][1];
temp = RNG_NEXT(temp);
v = (temp >> 32)|(temp << 32);
f1 = v*p[i+3][0] + p[i+3][1];
arr[i+2] = f0; arr[i+3] = f1;
}
for( ; i < len; i++ )
{
temp = RNG_NEXT(temp);
v = (temp >> 32)|(temp << 32);
arr[i] = v*p[i][0] + p[i][1];
}
*state = temp;
}
typedef void (*RandFunc)(uchar* arr, int len, uint64* state, const void* p, bool small_flag);
static RandFunc randTab[][8] =
{
{
(RandFunc)randi_8u, (RandFunc)randi_8s, (RandFunc)randi_16u, (RandFunc)randi_16s,
(RandFunc)randi_32s, (RandFunc)randf_32f, (RandFunc)randf_64f, 0
},
{
(RandFunc)randBits_8u, (RandFunc)randBits_8s, (RandFunc)randBits_16u, (RandFunc)randBits_16s,
(RandFunc)randBits_32s, 0, 0, 0
}
};
/*
The code below implements the algorithm described in
"The Ziggurat Method for Generating Random Variables"
by Marsaglia and Tsang, Journal of Statistical Software.
*/
static void
randn_0_1_32f( float* arr, int len, uint64* state )
{
const float r = 3.442620f; // The start of the right tail
const float rng_flt = 2.3283064365386962890625e-10f; // 2^-32
static unsigned kn[128];
static float wn[128], fn[128];
uint64 temp = *state;
static bool initialized=false;
int i;
if( !initialized )
{
const double m1 = 2147483648.0;
double dn = 3.442619855899, tn = dn, vn = 9.91256303526217e-3;
// Set up the tables
double q = vn/std::exp(-.5*dn*dn);
kn[0] = (unsigned)((dn/q)*m1);
kn[1] = 0;
wn[0] = (float)(q/m1);
wn[127] = (float)(dn/m1);
fn[0] = 1.f;
fn[127] = (float)std::exp(-.5*dn*dn);
for(i=126;i>=1;i--)
{
dn = std::sqrt(-2.*std::log(vn/dn+std::exp(-.5*dn*dn)));
kn[i+1] = (unsigned)((dn/tn)*m1);
tn = dn;
fn[i] = (float)std::exp(-.5*dn*dn);
wn[i] = (float)(dn/m1);
}
initialized = true;
}
for( i = 0; i < len; i++ )
{
float x, y;
for(;;)
{
int hz = (int)temp;
temp = RNG_NEXT(temp);
int iz = hz & 127;
x = hz*wn[iz];
if( (unsigned)std::abs(hz) < kn[iz] )
break;
if( iz == 0) // iz==0, handles the base strip
{
do
{
x = (unsigned)temp*rng_flt;
temp = RNG_NEXT(temp);
y = (unsigned)temp*rng_flt;
temp = RNG_NEXT(temp);
x = (float)(-std::log(x+FLT_MIN)*0.2904764);
y = (float)-std::log(y+FLT_MIN);
} // .2904764 is 1/r
while( y + y < x*x );
x = hz > 0 ? r + x : -r - x;
break;
}
// iz > 0, handle the wedges of other strips
y = (unsigned)temp*rng_flt;
temp = RNG_NEXT(temp);
if( fn[iz] + y*(fn[iz - 1] - fn[iz]) < std::exp(-.5*x*x) )
break;
}
arr[i] = x;
}
*state = temp;
}
double RNG::gaussian(double sigma)
{
float temp;
randn_0_1_32f( &temp, 1, &state );
return temp*sigma;
}
template<typename T, typename PT> static void
randnScale_( const float* src, T* dst, int len, int cn, const PT* mean, const PT* stddev, bool stdmtx )
{
int i, j, k;
if( !stdmtx )
{
if( cn == 1 )
{
PT b = mean[0], a = stddev[0];
for( i = 0; i < len; i++ )
dst[i] = saturate_cast<T>(src[i]*a + b);
}
else
{
for( i = 0; i < len; i++, src += cn, dst += cn )
