feat: QR 码解码器 — 从零手写的完整解码流水线
新增 core/src/decoder/ 模块（9 文件，~1500 行）： - bch.rs: BCH(15,5)+BCH(18,6) 查表解码（32+64 有效码字，t≤3） - format.rs: 从矩阵读取格式信息(EC+掩码)+版本信息（2 副本容错） - extract.rs: 逆向蛇形排列提取数据码字 - deinterleave.rs: 逆向 RS 数据交错 - rs_decode.rs: RS 纠错流水线（伴随式→BM→Chien→Forney） - mode_decode.rs: 逆向 4 种编码模式（数字/字母/字节/汉字 Shift JIS） - detect.rs: 定位图案检测（1:1:3:1:1 比例+交叉验证+聚类） - image.rs: 图像加载+灰度二值化（PNG/JPEG/WebP） - mod.rs: 顶层 API（decode_image + decode_matrix） 修改已有文件： - core: galois.rs 表 pub(crate), 新增 poly_eval(); reed_solomon 公开内部函数 - cli: 新增 --decode <file> 解码模式 - web: 新增 POST /api/decode（multipart file upload） 测试: 72 passed (58 原有 + 14 新增 decoder 测试)
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//! QR 码定位图案检测与采样
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//!
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//! 在二值化图像中检测 3 个定位图案（1:1:3:1:1 比例），
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//! 构建模块采样网格，提取布尔矩阵。
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/// 定位图案检测结果（图像坐标，原点左上角）
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struct FinderMatch {
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cx: usize, // 中心 X
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cy: usize, // 中心 Y
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size: usize, // 探测器边长（像素）
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}
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/// QR 码检测结果
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#[allow(dead_code)]
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pub(crate) struct DetectResult {
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pub modules: Vec<Vec<bool>>, // 布尔矩阵（含静区）
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pub version_estimate: u8,
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}
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/// 水平扫描查找 1:1:3:1:1 比例
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fn scan_row(gray: &[Vec<bool>], row: usize) -> Vec<(usize, usize)> {
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// （列号，运行长度）
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let mut runs: Vec<(usize, usize)> = Vec::new();
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let width = if gray.is_empty() { 0 } else { gray[0].len() };
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let mut col = 0;
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while col < width {
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let current = gray[row][col];
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let mut run_len = 0;
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while col < width && gray[row][col] == current {
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run_len += 1;
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col += 1;
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}
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runs.push((col - run_len, run_len));
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}
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// 找 5 连段符合 1:1:3:1:1 比例
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let mut centers: Vec<(usize, usize)> = Vec::new();
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for i in 0..runs.len().saturating_sub(4) {
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let r0 = runs[i].1 as f32;
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let r1 = runs[i + 1].1 as f32;
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let r2 = runs[i + 2].1 as f32;
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let r3 = runs[i + 3].1 as f32;
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let r4 = runs[i + 4].1 as f32;
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let avg = (r0 + r1 + r2 + r3 + r4) / 5.0;
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if avg < 2.0 {
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continue;
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}
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// 检查比例容差 ±40%
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let tolerance = 0.4;
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let check = |v: f32, expected: f32| (v - expected * avg).abs() < avg * tolerance;
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if check(r0, 1.0) && check(r1, 1.0) && check(r2, 3.0) && check(r3, 1.0) && check(r4, 1.0) {
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let cx = runs[i + 2].0 + runs[i + 2].1 / 2;
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centers.push((cx, row));
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}
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}
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centers
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}
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/// 垂直扫描查找 1:1:3:1:1 比例
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fn scan_col(gray: &[Vec<bool>], col: usize) -> Vec<(usize, usize)> {
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let height = gray.len();
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let mut runs: Vec<(usize, usize)> = Vec::new();
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let mut row = 0;
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while row < height {
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let current = gray[row][col];
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let mut run_len = 0;
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while row < height && gray[row][col] == current {
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run_len += 1;
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row += 1;
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}
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runs.push((row - run_len, run_len));
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}
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let mut centers: Vec<(usize, usize)> = Vec::new();
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for i in 0..runs.len().saturating_sub(4) {
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let r0 = runs[i].1 as f32;
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let r1 = runs[i + 1].1 as f32;
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let r2 = runs[i + 2].1 as f32;
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let r3 = runs[i + 3].1 as f32;
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let r4 = runs[i + 4].1 as f32;
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let avg = (r0 + r1 + r2 + r3 + r4) / 5.0;
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if avg < 2.0 {
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continue;
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}
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let tolerance = 0.4;
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let check = |v: f32, expected: f32| (v - expected * avg).abs() < avg * tolerance;
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if check(r0, 1.0) && check(r1, 1.0) && check(r2, 3.0) && check(r3, 1.0) && check(r4, 1.0) {
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let cy = runs[i + 2].0 + runs[i + 2].1 / 2;
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centers.push((col, cy));
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}
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}
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centers
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}
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/// 检测 3 个定位图案（交叉验证水平+垂直扫描）
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fn find_finders(gray: &[Vec<bool>]) -> Option<[FinderMatch; 3]> {
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let height = gray.len();
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let width = if height > 0 { gray[0].len() } else { 0 };
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if width < 21 || height < 21 {
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return None;
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}
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// 水平扫描
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let mut h_centers: Vec<(usize, usize, usize)> = Vec::new(); // (cx, cy, size)
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for row in (0..height).step_by(2) {
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for (cx, cy) in scan_row(gray, row) {
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// 交叉验证：垂直扫描
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let v_matches = scan_col(gray, cx);
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if v_matches
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.iter()
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.any(|&(_, vy)| (vy as i32 - cy as i32).abs() < 5)
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{
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let size = estimate_finder_size(gray, cx, cy);
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h_centers.push((cx, cy, size));
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}
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}
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}
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if h_centers.len() < 3 {
