diff --git a/openpilot/common/qrcode.py b/openpilot/common/qrcode.py
index 634beb94e0..895daece39 100644
--- a/openpilot/common/qrcode.py
+++ b/openpilot/common/qrcode.py
@@ -1,4 +1,7 @@
-"""Small QR encoder for the UI's byte-mode, error-correction-level-L codes."""
+"""QR code encoding, decoding, and UI textures."""
+
+import functools
+import itertools
 
 import numpy as np
 import pyray as rl
@@ -241,3 +244,496 @@ def make_texture(data: str, inverted: bool = False) -> rl.Texture:
   rl_image.mipmaps = 1
   rl_image.format = rl.PixelFormat.PIXELFORMAT_UNCOMPRESSED_R8G8B8A8
   return rl.load_texture_from_image(rl_image)
+
+
+# ---- Symbol structure for decoding ----
+
+
+class QRError(Exception):
+  pass
+
+
+_LEVELS = (1, 0, 3, 2)  # format info level bits -> column in _EC
+
+_MASKS = [
+  lambda i, j: (i + j) % 2 == 0,
+  lambda i, j: i % 2 == 0,
+  lambda i, j: j % 3 == 0,
+  lambda i, j: (i + j) % 3 == 0,
+  lambda i, j: (i // 2 + j // 3) % 2 == 0,
+  lambda i, j: (i * j) % 2 + (i * j) % 3 == 0,
+  lambda i, j: ((i * j) % 2 + (i * j) % 3) % 2 == 0,
+  lambda i, j: ((i + j) % 2 + (i * j) % 3) % 2 == 0,
+]
+
+_ALIGNMENT = np.ones((5, 5), dtype=bool)
+_ALIGNMENT[1:4, 1:4] = False
+_ALIGNMENT[2, 2] = True
+
+
+def _gf_inv(a: int) -> int:
+  return _EXP[-_LOG[a] % 255]
+
+
+@functools.lru_cache
+def _data_coords(version: int) -> tuple[np.ndarray, np.ndarray]:
+  """(rows, cols) of the data and error correction modules in placement order: two-column zigzag from the right."""
+  dim = version * 4 + 17
+  func = np.zeros((dim, dim), dtype=bool)  # finder, timing, alignment, format, and version modules
+  func[:9, :9] = func[:9, dim - 8:] = func[dim - 8:, :9] = True
+  func[6, :] = func[:, 6] = True
+  positions = _alignment_positions(version)
+  for r, c in itertools.product(positions, positions):
+    if (r, c) not in ((6, 6), (6, dim - 7), (dim - 7, 6)):
+      func[r - 2:r + 3, c - 2:c + 3] = True
+  if version >= 7:
+    func[:6, dim - 11:dim - 8] = func[dim - 11:dim - 8, :6] = True
+  ys = np.arange(dim)
+  rows, cols = [], []
+  # the vertical timing column is skipped, so the pairs left of it start at odd columns
+  for i, right in enumerate(col if col > 6 else col - 1 for col in range(dim - 1, 0, -2)):
+    r = np.repeat(ys[::-1] if i % 2 == 0 else ys, 2)
+    c = np.tile((right, right - 1), dim)
+    keep = ~func[r, c]
+    rows.append(r[keep])
+    cols.append(c[keep])
+  return np.concatenate(rows), np.concatenate(cols)
+
+
+# ---- Matrix decoding ----
+
+
+def _poly_eval(p: list[int], x: int) -> int:
+  # p is highest degree first
+  y = 0
+  for c in p:
+    y = _multiply(y, x) ^ c
+  return y
+
+
+_EXP_TABLE = np.array(_EXP)
+_LOG_TABLE = np.array([_LOG.get(v, 0) for v in range(256)])
+
+
+def _syndromes(msg: list[int], nsym: int) -> list[int]:
+  """syn[i] = msg(alpha^i), msg highest degree first."""
+  m = np.array(msg)
+  exponents = np.arange(nsym)[:, None] * (len(msg) - 1 - np.arange(len(msg)))
+  return np.bitwise_xor.reduce(_EXP_TABLE[(_LOG_TABLE[m] + exponents) % 255] * (m != 0), axis=1).tolist()
+
+
+def _rs_correct(msg: list[int], nsym: int) -> list[int]:
+  """Corrects up to nsym // 2 errors in a Reed-Solomon codeword, in place."""
