diff options
Diffstat (limited to 'vendor/color_quant')
| -rw-r--r-- | vendor/color_quant/.cargo-checksum.json | 1 | ||||
| -rw-r--r-- | vendor/color_quant/CHANGELOG.md | 7 | ||||
| -rw-r--r-- | vendor/color_quant/Cargo.toml | 20 | ||||
| -rw-r--r-- | vendor/color_quant/LICENSE | 21 | ||||
| -rw-r--r-- | vendor/color_quant/README.md | 11 | ||||
| -rw-r--r-- | vendor/color_quant/src/lib.rs | 480 | ||||
| -rw-r--r-- | vendor/color_quant/src/math.rs | 10 |
7 files changed, 0 insertions, 550 deletions
diff --git a/vendor/color_quant/.cargo-checksum.json b/vendor/color_quant/.cargo-checksum.json deleted file mode 100644 index ea192c1..0000000 --- a/vendor/color_quant/.cargo-checksum.json +++ /dev/null @@ -1 +0,0 @@ -{"files":{"CHANGELOG.md":"0647ef6e3629446892fb530dc6f49977a3a81c4d943d0028b697ffa0d98565ad","Cargo.toml":"4ec438714084877d63c79e99191599a3694839c74842f81ca69de836a5889c75","LICENSE":"592dc80f1a865d20d61a2006a2d29ce34a2bc28cd7e868ab300fdeed6da154ca","README.md":"af03438f3b349f8e32ae2cf77c026948bd6493e7631ddd908ee0d225385c7894","src/lib.rs":"17a7ed7a6c994b475976558f3492c8890d089c1ee19f4ea3cd246c28145c895a","src/math.rs":"1fef0855d7d7defb8af69a033a2ce7e5f64367f48ba673cb4ce8e85e2006a124"},"package":"3d7b894f5411737b7867f4827955924d7c254fc9f4d91a6aad6b097804b1018b"}
\ No newline at end of file diff --git a/vendor/color_quant/CHANGELOG.md b/vendor/color_quant/CHANGELOG.md deleted file mode 100644 index 5609cf5..0000000 --- a/vendor/color_quant/CHANGELOG.md +++ /dev/null @@ -1,7 +0,0 @@ -## 1.1.0 - -- Unify with `image::math::nq` as per https://github.com/image-rs/image/issues/1338 (https://github.com/image-rs/color_quant/pull/10) - - A new method `lookup` from `image::math::nq` is added - - More references in docs - - Some style improvements and better names for functions borrowed from `image::math::nq` -- Replace the internal `clamp!` macro with the `clamp` function (https://github.com/image-rs/color_quant/pull/8) diff --git a/vendor/color_quant/Cargo.toml b/vendor/color_quant/Cargo.toml deleted file mode 100644 index 3bd3f2d..0000000 --- a/vendor/color_quant/Cargo.toml +++ /dev/null @@ -1,20 +0,0 @@ -# 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 = "1.1.0" -authors = ["nwin <nwin@users.noreply.github.com>"] -description = "Color quantization library to reduce n colors to 256 colors." -readme = "README.md" -license = "MIT" -repository = "https://github.com/image-rs/color_quant.git" diff --git a/vendor/color_quant/LICENSE b/vendor/color_quant/LICENSE deleted file mode 100644 index 2177b23..0000000 --- a/vendor/color_quant/LICENSE +++ /dev/null @@ -1,21 +0,0 @@ -The 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. diff --git a/vendor/color_quant/README.md b/vendor/color_quant/README.md deleted file mode 100644 index 0644ad6..0000000 --- a/vendor/color_quant/README.md +++ /dev/null @@ -1,11 +0,0 @@ -# Color quantization library -This library provides a color quantizer based on the [NEUQUANT](https://scientificgems.wordpress.com/stuff/neuquant-fast-high-quality-image-quantization/) -quantization algorithm by Anthony Dekker. - -### 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(); - diff --git a/vendor/color_quant/src/lib.rs b/vendor/color_quant/src/lib.rs deleted file mode 100644 index afd0d93..0000000 --- a/vendor/color_quant/src/lib.rs +++ /dev/null @@ -1,480 +0,0 @@ -/* -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) -//! -//! 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(); -//! ``` - -mod math; -use crate::math::clamp; - -use std::cmp::{max, min}; - -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<T> { - r: T, - g: T, - b: T, - a: T, -} - -type Neuron = Quad<f64>; -type Color = Quad<i32>; - -pub struct NeuQuant { - network: Vec<Neuron>, - colormap: Vec<Color>, - netindex: Vec<usize>, - bias: Vec<f64>, // bias and freq arrays for learning - freq: Vec<f64>, - 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.build_netindex(); - } - - /// 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); - let (r, g, b, a) = (pixel[0], pixel[1], pixel[2], pixel[3]); - let i = self.search_netindex(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); - let (r, g, b, a) = (pixel[0], pixel[1], pixel[2], pixel[3]); - self.search_netindex(b, g, r, a) - } - - /// Lookup pixel values for color at `idx` in the colormap. - pub fn lookup(&self, idx: usize) -> Option<[u8; 4]> { - self.colormap - .get(idx) - .map(|p| [p.r as u8, p.g as u8, p.b as u8, p.a as u8]) - } - - /// Returns the RGBA color map calculated from the sample. - pub fn color_map_rgba(&self) -> Vec<u8> { - 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<u8> { - 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 salter_single(&mut self, alpha: f64, i: i32, quad: Quad<f64>) { - 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 alter_neighbour(&mut self, alpha: f64, rad: i32, i: i32, quad: Quad<f64>) { - 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.salter_single(alpha_, j, Quad { b, g, r, a }); - if rad > 0 { - self.alter_neighbour(alpha_, rad, j, Quad { b, g, r, 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 build_netindex(&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 { - ::std::mem::swap(&mut p, &mut q); - 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 search_netindex(&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 - } -} diff --git a/vendor/color_quant/src/math.rs b/vendor/color_quant/src/math.rs deleted file mode 100644 index 369c2ad..0000000 --- a/vendor/color_quant/src/math.rs +++ /dev/null @@ -1,10 +0,0 @@ -#[inline] -pub(crate) fn clamp(a: i32) -> i32 { - if a < 0 { - 0 - } else if a > 255 { - 255 - } else { - a - } -} |