mirror of
https://github.com/storytold/vectorcraft.git
synced 2026-10-25 10:17:02 -06:00
andeecollardandClaude Opus 5.5
1e06c82800
The Automatic palette counts flat art's colours by area (a colour needs about 1% of the picture, 0.5% at Colors 100) and fits that count with median cut and area-weighted k-means. A stem, a highlight, or a near white beside a pale background (within 24 levels, so in one box) was averaged into a large neighbour; mixed-colour merging and Noise could then remove what was left. On the flat route only, after the count is fitted, rare_colours finds the colours too rare or too close for it and keeps those that are distinct (at least 2.5 in Oklab x 100 from every palette colour and from the main colour each stands for), flat (mostly one histogram bin) and drawn (a 4-connected region of their own unblended pixels of at least 8 pixels, or three of at least 4); a colour lying between two others, as anti-aliasing mixes them, must also have an inside. At most six, largest region first. They stay fixed while the other colours are refitted around them; merge_mixed leaves them alone; denoise_keeping keeps their regions of 4 pixels or more whatever Noise is (smaller specks still go). Full Tone, Limited and Automatic's continuous-tone route are unchanged, and denoise without kept labels is as before. Methods cited in the code: median cut (Heckbert 1982), k-means (Lloyd 1982), Oklab (Ottosson 2020), salient-colour preservation (Fukuda et al. 2019, doi:10.1117/12.2521607). The rule and its numbers are VectorCraft's own; Adobe does not document how its palettes are chosen. Tests (code-drawn): a rare stem or patch over 2-8 flat colours, hard and anti-aliased at 1x and 2x; near-white highlights on #FFF4E0 kept and still paths at Noise 16; a 3 px #909090 stem on #808080; scattered single pixels of the same areas add no colour; denoise_keeping at Noise 0-32. Measured on 66 synthetic flat logos (22 designs x hard 1x, anti-aliased 1x and 2x; local inputs): every key colour and no spurious one in 66 (was 39); missing key colours 0 (was 43), spurious 0 (was 2), merged pairs 0 (was 46); rendered dE00 0.348 -> 0.138. Four public-domain scan crops take the continuous-tone route and are unchanged. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_018tBcdwNhptqtmX32JCGFzB
1032 lines
44 KiB
Rust
1032 lines
44 KiB
Rust
//! Colour reduction and noise removal. Colours are compared in Oklab, where equal distances look
|
||
//! equally different.
|
||
|
||
use crate::{Mode, Palette, Raster, TraceParams};
|
||
|
||
/// Label of a transparent (untraced) pixel.
|
||
pub const TRANSPARENT: u16 = u16::MAX;
|
||
|
||
/// A palette-indexed image.
|
||
#[derive(Clone, Debug, PartialEq)]
|
||
pub struct Quantized {
|
||
/// One palette index per pixel (row-major), or [`TRANSPARENT`].
|
||
pub labels: Vec<u16>,
|
||
pub palette: Vec<[u8; 3]>,
|
||
}
|
||
|
||
/// A pixel's gray (Rec. 601 luma): what Grayscale traces it as.
|
||
pub(crate) fn luma(p: [u8; 4]) -> u8 {
|
||
((p[0] as u32 * 299 + p[1] as u32 * 587 + p[2] as u32 * 114 + 500) / 1000) as u8
|
||
}
|
||
|
||
/// Reduce `img` to a palette according to `params.mode`.
|
||
pub fn quantize(img: &Raster, params: &TraceParams) -> Quantized {
|
||
quantize_keeping(img, params).0
|
||
}
|
||
|
||
/// [`quantize`], and the palette colours that are rare but kept on purpose (see [`rare_colours`]):
|
||
/// noise removal must not erase their regions.
|
||
pub(crate) fn quantize_keeping(img: &Raster, params: &TraceParams) -> (Quantized, Vec<[u8; 3]>) {
|
||
let px = img.rgba.as_chunks::<4>().0.iter().map(|c| [c[0], c[1], c[2], c[3]]);
|
||
match params.mode {
|
||
Mode::BlackAndWhite => {
|
||
let t = params.threshold;
|
||
let labels = px
|
||
.map(|p| {
|
||
if p[3] < 128 {
|
||
TRANSPARENT
|
||
} else if luma(p) < t {
|
||
1
|
||
} else {
|
||
0
|
||
}
|
||
})
|
||
.collect();
|
||
(Quantized { labels, palette: vec![[255, 255, 255], [0, 0, 0]] }, vec![])
|
||
}
|
||
Mode::Grayscale => {
|
||
let own = own_colours(img);
|
||
let histogram = |only_own: bool| {
|
||
let mut hist = [0u64; 256];
|
||
for (p, &f) in img.rgba.as_chunks::<4>().0.iter().zip(&own) {
|
||
if p[3] >= 128 && (f || !only_own) {
|
||
hist[luma([p[0], p[1], p[2], p[3]]) as usize] += 1;
|
||
}
|
||
}
|
||
hist
|
||
};
|
||
let (every, calm) = (histogram(false), histogram(true));
|
||
let hist = if palette_from_own(calm.iter().sum::<u64>() as f64, every.iter().sum::<u64>() as f64) { calm } else { every };
|
||
let levels = kmeans_1d(&hist, params.colors.clamp(2, 256) as usize);
|
||
let mut lut = [0u16; 256];
|
||
for (v, slot) in lut.iter_mut().enumerate() {
|
||
*slot = nearest_1d(&levels, v as f64) as u16;
|
||
}
|
||
let labels = px.map(|p| if p[3] < 128 { TRANSPARENT } else { lut[luma(p) as usize] }).collect();
|
||
let palette = levels.iter().map(|&l| {
|
||
let g = l.round().clamp(0.0, 255.0) as u8;
|
||
[g, g, g]
|
||
});
|
||
(settled(merge_mixed(compact(labels, palette.collect()), img.width as usize, img.height as usize, &[]), img, &own), vec![])
|
||
}
|
||
// Logo mode traces without quantising; asked directly, it quantises like Color.
|
||
Mode::Color | Mode::Logo => {
|
||
// 15-bit colour histogram with per-bin sums (exact bin means), fitted to the image's
|
||
// range per channel so that close tones (a dark, low-contrast photo) stay apart.
|
||
let hk = HistKey::new(img);
|
||
let own = own_colours(img);
|
||
let (mut every, mut calm) = (vec![Bin::default(); 1 << 15], vec![Bin::default(); 1 << 15]);
|
||
for (p, &f) in img.rgba.as_chunks::<4>().0.iter().zip(&own) {
|
||
if p[3] >= 128 {
|
||
let k = hk.key(p[0], p[1], p[2]);
|
||
every[k].add(p);
|
||
if f {
|
||
calm[k].add(p);
|
||
}
|
||
}
|
||
}
|
||
let used =
|
||
|bins: &[Bin]| -> Vec<Used> { bins.iter().enumerate().filter(|(_, b)| b.n > 0).map(|(i, b)| (i, b.mean(), b.n as f64)).collect() };
|
||
let (every, calm) = (used(&every), used(&calm));
|
||
let count = |u: &[Used]| u.iter().map(|b| b.2).sum::<f64>();
|
||
let from = if palette_from_own(count(&calm), count(&every)) { &calm } else { &every };
|
||
// Automatic on flat art: colours too rare for its count, but distinct and drawn as
|
||
// regions of their own, are added (see `rare_colours`).
