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opencv/modules/core/src/rand.cpp

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
/* ////////////////////////////////////////////////////////////////////
//
// Filling CvMat/IplImage instances with random numbers
//
// */
#include "precomp.hpp"
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_( Mat& _arr, uint64* state, const void* _param )
{
uint64 temp = *state;
const int* param = (const int*)_param;
int small_flag = (param[12]|param[13]|param[14]|param[15]) <= 255;
Size size = getContinuousSize(_arr,_arr.channels());
for( int y = 0; y < size.height; y++ )
{
T* arr = (T*)(_arr.data + _arr.step*y);
int i, k = 3;
const int* p = param;
if( !small_flag )
{
for( i = 0; i <= size.width - 4; i += 4 )
{
int t0, t1;
temp = RNG_NEXT(temp);
t0 = ((int)temp & p[i + 12]) + p[i];
temp = RNG_NEXT(temp);
t1 = ((int)temp & p[i + 13]) + p[i+1];
arr[i] = saturate_cast<T>(t0);
arr[i+1] = saturate_cast<T>(t1);
temp = RNG_NEXT(temp);
t0 = ((int)temp & p[i + 14]) + p[i+2];
temp = RNG_NEXT(temp);
t1 = ((int)temp & p[i + 15]) + p[i+3];
arr[i+2] = saturate_cast<T>(t0);
arr[i+3] = saturate_cast<T>(t1);
if( !--k )
{
k = 3;
p -= 12;
}
}
}
else
{
for( i = 0; i <= size.width - 4; i += 4 )
{
int t0, t1, t;
temp = RNG_NEXT(temp);
t = (int)temp;
t0 = (t & p[i + 12]) + p[i];
t1 = ((t >> 8) & p[i + 13]) + p[i+1];
arr[i] = saturate_cast<T>(t0);
arr[i+1] = saturate_cast<T>(t1);
t0 = ((t >> 16) & p[i + 14]) + p[i + 2];
t1 = ((t >> 24) & p[i + 15]) + p[i + 3];
arr[i+2] = saturate_cast<T>(t0);
arr[i+3] = saturate_cast<T>(t1);
if( !--k )
{
k = 3;
p -= 12;
}
}
}
for( ; i < size.width; i++ )
{
int t0;
temp = RNG_NEXT(temp);
t0 = ((int)temp & p[i + 12]) + p[i];
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_( Mat& _arr, uint64* state, const void* _param )
{
uint64 temp = *state;
const int* param = (const int*)_param;
Size size = getContinuousSize(_arr,_arr.channels());
int i, k, cn = _arr.channels();
DivStruct ds[12];
for( k = 0; k < cn; k++ )
{
ds[k].delta = param[k];
ds[k].d = (unsigned)(param[k+12] - param[k]);
int l = 0;
while(((uint64)1 << l) < ds[k].d)
l++;
ds[k].M = (unsigned)(((uint64)1 << 32)*(((uint64)1 << l) - ds[k].d)/ds[k].d) + 1;
ds[k].sh1 = min(l, 1);
ds[k].sh2 = max(l - 1, 0);
}
for( ; k < 12; k++ )
ds[k] = ds[k - cn];
for( int y = 0; y < size.height; y++ )
{
T* arr = (T*)(_arr.data + _arr.step*y);
const DivStruct* p = ds;
unsigned t0, t1, v0, v1;
for( i = 0, k = 3; i <= size.width - 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);
if( !--k )
{
k = 3;
p -= 12;
}
}
for( ; i < size.width; 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;
}
static void Randf_( Mat& _arr, uint64* state, const void* _param )
{
uint64 temp = *state;
const float* param = (const float*)_param;
Size size = getContinuousSize(_arr,_arr.channels());
for( int y = 0; y < size.height; y++ )
{
float* arr = (float*)(_arr.data + _arr.step*y);
int i, k = 3;
const float* p = param;
for( i = 0; i <= size.width - 4; i += 4 )
{