for( k = 0; k < cn; k++ )
dst[k] = saturate_cast<T>(src[k]*stddev[k] + mean[k]);
}
}
else
{
for( i = 0; i < len; i++, src += cn, dst += cn )
{
for( j = 0; j < cn; j++ )
{
PT s = mean[j];
for( k = 0; k < cn; k++ )
s += src[k]*stddev[j*cn + k];
dst[j] = saturate_cast<T>(s);
}
}
}
}
static void randnScale_8u( const float* src, uchar* dst, int len, int cn,
const float* mean, const float* stddev, bool stdmtx )
{ randnScale_(src, dst, len, cn, mean, stddev, stdmtx); }
static void randnScale_8s( const float* src, schar* dst, int len, int cn,
const float* mean, const float* stddev, bool stdmtx )
{ randnScale_(src, dst, len, cn, mean, stddev, stdmtx); }
static void randnScale_16u( const float* src, ushort* dst, int len, int cn,
const float* mean, const float* stddev, bool stdmtx )
{ randnScale_(src, dst, len, cn, mean, stddev, stdmtx); }
static void randnScale_16s( const float* src, short* dst, int len, int cn,
const float* mean, const float* stddev, bool stdmtx )
{ randnScale_(src, dst, len, cn, mean, stddev, stdmtx); }
static void randnScale_32s( const float* src, int* dst, int len, int cn,
const float* mean, const float* stddev, bool stdmtx )
{ randnScale_(src, dst, len, cn, mean, stddev, stdmtx); }
static void randnScale_32f( const float* src, float* dst, int len, int cn,
const float* mean, const float* stddev, bool stdmtx )
{ randnScale_(src, dst, len, cn, mean, stddev, stdmtx); }
static void randnScale_64f( const float* src, double* dst, int len, int cn,
const double* mean, const double* stddev, bool stdmtx )
{ randnScale_(src, dst, len, cn, mean, stddev, stdmtx); }
typedef void (*RandnScaleFunc)(const float* src, uchar* dst, int len, int cn,
const uchar*, const uchar*, bool);
static RandnScaleFunc randnScaleTab[] =
{
(RandnScaleFunc)randnScale_8u, (RandnScaleFunc)randnScale_8s, (RandnScaleFunc)randnScale_16u,
(RandnScaleFunc)randnScale_16s, (RandnScaleFunc)randnScale_32s, (RandnScaleFunc)randnScale_32f,
(RandnScaleFunc)randnScale_64f, 0
};
void RNG::fill( InputOutputArray _mat, int disttype,
InputArray _param1arg, InputArray _param2arg, bool saturateRange )
{
Mat mat = _mat.getMat(), _param1 = _param1arg.getMat(), _param2 = _param2arg.getMat();
int depth = mat.depth(), cn = mat.channels();
AutoBuffer<double> _parambuf;
int j, k, fast_int_mode = 0, smallFlag = 1;
RandFunc func = 0;
RandnScaleFunc scaleFunc = 0;
CV_Assert(_param1.channels() == 1 && (_param1.rows == 1 || _param1.cols == 1) &&
(_param1.rows + _param1.cols - 1 == cn || _param1.rows + _param1.cols - 1 == 1 ||
(_param1.size() == Size(1, 4) && _param1.type() == CV_64F && cn <= 4)));
CV_Assert( _param2.channels() == 1 &&
(((_param2.rows == 1 || _param2.cols == 1) &&
(_param2.rows + _param2.cols - 1 == cn || _param2.rows + _param2.cols - 1 == 1 ||