|
||
let detail = (params.color_detail / 100.0).clamp(0.0, 1.0);
|
||
let flat_auto = params.palette == Palette::Automatic && flat_colors(from, detail).is_some();
|
||
let mut rare: Vec<[u8; 3]> = vec![];
|
||
// The picture with `colors` colours asked for; fewer come back when it has fewer, or
|
||
// when noise made several of one (see `merge_mixed`).
|
||
let mut reduce = |colors: u32| {
|
||
let mut centers = color_palette(from, &TraceParams { colors, ..params.clone() });
|
||
if flat_auto {
|
||
let found = rare_colours(img, &own, &hk, ¢ers);
|
||
if !found.is_empty() {
|
||
let common = centers.len();
|
||
centers.extend(found);
|
||
centers = refine_around(from, centers, common);
|
||
rare = centers.iter().skip(common).map(|c| c.map(|v| v.round().clamp(0.0, 255.0) as u8)).collect();
|
||
}
|
||
}
|
||
let mut lut = vec![0u16; 1 << 15];
|
||
let center_labs = labs(¢ers);
|
||
for (i, c, _) in &every {
|
||
lut[*i] = nearest_lab(¢er_labs, oklab(*c)) as u16;
|
||
}
|
||
let labels =
|
||
img.rgba.as_chunks::<4>().0.iter().map(|p| if p[3] < 128 { TRANSPARENT } else { lut[hk.key(p[0], p[1], p[2])] }).collect();
|
||
let palette = centers.iter().map(|c| [c[0].round() as u8, c[1].round() as u8, c[2].round() as u8]).collect();
|
||
let q = compact(labels, palette);
|
||
// A swatch library's colours are used as they are.
|
||
if params.palette == Palette::DocumentLibrary && !params.swatches.is_empty() {
|
||
q
|
||
} else {
|
||
merge_mixed(q, img.width as usize, img.height as usize, &rare)
|
||
}
|
||
};
|
||
let q = reduce(params.colors);
|
||
let q = settled(q, img, &own);
|
||
rare.retain(|c| q.palette.contains(c));
|
||
(q, rare)
|
||
}
|
||
}
|
||
}
|
||
|
||
/// The opaque pixels whose colour is the picture's own, not a blend: a pixel is a blend when it
|
||
/// lies between the two pixels on opposite sides of it (left and right, above and below, or
|
||
/// across a diagonal) that differ by more than 20 levels, which is what anti-aliasing and blur
|
||
/// leave along an edge. A line one pixel wide is no blend (the pixels on its two sides are alike),
|
||
/// nor is noise.
|
||
fn own_colours(img: &Raster) -> Vec<bool> {
|
||
let (w, h) = (img.width as isize, img.height as isize);
|
||
let px = img.rgba.as_chunks::<4>().0;
|
||
let at = |x: isize, y: isize| {
|
||
let p = (x >= 0 && y >= 0 && x < w && y < h).then(|| px.get((y * w + x) as usize)).flatten()?;
|
||
(p[3] >= 128).then(|| [f64::from(p[0]), f64::from(p[1]), f64::from(p[2])])
|
||
};
|
||
let mut out = Vec::with_capacity(px.len());
|
||
for y in 0..h {
|
||
for x in 0..w {
|
||
let Some(p) = at(x, y) else {
|
||
out.push(false);
|
||
continue;
|
||
};
|
||
let blend = [(1, 0), (0, 1), (1, 1), (1, -1)].into_iter().any(|(dx, dy)| {
|
||
let (Some(a), Some(b)) = (at(x - dx, y - dy), at(x + dx, y + dy)) else { return false };
|
||
let d = [0, 1, 2].map(|k| b[k] - a[k]);
|
||
let len2: f64 = d.iter().map(|v| v * v).sum();
|
||
if len2 <= 400.0 {
|
||
return false;
|
||
}
|
||
let t = (0..3).map(|k| (p[k] - a[k]) * d[k]).sum::<f64>() / len2;
|
||
let off2: f64 = (0..3).map(|k| (p[k] - a[k] - t * d[k]).powi(2)).sum();
|
||
t > 0.06 && t < 0.94 && off2 < len2 / 16.0
|
||
});
|
||
out.push(!blend);
|
||
}
|
||
}
|
||
out
|
||
}
|
||
|
||
/// Whether the palette is made from the picture's own colours alone (`own` of `all` opaque
|
||
/// pixels), so that the blends along edges take none of its colours. A picture with next to none
|
||
/// is all soft edges: there every pixel counts.
|
||
fn palette_from_own(own: f64, all: f64) -> bool {
|
||
own >= 0.05 * all && own >= 16.0
|
||
}
|
||
|
||
/// sRGB (0–255) to Oklab × 100 (Björn Ottosson, "A perceptual color space for image processing",
|
||
/// 2020): equal distances look equally different, and 2 or 3 is about the least one sees.
|
||
fn oklab(c: [f64; 3]) -> [f64; 3] {
|
||
let lin = c.map(|v| {
|
||
let v = (v / 255.0).clamp(0.0, 1.0);
|
||
if v <= 0.04045 { v / 12.92 } else { ((v + 0.055) / 1.055).powf(2.4) }
|
||
});
|
||
let l = (0.412_221_470_8 * lin[0] + 0.536_332_536_3 * lin[1] + 0.051_445_992_9 * lin[2]).cbrt();
|
||
let m = (0.211_903_498_2 * lin[0] + 0.680_699_545_1 * lin[1] + 0.107_396_956_6 * lin[2]).cbrt();
|
||
let s = (0.088_302_461_9 * lin[0] + 0.281_718_837_6 * lin[1] + 0.629_978_700_5 * lin[2]).cbrt();
|
||
[
|
||
100.0 * (0.210_454_255_3 * l + 0.793_617_785 * m - 0.004_072_046_8 * s),
|
||
100.0 * (1.977_998_495_1 * l - 2.428_592_205 * m + 0.450_593_709_9 * s),
|
||
100.0 * (0.025_904_037_1 * l + 0.782_771_766_2 * m - 0.808_675_766 * s),
|
||
]
|
||
}
|
||
|
||
/// [`oklab`] of an sRGB colour, for the tests.
|
||
#[cfg(test)]
|
||
pub(crate) fn tests_oklab(c: [u8; 3]) -> [f64; 3] {
|
||
oklab(c.map(f64::from))
|
||
}
|
||
|
||
/// `q` with the colours that noise made of one colour merged, largest mix first.
|
||
///
|
||
/// Asked for more colours than a picture has, k-means splits a flat colour along its noise, and
|
||
/// the two halves would trace as specks of one all over the other. Such halves are close (under 30
|
||
/// levels apart: noise is about as strong in a dark colour as in a light one, so the distance is
|
||
/// taken in RGB) and thoroughly mixed: the pixels of the lesser one have, on average, more than
|
||
/// half a neighbour of the other each; or they are closer still (under 12 levels) and mixed in
|
||
/// patches (compression blocks). The bands of a gradient are close too, but each keeps to itself
|
||
/// and they stay apart. The colours in `keep` merge with none.
