chess-corners 0.9.0

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.ChessConfig.multiscale()
cfg.threshold_value = 0.15
cfg.refiner.kind = chess_corners.RefinementMethod.FORSTNER

corners = chess_corners.find_chess_corners(img, cfg)
print(corners.shape, corners.dtype)
print(cfg)

find_chess_corners(image, cfg=None) returns a NumPy float32 array of shape (N, 9) with columns:

1. x — subpixel corner x in input pixels
2. y — subpixel corner y in input pixels
3. response — raw ChESS response at the detected peak
4. contrast — amplitude of the fitted bright/dark structure
5. fit_rms — RMS residual of the two-axis intensity fit (gray levels)
6. axis0_angle — angle of the first local grid axis, radians in [0, π)
7. axis0_sigma — 1σ uncertainty of axis0_angle, radians
8. axis1_angle — angle of the second local grid axis, radians in (axis0_angle, axis0_angle + π)
9. axis1_sigma — 1σ uncertainty of axis1_angle, radians

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.

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

The public config shape is intentionally flat. There is no params section and no nested pyramid object.

cfg = chess_corners.ChessConfig()
cfg.detector_mode = chess_corners.DetectorMode.BROAD
cfg.descriptor_mode = chess_corners.DescriptorMode.FOLLOW_DETECTOR
cfg.threshold_mode = chess_corners.ThresholdMode.RELATIVE
cfg.threshold_value = 0.2
cfg.nms_radius = 2
cfg.min_cluster_size = 2
cfg.pyramid_levels = 3
cfg.pyramid_min_size = 128
cfg.refinement_radius = 3
cfg.merge_radius = 3.0

All nested objects are default-initialized, so you can always do:

cfg = chess_corners.ChessConfig()
cfg.refiner.kind = chess_corners.RefinementMethod.FORSTNER
cfg.refiner.forstner.max_offset = 2.0

Supported enums:

• DetectorMode: canonical, broad
• DescriptorMode: follow_detector, canonical, broad
• ThresholdMode: relative, absolute
• RefinementMethod: center_of_mass, forstner, saddle_point

broad uses the wider, blur-tolerant detector sampling pattern. Leave descriptor_mode at follow_detector unless you have a reason to override descriptor or orientation sampling separately.

Refiner configuration

cfg.refiner always contains every leaf config:

• cfg.refiner.center_of_mass
• cfg.refiner.forstner
• cfg.refiner.saddle_point

Only cfg.refiner.kind selects which one is active.

cfg = chess_corners.ChessConfig()
cfg.refiner.kind = chess_corners.RefinementMethod.SADDLE_POINT
cfg.refiner.saddle_point.radius = 3
cfg.refiner.saddle_point.max_offset = 2.0

JSON helpers and printing

Every public config object supports:

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

Example:

cfg = chess_corners.ChessConfig.multiscale()
text = cfg.to_json(indent=2)
restored = chess_corners.ChessConfig.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:

{
  "detector_mode": "broad",
  "descriptor_mode": "canonical",
  "threshold_mode": "absolute",
  "threshold_value": 0.5,
  "nms_radius": 3,
  "min_cluster_size": 1,
  "refiner": {
    "kind": "forstner",
    "center_of_mass": {
      "radius": 2
    },
    "forstner": {
      "radius": 3,
      "min_trace": 20.0,
      "min_det": 0.001,
      "max_condition_number": 60.0,
      "max_offset": 2.0
    },
    "saddle_point": {
      "radius": 3,
      "det_margin": 0.002,
      "max_offset": 1.75,
      "min_abs_det": 0.0002
    }
  },
  "pyramid_levels": 3,
  "pyramid_min_size": 96,
  "refinement_radius": 4,
  "merge_radius": 2.5
}

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

If the bindings are built with the ml-refiner feature, the package also exports:

corners = chess_corners.find_chess_corners_with_ml(img, cfg)

That toggles the separate ML-backed refinement path. It does not change the canonical ChessConfig schema.