ringgrid 0.10.0

Python bindings for ringgrid detector

ringgrid (Python)

Python bindings for the ringgrid detector (PyO3 + maturin).

Install

From PyPI:

pip install ringgrid

With plotting helpers:

pip install "ringgrid[viz]"

From source (repository checkout):

pip install maturin
maturin develop -m crates/ringgrid-py/Cargo.toml --release

Fast Start: Generate target_spec.json + Printable SVG/PNG/DXF

Installed-package target generation is available directly from import ringgrid via the typed TargetLayout API:

from pathlib import Path
import ringgrid

target = ringgrid.TargetLayout.coded_hex(
    pitch_mm=8.0,
    rows=15,
    long_row_cols=14,
    outer_radius_mm=4.8,
    inner_radius_mm=3.2,
    ring_width_mm=1.152,
)

Path("target_spec.json").write_text(target.to_spec_json())
target.write_svg(Path("target_print.svg"), margin_mm=5.0)
target.write_png(Path("target_print.png"), dpi=600.0, margin_mm=5.0)
target.write_dxf(Path("target_print.dxf"))

Key knobs:

API	What it controls	Typical value
TargetLayout.coded_hex(...)	Hex geometry (pitch_mm, rows, long_row_cols, radii, ring width)	8.0, 15, 14, 4.8, 3.2, 1.152
TargetLayout.rect_24x24() / TargetLayout(...)	Presets and the full compositional constructor (hex/rect × coded/plain × fiducials)	—
write_svg(..., margin_mm=...)	Extra white border around the printable page	3-10
write_png(..., dpi=...)	PNG raster resolution and embedded print metadata	300 or 600
write_png(..., include_scale_bar=...)	Include or omit the default scale bar	True

Outputs:

• target_spec.json (schema v5)
• target_print.svg
• target_print.png
• target_print.dxf (2D CAD, millimeters — for laser/CNC fabrication)

Equivalent paths for the same geometry (identical SVG/PNG/DXF; all write a v5 target_spec.json):

• Rust CLI: ringgrid gen-target hex --out_dir ... --pitch_mm 8 --rows 15 --long_row_cols 14 --marker_outer_radius_mm 4.8 --marker_inner_radius_mm 3.2 --marker_ring_width_mm 1.152 --name ringgrid_200mm_hex --dpi 600 --margin_mm 5
• Python script from the repo: tools/gen_target.py with the same arguments
• Rust API: TargetLayout::coded_hex(...) / TargetLayout::new(...) plus write_json_file, write_target_svg, write_target_png, and write_target_dxf

Legacy v4 board_spec.json files still load — ringgrid.TargetLayout.from_json(...) auto-migrates the v4 schema to v5. (See migration notes.)

Load this target in Python:

from pathlib import Path
import ringgrid

target = ringgrid.TargetLayout.from_json(Path("tools/out/target_faststart/target_spec.json"))
cfg = ringgrid.DetectConfig(target)
detector = ringgrid.Detector(cfg)
# Convenience default (config-free): detector = ringgrid.Detector.from_target(target)

If you are working from a repository checkout and also need synthetic images or ground truth, the repo tools under tools/ still provide the combined generation/evaluation workflow. The installed package target-generation API is for target JSON + printable SVG/PNG/DXF only. The repo-level tools/gen_target.py is a thin wrapper over this same installed-package surface.

Complete target-generation tutorial and full flag reference:

• https://vitalyvorobyev.github.io/ringgrid/book/target-generation.html

Target layouts

TargetLayout is the typed, first-class target model — the Python mirror of the Rust ringgrid.target.v5 schema. It expresses hex and rectangular lattices, coded and plain (uncoded) markers, and optional origin fiducials.

import ringgrid

# Presets (geometry comes from the native library — no duplicated constants):
hex_target = ringgrid.TargetLayout.default_hex()        # 15-row coded hex, 203 markers
rect_target = ringgrid.TargetLayout.rect_24x24()   # 24x24 plain rect + origin dots

