psychopath/sub_crates/blue_noise_mask/build.rs

// Generate Blue Noise Mask tables.

use std::cmp::Ordering;
use std::env;
use std::fs::File;
use std::io::Write;
use std::ops::{Index, IndexMut};
use std::path::Path;


const WINDOW_RADIUS: isize = 6;
const FILTER_WIDTH: f32 = 1.0;

// These are specified in powers of two (2^N) for fast wrapping
// in the generated Rust code.
const NUM_MASKS_POW: usize = 7;  // 128
const MASK_SIZE_POW: usize = 7;  // 128

fn main() {
    let out_dir = env::var("OUT_DIR").unwrap();
    let dest_path = Path::new(&out_dir).join("blue_noise_masks.rs");
    let mut f = File::create(&dest_path).unwrap();

    // Generate masks
    let masks = (0..(1 << NUM_MASKS_POW) as u32).map(|i| blue_noise_mask(1 << MASK_SIZE_POW, i)).collect::<Vec<_>>();

    // Write the beginning bits of the file
    f.write_all(format!(r#"
// This file is automatically generated.

// Compute points of the Halton sequence with with Faure-permutations for different bases.

pub const NUM_MASKS: u32 = {};
pub const MASK_SIZE: u32 = {};
const NUM_MASKS_WRAP_BITMASK: u32 = {};
const MASK_SIZE_WRAP_BITMASK: u32 = {};
const MASK_POINTS: u32 = {};

pub fn get_point(mask_i: u32, x: u32, y: u32) -> f32 {{
    let mask_i = mask_i & NUM_MASKS_WRAP_BITMASK;
    let x = x & MASK_SIZE_WRAP_BITMASK;
    let y = y & MASK_SIZE_WRAP_BITMASK;

    unsafe {{ *MASKS.get_unchecked(((mask_i * MASK_POINTS) + (y * MASK_SIZE) + x) as usize) }}
}}
"#,
    1 << NUM_MASKS_POW,
    1 << MASK_SIZE_POW,
    (1 << NUM_MASKS_POW) - 1,
    (1 << MASK_SIZE_POW) - 1,
    (1 << MASK_SIZE_POW) * (1 << MASK_SIZE_POW),
    ).as_bytes()).unwrap();

    // Write the mask data
    f.write_all(format!(r#"
const MASKS: [f32; {}] = [
    "#, (1 << MASK_SIZE_POW) * (1 << MASK_SIZE_POW) * (1 << NUM_MASKS_POW)).as_bytes()).unwrap();

    for mask in masks.iter() {
        for v in mask.data.iter() {
            f.write_all(format!(r#"    {:.8},
"#, v).as_bytes()).unwrap();
        }
    }

    f.write_all(format!(r#"
];
"#).as_bytes()).unwrap();
}



/// Creates a blue noise mask
fn blue_noise_mask(tile_size: usize, seed: u32) -> Image {
    let mut image = Image::new(tile_size, tile_size);

    for (i, v) in image.data.iter_mut().enumerate() {
        *v = hash_u32_to_f32(i as u32, seed);
    }

    // High pass and remap
    for _ in 0..2 {
        high_pass_filter(&mut image, WINDOW_RADIUS, FILTER_WIDTH);
        remap_values(&mut image);
    }

    image
}

/// High pass filter for an Image
fn high_pass_filter(image: &mut Image, window_radius: isize, filter_width: f32) {
    // Precompute filter convolution matrix
    let conv = {
        let mut conv = Image::new(window_radius as usize * 2 + 1, window_radius as usize * 2 + 1);
        for j in (-window_radius)..window_radius {
            for i in (-window_radius)..window_radius {
                let n = (((j*j) + (i*i)) as f32).sqrt();
                //let n = (j.abs() + i.abs()) as f32;
                conv.set_wrapped(gauss(n, filter_width), i,j);
            }
        }

        let inv_total = 1.0f32 / conv.data.iter().sum::<f32>();
        for v in conv.data.iter_mut() {
            *v *= inv_total;
        }

        conv
    };

    // Compute the low-pass image
    let mut low_pass_img = Image::new(image.width, image.height);
    for y in 0..image.height {
        for x in 0..image.width {
            for j in (-window_radius as isize)..window_radius {
                for i in (-window_radius as isize)..window_radius {
                    let b = y as isize + j;
                    let a = x as isize + i;
                    let alpha = conv.get_wrapped(i, j);
                    low_pass_img[(x as isize, y as isize)] += image.get_wrapped(a, b) * alpha;
                }
            }
        }
    }

    // Subtract low pass from original
    for i in 0..image.data.len() {
        image.data[i] -= low_pass_img.data[i] - 0.5;
    }
}

/// Remaps the values in an Image to be linearly distributed within [0, 1]
fn remap_values(image: &mut Image) {
    let mut vals = Vec::with_capacity(image.width * image.height);
    for y in 0..image.height {
        for x in 0..image.width {
            vals.push((image[(x as isize, y as isize)], x, y));
        }
    }
    vals.sort_by(|a, b| {
        if a < b {
            Ordering::Less
        } else {
            Ordering::Greater
        }   
    });
    let inc = 1.0 / (image.data.len() - 1) as f32;
    let mut nor_v = 0.0;
    for v in vals.iter() {
        image[(v.1 as isize, v.2 as isize)] = nor_v;
        nor_v += inc;
    }
}

/// Normal distribution
fn gauss(x: f32, sd: f32) -> f32 {
    let norm = 1.0 / (2.0 * std::f32::consts::PI * sd * sd).sqrt();
    let dist = ((-x * x) / (2.0 * sd * sd)).exp();
    norm * dist
}

/// Returns a random float in [0, 1] based on 'n' and a seed.
/// Generally use n for getting a bunch of different random
/// numbers, and use seed to vary between runs.
pub fn hash_u32_to_f32(n: u32, seed: u32) -> f32 {
    let mut hash = n;

    for _ in 0..8 {
        hash = hash.wrapping_mul(1936502639);
        hash ^= hash.wrapping_shr(16);
        hash = hash.wrapping_add(seed);
    }

    const INV_MAX: f32 = 1.0 / std::u32::MAX as f32;
    return hash as f32 * INV_MAX;
}

struct Image {
    data: Vec<f32>,
    width: usize,
    height: usize,
}

impl Image {
    fn new(width: usize, height: usize) -> Image {
        Image {
            data: vec![0.0; width * height],
            width: width,
            height: height,
        }
    }

    fn get_wrapped(&self, mut ix: isize, mut iy: isize) -> f32 {
        while ix < 0 {
            ix += self.width as isize;
        }
        while iy < 1 {
            iy += self.height as isize;
        }

        let x = ix as usize % self.width;
        let y = iy as usize % self.height;

        self.data[y * self.width + x]
    }

    fn set_wrapped(&mut self, v: f32, mut ix: isize, mut iy: isize){
        while ix < 0 {
            ix += self.width as isize;
        }
        while iy < 1 {
            iy += self.height as isize;
        }

        let x = ix as usize % self.width;
        let y = iy as usize % self.height;

        self.data[y * self.width + x] = v
    }
}

impl Index<(isize, isize)> for Image {
    type Output = f32;
    fn index(&self, index: (isize, isize)) -> &f32 {
        &self.data[index.1 as usize * self.width + index.0 as usize]
    }
}

impl IndexMut<(isize, isize)> for Image {
    fn index_mut(&mut self, index: (isize, isize)) -> &mut f32 {
        &mut self.data[index.1 as usize * self.width + index.0 as usize]
    }
}