float f0, f1;
temp = RNG_NEXT(temp);
f0 = (int)temp*p[i+12] + p[i];
temp = RNG_NEXT(temp);
f1 = (int)temp*p[i+13] + p[i+1];
arr[i] = f0; arr[i+1] = f1;
temp = RNG_NEXT(temp);
f0 = (int)temp*p[i+14] + p[i+2];
temp = RNG_NEXT(temp);
f1 = (int)temp*p[i+15] + p[i+3];
arr[i+2] = f0; arr[i+3] = f1;
if( !--k )
{
k = 3;
p -= 12;
}
}
for( ; i < size.width; i++ )
{
temp = RNG_NEXT(temp);
arr[i] = (int)temp*p[i+12] + p[i];
}
}
*state = temp;
}
static void
Randd_( Mat& _arr, uint64* state, const void* _param )
{
uint64 temp = *state;
const double* param = (const double*)_param;
Size size = getContinuousSize(_arr,_arr.channels());
int64 v = 0;
for( int y = 0; y < size.height; y++ )
{
double* arr = (double*)(_arr.data + _arr.step*y);
int i, k = 3;
const double* p = param;
for( i = 0; i <= size.width - 4; i += 4 )
{
double f0, f1;
temp = RNG_NEXT(temp);
v = (temp >> 32)|(temp << 32);
f0 = v*p[i+12] + p[i];
temp = RNG_NEXT(temp);
v = (temp >> 32)|(temp << 32);
f1 = v*p[i+13] + p[i+1];
arr[i] = f0; arr[i+1] = f1;
temp = RNG_NEXT(temp);
v = (temp >> 32)|(temp << 32);
f0 = v*p[i+14] + p[i+2];
temp = RNG_NEXT(temp);
v = (temp >> 32)|(temp << 32);
f1 = v*p[i+15] + p[i+3];
arr[i+2] = f0; arr[i+3] = f1;
if( !--k )
{
k = 3;
p -= 12;
}
}
for( ; i < size.width; i++ )
{
temp = RNG_NEXT(temp);
v = (temp >> 32)|(temp << 32);
arr[i] = v*p[i+12] + p[i];
}
}
*state = temp;
}
/*
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_C1R( 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_C1R( &temp, 1, &state );
return temp*sigma;
}
template<typename T, typename PT> static void
Randn_( Mat& _arr, uint64* state, const void* _param )
{
const int RAND_BUF_SIZE = 96;
float buffer[RAND_BUF_SIZE];
int pidx[RAND_BUF_SIZE];
const PT* param = (const PT*)_param;
Size size = getContinuousSize(_arr, _arr.channels());
int i, n = std::min(size.width, RAND_BUF_SIZE);
for( i = 0; i < 12; i++ )
pidx[i] = i;
for( ; i < n; i++ )
pidx[i] = pidx[i - 12];
for( int y = 0; y < size.height; y++ )
{
T* arr = (T*)(_arr.data + _arr.step*y);
int len = RAND_BUF_SIZE;
for( i = 0; i < size.width; i += RAND_BUF_SIZE )
{
if( i + len > size.width )
len = size.width - i;
Randn_0_1_32f_C1R( buffer, len, state );
for( int j = 0; j < len; j++ )
arr[i+j] = saturate_cast<T>(buffer[j]*param[pidx[j]+12] + param[pidx[j]]);
}
}
}
typedef void (*RandFunc)(Mat& dst, uint64* state, const void* param);
void RNG::fill( Mat& mat, int disttype, const Scalar& param1, const Scalar& param2 )
{
static RandFunc rngtab[3][8] =
{
{
RandBits_<uchar>,
RandBits_<schar>,
RandBits_<ushort>,
RandBits_<short>,
RandBits_<int>, 0, 0, 0},
{Randi_<uchar>,
Randi_<schar>,
Randi_<ushort>,
Randi_<short>,
Randi_<int>,
Randf_, Randd_, 0},
{Randn_<uchar,float>,
Randn_<schar,float>,
Randn_<ushort,float>,
Randn_<short,float>,
Randn_<int,float>,
Randn_<float,float>,
Randn_<double,double>, 0}
};
int depth = mat.depth(), channels = mat.channels();
double dparam[2][12];
float fparam[2][12];
int iparam[2][12];
void* param = dparam;
int i, fast_int_mode = 0;
