OpenCV
4.0.0pre
Open Source Computer Vision

Random Number Generator. More...
#include "core.hpp"
Public Types  
enum  { UNIFORM = 0, NORMAL = 1 } 
Public Member Functions  
RNG ()  
constructor More...  
RNG (uint64 state)  
void  fill (InputOutputArray mat, int distType, InputArray a, InputArray b, bool saturateRange=false) 
Fills arrays with random numbers. More...  
double  gaussian (double sigma) 
Returns the next random number sampled from the Gaussian distribution. More...  
unsigned  next () 
operator double ()  
operator float ()  
operator schar ()  
operator short ()  
operator uchar ()  
operator unsigned ()  
operator ushort ()  
unsigned  operator() () 
returns a random integer sampled uniformly from [0, N). More...  
unsigned  operator() (unsigned N) 
bool  operator== (const RNG &other) const 
int  uniform (int a, int b) 
returns uniformly distributed integer random number from [a,b) range More...  
float  uniform (float a, float b) 
double  uniform (double a, double b) 
Public Attributes  
uint64  state 
Random Number Generator.
Random number generator. It encapsulates the state (currently, a 64bit integer) and has methods to return scalar random values and to fill arrays with random values. Currently it supports uniform and Gaussian (normal) distributions. The generator uses MultiplyWithCarry algorithm, introduced by G. Marsaglia ( http://en.wikipedia.org/wiki/Multiplywithcarry ). Gaussiandistribution random numbers are generated using the Ziggurat algorithm ( http://en.wikipedia.org/wiki/Ziggurat_algorithm ), introduced by G. Marsaglia and W. W. Tsang.
cv::RNG::RNG  (  ) 
constructor
These are the RNG constructors. The first form sets the state to some predefined value, equal to 2**321 in the current implementation. The second form sets the state to the specified value. If you passed state=0 , the constructor uses the above default value instead to avoid the singular random number sequence, consisting of all zeros.
cv::RNG::RNG  (  uint64  state  ) 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
state  64bit value used to initialize the RNG. 
void cv::RNG::fill  (  InputOutputArray  mat, 
int  distType,  
InputArray  a,  
InputArray  b,  
bool  saturateRange = false 

) 
Fills arrays with random numbers.
mat  2D or Ndimensional matrix; currently matrices with more than 4 channels are not supported by the methods, use Mat::reshape as a possible workaround. 
distType  distribution type, RNG::UNIFORM or RNG::NORMAL. 
a  first distribution parameter; in case of the uniform distribution, this is an inclusive lower boundary, in case of the normal distribution, this is a mean value. 
b  second distribution parameter; in case of the uniform distribution, this is a noninclusive upper boundary, in case of the normal distribution, this is a standard deviation (diagonal of the standard deviation matrix or the full standard deviation matrix). 
saturateRange  presaturation flag; for uniform distribution only; if true, the method will first convert a and b to the acceptable value range (according to the mat datatype) and then will generate uniformly distributed random numbers within the range [saturate(a), saturate(b)), if saturateRange=false, the method will generate uniformly distributed random numbers in the original range [a, b) and then will saturate them, it means, for example, that theRNG().fill(mat_8u, RNG::UNIFORM, DBL_MAX, DBL_MAX) will likely produce array mostly filled with 0's and 255's, since the range (0, 255) is significantly smaller than [DBL_MAX, DBL_MAX). 
Each of the methods fills the matrix with the random values from the specified distribution. As the new numbers are generated, the RNG state is updated accordingly. In case of multiplechannel images, every channel is filled independently, which means that RNG cannot generate samples from the multidimensional Gaussian distribution with nondiagonal covariance matrix directly. To do that, the method generates samples from multidimensional standard Gaussian distribution with zero mean and identity covariation matrix, and then transforms them using transform to get samples from the specified Gaussian distribution.
double cv::RNG::gaussian  (  double  sigma  ) 
Returns the next random number sampled from the Gaussian distribution.
sigma  standard deviation of the distribution. 
The method transforms the state using the MWC algorithm and returns the next random number from the Gaussian distribution N(0,sigma) . That is, the mean value of the returned random numbers is zero and the standard deviation is the specified sigma .
unsigned cv::RNG::next  (  ) 
The method updates the state using the MWC algorithm and returns the next 32bit random number.
cv::RNG::operator double  (  ) 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
cv::RNG::operator float  (  ) 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
cv::RNG::operator schar  (  ) 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
cv::RNG::operator short  (  ) 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
cv::RNG::operator uchar  (  ) 
Each of the methods updates the state using the MWC algorithm and returns the next random number of the specified type. In case of integer types, the returned number is from the available value range for the specified type. In case of floatingpoint types, the returned value is from [0,1) range.
cv::RNG::operator unsigned  (  ) 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
cv::RNG::operator ushort  (  ) 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
unsigned cv::RNG::operator()  (  ) 
returns a random integer sampled uniformly from [0, N).
The methods transform the state using the MWC algorithm and return the next random number. The first form is equivalent to RNG::next . The second form returns the random number modulo N , which means that the result is in the range [0, N) .
unsigned cv::RNG::operator()  (  unsigned  N  ) 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
N  upper noninclusive boundary of the returned random number. 
bool cv::RNG::operator==  (  const RNG &  other  )  const 
int cv::RNG::uniform  (  int  a, 
int  b  
) 
returns uniformly distributed integer random number from [a,b) range
The methods transform the state using the MWC algorithm and return the next uniformlydistributed random number of the specified type, deduced from the input parameter type, from the range [a, b) . There is a nuance illustrated by the following sample:
The compiler does not take into account the type of the variable to which you assign the result of RNG::uniform . The only thing that matters to the compiler is the type of a and b parameters. So, if you want a floatingpoint random number, but the range boundaries are integer numbers, either put dots in the end, if they are constants, or use explicit type cast operators, as in the a1 initialization above.
a  lower inclusive boundary of the returned random number. 
b  upper noninclusive boundary of the returned random number. 
float cv::RNG::uniform  (  float  a, 
float  b  
) 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
double cv::RNG::uniform  (  double  a, 
double  b  
) 
This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.
uint64 cv::RNG::state 