color_quant-1.0.1/.gitignore010064400007650000024000000000251331026036000142350ustar0000000000000000target Cargo.lock .* color_quant-1.0.1/Cargo.toml.orig010064400007650000024000000004311331026062600151420ustar0000000000000000[package] name = "color_quant" license = "MIT" version = "1.0.1" authors = ["nwin "] readme = "README.md" description = "Color quantization library to reduce n colors to 256 colors." repository = "https://github.com/PistonDevelopers/color_quant.git" color_quant-1.0.1/Cargo.toml0000644000000014450000000000000114220ustar00# THIS FILE IS AUTOMATICALLY GENERATED BY CARGO # # When uploading crates to the registry Cargo will automatically # "normalize" Cargo.toml files for maximal compatibility # with all versions of Cargo and also rewrite `path` dependencies # to registry (e.g. crates.io) dependencies # # If you believe there's an error in this file please file an # issue against the rust-lang/cargo repository. If you're # editing this file be aware that the upstream Cargo.toml # will likely look very different (and much more reasonable) [package] name = "color_quant" version = "1.0.1" authors = ["nwin "] description = "Color quantization library to reduce n colors to 256 colors." readme = "README.md" license = "MIT" repository = "https://github.com/PistonDevelopers/color_quant.git" color_quant-1.0.1/LICENSE010064400007650000024000000020731331026060700132630ustar0000000000000000The MIT License (MIT) Copyright (c) 2016 PistonDevelopers 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. color_quant-1.0.1/README.md010064400007650000024000000006361331026036000135340ustar0000000000000000# Color quantization library This library provides a color quantizer based on the [NEUQUANT](http://members.ozemail.com.au/~dekker/NEUQUANT.HTML) quantization algorithm by Anthony Dekker. ### Usage let data = vec![0; 40]; let nq = color_quant::NeuQuant::new(10, 256, &data); let indixes: Vec = data.chunks(4).map(|pix| nq.index_of(pix) as u8).collect(); let color_map = nq.color_map_rgba(); color_quant-1.0.1/src/lib.rs010064400007650000024000000374251331026036000141660ustar0000000000000000/* 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. */ //! # Color quantization library //! This library provides a color quantizer based on the [NEUQUANT](http://members.ozemail.com.au/~dekker/NEUQUANT.HTML) //! quantization algorithm by Anthony Dekker. //! ### Usage //! ``` //! let data = vec![0; 40]; //! let nq = color_quant::NeuQuant::new(10, 256, &data); //! let indixes: Vec = data.chunks(4).map(|pix| nq.index_of(pix) as u8).collect(); //! let color_map = nq.color_map_rgba(); //! ``` //! use std::cmp::{ max, min }; macro_rules! clamp( ($x:expr) => (match $x { x if x < 0 => 0, x if x > 255 => 255, x => x }) ); const CHANNELS: usize = 4; const RADIUS_DEC: i32 = 30; // factor of 1/30 each cycle const ALPHA_BIASSHIFT: i32 = 10; // alpha starts at 1 const INIT_ALPHA: i32 = 1 << ALPHA_BIASSHIFT; // biased by 10 bits const GAMMA: f64 = 1024.0; const BETA: f64 = 1.0 / GAMMA; const BETAGAMMA: f64 = BETA * GAMMA; // four primes near 500 - assume no image has a length so large // that it is divisible by all four primes const PRIMES: [usize; 4] = [499, 491, 478, 503]; #[derive(Clone, Copy)] struct Quad { r: T, g: T, b: T, a: T, } type Neuron = Quad; type Color = Quad; /// Neural network based color quantizer. pub struct NeuQuant { network: Vec, colormap: Vec, netindex: Vec, bias: Vec, // bias and freq arrays for learning freq: Vec, samplefac: i32, netsize: usize, } impl NeuQuant { /// 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. pub fn new(samplefac: i32, colors: usize, pixels: &[u8]) -> Self { let netsize = colors; let mut this = NeuQuant { network: Vec::with_capacity(netsize), colormap: Vec::with_capacity(netsize), netindex: vec![0; 