# Coded hex from direct geometry (deterministic, geometry-derived name):
custom = ringgrid.TargetLayout.coded_hex(
    pitch_mm=8.0, rows=15, long_row_cols=14,
    outer_radius_mm=4.8, inner_radius_mm=3.2, ring_width_mm=1.152,
)

# Compose one explicitly — a tagged union mirrors the v5 schema verbatim:
rect_plain = ringgrid.TargetLayout(
    name="my_rect",
    lattice=ringgrid.RectGeometry(rows=24, cols=24, pitch_mm=14.0),
    marker=ringgrid.RingGeometry(outer_radius_mm=5.6, inner_radius_mm=2.8),
    coding=ringgrid.Plain(),
    fiducials=ringgrid.OriginFiducials(
        dot_radius_mm=1.4,
        dots_mm=[[161.0, 161.0], [147.0, 161.0], [161.0, 175.0]],
    ),
)

# Detector / DetectConfig accept a TargetLayout directly:
detector = ringgrid.Detector.from_target(hex_target)
# ...or: ringgrid.Detector(ringgrid.DetectConfig(hex_target))

to_dict() / from_dict() round-trip the v5 schema verbatim; from_json(...) loads v5 (or legacy v4, auto-migrated) text or a file path; to_spec_json() returns canonical, validated v5 JSON. Invalid geometry raises ValueError with the native error message:

lattice: ringgrid.LatticeGeometry = ringgrid.HexGeometry(15, 14, 8.0)
coding: ringgrid.MarkerCoding = ringgrid.Coded16(ring_width_mm=1.152)

restored = ringgrid.TargetLayout.from_json("target.json")
assert ringgrid.TargetLayout.from_dict(restored.to_dict()) == restored

Features

• Typed TargetLayout (ringgrid.target.v5) — hex/rect lattices, coded/plain markers, origin fiducials
• Native TargetLayout target generation for canonical spec JSON + printable SVG/PNG/DXF
• Native Detector API with NumPy input support
• Slim DetectionResult model objects with JSON round-trips
• Opt-in DetectionDiagnostics channel (detector.detect_with_diagnostics(...)) for per-marker fit/decode internals, edge points, and homography RANSAC stats
• Optional plotting helpers in ringgrid.viz (pip install ringgrid[viz])

Input Rules

• Detector.detect(...) accepts:
  • np.ndarray with dtype=uint8 and shape (H, W) (grayscale)
  • np.ndarray with dtype=uint8 and shape (H, W, 3|4) (RGB/RGBA, auto-converted to grayscale)
  • image file path (str or pathlib.Path)
• Other dtypes/shapes raise TypeError.

Proposal-Only Diagnostics

You can run just the proposal stage and inspect the heatmap used for proposal localization:

import ringgrid
from ringgrid import viz

proposals = ringgrid.propose("photo.png")
diagnostics = ringgrid.propose_with_heatmap("photo.png")

print(len(proposals))
print(diagnostics.heatmap.shape)  # (H, W), float32

viz.plot_proposal_diagnostics(
    image="photo.png",
    diagnostics=diagnostics,
    out="proposal_diagnostics.png",
)

If you want proposal generation to follow the detector's existing scale tuning, use the detector-bound methods instead:

target = ringgrid.TargetLayout.default_hex()
cfg = ringgrid.DetectConfig(target)
detector = ringgrid.Detector(cfg)

diagnostics = detector.propose_with_heatmap("photo.png")

ProposalResult.heatmap is the post-Gaussian-smoothed vote accumulator that the proposal stage uses for thresholding and NMS.

Full tutorial and repo tool workflow:

• Book: Proposal Diagnostics
• python tools/plot_proposal.py --image ... --gt ... --out ...

