[−][src]Trait opencv::prelude::MatExprTrait
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_
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
pub fn as_raw_MatExpr(&self) -> *const c_void
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pub fn as_raw_mut_MatExpr(&mut self) -> *mut c_void
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Provided methods
pub fn flags(&self) -> i32
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pub fn set_flags(&mut self, val: i32)
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pub fn a(&mut self) -> Mat
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pub fn set_a(&mut self, val: Mat)
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pub fn b(&mut self) -> Mat
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pub fn set_b(&mut self, val: Mat)
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pub fn c(&mut self) -> Mat
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pub fn set_c(&mut self, val: Mat)
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pub fn alpha(&self) -> f64
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pub fn set_alpha(&mut self, val: f64)
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pub fn beta(&self) -> f64
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pub fn set_beta(&mut self, val: f64)
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pub fn s(&self) -> Scalar
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pub fn set_s(&mut self, val: Scalar)
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pub fn to_mat(&self) -> Result<Mat>
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pub fn size(&self) -> Result<Size>
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pub fn typ(&self) -> Result<i32>
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pub fn row(&self, y: i32) -> Result<MatExpr>
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pub fn col(&self, x: i32) -> Result<MatExpr>
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pub fn diag(&self, d: i32) -> Result<MatExpr>
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pub fn t(&self) -> Result<MatExpr>
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pub fn inv(&self, method: i32) -> Result<MatExpr>
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C++ default parameters
- method: DECOMP_LU
pub fn mul_matexpr(&self, e: &MatExpr, scale: f64) -> Result<MatExpr>
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C++ default parameters
- scale: 1
pub fn mul(&self, m: &Mat, scale: f64) -> Result<MatExpr>
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C++ default parameters
- scale: 1