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95e26819d9
In the future there will be different methods of creating these use-time easing functions (e.g. from `KeywordStyleValue`s)
259 lines
11 KiB
C++
259 lines
11 KiB
C++
/*
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* Copyright (c) 2025, Callum Law <callumlaw1709@outlook.com>
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*
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* SPDX-License-Identifier: BSD-2-Clause
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*/
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#include "EasingFunction.h"
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#include <AK/BinarySearch.h>
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#include <LibWeb/CSS/StyleValues/EasingStyleValue.h>
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namespace Web::CSS {
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// https://drafts.csswg.org/css-easing/#linear-easing-function-output
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double LinearEasingFunction::evaluate_at(double input_progress, bool before_flag) const
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{
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// To calculate linear easing output progress for a given linear easing function func,
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// an input progress value inputProgress, and an optional before flag (defaulting to false),
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// perform the following:
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// 1. Let points be func’s control points.
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// 2. If points holds only a single item, return the output progress value of that item.
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if (control_points.size() == 1)
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return control_points[0].output;
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// 3. If inputProgress matches the input progress value of the first point in points,
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// and the before flag is true, return the first point’s output progress value.
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if (input_progress == control_points[0].input.value() && before_flag)
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return control_points[0].output;
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// 4. If inputProgress matches the input progress value of at least one point in points,
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// return the output progress value of the last such point.
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auto maybe_match = control_points.last_matching([&](auto& stop) { return input_progress == stop.input; });
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if (maybe_match.has_value())
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return maybe_match->output;
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// 5. Otherwise, find two control points in points, A and B, which will be used for interpolation:
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ControlPoint A;
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ControlPoint B;
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if (input_progress < control_points[0].input.value()) {
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// 1. If inputProgress is smaller than any input progress value in points,
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// let A and B be the first two items in points.
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// If A and B have the same input progress value, return A’s output progress value.
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A = control_points[0];
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B = control_points[1];
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if (A.input == B.input.value())
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return A.output;
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} else if (input_progress > control_points.last().input.value()) {
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// 2. If inputProgress is larger than any input progress value in points,
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// let A and B be the last two items in points.
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// If A and B have the same input progress value, return B’s output progress value.
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A = control_points[control_points.size() - 2];
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B = control_points[control_points.size() - 1];
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if (A.input == B.input.value())
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return B.output;
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} else {
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// 3. Otherwise, let A be the last control point whose input progress value is smaller than inputProgress,
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// and let B be the first control point whose input progress value is larger than inputProgress.
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A = control_points.last_matching([&](ControlPoint const& stop) { return stop.input.value() < input_progress; }).value();
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B = control_points.first_matching([&](ControlPoint const& stop) { return stop.input.value() > input_progress; }).value();
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}
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// 6. Linearly interpolate (or extrapolate) inputProgress along the line defined by A and B, and return the result.
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auto factor = (input_progress - A.input.value()) / (B.input.value() - A.input.value());
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return A.output + factor * (B.output - A.output);
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}
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// https://www.w3.org/TR/css-easing-1/#cubic-bezier-algo
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double CubicBezierEasingFunction::evaluate_at(double input_progress, bool) const
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{
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constexpr static auto cubic_bezier_at = [](double x1, double x2, double t) {
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auto a = 1.0 - 3.0 * x2 + 3.0 * x1;
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auto b = 3.0 * x2 - 6.0 * x1;
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auto c = 3.0 * x1;
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auto t2 = t * t;
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auto t3 = t2 * t;
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return (a * t3) + (b * t2) + (c * t);
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};
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// For input progress values outside the range [0, 1], the curve is extended infinitely using tangent of the curve
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// at the closest endpoint as follows:
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// - For input progress values less than zero,
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if (input_progress < 0.0) {
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// 1. If the x value of P1 is greater than zero, use a straight line that passes through P1 and P0 as the
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// tangent.
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if (x1 > 0.0)
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return y1 / x1 * input_progress;
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// 2. Otherwise, if the x value of P2 is greater than zero, use a straight line that passes through P2 and P0 as
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// the tangent.
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if (x2 > 0.0)
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return y2 / x2 * input_progress;
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// 3. Otherwise, let the output progress value be zero for all input progress values in the range [-∞, 0).
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return 0.0;
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}
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// - For input progress values greater than one,
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if (input_progress > 1.0) {
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// 1. If the x value of P2 is less than one, use a straight line that passes through P2 and P3 as the tangent.
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if (x2 < 1.0)
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return (1.0 - y2) / (1.0 - x2) * (input_progress - 1.0) + 1.0;
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// 2. Otherwise, if the x value of P1 is less than one, use a straight line that passes through P1 and P3 as the
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// tangent.
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if (x1 < 1.0)
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return (1.0 - y1) / (1.0 - x1) * (input_progress - 1.0) + 1.0;
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// 3. Otherwise, let the output progress value be one for all input progress values in the range (1, ∞].
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return 1.0;
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}
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// Note: The spec does not specify the precise algorithm for calculating values in the range [0, 1]:
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// "The evaluation of this curve is covered in many sources such as [FUND-COMP-GRAPHICS]."