|
||
fn merge_mixed(q: Quantized, w: usize, h: usize, keep: &[[u8; 3]]) -> Quantized {
|
||
let k = q.palette.len();
|
||
// A rare colour kept on purpose (see `rare_colours`) is distinct by choice: it merges with none.
|
||
let kept: Vec<bool> = q.palette.iter().map(|c| keep.contains(c)).collect();
|
||
if k < 2 || q.labels.len() != w * h {
|
||
return q;
|
||
}
|
||
// How many pixels each colour has, and how many neighbouring pairs each pair of colours.
|
||
let mut count = vec![0.0f64; k];
|
||
let mut touch = vec![0.0f64; k * k];
|
||
let label = |i: usize| q.labels.get(i).map(|&l| l as usize).filter(|&l| l < k);
|
||
for i in 0..w * h {
|
||
let Some(a) = label(i) else { continue };
|
||
count[a] += 1.0;
|
||
for j in [(i % w + 1 < w).then_some(i + 1), (i + w < w * h).then_some(i + w)].into_iter().flatten() {
|
||
if let Some(b) = label(j).filter(|&b| b != a) {
|
||
touch[a * k + b] += 1.0;
|
||
touch[b * k + a] += 1.0;
|
||
}
|
||
}
|
||
}
|
||
let mut color: Vec<[f64; 3]> = q.palette.iter().map(|c| c.map(f64::from)).collect();
|
||
// Which colour each has merged into (itself when none).
|
||
let mut into: Vec<usize> = (0..k).collect();
|
||
loop {
|
||
let mut best: Option<(f64, usize, usize)> = None;
|
||
for a in 0..k {
|
||
for b in a + 1..k {
|
||
if into[a] != a || into[b] != b || count[a] <= 0.0 || count[b] <= 0.0 || kept[a] || kept[b] {
|
||
continue;
|
||
}
|
||
let mixed = touch[a * k + b] / count[a].min(count[b]);
|
||
let apart = (0..3).map(|c| (color[a][c] - color[b][c]).powi(2)).sum::<f64>().sqrt();
|
||
if (mixed > 0.5 && apart < 30.0 || mixed > 0.15 && apart < 12.0) && best.is_none_or(|x| mixed > x.0) {
|
||
best = Some((mixed, a, b));
|
||
}
|
||
}
|
||
}
|
||
let Some((_, a, b)) = best else { break };
|
||
color[a] = [0, 1, 2].map(|c| (color[a][c] * count[a] + color[b][c] * count[b]) / (count[a] + count[b]));
|
||
count[a] += count[b];
|
||
count[b] = 0.0;
|
||
into[b] = a;
|
||
for c in 0..k {
|
||
if c != a && c != b {
|
||
touch[a * k + c] += touch[b * k + c];
|
||
touch[c * k + a] = touch[a * k + c];
|
||
}
|
||
}
|
||
}
|
||
if into.iter().enumerate().all(|(i, &t)| i == t) {
|
||
return q;
|
||
}
|
||
// A colour may have merged into one that merged again: follow each to where it ended up.
|
||
for i in 0..k {
|
||
let mut to = into[i];
|
||
while into[to] != to {
|
||
to = into[to];
|
||
}
|
||
into[i] = to;
|
||
}
|
||
let labels = q.labels.iter().map(|&l| into.get(l as usize).map_or(l, |&t| t as u16)).collect();
|
||
compact(labels, color.iter().map(|c| [c[0].round() as u8, c[1].round() as u8, c[2].round() as u8]).collect())
|
||
}
|
||
|
||
/// `q` with its blend pixels settled, and the colours that leaves unused dropped.
|
||
///
|
||
/// A pixel on an edge is a blend of the colours on its two sides, and the palette colour nearest
|
||
/// to a blend can be a third colour altogether (halfway from navy to yellow lies an olive that is
|
||
/// nearer to a green elsewhere in the picture), which would trace as a sliver of that colour along
|
||
/// the edge. A blend pixel (see [`own_colours`]) whose colour is none of those of the picture's
|
||
/// own pixels within two pixels of it takes the side it is nearer to of the pair of those colours
|
||
/// it is best a blend of, when that explains it twice as well as its own colour does. With one
|
||
/// colour around it, it keeps its own.
|
||
fn settled(mut q: Quantized, img: &Raster, own: &[bool]) -> Quantized {
|
||
let (w, h) = (img.width as usize, img.height as usize);
|
||
if q.labels.len() != w * h || own.len() != w * h {
|
||
return q;
|
||
}
|
||
let before = q.labels.clone();
|
||
let color = |l: u16| q.palette.get(l as usize).map(|c| c.map(f64::from));
|
||
let dist = |a: [f64; 3], b: [f64; 3]| (0..3).map(|k| (a[k] - b[k]).powi(2)).sum::<f64>().sqrt();
|
||
let mut changed = false;
|
||
for i in 0..w * h {
|
||
let mine_l = before[i];
|
||
if own[i] || mine_l == TRANSPARENT {
|
||
continue;
|
||
}
|
||
let (x, y) = (i % w, i / w);
|
||
// The colours of the picture's own pixels within two pixels (seldom more than three).
|
||
let mut near: Vec<u16> = Vec::new();
|
||
for yy in y.saturating_sub(2)..=(y + 2).min(h - 1) {
|
||
for xx in x.saturating_sub(2)..=(x + 2).min(w - 1) {
|
||
let j = yy * w + xx;
|
||
if own[j] && before[j] != TRANSPARENT && !near.contains(&before[j]) {
|
||
near.push(before[j]);
|
||
}
|
||
}
|
||
}
|
||
let (Some(p), Some(mine)) = (img.rgba.get(i * 4..i * 4 + 3).map(|p| [f64::from(p[0]), f64::from(p[1]), f64::from(p[2])]), color(mine_l))
|
||
else {
|
||
continue;
|
||
};
|
||
if near.len() < 2 || near.contains(&mine_l) {
|
||
continue;
|
||
}
|
||
let mut best: Option<(f64, u16)> = None;
|
||
for (n, &a) in near.iter().enumerate() {
|
||
for &b in &near[n + 1..] {
|
||
let (Some(ca), Some(cb)) = (color(a), color(b)) else { continue };
|
||
let d = [0, 1, 2].map(|k| cb[k] - ca[k]);
|
||
let len2: f64 = d.iter().map(|v| v * v).sum();
|
||
let t = if len2 > 0.0 { ((0..3).map(|k| (p[k] - ca[k]) * d[k]).sum::<f64>() / len2).clamp(0.0, 1.0) } else { 0.0 };
|
||
let off = dist(p, [0, 1, 2].map(|k| ca[k] + t * d[k]));
|
||
if best.is_none_or(|x| off < x.0) {
|
||
best = Some((off, if t < 0.5 { a } else { b }));
|
||
}
|
||
}
|
||
}
|
||
if let Some((off, to)) = best
|
||
&& off < 0.5 * dist(p, mine)
|
||
{
|
||
q.labels[i] = to;
|
||
changed = true;
|
||
}
|
||
}
|
||
if changed { compact(q.labels, q.palette) } else { q }
|
||
}
|
||
|
||
/// Whether `img` is continuous tone (a photo, a gradient) rather than flat art, however noisy
|
||
/// or unevenly lit (a scan): whether eight colours leave much of it unsaid. Flat art is a few
|
||
/// colours and what noise and lighting make of them, and eight colours come within a few levels
|
||
/// of every pixel; a photo's tones spread all through its range, and eight colours stay far from
|
||
/// most of them (more than 6 levels, and more than a tenth of how far a single colour stays).
