| <!DOCTYPE html><html lang="en"><head><meta charset="utf-8"><meta name="viewport" content="width=device-width, initial-scale=1.0"><meta name="generator" content="rustdoc"><meta name="description" content="Source of the Rust file `/root/.cargo/registry/src/github.com-1ecc6299db9ec823/color_quant-1.1.0/src/lib.rs`."><meta name="keywords" content="rust, rustlang, rust-lang"><title>lib.rs - source</title><link rel="preload" as="font" type="font/woff2" crossorigin href="../../SourceSerif4-Regular.ttf.woff2"><link rel="preload" as="font" type="font/woff2" crossorigin href="../../FiraSans-Regular.woff2"><link rel="preload" as="font" type="font/woff2" crossorigin href="../../FiraSans-Medium.woff2"><link rel="preload" as="font" type="font/woff2" crossorigin href="../../SourceCodePro-Regular.ttf.woff2"><link rel="preload" as="font" type="font/woff2" crossorigin href="../../SourceSerif4-Bold.ttf.woff2"><link rel="preload" as="font" type="font/woff2" crossorigin href="../../SourceCodePro-Semibold.ttf.woff2"><link rel="stylesheet" href="../../normalize.css"><link rel="stylesheet" href="../../rustdoc.css" id="mainThemeStyle"><link rel="stylesheet" href="../../ayu.css" disabled><link rel="stylesheet" href="../../dark.css" disabled><link rel="stylesheet" href="../../light.css" id="themeStyle"><script id="default-settings" ></script><script src="../../storage.js"></script><script defer src="../../source-script.js"></script><script defer src="../../source-files.js"></script><script defer src="../../main.js"></script><noscript><link rel="stylesheet" href="../../noscript.css"></noscript><link rel="alternate icon" type="image/png" href="../../favicon-16x16.png"><link rel="alternate icon" type="image/png" href="../../favicon-32x32.png"><link rel="icon" type="image/svg+xml" href="../../favicon.svg"></head><body class="rustdoc source"><!--[if lte IE 11]><div class="warning">This old browser is unsupported and will most likely display funky things.</div><![endif]--><nav class="sidebar"><a class="sidebar-logo" href="../../color_quant/index.html"><div class="logo-container"><img class="rust-logo" src="../../rust-logo.svg" alt="logo"></div></a></nav><main><div class="width-limiter"><nav class="sub"><a class="sub-logo-container" href="../../color_quant/index.html"><img class="rust-logo" src="../../rust-logo.svg" alt="logo"></a><form class="search-form"><div class="search-container"><span></span><input class="search-input" name="search" autocomplete="off" spellcheck="false" placeholder="Click or press ‘S’ to search, ‘?’ for more options…" type="search"><div id="help-button" title="help" tabindex="-1"><a href="../../help.html">?</a></div><div id="settings-menu" tabindex="-1"><a href="../../settings.html" title="settings"><img width="22" height="22" alt="Change settings" src="../../wheel.svg"></a></div></div></form></nav><section id="main-content" class="content"><div class="example-wrap"><pre class="src-line-numbers"><span id="1">1</span> |
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| </pre><pre class="rust"><code><span class="comment">/* |
| NeuQuant Neural-Net Quantization Algorithm by Anthony Dekker, 1994. |
| See "Kohonen neural networks for optimal colour quantization" |
| in "Network: Computation in Neural Systems" Vol. 5 (1994) pp 351-367. |
| for a discussion of the algorithm. |
| See also http://members.ozemail.com.au/~dekker/NEUQUANT.HTML |
| |
| Incorporated bugfixes and alpha channel handling from pngnq |
| http://pngnq.sourceforge.net |
| |
| Copyright (c) 2014 The Piston Developers |
| |
| Permission is hereby granted, free of charge, to any person obtaining a copy |
| of this software and associated documentation files (the "Software"), to deal |
| in the Software without restriction, including without limitation the rights |
| to use, copy, modify, merge, publish, distribute, sublicense, and/or sell |
