threadpool-fractal.zh.md

将绘制分形工作，分派到线程池

[![threadpool-badge]][threadpool] [![num-badge]][num] [![num_cpus-badge]][num_cpus] [![image-badge]][image] [![cat-concurrency-badge]][cat-concurrency][![cat-science-badge]][cat-science][![cat-rendering-badge]][cat-rendering]

本示例：绘制[朱利亚集合]的一个分形，生成一个图像，会用到分布式计算的线程池。

通过ImageBuffer::new，为给定宽度和高度的输出图像，分配内存。Rgb::from_channels计算 RGB 像素值。创建ThreadPool线程数，等于num_cpus::get核心数。ThreadPool::execute收到的每个像素，都作为一个单独的工作。

mpsc::channel收到工作，还有Receiver::recv会检索它们。ImageBuffer::put_pixel使用数据，设置像素颜色。ImageBuffer::save就将图像写入output.png。

# #[macro_use]
# extern crate error_chain;
extern crate threadpool;
extern crate num;
extern crate num_cpus;
extern crate image;

use std::sync::mpsc::{channel, RecvError};
use threadpool::ThreadPool;
use num::complex::Complex;
use image::{ImageBuffer, Pixel, Rgb};
#
# error_chain! {
#     foreign_links {
#         MpscRecv(RecvError);
#         Io(std::io::Error);
#     }
# }
#
# // Function converting intensity values to RGB
# // Based on http://www.efg2.com/Lab/ScienceAndEngineering/Spectra.htm
# fn wavelength_to_rgb(wavelength: u32) -> Rgb<u8> {
#     let wave = wavelength as f32;
#
#     let (r, g, b) = match wavelength {
#         380...439 => ((440. - wave)/(440. - 380.), 0.0, 1.0),
#         440...489 => (0.0, (wave - 440.)/(490. - 440.), 1.0),
#         490...509 => (0.0, 1.0, (510. - wave)/(510. - 490.)),
#         510...579 => ((wave - 510.)/(580. - 510.), 1.0, 0.0),
#         580...644 => (1.0, (645. - wave)/(645. - 580.), 0.0),
#         645...780 => (1.0, 0.0, 0.0),
#         _ => (0.0, 0.0, 0.0),
#     };
#
#     let factor = match wavelength {
#         380...419 => 0.3 + 0.7 * (wave - 380.)/(420. - 380.),
#         701...780 => 0.3 + 0.7 * (780. - wave)/(780. - 700.),
#         _ => 1.0,
#     };
#
#     let (r, g, b) = (normalize(r, factor), normalize(g, factor), normalize(b, factor));
#     Rgb::from_channels(r, g, b, 0)
# }
#
# // Maps Julia set distance estimation to intensity values
# fn julia(c: Complex<f32>, x: u32, y: u32, width: u32, height: u32, max_iter: u32) -> u32 {
#     let width = width as f32;
#     let height = height as f32;
#
#     let mut z = Complex {
#         // scale and translate the point to image coordinates
#         re: 3.0 * (x as f32 - 0.5 * width)/width,
#         im: 2.0 * (y as f32 - 0.5 * height)/height,
#     };
#
#     let mut i = 0;
#     for t in 0..max_iter {
#         if z.norm() >= 2.0 {
#             break;
#         }
#         z = z * z + c;
#         i = t;
#     }
#     i
# }
#
# // Normalizes color intensity values within RGB range
# fn normalize(color: f32, factor: f32) -> u8 {
#     ((color * factor).powf(0.8) * 255.) as u8
# }

fn run() -> Result<()> {
    let (width, height) = (1920, 1080);
    let mut img = ImageBuffer::new(width, height);
    let iterations = 300;

    let c = Complex::new(-0.8, 0.156);

    let pool = ThreadPool::new(num_cpus::get());
    let (tx, rx) = channel();

    for y in 0..height {
        let tx = tx.clone();
        pool.execute(move || for x in 0..width {
                         let i = julia(c, x, y, width, height, iterations);
                         let pixel = wavelength_to_rgb(380 + i * 400/iterations);
                         tx.send((x, y, pixel)).expect("Could not send data!");
                     });
    }

    for _ in 0..(width * height) {
        let (x, y, pixel) = rx.recv()?;
        img.put_pixel(x, y, pixel);
    }
    let _ = img.save("output.png")?;
    Ok(())
}
#
# quick_main!(run);