Function opencv::core::norm_sparse

pub fn norm_sparse(src: &SparseMat, norm_type: i32) -> Result<f64>

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 . The and norm for the sample value 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 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 and . It is notable that the norm forms the upper and the norm forms the lower border for the example function .

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.

Overloaded parameters

• src: first input array.
• normType: type of the norm (see #NormTypes).