| copies of the Software, and to permit persons to whom the Software is |
| furnished to do so, subject to the following conditions: |
| |
| The above copyright notice and this permission notice shall be included in |
| all copies or substantial portions of the Software. |
| |
| THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
| IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, |
| FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE |
| AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER |
| LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, |
| OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN |
| THE SOFTWARE. |
| |
| NeuQuant Neural-Net Quantization Algorithm |
| ------------------------------------------ |
| |
| Copyright (c) 1994 Anthony Dekker |
| |
| NEUQUANT Neural-Net quantization algorithm by Anthony Dekker, 1994. |
| See "Kohonen neural networks for optimal colour quantization" |
| in "Network: Computation in Neural Systems" Vol. 5 (1994) pp 351-367. |
| for a discussion of the algorithm. |
| See also http://members.ozemail.com.au/~dekker/NEUQUANT.HTML |
| |
| Any party obtaining a copy of these files from the author, directly or |
| indirectly, is granted, free of charge, a full and unrestricted irrevocable, |
| world-wide, paid up, royalty-free, nonexclusive right and license to deal |
| in this software and documentation files (the "Software"), including without |
| limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, |
| and/or sell copies of the Software, and to permit persons who receive |
| copies from any such party to do so, with the only requirement being |
| that this copyright notice remain intact. |
| |
| */ |
| |
| </span><span class="doccomment">//! # Color quantization library |
| //! |
| //! This library provides a color quantizer based on the [NEUQUANT](http://members.ozemail.com.au/~dekker/NEUQUANT.HTML) |
| //! |
| //! Original literature: Dekker, A. H. (1994). Kohonen neural networks for |
| //! optimal colour quantization. *Network: Computation in Neural Systems*, 5(3), 351-367. |
| //! [doi: 10.1088/0954-898X_5_3_003](https://doi.org/10.1088/0954-898X_5_3_003) |
| //! |
| //! See also <https://scientificgems.wordpress.com/stuff/neuquant-fast-high-quality-image-quantization/> |
| //! |
| //! ## Usage |
| //! |
| //! ``` |
| //! let data = vec![0; 40]; |
| //! let nq = color_quant::NeuQuant::new(10, 256, &data); |
| //! let indixes: Vec<u8> = data.chunks(4).map(|pix| nq.index_of(pix) as u8).collect(); |
| //! let color_map = nq.color_map_rgba(); |
| //! ``` |
| |
| </span><span class="kw">mod </span>math; |
| <span class="kw">use </span><span class="kw">crate</span>::math::clamp; |
| |
| <span class="kw">use </span>std::cmp::{max, min}; |
| |
| <span class="kw">const </span>CHANNELS: usize = <span class="number">4</span>; |
| |
| <span class="kw">const </span>RADIUS_DEC: i32 = <span class="number">30</span>; <span class="comment">// factor of 1/30 each cycle |
| |
| </span><span class="kw">const </span>ALPHA_BIASSHIFT: i32 = <span class="number">10</span>; <span class="comment">// alpha starts at 1 |
| </span><span class="kw">const </span>INIT_ALPHA: i32 = <span class="number">1 </span><< ALPHA_BIASSHIFT; <span class="comment">// biased by 10 bits |
| |
| </span><span class="kw">const </span>GAMMA: f64 = <span class="number">1024.0</span>; |
| <span class="kw">const </span>BETA: f64 = <span class="number">1.0 </span>/ GAMMA; |
| <span class="kw">const </span>BETAGAMMA: f64 = BETA * GAMMA; |
| |
| <span class="comment">// four primes near 500 - assume no image has a length so large |
| // that it is divisible by all four primes |
| </span><span class="kw">const </span>PRIMES: [usize; <span class="number">4</span>] = [<span class="number">499</span>, <span class="number">491</span>, <span class="number">478</span>, <span class="number">503</span>]; |
