Struct Kernel

pub struct Kernel { /* private fields */ }

2-dimensional convolution kernel

Implementations

impl Kernel

pub fn new(rows: usize, cols: usize) -> Kernel

Create a new kernel with the given number of rows and columns

pub fn square(x: usize) -> Kernel

Create a new, square kernel

pub fn normalize(&mut self)

Ensures the sum of the kernel is <= 1

pub fn create<F: Fn(usize, usize) -> f64>( rows: usize, cols: usize, f: F, ) -> Kernel

Create a new kernel and fill it by executing f with each possible (row, col) pair

pub fn gaussian(n: usize, std: f64) -> Kernel

Generate gaussian blur kernel

pub fn gaussian_3x3() -> Kernel

3x3 pixel gaussian blur

pub fn gaussian_5x5() -> Kernel

5x5 pixel gaussian blur

pub fn gaussian_7x7() -> Kernel

7x7 pixel gaussian blur

pub fn gaussian_9x9() -> Kernel

9x9 pixel gaussian blur

pub fn sobel_x() -> Kernel

Sobel X

pub fn sobel_y() -> Kernel

Sobel Y

pub fn laplacian() -> Kernel

Laplacian

pub fn sobel() -> Kernel

Sobel X and Y combined

Trait Implementations

impl Add for Kernel

type Output = Kernel

The resulting type after applying the + operator.

fn add(self, other: Kernel) -> Kernel

Performs the + operation.

impl Clone for Kernel

fn clone(&self) -> Kernel

Returns a duplicate of the value.

const fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for Kernel

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl<'de> Deserialize<'de> for Kernel

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Div for Kernel

type Output = Kernel

The resulting type after applying the / operator.

fn div(self, other: Kernel) -> Kernel

Performs the / operation.

impl<T: Type, C: Color, U: Type, D: Color> Filter<T, C, U, D> for Kernel

fn schedule(&self) -> Schedule

Determines whether a filter should be executed one pixel at a time, or a whole image at a time

fn compute_at( &self, pt: Point, input: &Input<'_, T, C>, dest: &mut DataMut<'_, U, D>, )

Compute filter at the given point for the provided input

fn output_size(&self, _input: &Input<'_, T, C>, dest: &mut Image<U, D>) -> Size

Get filter output size, this is typically the destination image size, however when used as part of a pipeline a single filter might have a different output size

fn eval_partial( &self, roi: Region, input: &[&Image<T, C>], output: &mut Image<U, D>, )

Evaluate a filter on part of an image

fn eval_partial_in_place(&self, roi: Region, output: &mut Image<U, D>)

Evaluate filter on part of an image using the same image for input and output

fn eval(&self, input: &[&Image<T, C>], output: &mut Image<U, D>)

Evaluate filter

fn eval_in_place(&self, output: &mut Image<U, D>)

Evaluate filter using the same image for input and output

impl<'a> From<&'a [&'a [f64]]> for Kernel

fn from(data: &'a [&'a [f64]]) -> Kernel

Converts to this type from the input type.

impl<const N: usize> From<[[f64; N]; N]> for Kernel

fn from(data: [[f64; N]; N]) -> Kernel

Converts to this type from the input type.

impl From<Vec<Vec<f64>>> for Kernel

fn from(data: Vec<Vec<f64>>) -> Kernel

Converts to this type from the input type.

impl Mul for Kernel

type Output = Kernel

The resulting type after applying the * operator.

fn mul(self, other: Kernel) -> Kernel

Performs the * operation.

impl PartialEq for Kernel

fn eq(&self, other: &Kernel) -> bool

Tests for self and other values to be equal, and is used by ==.

const fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl Serialize for Kernel

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl Sub for Kernel

type Output = Kernel

The resulting type after applying the - operator.

fn sub(self, other: Kernel) -> Kernel

Performs the - operation.

impl StructuralPartialEq for Kernel