RapidTag

⚠️ Work in progress — not production-ready. RapidTag is under active development and pre-1.0. APIs, behavior, and results may change without notice. Use it for research, evaluation, and prototyping, and validate it against your own data before relying on it for anything critical.

Fast, pure-Rust fiducial marker detection for realtime. RapidTag is a from-scratch Rust reimplementation of OpenCV's ArUco / AprilTag marker detector, exposed to Python via maturin / PyO3 — with no OpenCV runtime dependency. It reproduces OpenCV's detections down to the pixel while running substantially faster.

Why RapidTag

• ⚡ Faster than OpenCV — ~1.6× faster for realtime single-camera detection, and up to ~3.4× faster for multi-camera and offline batches (scales across cores).
• 🎯 A true drop-in for accuracy — corners match OpenCV to 0.0000 px, so any downstream pose or tracking is identical (see below).
• 🧵 Scales with your cores — a batch API processes many frames (a stereo pair, or a whole recording) across all cores with the GIL released.
• 📦 No OpenCV needed at runtime — the detection pipeline and marker dictionaries are implemented in pure Rust on top of image / nalgebra.

Performance

Measured on real dual-camera data (1280×800 monochrome, AprilTag 36h11):

Same detections, less time — the speedup comes from a leaner detection pipeline, not from skipping work.

Heterogeneous (big.LITTLE) ARM CPUs

On big.LITTLE SoCs, RapidTag pins its worker pool to the fast cores automatically. Every detect call waits on its slowest parallel task, so letting the OS place even one task on a little core caps the whole batch at little-core speed — on a Radxa Dragon Q6A (QCS6490: 4× A55 @1.9 GHz + 4× A78 @2.4–2.7 GHz) auto-pinning takes a single 1280×800 detect from 68 fps to 211 fps, and a dual-camera pair from 66 to 134 pairs/s. The calling thread and any capture/IO threads are left unpinned, keeping the little cores for them. Homogeneous CPUs and non-Linux hosts are unaffected.

Override with RAPIDTAG_CORES: an explicit core list (4-7, 0,2,4) or all to disable pinning. RAYON_NUM_THREADS still controls pool size when set.

Two further board-level settings are worth it on embedded targets:

• build for the exact CPU with scripts/build-board.sh (-C target-cpu=native)
• switch the fast cores' cpufreq governor to performance — bursty per-frame work never keeps schedutil clocked up (on the Q6A this is another ~1.7×: 123 → 211 fps single, 102 → 134 pairs/s dual)

Accuracy — verified as a drop-in replacement

Because RapidTag's corners are pixel-identical to OpenCV's, feeding them into the exact same pose pipeline (cv2.solvePnP) yields the same trajectory. Across a ~1,700-frame stereo recording of a moving AprilTag, the recovered position from RapidTag vs OpenCV corners is indistinguishable — a median difference of 0.00 mm on both cameras.

Install

pip install rapidtag

Prebuilt wheels are published for:

Wheels are abi3 (one wheel works on CPython 3.9+). If no wheel matches, pip builds from the source distribution (needs a Rust toolchain).

Building from source in your own project (opt-in)

By default uv add rapidtag / pip install rapidtag install the prebuilt portable wheel — no Rust toolchain needed. There is no package-side flag or extra (e.g. rapidtag[native]) that changes this; forcing a source build is always an installer-side opt-in on your side, and it needs a Rust toolchain (rustc/cargo) installed.

To make your project always compile rapidtag from source, add this to your pyproject.toml before installing:

[tool.uv]
no-binary-package = ["rapidtag"]

Then, to get CPU-native codegen for the machine you're installing on, set RUSTFLAGS at install time:

RUSTFLAGS="-C target-cpu=native" uv sync

Without RUSTFLAGS this still compiles a portable binary — functionally the same as the prebuilt wheel, just slower to install. The target-cpu=native build is not portable; don't redistribute it.

pip equivalents: pip install rapidtag --no-binary rapidtag, optionally prefixed with the same RUSTFLAGS.

