1229 lines
39 KiB
Rust
1229 lines
39 KiB
Rust
//! Functions and filters for the sampling of pixels.
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// See http://cs.brown.edu/courses/cs123/lectures/08_Image_Processing_IV.pdf
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// for some of the theory behind image scaling and convolution
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use std::f32;
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use num_traits::{NumCast, ToPrimitive, Zero};
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use crate::image::{GenericImage, GenericImageView};
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use crate::traits::{Enlargeable, Pixel, Primitive};
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use crate::utils::clamp;
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use crate::{ImageBuffer, Rgba32FImage};
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/// Available Sampling Filters.
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///
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/// ## Examples
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///
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/// To test the different sampling filters on a real example, you can find two
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/// examples called
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/// [`scaledown`](https://github.com/image-rs/image/tree/master/examples/scaledown)
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/// and
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/// [`scaleup`](https://github.com/image-rs/image/tree/master/examples/scaleup)
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/// in the `examples` directory of the crate source code.
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///
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/// Here is a 3.58 MiB
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/// [test image](https://github.com/image-rs/image/blob/master/examples/scaledown/test.jpg)
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/// that has been scaled down to 300x225 px:
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///
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/// <!-- NOTE: To test new test images locally, replace the GitHub path with `../../../docs/` -->
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/// <div style="display: flex; flex-wrap: wrap; align-items: flex-start;">
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/// <div style="margin: 0 8px 8px 0;">
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/// <img src="https://raw.githubusercontent.com/image-rs/image/master/examples/scaledown/scaledown-test-near.png" title="Nearest"><br>
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/// Nearest Neighbor
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/// </div>
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/// <div style="margin: 0 8px 8px 0;">
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/// <img src="https://raw.githubusercontent.com/image-rs/image/master/examples/scaledown/scaledown-test-tri.png" title="Triangle"><br>
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/// Linear: Triangle
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/// </div>
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/// <div style="margin: 0 8px 8px 0;">
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/// <img src="https://raw.githubusercontent.com/image-rs/image/master/examples/scaledown/scaledown-test-cmr.png" title="CatmullRom"><br>
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/// Cubic: Catmull-Rom
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/// </div>
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/// <div style="margin: 0 8px 8px 0;">
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/// <img src="https://raw.githubusercontent.com/image-rs/image/master/examples/scaledown/scaledown-test-gauss.png" title="Gaussian"><br>
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/// Gaussian
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/// </div>
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/// <div style="margin: 0 8px 8px 0;">
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/// <img src="https://raw.githubusercontent.com/image-rs/image/master/examples/scaledown/scaledown-test-lcz2.png" title="Lanczos3"><br>
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/// Lanczos with window 3
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/// </div>
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/// </div>
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///
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/// ## Speed
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///
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/// Time required to create each of the examples above, tested on an Intel
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/// i7-4770 CPU with Rust 1.37 in release mode:
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///
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/// <table style="width: auto;">
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/// <tr>
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/// <th>Nearest</th>
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/// <td>31 ms</td>
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/// </tr>
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/// <tr>
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/// <th>Triangle</th>
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/// <td>414 ms</td>
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/// </tr>
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/// <tr>
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/// <th>CatmullRom</th>
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/// <td>817 ms</td>
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/// </tr>
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/// <tr>
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/// <th>Gaussian</th>
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/// <td>1180 ms</td>
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/// </tr>
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/// <tr>
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/// <th>Lanczos3</th>
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/// <td>1170 ms</td>
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/// </tr>
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/// </table>
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#[derive(Clone, Copy, Debug, PartialEq)]
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pub enum FilterType {
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/// Nearest Neighbor
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Nearest,
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/// Linear Filter
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Triangle,
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/// Cubic Filter
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CatmullRom,
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/// Gaussian Filter
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Gaussian,
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/// Lanczos with window 3
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Lanczos3,
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}
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/// A Representation of a separable filter.
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pub(crate) struct Filter<'a> {
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/// The filter's filter function.
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pub(crate) kernel: Box<dyn Fn(f32) -> f32 + 'a>,
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/// The window on which this filter operates.
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pub(crate) support: f32,
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}
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struct FloatNearest(f32);
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// to_i64, to_u64, and to_f64 implicitly affect all other lower conversions.
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// Note that to_f64 by default calls to_i64 and thus needs to be overridden.
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impl ToPrimitive for FloatNearest {
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// to_{i,u}64 is required, to_{i,u}{8,16} are useful.
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// If a usecase for full 32 bits is found its trivial to add
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fn to_i8(&self) -> Option<i8> {
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self.0.round().to_i8()
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}
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fn to_i16(&self) -> Option<i16> {
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self.0.round().to_i16()
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}
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fn to_i64(&self) -> Option<i64> {
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self.0.round().to_i64()
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}
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fn to_u8(&self) -> Option<u8> {
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self.0.round().to_u8()
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}
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fn to_u16(&self) -> Option<u16> {
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self.0.round().to_u16()
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}
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fn to_u64(&self) -> Option<u64> {
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self.0.round().to_u64()
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}
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fn to_f64(&self) -> Option<f64> {
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self.0.to_f64()
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}
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}
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// sinc function: the ideal sampling filter.
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fn sinc(t: f32) -> f32 {
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let a = t * f32::consts::PI;
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if t == 0.0 {
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1.0
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} else {
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a.sin() / a
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}
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}
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// lanczos kernel function. A windowed sinc function.
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fn lanczos(x: f32, t: f32) -> f32 {
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if x.abs() < t {
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sinc(x) * sinc(x / t)
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} else {
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0.0
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}
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}
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// Calculate a splice based on the b and c parameters.
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// from authors Mitchell and Netravali.
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fn bc_cubic_spline(x: f32, b: f32, c: f32) -> f32 {
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let a = x.abs();
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let k = if a < 1.0 {
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(12.0 - 9.0 * b - 6.0 * c) * a.powi(3)
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+ (-18.0 + 12.0 * b + 6.0 * c) * a.powi(2)
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+ (6.0 - 2.0 * b)
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} else if a < 2.0 {
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(-b - 6.0 * c) * a.powi(3)
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+ (6.0 * b + 30.0 * c) * a.powi(2)
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+ (-12.0 * b - 48.0 * c) * a
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+ (8.0 * b + 24.0 * c)
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} else {
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0.0
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};
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k / 6.0
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}
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/// The Gaussian Function.