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return None;
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}
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// 聚类取前 3 个
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let mut clusters: Vec<Vec<&(usize, usize, usize)>> = Vec::new();
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for c in &h_centers {
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let mut found = false;
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for cluster in &mut clusters {
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let avg_x: f64 = cluster.iter().map(|c| c.0 as f64).sum::<f64>() / cluster.len() as f64;
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let avg_y: f64 = cluster.iter().map(|c| c.1 as f64).sum::<f64>() / cluster.len() as f64;
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let dx = c.0 as f64 - avg_x;
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let dy = c.1 as f64 - avg_y;
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if (dx * dx + dy * dy).sqrt() < 20.0 {
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cluster.push(c);
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found = true;
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break;
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}
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}
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if !found {
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clusters.push(vec![c]);
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}
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}
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// 按聚类大小排序，取前 3
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clusters.sort_by_key(|c| -(c.len() as i32));
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if clusters.len() < 3 {
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return None;
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}
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let mut finders: Vec<FinderMatch> = Vec::new();
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for cluster in clusters.iter().take(3) {
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let avg_x = cluster.iter().map(|c| c.0).sum::<usize>() / cluster.len();
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let avg_y = cluster.iter().map(|c| c.1).sum::<usize>() / cluster.len();
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let avg_size = cluster.iter().map(|c| c.2).sum::<usize>() / cluster.len();
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finders.push(FinderMatch {
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cx: avg_x,
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cy: avg_y,
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size: avg_size,
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});
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}
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// 排序：左上、右上、左下
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finders.sort_by(|a, b| {
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let da = a.cx * a.cx + a.cy * a.cy; // 到原点的距离
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let db = b.cx * b.cx + b.cy * b.cy;
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da.cmp(&db)
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});
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// 区分右上（X 最大）和左下（Y 最大）
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if finders[1].cx < finders[2].cx {
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finders.swap(1, 2);
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}
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let f0 = finders.remove(0);
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let f1 = finders.remove(0);
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let f2 = finders.remove(0);
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Some([f0, f1, f2])
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}
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/// 估算定位图案大小（像素）
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fn estimate_finder_size(gray: &[Vec<bool>], cx: usize, cy: usize) -> usize {
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// 从中心点水平扫描连续暗像素
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let mut left = cx;
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while left > 0 {
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if cy < gray.len() && left < gray[0].len() && gray[cy][left] {
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left -= 1;
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} else {
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break;
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}
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}
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let mut right = cx;
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while right + 1 < gray[0].len() {
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if cy < gray.len() && gray[cy][right] {
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right += 1;
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} else {
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break;
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}
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}
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right - left
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}
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/// 从二值化图像中提取 QR 布尔矩阵
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pub(crate) fn detect_and_extract(gray: &[Vec<bool>]) -> Result<DetectResult, String> {
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let finders = find_finders(gray).ok_or("未找到 QR 码定位图案")?;
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let tl = &finders[0]; // top-left
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let tr = &finders[1]; // top-right
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let _bl = &finders[2]; // bottom-left
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// 估算模块大小
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let module_size = (tl.size + tr.size) / 14; // finder = 7 modules wide
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if module_size == 0 {
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return Err("模块大小估算为零".into());
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}
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// 估算版本
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let dx = tr.cx as f64 - tl.cx as f64;
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let dy = tr.cy as f64 - tl.cy as f64;
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let dist_px = (dx * dx + dy * dy).sqrt() as f32;
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let dist_modules = dist_px / module_size as f32;
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let ver = ((dist_modules as i32 - 14) / 4) as u8;
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let version = ver.clamp(1, 40);
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let size = 17 + version as usize * 4;
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// 从采样网格构建模块矩阵
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let mut modules: Vec<Vec<bool>> = Vec::with_capacity(size);
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for my in 0..size {
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let mut row = Vec::with_capacity(size);
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for mx in 0..size {
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// 从采样网格映射到图像像素坐标
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let px = tl.cx as f32 + (mx as f32 - 3.5) * module_size as f32;
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let py = tl.cy as f32 + (my as f32 - 3.5) * module_size as f32;
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let px = px.round() as i32;
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let py = py.round() as i32;
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let sample = if px >= 0
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&& py >= 0
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&& (py as usize) < gray.len()
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&& (px as usize) < gray[0].len()
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{
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gray[py as usize][px as usize]
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} else {
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false
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};
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row.push(sample);
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}
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modules.push(row);
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}
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Ok(DetectResult {
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modules,
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version_estimate: version,
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})
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}
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