DetectConfig Field Guide

DetectConfig is the full Python tuning surface for Detector.detect(...), detect_adaptive(...), and detect_multiscale(...).

import ringgrid

target = ringgrid.TargetLayout.default_hex()
cfg = ringgrid.DetectConfig(target)

# Section properties return copies: mutate, then reassign.
decode = cfg.decode
decode.codebook_profile = "extended"
decode.min_decode_margin = 2
cfg.decode = decode

# Or use convenience aliases for common one-field tweaks.
cfg.completion_enable = False
cfg.decode_min_confidence = 0.4

snapshot = cfg.to_dict()
# Stage tuning nests under "advanced" in the wire view.
print(snapshot["advanced"]["decode"]["codebook_profile"])  # "extended"

How Python DetectConfig behaves:

• cfg.target is the constructor input and stays read-only. It is not included in cfg.to_dict().
• cfg.to_dict() returns the resolved native wire view. That is the easiest way to inspect the exact config the Rust detector will use. Its top-level keys are marker_scale, circle_refinement, self_undistort, and advanced; every stage-tuning section (decode, inner_fit, outer_fit, completion, proposal, id_correction, …) lives under the advanced object.
• Section getters such as cfg.decode, cfg.inner_fit, and cfg.self_undistort stay flat on the Python DetectConfig object — they return copies. Reassign the section after editing it, or use a convenience alias such as cfg.decode_min_margin = 2. (The flat Python accessors map onto the nested advanced wire fields automatically.)
• cfg.marker_scale defaults to 14-66 px outer diameter and re-derives the scale-coupled search windows when you replace it.
• Target geometry derives cfg.marker_spec.r_inner_expected and cfg.decode.code_band_ratio. For TargetLayout.default_hex(), those resolve to 0.48809522 and 0.74404764.
• cfg.circle_refinement uses the Python enum ringgrid.CircleRefinementMethod, while cfg.to_dict()["circle_refinement"] stores the native wire strings "ProjectiveCenter" or "None".

Default marker_scale derivations for DetectConfig(TargetLayout.default_hex()) (stage sections shown as nested advanced wire keys):

• advanced.proposal.r_min — spacing-aware, max(0.15 * spacing_min_px, 2.0) -> 3.0310888
• advanced.proposal.r_max — spacing-aware, min(0.45 * spacing_max_px, 1.35 * radius_max_px) -> 42.86825
• advanced.proposal.min_distance — derived from marker spacing and diameter prior
• advanced.edge_sample.r_max = 2.0 * radius_max_px -> 66.0
• advanced.outer_estimation.search_halfwidth_px = max(max((radius_max_px - radius_min_px) * 0.5, 2.0), 13.0) -> 13.0
• advanced.completion.roi_radius_px = clamp(0.75 * nominal_diameter_px, 24.0, 80.0) -> 30.0
• advanced.projective_center.max_correction_shift_px — stays None ("auto"): the gate falls back to the nominal marker diameter (40.0 px here) at use time

Deeper theory and Rust-side derivation details:

• Book: DetectConfig
• Book: MarkerScalePrior

Surface Map

Surface	Type	What it controls
cfg.target	TargetLayout	Target geometry used to derive geometry-coupled defaults
cfg.marker_scale	MarkerScalePrior	Expected marker diameter range in working pixels
cfg.proposal	ProposalConfig	Scharr-vote proposal generation
cfg.edge_sample	EdgeSampleConfig	Radial edge sampling limits and density
cfg.outer_estimation	OuterEstimationConfig	Outer-radius hypothesis generation from radial peaks
cfg.marker_spec	MarkerSpecConfig	Board-driven ring geometry assumptions
cfg.outer_fit	OuterFitConfig	Outer ellipse fit acceptance and scoring
cfg.inner_fit	InnerFitConfig	Inner ellipse fit acceptance and penalties
cfg.decode	DecodeConfig	Code-band sampling and decode strictness
cfg.seed_proposals	SeedProposalConfig	Seed-injected proposals for multi-pass flows
cfg.projective_center	ProjectiveCenterConfig	Projective-center recovery gates
cfg.completion	CompletionConfig	Homography-guided recovery of missing IDs
cfg.ransac_homography	RansacConfig	Global homography fitting thresholds
cfg.self_undistort	SelfUndistortConfig	Division-model self-undistort estimation
cfg.id_correction	IdCorrectionConfig	Hex-lattice ID verification and recovery
cfg.inner_as_outer_recovery	InnerAsOuterRecoveryConfig	Recovery when outer fit locked onto the inner ring