+  n = len(msg)
+  syn = _syndromes(msg, nsym)
+  if not any(syn):
+    return msg
+
+  # Berlekamp-Massey, sigma is lowest degree first
+  sigma, prev, L, m, b = [1], [1], 0, 1, 1
+  for r in range(nsym):
+    d = syn[r]
+    for i in range(1, L + 1):
+      d ^= _multiply(sigma[i], syn[r - i])
+    if d == 0:
+      m += 1
+      continue
+    coef = _multiply(d, _gf_inv(b))
+    shifted = [0] * m + prev
+    saved = sigma[:]
+    sigma = sigma + [0] * max(0, len(shifted) - len(sigma))
+    for i, c in enumerate(shifted):
+      sigma[i] ^= _multiply(coef, c)
+    if 2 * L <= r:
+      L, prev, b, m = r + 1 - L, saved, d, 1
+    else:
+      m += 1
+  sigma = sigma[:L + 1]
+  if 2 * L > nsym:
+    raise QRError("too many errors")
+
+  # Chien search: codeword position p has locator alpha^(n-1-p)
+  positions = [p for p in range(n) if _poly_eval(sigma[::-1], _EXP[(p - n + 1) % 255]) == 0]
+  if len(positions) != L:
+    raise QRError("error locator mismatch")
+
+  # solve syn[i] = sum_k e_k * X_k^i for the magnitudes e_k
+  xlog = [(n - 1 - p) % 255 for p in positions]
+  A = [[_EXP[(xlog[k] * i) % 255] for k in range(L)] + [syn[i]] for i in range(L)]
+  for col in range(L):
+    piv = next((r for r in range(col, L) if A[r][col]), None)
+    if piv is None:
+      raise QRError("singular")
+    A[col], A[piv] = A[piv], A[col]
+    inv = _gf_inv(A[col][col])
+    A[col] = [_multiply(inv, v) for v in A[col]]
+    for r in range(L):
+      if r != col and A[r][col]:
+        f = A[r][col]
+        A[r] = [a ^ _multiply(f, c) for a, c in zip(A[r], A[col], strict=True)]
+  for k, p in enumerate(positions):
+    msg[p] ^= A[k][L]
+
+  if any(_syndromes(msg, nsym)):
+    raise QRError("uncorrectable")
+  return msg
+
+
+def _read_format(m: np.ndarray) -> int:
+  """Returns the closest format info (level bits << 3 | mask) from either copy."""
+  dim = m.shape[0]
+  copies = ([(8, i) for i in range(6)] + [(8, 7), (8, 8), (7, 8)] + [(5 - i, 8) for i in range(6)],
+            [(dim - 1 - i, 8) for i in range(7)] + [(8, dim - 8 + i) for i in range(8)])  # (row, col), msb first
+  candidates = []
+  for coords in copies:
+    bits = int("".join(str(int(m[r, c])) for r, c in coords), 2)
+    candidates += [((bits ^ f).bit_count(), i) for i, f in enumerate(_FORMATS)]
+  distance, fmt = min(candidates)
+  if distance > 3:
+    raise QRError("bad format info")
+  return fmt
+
+
+_ALNUM = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ $%*+-./:"
+
+_ECI_ENCODINGS = {
+  0: "cp437", 2: "cp437", 1: "iso8859-1", 3: "iso8859-1",
+  **{i + 2: f"iso8859-{i}" for i in range(2, 17) if i != 12},
+  20: "shift_jis", 21: "cp1250", 22: "cp1251", 23: "cp1252", 24: "cp1256",
+  25: "utf-16-be", 26: "utf-8", 27: "ascii", 170: "ascii", 28: "big5", 29: "gb18030", 30: "euc_kr",
+}
+
+
+class _Bits:
+  def __init__(self, data: list[int]):
+    self._value = int.from_bytes(bytes(data), "big")
+    self.remaining = len(data) * 8
+
+  def read(self, n: int) -> int:
+    if n > self.remaining:
+      raise QRError("bitstream underflow")
+    self.remaining -= n
+    return self._value >> self.remaining & (1 << n) - 1
+
+  def read_below(self, n: int, limit: int) -> int:
+    v = self.read(n)
+    if v >= limit:
+      raise QRError("value out of range")
+    return v
+
+
+def _parse_data(data: list[int], version: int) -> str:
+  bits = _Bits(data)
+  out: list[str] = []
+  encoding = None
+  band = 0 if version <= 9 else 1 if version <= 26 else 2
+  while bits.remaining >= 4:
+    mode = bits.read(4)
+    if mode == 0:
+      break
+    if mode == 7:  # ECI character set assignment
+      first = bits.read(8)
+      extra = 0 if first < 0x80 else 8 if first < 0xC0 else 16 if first < 0xE0 else -1  # 1, 2, or 3 byte assignment
+      if extra < 0:
+        raise QRError("bad ECI assignment")
+      assignment = (first & 0x7F >> extra // 8) << extra | bits.read(extra)