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/// ```r``` is the standard deviation.
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pub(crate) fn gaussian(x: f32, r: f32) -> f32 {
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((2.0 * f32::consts::PI).sqrt() * r).recip() * (-x.powi(2) / (2.0 * r.powi(2))).exp()
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}
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/// Calculate the lanczos kernel with a window of 3
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pub(crate) fn lanczos3_kernel(x: f32) -> f32 {
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lanczos(x, 3.0)
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}
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/// Calculate the gaussian function with a
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/// standard deviation of 0.5
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pub(crate) fn gaussian_kernel(x: f32) -> f32 {
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gaussian(x, 0.5)
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}
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/// Calculate the Catmull-Rom cubic spline.
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/// Also known as a form of `BiCubic` sampling in two dimensions.
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pub(crate) fn catmullrom_kernel(x: f32) -> f32 {
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bc_cubic_spline(x, 0.0, 0.5)
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}
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/// Calculate the triangle function.
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/// Also known as `BiLinear` sampling in two dimensions.
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pub(crate) fn triangle_kernel(x: f32) -> f32 {
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if x.abs() < 1.0 {
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1.0 - x.abs()
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} else {
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0.0
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}
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}
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/// Calculate the box kernel.
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/// Only pixels inside the box should be considered, and those
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/// contribute equally. So this method simply returns 1.
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pub(crate) fn box_kernel(_x: f32) -> f32 {
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1.0
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}
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// Sample the rows of the supplied image using the provided filter.
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// The height of the image remains unchanged.
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// ```new_width``` is the desired width of the new image
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// ```filter``` is the filter to use for sampling.
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// ```image``` is not necessarily Rgba and the order of channels is passed through.
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fn horizontal_sample<P, S>(
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image: &Rgba32FImage,
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new_width: u32,
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filter: &mut Filter,
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) -> ImageBuffer<P, Vec<S>>
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where
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P: Pixel<Subpixel = S> + 'static,
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S: Primitive + 'static,
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{
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let (width, height) = image.dimensions();
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let mut out = ImageBuffer::new(new_width, height);
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let mut ws = Vec::new();
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let max: f32 = NumCast::from(S::DEFAULT_MAX_VALUE).unwrap();
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let min: f32 = NumCast::from(S::DEFAULT_MIN_VALUE).unwrap();
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let ratio = width as f32 / new_width as f32;
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let sratio = if ratio < 1.0 { 1.0 } else { ratio };
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let src_support = filter.support * sratio;
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for outx in 0..new_width {
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// Find the point in the input image corresponding to the centre
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// of the current pixel in the output image.
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let inputx = (outx as f32 + 0.5) * ratio;
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// Left and right are slice bounds for the input pixels relevant
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// to the output pixel we are calculating. Pixel x is relevant
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// if and only if (x >= left) && (x < right).
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// Invariant: 0 <= left < right <= width
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let left = (inputx - src_support).floor() as i64;
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let left = clamp(left, 0, <i64 as From<_>>::from(width) - 1) as u32;
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let right = (inputx + src_support).ceil() as i64;
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let right = clamp(
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right,
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<i64 as From<_>>::from(left) + 1,
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<i64 as From<_>>::from(width),
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) as u32;
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// Go back to left boundary of pixel, to properly compare with i
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// below, as the kernel treats the centre of a pixel as 0.
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let inputx = inputx - 0.5;
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ws.clear();
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let mut sum = 0.0;
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for i in left..right {
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let w = (filter.kernel)((i as f32 - inputx) / sratio);
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ws.push(w);
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sum += w;
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}
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ws.iter_mut().for_each(|w| *w /= sum);
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for y in 0..height {
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let mut t = (0.0, 0.0, 0.0, 0.0);
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for (i, w) in ws.iter().enumerate() {
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let p = image.get_pixel(left + i as u32, y);
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#[allow(deprecated)]
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let vec = p.channels4();
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t.0 += vec.0 * w;
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t.1 += vec.1 * w;
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t.2 += vec.2 * w;
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t.3 += vec.3 * w;
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}
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#[allow(deprecated)]
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let t = Pixel::from_channels(
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NumCast::from(FloatNearest(clamp(t.0, min, max))).unwrap(),
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NumCast::from(FloatNearest(clamp(t.1, min, max))).unwrap(),
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NumCast::from(FloatNearest(clamp(t.2, min, max))).unwrap(),
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NumCast::from(FloatNearest(clamp(t.3, min, max))).unwrap(),
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);
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out.put_pixel(outx, y, t);
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}
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}
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out
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}
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/// Linearly sample from an image using coordinates in [0, 1].
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pub fn sample_bilinear<P: Pixel>(
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img: &impl GenericImageView<Pixel = P>,
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u: f32,
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v: f32,
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) -> Option<P> {
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if ![u, v].iter().all(|c| (0.0..=1.0).contains(c)) {
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return None;
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}
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let (w, h) = img.dimensions();
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if w == 0 || h == 0 {
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return None;
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}
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let ui = w as f32 * u - 0.5;
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let vi = h as f32 * v - 0.5;
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interpolate_bilinear(
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img,
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ui.max(0.).min((w - 1) as f32),
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vi.max(0.).min((h - 1) as f32),
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)
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}
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/// Sample from an image using coordinates in [0, 1], taking the nearest coordinate.
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pub fn sample_nearest<P: Pixel>(
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img: &impl GenericImageView<Pixel = P>,
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u: f32,
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v: f32,
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) -> Option<P> {
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if ![u, v].iter().all(|c| (0.0..=1.0).contains(c)) {
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return None;
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}
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let (w, h) = img.dimensions();
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let ui = w as f32 * u - 0.5;
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let ui = ui.max(0.).min((w.saturating_sub(1)) as f32);
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let vi = h as f32 * v - 0.5;
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let vi = vi.max(0.).min((h.saturating_sub(1)) as f32);
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interpolate_nearest(img, ui, vi)
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}
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/// Sample from an image using coordinates in [0, w-1] and [0, h-1], taking the
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/// nearest pixel.