Top-Level Controls

Property	Default	Practical notes
cfg.target	constructor input	Read-only target layout. Replace the whole config if you need a different target.
cfg.circle_refinement	ringgrid.CircleRefinementMethod.PROJECTIVE_CENTER	Use NONE for raw ellipse centers, or keep PROJECTIVE_CENTER for the accuracy-oriented default.
cfg.dedup_radius	6.0	Final marker merge radius in pixels. Raise only if duplicate fits survive; lower if nearby valid markers merge incorrectly.
cfg.max_aspect_ratio	3.0	Rejects very elongated ellipses. Tighten when false positives are obviously non-circular; loosen only for extreme perspective.
cfg.use_global_filter	True	Enables homography-based outlier rejection. Turn it off when debugging local fits or when you want to inspect raw pre-homography detections.

Convenience Aliases

Alias	Expands to	When to use it
cfg.completion_enable	cfg.completion.enable	Quick toggle for homography-guided completion
cfg.self_undistort_enable	cfg.self_undistort.enable	Quick toggle for division-model self-undistort inside detect()
cfg.inner_fit_required	cfg.inner_fit.require_inner_fit	Promote missing inner fits from soft penalty to hard reject
cfg.homography_inlier_threshold_px	cfg.ransac_homography.inlier_threshold	Tighten or loosen global homography inlier gating
cfg.decode_min_margin	cfg.decode.min_decode_margin	Reject ambiguous decodes more aggressively
cfg.decode_max_dist	cfg.decode.max_decode_dist	Limit how many bit errors a decode may contain
cfg.decode_min_confidence	cfg.decode.min_decode_confidence	Raise or lower overall decode strictness without rebuilding the section

marker_scale

This is the highest-leverage tuning section. Replacing cfg.marker_scale recomputes the scale-coupled defaults in proposal, edge_sample, outer_estimation.search_halfwidth_px, and completion.roi_radius_px; the projective_center.max_correction_shift_px "auto" fallback also tracks it.

Field	Default	Practical notes
diameter_min_px	14.0	Minimum expected outer diameter in working pixels. Raise it if markers are never tiny.
diameter_max_px	66.0	Maximum expected outer diameter. Narrow it to cut false positives; widen it only if markers truly get larger.

If markers span a very wide range, prefer detect_adaptive(...) or detect_multiscale(...) over one very wide marker_scale.

proposal

Controls the first Scharr-gradient voting stage that proposes candidate marker centers before local fitting.

Field	Default	Practical notes
r_min	derived -> 3.0310888	Minimum vote radius. Lower only for genuinely tiny markers. Re-derived (spacing-aware) from cfg.marker_scale and board geometry.
r_max	derived -> 42.86825	Maximum vote radius. Raise only if markers exceed your current size prior. Re-derived (spacing-aware) from cfg.marker_scale and board geometry.
grad_threshold	0.05	Fraction of max gradient magnitude used to keep votes. Raise it in noisy scenes; lower it for low-contrast imagery.
min_distance	derived	Minimum distance between proposals (px). Re-derived from cfg.marker_scale.
min_vote_frac	0.1	Minimum accumulator peak fraction relative to the best proposal. Raise to be stricter, lower to keep weaker peaks.
max_candidates	None	Optional hard cap on proposals. Use only when you must bound runtime in cluttered scenes.
radius_step	1	Stride between voting radii. 1 (default) tests every integer radius; 2+ subsamples (≈ halves proposal cost at 2) but lowers recall on blurry/real scenes. The max radius is always included.

edge_sample

Controls the radial rays used to collect inner and outer edge evidence around a proposal.

Field	Default	Practical notes
n_rays	48	Angular sampling density. More rays improve stability on oblique markers at extra cost.
r_max	derived -> 66.0	Maximum sampling radius. Re-derived from cfg.marker_scale.
r_min	1.5	Minimum sampling radius. Rarely changed directly.
r_step	0.5	Radial step in pixels. Lower values sample more densely but cost more.
min_ring_depth	0.08	Minimum signed edge depth kept during sampling. Raise for noisy false edges; lower for low-contrast targets.
min_rays_with_ring	16	Minimum rays that must see a valid ring-like response. Raise for stricter geometry, lower for partial occlusion.

outer_estimation

Controls the radial profile stage that predicts an outer radius before the ellipse fit. This stage is cheaper than a full fit, so it is a good place to reject weak hypotheses early.