+      encoding = _ECI_ENCODINGS.get(assignment)
+      if encoding is None:
+        raise QRError(f"unsupported ECI assignment {assignment}")
+    elif mode == 1:
+      n = bits.read((10, 12, 14)[band])
+      while n > 0:
+        k = min(n, 3)  # 3 digits in 10 bits, the last 2 or 1 in 7 or 4
+        out.append(f"{bits.read_below((4, 7, 10)[k - 1], 10 ** k):0{k}d}")
+        n -= k
+    elif mode == 2:
+      n = bits.read((9, 11, 13)[band])
+      while n > 0:
+        k = min(n, 2)  # 2 characters in 11 bits, a last one in 6
+        v = bits.read_below((6, 11)[k - 1], 45 ** k)
+        out.append(_ALNUM[v // 45] * (k - 1) + _ALNUM[v % 45])
+        n -= k
+    elif mode == 4:
+      n = bits.read((8, 16, 16)[band])
+      segment = bytes(bits.read(8) for _ in range(n))
+      try:
+        out.append(segment.decode(encoding or "utf-8"))
+      except UnicodeDecodeError as e:
+        if encoding is not None:
+          raise QRError("invalid ECI byte segment") from e
+        out.append(segment.decode("latin-1"))
+    elif mode == 8:
+      n = bits.read((8, 10, 12)[band])
+      for _ in range(n):
+        v = bits.read(13)
+        c = (v // 0xC0) << 8 | v % 0xC0
+        c += 0x8140 if c < 0x1F00 else 0xC140
+        try:
+          out.append(c.to_bytes(2, "big").decode("shift_jis"))
+        except UnicodeDecodeError as e:
+          raise QRError("invalid Kanji character") from e
+    else:
+      raise QRError(f"unsupported mode {mode}")
+  return "".join(out)
+
+
+def decode_matrix(m: np.ndarray) -> str:
+  """Decodes a square boolean module matrix (True = dark) without a quiet zone."""
+  dim = m.shape[0]
+  if m.shape != (dim, dim) or dim % 4 != 1 or not 21 <= dim <= 177:
+    raise QRError("bad matrix size")
+  version = (dim - 17) // 4
+
+  fmt = _read_format(m)
+  level = _LEVELS[fmt >> 3]
+  rows, cols = _data_coords(version)
+  bits = m[rows, cols] ^ _MASKS[fmt & 7](rows, cols)
+  codewords = np.packbits(bits[:len(bits) // 8 * 8]).tolist()
+
+  ec, _ = _EC[version - 1][level]
+  lens = _block_lengths(version, level)
+  blocks = [[0] * (n + ec) for n in lens]
+  for (b, i), codeword in zip(_interleaved(version, level), codewords, strict=True):
+    blocks[b][i] = codeword
+
+  data: list[int] = []
+  for block, n in zip(blocks, lens, strict=True):
+    data += _rs_correct(block, ec)[:n]
+  return _parse_data(data, version)
+
+
+# ---- Image decoding ----
+
+
+def _box_sums(a: np.ndarray, radii: tuple[int, ...]) -> list[np.ndarray]:
+  """Sums over (2r + 1)^2 neighborhoods of the last two axes, edge padded, from one integral image."""
+  P = max(radii)
+  lead = [(0, 0)] * (a.ndim - 2)
+  cs = np.pad(np.cumsum(np.cumsum(np.pad(a, lead + [(P, P), (P, P)], mode="edge"), -2), -1), lead + [(1, 0), (1, 0)])
+  H, W = a.shape[-2:]
+  out = []
+  for r in radii:
+    lo, hi = P - r, P + r + 1
+    out.append(cs[..., hi:hi + H, hi:hi + W] - cs[..., lo:lo + H, hi:hi + W] - cs[..., hi:hi + H, lo:lo + W] + cs[..., lo:lo + H, lo:lo + W])
+  return out
+
+
+def _binarize(gray: np.ndarray) -> np.ndarray:
+  """Adaptive threshold: each pixel against the mean of the surrounding tiles that have contrast."""
+  h, w = gray.shape
+  if h < 21 or w < 21:
+    raise QRError("image too small")
+  B = max(8, min(h, w) // 128 * 2)
+  H, W = -(-h // B), -(-w // B)
+  padded = np.pad(gray, ((0, H * B - h), (0, W * B - w)), mode="edge")
+  # block statistics from a subsample are plenty
+  sub = np.ascontiguousarray(padded[::2, ::2].reshape(H, B // 2, W, B // 2).transpose(0, 2, 1, 3)).reshape(H, W, -1)
+  blocks = sub.sum(axis=2, dtype=np.uint32) / sub.shape[2]
+  known = sub.max(axis=2) - sub.min(axis=2) >= 32
+  # Flat tiles cannot estimate their own threshold: use the tiles with contrast nearby, then
+  # further out, then the global midrange. A flat tile is then all dark or all light.