|
||
pub(crate) fn continuous_tone(img: &Raster) -> bool {
|
||
let hk = HistKey::new(img);
|
||
let mut bins = vec![Bin::default(); 1 << 15];
|
||
for p in img.rgba.as_chunks::<4>().0 {
|
||
if p[3] >= 128 {
|
||
bins[hk.key(p[0], p[1], p[2])].add(p);
|
||
}
|
||
}
|
||
let used: Vec<Used> = bins.iter().enumerate().filter(|(_, b)| b.n > 0).map(|(i, b)| (i, b.mean(), b.n as f64)).collect();
|
||
// The root-mean-square distance (levels) from the pixels to the nearest of `k` colours.
|
||
let unsaid = |k: usize| {
|
||
let colors = centers(&median_cut(&used, k, 0.0));
|
||
let (mut sum, mut n) = (0.0, 0.0);
|
||
for (_, c, weight) in &used {
|
||
let d2 = colors.iter().map(|m| (0..3).map(|ch| (m[ch] - c[ch]).powi(2)).sum::<f64>()).fold(f64::INFINITY, f64::min);
|
||
sum += d2 * weight;
|
||
n += weight;
|
||
}
|
||
if n > 0.0 { (sum / n).sqrt() } else { 0.0 }
|
||
};
|
||
let eight = unsaid(8);
|
||
eight > 6.0 && eight > 0.1 * unsaid(1)
|
||
}
|
||
|
||
/// Drop unused palette entries and merge duplicates.
|
||
fn compact(mut labels: Vec<u16>, palette: Vec<[u8; 3]>) -> Quantized {
|
||
let mut used = vec![false; palette.len()];
|
||
for &l in &labels {
|
||
if l != TRANSPARENT {
|
||
used[l as usize] = true;
|
||
}
|
||
}
|
||
let mut remap = vec![0u16; palette.len()];
|
||
let mut out: Vec<[u8; 3]> = vec![];
|
||
for (i, c) in palette.iter().enumerate() {
|
||
if !used[i] {
|
||
continue;
|
||
}
|
||
remap[i] = match out.iter().position(|o| o == c) {
|
||
Some(j) => j as u16,
|
||
None => {
|
||
out.push(*c);
|
||
(out.len() - 1) as u16
|
||
}
|
||
};
|
||
}
|
||
for l in &mut labels {
|
||
if *l != TRANSPARENT {
|
||
*l = remap[*l as usize];
|
||
}
|
||
}
|
||
Quantized { labels, palette: out }
|
||
}
|
||
|
||
#[derive(Clone, Copy, Default)]
|
||
struct Bin {
|
||
n: u64,
|
||
sum: [u64; 3],
|
||
}
|
||
|
||
impl Bin {
|
||
fn add(&mut self, p: &[u8; 4]) {
|
||
self.n += 1;
|
||
for (sum, v) in self.sum.iter_mut().zip(p) {
|
||
*sum += u64::from(*v);
|
||
}
|
||
}
|
||
fn mean(&self) -> [f64; 3] {
|
||
let n = self.n.max(1) as f64;
|
||
[self.sum[0] as f64 / n, self.sum[1] as f64 / n, self.sum[2] as f64 / n]
|
||
}
|
||
}
|
||
|
||
/// The colour histogram's bin index: 5 bits per channel over that channel's range in the image.
|
||
/// A channel spanning at most 32 values keeps every value apart; a full-range one keeps the
|
||
/// plain top 5 bits.
|
||
struct HistKey {
|
||
lo: [u8; 3],
|
||
shift: [u8; 3],
|
||
}
|
||
|
||
impl HistKey {
|
||
/// Fit the bins to the opaque pixels (alpha >= 128) of `img`.
|
||
fn new(img: &Raster) -> Self {
|
||
let (mut lo, mut hi) = ([u8::MAX; 3], [0u8; 3]);
|
||
for p in img.rgba.as_chunks::<4>().0 {
|
||
if p[3] >= 128 {
|
||
for ch in 0..3 {
|
||
lo[ch] = lo[ch].min(p[ch]);
|
||
hi[ch] = hi[ch].max(p[ch]);
|
||
}
|
||
}
|
||
}
|
||
let mut shift = [0u8; 3];
|
||
for ch in 0..3 {
|
||
if lo[ch] > hi[ch] {
|
||
// No opaque pixel: nothing is looked up.
|
||
lo[ch] = 0;
|
||
continue;
|
||
}
|
||
let range = hi[ch] - lo[ch];
|
||
while shift[ch] < 3 && (range >> shift[ch]) > 31 {
|
||
shift[ch] += 1;
|
||
}
|
||
}
|
||
Self { lo, shift }
|
||
}
|
||
|
||
fn key(&self, r: u8, g: u8, b: u8) -> usize {
|
||
let c = |v: u8, ch: usize| (v.saturating_sub(self.lo[ch]) >> self.shift[ch]).min(31) as usize;
|
||
(c(r, 0) << 10) | (c(g, 1) << 5) | c(b, 2)
|
||
}
|
||
}
|
||
|
||
fn nearest_1d(levels: &[f64], v: f64) -> usize {
|
||
let mut best = (f64::INFINITY, 0);
|
||
for (i, &l) in levels.iter().enumerate() {
|
||
let d = (l - v).abs();
|
||
if d < best.0 {
|
||
best = (d, i);
|
||
}
|
||
}
|
||
best.1
|
||
}
|
||
|
||
/// 1-D k-means over a 256-bin histogram (Lloyd iterations, evenly spaced start).
|
||
fn kmeans_1d(hist: &[u64; 256], k: usize) -> Vec<f64> {
|
||
let lo = hist.iter().position(|&n| n > 0).unwrap_or(0) as f64;
|
||
let hi = hist.iter().rposition(|&n| n > 0).unwrap_or(255) as f64;
|
||
let mut levels: Vec<f64> = (0..k).map(|i| if k == 1 { lo } else { lo + (hi - lo) * i as f64 / (k - 1) as f64 }).collect();
|
||
for _ in 0..20 {
|
||
let mut sum = vec![0.0; k];
|
||
let mut cnt = vec![0.0; k];
|
||
for (v, &n) in hist.iter().enumerate() {
|
||
if n > 0 {
|
||
let j = nearest_1d(&levels, v as f64);
|
||
sum[j] += v as f64 * n as f64;
|
||
cnt[j] += n as f64;
|
||
}
|
||
}
|
||
let mut moved = false;
|
||
for j in 0..k {
|
||
if cnt[j] > 0.0 {
|
||
let m = sum[j] / cnt[j];
|
||
moved |= (m - levels[j]).abs() > 0.01;
|
||
levels[j] = m;
|
||
}
|
||
}
|
||
if !moved {
|
||
break;
|
||
}
|
||
}
|
||
levels
|
||
}
|
||
|
||
/// The index of the colour of `labs` (Oklab) nearest to `c` (Oklab): the one that looks most like it.
