Language Bindings

ringgrid is one detection engine with several surfaces. The Rust crate is the reference; the bindings wrap it with the same detection results and the same JSON-at-the-boundary convention (targets, configs, and results cross as JSON; pixels as raw buffers), so switching languages does not change behavior.

There is also a command-line tool — see the CLI Guide — and an in-browser interactive demo.

Python

pip install ringgrid          # detector + typed TargetLayout API
pip install "ringgrid[viz]"   # + optional matplotlib/Pillow visualization helpers

import ringgrid

detector = ringgrid.Detector.from_target(ringgrid.TargetLayout.coded_hex())
result = detector.detect_path("photo.png")
for m in result.detected_markers:
    print(m.id, m.center)

The Python package ships type stubs (py.typed) and a typed TargetLayout API that can also render printable SVG/PNG/DXF targets. See the ringgrid-py README for the full DetectConfig field guide.

C / C++

A stable C ABI plus a header-only C++ RAII wrapper, distributed through vcpkg and CMake find_package. See C & C++ for the full guide.

JavaScript / WebAssembly

npm install @vitavision/ringgrid

import init, { RinggridDetector, default_board_json } from "@vitavision/ringgrid";

await init();
const detector = new RinggridDetector(default_board_json());
const json = detector.detect_rgba(imageData.data, imageData.width, imageData.height);
const result = JSON.parse(json);

The WASM package powers the interactive demo; it accepts grayscale or RGBA (canvas ImageData) buffers and returns the same DetectionResult JSON.