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///
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/// Coordinates outside the image bounds will return `None`, however the
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/// behavior for points within half a pixel of the image bounds may change in
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/// the future.
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pub fn interpolate_nearest<P: Pixel>(
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img: &impl GenericImageView<Pixel = P>,
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x: f32,
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y: f32,
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) -> Option<P> {
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let (w, h) = img.dimensions();
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if w == 0 || h == 0 {
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return None;
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}
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if !(0.0..=((w - 1) as f32)).contains(&x) {
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return None;
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}
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if !(0.0..=((h - 1) as f32)).contains(&y) {
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return None;
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}
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Some(img.get_pixel(x.round() as u32, y.round() as u32))
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}
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/// Linearly sample from an image using coordinates in [0, w-1] and [0, h-1].
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pub fn interpolate_bilinear<P: Pixel>(
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img: &impl GenericImageView<Pixel = P>,
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x: f32,
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y: f32,
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) -> Option<P> {
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let (w, h) = img.dimensions();
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if w == 0 || h == 0 {
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return None;
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}
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if !(0.0..=((w - 1) as f32)).contains(&x) {
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return None;
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}
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if !(0.0..=((h - 1) as f32)).contains(&y) {
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return None;
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}
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let uf = x.floor();
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let vf = y.floor();
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let uc = (x + 1.).min((w - 1) as f32);
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let vc = (y + 1.).min((h - 1) as f32);
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// clamp coords to the range of the image
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let coords = [[uf, vf], [uf, vc], [uc, vf], [uc, vc]];
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assert!(coords
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.iter()
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.all(|&[u, v]| { img.in_bounds(u as u32, v as u32) }));
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let samples = coords.map(|[u, v]| img.get_pixel(u as u32, v as u32));
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assert!(P::CHANNEL_COUNT <= 4);
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// convert samples to f32
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// currently rgba is the largest one,
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// so just store as many items as necessary,
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// because there's not a simple way to be generic over all of them.
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let [sff, sfc, scf, scc] = samples.map(|s| {
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let mut out = [0.; 4];
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for (i, c) in s.channels().iter().enumerate() {
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out[i] = c.to_f32().unwrap();
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}
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out
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});
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// weights
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let [ufw, vfw] = [x - uf, y - vf];
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let [ucw, vcw] = [1. - ufw, 1. - vfw];
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// https://en.wikipedia.org/wiki/Bilinear_interpolation#Weighted_mean
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// the distance between pixels is 1 so there is no denominator
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let wff = ucw * vcw;
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let wfc = ucw * vfw;
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let wcf = ufw * vcw;
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let wcc = ufw * vfw;
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assert!(f32::abs((wff + wfc + wcf + wcc) - 1.) < 1e-3);
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// hack to get around not being able to construct a generic Pixel
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let mut out = samples[0];
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for (i, c) in out.channels_mut().iter_mut().enumerate() {
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let v = wff * sff[i] + wfc * sfc[i] + wcf * scf[i] + wcc * scc[i];
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// this rounding may introduce quantization errors,
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// but cannot do anything about it.
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*c = <P::Subpixel as NumCast>::from(v.round()).unwrap_or({
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if v < 0.0 {
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P::Subpixel::DEFAULT_MIN_VALUE
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} else {
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P::Subpixel::DEFAULT_MAX_VALUE
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}
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})
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}
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Some(out)
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}
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// Sample the columns of the supplied image using the provided filter.
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// The width of the image remains unchanged.
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// ```new_height``` is the desired height of the new image
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// ```filter``` is the filter to use for sampling.
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// The return value is not necessarily Rgba, the underlying order of channels in ```image``` is
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// preserved.
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fn vertical_sample<I, P, S>(image: &I, new_height: u32, filter: &mut Filter) -> Rgba32FImage
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where
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I: GenericImageView<Pixel = P>,
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P: Pixel<Subpixel = S> + 'static,
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S: Primitive + 'static,
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{
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let (width, height) = image.dimensions();
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let mut out = ImageBuffer::new(width, new_height);
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let mut ws = Vec::new();
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let ratio = height as f32 / new_height as f32;
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let sratio = if ratio < 1.0 { 1.0 } else { ratio };
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let src_support = filter.support * sratio;
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for outy in 0..new_height {
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// For an explanation of this algorithm, see the comments
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// in horizontal_sample.
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let inputy = (outy as f32 + 0.5) * ratio;
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let left = (inputy - src_support).floor() as i64;
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let left = clamp(left, 0, <i64 as From<_>>::from(height) - 1) as u32;
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let right = (inputy + src_support).ceil() as i64;
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let right = clamp(
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right,
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<i64 as From<_>>::from(left) + 1,
|
|
<i64 as From<_>>::from(height),
|
|
) as u32;
|
|
|
|
let inputy = inputy - 0.5;
|
|
|
|
ws.clear();
|
|
let mut sum = 0.0;
|
|
for i in left..right {
|
|
let w = (filter.kernel)((i as f32 - inputy) / sratio);
|
|
ws.push(w);
|
|
sum += w;
|
|
}
|
|
ws.iter_mut().for_each(|w| *w /= sum);
|
|
|
|
for x in 0..width {
|
|
let mut t = (0.0, 0.0, 0.0, 0.0);
|
|
|
|
for (i, w) in ws.iter().enumerate() {
|
|
let p = image.get_pixel(x, left + i as u32);
|
|
|
|
#[allow(deprecated)]
|
|
let (k1, k2, k3, k4) = p.channels4();
|
|
let vec: (f32, f32, f32, f32) = (
|
|
NumCast::from(k1).unwrap(),
|
|
NumCast::from(k2).unwrap(),
|
|
NumCast::from(k3).unwrap(),
|
|
NumCast::from(k4).unwrap(),
|
|
);
|
|
|
|
t.0 += vec.0 * w;
|
|
t.1 += vec.1 * w;
|
|
t.2 += vec.2 * w;
|
|
t.3 += vec.3 * w;
|
|
}
|
|
|
|
#[allow(deprecated)]
|
|
// This is not necessarily Rgba.