| |
| <span class="attribute">#[derive(Clone, Copy)] |
| </span><span class="kw">struct </span>Quad<T> { |
| r: T, |
| g: T, |
| b: T, |
| a: T, |
| } |
| |
| <span class="kw">type </span>Neuron = Quad<f64>; |
| <span class="kw">type </span>Color = Quad<i32>; |
| |
| <span class="kw">pub struct </span>NeuQuant { |
| network: Vec<Neuron>, |
| colormap: Vec<Color>, |
| netindex: Vec<usize>, |
| bias: Vec<f64>, <span class="comment">// bias and freq arrays for learning |
| </span>freq: Vec<f64>, |
| samplefac: i32, |
| netsize: usize, |
| } |
| |
| <span class="kw">impl </span>NeuQuant { |
| <span class="doccomment">/// Creates a new neuronal network and trains it with the supplied data. |
| /// |
| /// Pixels are assumed to be in RGBA format. |
| /// `colors` should be $>=64$. `samplefac` determines the faction of |
| /// the sample that will be used to train the network. Its value must be in the |
| /// range $[1, 30]$. A value of $1$ thus produces the best result but is also |
| /// slowest. $10$ is a good compromise between speed and quality. |
| </span><span class="kw">pub fn </span>new(samplefac: i32, colors: usize, pixels: <span class="kw-2">&</span>[u8]) -> <span class="self">Self </span>{ |
| <span class="kw">let </span>netsize = colors; |
| <span class="kw">let </span><span class="kw-2">mut </span>this = NeuQuant { |
| network: Vec::with_capacity(netsize), |
| colormap: Vec::with_capacity(netsize), |
| netindex: <span class="macro">vec!</span>[<span class="number">0</span>; <span class="number">256</span>], |
| bias: Vec::with_capacity(netsize), |
| freq: Vec::with_capacity(netsize), |
| samplefac: samplefac, |
| netsize: colors, |
| }; |
| this.init(pixels); |
| this |
| } |
| |
| <span class="doccomment">/// Initializes the neuronal network and trains it with the supplied data. |
| /// |
| /// This method gets called by `Self::new`. |
| </span><span class="kw">pub fn </span>init(<span class="kw-2">&mut </span><span class="self">self</span>, pixels: <span class="kw-2">&</span>[u8]) { |
| <span class="self">self</span>.network.clear(); |
| <span class="self">self</span>.colormap.clear(); |
| <span class="self">self</span>.bias.clear(); |
| <span class="self">self</span>.freq.clear(); |
| <span class="kw">let </span>freq = (<span class="self">self</span>.netsize <span class="kw">as </span>f64).recip(); |
| <span class="kw">for </span>i <span class="kw">in </span><span class="number">0</span>..<span class="self">self</span>.netsize { |
| <span class="kw">let </span>tmp = (i <span class="kw">as </span>f64) * <span class="number">256.0 </span>/ (<span class="self">self</span>.netsize <span class="kw">as </span>f64); |
| <span class="comment">// Sets alpha values at 0 for dark pixels. |
| </span><span class="kw">let </span>a = <span class="kw">if </span>i < <span class="number">16 </span>{ i <span class="kw">as </span>f64 * <span class="number">16.0 </span>} <span class="kw">else </span>{ <span class="number">255.0 </span>}; |
| <span class="self">self</span>.network.push(Neuron { |
| r: tmp, |
| g: tmp, |
| b: tmp, |
| a: a, |
| }); |
| <span class="self">self</span>.colormap.push(Color { |
| r: <span class="number">0</span>, |
| g: <span class="number">0</span>, |
| b: <span class="number">0</span>, |
| a: <span class="number">255</span>, |
| }); |
| <span class="self">self</span>.freq.push(freq); |
| <span class="self">self</span>.bias.push(<span class="number">0.0</span>); |
| } |
| <span class="self">self</span>.learn(pixels); |
| <span class="self">self</span>.build_colormap(); |
| <span class="self">self</span>.build_netindex(); |
| } |
| |
| <span class="doccomment">/// Maps the rgba-pixel in-place to the best-matching color in the color map. |
| </span><span class="attribute">#[inline(always)] |
