MatExpr Class Reference
[Basic structures]

Matrix expression representation. More...

#include <mat.hpp>

Detailed Description

Matrix expression representation.

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 0.
• 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_<T>() 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);
  

Definition at line 3281 of file mat.hpp.

Friends And Related Function Documentation