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auto x = input_progress;
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auto solve = [&](auto t) {
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auto x = cubic_bezier_at(x1, x2, t);
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auto y = cubic_bezier_at(y1, y2, t);
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return CachedSample { x, y, t };
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};
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if (m_cached_x_samples.is_empty())
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m_cached_x_samples.append(solve(0.));
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size_t nearby_index = 0;
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if (auto found = binary_search(m_cached_x_samples, x, &nearby_index, [](auto x, auto& sample) {
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if (x - sample.x >= NumericLimits<double>::epsilon())
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return 1;
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if (x - sample.x <= NumericLimits<double>::epsilon())
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return -1;
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return 0;
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}))
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return found->y;
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if (nearby_index == m_cached_x_samples.size() || nearby_index + 1 == m_cached_x_samples.size()) {
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// Produce more samples until we have enough.
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auto last_t = m_cached_x_samples.last().t;
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auto last_x = m_cached_x_samples.last().x;
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while (last_x <= x && last_t < 1.0) {
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last_t += 1. / 60.;
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auto solution = solve(last_t);
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m_cached_x_samples.append(solution);
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last_x = solution.x;
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}
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if (auto found = binary_search(m_cached_x_samples, x, &nearby_index, [](auto x, auto& sample) {
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if (x - sample.x >= NumericLimits<double>::epsilon())
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return 1;
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if (x - sample.x <= NumericLimits<double>::epsilon())
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return -1;
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return 0;
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}))
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return found->y;
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}
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// We have two samples on either side of the x value we want, so we can linearly interpolate between them.
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auto& sample1 = m_cached_x_samples[nearby_index];
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auto& sample2 = m_cached_x_samples[nearby_index + 1];
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auto factor = (x - sample1.x) / (sample2.x - sample1.x);
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return sample1.y + factor * (sample2.y - sample1.y);
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}
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// https://www.w3.org/TR/css-easing-1/#step-easing-algo
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double StepsEasingFunction::evaluate_at(double input_progress, bool before_flag) const
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{
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auto current_step = floor(input_progress * interval_count);
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// 2. If the step position property is one of:
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// - jump-start,
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// - jump-both,
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// increment current step by one.
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if (position == StepPosition::JumpStart || position == StepPosition::Start || position == StepPosition::JumpBoth)
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current_step += 1;
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// 3. If both of the following conditions are true:
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// - the before flag is set, and
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// - input progress value × steps mod 1 equals zero (that is, if input progress value × steps is integral), then
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// decrement current step by one.
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auto step_progress = input_progress * interval_count;
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if (before_flag && trunc(step_progress) == step_progress)
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current_step -= 1;
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// 4. If input progress value ≥ 0 and current step < 0, let current step be zero.
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if (input_progress >= 0.0 && current_step < 0.0)
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current_step = 0.0;
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// 5. Calculate jumps based on the step position as follows:
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// jump-start or jump-end -> steps
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// jump-none -> steps - 1
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// jump-both -> steps + 1
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auto jumps = interval_count;
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if (position == StepPosition::JumpNone) {
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jumps--;
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} else if (position == StepPosition::JumpBoth) {
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jumps++;
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}
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// 6. If input progress value ≤ 1 and current step > jumps, let current step be jumps.
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if (input_progress <= 1.0 && current_step > jumps)
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current_step = jumps;
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// 7. The output progress value is current step / jumps.
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return current_step / jumps;
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}
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EasingFunction EasingFunction::from_style_value(StyleValue const& style_value)
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{
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if (style_value.is_easing()) {
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return style_value.as_easing().function().visit(
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[](EasingStyleValue::Linear const& linear) -> EasingFunction {
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Vector<LinearEasingFunction::ControlPoint> resolved_control_points;
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for (auto const& control_point : linear.stops)
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resolved_control_points.append({ control_point.input, control_point.output });
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return LinearEasingFunction { resolved_control_points, linear.to_string(SerializationMode::ResolvedValue) };
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},
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[](EasingStyleValue::CubicBezier const& cubic_bezier) -> EasingFunction {
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auto resolved_x1 = clamp(cubic_bezier.x1.resolved({}).value_or(0.0), 0.0, 1.0);
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auto resolved_y1 = cubic_bezier.y1.resolved({}).value_or(0.0);
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auto resolved_x2 = clamp(cubic_bezier.x2.resolved({}).value_or(0.0), 0.0, 1.0);
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auto resolved_y2 = cubic_bezier.y2.resolved({}).value_or(0.0);
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return CubicBezierEasingFunction { resolved_x1, resolved_y1, resolved_x2, resolved_y2, cubic_bezier.to_string(SerializationMode::Normal) };
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},
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[](EasingStyleValue::Steps const& steps) -> EasingFunction {
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auto resolved_interval_count = max(steps.number_of_intervals.resolved({}).value_or(1), steps.position == StepPosition::JumpNone ? 2 : 1);
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return StepsEasingFunction { resolved_interval_count, steps.position, steps.to_string(SerializationMode::ResolvedValue) };
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});
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}
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VERIFY_NOT_REACHED();
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}
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double EasingFunction::evaluate_at(double input_progress, bool before_flag) const
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{
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return visit(
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[&](auto const& function) {
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return function.evaluate_at(input_progress, before_flag);
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});
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}
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String EasingFunction::to_string() const
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{
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return visit(
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[](auto const& function) {
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return function.stringified;
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});
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}
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}
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