Fullseye Documentation Index
Language: 日本語 · English · 简体中文 · 繁體中文 · 한국어 · Deutsch
Please note: only this index page is translated. The individual documents it links to are, for the moment, available in Japanese only.
Fullseye (working name: imgevolve) is a HALCON/HDevelop-class production tool. It combines a numpy-native library of image-processing operators with an HDevelop-style visual pipeline design environment (Fullseye Studio) and an execution runtime (FullseyeEngine). It carries roughly 521 operators (as counted in the registry), provides genuine implementations of 269/2313 actual HALCON operators, and spans 31 categories.
Start here → GETTING_STARTED.md (up and running in 5 minutes)
Usage (for users — these four first)
| Document |
Contents |
| GETTING_STARTED.md |
Get going in 5 minutes: install → your first pipeline → run it from Studio, the CLI or code → look at the result |
| INSTALL.md |
The complete setup guide: prerequisites, pip install -e . and which extras to choose, the Windows/Linux installers, minimal and embedded configurations, troubleshooting |
| STUDIO_GUIDE.md |
The complete Fullseye Studio guide: the three panels, operator browser, step execution, knobs, Inspector, perception panel, command palette, keyboard shortcuts, export |
| ENGINE.md |
FullseyeEngine (design → execution): every method, use from Python, the run CLI, calling it from another project |
Operator / API reference
| Document |
Contents |
| OPERATORS.md |
Catalogue of all 521 operators (31 categories, grouped by sort, with the matching HALCON/OpenCV/scikit-image/MATLAB APIs) |
| EXAMPLES.md |
Sample code for each operator, with the equivalent call in other libraries |
| OP_INDEX.json |
Machine-readable operator index (regenerate it with imgevolve.py index) |
| ADDING_OPS.md |
How to add a new operator (evolution, codegen, catalogue and index all follow automatically) |
| ../examples/README.md |
Runnable end-to-end example scripts |
Perception stack (robotics / vision)
| Document |
Contents |
| PERCEPTION.md |
One-page reference for the perception stack (stereo / terrain / detect / registration / pose / flow / motion) |
| PERCEPTION_REALDATA.md |
Measurements on real camera footage (video I/O plus honestly reported figures) |
HALCON parity / coverage (honest disclosure)
| Document |
Contents |
| HALCON_PARITY.md |
Genuine implementation status (269/2313): whether an operator truly does the same work, rather than merely sharing a name |
| HALCON_COVERAGE.md |
Coverage measured by actually scraping the official reference (v2605) |
| LIB_COVERAGE.md |
Cross-library coverage (distinctive operators taken in from beyond HALCON) |
| PARITY_CROSSBACKEND.md |
Parity evidenced by cross-backend agreement between independent implementations (scipy/cv2/skimage) |
Quality / provenance / reproduction
| Document |
Contents |
| ACCURACY_BENCH.md |
The standing accuracy table: evolved champion vs null baseline (holdout) |
| CHAIN_FUZZ.md |
The chain fuzzer — a third quality-assurance layer that shakes operators chained together (diffuse → converge → minimal reproduction) |
| EVOLUTION_ENVIRONMENT.md |
The evolutionary algorithm development environment (diffuse → contract → promote; the counterfactual-utility gate and the bridge between the two operator universes) |
| PROVENANCE.md |
Provenance: the record that these are our own work, built from published algorithms |
| REFERENCES.md |
The literature backing each operator |
| REPRODUCE.md |
How to reproduce the figures: seed-driven and deterministic |
| STATUS.md |
Where the project stands and what is planned (plan_ref) |
Release notes / design
| Document |
Contents |
| V13.md |
v13 = production readiness + cross-project packaging + the perception stack |
| V14.md |
v14 = the perception stack completed (motion + hardening) |
| STUDIO_UX.md |
The intent and the reasoning behind Fullseye Studio’s UX/design improvements |
Quick commands
py -3.11 -m pip install -e ".[opencv,gui]" # install (image I/O + Studio)
py -3.11 studio.py # launch Fullseye Studio (= fullseye-studio)
py -3.11 imgevolve.py ops --search edge # search operators (= fullseye ops --search edge)
py -3.11 imgevolve.py apply gauss_filter in.png out.png --a 0.6
py -3.11 imgevolve.py run pipeline.json in.png --out result.png
py -3.11 imgevolve.py coverage # honest coverage figures
From Python:
import fullseye, numpy as np
out = fullseye.run_pipeline(frame, ["gaussian", "sobel_amp", "otsu"])
eng = fullseye.FullseyeEngine.load("pipeline.json"); result = eng.run(frame)