| </span><span class="kw">pub fn </span>map_pixel(<span class="kw-2">&</span><span class="self">self</span>, pixel: <span class="kw-2">&mut </span>[u8]) { |
| <span class="macro">assert!</span>(pixel.len() == <span class="number">4</span>); |
| <span class="kw">let </span>(r, g, b, a) = (pixel[<span class="number">0</span>], pixel[<span class="number">1</span>], pixel[<span class="number">2</span>], pixel[<span class="number">3</span>]); |
| <span class="kw">let </span>i = <span class="self">self</span>.search_netindex(b, g, r, a); |
| pixel[<span class="number">0</span>] = <span class="self">self</span>.colormap[i].r <span class="kw">as </span>u8; |
| pixel[<span class="number">1</span>] = <span class="self">self</span>.colormap[i].g <span class="kw">as </span>u8; |
| pixel[<span class="number">2</span>] = <span class="self">self</span>.colormap[i].b <span class="kw">as </span>u8; |
| pixel[<span class="number">3</span>] = <span class="self">self</span>.colormap[i].a <span class="kw">as </span>u8; |
| } |
| |
| <span class="doccomment">/// Finds the best-matching index in the color map. |
| /// |
| /// `pixel` is assumed to be in RGBA format. |
| </span><span class="attribute">#[inline(always)] |
| </span><span class="kw">pub fn </span>index_of(<span class="kw-2">&</span><span class="self">self</span>, pixel: <span class="kw-2">&</span>[u8]) -> usize { |
| <span class="macro">assert!</span>(pixel.len() == <span class="number">4</span>); |
| <span class="kw">let </span>(r, g, b, a) = (pixel[<span class="number">0</span>], pixel[<span class="number">1</span>], pixel[<span class="number">2</span>], pixel[<span class="number">3</span>]); |
| <span class="self">self</span>.search_netindex(b, g, r, a) |
| } |
| |
| <span class="doccomment">/// Lookup pixel values for color at `idx` in the colormap. |
| </span><span class="kw">pub fn </span>lookup(<span class="kw-2">&</span><span class="self">self</span>, idx: usize) -> <span class="prelude-ty">Option</span><[u8; <span class="number">4</span>]> { |
| <span class="self">self</span>.colormap |
| .get(idx) |
| .map(|p| [p.r <span class="kw">as </span>u8, p.g <span class="kw">as </span>u8, p.b <span class="kw">as </span>u8, p.a <span class="kw">as </span>u8]) |
| } |
| |
| <span class="doccomment">/// Returns the RGBA color map calculated from the sample. |
| </span><span class="kw">pub fn </span>color_map_rgba(<span class="kw-2">&</span><span class="self">self</span>) -> Vec<u8> { |
| <span class="kw">let </span><span class="kw-2">mut </span>map = Vec::with_capacity(<span class="self">self</span>.netsize * <span class="number">4</span>); |
| <span class="kw">for </span>entry <span class="kw">in </span><span class="kw-2">&</span><span class="self">self</span>.colormap { |
| map.push(entry.r <span class="kw">as </span>u8); |
| map.push(entry.g <span class="kw">as </span>u8); |
| map.push(entry.b <span class="kw">as </span>u8); |
| map.push(entry.a <span class="kw">as </span>u8); |
| } |
| map |
| } |
| |
| <span class="doccomment">/// Returns the RGBA color map calculated from the sample. |
| </span><span class="kw">pub fn </span>color_map_rgb(<span class="kw-2">&</span><span class="self">self</span>) -> Vec<u8> { |
| <span class="kw">let </span><span class="kw-2">mut </span>map = Vec::with_capacity(<span class="self">self</span>.netsize * <span class="number">3</span>); |
| <span class="kw">for </span>entry <span class="kw">in </span><span class="kw-2">&</span><span class="self">self</span>.colormap { |
| map.push(entry.r <span class="kw">as </span>u8); |
| map.push(entry.g <span class="kw">as </span>u8); |
| map.push(entry.b <span class="kw">as </span>u8); |
| } |
| map |
| } |
| |
| <span class="doccomment">/// Move neuron i towards biased (a,b,g,r) by factor alpha |
| </span><span class="kw">fn </span>salter_single(<span class="kw-2">&mut </span><span class="self">self</span>, alpha: f64, i: i32, quad: Quad<f64>) { |