|
|
let t = Pixel::from_channels(t.0, t.1, t.2, t.3);
|
|
|
|
out.put_pixel(x, outy, t);
|
|
}
|
|
}
|
|
|
|
out
|
|
}
|
|
|
|
/// Local struct for keeping track of pixel sums for fast thumbnail averaging
|
|
struct ThumbnailSum<S: Primitive + Enlargeable>(S::Larger, S::Larger, S::Larger, S::Larger);
|
|
|
|
impl<S: Primitive + Enlargeable> ThumbnailSum<S> {
|
|
fn zeroed() -> Self {
|
|
ThumbnailSum(
|
|
S::Larger::zero(),
|
|
S::Larger::zero(),
|
|
S::Larger::zero(),
|
|
S::Larger::zero(),
|
|
)
|
|
}
|
|
|
|
fn sample_val(val: S) -> S::Larger {
|
|
<S::Larger as NumCast>::from(val).unwrap()
|
|
}
|
|
|
|
fn add_pixel<P: Pixel<Subpixel = S>>(&mut self, pixel: P) {
|
|
#[allow(deprecated)]
|
|
let pixel = pixel.channels4();
|
|
self.0 += Self::sample_val(pixel.0);
|
|
self.1 += Self::sample_val(pixel.1);
|
|
self.2 += Self::sample_val(pixel.2);
|
|
self.3 += Self::sample_val(pixel.3);
|
|
}
|
|
}
|
|
|
|
/// Resize the supplied image to the specific dimensions.
|
|
///
|
|
/// For downscaling, this method uses a fast integer algorithm where each source pixel contributes
|
|
/// to exactly one target pixel. May give aliasing artifacts if new size is close to old size.
|
|
///
|
|
/// In case the current width is smaller than the new width or similar for the height, another
|
|
/// strategy is used instead. For each pixel in the output, a rectangular region of the input is
|
|
/// determined, just as previously. But when no input pixel is part of this region, the nearest
|
|
/// pixels are interpolated instead.
|
|
///
|
|
/// For speed reasons, all interpolation is performed linearly over the colour values. It will not
|
|
/// take the pixel colour spaces into account.
|
|
pub fn thumbnail<I, P, S>(image: &I, new_width: u32, new_height: u32) -> ImageBuffer<P, Vec<S>>
|
|
where
|
|
I: GenericImageView<Pixel = P>,
|
|
P: Pixel<Subpixel = S> + 'static,
|
|
S: Primitive + Enlargeable + 'static,
|
|
{
|
|
let (width, height) = image.dimensions();
|
|
let mut out = ImageBuffer::new(new_width, new_height);
|
|
|
|
let x_ratio = width as f32 / new_width as f32;
|
|
let y_ratio = height as f32 / new_height as f32;
|
|
|
|
for outy in 0..new_height {
|
|
let bottomf = outy as f32 * y_ratio;
|
|
let topf = bottomf + y_ratio;
|
|
|
|
let bottom = clamp(bottomf.ceil() as u32, 0, height - 1);
|
|
let top = clamp(topf.ceil() as u32, bottom, height);
|
|
|
|
for outx in 0..new_width {
|
|
let leftf = outx as f32 * x_ratio;
|
|
let rightf = leftf + x_ratio;
|
|
|
|
let left = clamp(leftf.ceil() as u32, 0, width - 1);
|
|
let right = clamp(rightf.ceil() as u32, left, width);
|
|
|
|
let avg = if bottom != top && left != right {
|
|
thumbnail_sample_block(image, left, right, bottom, top)
|
|
} else if bottom != top {
|
|
// && left == right
|
|
// In the first column we have left == 0 and right > ceil(y_scale) > 0 so this
|
|
// assertion can never trigger.
|
|
debug_assert!(
|
|
left > 0 && right > 0,
|
|
"First output column must have corresponding pixels"
|
|
);
|
|
|
|
let fraction_horizontal = (leftf.fract() + rightf.fract()) / 2.;
|
|
thumbnail_sample_fraction_horizontal(
|
|
image,
|
|
right - 1,
|
|
fraction_horizontal,
|
|
bottom,
|
|
top,
|
|
)
|
|
} else if left != right {
|
|
// && bottom == top
|
|
// In the first line we have bottom == 0 and top > ceil(x_scale) > 0 so this
|
|
// assertion can never trigger.
|
|
debug_assert!(
|
|
bottom > 0 && top > 0,
|
|
"First output row must have corresponding pixels"
|
|
);
|
|
|
|
let fraction_vertical = (topf.fract() + bottomf.fract()) / 2.;
|
|
thumbnail_sample_fraction_vertical(image, left, right, top - 1, fraction_vertical)
|
|
} else {
|
|
// bottom == top && left == right
|
|
let fraction_horizontal = (topf.fract() + bottomf.fract()) / 2.;
|
|
let fraction_vertical = (leftf.fract() + rightf.fract()) / 2.;
|
|
|
|
thumbnail_sample_fraction_both(
|
|
image,
|
|
right - 1,
|
|
fraction_horizontal,
|
|
top - 1,
|
|
fraction_vertical,
|
|
)
|
|
};
|
|
|
|
#[allow(deprecated)]
|
|
let pixel = Pixel::from_channels(avg.0, avg.1, avg.2, avg.3);
|
|
out.put_pixel(outx, outy, pixel);
|
|
}
|
|
}
|
|
|
|
out
|
|
}
|
|
|
|
/// Get a pixel for a thumbnail where the input window encloses at least a full pixel.
|
|
fn thumbnail_sample_block<I, P, S>(
|
|
image: &I,
|
|
left: u32,
|
|
right: u32,
|
|
bottom: u32,
|
|
top: u32,
|
|
) -> (S, S, S, S)
|
|
where
|
|
I: GenericImageView<Pixel = P>,
|
|
P: Pixel<Subpixel = S>,
|
|
S: Primitive + Enlargeable,
|
|
{
|
|
let mut sum = ThumbnailSum::zeroed();
|
|
|
|
for y in bottom..top {
|
|
for x in left..right {
|
|
let k = image.get_pixel(x, y);
|
|
sum.add_pixel(k);
|
|
}
|
|
}
|
|
|
|
let n = <S::Larger as NumCast>::from((right - left) * (top - bottom)).unwrap();
|
|
let round = <S::Larger as NumCast>::from(n / NumCast::from(2).unwrap()).unwrap();
|
|
(
|
|
S::clamp_from((sum.0 + round) / n),
|
|
S::clamp_from((sum.1 + round) / n),
|
|
S::clamp_from((sum.2 + round) / n),
|
|
S::clamp_from((sum.3 + round) / n),
|
|
)
|
|
}
|
|
|
|
/// Get a thumbnail pixel where the input window encloses at least a vertical pixel.
