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pub fn find_nearest(pix: &[u8], pal: &[[u8; 3]; 256]) -> usize {
let mut bestidx = 0;
let mut bestdist = std::i32::MAX;
for (idx, entry) in pal.iter().enumerate() {
let dist0 = i32::from(pix[0]) - i32::from(entry[0]);
let dist1 = i32::from(pix[1]) - i32::from(entry[1]);
let dist2 = i32::from(pix[2]) - i32::from(entry[2]);
if (dist0 | dist1 | dist2) == 0 {
return idx;
}
let dist = dist0 * dist0 + dist1 * dist1 + dist2 * dist2;
if bestdist > dist {
bestdist = dist;
bestidx = idx;
}
}
bestidx
}
pub struct LocalSearch {
pal: [[u8; 3]; 256],
db: Vec<Vec<[u8; 4]>>,
}
impl LocalSearch {
fn quant(key: [u8; 3]) -> usize {
(((key[0] >> 3) as usize) << 10) |
(((key[1] >> 3) as usize) << 5) |
((key[2] >> 3) as usize)
}
pub fn new(in_pal: &[[u8; 3]; 256]) -> Self {
let mut db = Vec::with_capacity(1 << 15);
let pal = *in_pal;
for _ in 0..(1 << 15) {
db.push(Vec::new());
}
for (i, palentry) in pal.iter().enumerate() {
let r0 = (palentry[0] >> 3) as usize;
let g0 = (palentry[1] >> 3) as usize;
let b0 = (palentry[2] >> 3) as usize;
for r in r0.saturating_sub(1)..=(r0 + 1).min(31) {
for g in g0.saturating_sub(1)..=(g0 + 1).min(31) {
for b in b0.saturating_sub(1)..=(b0 + 1).min(31) {
let idx = (r << 10) | (g << 5) | b;
db[idx].push([palentry[0], palentry[1], palentry[2], i as u8]);
}
}
}
}
Self { pal, db }
}
fn dist(a: &[u8; 4], b: [u8; 3]) -> u32 {
let d0 = i32::from(a[0]) - i32::from(b[0]);
let d1 = i32::from(a[1]) - i32::from(b[1]);
let d2 = i32::from(a[2]) - i32::from(b[2]);
(d0 * d0 + d1 * d1 + d2 * d2) as u32
}
pub fn search(&self, pix: [u8; 3]) -> usize {
let idx = Self::quant(pix);
let mut best_dist = std::u32::MAX;
let mut best_idx = 0;
let mut count = 0;
for clr in self.db[idx].iter() {
let dist = Self::dist(clr, pix);
count += 1;
if best_dist > dist {
best_dist = dist;
best_idx = clr[3] as usize;
if dist == 0 { break; }
}
}
if count > 0 {
best_idx
} else {
find_nearest(&pix, &self.pal)
}
}
}
struct KDNode {
key: [u8; 3],
comp: u8,
idx: u8,
child0: usize,
child1: usize,
}
pub struct KDTree {
nodes: Vec<KDNode>,
}
fn avg_u8(a: u8, b: u8) -> u8 {
(a & b) + ((a ^ b) >> 1)
}
impl KDTree {
pub fn new(pal: &[[u8; 3]; 256]) -> Self {
let mut npal = [[0; 4]; 256];
for i in 0..256 {
npal[i][0] = pal[i][0];
npal[i][1] = pal[i][1];
npal[i][2] = pal[i][2];
npal[i][3] = i as u8;
}
let mut tree = Self { nodes: Vec::with_capacity(512) };
tree.build(&mut npal, 0, 256, 1024, false);
tree
}
fn build(&mut self, pal: &mut [[u8; 4]; 256], start: usize, end: usize, root: usize, child0: bool) {
if start + 1 == end {
let key = [pal[start][0], pal[start][1], pal[start][2]];
let newnode = KDNode { key, comp: 0, idx: pal[start][3], child0: 0, child1: 0 };
let cur_node = self.nodes.len();
self.nodes.push(newnode);
if child0 {
self.nodes[root].child0 = cur_node;
} else {
self.nodes[root].child1 = cur_node;
}
return;
}
let mut min = [255u8; 3];
let mut max = [0u8; 3];
for i in start..end {
for comp in 0..3 {
min[comp] = min[comp].min(pal[i][comp]);
max[comp] = max[comp].max(pal[i][comp]);
}
}
let dr = max[0] - min[0];
let dg = max[1] - min[1];
let db = max[2] - min[2];
let med = [avg_u8(min[0], max[0]), avg_u8(min[1], max[1]), avg_u8(min[2], max[2])];
let comp = if dr > dg && dr > db {
0
} else if db > dr && db > dg {
2
} else {
1
};
let pivot = Self::reorder(&mut pal[start..end], comp, med[comp]) + start;
let newnode = KDNode { key: med, comp: comp as u8, idx: 0, child0: 0, child1: 0 };
let cur_node = self.nodes.len();
self.nodes.push(newnode);
if root != 1024 {
if child0 {
self.nodes[root].child0 = cur_node;
} else {
self.nodes[root].child1 = cur_node;
}
}
self.build(pal, start, pivot, cur_node, true);
self.build(pal, pivot, end, cur_node, false);
}
fn reorder(pal: &mut[[u8; 4]], comp: usize, med: u8) -> usize {
let mut start = 0;
let mut end = pal.len() - 1;
while start < end {
while start < end && pal[start][comp] <= med {
start += 1;
}
while start < end && pal[end][comp] > med {
end -= 1;
}
if start < end {
pal.swap(start, end);
start += 1;
end -= 1;
}
}
start
}
pub fn search(&self, pix: [u8; 3]) -> usize {
let mut idx = 0;
loop {
let cnode = &self.nodes[idx];
if cnode.child0 == 0 {
return cnode.idx as usize;
}
let nidx = if cnode.key[cnode.comp as usize] >= pix[cnode.comp as usize] { cnode.child0 } else { cnode.child1 };
idx = nidx;
}
}
}
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