| <span class="kw">let </span>n = <span class="kw-2">&mut </span><span class="self">self</span>.network[i <span class="kw">as </span>usize]; |
| n.b -= alpha * (n.b - quad.b); |
| n.g -= alpha * (n.g - quad.g); |
| n.r -= alpha * (n.r - quad.r); |
| n.a -= alpha * (n.a - quad.a); |
| } |
| |
| <span class="doccomment">/// Move neuron adjacent neurons towards biased (a,b,g,r) by factor alpha |
| </span><span class="kw">fn </span>alter_neighbour(<span class="kw-2">&mut </span><span class="self">self</span>, alpha: f64, rad: i32, i: i32, quad: Quad<f64>) { |
| <span class="kw">let </span>lo = max(i - rad, <span class="number">0</span>); |
| <span class="kw">let </span>hi = min(i + rad, <span class="self">self</span>.netsize <span class="kw">as </span>i32); |
| <span class="kw">let </span><span class="kw-2">mut </span>j = i + <span class="number">1</span>; |
| <span class="kw">let </span><span class="kw-2">mut </span>k = i - <span class="number">1</span>; |
| <span class="kw">let </span><span class="kw-2">mut </span>q = <span class="number">0</span>; |
| |
| <span class="kw">while </span>(j < hi) || (k > lo) { |
| <span class="kw">let </span>rad_sq = rad <span class="kw">as </span>f64 * rad <span class="kw">as </span>f64; |
| <span class="kw">let </span>alpha = (alpha * (rad_sq - q <span class="kw">as </span>f64 * q <span class="kw">as </span>f64)) / rad_sq; |
| q += <span class="number">1</span>; |
| <span class="kw">if </span>j < hi { |
| <span class="kw">let </span>p = <span class="kw-2">&mut </span><span class="self">self</span>.network[j <span class="kw">as </span>usize]; |
| p.b -= alpha * (p.b - quad.b); |
| p.g -= alpha * (p.g - quad.g); |
| p.r -= alpha * (p.r - quad.r); |
| p.a -= alpha * (p.a - quad.a); |
| j += <span class="number">1</span>; |
| } |
| <span class="kw">if </span>k > lo { |
| <span class="kw">let </span>p = <span class="kw-2">&mut </span><span class="self">self</span>.network[k <span class="kw">as </span>usize]; |
| p.b -= alpha * (p.b - quad.b); |
| p.g -= alpha * (p.g - quad.g); |
| p.r -= alpha * (p.r - quad.r); |
| p.a -= alpha * (p.a - quad.a); |
| k -= <span class="number">1</span>; |
| } |
| } |
| } |
| |
| <span class="doccomment">/// Search for biased BGR values |
| /// finds closest neuron (min dist) and updates freq |
| /// finds best neuron (min dist-bias) and returns position |
| /// for frequently chosen neurons, freq[i] is high and bias[i] is negative |
| /// bias[i] = gamma*((1/self.netsize)-freq[i]) |
| </span><span class="kw">fn </span>contest(<span class="kw-2">&mut </span><span class="self">self</span>, b: f64, g: f64, r: f64, a: f64) -> i32 { |
| <span class="kw">use </span>std::f64; |
| |
| <span class="kw">let </span><span class="kw-2">mut </span>bestd = f64::MAX; |
| <span class="kw">let </span><span class="kw-2">mut </span>bestbiasd: f64 = bestd; |
| <span class="kw">let </span><span class="kw-2">mut </span>bestpos = -<span class="number">1</span>; |
| <span class="kw">let </span><span class="kw-2">mut </span>bestbiaspos: i32 = bestpos; |
| |
| <span class="kw">for </span>i <span class="kw">in </span><span class="number">0</span>..<span class="self">self</span>.netsize { |
| <span class="kw">let </span>bestbiasd_biased = bestbiasd + <span class="self">self</span>.bias[i]; |
| <span class="kw">let </span><span class="kw-2">mut </span>dist; |
| <span class="kw">let </span>n = <span class="kw-2">&</span><span class="self">self</span>.network[i]; |
| dist = (n.b - b).abs(); |
| dist += (n.r - r).abs(); |
| <span class="kw">if </span>dist < bestd || dist < bestbiasd_biased { |
| dist += (n.g - g).abs(); |
| dist += (n.a - a).abs(); |
| <span class="kw">if </span>dist < bestd { |
| bestd = dist; |
| bestpos = i <span class="kw">as </span>i32; |
| } |
| <span class="kw">let </span>biasdist = dist - <span class="self">self</span>.bias[i]; |