+  est = np.full((H, W), (blocks.min() + blocks.max()) / 2)
+  filled = np.zeros((H, W), dtype=bool)
+  for total, count in _box_sums(np.stack((known * blocks, known.astype(float))), (2, 6)):
+    fill = ~filled & (count > 0)
+    est[fill] = total[fill] / count[fill]
+    filled |= fill
+  thr = np.where(known, np.minimum(est, 254) + 1, np.where(blocks <= est, 255, 0)).astype(np.uint8)
+  return (padded.reshape(H, B, W, B) < thr[:, None, :, None]).reshape(H * B, W * B)[:h, :w]
+
+
+class _Runs:
+  """Run-length table of a padded, flattened binary image with a per-pixel run index."""
+
+  def __init__(self, padded: np.ndarray):
+    self.flat = padded.ravel()
+    self.lines, self.stride = padded.shape
+    change = self.flat[1:] != self.flat[:-1]
+    self.starts = np.concatenate(([0], np.flatnonzero(change) + 1))
+    self.lengths = np.diff(np.append(self.starts, self.flat.size)).astype(np.int32)
+
+  def run_at(self, line: np.ndarray, pos: np.ndarray) -> np.ndarray:
+    """Index of the run containing the pixel at `pos` along `line`."""
+    return np.searchsorted(self.starts, line * self.stride + pos + 1, side="right") - 1
+
+  @staticmethod
+  def _match(lengths: list[np.ndarray], ratios: tuple[int, ...]) -> tuple[np.ndarray, np.ndarray]:
+    """Checks windows of runs against the ratios, given the length of each run. Returns (ok, module size)."""
+    S = sum(ratios)
+    total = sum(lengths[1:], start=lengths[0])
+    ok = total >= 2 * S  # modules need to be at least 2 px
+    for L, r in zip(lengths, ratios, strict=True):
+      ok &= np.abs(2 * S * L - 2 * r * total) <= r * total  # integer form of |L - r * total / S| <= r * total / (2 * S)
+    return ok, total / S
+
+  def scan(self, ratios: tuple[int, ...]) -> np.ndarray:
+    """Returns the indices of all dark runs starting a window of runs matching the ratios."""
+    n = len(ratios)
+    N = len(self.lengths) - n + 1
+    if N <= 0:
+      return np.zeros(0, dtype=int)
+    ok, _ = self._match([self.lengths[k:N + k] for k in range(n)], ratios)
+    ok &= self.flat[self.starts[:N]]
+    first = np.flatnonzero(ok)
+    return first[self.starts[first] // self.stride == self.starts[first + n - 1] // self.stride]
+
+  def check(self, first: np.ndarray, ratios: tuple[int, ...]) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+    """Checks the run windows starting at run index `first`. Returns (ok, center position along the line, module size)."""
+    n, half = len(ratios), len(ratios) // 2
+    ok = (first >= 0) & (first + n <= len(self.starts))
+    idx = np.clip(first[:, None] + np.arange(n), 0, len(self.starts) - 1)
+    matched, module = self._match([self.lengths[idx[:, k]] for k in range(n)], ratios)
+    ok &= matched & self.flat[self.starts[idx[:, 0]]]
+    ok &= self.starts[idx[:, 0]] // self.stride == self.starts[idx[:, -1]] // self.stride
+    center = self.starts[idx[:, half]] % self.stride - 1 + self.lengths[idx[:, half]] / 2
+    return ok, center, module
+
+
+def _find_patterns(binary: np.ndarray, ratios: tuple[int, ...]) -> list[tuple[float, float, float]]:
+  """Finds dark/light run patterns with the given module ratios. Returns (x, y, module size)."""
