pub fn norm(src1: &dyn core::ToInputArray, norm_type: i32, mask: &dyn core::ToInputArray) -> Result<f64> {
	input_array_arg!(src1);
	input_array_arg!(mask);
	unsafe { sys::cv_norm_const__InputArrayX_int_const__InputArrayX(src1.as_raw__InputArray(), norm_type, mask.as_raw__InputArray()) }.into_result()
}
/// Calculates the  absolute norm of an array.
/// 
/// This version of #norm calculates the absolute norm of src1. The type of norm to calculate is specified using #NormTypes.
/// 
/// As example for one array consider the function ![inline formula](https://latex.codecogs.com/png.latex?r%28x%29%3D%20%5Cbegin%7Bpmatrix%7D%20x%20%5C%5C%201%2Dx%20%5Cend%7Bpmatrix%7D%2C%20x%20%5Cin%20%5B%2D1%3B1%5D).
/// The ![inline formula](https://latex.codecogs.com/png.latex?%20L%5F%7B1%7D%2C%20L%5F%7B2%7D%20) and ![inline formula](https://latex.codecogs.com/png.latex?%20L%5F%7B%5Cinfty%7D%20) norm for the sample value ![inline formula](https://latex.codecogs.com/png.latex?r%28%2D1%29%20%3D%20%5Cbegin%7Bpmatrix%7D%20%2D1%20%5C%5C%202%20%5Cend%7Bpmatrix%7D)
/// is calculated as follows
/// \f{align*}
///    \| r(-1) \|_{L_1} &= |-1| + |2| = 3 \\
///    \| r(-1) \|_{L_2} &= \sqrt{(-1)^{2} + (2)^{2}} = \sqrt{5} \\
///    \| r(-1) \|_{L_\infty} &= \max(|-1|,|2|) = 2
/// \f}
/// and for ![inline formula](https://latex.codecogs.com/png.latex?r%280%2E5%29%20%3D%20%5Cbegin%7Bpmatrix%7D%200%2E5%20%5C%5C%200%2E5%20%5Cend%7Bpmatrix%7D) the calculation is
/// \f{align*}
///    \| r(0.5) \|_{L_1} &= |0.5| + |0.5| = 1 \\
///    \| r(0.5) \|_{L_2} &= \sqrt{(0.5)^{2} + (0.5)^{2}} = \sqrt{0.5} \\
///    \| r(0.5) \|_{L_\infty} &= \max(|0.5|,|0.5|) = 0.5.
/// \f}
/// The following graphic shows all values for the three norm functions ![inline formula](https://latex.codecogs.com/png.latex?%5C%7C%20r%28x%29%20%5C%7C%5F%7BL%5F1%7D%2C%20%5C%7C%20r%28x%29%20%5C%7C%5F%7BL%5F2%7D) and ![inline formula](https://latex.codecogs.com/png.latex?%5C%7C%20r%28x%29%20%5C%7C%5F%7BL%5F%5Cinfty%7D).
/// It is notable that the ![inline formula](https://latex.codecogs.com/png.latex?%20L%5F%7B1%7D%20) norm forms the upper and the ![inline formula](https://latex.codecogs.com/png.latex?%20L%5F%7B%5Cinfty%7D%20) norm forms the lower border for the example function ![inline formula](https://latex.codecogs.com/png.latex?%20r%28x%29%20).
/// ![Graphs for the different norm functions from the above example](https://docs.opencv.org/3.4.10/NormTypes_OneArray_1-2-INF.png)
/// 
/// When the mask parameter is specified and it is not empty, the norm is
/// 
/// If normType is not specified, #NORM_L2 is used.
/// calculated only over the region specified by the mask.
/// 
/// Multi-channel input arrays are treated as single-channel arrays, that is,
/// the results for all channels are combined.
/// 
/// Hamming norms can only be calculated with CV_8U depth arrays.
/// 
/// ## Parameters
/// * src1: first input array.
/// * normType: type of the norm (see #NormTypes).
/// * mask: optional operation mask; it must have the same size as src1 and CV_8UC1 type.
/// 
/// ## C++ default parameters
/// * norm_type: NORM_L2
/// * mask: noArray()
Calculates the  absolute norm of an array.
This version of #norm calculates the absolute norm of src1. The type of norm to calculate is specified using #NormTypes.

As example for one array consider the function ![inline formula].

When the mask parameter is specified and it is not empty, the norm is

If normType is not specified, #NORM_L2 is used.
calculated only over the region specified by the mask.

Multi-channel input arrays are treated as single-channel arrays, that is,
the results for all channels are combined.

Hamming norms can only be calculated with CV_8U depth arrays.
Calculates the absolute norm of an array . This version of #norm calculates the absolute norm of src1 . The type of norm to calculate is specified using #NormTypes . As example for one array consider the function ! [ inline formula ] . When the mask parameter is specified and it is not empty the norm is If normType is not specified #NORM_L2 is used . calculated only over the region specified by the mask . Multi - channel input arrays are treated as single - channel arrays that is the results for all channels are combined . Hamming norms can only be calculated with CV_8U depth arrays .
pub fn norm(src1: &dyn core::ToInputArray, norm_type: i32, mask: &dyn core::ToInputArray) -> Result<f64> {
	input_array_arg!(src1);
	input_array_arg!(mask);
	unsafe { sys::cv_norm_const__InputArrayX_int_const__InputArrayX(src1.as_raw__InputArray(), norm_type, mask.as_raw__InputArray()) }.into_result()
}
pub fn norm ( src1 : & dyn core :: ToInputArray , norm_type : i32 , mask : & dyn core :: ToInputArray ) -> Result < f64 > { input_array_arg ! ( src1 ) ; input_array_arg ! ( mask ) ; unsafe { sys :: cv_norm_const__InputArrayX_int_const__InputArrayX ( src1 . as_raw__InputArray ( ) , norm_type , mask . as_raw__InputArray ( ) ) } . into_result ( ) }
Calculates the  absolute norm of an array.
Calculates the absolute norm of an array .
src1
&dyn core::ToInputArray
norm_type
i32
mask
&dyn core::ToInputArray
c++ default parameters
NORM_L2
mask: noArray()
NORM_L2 mask : noArray ()