| <span class="kw">if </span>biasdist < bestbiasd { |
| bestbiasd = biasdist; |
| bestbiaspos = i <span class="kw">as </span>i32; |
| } |
| } |
| <span class="self">self</span>.freq[i] -= BETA * <span class="self">self</span>.freq[i]; |
| <span class="self">self</span>.bias[i] += BETAGAMMA * <span class="self">self</span>.freq[i]; |
| } |
| <span class="self">self</span>.freq[bestpos <span class="kw">as </span>usize] += BETA; |
| <span class="self">self</span>.bias[bestpos <span class="kw">as </span>usize] -= BETAGAMMA; |
| <span class="kw">return </span>bestbiaspos; |
| } |
| |
| <span class="doccomment">/// Main learning loop |
| /// Note: the number of learning cycles is crucial and the parameters are not |
| /// optimized for net sizes < 26 or > 256. 1064 colors seems to work fine |
| </span><span class="kw">fn </span>learn(<span class="kw-2">&mut </span><span class="self">self</span>, pixels: <span class="kw-2">&</span>[u8]) { |
| <span class="kw">let </span>initrad: i32 = <span class="self">self</span>.netsize <span class="kw">as </span>i32 / <span class="number">8</span>; <span class="comment">// for 256 cols, radius starts at 32 |
| </span><span class="kw">let </span>radiusbiasshift: i32 = <span class="number">6</span>; |
| <span class="kw">let </span>radiusbias: i32 = <span class="number">1 </span><< radiusbiasshift; |
| <span class="kw">let </span>init_bias_radius: i32 = initrad * radiusbias; |
| <span class="kw">let </span><span class="kw-2">mut </span>bias_radius = init_bias_radius; |
| <span class="kw">let </span>alphadec = <span class="number">30 </span>+ ((<span class="self">self</span>.samplefac - <span class="number">1</span>) / <span class="number">3</span>); |
| <span class="kw">let </span>lengthcount = pixels.len() / CHANNELS; |
| <span class="kw">let </span>samplepixels = lengthcount / <span class="self">self</span>.samplefac <span class="kw">as </span>usize; |
| <span class="comment">// learning cycles |
| </span><span class="kw">let </span>n_cycles = <span class="kw">match </span><span class="self">self</span>.netsize >> <span class="number">1 </span>{ |
| n <span class="kw">if </span>n <= <span class="number">100 </span>=> <span class="number">100</span>, |
| n => n, |
| }; |
| <span class="kw">let </span>delta = <span class="kw">match </span>samplepixels / n_cycles { |
| <span class="number">0 </span>=> <span class="number">1</span>, |
| n => n, |
| }; |
| <span class="kw">let </span><span class="kw-2">mut </span>alpha = INIT_ALPHA; |
| |
| <span class="kw">let </span><span class="kw-2">mut </span>rad = bias_radius >> radiusbiasshift; |
| <span class="kw">if </span>rad <= <span class="number">1 </span>{ |
| rad = <span class="number">0 |
| </span>}; |
| |
| <span class="kw">let </span><span class="kw-2">mut </span>pos = <span class="number">0</span>; |
| <span class="kw">let </span>step = <span class="kw-2">*</span>PRIMES |
| .iter() |
| .find(|&&prime| lengthcount % prime != <span class="number">0</span>) |
| .unwrap_or(<span class="kw-2">&</span>PRIMES[<span class="number">3</span>]); |
| |
| <span class="kw">let </span><span class="kw-2">mut </span>i = <span class="number">0</span>; |
| <span class="kw">while </span>i < samplepixels { |
| <span class="kw">let </span>(r, g, b, a) = { |
| <span class="kw">let </span>p = <span class="kw-2">&</span>pixels[CHANNELS * pos..][..CHANNELS]; |
| (p[<span class="number">0</span>] <span class="kw">as </span>f64, p[<span class="number">1</span>] <span class="kw">as </span>f64, p[<span class="number">2</span>] <span class="kw">as </span>f64, p[<span class="number">3</span>] <span class="kw">as </span>f64) |
| }; |
| |
| <span class="kw">let </span>j = <span class="self">self</span>.contest(b, g, r, a); |
| |
| <span class="kw">let </span>alpha_ = (<span class="number">1.0 </span>* alpha <span class="kw">as </span>f64) / INIT_ALPHA <span class="kw">as </span>f64; |
