nanofractal 0.1.1

High-performance fiducial marker detection (ArUco Nano v6 + Fractal)

nanofractal

High-performance fiducial-marker detection for Python. nanofractal wraps two compact, header-only C++ detectors with nanobind:

• ArUco Nano v6 — square markers (ARUCO_MIP_36h12 and AprilTag 36h11).
• Fractal markers — nested markers that stay detectable under heavy occlusion and expose many inner corner correspondences for accurate, long-range pose.

It is built for speed: zero-copy NumPy ↔ cv::Mat, the GIL is released during detection, and a parallel batch API scales across cores.

single-frame detect():   ~0.43 ms @ 640x480   ~1.3 ms @ 1280x720   ~2.9 ms @ 1920x1080
batch detect_batch():    ~3.2x throughput on 4 threads

Measured on a desktop CPU with max_attempts=1; your numbers will vary.

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Installation

pip install nanofractal

Wheels bundle a minimal OpenCV, so no system OpenCV is required at runtime.

Build from source

You need a C++17 compiler, CMake ≥ 3.18 and a development OpenCV (core, imgproc, calib3d, features2d):

# Debian/Ubuntu
sudo apt-get install -y build-essential cmake libopencv-dev

pip install .

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Quick start

Inputs are plain NumPy uint8 arrays — either (H, W) grayscale or (H, W, 3) BGR, and C-contiguous (use np.ascontiguousarray if unsure). Any image loader works; the examples use OpenCV.

Detect ArUco / AprilTag markers

import cv2
import nanofractal as nf

image = cv2.imread("scene.png")                  # (H, W, 3) uint8 BGR
det = nf.ArucoDetector(nf.Dict.ARUCO_MIP_36h12)  # or nf.Dict.APRILTAG_36h11

res = det.detect(image)
print(res.ids)        # int32   (N,)       e.g. [ 7 42]
print(res.corners)    # float32 (N, 4, 2)  clockwise corners, subpixel

Estimate pose

estimate_pose runs solvePnP (IPPE) for every detected marker at once.

import numpy as np

camera_matrix = np.array([[600, 0, 320],
                          [0, 600, 240],
                          [0,   0,   1]], dtype=np.float64)
dist_coeffs = np.zeros(5, dtype=np.float64)

rvecs, tvecs = det.estimate_pose(res.corners, camera_matrix, dist_coeffs,
                                 marker_size=0.05)   # marker side in metres
# rvecs, tvecs: float64 (N, 3) — rotation (Rodrigues) and translation per marker

Detect fractal markers

fdet = nf.FractalDetector("FRACTAL_5L_6", marker_size=0.85)  # size in metres (optional)

res = fdet.detect(image)
print(res.ids, res.corners.shape)   # outer 4 corners of each fractal marker

Fractal pose + visualization (occlusion-robust)

FractalDetector.estimate_pose returns one marker pose (rvec, tvec, reproj_err) or None. It uses every visible inner and outer corner correspondence when available (accurate, robust to occlusion) and otherwise falls back to the four outer corners — so you never call solvePnP yourself or worry about the empty-inner-points case. reproj_err (RMS pixels) lets you gate noisy poses.

fdet = nf.FractalDetector("FRACTAL_5L_6", marker_size=0.85)  # size in metres

res = fdet.detect(image, with_inner_points=True)
pose = fdet.estimate_pose(res, camera_matrix, dist_coeffs)
if pose is not None:
    rvec, tvec, reproj_err = pose      # rvec, tvec: float64 (3,); reproj_err: px
    fdet.draw(image, res, camera_matrix, dist_coeffs, rvec, tvec)  # corners + axes

draw(image, result, ...) overlays marker outlines, ids and (given a pose) the frame axes in place — no cv2.polylines/drawFrameAxes boilerplate. Without a pose, fdet.draw(image, res) just draws the outlines.

