//! 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://www.acm.org/~dekker/NEUQUANT.HTML

/* 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://www.acm.org/~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.
 *
 *
 * Incorporated bugfixes and alpha channel handling from pngnq
 * http://pngnq.sourceforge.net
 *
 */

use std::num::Float;

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 = 1.0/1024.0;
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(Copy)]
struct Neuron {
    r: f64,
    g: f64,
    b: f64,
    a: f64,
}

#[derive(Copy)]
struct Color {
    r: i32,
    g: i32,
    b: i32,
    a: i32,
}

macro_rules! clamp(
    ($x:expr) => (match $x {
        x if x < 0 => 0,
        x if x > 255 => 255,
        x => x
    })
);

macro_rules! abs(
    ($x:expr) => (match $x {
        x if x < 0.0 => -x,
        x => x
    })
);

/// Neural network color quantizer
pub struct NeuQuant {
    network: Vec<Neuron>,
    colormap: Vec<Color>,
    netindex: Vec<usize>, // for network lookup - really 256
    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
    pub fn new(samplefac: i32, colors: usize, pixels: &[u8]) -> NeuQuant {
        let netsize = colors;
        let mut this = NeuQuant {
            network: Vec::with_capacity(netsize),
            colormap: Vec::with_capacity(netsize),
            netindex: Vec::with_capacity(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
    pub fn init(&mut self, pixels: &[u8]) {
        self.network.clear();
        self.colormap.clear();
        self.netindex.clear();
        self.bias.clear();
        self.freq.clear();
        let freq = 1.0/self.netsize as f64;
        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);
        }
        for _ in 0..256 {
            self.netindex.push(0);
        }
        self.learn(pixels);
        self.build_colormap();
        self.inxbuild();
    }

    /// Maps the pixel in-place to the best-matching color in the color map
    #[inline(always)]
    pub fn map_pixel(&self, pixel: &mut [u8]) {
        match pixel {
            [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;
            }
            _ => panic!()
        }
    }

    /// Finds the best-matching index in the color map for `pixel`
    #[inline(always)]
    pub fn index_of(&self, pixel: &[u8]) -> usize {
        match pixel {
            [r, g, b, a] => {
                self.inxsearch(b, g, r, a)
            }
            _ => panic!()
        }
    }

    /*
    /// Returns the color map in the format [r, g, b, r, g, ...]
    pub fn colormap_rgb(&mut self, pixel: &mut [u8]) -> Vec<u8> {
        self.colormap.iter().flat_map(|&c| [c.r as u8, c.g as u8, c.b as u8].iter().map(|&v| v)).collect()
    }*/

    /// Move neuron i towards biased (a,b,g,r) by factor alpha
    fn altersingle(&mut self, alpha: f64, i: i32, b: f64, g: f64, r: f64, a: f64) {
        // Move neuron i towards biased (b,g,r) by factor alpha
        let n = &mut self.network[i as usize]; // alter hit neuron
        n.b -= alpha*(n.b - b);
        n.g -= alpha*(n.g - g);
        n.r -= alpha*(n.r - r);
        n.a -= alpha*(n.a - a);
    }

    /// Move neuron adjacent neurons towards biased (a,b,g,r) by factor alpha
    fn alterneigh(&mut self, alpha: f64, rad: i32, i: i32, b: f64, g: f64, r: f64, a: f64) {

        let mut lo = i-rad;   if lo<0 {lo=0};
        let mut hi = i+rad;   if hi>self.netsize as i32 {hi=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 - b);
                p.g -= alpha*(p.g - g);
                p.r -= alpha*(p.r - r);
                p.a -= alpha*(p.a - a);
                j += 1;
            }
            if k>lo {
                let p = &mut self.network[k as usize];
                p.b -= alpha*(p.b - b);
                p.g -= alpha*(p.g - g);
                p.r -= alpha*(p.r - r);
                p.a -= alpha*(p.a - 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 {
        let mut bestd = Float::max_value();
        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  = abs!(n.b - b);
            dist += abs!(n.r - r);
            if dist < bestd || dist < bestbiasd_biased {
                dist += abs!(n.g - g);
                dist += abs!(n.a - a);
                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, b, g, r, a);
            if rad > 0 {self.alterneigh (alpha_, rad, j, b, g, r, a)};   // alter neighbours

            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!((0.5 + self.network[i].b) as i32);
            self.colormap[i].g = clamp!((0.5 + self.network[i].g) as i32);
            self.colormap[i].r = clamp!((0.5 + self.network[i].r) as i32);
            self.colormap[i].a = clamp!((0.5 + self.network[i].a) 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
    }
}
