calib-targets 0.8.0

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calib-targets — Python bindings

Native-feeling Python API for the calib-targets Rust workspace. Detects chessboards, ChArUco, PuzzleBoard, and marker boards, and generates printable target bundles (JSON + SVG + PNG). Built with PyO3 + maturin.

Python package name: calib_targets (the Rust crate is calib-targets-py).

Install

# From source — this repo:
uv pip install maturin
uv run maturin develop --release -m crates/calib-targets-py/Cargo.toml

# Or from PyPI (pre-built wheels):
pip install calib-targets

Hello world

import numpy as np
from PIL import Image
import calib_targets as ct

image = np.asarray(Image.open("board.png").convert("L"), dtype=np.uint8)
result = ct.detect_chessboard_best(image, [ct.ChessboardParams()])
if result is not None:
    print(f"labelled {len(result.detection.corners)} corners")

End-to-end round-trip per target type

Each snippet covers: generate a printable target → load the PNG → detect → export detection to JSON.

Runnable scripts at crates/calib-targets-py/examples/. Use any of them as a starting point.

Chessboard

import io, json
import numpy as np
from PIL import Image
import calib_targets as ct

# 1. Generate target.
doc = ct.PrintableTargetDocument(
    target=ct.ChessboardTargetSpec(inner_rows=7, inner_cols=9, square_size_mm=20.0),
    page=ct.PageSpec(size=ct.PageSize.custom(width_mm=220.0, height_mm=180.0), margin_mm=10.0),
    render=ct.RenderOptions(png_dpi=150),
)
bundle = ct.render_target_bundle(doc)

# 2. Load as grayscale numpy array.
image = np.asarray(Image.open(io.BytesIO(bundle.png_bytes)).convert("L"), dtype=np.uint8)

# 3. Detect — prefer *_best for robustness.
result = ct.detect_chessboard_best(image, [
    ct.ChessboardParams(),
    ct.ChessboardParams(chess=ct.ChessConfig(threshold_value=0.15)),
    ct.ChessboardParams(chess=ct.ChessConfig(threshold_value=0.08)),
])

# 4. Export detection to JSON.
print(json.dumps(result.to_dict(), indent=2)[:200])

Runnable: examples/chessboard_roundtrip.py.

ChArUco

import calib_targets as ct
# (synthesise PNG as above; build matching board spec)
board = ct.CharucoBoardSpec(
    rows=5, cols=7, cell_size=1.0, marker_size_rel=0.75,
    dictionary="DICT_4X4_50", marker_layout=ct.MarkerLayout.OPENCV_CHARUCO,
)
params = ct.CharucoParams(
    board=board, px_per_square=60.0,
    chessboard=ct.ChessboardParams(),
    max_hamming=2, min_marker_inliers=4,
)
result = ct.detect_charuco(image, params=params)   # raises on failure
print(len(result.detection.corners), "corners,", len(result.markers), "markers")

Runnable: examples/charuco_roundtrip.py.

Marker board

circles = (
    ct.MarkerCircleSpec(i=3, j=2, polarity=ct.CirclePolarity.WHITE),
    ct.MarkerCircleSpec(i=4, j=2, polarity=ct.CirclePolarity.BLACK),
    ct.MarkerCircleSpec(i=4, j=3, polarity=ct.CirclePolarity.WHITE),
)
layout = ct.MarkerBoardLayout(rows=6, cols=8, cell_size=1.0, circles=circles)
params = ct.MarkerBoardParams(layout=layout, chessboard=ct.ChessboardParams())
result = ct.detect_marker_board(image, params=params)

Runnable: examples/markerboard_roundtrip.py.

PuzzleBoard

params = ct.default_puzzleboard_params(rows=10, cols=10)
params.decode.search_mode = ct.PuzzleBoardSearchMode.fixed_board()
params.decode.scoring_mode = ct.PuzzleBoardScoringMode.soft_log_likelihood()
result = ct.detect_puzzleboard(image, params=params)
# Every corner has an absolute master ID: result.detection.corners[0].id
# Soft mode also exposes result.decode.score_margin and the runner-up hypothesis.

Runnable: examples/puzzleboard_roundtrip.py.