|
|
fn thumbnail_sample_fraction_horizontal<I, P, S>(
|
|
image: &I,
|
|
left: u32,
|
|
fraction_horizontal: f32,
|
|
bottom: u32,
|
|
top: u32,
|
|
) -> (S, S, S, S)
|
|
where
|
|
I: GenericImageView<Pixel = P>,
|
|
P: Pixel<Subpixel = S>,
|
|
S: Primitive + Enlargeable,
|
|
{
|
|
let fract = fraction_horizontal;
|
|
|
|
let mut sum_left = ThumbnailSum::zeroed();
|
|
let mut sum_right = ThumbnailSum::zeroed();
|
|
for x in bottom..top {
|
|
let k_left = image.get_pixel(left, x);
|
|
sum_left.add_pixel(k_left);
|
|
|
|
let k_right = image.get_pixel(left + 1, x);
|
|
sum_right.add_pixel(k_right);
|
|
}
|
|
|
|
// Now we approximate: left/n*(1-fract) + right/n*fract
|
|
let fact_right = fract / ((top - bottom) as f32);
|
|
let fact_left = (1. - fract) / ((top - bottom) as f32);
|
|
|
|
let mix_left_and_right = |leftv: S::Larger, rightv: S::Larger| {
|
|
<S as NumCast>::from(
|
|
fact_left * leftv.to_f32().unwrap() + fact_right * rightv.to_f32().unwrap(),
|
|
)
|
|
.expect("Average sample value should fit into sample type")
|
|
};
|
|
|
|
(
|
|
mix_left_and_right(sum_left.0, sum_right.0),
|
|
mix_left_and_right(sum_left.1, sum_right.1),
|
|
mix_left_and_right(sum_left.2, sum_right.2),
|
|
mix_left_and_right(sum_left.3, sum_right.3),
|
|
)
|
|
}
|
|
|
|
/// Get a thumbnail pixel where the input window encloses at least a horizontal pixel.
|
|
fn thumbnail_sample_fraction_vertical<I, P, S>(
|
|
image: &I,
|
|
left: u32,
|
|
right: u32,
|
|
bottom: u32,
|
|
fraction_vertical: f32,
|
|
) -> (S, S, S, S)
|
|
where
|
|
I: GenericImageView<Pixel = P>,
|
|
P: Pixel<Subpixel = S>,
|
|
S: Primitive + Enlargeable,
|
|
{
|
|
let fract = fraction_vertical;
|
|
|
|
let mut sum_bot = ThumbnailSum::zeroed();
|
|
let mut sum_top = ThumbnailSum::zeroed();
|
|
for x in left..right {
|
|
let k_bot = image.get_pixel(x, bottom);
|
|
sum_bot.add_pixel(k_bot);
|
|
|
|
let k_top = image.get_pixel(x, bottom + 1);
|
|
sum_top.add_pixel(k_top);
|
|
}
|
|
|
|
// Now we approximate: bot/n*fract + top/n*(1-fract)
|
|
let fact_top = fract / ((right - left) as f32);
|
|
let fact_bot = (1. - fract) / ((right - left) as f32);
|
|
|
|
let mix_bot_and_top = |botv: S::Larger, topv: S::Larger| {
|
|
<S as NumCast>::from(fact_bot * botv.to_f32().unwrap() + fact_top * topv.to_f32().unwrap())
|
|
.expect("Average sample value should fit into sample type")
|
|
};
|
|
|
|
(
|
|
mix_bot_and_top(sum_bot.0, sum_top.0),
|
|
mix_bot_and_top(sum_bot.1, sum_top.1),
|
|
mix_bot_and_top(sum_bot.2, sum_top.2),
|
|
mix_bot_and_top(sum_bot.3, sum_top.3),
|
|
)
|
|
}
|
|
|
|
/// Get a single pixel for a thumbnail where the input window does not enclose any full pixel.
|
|
fn thumbnail_sample_fraction_both<I, P, S>(
|
|
image: &I,
|
|
left: u32,
|
|
fraction_vertical: f32,
|
|
bottom: u32,
|
|
fraction_horizontal: f32,
|
|
) -> (S, S, S, S)
|
|
where
|
|
I: GenericImageView<Pixel = P>,
|
|
P: Pixel<Subpixel = S>,
|
|
S: Primitive + Enlargeable,
|
|
{
|
|
#[allow(deprecated)]
|
|
let k_bl = image.get_pixel(left, bottom).channels4();
|
|
#[allow(deprecated)]
|
|
let k_tl = image.get_pixel(left, bottom + 1).channels4();
|
|
#[allow(deprecated)]
|
|
let k_br = image.get_pixel(left + 1, bottom).channels4();
|
|
#[allow(deprecated)]
|
|
let k_tr = image.get_pixel(left + 1, bottom + 1).channels4();
|
|
|
|
let frac_v = fraction_vertical;
|
|
let frac_h = fraction_horizontal;
|
|
|
|
let fact_tr = frac_v * frac_h;
|
|
let fact_tl = frac_v * (1. - frac_h);
|
|
let fact_br = (1. - frac_v) * frac_h;
|
|
let fact_bl = (1. - frac_v) * (1. - frac_h);
|
|
|
|
let mix = |br: S, tr: S, bl: S, tl: S| {
|
|
<S as NumCast>::from(
|
|
fact_br * br.to_f32().unwrap()
|
|
+ fact_tr * tr.to_f32().unwrap()
|
|
+ fact_bl * bl.to_f32().unwrap()
|
|
+ fact_tl * tl.to_f32().unwrap(),
|
|
)
|
|
.expect("Average sample value should fit into sample type")
|
|
};
|
|
|
|
(
|
|
mix(k_br.0, k_tr.0, k_bl.0, k_tl.0),
|
|
mix(k_br.1, k_tr.1, k_bl.1, k_tl.1),
|
|
mix(k_br.2, k_tr.2, k_bl.2, k_tl.2),
|
|
mix(k_br.3, k_tr.3, k_bl.3, k_tl.3),
|
|
)
|
|
}
|
|
|
|
/// Perform a 3x3 box filter on the supplied image.