+  half = len(ratios) // 2
+  step = 2  # the center rows of a 2 px finder pattern still get scanned twice
+  rows_t = _Runs(np.pad(binary[::step], ((0, 0), (1, 1))))
+  first = rows_t.scan(ratios)
+  if len(first) == 0:
+    return []
+  _, cx, hmod = rows_t.check(first, ratios)
+  row = rows_t.starts[first] // rows_t.stride * step
+
+  xi = cx.astype(int)
+  xs, col = np.unique(xi, return_inverse=True)
+  cols_t = _Runs(np.pad(binary[:, xs].T, ((0, 0), (1, 1))))
+  ok, cy, vmod = cols_t.check(cols_t.run_at(col, row) - half, ratios)
+  ok &= (0.5 <= vmod / hmod) & (vmod / hmod <= 2)
+  line = np.clip(np.rint(cy / step), 0, rows_t.lines - 1).astype(int)
+  ok2, cx2, hmod2 = rows_t.check(rows_t.run_at(line, xi) - half, ratios)
+  ok &= ok2 & (0.5 <= hmod2 / vmod) & (hmod2 / vmod <= 2)
+
+  found: list[list[float]] = []  # [x, y, module, count]
+  for x, y, module in zip(cx2[ok], cy[ok], (hmod2[ok] + vmod[ok]) / 2, strict=True):
+    for f in found:
+      if abs(f[0] - x) <= f[2] and abs(f[1] - y) <= f[2] and 0.5 <= f[2] / module <= 2:
+        c = f[3]
+        f[0], f[1], f[2], f[3] = (f[0] * c + x) / (c + 1), (f[1] * c + y) / (c + 1), (f[2] * c + module) / (c + 1), c + 1
+        break
+    else:
+      found.append([x, y, module, 1])
+  found.sort(key=lambda f: -f[3])
+  return [(f[0], f[1], f[2]) for f in found if f[3] >= 2]
+
+
+def _pick_finders(patterns: list[tuple[float, float, float]]) -> tuple[np.ndarray, np.ndarray, np.ndarray, float]:
+  """Returns (top-left, top-right, bottom-left) centers and the module size of the most square-looking triple."""
+  best = None
+  for a, b, c in itertools.combinations(patterns[:10], 3):
+    mods = sorted((a[2], b[2], c[2]))
+    if mods[2] / mods[0] > 1.5:
+      continue
+    pts = [np.array(p[:2]) for p in (a, b, c)]
+    d = [np.linalg.norm(pts[(i + 1) % 3] - pts[(i + 2) % 3]) for i in range(3)]
+    tl = int(np.argmax(d))  # opposite the hypotenuse
+    p1, p2 = pts[(tl + 1) % 3], pts[(tl + 2) % 3]
+    v1, v2 = p1 - pts[tl], p2 - pts[tl]
+    n1, n2 = np.linalg.norm(v1), np.linalg.norm(v2)
+    if n1 == 0 or n2 == 0:
+      continue
+    cos = abs(np.dot(v1, v2)) / (n1 * n2)
+    if cos > 0.35 or not 0.6 <= n1 / n2 <= 1.6:
+      continue
+    score = cos + abs(np.log(n1 / n2)) + np.log(mods[2] / mods[0])
+    if best is not None and score >= best[0]:
+      continue
+    if v1[0] * v2[1] - v1[1] * v2[0] < 0:
+      p1, p2 = p2, p1
+    best = (score, pts[tl], p1, p2, float(sum(mods) / 3))
+  if best is None:
+    raise QRError("no finder patterns")
+  return best[1:]
+
+
+def _perspective(src: np.ndarray, dst: np.ndarray) -> np.ndarray:
+  """Homography mapping the four src points onto the four dst points."""
+  A = [row for (x, y), (u, v) in zip(src, dst, strict=True)
+       for row in ([x, y, 1, 0, 0, 0, -u * x, -u * y], [0, 0, 0, x, y, 1, -v * x, -v * y])]
+  try:
+    h = np.linalg.solve(np.array(A, dtype=float), np.asarray(dst, dtype=float).ravel())
+  except np.linalg.LinAlgError as e:
+    raise QRError("degenerate geometry") from e
+  return np.append(h, 1).reshape(3, 3)
+
+
+def _transform(H: np.ndarray, pts: np.ndarray) -> np.ndarray:
+  p = np.column_stack((pts, np.ones(len(pts)))) @ H.T
+  return p[:, :2] / p[:, 2:3]
+
+
+def _match_alignment(binary: np.ndarray, est: np.ndarray, offs: np.ndarray, r: int, module: float) -> np.ndarray | None:
+  h, w = binary.shape
+  dy = np.arange(max(0, int(est[1]) - r), min(h, int(est[1]) + r)) - est[1]
+  dx = np.arange(max(0, int(est[0]) - r), min(w, int(est[0]) + r)) - est[0]
+  if len(dy) == 0 or len(dx) == 0:
+    return None
+  y = np.rint(est[1] + dy[:, None, None] + offs[None, None, :, 1]).astype(int)
+  x = np.rint(est[0] + dx[None, :, None] + offs[None, None, :, 0]).astype(int)
+  valid = ((y >= 0) & (y < h) & (x >= 0) & (x < w)).all(axis=2)
+  samples = binary[np.clip(y, 0, h - 1), np.clip(x, 0, w - 1)]
+  score = np.where(valid, (samples == _ALIGNMENT.ravel()).sum(axis=2), 0)
+  if score.max() < 23:
+    return None
+  hits = np.argwhere(score == score.max())
+  centers = np.column_stack((est[0] + dx[hits[:, 1]], est[1] + dy[hits[:, 0]]))
+  closest = centers[np.argmin(np.linalg.norm(centers - est, axis=1))]
+  return centers[np.linalg.norm(centers - closest, axis=1) <= module / 2].mean(axis=0)
+
+
+def _locate_alignment(binary: np.ndarray, H: np.ndarray, center: float, module: float) -> np.ndarray | None:
+  """Template matches the 5x5 alignment pattern around its position estimated from H."""