(_param1.size() == Size(1, 4) && _param1.type() == CV_64F && cn <= 4))) ||
(_param2.rows == cn && _param2.cols == cn && disttype == NORMAL)));
Vec2i* ip = 0;
Vec2d* dp = 0;
Vec2f* fp = 0;
DivStruct* ds = 0;
uchar* mean = 0;
uchar* stddev = 0;
bool stdmtx = false;
int n1 = (int)_param1.total();
int n2 = (int)_param2.total();
if( disttype == UNIFORM )
{
_parambuf.allocate(cn*8 + n1 + n2);
double* parambuf = _parambuf;
double* p1 = (double*)_param1.data;
double* p2 = (double*)_param2.data;
if( !_param1.isContinuous() || _param1.type() != CV_64F || n1 != cn )
{
Mat tmp(_param1.size(), CV_64F, parambuf);
_param1.convertTo(tmp, CV_64F);
p1 = parambuf;
if( n1 < cn )
for( j = n1; j < cn; j++ )
p1[j] = p1[j-n1];
}
if( !_param2.isContinuous() || _param2.type() != CV_64F || n2 != cn )
{
Mat tmp(_param2.size(), CV_64F, parambuf + cn);
_param2.convertTo(tmp, CV_64F);
p2 = parambuf + cn;
if( n2 < cn )
for( j = n2; j < cn; j++ )
p2[j] = p2[j-n2];
}
if( depth <= CV_32S )
{
ip = (Vec2i*)(parambuf + cn*2);
for( j = 0, fast_int_mode = 1; j < cn; j++ )
{
double a = min(p1[j], p2[j]);
double b = max(p1[j], p2[j]);
if( saturateRange )
{
a = max(a, depth == CV_8U || depth == CV_16U ? 0. :
depth == CV_8S ? -128. : depth == CV_16S ? -32768. : (double)INT_MIN);
b = min(b, depth == CV_8U ? 256. : depth == CV_16U ? 65536. :
depth == CV_8S ? 128. : depth == CV_16S ? 32768. : (double)INT_MAX);
}
ip[j][1] = cvCeil(a);
int idiff = ip[j][0] = cvFloor(b) - ip[j][1] - 1;
double diff = b - a;
fast_int_mode &= diff <= 4294967296. && (idiff & (idiff+1)) == 0;
if( fast_int_mode )
smallFlag &= idiff <= 255;
else
{
if( diff > INT_MAX )
ip[j][0] = INT_MAX;
if( a < INT_MIN/2 )
ip[j][1] = INT_MIN/2;
}
}
if( !fast_int_mode )
{
ds = (DivStruct*)(ip + cn);
for( j = 0; j < cn; j++ )
{
ds[j].delta = ip[j][1];
unsigned d = ds[j].d = (unsigned)(ip[j][0]+1);
int l = 0;
while(((uint64)1 << l) < d)
l++;
ds[j].M = (unsigned)(((uint64)1 << 32)*(((uint64)1 << l) - d)/d) + 1;
ds[j].sh1 = min(l, 1);
ds[j].sh2 = max(l - 1, 0);
}
}
func = randTab[fast_int_mode][depth];
}
else
{
double scale = depth == CV_64F ?
5.4210108624275221700372640043497e-20 : // 2**-64
2.3283064365386962890625e-10; // 2**-32
double maxdiff = saturateRange ? (double)FLT_MAX : DBL_MAX;
// for each channel i compute such dparam[0][i] & dparam[1][i],
// so that a signed 32/64-bit integer X is transformed to
// the range [param1.val[i], param2.val[i]) using
// dparam[1][i]*X + dparam[0][i]
if( depth == CV_32F )
{
fp = (Vec2f*)(parambuf + cn*2);
for( j = 0; j < cn; j++ )
{
fp[j][0] = (float)(std::min(maxdiff, p2[j] - p1[j])*scale);
fp[j][1] = (float)((p2[j] + p1[j])*0.5);
}
}
else
{
dp = (Vec2d*)(parambuf + cn*2);
for( j = 0; j < cn; j++ )
{
dp[j][0] = std::min(DBL_MAX, p2[j] - p1[j])*scale;