256], bias: Vec::with_capacity(netsize), freq: Vec::with_capacity(netsize), samplefac: samplefac, netsize: colors }; this.init(pixels); this } /// Initializes the neuronal network and trains it with the supplied data. /// /// This method gets called by `Self::new`. pub fn init(&mut self, pixels: &[u8]) { self.network.clear(); self.colormap.clear(); self.bias.clear(); self.freq.clear(); let freq = (self.netsize as f64).recip(); for i in 0..self.netsize { let tmp = (i as f64) * 256.0 / (self.netsize as f64); // Sets alpha values at 0 for dark pixels. let a = if i < 16 { i as f64 * 16.0 } else { 255.0 }; self.network.push(Neuron { r: tmp, g: tmp, b: tmp, a: a}); self.colormap.push(Color { r: 0, g: 0, b: 0, a: 255 }); self.freq.push(freq); self.bias.push(0.0); } self.learn(pixels); self.build_colormap(); self.inxbuild(); } /// Maps the rgba-pixel in-place to the best-matching color in the color map. #[inline(always)] pub fn map_pixel(&self, pixel: &mut [u8]) { assert!(pixel.len() == 4); match (pixel[0], pixel[1], pixel[2], pixel[3]) { (r, g, b, a) => { let i = self.inxsearch(b, g, r, a); pixel[0] = self.colormap[i].r as u8; pixel[1] = self.colormap[i].g as u8; pixel[2] = self.colormap[i].b as u8; pixel[3] = self.colormap[i].a as u8; } } } /// Finds the best-matching index in the color map. /// /// `pixel` is assumed to be in RGBA format. #[inline(always)] pub fn index_of(&self, pixel: &[u8]) -> usize { assert!(pixel.len() == 4); match (pixel[0], pixel[1], pixel[2], pixel[3]) { (r, g, b, a) => { self.inxsearch(b, g, r, a) } } } /// Returns the RGBA color map calculated from the sample. pub fn color_map_rgba(&self) -> Vec { let mut map = Vec::with_capacity(self.netsize * 4); for entry in &self.colormap { map.push(entry.r as u8); map.push(entry.g as u8); map.push(entry.b as u8); map.push(entry.a as u8); } map } /// Returns the RGBA color map calculated from the sample. pub fn color_map_rgb(&self) -> Vec { let mut map = Vec::with_capacity(self.netsize * 3); for entry in &self.colormap { map.push(entry.r as u8); map.push(entry.g as u8); map.push(entry.b as u8); } map } /// Move neuron i towards biased (a,b,g,r) by factor alpha fn altersingle(&mut self, alpha: f64, i: i32, quad: Quad) { let n = &mut self.network[i as 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); } /// Move neuron adjacent neurons towards biased (a,b,g,r) by factor alpha fn alterneigh(&mut self, alpha: f64, rad: i32, i: i32, quad: Quad) { let lo = max(i - rad, 0); let hi = min(i + rad, self.netsize as i32); let mut j = i + 1; let mut k = i - 1; let mut q = 0; while (j < hi) || (k > lo) { let rad_sq = rad as f64 * rad as f64; let alpha = (alpha * (rad_sq - q as f64 * q as f64)) / rad_sq; q += 1; if j < hi { let p = &mut self.network[j as 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 += 1; } if k > lo { let p = &mut self.network[k as 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 -= 1; } } } /// 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]) fn contest (&mut self, b: f64, g: f64, r: f64, a: f64) -> i32 { use std::f64; let mut bestd = f64::MAX; let mut bestbiasd: f64 = bestd; let mut bestpos = -1; let mut bestbiaspos: i32 = bestpos; for i in 0..self.netsize { let bestbiasd_biased = bestbiasd + self.bias[i]; let mut dist; let n = &self.network[i]; dist = (n.b - b).abs(); dist += (n.r - r).abs(); if dist < bestd || dist < bestbiasd_biased { dist += (n.g - g).abs(); dist += (n.a - a).abs(); if dist < bestd {bestd=dist; bestpos=i as i32;} let biasdist = dist - self.bias [i]; if biasdist < bestbiasd {bestbiasd=biasdist; bestbiaspos=i as i32;} } self.freq[i] -= BETA * self.freq[i]; self.bias[i] += BETAGAMMA * self.freq[i]; } self.freq[bestpos as usize] += BETA; self.bias[bestpos as usize] -= BETAGAMMA; return bestbiaspos; } /// 