chess-corners 1.0.0

Python bindings for the fast ChESS chessboard corner detector (Rust backend)

chess_corners (Python)

Python-first bindings for the chess-corners detector.

The installed package is a mixed Rust/Python package:

• chess_corners is a pure-Python public API with type hints, docstrings, JSON helpers, and readable config objects.
• chess_corners._native is the private PyO3 extension module that runs the detector.

Quick start

import numpy as np
import chess_corners

img = np.zeros((128, 128), dtype=np.uint8)

cfg = chess_corners.DetectorConfig.chess_multiscale()
cfg.threshold = 60.0  # ChESS: absolute floor on the raw response (default 30)
cfg.strategy.chess.refiner = chess_corners.ChessRefiner.forstner()

detector = chess_corners.Detector(cfg)
det = detector.detect(img)
print(det.xy.shape, det.xy.dtype)  # (N, 2) float32
if det.angles is not None:
    print(det.angles.shape)        # (N, 2) float32
print(cfg)

Detector(cfg).detect(image) returns a Detections object with named arrays:

• det.xy — (N, 2) float32, subpixel corner positions (x, y) in input pixels
• det.response — (N,) float32, raw detector response at each peak
• det.angles — (N, 2) float32, [axis0_angle, axis1_angle] in radians [0, π), or None when orientation is disabled
• det.sigmas — (N, 2) float32, 1σ uncertainty per axis in radians, or None when orientation is disabled

Rotating CCW from axis0_angle toward axis1_angle (by less than π) traverses a dark sector of the corner; the two grid axes are not assumed to be orthogonal, so this output correctly captures projective warp and lens distortion.

The orientation fit is the dominant per-corner cost, and it is optional. A pipeline that recovers board geometry from corner positions alone can skip it with cfg.without_orientation(); in that case det.angles and det.sigmas are None.

Input requirements:

• image must be a 2D uint8 NumPy array with shape (H, W)
• it must be C-contiguous

The rows are sorted deterministically by response descending, then x, then y.

Public config API

DetectorConfig is strategy-typed: detector-specific tuning lives inside a DetectionStrategy variant. Top-level fields are threshold, multiscale, upscale, orientation_method, and merge_radius.

cfg = chess_corners.DetectorConfig.chess()  # ChESS, no pyramid
cfg.threshold = 60.0  # plain float; ChESS = absolute response floor (default 30), Radon = fraction of per-frame max (default 0.28)
cfg.merge_radius = 3.0

# Enable the coarse-to-fine pyramid (both detectors honour this):
cfg.multiscale = chess_corners.MultiscaleConfig.pyramid(
    levels=3, min_size=128, refinement_radius=3,
)

# Detector-specific knobs live inside the strategy. Nested getters
# return the live shared object, so direct attribute assignment
# propagates back to `cfg` — no rebuild needed:
cfg.strategy.chess.ring = chess_corners.ChessRing.BROAD
cfg.detection.nms_radius = 2
cfg.detection.min_cluster_size = 2

# Switch the active strategy by assigning a new one:
cfg.strategy = chess_corners.DetectionStrategy.from_radon(
    chess_corners.RadonConfig()
)

For one-shot configuration, the chainable with_chess(**kwargs) / with_radon(**kwargs) builders return a new config with only the named fields replaced:

cfg = (
    chess_corners.DetectorConfig.chess_multiscale()
    .with_chess(
        refiner=chess_corners.ChessRefiner.forstner(),
        ring=chess_corners.ChessRing.BROAD,
    )
    .with_detection(nms_radius=2, min_cluster_size=2)
)

Refiners are per-detector: ChessRefiner carries one of center_of_mass, forstner, saddle_point, or ml (with the ml-refiner feature). The Radon detector uses its built-in Gaussian peak fit (PeakFitMode); it does not expose a pluggable refiner. The active ChessRefiner variant's tuning is reachable via the payload property:

fcfg = chess_corners.ForstnerConfig()
fcfg.max_offset = 2.0
cfg.strategy.chess.refiner = chess_corners.ChessRefiner.forstner(fcfg)

assert cfg.strategy.chess.refiner.kind == "forstner"
assert cfg.strategy.chess.refiner.payload.max_offset == 2.0

Tagged classes:

• MultiscaleConfig: MultiscaleConfig.single_scale() / MultiscaleConfig.pyramid(levels=, min_size=, refinement_radius=); read cfg.multiscale.kind and (when pyramid) levels, min_size, refinement_radius.
• UpscaleConfig: UpscaleConfig.disabled() / UpscaleConfig.fixed(factor); read cfg.upscale.kind and (when fixed) factor.
• ChessRefiner: center_of_mass(), forstner(), saddle_point(), ml() (with the ml-refiner feature).

Enums:

• ChessRing: CANONICAL, BROAD
• PeakFitMode: PARABOLIC, GAUSSIAN
• OrientationMethod: RING_FIT, DISK_FIT; disable the fit entirely with cfg.without_orientation() (then det.angles and det.sigmas are None)

ChessRing.BROAD uses the wider radius-10 detector sampling pattern. Descriptors always sample at the detector ring radius.

JSON helpers and printing

Every public config object supports:

• to_dict()
• from_dict(...)
• to_json()
• from_json(...)
• pretty()
• print()

Example:

cfg = chess_corners.DetectorConfig.chess_multiscale()
text = cfg.to_json(indent=2)
restored = chess_corners.DetectorConfig.from_json(text)

print(restored)
restored.print()

If rich is installed, .print() uses it automatically and the config objects also expose a Rich render hook.

Canonical JSON schema

The same algorithm config schema is used by Rust, Python, docs, and the CLI:

{
  "strategy": {
    "chess": {
      "ring": "broad",
      "refiner": {
        "forstner": {
          "radius": 3,
          "min_trace": 20.0,
          "min_det": 0.001,
          "max_condition_number": 60.0,
          "max_offset": 2.0
        }
      }
    }
  },
  "threshold": 60.0,
  "detection": { "nms_radius": 3, "min_cluster_size": 1 },
  "multiscale": {
    "pyramid": {
      "levels": 3,
      "min_size": 96,
      "refinement_radius": 4
    }
  },
  "upscale": "disabled",
  "orientation_method": "ring_fit",
  "merge_radius": 2.5
}

Switch to the Radon strategy by replacing the strategy object and setting the shared detection params:

{
  "strategy": {
    "radon": {
      "ray_radius": 4,
      "image_upsample": 2,
      "response_blur_radius": 1,
      "peak_fit": "gaussian"
    }
  },
  "detection": { "nms_radius": 4, "min_cluster_size": 2 }
}

Unknown keys are rejected with a clear ConfigError.

Example runners

For a complete Pillow-based example that loads the full config from JSON, run:

uv run --python .venv/bin/python python crates/chess-corners-py/examples/run_with_full_config.py \
  testimages/mid.png \
  config/chess_algorithm_config_example.json

For a complete Pillow-based example that defines the entire config directly in Python code and only takes the image path as an argument, run:

uv run --python .venv/bin/python python crates/chess-corners-py/examples/run_with_code_config.py \
  testimages/mid.png

Both examples use Pillow only for image loading:

uv pip install --python .venv/bin/python Pillow

ML refiner

The published wheel is built with the ml-refiner feature on by default (pip install cannot toggle Cargo features), so ChessRefiner.ml() is always available out of the box. The ML pipeline is selected by passing ChessRefiner.ml() as the active variant on the ChESS strategy. The ML refiner runs a small ONNX model on normalized intensity patches around each candidate.