Struct opencv::core::SVD

pub struct SVD { /* fields omitted */ }

Singular Value Decomposition

Class for computing Singular Value Decomposition of a floating-point matrix. The Singular Value Decomposition is used to solve least-square problems, under-determined linear systems, invert matrices, compute condition numbers, and so on.

If you want to compute a condition number of a matrix or an absolute value of its determinant, you do not need u and vt. You can pass flags=SVD::NO_UV|... . Another flag SVD::FULL_UV indicates that full-size u and vt must be computed, which is not necessary most of the time.

See also

invert, solve, eigen, determinant

Implementations

impl SVD

pub fn as_raw_SVD(&self) -> *const c_void

pub fn as_raw_mut_SVD(&mut self) -> *mut c_void

impl SVD

pub fn default() -> Result<SVD>

the default constructor

initializes an empty SVD structure

pub fn new(src: &dyn ToInputArray, flags: i32) -> Result<SVD>

the default constructor

initializes an empty SVD structure

Overloaded parameters

initializes an empty SVD structure and then calls SVD::operator()

Parameters

• src: decomposed matrix. The depth has to be CV_32F or CV_64F.
• flags: operation flags (SVD::Flags)

C++ default parameters

• flags: 0

pub fn compute_ext(
    src: &dyn ToInputArray,
    w: &mut dyn ToOutputArray,
    u: &mut dyn ToOutputArray,
    vt: &mut dyn ToOutputArray,
    flags: i32
) -> Result<()>

decomposes matrix and stores the results to user-provided matrices

The methods/functions perform SVD of matrix. Unlike SVD::SVD constructor and SVD::operator(), they store the results to the user-provided matrices:

Mat A, w, u, vt;
SVD::compute(A, w, u, vt);

Parameters

• src: decomposed matrix. The depth has to be CV_32F or CV_64F.
• w: calculated singular values
• u: calculated left singular vectors
• vt: transposed matrix of right singular vectors
• flags: operation flags - see SVD::Flags.

C++ default parameters

• flags: 0

pub fn compute(
    src: &dyn ToInputArray,
    w: &mut dyn ToOutputArray,
    flags: i32
) -> Result<()>

@todo document

Overloaded parameters

computes singular values of a matrix

Parameters

• src: decomposed matrix. The depth has to be CV_32F or CV_64F.
• w: calculated singular values
• flags: operation flags - see SVD::Flags.

C++ default parameters

• flags: 0

pub fn back_subst_multi(
    w: &dyn ToInputArray,
    u: &dyn ToInputArray,
    vt: &dyn ToInputArray,
    rhs: &dyn ToInputArray,
    dst: &mut dyn ToOutputArray
) -> Result<()>

performs back substitution

pub fn solve_z(
    src: &dyn ToInputArray,
    dst: &mut dyn ToOutputArray
) -> Result<()>

solves an under-determined singular linear system

The method finds a unit-length solution x of a singular linear system A*x = 0. Depending on the rank of A, there can be no solutions, a single solution or an infinite number of solutions. In general, the algorithm solves the following problem:

Parameters

• src: left-hand-side matrix.
• dst: found solution.

Trait Implementations

impl Boxed for SVD

unsafe fn from_raw(ptr: *mut c_void) -> Self

Wrap the specified raw pointer

fn into_raw(self) -> *mut c_void

Return an the underlying raw pointer while consuming this wrapper.

fn as_raw(&self) -> *const c_void

Return the underlying raw pointer.

fn as_raw_mut(&mut self) -> *mut c_void

Return the underlying mutable raw pointer

impl Drop for SVD

fn drop(&mut self)

Executes the destructor for this type.

impl SVDTrait for SVD

fn as_raw_SVD(&self) -> *const c_void

fn as_raw_mut_SVD(&mut self) -> *mut c_void

fn u(&mut self) -> Mat

fn set_u(&mut self, mut val: Mat)

fn w(&mut self) -> Mat

fn set_w(&mut self, mut val: Mat)

fn vt(&mut self) -> Mat

fn set_vt(&mut self, mut val: Mat)

fn back_subst(
    &self,
    rhs: &dyn ToInputArray,
    dst: &mut dyn ToOutputArray
) -> Result<()>

performs a singular value back substitution.

impl Send for SVD