Introduction

calib-targets-rs is a workspace of Rust crates for detecting and modeling planar calibration targets from corner clouds (for example, ChESS corners). The focus is geometry-first: target modeling, grid fitting, and rectification live here, while image I/O and corner detection are intentionally out of scope.

ChArUco detection overlay on a small board.

What it is:

• A small, composable set of crates for chessboard, ChArUco, PuzzleBoard, and marker-style targets.
• A set of geometric primitives (homographies, rectified views, grid coords).
• Practical examples and tests based on the chess-corners crate.

What it is not:

• A replacement for your corner detector or image pipeline.
• A full calibration stack (no camera calibration or PnP here).

Recommended reading order:

1. Project Overview and Conventions
2. Algorithms — the building blocks every detector shares
3. Pipelines — each target’s end-to-end detection flow
4. Crate chapters, starting with calib-targets-core and calib-targets-chessboard

API docs.

Quickstart

Install the facade crate (the image feature is enabled by default):

cargo add calib-targets image

Minimal chessboard detection:

use calib_targets::detect;
use calib_targets::chessboard::ChessboardParams;
use calib_targets::image::ImageReader; // re-exported; version always matches

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let img = ImageReader::open("board.png")?.decode()?.to_luma8();
    let params = ChessboardParams::default();

    let result = detect::detect_chessboard(&img, &detect::default_chess_config(), &params);
    println!("detected: {}", result.is_ok());
    Ok(())
}

Python bindings

Python bindings are built with maturin:

pip install maturin
maturin develop
python crates/calib-targets-py/examples/detect_chessboard.py path/to/image.png

The calib_targets module exposes detect_chessboard, detect_charuco, detect_puzzleboard, and detect_marker_board, plus diagnose_charuco, diagnose_puzzleboard, and diagnose_marker_board for the diagnostics-returning counterparts. The public API is dataclass-first: config inputs are typed models and detector results are typed dataclasses with to_dict()/from_dict(...) helpers for JSON interoperability. detect_charuco requires params and the board lives in params.board. For marker boards, target_position is populated only when params.board.cell_size is set and alignment succeeds.

MSRV: Rust 1.91 (stable).