+  grid = np.mgrid[-2:3, -2:3].reshape(2, -1).T[:, ::-1] + center  # (25, 2) module coords (x, y)
+  pts = _transform(H, grid)
+  # the affine estimate can be off in both position and local scale under perspective
+  for radius in (2, 4, 8, 16):
+    for scale in (1.0, 0.8, 1.25, 0.65, 1.5):
+      found = _match_alignment(binary, pts[12], (pts - pts[12]) * scale, int(module * radius), module)
+      if found is not None:
+        return found
+  return None
+
+
+def _sample(binary: np.ndarray, tl: np.ndarray, tr: np.ndarray, bl: np.ndarray, module: float, dim: int, use_alignment: bool) -> np.ndarray:
+  src = np.array([(3.5, 3.5), (dim - 3.5, 3.5), (3.5, dim - 3.5), (dim - 3.5, dim - 3.5)])
+  dst = np.array([tl, tr, bl, tr + bl - tl])
+  H = _perspective(src, dst)
+  if use_alignment and dim > 21:
+    align = _locate_alignment(binary, H, dim - 6.5, module)
+    if align is not None:
+      src[3], dst[3] = (dim - 6.5, dim - 6.5), align
+      H = _perspective(src, dst)
+
+  rows, cols = np.mgrid[0:dim, 0:dim]
+  pts = _transform(H, np.column_stack((cols.ravel() + 0.5, rows.ravel() + 0.5)))
+  xy = np.rint(pts).astype(int)
+  h, w = binary.shape
+  if (xy < 0).any() or (xy[:, 0] >= w).any() or (xy[:, 1] >= h).any():
+    raise QRError("code extends outside image")
+  return binary[xy[:, 1], xy[:, 0]].reshape(dim, dim)
+
+
+def decode(gray: np.ndarray) -> str | None:
+  """Decodes the QR code in a 2D uint8 grayscale image. Modules need to be at least 2 px.
+  Returns None if nothing could be decoded."""
+  try:
+    binary = _binarize(gray)
+    tl, tr, bl, module = _pick_finders(_find_patterns(binary, (1, 1, 3, 1, 1)))
+  except QRError:
+    return None
+
+  d = (np.linalg.norm(tr - tl) + np.linalg.norm(bl - tl)) / 2
+  dim = int(round((d / module + 7 - 17) / 4)) * 4 + 17
+  dims = [cand for cand in (dim, dim - 4, dim + 4) if 21 <= cand <= 177]
+  for cand, use_alignment, transpose in itertools.product(dims, (True, False), (False, True)):
+    try:
+      m = _sample(binary, tl, tr, bl, module, cand, use_alignment)
+      return decode_matrix(m.T if transpose else m)
+    except QRError:
+      pass
+  return None
diff --git a/openpilot/common/tests/fixtures/qrcode_0.npz b/openpilot/common/tests/fixtures/qrcode_0.npz
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index 0000000000..96f550e5f4
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diff --git a/openpilot/common/tests/fixtures/qrcode_1.npz b/openpilot/common/tests/fixtures/qrcode_1.npz
new file mode 100644
index 0000000000..8ddd170935
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diff --git a/openpilot/common/tests/fixtures/qrcode_2.npz b/openpilot/common/tests/fixtures/qrcode_2.npz
new file mode 100644
index 0000000000..e0c62e9f63
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diff --git a/openpilot/common/tests/fixtures/qrcode_3.npz b/openpilot/common/tests/fixtures/qrcode_3.npz
new file mode 100644
index 0000000000..8991156679
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diff --git a/openpilot/common/tests/fixtures/qrcode_eci.npz b/openpilot/common/tests/fixtures/qrcode_eci.npz
new file mode 100644
index 0000000000..93abae0bbe
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diff --git a/openpilot/common/tests/test_qrcode.py b/openpilot/common/tests/test_qrcode.py
index 6c7d8c6f47..bc664d2ee1 100644
--- a/openpilot/common/tests/test_qrcode.py
+++ b/openpilot/common/tests/test_qrcode.py
@@ -1,9 +1,178 @@
+import hashlib
+import math
+from pathlib import Path
+
+import numpy as np
+
 from openpilot.common import qrcode as qr
 from openpilot.common.test import OpenpilotTestCase
 
+LPA = "LPA:1$rsp.truphone.com$QRF-BETTERROAMING-PMRDGIR2EARDEIT5"
+
+
+# Matrices generated with python-qrcode 8.2, covering all versions and EC levels.
