| 1 | use super::Pixel; |
| 2 | |
| 3 | pub struct NeuQuantQuantiser { |
| 4 | weights: [[f64; 3]; 256], |
| 5 | freq: [f64; 256], |
| 6 | bias: [f64; 256], |
| 7 | factor: usize, |
| 8 | } |
| 9 | |
| 10 | const SPECIAL_NODES: usize = 2; |
| 11 | impl NeuQuantQuantiser { |
| 12 | pub fn new(factor: usize) -> Self { |
| 13 | let mut weights = [[0.0; 3]; 256]; |
| 14 | if SPECIAL_NODES > 1 { |
| 15 | weights[1] = [255.0; 3]; // for white |
| 16 | } |
| 17 | for i in SPECIAL_NODES..256 { |
| 18 | let w = 255.0 * ((i - SPECIAL_NODES) as f64) / ((256 - SPECIAL_NODES) as f64); |
| 19 | weights[i] = [w, w, w]; |
| 20 | } |
| 21 | Self { |
| 22 | weights, |
| 23 | freq: [1.0 / 256.0; 256], |
| 24 | bias: [0.0; 256], |
| 25 | factor, |
| 26 | } |
| 27 | } |
| 28 | fn update_node(&mut self, idx: usize, clr: &[f64; 3], alpha: f64) { |
| 29 | self.weights[idx][0] -= alpha * (self.weights[idx][0] - clr[0]); |
| 30 | self.weights[idx][1] -= alpha * (self.weights[idx][1] - clr[1]); |
| 31 | self.weights[idx][2] -= alpha * (self.weights[idx][2] - clr[2]); |
| 32 | } |
| 33 | fn update_neighbours(&mut self, idx: usize, clr: &[f64; 3], alpha: f64, radius: usize) { |
| 34 | let low = idx.saturating_sub(radius).max(SPECIAL_NODES - 1); |
| 35 | let high = (idx + radius).min(self.weights.len() - 1); |
| 36 | |
| 37 | let mut idx0 = idx + 1; |
| 38 | let mut idx1 = idx - 1; |
| 39 | let mut range = 0; |
| 40 | let sqradius = (radius * radius) as f64; |
| 41 | while (idx0 < high) || (idx1 > low) { |
| 42 | let sqrng = f64::from(range * range); |
| 43 | let a = alpha * (sqradius - sqrng) / sqradius; |
| 44 | range += 1; |
| 45 | if idx0 < high { |
| 46 | self.update_node(idx0, clr, a); |
| 47 | idx0 += 1; |
| 48 | } |
| 49 | if idx1 > low { |
| 50 | self.update_node(idx1, clr, a); |
| 51 | idx1 -= 1; |
| 52 | } |
| 53 | } |
| 54 | } |
| 55 | fn find_node(&mut self, clr: &[f64; 3]) -> usize { |
| 56 | for i in 0..SPECIAL_NODES { |
| 57 | if &self.weights[i] == clr { |
| 58 | return i; |
| 59 | } |
| 60 | } |
| 61 | let mut bestdist = std::f64::MAX; |
| 62 | let mut distidx = 0; |
| 63 | let mut bestbias = std::f64::MAX; |
| 64 | let mut biasidx = 0; |
| 65 | for i in SPECIAL_NODES..256 { |
| 66 | let dist = (self.weights[i][0] - clr[0]) * (self.weights[i][0] - clr[0]) |
| 67 | + (self.weights[i][1] - clr[1]) * (self.weights[i][1] - clr[1]) |
| 68 | + (self.weights[i][2] - clr[2]) * (self.weights[i][2] - clr[2]); |
| 69 | if bestdist > dist { |
| 70 | bestdist = dist; |
| 71 | distidx = i; |
| 72 | } |
| 73 | let biasdiff = dist - self.bias[i]; |
| 74 | if bestbias > biasdiff { |
| 75 | bestbias = biasdiff; |
| 76 | biasidx = i; |
| 77 | } |
| 78 | self.freq[i] -= self.freq[i] / 1024.0; |
| 79 | self.bias[i] += self.freq[i]; |
| 80 | } |
| 81 | self.freq[distidx] += 1.0 / 1024.0; |
| 82 | self.bias[distidx] -= 1.0; |
| 83 | biasidx |
| 84 | } |
| 85 | pub fn learn(&mut self, src: &[Pixel]) { |
| 86 | let mut bias_radius = (256 / 8) << 6; |
| 87 | let alphadec = (30 + (self.factor - 1) / 3) as f64; |
| 88 | let initial_alpha = f64::from(1 << 10); |
| 89 | |
| 90 | let npixels = src.len(); |
| 91 | |
| 92 | let mut radius = bias_radius >> 6; |
| 93 | if radius == 1 { radius = 0 }; |
| 94 | let samples = npixels / self.factor; |
| 95 | let delta = samples / 100; |
| 96 | let mut alpha = initial_alpha; |
| 97 | |
| 98 | let mut pos = 0; |
| 99 | const PRIMES: [usize; 4] = [ 499, 491, 487, 503 ]; |
| 100 | let mut step = PRIMES[3]; |
| 101 | for prime in PRIMES.iter().rev() { |
| 102 | if npixels % *prime != 0 { |
| 103 | step = *prime; |
| 104 | } |
| 105 | } |
| 106 | |
| 107 | for i in 0..samples { |
| 108 | let clr = [f64::from(src[pos].r), f64::from(src[pos].g), f64::from(src[pos].b)]; |
| 109 | let idx = self.find_node(&clr); |
| 110 | if idx >= SPECIAL_NODES { |
| 111 | let new_alpha = alphadec / initial_alpha; |
| 112 | self.update_node(idx, &clr, new_alpha); |
| 113 | if radius > 0 { |
| 114 | self.update_neighbours(idx, &clr, new_alpha, radius); |
| 115 | } |
| 116 | } |
| 117 | pos = (pos + step) % npixels; |
| 118 | if (i + 1) % delta == 0 { |
| 119 | alpha -= alpha / alphadec; |
| 120 | bias_radius -= bias_radius / 30; |
| 121 | radius = bias_radius >> 6; |
| 122 | if radius == 1 { radius = 0 }; |
| 123 | } |
| 124 | } |
| 125 | } |
| 126 | pub fn make_pal(&self, pal: &mut [[u8; 3]; 256]) { |
| 127 | for (pal, node) in pal.iter_mut().zip(self.weights.iter()) { |
| 128 | pal[0] = (node[0] + 0.5).max(0.0).min(255.0) as u8; |
| 129 | pal[1] = (node[1] + 0.5).max(0.0).min(255.0) as u8; |
| 130 | pal[2] = (node[2] + 0.5).max(0.0).min(255.0) as u8; |
| 131 | } |
| 132 | } |
| 133 | } |