|
||
fn nearest_lab(labs: &[[f64; 3]], c: [f64; 3]) -> usize {
|
||
let mut best = (f64::INFINITY, 0);
|
||
for (i, m) in labs.iter().enumerate() {
|
||
let d = (m[0] - c[0]).powi(2) + (m[1] - c[1]).powi(2) + (m[2] - c[2]).powi(2);
|
||
if d < best.0 {
|
||
best = (d, i);
|
||
}
|
||
}
|
||
best.1
|
||
}
|
||
|
||
fn labs(colors: &[[f64; 3]]) -> Vec<[f64; 3]> {
|
||
colors.iter().map(|c| oklab(*c)).collect()
|
||
}
|
||
|
||
/// The most colours Full Tone (and Automatic on continuous-tone images) makes.
|
||
pub const FULL_TONE_MAX: usize = 256;
|
||
|
||
/// A histogram bin in use: its index, mean colour and pixel count.
|
||
type Used = (usize, [f64; 3], f64);
|
||
|
||
/// Color mode's palette for the histogram bins `used`, as `params.palette` asks.
|
||
fn color_palette(used: &[Used], params: &TraceParams) -> Vec<[f64; 3]> {
|
||
let k = params.colors.clamp(2, 256) as usize;
|
||
let detail = (params.color_detail / 100.0).clamp(0.0, 1.0);
|
||
// Full Tone: split until no colour box is wider than 96 (few colours) … 8 (many) levels per
|
||
// channel, which is one histogram bin.
|
||
let full_tone = || refine(used, centers(&median_cut(used, FULL_TONE_MAX, 96.0 - 88.0 * detail)));
|
||
match params.palette {
|
||
Palette::Limited => kmeans_rgb(used, k),
|
||
Palette::FullTone => full_tone(),
|
||
Palette::Automatic => match flat_colors(used, detail) {
|
||
Some(n) => kmeans_rgb(used, n),
|
||
None => full_tone(),
|
||
},
|
||
Palette::DocumentLibrary if !params.swatches.is_empty() => library_colors(used, ¶ms.swatches, k),
|
||
// No library colours to use: as Limited.
|
||
Palette::DocumentLibrary => kmeans_rgb(used, k),
|
||
}
|
||
}
|
||
|
||
/// Automatic: how many colours flat art is made of, or `None` for continuous tone (photos,
|
||
/// gradients). Flat art has most of its pixels in a few tight colours (anti-aliased edges and
|
||
/// noise aside): the bins holding 90% of the pixels are few, and each colour's pixels sit mostly
|
||
/// in one bin. `detail` (0–1) lets smaller colour areas count as colours of their own.
|
||
fn flat_colors(used: &[Used], detail: f64) -> Option<usize> {
|
||
let total: f64 = used.iter().map(|u| u.2).sum();
|
||
if total <= 0.0 {
|
||
return Some(2);
|
||
}
|
||
let mut by_count: Vec<&Used> = used.iter().collect();
|
||
by_count.sort_by(|a, b| b.2.total_cmp(&a.2));
|
||
let mut covered = 0.0;
|
||
let main = by_count
|
||
.iter()
|
||
.take_while(|u| {
|
||
let before = covered;
|
||
covered += u.2;
|
||
before < 0.9 * total
|
||
})
|
||
.count();
|
||
if main > FLAT_BINS {
|
||
return None;
|
||
}
|
||
let boxes = median_cut(used, 64, 24.0);
|
||
let min_share = (0.04 - 0.035 * detail) * total;
|
||
let colours: Vec<&ColorBox> = boxes.iter().filter(|b| b.weight >= min_share).collect();
|
||
let weight: f64 = colours.iter().map(|b| b.weight).sum();
|
||
let peaks: f64 = colours.iter().map(|b| b.peak).sum();
|
||
(weight >= 0.9 * total && peaks >= 0.5 * weight && colours.len() <= 32).then_some(colours.len().max(2))
|
||
}
|
||
|
||
/// The most histogram bins flat art's main colours spread over (a few bins per colour).
|
||
const FLAT_BINS: usize = 96;
|
||
|
||
/// Document Library: the `k` swatches (at most) that the most pixels are nearest to.
|
||
fn library_colors(used: &[Used], swatches: &[[u8; 3]], k: usize) -> Vec<[f64; 3]> {
|
||
let mut all: Vec<[f64; 3]> = vec![];
|
||
for s in swatches {
|
||
let c = s.map(f64::from);
|
||
if !all.contains(&c) {
|
||
all.push(c);
|
||
}
|
||
}
|
||
let mut weight = vec![0.0; all.len()];
|
||
let all_labs = labs(&all);
|
||
for (_, c, n) in used {
|
||
if let Some(w) = weight.get_mut(nearest_lab(&all_labs, oklab(*c))) {
|
||
*w += n;
|
||
}
|
||
}
|
||
let mut order: Vec<usize> = (0..all.len()).collect();
|
||
order.sort_by(|a, b| weight[*b].total_cmp(&weight[*a]).then(a.cmp(b)));
|
||
order.into_iter().take(k.max(1)).filter_map(|i| all.get(i).copied()).collect()
|
||
}
|
||
|
||
/// A median-cut box: its weighted mean colour, pixel count and the pixel count of its heaviest bin.
|
||
struct ColorBox {
|
||
mean: [f64; 3],
|
||
weight: f64,
|
||
peak: f64,
|
||
}
|
||
|
||
fn centers(boxes: &[ColorBox]) -> Vec<[f64; 3]> {
|
||
boxes.iter().map(|b| b.mean).collect()
|
||
}
|
||
|
||
/// Median cut to `k` colours, then weighted k-means refinement over histogram bins.
|
||
fn kmeans_rgb(used: &[Used], k: usize) -> Vec<[f64; 3]> {
|
||
refine(used, centers(&median_cut(used, k, 0.0)))
|
||
}
|
||
|
||
/// Divisive clustering in the manner of median cut, but each box is cut where that leaves its two
|
||
/// sides most alike within themselves (the cut, along one channel, with the largest between-side
|
||
/// variance: Otsu's criterion), and the box cut next is the one whose cut gains most. A cut at the
|
||
/// median, as in classic median cut, splits the largest colour of flat art in two along its noise
|
||
/// and leaves two smaller colours in one box. Boxes are cut while there are fewer than `k` and
|
||
/// some box spans more than `spread` levels of a channel.
|
||
fn median_cut(used: &[Used], k: usize, spread: f64) -> Vec<ColorBox> {
|
||
if used.is_empty() {
|
||
return vec![ColorBox { mean: [255.0; 3], weight: 0.0, peak: 0.0 }];
|
||
}
|
||
/// A box's bins and its best cut: (gain, bins sorted along the cut's channel, bins on the first side).
