Trait opencv::hub_prelude::MatExprTraitConst

source · 
pub trait MatExprTraitConst {

Show 20 methods    fn as_raw_MatExpr(&self) -> *const c_void;

    fn flags(&self) -> i32 { ... }
    fn a(&self) -> Mat { ... }
    fn b(&self) -> Mat { ... }
    fn c(&self) -> Mat { ... }
    fn alpha(&self) -> f64 { ... }
    fn beta(&self) -> f64 { ... }
    fn s(&self) -> Scalar { ... }
    fn to_mat(&self) -> Result<Mat> { ... }
    fn size(&self) -> Result<Size> { ... }
    fn typ(&self) -> Result<i32> { ... }
    fn row(&self, y: i32) -> Result<MatExpr> { ... }
    fn col(&self, x: i32) -> Result<MatExpr> { ... }
    fn diag(&self, d: i32) -> Result<MatExpr> { ... }
    fn t(&self) -> Result<MatExpr> { ... }
    fn inv(&self, method: i32) -> Result<MatExpr> { ... }
    fn mul_matexpr(&self, e: &MatExpr, scale: f64) -> Result<MatExpr> { ... }
    fn mul(&self, m: &Mat, scale: f64) -> Result<MatExpr> { ... }
    fn cross(&self, m: &Mat) -> Result<Mat> { ... }
    fn dot(&self, m: &Mat) -> Result<f64> { ... }

}
Expand description

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 AT)
  • 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);

Required Methods

Provided Methods

C++ default parameters
  • d: 0
C++ default parameters
  • method: DECOMP_LU
C++ default parameters
  • scale: 1
C++ default parameters
  • scale: 1

Implementors