+# Packed fixtures keep the decoder tests independent of our encoder.
+FIXTURES = {}
+for path in Path(__file__).with_name("fixtures").glob("qrcode_*.npz"):
+  with np.load(path) as fixtures:
+    FIXTURES.update({key: fixtures[key] for key in fixtures.files})
+
+
+def fixture(key: str) -> np.ndarray:
+  bits = np.unpackbits(FIXTURES[key])
+  size = math.isqrt(len(bits))
+  return bits[:size * size].reshape(size, size).astype(bool)
+
+
+def render(matrix: np.ndarray, box: int = 6, border: int = 4) -> np.ndarray:
+  img = np.repeat(np.repeat(np.pad(matrix, border), box, axis=0), box, axis=1)
+  return np.where(img, 0, 255).astype(np.uint8)
+
+
+def make(data: str, version: int | None = None, level: int = 0, box: int = 6, border: int = 4):
+  matrix = fixture(hashlib.sha256(f"{version}:{level}:{data}".encode()).hexdigest())
+  return matrix, render(matrix, box, border)
+
+
+def warp(img: np.ndarray, H: np.ndarray) -> np.ndarray:
+  """Bilinear resampling through the output -> input homography H, white outside the image."""
+  h, w = img.shape
+  rows, cols = np.mgrid[0:h, 0:w]
+  pts = qr._transform(H, np.column_stack((cols.ravel() + 0.5, rows.ravel() + 0.5))) - 0.5
+  x0, y0 = np.floor(pts[:, 0]).astype(int), np.floor(pts[:, 1]).astype(int)
+  fx, fy = pts[:, 0] - x0, pts[:, 1] - y0
+  padded = np.pad(img.astype(float), 1, constant_values=255)
+
+  def at(y, x):
+    return padded[np.clip(y + 1, 0, h + 1), np.clip(x + 1, 0, w + 1)]
+
+  out = at(y0, x0) * (1 - fx) * (1 - fy) + at(y0, x0 + 1) * fx * (1 - fy) + at(y0 + 1, x0) * (1 - fx) * fy + at(y0 + 1, x0 + 1) * fx * fy
+  return np.clip(out, 0, 255).astype(np.uint8).reshape(h, w)
+
+
+def rotate(img: np.ndarray, angle: float) -> np.ndarray:
+  h, w = img.shape
+  t = np.radians(angle)
+  R = np.array([[np.cos(t), -np.sin(t)], [np.sin(t), np.cos(t)]])
+  center = np.array([w / 2, h / 2])
+  corners = np.array([(0, 0), (w, 0), (w, h), (0, h)], dtype=float)
+  return warp(img, qr._perspective((corners - center) @ R.T + center, corners))
+
 
 class TestQRCode(OpenpilotTestCase):
   def test_alignment_positions(self):
     assert qr._alignment_positions(7) == [6, 22, 38]
     assert qr._alignment_positions(32) == [6, 34, 60, 86, 112, 138]
     assert qr._alignment_positions(40) == [6, 30, 58, 86, 114, 142, 170]
+
+  def test_all_versions(self):
+    for version in range(1, 41):
+      for level in range(4):
+        with self.subTest(version=version, level=level):
+          data = "".join(chr(ord("a") + i % 26) for i in range(version))
+          matrix, img = make(data, version, level, box=3)
+          assert qr.decode_matrix(matrix) == data
+          assert qr.decode(img) == data
+
+  def test_modes(self):
+    for data in ["0123456789012345", "HELLO WORLD $1.50", LPA, "こんにちは", "ünïcødé", "mixed 123 ABC xyz"]:
+      with self.subTest(data=data):
+        matrix, img = make(data)
+        assert qr.decode_matrix(matrix) == data
+        assert qr.decode(img) == data
+
+  def test_error_correction(self):
+    matrix, _ = make(LPA, level=2)
+    rng = np.random.default_rng(0)
+    flipped = matrix.copy()
+    for r, c in rng.integers(9, matrix.shape[0] - 9, size=(40, 2)):
+      flipped[r, c] ^= True
+    assert qr.decode_matrix(flipped) == LPA
+
+  def test_large_modules(self):
+    for data in ["0123456789012345", "HELLO WORLD $1.50", LPA, "mixed 123 ABC xyz"]:
+      for box in [16, 20, 24, 32]:
+        for dark, light in [(0, 255), (60, 200), (140, 250)]:
+          with self.subTest(data=data, box=box, dark=dark):
+            _, img = make(data, box=box)
+            img = np.where(img == 0, dark, light).astype(np.uint8)
+            assert qr.decode(img) == data
+
+  def test_image_edges(self):
+    # a code touching the image edge must not lose the rows and columns left over from tiling
+    matrix, _ = make(LPA)
+    for size in (200, 203):
+      with self.subTest(size=size):
+        img = np.full((size, size), 255, dtype=np.uint8)
+        code = render(matrix, box=5, border=0)
+        img[size - code.shape[0]:, size - code.shape[1]:] = code
+        assert qr.decode(img) == LPA
+
+  def test_eci(self):
+    # qrcode_eci.npz: packed Segno 1.6.6 matrices, generated with mode='byte',
+    # eci=True, micro=False and the named encoding. Mixed also includes numeric,
+    # alphanumeric, and Kanji segments after changing the byte encoding twice.