Usage

import cv2            # only to load/generate images
import rapidtag

img = cv2.imread("scene.png")          # HxWx3 BGR, or HxW grayscale uint8

# --- realtime: one frame ---
corners, ids = rapidtag.detect_markers(img, "DICT_APRILTAG_36h11")
# corners: list of 4x2 [(x, y), ...] per marker (clockwise)
# ids:     list of marker ids, aligned with corners

# --- multi-camera / batch: process many frames across all cores ---
results = rapidtag.detect_markers_batch([cam0, cam1], "DICT_APRILTAG_36h11")
(c0, i0), (c1, i1) = results

# --- tunable parameters (same names/defaults as cv2.aruco.DetectorParameters) ---
p = rapidtag.DetectorParameters()
p.adaptive_thresh_constant = 7.0
p.detect_inverted_marker = True
corners, ids = rapidtag.detect_markers(img, "DICT_6X6_250", p)

print(rapidtag.predefined_dictionaries())   # list supported dictionary names

# --- generic calibration chessboard (9 columns x 6 rows of interior corners) ---
found, chessboard_corners = rapidtag.find_chessboard_corners(img, (9, 6))

# --- robust board / multi-marker pose with bad-correspondence rejection ---
pose = rapidtag.solve_pnp_ransac(
    object_points, image_points, camera_matrix, dist_coeffs,
    iterations=100, reprojection_error=3.0, confidence=0.99, seed=0,
)
if pose is not None:
    rvec, tvec, inlier_indices, reprojection_rmse = pose

# --- reusable rigid-body geometry; construct once from your calibration ---
marker_ids = sorted(int(marker_id) for marker_id in rigid_body_config["markers"])
body = rapidtag.RigidBody(
    rigid_body_config["meta"]["tag_size_m"],
    marker_ids,
    [rigid_body_config["markers"][str(i)]["rotation_marker_to_reference"]
     for i in marker_ids],
    [rigid_body_config["markers"][str(i)]["translation_marker_to_reference_m"]
     for i in marker_ids],
)
body_pose = rapidtag.estimate_rigid_body_pose(
    corners, ids, body, camera_matrix, dist_coeffs,
    iterations=100, reprojection_error=3.0,
)
if body_pose is not None:
    print(body_pose.rvec, body_pose.tvec)
    print(body_pose.used_marker_ids, body_pose.inlier_marker_ids)

Documentation

• Installation and source builds
• Marker and ChArUco detection
• Pose estimation and RANSAC
• Rigid-body multi-marker pose
• Radxa Dragon Q6A deployment
• Complete Python API reference
• Runnable examples

Python signatures are also shipped in rapidtag.pyi for IDEs and type checkers. Maintainers can build the internal Rust documentation with cargo doc --no-deps --document-private-items --open.

Supported dictionaries: all DICT_{4,5,6,7}X{4,5,6,7}_{50,100,250,1000}, DICT_ARUCO_ORIGINAL, DICT_ARUCO_MIP_36h12, and AprilTag DICT_APRILTAG_{16h5,25h9,36h10,36h11}.

Status

Implemented:

• ArUco / AprilTag marker detection (detectMarkers, CORNER_REFINE_NONE)
• Generic chessboard detection with sub-pixel corners (findChessboardCorners)
• ChArUco board detection using local marker homographies
• Iterative PnP, RANSAC PnP, rigid-body multi-marker pose, Rodrigues, point projection, and ChArUco board pose estimation

Not yet implemented: marker-corner refinement, grid boards, camera calibration, refineDetectedMarkers, and ChArUco's camera-aware interpolation path.

Build from source

maturin develop --release        # dev install into the current virtualenv
maturin build --release          # or build a wheel

The committed build uses portable CPU baselines so one wheel works everywhere. For a max-performance build tuned to your own machine (not portable — don't redistribute it):

RUSTFLAGS="-C target-cpu=native" maturin build --release

For a wheel tuned to a specific ARM64 board, use ./scripts/build-board.sh (it picks the right CPU flags so the published portable wheels stay CPU-agnostic).

Tests

python tests/crosscheck.py     # cross-validate vs cv2.aruco on synthetic scenes
python tests/crosscheck_chessboard.py  # generic chessboard parity vs OpenCV
python tests/crosscheck_charuco.py     # ChArUco parity vs OpenCV
python tests/crosscheck_pnp.py         # geometry and end-to-end pose parity
python tests/bench.py          # benchmark + parity on real camera data
python scripts/bench_ransac_dome.py    # multi-marker RANSAC benchmark on dome recordings

The verification figures above are reproduced by the scripts in scripts/ (pnp_opencv_vs_rapidtag.py, pnp_sanity.py).