|
|
/// ```kernel``` is an array of the filter weights of length 9.
|
|
pub fn filter3x3<I, P, S>(image: &I, kernel: &[f32]) -> ImageBuffer<P, Vec<S>>
|
|
where
|
|
I: GenericImageView<Pixel = P>,
|
|
P: Pixel<Subpixel = S> + 'static,
|
|
S: Primitive + 'static,
|
|
{
|
|
// The kernel's input positions relative to the current pixel.
|
|
let taps: &[(isize, isize)] = &[
|
|
(-1, -1),
|
|
(0, -1),
|
|
(1, -1),
|
|
(-1, 0),
|
|
(0, 0),
|
|
(1, 0),
|
|
(-1, 1),
|
|
(0, 1),
|
|
(1, 1),
|
|
];
|
|
|
|
let (width, height) = image.dimensions();
|
|
|
|
let mut out = ImageBuffer::new(width, height);
|
|
|
|
let max = S::DEFAULT_MAX_VALUE;
|
|
let max: f32 = NumCast::from(max).unwrap();
|
|
|
|
let sum = match kernel.iter().fold(0.0, |s, &item| s + item) {
|
|
x if x == 0.0 => 1.0,
|
|
sum => sum,
|
|
};
|
|
let sum = (sum, sum, sum, sum);
|
|
|
|
for y in 1..height - 1 {
|
|
for x in 1..width - 1 {
|
|
let mut t = (0.0, 0.0, 0.0, 0.0);
|
|
|
|
// TODO: There is no need to recalculate the kernel for each pixel.
|
|
// Only a subtract and addition is needed for pixels after the first
|
|
// in each row.
|
|
for (&k, &(a, b)) in kernel.iter().zip(taps.iter()) {
|
|
let k = (k, k, k, k);
|
|
let x0 = x as isize + a;
|
|
let y0 = y as isize + b;
|
|
|
|
let p = image.get_pixel(x0 as u32, y0 as u32);
|
|
|
|
#[allow(deprecated)]
|
|
let (k1, k2, k3, k4) = p.channels4();
|
|
|
|
let vec: (f32, f32, f32, f32) = (
|
|
NumCast::from(k1).unwrap(),
|
|
NumCast::from(k2).unwrap(),
|
|
NumCast::from(k3).unwrap(),
|
|
NumCast::from(k4).unwrap(),
|
|
);
|
|
|
|
t.0 += vec.0 * k.0;
|
|
t.1 += vec.1 * k.1;
|
|
t.2 += vec.2 * k.2;
|
|
t.3 += vec.3 * k.3;
|
|
}
|
|
|
|
let (t1, t2, t3, t4) = (t.0 / sum.0, t.1 / sum.1, t.2 / sum.2, t.3 / sum.3);
|
|
|
|
#[allow(deprecated)]
|
|
let t = Pixel::from_channels(
|
|
NumCast::from(clamp(t1, 0.0, max)).unwrap(),
|
|
NumCast::from(clamp(t2, 0.0, max)).unwrap(),
|
|
NumCast::from(clamp(t3, 0.0, max)).unwrap(),
|
|
NumCast::from(clamp(t4, 0.0, max)).unwrap(),
|
|
);
|
|
|
|
out.put_pixel(x, y, t);
|
|
}
|
|
}
|
|
|
|
out
|
|
}
|
|
|
|
/// Resize the supplied image to the specified dimensions.
|
|
/// ```nwidth``` and ```nheight``` are the new dimensions.
|
|
/// ```filter``` is the sampling filter to use.
|
|
pub fn resize<I: GenericImageView>(
|
|
image: &I,
|
|
nwidth: u32,
|
|
nheight: u32,
|
|
filter: FilterType,
|
|
) -> ImageBuffer<I::Pixel, Vec<<I::Pixel as Pixel>::Subpixel>>
|
|
where
|
|
I::Pixel: 'static,
|
|
<I::Pixel as Pixel>::Subpixel: 'static,
|
|
{
|
|
// check if the new dimensions are the same as the old. if they are, make a copy instead of resampling
|
|
if (nwidth, nheight) == image.dimensions() {
|
|
let mut tmp = ImageBuffer::new(image.width(), image.height());
|
|
tmp.copy_from(image, 0, 0).unwrap();
|
|
return tmp;
|
|
}
|
|
|
|
let mut method = match filter {
|
|
FilterType::Nearest => Filter {
|
|
kernel: Box::new(box_kernel),
|
|
support: 0.0,
|
|
},
|
|
FilterType::Triangle => Filter {
|
|
kernel: Box::new(triangle_kernel),
|
|
support: 1.0,
|
|
},
|
|
FilterType::CatmullRom => Filter {
|
|
kernel: Box::new(catmullrom_kernel),
|
|
support: 2.0,
|
|
},
|
|
FilterType::Gaussian => Filter {
|
|
kernel: Box::new(gaussian_kernel),
|
|
support: 3.0,
|
|
},
|
|
FilterType::Lanczos3 => Filter {
|
|
kernel: Box::new(lanczos3_kernel),
|
|
support: 3.0,
|
|
},
|
|
};
|
|
|
|
// Note: tmp is not necessarily actually Rgba
|
|
let tmp: Rgba32FImage = vertical_sample(image, nheight, &mut method);
|
|
horizontal_sample(&tmp, nwidth, &mut method)
|
|
}
|
|
|
|
/// Performs a Gaussian blur on the supplied image.
|
|
/// ```sigma``` is a measure of how much to blur by.
|
|
pub fn blur<I: GenericImageView>(
|
|
image: &I,
|
|
sigma: f32,
|
|
) -> ImageBuffer<I::Pixel, Vec<<I::Pixel as Pixel>::Subpixel>>
|
|
where
|
|
I::Pixel: 'static,
|
|
{
|
|
let sigma = if sigma <= 0.0 { 1.0 } else { sigma };
|
|
|
|
let mut method = Filter {
|
|
kernel: Box::new(|x| gaussian(x, sigma)),
|
|
support: 2.0 * sigma,
|
|
};
|
|
|
|
let (width, height) = image.dimensions();
|
|
|
|
// Keep width and height the same for horizontal and
|
|
// vertical sampling.
