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use std::cmp::min;
use std::f64;
use markup5ever::{expanded_name, local_name, namespace_url, ns};
use nalgebra::{DMatrix, Dyn, VecStorage};
use crate::document::AcquiredNodes;
use crate::drawing_ctx::DrawingCtx;
use crate::element::{set_attribute, ElementTrait};
use crate::node::{CascadedValues, Node};
use crate::parsers::{NumberOptionalNumber, ParseValue};
use crate::properties::ColorInterpolationFilters;
use crate::rect::IRect;
use crate::session::Session;
use crate::surface_utils::{
shared_surface::{BlurDirection, Horizontal, SharedImageSurface, Vertical},
EdgeMode,
};
use crate::xml::Attributes;
use super::bounds::BoundsBuilder;
use super::context::{FilterContext, FilterOutput};
use super::{
FilterEffect, FilterError, FilterResolveError, Input, Primitive, PrimitiveParams,
ResolvedPrimitive,
/// The maximum gaussian blur kernel size.
///
/// The value of 500 is used in webkit.
const MAXIMUM_KERNEL_SIZE: usize = 500;
/// The `feGaussianBlur` filter primitive.
#[derive(Default)]
pub struct FeGaussianBlur {
base: Primitive,
params: GaussianBlur,
}
/// Resolved `feGaussianBlur` primitive for rendering.
#[derive(Clone)]
pub struct GaussianBlur {
pub in1: Input,
pub std_deviation: NumberOptionalNumber<f64>,
pub edge_mode: EdgeMode,
pub color_interpolation_filters: ColorInterpolationFilters,
// We need this because NumberOptionalNumber doesn't impl Default
impl Default for GaussianBlur {
fn default() -> GaussianBlur {
GaussianBlur {
in1: Default::default(),
std_deviation: NumberOptionalNumber(0.0, 0.0),
// Note that per the spec, `edgeMode` has a different initial value
// in feGaussianBlur than feConvolveMatrix.
edge_mode: EdgeMode::None,
color_interpolation_filters: Default::default(),
impl ElementTrait for FeGaussianBlur {
fn set_attributes(&mut self, attrs: &Attributes, session: &Session) {
self.params.in1 = self.base.parse_one_input(attrs, session);
for (attr, value) in attrs.iter() {
match attr.expanded() {
expanded_name!("", "stdDeviation") => {
set_attribute(&mut self.params.std_deviation, attr.parse(value), session)
expanded_name!("", "edgeMode") => {
set_attribute(&mut self.params.edge_mode, attr.parse(value), session)
_ => (),
/// Computes a gaussian kernel line for the given standard deviation.
fn gaussian_kernel(std_deviation: f64) -> Vec<f64> {
assert!(std_deviation > 0.0);
// Make sure there aren't any infinities.
let maximal_deviation = (MAXIMUM_KERNEL_SIZE / 2) as f64 / 3.0;
// Values further away than std_deviation * 3 are too small to contribute anything meaningful.
let radius = ((std_deviation.min(maximal_deviation) * 3.0) + 0.5) as usize;
// Clamp the radius rather than diameter because `MAXIMUM_KERNEL_SIZE` might be even and we
// want an odd-sized kernel.
let radius = min(radius, (MAXIMUM_KERNEL_SIZE - 1) / 2);
let diameter = radius * 2 + 1;
let mut kernel = Vec::with_capacity(diameter);
let gauss_point = |x: f64| (-x.powi(2) / (2.0 * std_deviation.powi(2))).exp();
// Fill the matrix by doing numerical integration approximation from -2*std_dev to 2*std_dev,
// sampling 50 points per pixel. We do the bottom half, mirror it to the top half, then compute
// the center point. Otherwise asymmetric quantization errors will occur. The formula to
// integrate is e^-(x^2/2s^2).
for i in 0..diameter / 2 {
let base_x = (diameter / 2 + 1 - i) as f64 - 0.5;
let mut sum = 0.0;
for j in 1..=50 {
let r = base_x + 0.02 * f64::from(j);
sum += gauss_point(r);
kernel.push(sum / 50.0);
// We'll compute the middle point later.
kernel.push(0.0);
// Mirror the bottom half to the top half.
let x = kernel[diameter / 2 - 1 - i];
kernel.push(x);
// Find center val -- calculate an odd number of quanta to make it symmetric, even if the
// center point is weighted slightly higher than others.
for j in 0..=50 {
let r = -0.5 + 0.02 * f64::from(j);
kernel[diameter / 2] = sum / 51.0;
// Normalize the distribution by scaling the total sum to 1.
let sum = kernel.iter().sum::<f64>();
kernel.iter_mut().for_each(|x| *x /= sum);
kernel
/// Returns a size of the box blur kernel to approximate the gaussian blur.
fn box_blur_kernel_size(std_deviation: f64) -> usize {
let d = (std_deviation * 3.0 * (2.0 * f64::consts::PI).sqrt() / 4.0 + 0.5).floor();
let d = d.min(MAXIMUM_KERNEL_SIZE as f64);
d as usize
/// Applies three box blurs to approximate the gaussian blur.
