opencv::core

Trait PCATrait

Source 
pub trait PCATrait: PCATraitConst {
    // Required method
    fn as_raw_mut_PCA(&mut self) -> *mut c_void;

    // Provided methods
    fn set_eigenvectors(&mut self, val: Mat) { ... }
    fn set_eigenvalues(&mut self, val: Mat) { ... }
    fn set_mean(&mut self, val: Mat) { ... }
    fn apply(
        &mut self,
        data: &impl ToInputArray,
        mean: &impl ToInputArray,
        flags: i32,
        max_components: i32,
    ) -> Result<PCA> { ... }
    fn apply_def(
        &mut self,
        data: &impl ToInputArray,
        mean: &impl ToInputArray,
        flags: i32,
    ) -> Result<PCA> { ... }
    fn apply_1(
        &mut self,
        data: &impl ToInputArray,
        mean: &impl ToInputArray,
        flags: i32,
        retained_variance: f64,
    ) -> Result<PCA> { ... }
    fn read(&mut self, fn_: &impl FileNodeTraitConst) -> Result<()> { ... }
}
Expand description

Mutable methods for core::PCA

Required Methods§

Source

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

Provided Methods§

Source

fn set_eigenvectors(&mut self, val: Mat)

eigenvectors of the covariation matrix

Source

fn set_eigenvalues(&mut self, val: Mat)

eigenvalues of the covariation matrix

Source

fn set_mean(&mut self, val: Mat)

mean value subtracted before the projection and added after the back projection

Source

fn apply( &mut self, data: &impl ToInputArray, mean: &impl ToInputArray, flags: i32, max_components: i32, ) -> Result<PCA>

performs %PCA

The operator performs %PCA of the supplied dataset. It is safe to reuse the same PCA structure for multiple datasets. That is, if the structure has been previously used with another dataset, the existing internal data is reclaimed and the new [eigenvalues], [eigenvectors] and mean are allocated and computed.

The computed [eigenvalues] are sorted from the largest to the smallest and the corresponding [eigenvectors] are stored as eigenvectors rows.

§Parameters
  • data: input samples stored as the matrix rows or as the matrix columns.
  • mean: optional mean value; if the matrix is empty (noArray()), the mean is computed from the data.
  • flags: operation flags; currently the parameter is only used to specify the data layout. (Flags)
  • maxComponents: maximum number of components that PCA should retain; by default, all the components are retained.
§C++ default parameters
  • max_components: 0
Source

fn apply_def( &mut self, data: &impl ToInputArray, mean: &impl ToInputArray, flags: i32, ) -> Result<PCA>

performs %PCA

The operator performs %PCA of the supplied dataset. It is safe to reuse the same PCA structure for multiple datasets. That is, if the structure has been previously used with another dataset, the existing internal data is reclaimed and the new [eigenvalues], [eigenvectors] and mean are allocated and computed.

The computed [eigenvalues] are sorted from the largest to the smallest and the corresponding [eigenvectors] are stored as eigenvectors rows.

§Parameters
  • data: input samples stored as the matrix rows or as the matrix columns.
  • mean: optional mean value; if the matrix is empty (noArray()), the mean is computed from the data.
  • flags: operation flags; currently the parameter is only used to specify the data layout. (Flags)
  • maxComponents: maximum number of components that PCA should retain; by default, all the components are retained.
§Note

This alternative version of PCATrait::apply function uses the following default values for its arguments:

  • max_components: 0
Source

fn apply_1( &mut self, data: &impl ToInputArray, mean: &impl ToInputArray, flags: i32, retained_variance: f64, ) -> Result<PCA>

performs %PCA

The operator performs %PCA of the supplied dataset. It is safe to reuse the same PCA structure for multiple datasets. That is, if the structure has been previously used with another dataset, the existing internal data is reclaimed and the new [eigenvalues], [eigenvectors] and mean are allocated and computed.

The computed [eigenvalues] are sorted from the largest to the smallest and the corresponding [eigenvectors] are stored as eigenvectors rows.

§Parameters
  • data: input samples stored as the matrix rows or as the matrix columns.
  • mean: optional mean value; if the matrix is empty (noArray()), the mean is computed from the data.
  • flags: operation flags; currently the parameter is only used to specify the data layout. (Flags)
  • maxComponents: maximum number of components that PCA should retain; by default, all the components are retained.
§Overloaded parameters
  • data: input samples stored as the matrix rows or as the matrix columns.
  • mean: optional mean value; if the matrix is empty (noArray()), the mean is computed from the data.
  • flags: operation flags; currently the parameter is only used to specify the data layout. (PCA::Flags)
  • retainedVariance: Percentage of variance that %PCA should retain. Using this parameter will let the %PCA decided how many components to retain but it will always keep at least 2.
Source

fn read(&mut self, fn_: &impl FileNodeTraitConst) -> Result<()>

load PCA objects

Loads [eigenvalues] [eigenvectors] and mean from specified FileNode

Dyn Compatibility§

This trait is not dyn compatible.

In older versions of Rust, dyn compatibility was called "object safety", so this trait is not object safe.

Implementors§