RandFunc func = 0;
CV_Assert( channels <= 4 );
if( disttype == UNIFORM )
{
if( depth <= CV_32S )
{
for( i = 0, fast_int_mode = 1; i < channels; i++ )
{
double a = min(param1.val[i], param2.val[i]);
double b = max(param1.val[i], param2.val[i]);
int t0 = iparam[0][i] = cvCeil(a);
int t1 = iparam[1][i] = cvFloor(b);
double diff = b - a;
fast_int_mode &= diff <= 4294967296. && ((t1-t0) & (t1-t0-1)) == 0;
}
if( fast_int_mode )
{
for( i = 0; i < channels; i++ )
iparam[1][i] = iparam[1][i] > iparam[0][i] ? iparam[1][i] - iparam[0][i] - 1 : 0;
}
for( ; i < 12; i++ )
{
int t0 = iparam[0][i - channels];
int t1 = iparam[1][i - channels];
iparam[0][i] = t0;
iparam[1][i] = t1;
}
func = rngtab[!fast_int_mode][depth];
param = iparam;
}
else
{
double scale = depth == CV_64F ?
5.4210108624275221700372640043497e-20 : // 2**-64
2.3283064365386962890625e-10; // 2**-32
// 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]
for( i = 0; i < channels; i++ )
{
double t0 = param1.val[i];
double t1 = param2.val[i];
dparam[0][i] = (t1 + t0)*0.5;
dparam[1][i] = (t1 - t0)*scale;
}
func = rngtab[1][depth];
param = dparam;
}
}
else if( disttype == CV_RAND_NORMAL )
{
for( i = 0; i < channels; i++ )
{
double t0 = param1.val[i];
double t1 = param2.val[i];
dparam[0][i] = t0;
dparam[1][i] = t1;
}
func = rngtab[2][depth];
param = dparam;
}
else
CV_Error( CV_StsBadArg, "Unknown distribution type" );
if( param == dparam )
{
for( i = channels; i < 12; i++ )
{
double t0 = dparam[0][i - channels];
double t1 = dparam[1][i - channels];
dparam[0][i] = t0;
dparam[1][i] = t1;
}
if( depth != CV_64F )
{
for( i = 0; i < 12; i++ )
{
fparam[0][i] = (float)dparam[0][i];
fparam[1][i] = (float)dparam[1][i];
}
param = fparam;
}
}
CV_Assert( func != 0);
if( mat.dims > 2 )
{
const Mat* arrays[] = {&mat, 0};
Mat planes[1];
NAryMatIterator it(arrays, planes);
for( int i = 0; i < it.nplanes; i++, ++it )
func( it.planes[0], &state, param );
}
else
func( mat, &state, param );
}
#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 void deleteRNG(void* data)
{
delete (RNG*)data;
}
RNG& theRNG()
{
if( !tlsRNGKey )
{
int errcode = pthread_key_create(&tlsRNGKey, deleteRNG);
CV_Assert(errcode == 0);
}
RNG* rng = (RNG*)pthread_getspecific(tlsRNGKey);
if( !rng )
{
rng = new RNG;
pthread_setspecific(tlsRNGKey, rng);
}
return *rng;
}
#endif
void randu(CV_OUT Mat& dst, const Scalar& low, const Scalar& high)
{
theRNG().fill(dst, RNG::UNIFORM, low, high);
}
void randn(CV_OUT Mat& dst, const Scalar& mean, const Scalar& stddev)
{
theRNG().fill(dst, RNG::NORMAL, mean, stddev);
}
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 randShuffle( Mat& 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
};
RNG& rng = _rng ? *_rng : theRNG();
CV_Assert( dst.elemSize() <= 32 );
RandShuffleFunc func = tab[dst.elemSize()];
CV_Assert( func != 0 );
func( dst, rng, 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, param1, 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. */
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