|
||
struct Cut {
|
||
bins: Vec<usize>,
|
||
best: Option<(f64, Vec<usize>, usize)>,
|
||
}
|
||
let plan = |bins: Vec<usize>| {
|
||
let mut best: Option<(f64, Vec<usize>, usize)> = None;
|
||
let widest = (0..3)
|
||
.map(|ch| {
|
||
let v = bins.iter().map(|&i| used[i].1[ch]);
|
||
v.clone().fold(f64::NEG_INFINITY, f64::max) - v.fold(f64::INFINITY, f64::min)
|
||
})
|
||
.fold(0.0, f64::max);
|
||
if bins.len() >= 2 && widest > spread.max(0.0) {
|
||
for ch in 0..3 {
|
||
let mut sorted = bins.clone();
|
||
sorted.sort_by(|x, y| used[*x].1[ch].total_cmp(&used[*y].1[ch]));
|
||
let (total, sum): (f64, f64) = sorted.iter().fold((0.0, 0.0), |a, &i| (a.0 + used[i].2, a.1 + used[i].1[ch] * used[i].2));
|
||
let (mut w, mut s) = (0.0, 0.0);
|
||
let mut cut: Option<(f64, usize)> = None;
|
||
for (j, &i) in sorted.iter().enumerate().take(sorted.len() - 1) {
|
||
w += used[i].2;
|
||
s += used[i].1[ch] * used[i].2;
|
||
let rest = total - w;
|
||
if w > 0.0 && rest > 0.0 {
|
||
let gain = w * rest / total * (s / w - (sum - s) / rest).powi(2);
|
||
if cut.is_none_or(|c| gain > c.0) {
|
||
cut = Some((gain, j + 1));
|
||
}
|
||
}
|
||
}
|
||
if let Some((gain, at)) = cut
|
||
&& best.as_ref().is_none_or(|b| gain > b.0)
|
||
{
|
||
best = Some((gain, sorted, at));
|
||
}
|
||
}
|
||
}
|
||
Cut { bins, best }
|
||
};
|
||
let mut boxes = vec![plan((0..used.len()).collect())];
|
||
while boxes.len() < k {
|
||
let gain = |c: &Cut| c.best.as_ref().map_or(f64::NEG_INFINITY, |b| b.0);
|
||
let Some(bi) = (0..boxes.len()).filter(|&i| boxes[i].best.is_some()).max_by(|&a, &b| gain(&boxes[a]).total_cmp(&gain(&boxes[b]))) else {
|
||
break;
|
||
};
|
||
let Some((_, mut sorted, at)) = boxes.swap_remove(bi).best else { break };
|
||
let tail = sorted.split_off(at);
|
||
boxes.push(plan(sorted));
|
||
boxes.push(plan(tail));
|
||
}
|
||
boxes
|
||
.iter()
|
||
.map(|b| {
|
||
let mut s = [0.0; 3];
|
||
let (mut w, mut peak) = (0.0, 0.0f64);
|
||
for &i in &b.bins {
|
||
for (acc, v) in s.iter_mut().zip(used[i].1) {
|
||
*acc += v * used[i].2;
|
||
}
|
||
w += used[i].2;
|
||
peak = peak.max(used[i].2);
|
||
}
|
||
ColorBox { mean: [s[0] / w, s[1] / w, s[2] / w], weight: w, peak }
|
||
})
|
||
.collect()
|
||
}
|
||
|
||
/// Lloyd refinement of `centers` over the bins `used`: each bin goes to the centre that looks most
|
||
/// like it (nearest in Oklab), and each centre becomes the mean colour of its bins.
|
||
fn refine(used: &[Used], mut centers: Vec<[f64; 3]>) -> Vec<[f64; 3]> {
|
||
let bins = labs(&used.iter().map(|u| u.1).collect::<Vec<_>>());
|
||
for _ in 0..12 {
|
||
let center_labs = labs(¢ers);
|
||
let mut sum = vec![[0.0; 3]; centers.len()];
|
||
let mut cnt = vec![0.0; centers.len()];
|
||
for ((_, c, n), lab) in used.iter().zip(&bins) {
|
||
let j = nearest_lab(¢er_labs, *lab);
|
||
for ch in 0..3 {
|
||
sum[j][ch] += c[ch] * n;
|
||
}
|
||
cnt[j] += n;
|
||
}
|
||
let mut moved = 0.0f64;
|
||
for j in 0..centers.len() {
|
||
if cnt[j] > 0.0 {
|
||
let m = [sum[j][0] / cnt[j], sum[j][1] / cnt[j], sum[j][2] / cnt[j]];
|
||
moved = moved.max((0..3).map(|ch| (m[ch] - centers[j][ch]).powi(2)).sum());
|
||
centers[j] = m;
|
||
}
|
||
}
|
||
if moved < 0.25 {
|
||
break;
|
||
}
|
||
}
|
||
centers
|
||
}
|
||
|
||
/// Automatic: the most rare colours added to a flat picture's count.
|
||
const RARE_BUDGET: usize = 6;
|
||
|
||
/// How far apart (Oklab × 100) a rare colour must be from every palette colour and from the main
|
||
/// colour each stands for: a little more than the least difference one sees (about 2).
|
||
const RARE_APART: f64 = 2.5;
|
||
|
||
/// The fewest pixels of a 4-connected region that make a rare colour a colour of the picture (a
|
||
/// stem one pixel wide and eight long): scattered noise makes none so large.
|
||
const RARE_REGION: usize = 8;
|
||
|
||
/// A rare colour drawn as several small regions (highlights) is a colour of the picture too when
|
||
/// at least [`RARE_ISLANDS`] of its regions have this many pixels; noise kept by Noise removal is
|
||
/// no smaller than this either.
|
||
pub(crate) const RARE_ISLAND: usize = 4;
|
||
|
||
/// How many regions of [`RARE_ISLAND`] pixels make a rare colour coherent.
|
||
const RARE_ISLANDS: usize = 3;
|
||
|
||
/// Automatic on flat art: the colours too rare to count among its colours (the count keeps colours
|
||
/// on at least about 1% of the picture), or too close to a large one to get a box of their own,
|
||
/// that are still colours of the picture: a highlight, a stem, a thin outline. A rare colour is
|
||
/// kept when it is distinct, at least [`RARE_APART`] in Oklab (Ottosson 2020) from every colour of
|
||
/// `centers` and from the main colour each stands for; flat, its pixels mostly of one histogram
|
||
/// bin; and drawn, some 4-connected region of its own (not blended) pixels of at least
|
||
/// [`RARE_REGION`] pixels, or [`RARE_ISLANDS`] regions of at least [`RARE_ISLAND`]. At most [`RARE_BUDGET`] are kept, those with the largest regions first.
|
||
///
|
||
/// The palette is otherwise median cut (Heckbert 1982) refined by k-means (Lloyd 1982); keeping
|
||
/// salient colours that are rare by area follows the idea of salient-colour preservation in
|
||
/// palette reduction (Fukuda et al. 2019, doi:10.1117/12.2521607). The rule and its numbers are
|
||
/// VectorCraft's own.
|
||
fn rare_colours(img: &Raster, own: &[bool], hk: &HistKey, centers: &[[f64; 3]]) -> Vec<[f64; 3]> {
|
||
let px = img.rgba.as_chunks::<4>().0;
|
||
if centers.is_empty() || own.len() != px.len() {
|
||
return vec![];
|
||
}
|
||
// The picture's own (not blended) pixels by histogram bin.
