ringgrid

Pure-Rust detector for dense coded ring calibration targets on a hex lattice. Detects markers with subpixel edge precision, decodes 16-sector binary IDs from a 893-codeword codebook, fits ellipses via Fitzgibbon's direct method with RANSAC, corrects projective center bias, and estimates a board-to-image homography. No OpenCV dependency.

Key Features

• Subpixel edge detection — gradient-based radial sampling produces edge points fed to a direct ellipse fit, yielding subpixel-accurate marker localization
• Projective center correction — recovers the true projected center from inner/outer conic pencil geometry, correcting the systematic bias of ellipse-fit centers
• Consistency-first ID correction — verifies decoded IDs against local hex-lattice structure, clears contradictory IDs, and recovers safe missing IDs before global filtering
• 893 unique IDs — 16-sector binary codebook with minimum cyclic Hamming distance of 5, enabling reliable identification under noise and partial occlusion
• Distortion-aware — supports external camera models (Brown-Conrady) via the PixelMapper trait, or blind single-parameter self-undistort estimation
• Pure Rust — no C/C++ dependencies, no OpenCV bindings

Pipeline Stages

Named stage order: proposal -> local fit/decode -> dedup -> projective center -> id_correction -> optional global filter -> optional completion -> final homography refit.

Installation

[dependencies]
ringgrid = "0.1"

Fast Start: Generate Target JSON + Printable SVG/PNG

Target generation scripts are in the repository root (tools/).

python3 -m venv .venv
./.venv/bin/python -m pip install -U pip
./.venv/bin/python -m pip install numpy

./.venv/bin/python tools/gen_synth.py \
  --out_dir tools/out/target_faststart \
  --n_images 0 \
  --board_mm 200 \
  --pitch_mm 8 \
  --print \
  --print_dpi 600 \
  --print_margin_mm 5 \
  --print_basename target_print

Key knobs:

This generates:

• tools/out/target_faststart/board_spec.json
• tools/out/target_faststart/target_print.svg
• tools/out/target_faststart/target_print.png

Use the generated JSON in detection:

use ringgrid::{BoardLayout, Detector};
use std::path::Path;

let board = BoardLayout::from_json_file(Path::new("tools/out/target_faststart/board_spec.json")).unwrap();
let detector = Detector::new(board);

Complete step-by-step target generation docs (all flags/config fields):

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

Simple Detection

use ringgrid::{BoardLayout, Detector};
use std::path::Path;

let board = BoardLayout::from_json_file(Path::new("target.json")).unwrap();
let image = image::open("photo.png").unwrap().to_luma8();

let detector = Detector::new(board);
let result = detector.detect(&image);

for marker in &result.detected_markers {
    if let Some(id) = marker.id {
        println!("Marker {id} at ({:.1}, {:.1})", marker.center[0], marker.center[1]);
    }
}

With a marker diameter hint for better scale tuning:

# use ringgrid::{BoardLayout, Detector};
# use std::path::Path;
# let board = BoardLayout::from_json_file(Path::new("target.json")).unwrap();
let detector = Detector::with_marker_diameter_hint(board, 32.0);

Adaptive Scale Detection

For scenes with large marker size variation, use adaptive multi-scale methods:

# use ringgrid::{BoardLayout, Detector, ScaleTiers};
# use std::path::Path;
# let board = BoardLayout::from_json_file(Path::new("target.json")).unwrap();
# let detector = Detector::new(board);
# let image = image::open("photo.png").unwrap().to_luma8();
let result = detector.detect_adaptive(&image);
let result = detector.detect_adaptive_with_hint(&image, Some(32.0));
let result = detector.detect_multiscale(&image, &ScaleTiers::four_tier_wide());

Which method to choose:

Inspect adaptive tiers before detecting:

# use ringgrid::{BoardLayout, Detector};
# use std::path::Path;
# let board = BoardLayout::from_json_file(Path::new("target.json")).unwrap();
# let detector = Detector::new(board);
# let image = image::open("photo.png").unwrap().to_luma8();
let tiers = detector.adaptive_tiers(&image, Some(32.0));
let result = detector.detect_multiscale(&image, &tiers);

Adaptive scale guide:

• https://vitalyvorobyev.github.io/ringgrid/book/detection-modes/adaptive-scale.html

Detection with Camera Model

When camera intrinsics and distortion coefficients are known, use detect_with_mapper for distortion-aware detection via a two-pass pipeline:

use ringgrid::{
    BoardLayout, CameraIntrinsics, CameraModel, Detector, RadialTangentialDistortion,
};
use std::path::Path;

let board = BoardLayout::from_json_file(Path::new("target.json")).unwrap();
let image = image::open("photo.png").unwrap().to_luma8();
let (w, h) = image.dimensions();

let camera = CameraModel {
    intrinsics: CameraIntrinsics {
        fx: 900.0, fy: 900.0,
        cx: w as f64 * 0.5, cy: h as f64 * 0.5,
    },
    distortion: RadialTangentialDistortion {
        k1: -0.15, k2: 0.05, p1: 0.001, p2: -0.001, k3: 0.0,
    },
};

let detector = Detector::new(board);
let result = detector.detect_with_mapper(&image, &camera);

for marker in &result.detected_markers {
    // center is always image-space
    println!("Image: ({:.1}, {:.1})", marker.center[0], marker.center[1]);
    // center_mapped is working-frame (undistorted)
    if let Some(mapped) = marker.center_mapped {
        println!("Working: ({:.1}, {:.1})", mapped[0], mapped[1]);
    }
}

Self-Undistort (No Calibration Required)

When camera calibration is unavailable, ringgrid can estimate a single-parameter division-model distortion correction from the detected markers:

use ringgrid::{BoardLayout, DetectConfig, Detector};
use std::path::Path;

let board = BoardLayout::from_json_file(Path::new("target.json")).unwrap();
let image = image::open("photo.png").unwrap().to_luma8();

let mut cfg = DetectConfig::from_target(board);
cfg.self_undistort.enable = true;

let detector = Detector::with_config(cfg);
let result = detector.detect(&image);

if let Some(su) = &result.self_undistort {
    println!("Lambda: {:.3e}, applied: {}", su.model.lambda, su.applied);
}

Custom PixelMapper

Implement the PixelMapper trait to plug in any distortion model:

use ringgrid::PixelMapper;

struct Identity;

impl PixelMapper for Identity {
    fn image_to_working_pixel(&self, p: [f64; 2]) -> Option<[f64; 2]> {
        Some(p)
    }
    fn working_to_image_pixel(&self, p: [f64; 2]) -> Option<[f64; 2]> {
        Some(p)
    }
}

Then use it with detector.detect_with_mapper(&image, &mapper).

Coordinate Frames

• DetectedMarker.center — always raw image pixel coordinates
• DetectedMarker.center_mapped — working-frame (undistorted) coordinates when a mapper is active
• DetectedMarker.board_xy_mm — board-space marker coordinates in millimeters for valid decoded IDs
• DetectionResult.center_frame / homography_frame — explicit frame metadata

Documentation

• User Guide — comprehensive mdbook covering marker design, detection pipeline, mathematical foundations, and configuration
• API Reference — rustdoc for all public types

License

Licensed under either of:

• Apache License, Version 2.0 (LICENSE-APACHE)
• MIT license (LICENSE-MIT)

at your option.