Field	Default	Practical notes
search_halfwidth_px	derived -> 13.0	Radius search window around the prior. Re-derived from cfg.marker_scale but never below the base default.
radial_samples	64	Samples per ray used to estimate radial peaks. Higher values help blurry targets at extra runtime.
aggregator	"median"	Cross-ray aggregation policy. The shipped value is the only one used in first-party docs/tests.
grad_polarity	"dark_to_light"	Expected outer-edge gradient direction. Match this only if you intentionally invert target contrast assumptions.
min_theta_coverage	0.6	Minimum fraction of rays with valid evidence. Raise it to reject partial arcs sooner.
min_theta_consistency	0.35	Minimum agreement fraction around the selected radius. Raise for stricter peak consensus.
allow_two_hypotheses	True	Lets the stage carry a strong secondary radius hypothesis into later scoring. Helpful for ambiguous profiles.
second_peak_min_rel	0.85	Relative strength required for that second hypothesis. Raise it to keep only nearly-tied alternatives.
refine_halfwidth_px	1.0	Per-ray refinement window around the selected peak. Raise slightly for blurrier edges.

marker_spec

Describes the expected ring geometry. The target layout drives r_inner_expected, while the remaining fields control radial/theta sampling and coverage checks.

Field	Default	Practical notes
r_inner_expected	derived -> 0.48809522	Expected inner/outer radius ratio after board-geometry padding. Usually change the board geometry, not this field.
inner_search_halfwidth	0.08	Half-width around r_inner_expected used to search for the inner ring. Widen only if the target design itself differs.
inner_grad_polarity	"light_to_dark"	Expected inner-edge polarity. Match this only if you intentionally invert the target rendering.
radial_samples	64	Samples per theta spoke. Raise for softer gradients; lower only if you are aggressively trading accuracy for runtime.
theta_samples	96	Angular samples around the ring. More samples help oblique or partially occluded markers.
aggregator	"median"	Cross-theta aggregation policy. The shipped value is the canonical path.
min_theta_coverage	0.6	Minimum angular coverage for a valid marker profile. Lower it only for partial visibility.
min_theta_consistency	0.25	Minimum angular consistency around the chosen profile. Raise to reject uneven or contaminated profiles earlier.

outer_fit

Controls the final outer-ellipse fit. Nested ransac is a ringgrid.RansacFitConfig.

Field	Default	Practical notes
min_direct_fit_points	6	Minimum points required for a direct algebraic fit. Rarely tuned.
min_ransac_points	8	Minimum points before RANSAC is attempted. Lower only for severe occlusion experiments.
ransac.max_iters	200	More iterations help with heavy outliers but cost runtime.
ransac.inlier_threshold	1.5	Sampson-distance threshold in pixels. Tighten for cleaner data; loosen for blur/distortion.
ransac.min_inliers	6	Minimum inlier count accepted by the outer fit.
ransac.seed	42	Deterministic RNG seed for the fit.
size_score_weight	0.15	Weight of size agreement in the outer-hypothesis score. Raise if size priors are highly reliable.
max_angular_gap_rad	1.5707963267948966	Largest allowed missing arc gap (pi/2 by default). Lower for stricter completeness, raise for partial arcs.

inner_fit

Controls the inner-ellipse fit that refines geometry and influences final confidence. Nested ransac is a ringgrid.RansacFitConfig.

