Deleted vendor folder

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
-    }
-}