Inputs

• image: numpy.ndarray[uint8] with shape (h, w). Grayscale only; convert RGB upstream (Image.convert("L")).
• chess_cfg: ChessConfig | None — overrides the default ChESS corner detector.
• params: *Params — typed dataclass matching the detector. Dict inputs are rejected; use the typed classes.

Outputs

Every detection result is a typed dataclass with full attribute access, editor autocomplete, and type stubs. Round-trip through JSON with to_dict() and from_dict(...) — the dict schema matches the Rust crate's serde_json output byte-for-byte.

payload = json.dumps(result.to_dict())
# ... later, elsewhere:
restored = ct.ChessboardDetectionResult.from_dict(json.loads(payload))

Every config / result type has these methods — ChessConfig, ChessboardParams, CharucoParams, PuzzleBoardParams, MarkerBoardParams, PrintableTargetDocument, and all result types.

Printable targets

One-liner helpers with sensible defaults (A4 portrait, 10 mm margins, 300 DPI):

doc = ct.charuco_document(rows=5, cols=7, square_size_mm=20.0,
                          marker_size_rel=0.75, dictionary="DICT_4X4_50")
written = ct.write_target_bundle(doc, "out/charuco_a4")
print(written.json_path, written.svg_path, written.png_path)

Other helpers: chessboard_document, puzzleboard_document, marker_board_document. Each accepts optional page= / render= overrides. For full control, construct PrintableTargetDocument directly with one of the target specs (ChessboardTargetSpec, CharucoTargetSpec, MarkerBoardTargetSpec, PuzzleBoardTargetSpec).

CLI

pip install calib-targets installs a calib-targets console script that mirrors the Rust CLI:

calib-targets gen puzzleboard --rows 8 --cols 10 --square-size-mm 15 \
    --out-stem puzzle
calib-targets list-dictionaries
calib-targets init chessboard --out spec.json \
    --inner-rows 6 --inner-cols 8 --square-size-mm 20
calib-targets generate --spec spec.json --out-stem my_board

See testdata/printable/*.json for ready-made spec files; every file is PrintableTargetDocument.from_dict( json.load(open(path)))-compatible.

Tuning difficult cases

1. Replace detect_* with detect_*_best and pass a 3-config sweep — this is the recommended default.
2. Increase rasterisation / input resolution if cells are smaller than ~20 px across.
3. Open the per-detector README for deeper guidance: chessboard, ChArUco, PuzzleBoard, marker. Python passes all parameters through to Rust, so tuning advice applies identically.

Limitations

• One target instance per image. Multiple simultaneous boards are not detected; pass cropped sub-images per target.
• Pinhole-ish optics only. Moderate radial / perspective distortion is handled gracefully; fisheye is not supported.
• Grayscale uint8 numpy arrays only. No torch tensors, no GPU.
• Board PNG / SVG generation for chessboard, ChArUco, marker board, and PuzzleBoard is supported; other target kinds are not.

Migration from pre-0.7 dict-based API

Old	New
detect_chessboard(img, params={"min_corner_strength": 0.5})	detect_chessboard(img, params=ChessboardParams(min_corner_strength=0.5))
detect_charuco(..., params={"board": {...}})	detect_charuco(..., params=CharucoParams(board=CharucoBoardSpec(...)))
result["detection"]["corners"]	result.detection.corners
json.dumps(result_dict)	json.dumps(result.to_dict())

Dict-based configuration is rejected in the new API; use the typed dataclasses.

Feature parity vs Rust facade

• detect_chessboard / _all / _best / _debug — ✔
• detect_charuco / _best, detect_puzzleboard / _best, detect_marker_board / _best — ✔
• Printable targets for all four target kinds — ✔
• to_dict / from_dict round-trip on every config + result type — ✔

Implementation note

The compiled Rust module is internal (calib_targets._core). Public API stability is guaranteed only for top-level calib_targets exports.

Links

• Book (getting started + per-target chapters): https://vitalyvorobyev.github.io/calib-targets-rs/
• Rust facade crate: https://docs.rs/calib-targets
• Source + issue tracker: https://github.com/VitalyVorobyev/calib-targets-rs