Field	Default	Practical notes
min_points	20	Minimum points before an inner fit is attempted. Lower only when many markers are partially cropped.
min_inlier_ratio	0.5	Required RANSAC inlier fraction. Raise for cleaner scenes, lower for blur/heavy distortion.
max_rms_residual	1.0	Maximum RMS Sampson residual. Tighten to reject sloppy inner fits.
max_center_shift_px	12.0	Largest allowed shift between outer and inner centers. Raise only if strong perspective or distortion genuinely moves the fitted center more.
max_ratio_abs_error	0.15	Maximum deviation from the expected inner/outer ratio. Tighten for well-calibrated, fixed targets.
local_peak_halfwidth_idx	3	Radial index half-width around the predicted inner peak.
ransac.max_iters	200	More iterations help when inner edges are noisy.
ransac.inlier_threshold	1.5	Inner-fit Sampson threshold in pixels.
ransac.min_inliers	8	Minimum inliers required for the inner fit.
ransac.seed	43	Deterministic RNG seed for the inner fit.
miss_confidence_factor	0.7	Confidence multiplier when the inner fit is missing. Lower values punish missing inner rings more strongly.
max_angular_gap_rad	1.5707963267948966	Largest missing inner-edge arc accepted before rejection.
require_inner_fit	False	Soft by default. Set to True when you want to reject any marker that lacks a trustworthy inner ellipse.

decode

Controls code-band sampling and codebook matching.

Field	Default	Practical notes
codebook_profile	"base"	String selector: "base" keeps the stable shipped IDs 0..892; "extended" opts into the additive larger profile.
code_band_ratio	derived -> 0.74404764	Sampling radius inside the outer ellipse. Derived from board geometry and usually not tuned directly.
samples_per_sector	5	Angular intensity samples per bit sector. Raise for blur, lower only for aggressive speed tradeoffs.
n_radial_rings	3	Radial samples across the code band. More rings improve robustness on soft edges.
max_decode_dist	3	Maximum Hamming distance accepted. Lower it to reject noisy decodes more aggressively.
min_decode_confidence	0.3	Overall decode-confidence floor. Raise this first when you want stricter decoding.
min_decode_margin	1	Rejects ambiguous ties by default. Raising it is a strong way to prefer only very clear decodes.
min_decode_contrast	0.03	Minimum sampled code-band contrast before decoding. Lower only for low-contrast images.
threshold_max_iters	10	Iteration cap for the internal 2-means threshold refinement.
threshold_convergence_eps	0.0001	Convergence epsilon for that refinement loop.

Typical decode tuning:

• Too many ambiguous IDs: raise cfg.decode_min_margin or lower cfg.decode_max_dist.
• Good geometry but noisy contrast: lower min_decode_contrast slightly before widening geometry gates.
• Need IDs beyond the stable shipped set: set codebook_profile to "extended" explicitly.

seed_proposals

Controls proposal injection from already-known seed centers during multi-pass or guided workflows.

Field	Default	Practical notes
merge_radius_px	3.0	Seed/proposal merge distance. Raise only if seed centers are systematically off by several pixels.
seed_score	1000000000000.0	Score assigned to injected seeds so they survive proposal ranking.
max_seeds	512	Optional cap on consumed seeds. Use it to bound runtime when external seed lists get large.

projective_center

Controls the center-refinement stage used when cfg.circle_refinement == ringgrid.CircleRefinementMethod.PROJECTIVE_CENTER.

Field	Default	Practical notes
use_expected_ratio	True	Uses the board-driven inner/outer ratio as a prior in the selector. Usually leave this on.
ratio_penalty_weight	1.0	Strength of that ratio prior. Lower it if you need the selector to trust raw conic evidence more.
max_correction_shift_px	None (auto)	Maximum accepted correction jump (pre-0.8 name: max_center_shift_px). None falls back to the nominal marker diameter; explicit values are honored as-is.
max_selected_residual	0.25	Rejects unstable projective-center candidates. Raise only if valid markers are failing this gate.
min_eig_separation	1e-06	Guards against unstable conic-pencil eigenpairs. Lower only if you have evidence the default is too strict.

completion

Completion tries to recover missing IDs at homography-projected board locations. It only runs when a valid homography is available.