|
|
// Note: tmp is not necessarily actually Rgba
|
|
let tmp: Rgba32FImage = vertical_sample(image, height, &mut method);
|
|
horizontal_sample(&tmp, width, &mut method)
|
|
}
|
|
|
|
/// Performs an unsharpen mask on the supplied image.
|
|
/// ```sigma``` is the amount to blur the image by.
|
|
/// ```threshold``` is the threshold for minimal brightness change that will be sharpened.
|
|
///
|
|
/// See <https://en.wikipedia.org/wiki/Unsharp_masking#Digital_unsharp_masking>
|
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pub fn unsharpen<I, P, S>(image: &I, sigma: f32, threshold: i32) -> ImageBuffer<P, Vec<S>>
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where
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I: GenericImageView<Pixel = P>,
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P: Pixel<Subpixel = S> + 'static,
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S: Primitive + 'static,
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{
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let mut tmp = blur(image, sigma);
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let max = S::DEFAULT_MAX_VALUE;
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let max: i32 = NumCast::from(max).unwrap();
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let (width, height) = image.dimensions();
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for y in 0..height {
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for x in 0..width {
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let a = image.get_pixel(x, y);
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let b = tmp.get_pixel_mut(x, y);
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let p = a.map2(b, |c, d| {
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let ic: i32 = NumCast::from(c).unwrap();
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let id: i32 = NumCast::from(d).unwrap();
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let diff = (ic - id).abs();
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if diff > threshold {
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let e = clamp(ic + diff, 0, max); // FIXME what does this do for f32? clamp 0-1 integers??
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NumCast::from(e).unwrap()
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} else {
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c
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}
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});
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*b = p;
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}
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}
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tmp
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}
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#[cfg(test)]
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mod tests {
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use super::{resize, sample_bilinear, sample_nearest, FilterType};
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use crate::{GenericImageView, ImageBuffer, RgbImage};
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#[cfg(feature = "benchmarks")]
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use test;
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#[bench]
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#[cfg(all(feature = "benchmarks", feature = "png"))]
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fn bench_resize(b: &mut test::Bencher) {
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use std::path::Path;
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let img = crate::open(&Path::new("./examples/fractal.png")).unwrap();
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b.iter(|| {
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test::black_box(resize(&img, 200, 200, FilterType::Nearest));
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});
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b.bytes = 800 * 800 * 3 + 200 * 200 * 3;
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}
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#[test]
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#[cfg(feature = "png")]
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fn test_resize_same_size() {
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use std::path::Path;
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let img = crate::open(&Path::new("./examples/fractal.png")).unwrap();
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let resize = img.resize(img.width(), img.height(), FilterType::Triangle);
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assert!(img.pixels().eq(resize.pixels()))
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}
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#[test]
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#[cfg(feature = "png")]
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fn test_sample_bilinear() {
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use std::path::Path;
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let img = crate::open(&Path::new("./examples/fractal.png")).unwrap();
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assert!(sample_bilinear(&img, 0., 0.).is_some());
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assert!(sample_bilinear(&img, 1., 0.).is_some());
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assert!(sample_bilinear(&img, 0., 1.).is_some());
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assert!(sample_bilinear(&img, 1., 1.).is_some());
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assert!(sample_bilinear(&img, 0.5, 0.5).is_some());
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assert!(sample_bilinear(&img, 1.2, 0.5).is_none());
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assert!(sample_bilinear(&img, 0.5, 1.2).is_none());
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assert!(sample_bilinear(&img, 1.2, 1.2).is_none());
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assert!(sample_bilinear(&img, -0.1, 0.2).is_none());
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assert!(sample_bilinear(&img, 0.2, -0.1).is_none());
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assert!(sample_bilinear(&img, -0.1, -0.1).is_none());
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}
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#[test]
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#[cfg(feature = "png")]
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fn test_sample_nearest() {
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use std::path::Path;
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let img = crate::open(&Path::new("./examples/fractal.png")).unwrap();
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assert!(sample_nearest(&img, 0., 0.).is_some());
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assert!(sample_nearest(&img, 1., 0.).is_some());
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assert!(sample_nearest(&img, 0., 1.).is_some());
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assert!(sample_nearest(&img, 1., 1.).is_some());
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assert!(sample_nearest(&img, 0.5, 0.5).is_some());
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assert!(sample_nearest(&img, 1.2, 0.5).is_none());
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assert!(sample_nearest(&img, 0.5, 1.2).is_none());
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assert!(sample_nearest(&img, 1.2, 1.2).is_none());
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assert!(sample_nearest(&img, -0.1, 0.2).is_none());
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assert!(sample_nearest(&img, 0.2, -0.1).is_none());
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assert!(sample_nearest(&img, -0.1, -0.1).is_none());
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}
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#[test]
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fn test_sample_bilinear_correctness() {
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use crate::Rgba;
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let img = ImageBuffer::from_fn(2, 2, |x, y| match (x, y) {
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(0, 0) => Rgba([255, 0, 0, 0]),
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(0, 1) => Rgba([0, 255, 0, 0]),
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(1, 0) => Rgba([0, 0, 255, 0]),
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(1, 1) => Rgba([0, 0, 0, 255]),
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_ => panic!(),
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});
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assert_eq!(sample_bilinear(&img, 0.5, 0.5), Some(Rgba([64; 4])));
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assert_eq!(sample_bilinear(&img, 0.0, 0.0), Some(Rgba([255, 0, 0, 0])));
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assert_eq!(sample_bilinear(&img, 0.0, 1.0), Some(Rgba([0, 255, 0, 0])));
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assert_eq!(sample_bilinear(&img, 1.0, 0.0), Some(Rgba([0, 0, 255, 0])));
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assert_eq!(sample_bilinear(&img, 1.0, 1.0), Some(Rgba([0, 0, 0, 255])));