| <span class="self">self</span>.salter_single(alpha_, j, Quad { b, g, r, a }); |
| <span class="kw">if </span>rad > <span class="number">0 </span>{ |
| <span class="self">self</span>.alter_neighbour(alpha_, rad, j, Quad { b, g, r, a }) |
| }; |
| |
| pos += step; |
| <span class="kw">while </span>pos >= lengthcount { |
| pos -= lengthcount |
| } |
| |
| i += <span class="number">1</span>; |
| <span class="kw">if </span>i % delta == <span class="number">0 </span>{ |
| alpha -= alpha / alphadec; |
| bias_radius -= bias_radius / RADIUS_DEC; |
| rad = bias_radius >> radiusbiasshift; |
| <span class="kw">if </span>rad <= <span class="number">1 </span>{ |
| rad = <span class="number">0 |
| </span>}; |
| } |
| } |
| } |
| |
| <span class="doccomment">/// initializes the color map |
| </span><span class="kw">fn </span>build_colormap(<span class="kw-2">&mut </span><span class="self">self</span>) { |
| <span class="kw">for </span>i <span class="kw">in </span><span class="number">0usize</span>..<span class="self">self</span>.netsize { |
| <span class="self">self</span>.colormap[i].b = clamp(<span class="self">self</span>.network[i].b.round() <span class="kw">as </span>i32); |
| <span class="self">self</span>.colormap[i].g = clamp(<span class="self">self</span>.network[i].g.round() <span class="kw">as </span>i32); |
| <span class="self">self</span>.colormap[i].r = clamp(<span class="self">self</span>.network[i].r.round() <span class="kw">as </span>i32); |
| <span class="self">self</span>.colormap[i].a = clamp(<span class="self">self</span>.network[i].a.round() <span class="kw">as </span>i32); |
| } |
| } |
| |
| <span class="doccomment">/// Insertion sort of network and building of netindex[0..255] |
| </span><span class="kw">fn </span>build_netindex(<span class="kw-2">&mut </span><span class="self">self</span>) { |
| <span class="kw">let </span><span class="kw-2">mut </span>previouscol = <span class="number">0</span>; |
| <span class="kw">let </span><span class="kw-2">mut </span>startpos = <span class="number">0</span>; |
| |
| <span class="kw">for </span>i <span class="kw">in </span><span class="number">0</span>..<span class="self">self</span>.netsize { |
| <span class="kw">let </span><span class="kw-2">mut </span>p = <span class="self">self</span>.colormap[i]; |
| <span class="kw">let </span><span class="kw-2">mut </span>q; |
| <span class="kw">let </span><span class="kw-2">mut </span>smallpos = i; |
| <span class="kw">let </span><span class="kw-2">mut </span>smallval = p.g <span class="kw">as </span>usize; <span class="comment">// index on g |
| // find smallest in i..netsize-1 |
| </span><span class="kw">for </span>j <span class="kw">in </span>(i + <span class="number">1</span>)..<span class="self">self</span>.netsize { |
| q = <span class="self">self</span>.colormap[j]; |
| <span class="kw">if </span>(q.g <span class="kw">as </span>usize) < smallval { |
| <span class="comment">// index on g |
| </span>smallpos = j; |
| smallval = q.g <span class="kw">as </span>usize; <span class="comment">// index on g |
| </span>} |
| } |
| q = <span class="self">self</span>.colormap[smallpos]; |
| <span class="comment">// swap p (i) and q (smallpos) entries |
| </span><span class="kw">if </span>i != smallpos { |
| ::std::mem::swap(<span class="kw-2">&mut </span>p, <span class="kw-2">&mut </span>q); |
| <span class="self">self</span>.colormap[i] = p; |
| <span class="self">self</span>.colormap[smallpos] = q; |
| } |
| <span class="comment">// smallval entry is now in position i |
| </span><span class="kw">if </span>smallval != previouscol { |
| <span class="self">self</span>.netindex[previouscol] = (startpos + i) >> <span class="number">1</span>; |
| <span class="kw">for </span>j <span class="kw">in </span>(previouscol + <span class="number">1</span>)..smallval { |
