Struct opencv::core::MatExpr

pub struct MatExpr { /* fields omitted */ }

Matrix expression representation @anchor MatrixExpressions This is a list of implemented matrix operations that can be combined in arbitrary complex expressions (here A, B stand for matrices ( Mat ), s for a scalar ( Scalar ), alpha for a real-valued scalar ( double )):

• Addition, subtraction, negation: A+B, A-B, A+s, A-s, s+A, s-A, -A
• Scaling: A*alpha
• Per-element multiplication and division: A.mul(B), A/B, alpha/A
• Matrix multiplication: A*B
• Transposition: A.t() (means A^T)
• Matrix inversion and pseudo-inversion, solving linear systems and least-squares problems: A.inv([method]) (~ A<sup>-1</sup>), A.inv([method])*B (~ X: AX=B)
• Comparison: A cmpop B, A cmpop alpha, alpha cmpop A, where cmpop is one of >, >=, ==, !=, <=, <. The result of comparison is an 8-bit single channel mask whose elements are set to 255 (if the particular element or pair of elements satisfy the condition) or

• Bitwise logical operations: A logicop B, A logicop s, s logicop A, ~A, where logicop is one of &, |, ^.
• Element-wise minimum and maximum: min(A, B), min(A, alpha), max(A, B), max(A, alpha)
• Element-wise absolute value: abs(A)
• Cross-product, dot-product: A.cross(B), A.dot(B)
• Any function of matrix or matrices and scalars that returns a matrix or a scalar, such as norm, mean, sum, countNonZero, trace, determinant, repeat, and others.
• Matrix initializers ( Mat::eye(), Mat::zeros(), Mat::ones() ), matrix comma-separated initializers, matrix constructors and operators that extract sub-matrices (see Mat description).
• Mat_<destination_type>() constructors to cast the result to the proper type.

Note: Comma-separated initializers and probably some other operations may require additional explicit Mat() or Mat_() constructor calls to resolve a possible ambiguity.

Here are examples of matrix expressions:

// compute pseudo-inverse of A, equivalent to A.inv(DECOMP_SVD)
SVD svd(A);
Mat pinvA = svd.vt.t()*Mat::diag(1./svd.w)*svd.u.t();

// compute the new vector of parameters in the Levenberg-Marquardt algorithm
x -= (A.t()*A + lambda*Mat::eye(A.cols,A.cols,A.type())).inv(DECOMP_CHOLESKY)*(A.t()*err);

// sharpen image using "unsharp mask" algorithm
Mat blurred; double sigma = 1, threshold = 5, amount = 1;
GaussianBlur(img, blurred, Size(), sigma, sigma);
Mat lowContrastMask = abs(img - blurred) < threshold;
Mat sharpened = img*(1+amount) + blurred*(-amount);
img.copyTo(sharpened, lowContrastMask);

Methods

impl MatExpr

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

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

impl MatExpr

pub fn default() -> Result<MatExpr>

pub fn from_mat(m: &Mat) -> Result<MatExpr>

pub fn new(
    _op: &dyn MatOp,
    _flags: i32,
    _a: &Mat,
    _b: &Mat,
    _c: &Mat,
    _alpha: f64,
    _beta: f64,
    _s: Scalar
) -> Result<MatExpr>

C++ default parameters

• _a: Mat()
• _b: Mat()
• _c: Mat()
• _alpha: 1
• _beta: 1
• _s: Scalar()

pub fn to_mat(&self) -> Result<Mat>

pub fn size(&self) -> Result<Size>

pub fn typ(&self) -> Result<i32>

pub fn row(&self, y: i32) -> Result<MatExpr>

pub fn col(&self, x: i32) -> Result<MatExpr>

pub fn diag(&self, d: i32) -> Result<MatExpr>

C++ default parameters

• d: 0

pub fn t(&self) -> Result<MatExpr>

pub fn inv(&self, method: i32) -> Result<MatExpr>

C++ default parameters

• method: DECOMP_LU

pub fn mul_matexpr(&self, e: &MatExpr, scale: f64) -> Result<MatExpr>

C++ default parameters

• scale: 1

pub fn mul(&self, m: &Mat, scale: f64) -> Result<MatExpr>

C++ default parameters

• scale: 1

pub fn cross(&self, m: &Mat) -> Result<Mat>

pub fn dot(&self, m: &Mat) -> Result<f64>

Trait Implementations

impl ToInputArray for MatExpr

fn input_array(&self) -> Result<_InputArray>

impl<'_> ToInputArray for &'_ MatExpr

fn input_array(&self) -> Result<_InputArray>

impl Send for MatExpr

impl Drop for MatExpr

fn drop(&mut self)

Executes the destructor for this type.