|
||
let mut bins = vec![Bin::default(); 1 << 15];
|
||
for (p, _) in px.iter().zip(own).filter(|(p, f)| **f && p[3] >= 128) {
|
||
bins[hk.key(p[0], p[1], p[2])].add(p);
|
||
}
|
||
let used: Vec<(usize, [f64; 3], f64)> = bins.iter().enumerate().filter(|(_, b)| b.n > 0).map(|(i, b)| (i, b.mean(), b.n as f64)).collect();
|
||
let used_labs = labs(&used.iter().map(|u| u.1).collect::<Vec<_>>());
|
||
let center_labs = labs(centers);
|
||
// Each palette colour's main colour: its heaviest bin.
|
||
let mut peak: Vec<Option<(f64, [f64; 3])>> = vec![None; centers.len()];
|
||
for ((_, _, n), lab) in used.iter().zip(&used_labs) {
|
||
let j = nearest_lab(¢er_labs, *lab);
|
||
if let Some(slot) = peak.get_mut(j)
|
||
&& slot.is_none_or(|(m, _)| *n > m)
|
||
{
|
||
*slot = Some((*n, *lab));
|
||
}
|
||
}
|
||
let dist = |a: [f64; 3], b: [f64; 3]| (0..3).map(|k| (a[k] - b[k]).powi(2)).sum::<f64>().sqrt();
|
||
let apart = |lab: [f64; 3]| center_labs.iter().chain(peak.iter().flatten().map(|(_, l)| l)).all(|c| dist(lab, *c) >= RARE_APART);
|
||
// The bins far from every colour, heaviest first, grouped: a bin joins the first group whose
|
||
// heaviest bin is nearer than RARE_APART.
|
||
let mut far: Vec<usize> = (0..used.len()).filter(|&u| used_labs.get(u).is_some_and(|l| apart(*l))).collect();
|
||
far.sort_by(|&a, &b| used[b].2.total_cmp(&used[a].2).then(a.cmp(&b)));
|
||
let mut group_of = vec![u16::MAX; 1 << 15];
|
||
let mut groups: Vec<Vec<usize>> = vec![];
|
||
for u in far {
|
||
let (Some(lab), Some(&(bin, ..))) = (used_labs.get(u), used.get(u)) else { continue };
|
||
let at = groups.iter().position(|g| g.first().and_then(|&f| used_labs.get(f)).is_some_and(|l| dist(*l, *lab) < RARE_APART));
|
||
let g = match at {
|
||
Some(g) => g,
|
||
None if groups.len() < 64 => {
|
||
groups.push(vec![]);
|
||
groups.len() - 1
|
||
}
|
||
None => continue,
|
||
};
|
||
if let Some(members) = groups.get_mut(g) {
|
||
members.push(u);
|
||
}
|
||
group_of[bin] = g as u16;
|
||
}
|
||
if groups.is_empty() {
|
||
return vec![];
|
||
}
|
||
// The largest 4-connected region of each group's own pixels.
|
||
let (w, h) = (img.width as usize, img.height as usize);
|
||
let group_at = |i: usize| -> Option<u16> {
|
||
let p = px.get(i)?;
|
||
(own.get(i).copied().unwrap_or(false) && p[3] >= 128).then(|| group_of[hk.key(p[0], p[1], p[2])]).filter(|&g| g != u16::MAX)
|
||
};
|
||
let mut largest = vec![0usize; groups.len()];
|
||
let mut islands = vec![0usize; groups.len()];
|
||
// Pixels of a group whose four neighbours are of it too: an edge's blend is a line one pixel
|
||
// wide and has next to none.
|
||
let mut inner = vec![0usize; groups.len()];
|
||
let mut seen = vec![false; w * h];
|
||
let mut stack = vec![];
|
||
for start in 0..w * h {
|
||
let Some(g) = group_at(start).filter(|_| !seen[start]) else { continue };
|
||
seen[start] = true;
|
||
stack.push(start);
|
||
let mut size = 0;
|
||
while let Some(i) = stack.pop() {
|
||
size += 1;
|
||
let (x, y) = (i % w, i / w);
|
||
let mut alike = 0;
|
||
for j in [(x > 0).then(|| i - 1), (x + 1 < w).then(|| i + 1), (y > 0).then(|| i - w), (y + 1 < h).then(|| i + w)].into_iter().flatten() {
|
||
if group_at(j) == Some(g) {
|
||
alike += 1;
|
||
if !seen[j] {
|
||
seen[j] = true;
|
||
stack.push(j);
|
||
}
|
||
}
|
||
}
|
||
if alike == 4
|
||
&& let Some(n) = inner.get_mut(g as usize)
|
||
{
|
||
*n += 1;
|
||
}
|
||
}
|
||
if let Some(l) = largest.get_mut(g as usize) {
|
||
*l = (*l).max(size);
|
||
}
|
||
if let Some(n) = islands.get_mut(g as usize).filter(|_| size >= RARE_ISLAND) {
|
||
*n += 1;
|
||
}
|
||
}
|
||
// Coherent and flat groups, largest region first.
|
||
let mut chosen: Vec<(usize, f64, [f64; 3])> = vec![];
|
||
for (g, members) in groups.iter().enumerate() {
|
||
let weight: f64 = members.iter().filter_map(|&u| used.get(u)).map(|u| u.2).sum();
|
||
let heaviest = members.first().and_then(|&u| used.get(u)).map_or(0.0, |u| u.2);
|
||
let region = largest.get(g).copied().unwrap_or(0);
|
||
let coherent = region >= RARE_REGION || islands.get(g).is_some_and(|&n| n >= RARE_ISLANDS);
|
||
if !coherent || heaviest < 0.5 * weight || weight <= 0.0 {
|
||
continue;
|
||
}
|
||
let mut mean = [0.0; 3];
|
||
for u in members.iter().filter_map(|&u| used.get(u)) {
|
||
for (m, c) in mean.iter_mut().zip(u.1) {
|
||
*m += c * u.2 / weight;
|
||
}
|
||
}
|
||
// Not an edge's blend of two colours that the blend test let through (a thin ring of
|
||
// anti-aliasing between two bands is coherent too): a colour that lies between two others
|
||
// is kept only with an inside, pixels whose neighbours are all of it.
|
||
if is_mix(mean, centers) && inner.get(g).is_none_or(|&n| n < RARE_ISLAND) {
|
||
continue;
|
||
}
|
||
chosen.push((region, weight, mean));
|
||
}
|
||
chosen.sort_by(|a, b| b.0.cmp(&a.0).then(b.1.total_cmp(&a.1)).then(a.2.partial_cmp(&b.2).unwrap_or(std::cmp::Ordering::Equal)));
|
||
let mut out: Vec<[f64; 3]> = vec![];
|
||
for (_, _, c) in chosen {
|
||
if out.len() >= RARE_BUDGET {
|
||
break;
|
||
}
|
||
let lab = oklab(c);
|
||
if out.iter().all(|o| dist(oklab(*o), lab) >= RARE_APART) {
|
||
out.push(c);
|
||
}
|
||
}
|
||
out
|
||
}
|
||
|
||
/// Whether `c` (sRGB) lies between two of `centers`, as anti-aliasing mixes them: within 12 levels
|
||
/// of the line between them, and at least an eighth of the way from each.
