maddo/AIFotoOnlus
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AIFotoOnlus/src/README.md
MaddoScientisto 769afc08fb Initial .NET scaffold: Core, Console, WPF projects 
Introduced solution structure for AIFotoONLUS migration to .NET. Added Core library with YOLO-based detection/recognition engine using OpenCvSharp, Console batch runner, and WPF demo frontend with MVVM. Implemented model loading, directory processing, progress reporting, and preferences. Added README with build/run instructions.
2026-02-15 15:16:56 +01:00

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AIFotoONLUS (.NET) — scaffold

This workspace contains the initial scaffold for migrating the YOLO-based number recognition Python script to .NET 10.

Projects:

  • AIFotoONLUS.Core — core inference library (OpenCvSharp)
  • AIFotoONLUS.Console — console batch runner
  • AIFotoONLUS.WPF — simple WPF demo frontend

Quick build & run (Windows):

  1. Ensure .NET 10 SDK is installed.
  2. From src run:
    • dotnet restore
    • dotnet build
  3. Copy the models/ folder (contains detection/recognition .cfg and .weights) to the src output folder or run the apps from repository root so models/ is accessible.

Console example: dotnet run --project src\AIFotoONLUS.Console -- -d "..\images\onlus" -c result.csv

Notes:

  • Core engine currently loads Darknet models and scaffolds the detection/recognition pipeline.
  • Parsing of network outputs (YOLO postprocessing) and recognition logic will be implemented next.
  • WPF app is a minimal demo (code-behind). We'll replace with MVVM as we iterate.