/// This is intended to be used in two steps, horizontal and vertical.
fn three_box_blurs<B: BlurDirection>(
surface: &SharedImageSurface,
bounds: IRect,
std_deviation: f64,
) -> Result<SharedImageSurface, FilterError> {
let d = box_blur_kernel_size(std_deviation);
if d == 0 {
return Ok(surface.clone());
let surface = if d % 2 == 1 {
// Odd kernel sizes just get three successive box blurs.
let mut surface = surface.clone();
for _ in 0..3 {
surface = surface.box_blur::<B>(bounds, d, d / 2)?;
surface
} else {
// Even kernel sizes have a more interesting scheme.
let surface = surface.box_blur::<B>(bounds, d, d / 2)?;
let surface = surface.box_blur::<B>(bounds, d, d / 2 - 1)?;
let d = d + 1;
surface.box_blur::<B>(bounds, d, d / 2)?
Ok(surface)
/// Applies the gaussian blur.
fn gaussian_blur(
input_surface: &SharedImageSurface,
edge_mode: EdgeMode,
vertical: bool,
let kernel = gaussian_kernel(std_deviation);
let (rows, cols) = if vertical {
(kernel.len(), 1)
(1, kernel.len())
let kernel = DMatrix::from_data(VecStorage::new(Dyn(rows), Dyn(cols), kernel));
Ok(input_surface.convolve(
bounds,
((cols / 2) as i32, (rows / 2) as i32),
&kernel,
edge_mode,
)?)
impl GaussianBlur {
pub fn render(
&self,
bounds_builder: BoundsBuilder,
ctx: &FilterContext,
acquired_nodes: &mut AcquiredNodes<'_>,
draw_ctx: &mut DrawingCtx,
) -> Result<FilterOutput, FilterError> {
let input_1 = ctx.get_input(
acquired_nodes,
draw_ctx,
&self.in1,
self.color_interpolation_filters,
)?;
let bounds: IRect = bounds_builder
.add_input(&input_1)
.compute(ctx)
.clipped
.into();
let NumberOptionalNumber(std_x, std_y) = self.std_deviation;
// "A negative value or a value of zero disables the effect of
// the given filter primitive (i.e., the result is the filter
// input image)."
if std_x <= 0.0 && std_y <= 0.0 {
return Ok(FilterOutput {
surface: input_1.surface().clone(),
});
let (std_x, std_y) = ctx.paffine().transform_distance(std_x, std_y);
// The deviation can become negative here due to the transform.
let std_x = std_x.abs();
let std_y = std_y.abs();
// Performance TODO: gaussian blur is frequently used for shadows, operating on SourceAlpha
// (so the image is alpha-only). We can use this to not waste time processing the other
// channels.
// Horizontal convolution.
let horiz_result_surface = if std_x >= 2.0 {
// The spec says for deviation >= 2.0 three box blurs can be used as an optimization.
three_box_blurs::<Horizontal>(input_1.surface(), bounds, std_x)?
} else if std_x != 0.0 {
gaussian_blur(input_1.surface(), bounds, std_x, self.edge_mode, false)?
input_1.surface().clone()
// Vertical convolution.
let output_surface = if std_y >= 2.0 {
three_box_blurs::<Vertical>(&horiz_result_surface, bounds, std_y)?
} else if std_y != 0.0 {
gaussian_blur(&horiz_result_surface, bounds, std_y, self.edge_mode, true)?
horiz_result_surface
Ok(FilterOutput {
surface: output_surface,
})
impl FilterEffect for FeGaussianBlur {
fn resolve(
_acquired_nodes: &mut AcquiredNodes<'_>,
node: &Node,
) -> Result<Vec<ResolvedPrimitive>, FilterResolveError> {
let cascaded = CascadedValues::new_from_node(node);
let values = cascaded.get();
let mut params = self.params.clone();
params.color_interpolation_filters = values.color_interpolation_filters();
Ok(vec![ResolvedPrimitive {
primitive: self.base.clone(),
params: PrimitiveParams::GaussianBlur(params),
}])