dp[j][1] = ((p2[j] + p1[j])*0.5);
}
}
func = randTab[0][depth];
}
CV_Assert( func != 0 );
}
else if( disttype == CV_RAND_NORMAL )
{
_parambuf.allocate(MAX(n1, cn) + MAX(n2, cn));
double* parambuf = _parambuf;
int ptype = depth == CV_64F ? CV_64F : CV_32F;
int esz = (int)CV_ELEM_SIZE(ptype);
if( _param1.isContinuous() && _param1.type() == ptype )
mean = _param1.data;
else
{
Mat tmp(_param1.size(), ptype, parambuf);
_param1.convertTo(tmp, ptype);
mean = (uchar*)parambuf;
}
if( n1 < cn )
for( j = n1*esz; j < cn*esz; j++ )
mean[j] = mean[j - n1*esz];
if( _param2.isContinuous() && _param2.type() == ptype )
stddev = _param2.data;
else
{
Mat tmp(_param2.size(), ptype, parambuf + cn);
_param2.convertTo(tmp, ptype);
stddev = (uchar*)(parambuf + cn);
}
if( n1 < cn )
for( j = n1*esz; j < cn*esz; j++ )
stddev[j] = stddev[j - n1*esz];
stdmtx = _param2.rows == cn && _param2.cols == cn;
scaleFunc = randnScaleTab[depth];
CV_Assert( scaleFunc != 0 );
}
else
CV_Error( CV_StsBadArg, "Unknown distribution type" );
const Mat* arrays[] = {&mat, 0};
uchar* ptr;
NAryMatIterator it(arrays, &ptr);
int total = (int)it.size, blockSize = std::min((BLOCK_SIZE + cn - 1)/cn, total);
size_t esz = mat.elemSize();
AutoBuffer<double> buf;
uchar* param = 0;
float* nbuf = 0;
if( disttype == UNIFORM )
{
buf.allocate(blockSize*cn*4);
param = (uchar*)(double*)buf;
if( ip )
{
if( ds )
{
DivStruct* p = (DivStruct*)param;
for( j = 0; j < blockSize*cn; j += cn )
for( k = 0; k < cn; k++ )
p[j + k] = ds[k];
}
else
{
Vec2i* p = (Vec2i*)param;
for( j = 0; j < blockSize*cn; j += cn )
for( k = 0; k < cn; k++ )
p[j + k] = ip[k];
}
}
else if( fp )
{
Vec2f* p = (Vec2f*)param;
for( j = 0; j < blockSize*cn; j += cn )
for( k = 0; k < cn; k++ )
p[j + k] = fp[k];
}
else
{
Vec2d* p = (Vec2d*)param;
for( j = 0; j < blockSize*cn; j += cn )
for( k = 0; k < cn; k++ )
p[j + k] = dp[k];
}
}
else
{
buf.allocate((blockSize*cn+1)/2);
nbuf = (float*)(double*)buf;
}
for( size_t i = 0; i < it.nplanes; i++, ++it )
{
for( j = 0; j < total; j += blockSize )
{
int len = std::min(total - j, blockSize);
if( disttype == CV_RAND_UNI )
func( ptr, len*cn, &state, param, smallFlag != 0 );
else
{
randn_0_1_32f(nbuf, len*cn, &state);
scaleFunc(nbuf, ptr, len, cn, mean, stddev, stdmtx);
}
ptr += len*esz;
}
}
}
#ifdef WIN32
#ifdef WINCE
# define TLS_OUT_OF_INDEXES ((DWORD)0xFFFFFFFF)
#endif
static DWORD tlsRNGKey = TLS_OUT_OF_INDEXES;
void deleteThreadRNGData()
{
if( tlsRNGKey != TLS_OUT_OF_INDEXES )
delete (RNG*)TlsGetValue( tlsRNGKey );
}
RNG& theRNG()
{
if( tlsRNGKey == TLS_OUT_OF_INDEXES )
{
tlsRNGKey = TlsAlloc();
CV_Assert(tlsRNGKey != TLS_OUT_OF_INDEXES);
}
RNG* rng = (RNG*)TlsGetValue( tlsRNGKey );
if( !rng )
{
rng = new RNG;
TlsSetValue( tlsRNGKey, rng );
}
return *rng;
}
#else
static pthread_key_t tlsRNGKey = 0;
