Star Trek Online screenshot recognition + ML training (standalone WARP/WARP CORE).
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Project description
STO-WARP
Star Trek Online screenshot recognition + ML training, distributed as a standalone Python package.
What is this?
STO-WARP combines two tools in one package:
WARP (Weaponry & Armament Recognition Platform) — reads your in-game screenshots and automatically fills in your SETS build. Detects equipment, traits, bridge officers, and ship information using computer vision and machine learning.
WARP CORE — trainer interface built into WARP. Review and correct recognition results, confirm annotations, and retrain the local ML models (Icon Classifier + Layout Regressor) on your own data to improve accuracy over time.
Technology
- WARP — recognition pipeline (OCR + layout detection + icon matching). Reads STO screenshots and emits structured slot/item results.
- WARP CORE — Qt trainer UI for reviewing recognition output and fine-tuning the EfficientNet / MobileNetV3 models that drive WARP.
Install
The recommended way to install sto-warp is via our universal installation scripts, which will automatically configure an isolated environment using pipx.
Linux / macOS:
curl -sSL https://raw.githubusercontent.com/raman78/sto-warp/main/install.sh | bash
Windows (PowerShell):
Invoke-WebRequest -Uri "https://raw.githubusercontent.com/raman78/sto-warp/main/install.ps1" -OutFile "install.ps1"; .\install.ps1; Remove-Item "install.ps1"
For manual installation methods and more details, see INSTALLATION.md.
Data and models
On first run sto-warp downloads:
- ML models (
icon_classifier.pt,screen_classifier.pt) from the Hugging Face hub. - Cargo / ship / trait JSON from the community
STOCD/SETS-Datarepository.
Everything is cached under ~/.config/warp/ (or $XDG_CONFIG_HOME/warp/
when set). Nothing is committed to this repository.
License
GPL-3.0 — see LICENSE.
Project docs
INSTALLATION.md— install methods (pipx, native packages).CHANGELOG.md— release notes.PROJECT_CONTEXT.md— design context.CLAUDE.md— repository rules for AI-assisted development.docs/— technical deep-dives (BOFF / trait detection, ML pipeline, …).
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