+    cases = {
+      "iso8859-5": "Привет", "utf-16-be": "héllo", "utf-8": "こんにちは",
+      "shift_jis": "日本語", "cp1251": "Привет", "iso8859-1": "héllo",
+      "mixed": "hélloПривет日本語123ABC漢字",
+    }
+    for encoding, expected in cases.items():
+      with self.subTest(encoding=encoding):
+        matrix = fixture(encoding)
+        assert qr.decode_matrix(matrix) == expected
+        assert qr.decode(render(matrix)) == expected
+
+  def test_parse_data(self):
+    def parse(stream: str) -> str:
+      stream += '0' * (-len(stream) % 8)
+      return qr._parse_data([int(stream[i:i + 8], 2) for i in range(0, len(stream), 8)], 1)
+
+    def eci(assignment: str, payload: bytes = b'A') -> str:
+      return parse('0111' + assignment + '0100' + f'{len(payload):08b}' + ''.join(f'{b:08b}' for b in payload) + '0000')
+
+    # ASCII assignment 170 uses the two-byte ECI representation.
+    assert eci('1000000010101010') == 'A'
+    for assignment in ['00001110', '1000001111100111', '110000010000000000000000', '11100000']:
+      with self.subTest(assignment=assignment), self.assertRaises(qr.QRError):
+        eci(assignment)
+    with self.assertRaises(qr.QRError):
+      eci('00011010', b'\xff')  # Invalid UTF-8 must not fall back to Latin-1.
+
+    # out-of-range numeric, alphanumeric, and Kanji values are format errors, not crashes
+    for stream in ['0001' + '0000000011' + '1111111111', '0001' + '0000000010' + '1111111',
+                   '0010' + '000000010' + '11111111111', '0010' + '000000001' + '111111',
+                   '1000' + '00000001' + '0000000111111']:
+      with self.subTest(stream=stream), self.assertRaises(qr.QRError):
+        parse(stream)
+
+  def test_rotation(self):
+    for angle in [0, 90, 180, 270, 25, 110]:
+      with self.subTest(angle=angle):
+        _, img = make(LPA, box=8, border=12)
+        assert qr.decode(rotate(img, angle)) == LPA
+
+  def test_mirrored(self):
+    _, img = make(LPA)
+    assert qr.decode(img[:, ::-1]) == LPA
+
+  def test_perspective_and_noise(self):
+    _, img = make(LPA, box=10, border=8)
+    h, w = img.shape
+    corners = np.array([(40, 60), (w - 20, 30), (w - 60, h - 40), (30, h - 90)])
+    arr = warp(img, qr._perspective(corners, np.array([(0, 0), (w, 0), (w, h), (0, h)]))).astype(float)
+    rng = np.random.default_rng(1)
+    arr = arr * 0.6 + 60 + rng.normal(0, 12, arr.shape)  # low contrast + noise
+    # uneven lighting
+    arr += np.linspace(-40, 40, w)[None, :]
+    assert qr.decode(np.clip(arr, 0, 255).astype(np.uint8)) == LPA
+
+  def test_no_code(self):
+    rng = np.random.default_rng(2)
+    assert qr.decode(rng.integers(0, 256, size=(240, 320), dtype=np.uint8)) is None
+    assert qr.decode(np.full((240, 320), 200, dtype=np.uint8)) is None
+
+  def test_encoder_roundtrip(self):
+    for version in range(1, 21):
+      with self.subTest(version=version):
+        assert qr.decode_matrix(np.array(qr._Qr(version, b"hello").modules)) == "hello"