Field	Default	Practical notes
enable	True	Set to False to inspect only directly fit-decoded markers.
roi_radius_px	derived -> 30.0	Radius of the completion search ROI. Re-derived from cfg.marker_scale.
reproj_gate_px	3.0	Maximum allowed distance between the fitted center and the projected board position.
min_fit_confidence	0.45	Minimum confidence for a recovered completion marker.
min_arc_coverage	0.35	Minimum fraction of rays that found both edges. Lower only for heavy occlusion.
max_attempts	None	Optional cap on attempted missing IDs.
image_margin_px	10.0	Skip projected centers too close to the image boundary.
require_perfect_decode	False	Strong safety gate for distortion-heavy scenes without a trusted mapper.
max_radii_std_ratio	0.35	Rejects fits with highly inconsistent outer radii across rays.

ransac_homography

Controls global homography fitting from decoded markers.

Field	Default	Practical notes
max_iters	2000	Iteration budget for the global RANSAC loop.
inlier_threshold	5.0	Pixel reprojection threshold for inliers. Tighten for cleaner data; loosen for more distortion or weak initial geometry.
min_inliers	6	Minimum correspondences accepted for a homography.
seed	0	Deterministic RNG seed for repeatable fitting.

self_undistort

Controls the optional one-parameter division-model self-undistort flow. cfg.self_undistort_enable = True affects Detector.detect(...), but Detector.detect_with_mapper(...) always uses the mapper you pass in instead.

Field	Default	Practical notes
enable	False	Turn on only when you want detect() to estimate distortion first.
lambda_range	[-8e-07, 8e-07]	Search interval for the division-model parameter. Widen only if you know distortion is stronger.
max_evals	40	Maximum objective evaluations during optimization.
min_markers	6	Minimum markers with usable edge data before self-undistort is attempted.
improvement_threshold	0.01	Relative improvement required before the estimate is considered useful.
min_abs_improvement	0.0001	Absolute improvement floor. Prevents tiny numerical wins from activating the model.
trim_fraction	0.1	Fraction of worst residuals trimmed in robust scoring.
min_lambda_abs	5e-09	Rejects near-zero solutions that do not meaningfully change the model.
reject_range_edge	True	Rejects solutions that land too close to the search-interval edge.
range_edge_margin_frac	0.02	Edge margin used by that range-edge rejection.
validation_min_markers	24	Marker count required for the homography validation pass.
validation_abs_improvement_px	0.05	Absolute reprojection improvement required by validation.
validation_rel_improvement	0.03	Relative reprojection improvement required by validation.

id_correction

Runs after local fit/decode to verify or recover IDs from the board's hex lattice structure.

Field	Default	Practical notes
enable	True	Leave on unless you are explicitly debugging raw decoder output.
auto_search_radius_outer_muls	[2.4, 2.9, 3.5, 4.2, 5.0]	Staged neighborhood radii for local search. Tighten only if incorrect neighbors dominate.
consistency_outer_mul	3.2	Neighborhood radius for structural consistency checks.
consistency_min_neighbors	1	Minimum neighbors required before a consistency check runs.
consistency_min_support_edges	1	Minimum supporting board-neighbor edges required to keep an ID.
consistency_max_contradiction_frac	0.5	Maximum allowed contradiction fraction before clearing an ID.
soft_lock_exact_decode	True	Protects exact decodes unless structure strongly contradicts them.
min_votes	2	Votes required to change an already-assigned ID.
min_votes_recover	1	Votes required to recover a missing ID.
min_vote_weight_frac	0.55	Minimum weighted-vote share for the winning candidate.
h_reproj_gate_px	30.0	Loose reprojection gate used by the fallback homography assignment.
homography_fallback_enable	True	Enables fallback ID recovery from a rough homography when local evidence is weak.
homography_min_trusted	24	Minimum trusted markers before that fallback is attempted.
homography_min_inliers	12	Minimum inliers required for the fallback homography.
max_iters	5	Maximum iterative correction passes.
remove_unverified	False	Default keeps the detection but clears the ID. Set to True to drop unverifiable markers entirely.
seed_min_decode_confidence	0.7	Minimum decode confidence used when bootstrapping trusted seeds without a homography.

inner_as_outer_recovery

Post-processing stage that tries to fix markers whose outer fit locked onto the inner ring.