The raw correspondences are still exposed if you prefer to run PnP yourself:

res.points_2d   # float32 (M, 2) image points  (None unless with_inner_points=True)
res.points_3d   # float32 (M, 3) object points (planar, z = 0)

Parallel batch (offline throughput)

Process many frames across a thread pool. The GIL is released, so it scales with cores. num_threads=0 uses all cores.

frames = [cv2.imread(p) for p in paths]            # list of uint8 arrays
results = det.detect_batch(frames, num_threads=0)  # list[DetectionResult]
for r in results:
    print(r.ids)

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API

ArucoDetector(dictionary=Dict.ARUCO_MIP_36h12, max_attempts=1)

• dictionary: Dict — ARUCO_MIP_36h12 or APRILTAG_36h11.
• max_attempts: int — retries per candidate with small corner jitter. 1 is fastest (real-time default); raise (up to ~10) for harder images.
• detect(image) -> DetectionResult
• detect_batch(images, num_threads=0) -> list[DetectionResult]
• estimate_pose(corners, camera_matrix, dist_coeffs, marker_size) -> (rvecs, tvecs) — corners is (N, 4, 2) float32; outputs are (N, 3) float64.

FractalDetector(config, marker_size=-1.0)

• config: str — one of FRACTAL_2L_6, FRACTAL_3L_6, FRACTAL_4L_6, FRACTAL_5L_6.
• marker_size: float — outer marker side in metres; if set, points_3d is returned in metres (otherwise normalized).
• detect(image, with_inner_points=False) -> DetectionResult
• detect_batch(images, num_threads=0) -> list[DetectionResult]
• estimate_pose(result, camera_matrix, dist_coeffs) -> (rvec, tvec, reproj_err) | None — single-marker pose; uses inner+outer points when ≥ 4, else the 4 outer corners; rvec/tvec are float64 (3,), reproj_err is RMS pixels.
• draw(image, result, camera_matrix=None, dist_coeffs=None, rvec=None, tvec=None, axis_length=None) -> image — draw outlines + ids (and frame axes when a pose is given) in place; image must be a writable BGR uint8 array.

DetectionResult

field	dtype / shape	meaning
ids	int32 (N,)	marker ids
corners	float32 (N, 4, 2)	outer corners (subpixel, clockwise)
points_2d	float32 (M, 2) or None	inner+outer image points (fractal, with_inner_points=True)
points_3d	float32 (M, 3) or None	matching object points

Empty results are returned as correctly-shaped empty arrays ((0,), (0, 4, 2)), never None.

Errors

• Wrong dtype / non-contiguous input → TypeError.
• Unsupported shape, empty frame, invalid dictionary or fractal config → ValueError.

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Performance notes

• Zero-copy input. A contiguous uint8 array is wrapped as a cv::Mat over the same buffer — no copy. Non-contiguous or wrong-dtype inputs raise instead of silently copying.
• GIL released during the native detection, so other Python threads keep running and detect_batch scales.
• Thread safety. The ArUco detector is stateless and shared across batch workers. The fractal detector is not thread-safe, so detect_batch uses a pool of independent detectors (one per worker). A single detector object is fine to call from one thread at a time.

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Citation

If you use this in research, please cite the original work:

• F. J. Romero-Ramirez, R. Muñoz-Salinas, R. Medina-Carnicer, "Speeded up detection of squared fiducial markers", Image and Vision Computing, 76, 2018.
• S. Garrido-Jurado, R. Muñoz-Salinas, F. J. Madrid-Cuevas, R. Medina-Carnicer, "Generation of fiducial marker dictionaries using mixed integer linear programming", Pattern Recognition, 51, 2016.
• F. J. Romero-Ramirez, R. Muñoz-Salinas, R. Medina-Carnicer, "Fractal Markers: A New Approach for Long-Range Marker Pose Estimation Under Occlusion", IEEE Access, 7, 2019.

License

Apache-2.0. The vendored detectors (ArUco Nano, Fractal markers) are © their authors and used under their terms; see third_party/ and PATCHES.md.