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@Eugene Sukhodolskiy Eugene Sukhodolskiy 2 days ago 2 KB feat: bootstrap Python project structure\n\n- pyproject.toml with FastAPI, YOLO, OpenCV, SQLite stack\n- src/sups_yolo modules: camera, preprocessing, models, core, data, training, api, web\n- config/example.json and config/local.json\n- tests/unit and tests/integration with pytest\n- CONTRIBUTING.md, .gitignore, data dirs\n- README tech stack and setup instructions
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sups_yolo

Intelligent information-measuring system for real-time control of geometric and physico-mechanical parameters of polyurethane shoe soles.

Goal

Detect and classify defects on polyurethane soles in several categories, despite moderate disturbances such as dust, glare, and varying lighting conditions.

Tech stack

Layer Choice Notes
Language Python 3.10+ Main inference and backend language.
Object detection Ultralytics YOLOv8 One detector instance per camera channel.
Web backend FastAPI Serves REST/WebSocket APIs for the UI and camera channels.
Web UI HTML + htmx / vanilla JS Lightweight, channel tabs, history, validation, retraining.
Database SQLite (production ready via aiosqlite) Stores events, images paths, labels, config snapshots.
Camera capture OpenCV + picamera2 / RTSP URLs IP cameras or Raspberry Pi cameras.
Image augmentation Albumentations Synthetic dust, lighting, rotation for training/testing.
Training loop Ultralytics Python API Fine-tune YOLO on collected verified data.
Configuration JSON files in config/ Per-channel preprocessing and model settings.
Testing pytest Unit and integration tests.
Environment Linux-like OS, RTX 2060 workstation 2–3 Raspberry Pi / IP cameras, Full HD.

System overview

  • Vision hardware: 2–3 Raspberry Pi or IP cameras with web access + a workstation with an RTX 2060; Full HD cameras.
  • Software: Linux-like OS, logging of processed data, YOLO-based detection instances per camera.
  • User web interface: history view, validation status, expert feedback (correct/incorrect), multi-channel tabs (camera 1/2/3 with independent YOLO instances), live camera preview for setup, settings section, retraining with date-restricted data.
  • Event record per sole: sole ID, defect photo, defect probability, annotated photo with defect zone.
  • Performance target: 15 seconds per image analysis and result description.

Documentation

Project documentation lives in docs/.

Setup

  1. Install uv (recommended) or pip.
  2. Create a virtual environment inside the project: 
    uv venv .venv
source .venv/bin/activate
  3. Install dependencies: 
    uv pip install -e ".[dev]"
  4. Copy config/example.json to config/local.json and adjust camera / model settings.
  5. Run tests: 
    pytest