Field	Default	Practical notes
enable	True	Leave on for the default blur-tolerant behavior.
ratio_threshold	0.75	Neighbor-radius ratio below which a marker is considered suspicious.
k_neighbors	6	Number of nearest neighbors used to estimate the expected outer radius.
min_theta_consistency	0.18	Lower-than-normal consistency gate used by this recovery path.
min_theta_coverage	0.4	Minimum angular coverage required during the re-fit.
min_ring_depth	0.02	Relaxed edge-depth gate used for blurry outer edges.
refine_halfwidth_px	2.5	Wider local radius refinement window for the recovery re-fit.
size_gate_tolerance	0.25	Prevents the relaxed recovery fit from re-locking onto the inner ring.

Typical tuning sequence:

• Known scale: tighten cfg.marker_scale before touching low-level proposal thresholds.
• Too many weak IDs: raise cfg.decode_min_confidence or cfg.decode_min_margin.
• Good local fits but unstable global cleanup: tighten cfg.homography_inlier_threshold_px.
• Distorted scenes without calibration: try cfg.self_undistort_enable = True.
• Want direct detections only: set cfg.completion_enable = False.

Adaptive Detection

Use adaptive detection when marker diameter varies substantially across the image (near/far perspective, zoom changes, mixed target scales).

Which Method Should I Use?

Situation	Recommended call	Why
You do not know marker size in advance	detector.detect_adaptive(image)	Probes scale and auto-selects tiers
You know approximate marker diameter (px)	detector.detect_adaptive(image, nominal_diameter_px=d)	Skips probe and uses focused two-tier bracket around d
You need fixed/reproducible tier policy	detector.detect_multiscale(image, tiers)	Full explicit control over tiers
Marker size range is tight and runtime is priority	detector.detect(image)	Single-pass (fastest)

Canonical adaptive entry point is:

• Detector.detect_adaptive(image, nominal_diameter_px: float | None = None)

Compatibility alias (deprecated, still supported):

• Detector.detect_adaptive_with_hint(image, nominal_diameter_px=...)

Tier objects:

• ScaleTier(diameter_min_px, diameter_max_px)
• ScaleTiers([...])
• Presets: ScaleTiers.four_tier_wide(), ScaleTiers.two_tier_standard()
• Single-pass equivalent: ScaleTiers.single(MarkerScalePrior(...))

Practical Recipes

Unknown scene scale:

from pathlib import Path
import ringgrid

target = ringgrid.TargetLayout.default_hex()
detector = ringgrid.Detector.from_target(target)
image = Path("testdata/target_3_split_00.png")

result = detector.detect_adaptive(image)

Known nominal diameter (for example, ~32 px):

result = detector.detect_adaptive(image, nominal_diameter_px=32.0)

Inspect tiers used by adaptive logic (debug/repro):

tiers = detector.adaptive_tiers(image, nominal_diameter_px=32.0)
for tier in tiers.tiers:
    print(tier.diameter_min_px, tier.diameter_max_px)

# Re-run exactly those tiers
result = detector.detect_multiscale(image, tiers)

Examples

Run from repository root:

python crates/ringgrid-py/examples/basic_detect.py \
  --image testdata/target_3_split_00.png \
  --out testdata/target_3_split_00_det_py.json

python crates/ringgrid-py/examples/detect_with_camera.py \
  --image testdata/target_3_split_00.png \
  --out testdata/target_3_split_00_det_cam_py.json

python crates/ringgrid-py/examples/detect_adaptive.py \
  --image testdata/target_3_split_00.png \
  --out testdata/target_3_split_00_det_adaptive_py.json

python crates/ringgrid-py/examples/detect_multiscale.py \
  --image testdata/target_3_split_00.png \
  --tiers four_tier_wide \
  --out testdata/target_3_split_00_det_multiscale_py.json

Plotting example:

python crates/ringgrid-py/examples/plot_detection.py \
  --image testdata/target_3_split_00.png \
  --out testdata/target_3_split_00_overlay_py.png