WebAssembly 实战：在浏览器里跑一个完整的图像处理管线

cargo new image-pipeline --lib
cd image-pipeline

# Cargo.toml
[lib]
crate-type = ["cdylib"]

[dependencies]
wasm-bindgen = "0.2"
image = "0.25"
js-sys = "0.3"

use wasm_bindgen::prelude::*;
use image::{ImageBuffer, Rgba};

#[wasm_bindgen]
pub fn grayscale(data: &[u8], width: u32, height: u32) -> Vec<u8> {
    let mut result = Vec::with_capacity(data.len());
    for chunk in data.chunks(4) {
        let gray = (0.299 * chunk[0] as f64 
                  + 0.587 * chunk[1] as f64 
                  + 0.114 * chunk[2] as f64) as u8;
        result.extend_from_slice(&[gray, gray, gray, chunk[3]]);
    }
    result
}

#[wasm_bindgen]
pub fn gaussian_blur(data: &[u8], width: u32, height: u32, radius: f32) -> Vec<u8> {
    // 5x5 kernel
    let kernel: [f32; 25] = [
        1.0, 4.0, 7.0, 4.0, 1.0,
        4.0, 16.0, 26.0, 16.0, 4.0,
        7.0, 26.0, 41.0, 26.0, 7.0,
        4.0, 16.0, 26.0, 16.0, 4.0,
        1.0, 4.0, 7.0, 4.0, 1.0,
    ];
    let kernel_sum: f32 = kernel.iter().sum();
    apply_convolution(data, width, height, &kernel, kernel_sum)
}