| <span class="self">self</span>.netindex[j] = i |
| } |
| previouscol = smallval; |
| startpos = i; |
| } |
| } |
| <span class="kw">let </span>max_netpos = <span class="self">self</span>.netsize - <span class="number">1</span>; |
| <span class="self">self</span>.netindex[previouscol] = (startpos + max_netpos) >> <span class="number">1</span>; |
| <span class="kw">for </span>j <span class="kw">in </span>(previouscol + <span class="number">1</span>)..<span class="number">256 </span>{ |
| <span class="self">self</span>.netindex[j] = max_netpos |
| } <span class="comment">// really 256 |
| </span>} |
| |
| <span class="doccomment">/// Search for best matching color |
| </span><span class="kw">fn </span>search_netindex(<span class="kw-2">&</span><span class="self">self</span>, b: u8, g: u8, r: u8, a: u8) -> usize { |
| <span class="kw">let </span><span class="kw-2">mut </span>bestd = <span class="number">1 </span><< <span class="number">30</span>; <span class="comment">// ~ 1_000_000 |
| </span><span class="kw">let </span><span class="kw-2">mut </span>best = <span class="number">0</span>; |
| <span class="comment">// start at netindex[g] and work outwards |
| </span><span class="kw">let </span><span class="kw-2">mut </span>i = <span class="self">self</span>.netindex[g <span class="kw">as </span>usize]; |
| <span class="kw">let </span><span class="kw-2">mut </span>j = <span class="kw">if </span>i > <span class="number">0 </span>{ i - <span class="number">1 </span>} <span class="kw">else </span>{ <span class="number">0 </span>}; |
| |
| <span class="kw">while </span>(i < <span class="self">self</span>.netsize) || (j > <span class="number">0</span>) { |
| <span class="kw">if </span>i < <span class="self">self</span>.netsize { |
| <span class="kw">let </span>p = <span class="self">self</span>.colormap[i]; |
| <span class="kw">let </span><span class="kw-2">mut </span>e = p.g - g <span class="kw">as </span>i32; |
| <span class="kw">let </span><span class="kw-2">mut </span>dist = e * e; <span class="comment">// inx key |
| </span><span class="kw">if </span>dist >= bestd { |
| <span class="kw">break</span>; |
| } <span class="kw">else </span>{ |
| e = p.b - b <span class="kw">as </span>i32; |
| dist += e * e; |
| <span class="kw">if </span>dist < bestd { |
| e = p.r - r <span class="kw">as </span>i32; |
| dist += e * e; |
| <span class="kw">if </span>dist < bestd { |
| e = p.a - a <span class="kw">as </span>i32; |
| dist += e * e; |
| <span class="kw">if </span>dist < bestd { |
| bestd = dist; |
| best = i; |
| } |
| } |
| } |
| i += <span class="number">1</span>; |
| } |
| } |
| <span class="kw">if </span>j > <span class="number">0 </span>{ |
| <span class="kw">let </span>p = <span class="self">self</span>.colormap[j]; |
| <span class="kw">let </span><span class="kw-2">mut </span>e = p.g - g <span class="kw">as </span>i32; |
| <span class="kw">let </span><span class="kw-2">mut </span>dist = e * e; <span class="comment">// inx key |
| </span><span class="kw">if </span>dist >= bestd { |
| <span class="kw">break</span>; |
| } <span class="kw">else </span>{ |
| e = p.b - b <span class="kw">as </span>i32; |
| dist += e * e; |
| <span class="kw">if </span>dist < bestd { |
| e = p.r - r <span class="kw">as </span>i32; |
| dist += e * e; |
| <span class="kw">if </span>dist < bestd { |
| e = p.a - a <span class="kw">as </span>i32; |
| dist += e * e; |
| <span class="kw">if </span>dist < bestd { |
| bestd = dist; |
| best = j; |
| } |
| } |
| } |
| j -= <span class="number">1</span>; |
| } |
| } |
| } |
| best |
| } |
| } |
| </code></pre></div> |
| </section></div></main><div id="rustdoc-vars" data-root-path="../../" data-current-crate="color_quant" data-themes="ayu,dark,light" data-resource-suffix="" data-rustdoc-version="1.66.0-nightly (5c8bff74b 2022-10-21)" ></div></body></html> |
