fn fill(&mut self, mat: &mut dyn core::ToInputOutputArray, dist_type: i32, a: &dyn core::ToInputArray, b: &dyn core::ToInputArray, saturate_range: bool) -> Result<()> {
		input_output_array_arg!(mat);
		input_array_arg!(a);
		input_array_arg!(b);
		unsafe { sys::cv_RNG_fill_const__InputOutputArrayX_int_const__InputArrayX_const__InputArrayX_bool(self.as_raw_mut_RNG(), mat.as_raw__InputOutputArray(), dist_type, a.as_raw__InputArray(), b.as_raw__InputArray(), saturate_range) }.into_result()
	}
/// Fills arrays with random numbers.
/// 
/// ## Parameters
/// * mat: 2D or N-dimensional 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 non-inclusive 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: pre-saturation 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
/// <tt>theRNG().fill(mat_8u, RNG::UNIFORM, -DBL_MAX, DBL_MAX)</tt> 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 multiple-channel images, every
/// channel is filled independently, which means that RNG cannot generate
/// samples from the multi-dimensional Gaussian distribution with
/// non-diagonal covariance matrix directly. To do that, the method
/// generates samples from multi-dimensional standard Gaussian distribution
/// with zero mean and identity covariation matrix, and then transforms them
/// using transform to get samples from the specified Gaussian distribution.
/// 
/// ## C++ default parameters
/// * saturate_range: false
Fills arrays with random numbers.
Fills arrays with random numbers .
fn fill(&mut self, mat: &mut dyn core::ToInputOutputArray, dist_type: i32, a: &dyn core::ToInputArray, b: &dyn core::ToInputArray, saturate_range: bool) -> Result<()> {
		input_output_array_arg!(mat);
		input_array_arg!(a);
		input_array_arg!(b);
		unsafe { sys::cv_RNG_fill_const__InputOutputArrayX_int_const__InputArrayX_const__InputArrayX_bool(self.as_raw_mut_RNG(), mat.as_raw__InputOutputArray(), dist_type, a.as_raw__InputArray(), b.as_raw__InputArray(), saturate_range) }.into_result()
	}
fn fill ( & mut self , mat : & mut dyn core :: ToInputOutputArray , dist_type : i32 , a : & dyn core :: ToInputArray , b : & dyn core :: ToInputArray , saturate_range : bool ) -> Result < ( ) > { input_output_array_arg ! ( mat ) ; input_array_arg ! ( a ) ; input_array_arg ! ( b ) ; unsafe { sys :: cv_RNG_fill_const__InputOutputArrayX_int_const__InputArrayX_const__InputArrayX_bool ( self . as_raw_mut_RNG ( ) , mat . as_raw__InputOutputArray ( ) , dist_type , a . as_raw__InputArray ( ) , b . as_raw__InputArray ( ) , saturate_range ) } . into_result ( ) }
Fills arrays with random numbers.
Fills arrays with random numbers .
&mut self
mat
&mut dyn core::ToInputOutputArray
dist_type
i32
a
&dyn core::ToInputArray
b
&dyn core::ToInputArray
saturate_range
bool
c++ default parameters
None