static pthread_once_t tlsRNGKeyOnce = PTHREAD_ONCE_INIT;
static void deleteRNG(void* data)
{
delete (RNG*)data;
}
static void makeRNGKey()
{
int errcode = pthread_key_create(&tlsRNGKey, deleteRNG);
CV_Assert(errcode == 0);
}
RNG& theRNG()
{
pthread_once(&tlsRNGKeyOnce, makeRNGKey);
RNG* rng = (RNG*)pthread_getspecific(tlsRNGKey);
if( !rng )
{
rng = new RNG;
pthread_setspecific(tlsRNGKey, rng);
}
return *rng;
}
#endif
}
void cv::randu(InputOutputArray dst, InputArray low, InputArray high)
{
theRNG().fill(dst, RNG::UNIFORM, low, high);
}
void cv::randn(InputOutputArray dst, InputArray mean, InputArray stddev)
{
theRNG().fill(dst, RNG::NORMAL, mean, stddev);
}
namespace cv
{
template<typename T> static void
randShuffle_( Mat& _arr, RNG& rng, double iterFactor )
{
int sz = _arr.rows*_arr.cols, iters = cvRound(iterFactor*sz);
if( _arr.isContinuous() )
{
T* arr = (T*)_arr.data;
for( int i = 0; i < iters; i++ )
{
int j = (unsigned)rng % sz, k = (unsigned)rng % sz;
std::swap( arr[j], arr[k] );
}
}
else
{
uchar* data = _arr.data;
size_t step = _arr.step;
int cols = _arr.cols;
for( int i = 0; i < iters; i++ )
{
int j1 = (unsigned)rng % sz, k1 = (unsigned)rng % sz;
int j0 = j1/cols, k0 = k1/cols;
j1 -= j0*cols; k1 -= k0*cols;
std::swap( ((T*)(data + step*j0))[j1], ((T*)(data + step*k0))[k1] );
}
}
}
typedef void (*RandShuffleFunc)( Mat& dst, RNG& rng, double iterFactor );
}
void cv::randShuffle( InputOutputArray _dst, double iterFactor, RNG* _rng )
{
RandShuffleFunc tab[] =
{
0,
randShuffle_<uchar>, // 1
randShuffle_<ushort>, // 2
randShuffle_<Vec<uchar,3> >, // 3
randShuffle_<int>, // 4
0,
randShuffle_<Vec<ushort,3> >, // 6
0,
randShuffle_<Vec<int,2> >, // 8
0, 0, 0,
randShuffle_<Vec<int,3> >, // 12
0, 0, 0,
randShuffle_<Vec<int,4> >, // 16
0, 0, 0, 0, 0, 0, 0,
randShuffle_<Vec<int,6> >, // 24
0, 0, 0, 0, 0, 0, 0,
randShuffle_<Vec<int,8> > // 32
};
Mat dst = _dst.getMat();
RNG& rng = _rng ? *_rng : theRNG();
CV_Assert( dst.elemSize() <= 32 );
RandShuffleFunc func = tab[dst.elemSize()];
CV_Assert( func != 0 );
func( dst, rng, iterFactor );
}
void cv::randShuffle_( InputOutputArray _dst, double iterFactor )
{
randShuffle(_dst, iterFactor);
}
CV_IMPL void
cvRandArr( CvRNG* _rng, CvArr* arr, int disttype, CvScalar param1, CvScalar param2 )
{
cv::Mat mat = cv::cvarrToMat(arr);
// !!! this will only work for current 64-bit MWC RNG !!!
cv::RNG& rng = _rng ? (cv::RNG&)*_rng : cv::theRNG();
rng.fill(mat, disttype == CV_RAND_NORMAL ?
cv::RNG::NORMAL : cv::RNG::UNIFORM, cv::Scalar(param1), cv::Scalar(param2) );
}
CV_IMPL void cvRandShuffle( CvArr* arr, CvRNG* _rng, double iter_factor )
{
cv::Mat dst = cv::cvarrToMat(arr);
cv::RNG& rng = _rng ? (cv::RNG&)*_rng : cv::theRNG();
cv::randShuffle( dst, iter_factor, &rng );
}
/* End of file. */