|
||
fn is_mix(c: [f64; 3], centers: &[[f64; 3]]) -> bool {
|
||
centers.iter().enumerate().any(|(i, a)| {
|
||
centers.iter().skip(i + 1).any(|b| {
|
||
let d = [0, 1, 2].map(|k| b[k] - a[k]);
|
||
let len2: f64 = d.iter().map(|v| v * v).sum();
|
||
if len2 <= 0.0 {
|
||
return false;
|
||
}
|
||
let t = (0..3).map(|k| (c[k] - a[k]) * d[k]).sum::<f64>() / len2;
|
||
let off2: f64 = (0..3).map(|k| (c[k] - a[k] - t * d[k]).powi(2)).sum();
|
||
(0.12..=0.88).contains(&t) && off2 < 144.0
|
||
})
|
||
})
|
||
}
|
||
|
||
/// `centers` refined over the bins `used` as k-means does, but the colours from `fixed` on (rare
|
||
/// colours, see [`rare_colours`]) stay where they are: they take their own bins from the colours
|
||
/// they were averaged into, and those move back to their own.
|
||
fn refine_around(used: &[Used], mut centers: Vec<[f64; 3]>, fixed: usize) -> Vec<[f64; 3]> {
|
||
let bins = labs(&used.iter().map(|u| u.1).collect::<Vec<_>>());
|
||
for _ in 0..4 {
|
||
let center_labs = labs(¢ers);
|
||
let mut sum = vec![[0.0; 3]; centers.len()];
|
||
let mut cnt = vec![0.0; centers.len()];
|
||
for ((_, c, n), lab) in used.iter().zip(&bins) {
|
||
let j = nearest_lab(¢er_labs, *lab);
|
||
if let (Some(s), Some(k)) = (sum.get_mut(j), cnt.get_mut(j)) {
|
||
for ch in 0..3 {
|
||
s[ch] += c[ch] * n;
|
||
}
|
||
*k += n;
|
||
}
|
||
}
|
||
for (j, center) in centers.iter_mut().enumerate().take(fixed) {
|
||
if let (Some(s), Some(&k)) = (sum.get(j), cnt.get(j))
|
||
&& k > 0.0
|
||
{
|
||
*center = [s[0] / k, s[1] / k, s[2] / k];
|
||
}
|
||
}
|
||
}
|
||
centers
|
||
}
|
||
|
||
/// Merge whole 4-connected regions, smallest first, into their most common neighbouring label.
|
||
/// The merge order does not depend on `min_area`: a higher threshold continues the same sequence
|
||
/// instead of splitting a region already merged at a lower threshold. Transparency is a barrier.
|
||
pub fn denoise(labels: &mut [u16], w: usize, h: usize, min_area: usize) {
|
||
denoise_keeping(labels, w, h, min_area, &[]);
|
||
}
|
||
|
||
/// [`denoise`], except that a region of a label `keep` marks (a rare colour kept on purpose, see
|
||
/// [`rare_colours`]) that is at least [`RARE_ISLAND`] pixels stays whatever `min_area` is: Noise
|
||
/// clears specks, not a colour's only regions. Smaller regions of it go as any other.
|
||
pub(crate) fn denoise_keeping(labels: &mut [u16], w: usize, h: usize, min_area: usize, keep: &[bool]) {
|
||
use std::cmp::Reverse;
|
||
use std::collections::{BTreeMap, BinaryHeap};
|
||
|
||
if min_area <= 1 || w == 0 || h == 0 || w.checked_mul(h) != Some(labels.len()) {
|
||
return;
|
||
}
|
||
struct Region {
|
||
label: u16,
|
||
pixels: Vec<usize>,
|
||
}
|
||
// Region membership is kept current after every merge. In particular, no stale `seen`
|
||
// flags can exclude previously recoloured pixels from a subsequent connected component.
|
||
let mut at = vec![usize::MAX; labels.len()];
|
||
let mut regions: Vec<Region> = Vec::new();
|
||
let neighbours = |i: usize| {
|
||
let (x, y) = (i % w, i / w);
|
||
[(x > 0).then(|| i - 1), (x + 1 < w).then(|| i + 1), (y > 0).then(|| i - w), (y + 1 < h).then(|| i + w)]
|
||
};
|
||
for start in 0..labels.len() {
|
||
if at[start] != usize::MAX || labels[start] == TRANSPARENT {
|
||
continue;
|
||
}
|
||
let id = regions.len();
|
||
let label = labels[start];
|
||
let mut pixels = vec![start];
|
||
at[start] = id;
|
||
let mut cursor = 0;
|
||
while let Some(&i) = pixels.get(cursor) {
|
||
for j in neighbours(i).into_iter().flatten() {
|
||
if at[j] == usize::MAX && labels[j] == label {
|
||
at[j] = id;
|
||
pixels.push(j);
|
||
}
|
||
}
|
||
cursor += 1;
|
||
}
|
||
regions.push(Region { label, pixels });
|
||
}
|
||
let mut pending: BinaryHeap<_> = regions.iter().enumerate().map(|(id, r)| Reverse((r.pixels.len(), id))).collect();
|
||
while let Some(Reverse((area, id))) = pending.pop() {
|
||
if area >= min_area {
|
||
break;
|
||
}
|
||
if regions[id].pixels.len() != area {
|
||
continue; // absorbed region or an old heap entry for a region that grew
|
||
}
|
||
if area >= RARE_ISLAND && keep.get(regions[id].label as usize).copied().unwrap_or(false) {
|
||
continue;
|
||
}
|
||
let mut borders: BTreeMap<usize, usize> = BTreeMap::new();
|
||
for &i in ®ions[id].pixels {
|
||
for j in neighbours(i).into_iter().flatten() {
|
||
let other = at[j];
|
||
if other != id && other != usize::MAX {
|
||
*borders.entry(other).or_default() += 1;
|
||
}
|
||
}
|
||
}
|
||
let mut colours: BTreeMap<u16, usize> = BTreeMap::new();
|
||
for (&other, &border) in &borders {
|
||
*colours.entry(regions[other].label).or_default() += border;
|
||
}
|
||
let Some((&colour, _)) = colours.iter().max_by_key(|(label, border)| (**border, Reverse(**label))) else {
|
||
continue; // an opaque island surrounded only by transparency stays intact
|
||
};
|
||
// All adjacent regions of the chosen colour become connected by this merge. Absorb
|
||
// them together, keeping the largest one's storage to avoid repeatedly moving it.
|
||
let Some(target) = borders
|
||
.keys()
|
||
.copied()
|
||
.filter(|&other| regions[other].label == colour)
|
||
.max_by_key(|&other| (regions[other].pixels.len(), Reverse(other)))
|
||
else {
|
||
continue;
|
||
};
|
||
let sources: Vec<_> =
|
||
std::iter::once(id).chain(borders.keys().copied().filter(|&other| other != target && regions[other].label == colour)).collect();
|
||
for source in sources {
|
||
let pixels = std::mem::take(&mut regions[source].pixels);
|
||
for &i in &pixels {
|
||
at[i] = target;
|
||
}
|
||
regions[target].pixels.extend(pixels);
|
||
}
|
||
pending.push(Reverse((regions[target].pixels.len(), target)));
|
||
}
|
||
for region in regions {
|
||
for i in region.pixels {
|
||
labels[i] = region.label;
|
||
}
|
||
}
|
||
}
|