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 fn learn(&mut self, pixels: &[u8]) { let initrad: i32 = self.netsize as i32/8; // for 256 cols, radius starts at 32 let radiusbiasshift: i32 = 6; let radiusbias: i32 = 1 << radiusbiasshift; let init_bias_radius: i32 = initrad*radiusbias; let mut bias_radius = init_bias_radius; let alphadec = 30 + ((self.samplefac-1)/3); let lengthcount = pixels.len() / CHANNELS; let samplepixels = lengthcount / self.samplefac as usize; // learning cycles let n_cycles = match self.netsize >> 1 { n if n <= 100 => 100, n => n}; let delta = match samplepixels / n_cycles { 0 => 1, n => n }; let mut alpha = INIT_ALPHA; let mut rad = bias_radius >> radiusbiasshift; if rad <= 1 {rad = 0}; let mut pos = 0; let step = *PRIMES.iter() .find(|&&prime| lengthcount % prime != 0) .unwrap_or(&PRIMES[3]); let mut i = 0; while i < samplepixels { let (r, g, b, a) = { let p = &pixels[CHANNELS * pos..][..CHANNELS]; (p[0] as f64, p[1] as f64, p[2] as f64, p[3] as f64) }; let j = self.contest (b, g, r, a); let alpha_ = (1.0 * alpha as f64) / INIT_ALPHA as f64; self.altersingle(alpha_, j, Quad { b: b, g: g, r: r, a: a }); if rad > 0 { self.alterneigh(alpha_, rad, j, Quad { b: b, g: g, r: r, a: a }) }; pos += step; while pos >= lengthcount { pos -= lengthcount }; i += 1; if i%delta == 0 { alpha -= alpha / alphadec; bias_radius -= bias_radius / RADIUS_DEC; rad = bias_radius >> radiusbiasshift; if rad <= 1 {rad = 0}; } } } /// initializes the color map fn build_colormap(&mut self) { for i in 0usize..self.netsize { self.colormap[i].b = clamp!(self.network[i].b.round() as i32); self.colormap[i].g = clamp!(self.network[i].g.round() as i32); self.colormap[i].r = clamp!(self.network[i].r.round() as i32); self.colormap[i].a = clamp!(self.network[i].a.round() as i32); } } /// Insertion sort of network and building of netindex[0..255] fn inxbuild(&mut self) { let mut previouscol = 0; let mut startpos = 0; for i in 0..self.netsize { let mut p = self.colormap[i]; let mut q; let mut smallpos = i; let mut smallval = p.g as usize; // index on g // find smallest in i..netsize-1 for j in (i + 1)..self.netsize { q = self.colormap[j]; if (q.g as usize) < smallval { // index on g smallpos = j; smallval = q.g as usize; // index on g } } q = self.colormap[smallpos]; // swap p (i) and q (smallpos) entries if i != smallpos { let mut j; j = q; q = p; p = j; self.colormap[i] = p; self.colormap[smallpos] = q; } // smallval entry is now in position i if smallval != previouscol { self.netindex[previouscol] = (startpos + i)>>1; for j in (previouscol + 1)..smallval { self.netindex[j] = i } previouscol = smallval; startpos = i; } } let max_netpos = self.netsize - 1; self.netindex[previouscol] = (startpos + max_netpos)>>1; for j in (previouscol + 1)..256 { self.netindex[j] = max_netpos }; // really 256 } /// Search for best matching color fn inxsearch(&self, b: u8, g: u8, r: u8, a: u8) -> usize { let mut bestd = 1 << 30; // ~ 1_000_000 let mut best = 0; // start at netindex[g] and work outwards let mut i = self.netindex[g as usize]; let mut j = if i > 0 { i - 1 } else { 0 }; while (i < self.netsize) || (j > 0) { if i < self.netsize { let p = self.colormap[i]; let mut e = p.g - g as i32; let mut dist = e*e; // inx key if dist >= bestd { break } else { e = p.b - b as i32; dist += e*e; if dist < bestd { e = p.r - r as i32; dist += e*e; if dist < bestd { e = p.a - a as i32; dist += e*e; if dist < bestd { bestd = dist; best = i;} } } i += 1; } } if j > 0 { let p = self.colormap[j]; let mut e = p.g - g as i32; let mut dist = e*e; // inx key if dist >= bestd { break } else { e = p.b - b as i32; dist += e*e; if dist < bestd { e = p.r - r as i32; dist += e*e; if dist < bestd { e = p.a - a as i32; dist += e*e; if dist < bestd { bestd = dist; best = j; } } } j -= 1; } } } best } }