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assert_eq!(
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sample_bilinear(&img, 0.5, 0.0),
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Some(Rgba([128, 0, 128, 0]))
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);
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assert_eq!(
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sample_bilinear(&img, 0.0, 0.5),
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Some(Rgba([128, 128, 0, 0]))
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);
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assert_eq!(
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sample_bilinear(&img, 0.5, 1.0),
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Some(Rgba([0, 128, 0, 128]))
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);
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assert_eq!(
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sample_bilinear(&img, 1.0, 0.5),
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Some(Rgba([0, 0, 128, 128]))
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);
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}
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#[test]
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fn test_sample_nearest_correctness() {
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use crate::Rgba;
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let img = ImageBuffer::from_fn(2, 2, |x, y| match (x, y) {
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(0, 0) => Rgba([255, 0, 0, 0]),
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(0, 1) => Rgba([0, 255, 0, 0]),
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(1, 0) => Rgba([0, 0, 255, 0]),
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(1, 1) => Rgba([0, 0, 0, 255]),
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_ => panic!(),
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});
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assert_eq!(sample_nearest(&img, 0.0, 0.0), Some(Rgba([255, 0, 0, 0])));
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assert_eq!(sample_nearest(&img, 0.0, 1.0), Some(Rgba([0, 255, 0, 0])));
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assert_eq!(sample_nearest(&img, 1.0, 0.0), Some(Rgba([0, 0, 255, 0])));
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assert_eq!(sample_nearest(&img, 1.0, 1.0), Some(Rgba([0, 0, 0, 255])));
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assert_eq!(sample_nearest(&img, 0.5, 0.5), Some(Rgba([0, 0, 0, 255])));
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assert_eq!(sample_nearest(&img, 0.5, 0.0), Some(Rgba([0, 0, 255, 0])));
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assert_eq!(sample_nearest(&img, 0.0, 0.5), Some(Rgba([0, 255, 0, 0])));
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assert_eq!(sample_nearest(&img, 0.5, 1.0), Some(Rgba([0, 0, 0, 255])));
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assert_eq!(sample_nearest(&img, 1.0, 0.5), Some(Rgba([0, 0, 0, 255])));
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}
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#[bench]
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#[cfg(all(feature = "benchmarks", feature = "tiff"))]
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fn bench_resize_same_size(b: &mut test::Bencher) {
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let path = concat!(
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env!("CARGO_MANIFEST_DIR"),
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"/tests/images/tiff/testsuite/mandrill.tiff"
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);
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let image = crate::open(path).unwrap();
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b.iter(|| {
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test::black_box(image.resize(image.width(), image.height(), FilterType::CatmullRom));
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});
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b.bytes = (image.width() * image.height() * 3) as u64;
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}
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#[test]
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fn test_issue_186() {
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let img: RgbImage = ImageBuffer::new(100, 100);
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let _ = resize(&img, 50, 50, FilterType::Lanczos3);
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}
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#[bench]
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#[cfg(all(feature = "benchmarks", feature = "tiff"))]
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fn bench_thumbnail(b: &mut test::Bencher) {
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|
let path = concat!(
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env!("CARGO_MANIFEST_DIR"),
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"/tests/images/tiff/testsuite/mandrill.tiff"
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);
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let image = crate::open(path).unwrap();
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b.iter(|| {
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test::black_box(image.thumbnail(256, 256));
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});
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b.bytes = 512 * 512 * 4 + 256 * 256 * 4;
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}
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|
#[bench]
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|
#[cfg(all(feature = "benchmarks", feature = "tiff"))]
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|
fn bench_thumbnail_upsize(b: &mut test::Bencher) {
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|
let path = concat!(
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env!("CARGO_MANIFEST_DIR"),
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"/tests/images/tiff/testsuite/mandrill.tiff"
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);
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|
let image = crate::open(path).unwrap().thumbnail(256, 256);
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b.iter(|| {
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|
test::black_box(image.thumbnail(512, 512));
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});
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b.bytes = 512 * 512 * 4 + 256 * 256 * 4;
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}
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|
|
#[bench]
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|
#[cfg(all(feature = "benchmarks", feature = "tiff"))]
|
|
fn bench_thumbnail_upsize_irregular(b: &mut test::Bencher) {
|
|
let path = concat!(
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|
env!("CARGO_MANIFEST_DIR"),
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|
"/tests/images/tiff/testsuite/mandrill.tiff"
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);
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|
let image = crate::open(path).unwrap().thumbnail(193, 193);
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b.iter(|| {
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|
test::black_box(image.thumbnail(256, 256));
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});
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b.bytes = 193 * 193 * 4 + 256 * 256 * 4;
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}
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|
|
#[test]
|
|
#[cfg(feature = "png")]
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|
fn resize_transparent_image() {
|
|
use super::FilterType::{CatmullRom, Gaussian, Lanczos3, Nearest, Triangle};
|
|
use crate::imageops::crop_imm;
|
|
use crate::RgbaImage;
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|
|
|
fn assert_resize(image: &RgbaImage, filter: FilterType) {
|
|
let resized = resize(image, 16, 16, filter);
|
|
let cropped = crop_imm(&resized, 5, 5, 6, 6).to_image();
|
|
for pixel in cropped.pixels() {
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|
let alpha = pixel.0[3];
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|
assert!(
|
|
alpha != 254 && alpha != 253,
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|
"alpha value: {}, {:?}",
|
|
alpha,
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|
filter
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|
);
|
|
}
|
|
}
|
|
|
|
let path = concat!(
|
|
env!("CARGO_MANIFEST_DIR"),
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|
"/tests/images/png/transparency/tp1n3p08.png"
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|
);
|
|
let img = crate::open(path).unwrap();
|
|
let rgba8 = img.as_rgba8().unwrap();
|
|
let filters = &[Nearest, Triangle, CatmullRom, Gaussian, Lanczos3];
|
|
for filter in filters {
|
|
assert_resize(rgba8, *filter);
|
|
}
|
|
}
|
|
|
|
#[test]
|
|
fn bug_1600() {
|
|
let image = crate::RgbaImage::from_raw(629, 627, vec![255; 629 * 627 * 4]).unwrap();
|
|
let result = resize(&image, 22, 22, FilterType::Lanczos3);
|
|
assert!(result.into_raw().into_iter().any(|c| c != 0));
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|
}
|
|
}
|