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Initial .NET scaffold: Core, Console, WPF projects

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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.
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MaddoScientisto 2026-02-15 15:16:56 +01:00
commit 769afc08fb
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42
AIFotoONLUS.sln Normal file
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Microsoft Visual Studio Solution File, Format Version 12.00
# Visual Studio Version 18
VisualStudioVersion = 18.3.11505.172 d18.3
MinimumVisualStudioVersion = 10.0.40219.1
Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "src", "src", "{827E0CD3-B72D-47B6-A68D-7590B98EB39B}"
EndProject
Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "AIFotoONLUS.Core", "src\AIFotoONLUS.Core\AIFotoONLUS.Core.csproj", "{E115F3D0-A9E5-B632-150F-3AE0FB7AC8A8}"
EndProject
Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "AIFotoONLUS.Console", "src\AIFotoONLUS.Console\AIFotoONLUS.Console.csproj", "{272F569E-4277-20A2-5482-A7FF36ECB248}"
EndProject
Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "AIFotoONLUS.WPF", "src\AIFotoONLUS.WPF\AIFotoONLUS.WPF.csproj", "{961196B2-0208-3C5E-D664-14EF48F3B2F4}"
EndProject
Global
GlobalSection(SolutionConfigurationPlatforms) = preSolution
Debug|Any CPU = Debug|Any CPU
Release|Any CPU = Release|Any CPU
EndGlobalSection
GlobalSection(ProjectConfigurationPlatforms) = postSolution
{E115F3D0-A9E5-B632-150F-3AE0FB7AC8A8}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
{E115F3D0-A9E5-B632-150F-3AE0FB7AC8A8}.Debug|Any CPU.Build.0 = Debug|Any CPU
{E115F3D0-A9E5-B632-150F-3AE0FB7AC8A8}.Release|Any CPU.ActiveCfg = Release|Any CPU
{E115F3D0-A9E5-B632-150F-3AE0FB7AC8A8}.Release|Any CPU.Build.0 = Release|Any CPU
{272F569E-4277-20A2-5482-A7FF36ECB248}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
{272F569E-4277-20A2-5482-A7FF36ECB248}.Debug|Any CPU.Build.0 = Debug|Any CPU
{272F569E-4277-20A2-5482-A7FF36ECB248}.Release|Any CPU.ActiveCfg = Release|Any CPU
{272F569E-4277-20A2-5482-A7FF36ECB248}.Release|Any CPU.Build.0 = Release|Any CPU
{961196B2-0208-3C5E-D664-14EF48F3B2F4}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
{961196B2-0208-3C5E-D664-14EF48F3B2F4}.Debug|Any CPU.Build.0 = Debug|Any CPU
{961196B2-0208-3C5E-D664-14EF48F3B2F4}.Release|Any CPU.ActiveCfg = Release|Any CPU
{961196B2-0208-3C5E-D664-14EF48F3B2F4}.Release|Any CPU.Build.0 = Release|Any CPU
EndGlobalSection
GlobalSection(SolutionProperties) = preSolution
HideSolutionNode = FALSE
EndGlobalSection
GlobalSection(NestedProjects) = preSolution
{E115F3D0-A9E5-B632-150F-3AE0FB7AC8A8} = {827E0CD3-B72D-47B6-A68D-7590B98EB39B}
{961196B2-0208-3C5E-D664-14EF48F3B2F4} = {827E0CD3-B72D-47B6-A68D-7590B98EB39B}
EndGlobalSection
GlobalSection(ExtensibilityGlobals) = postSolution
SolutionGuid = {D0584B69-5C15-4308-9677-0B13E4D08267}
EndGlobalSection
EndGlobal

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src/AIFotoONLUS.Console/AIFotoONLUS.Console.csproj Normal file
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<Project Sdk="Microsoft.NET.Sdk">
<PropertyGroup>
<OutputType>Exe</OutputType>
<TargetFramework>net10.0</TargetFramework>
<Nullable>enable</Nullable>
<ImplicitUsings>enable</ImplicitUsings>
</PropertyGroup>
<ItemGroup>
<PackageReference Include="NLog" Version="6.1.0" />
</ItemGroup>
<ItemGroup>
<ProjectReference Include="..\AIFotoONLUS.Core\AIFotoONLUS.Core.csproj" />
</ItemGroup>
</Project>

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src/AIFotoONLUS.Console/Program.cs Normal file
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using AIFotoONLUS.Core;
using System;
using System.IO;
using System.Linq;
namespace AIFotoONLUS.ConsoleApp
{
internal static class Program
{
private static int Main(string[] args)
{
if (args.Length == 0)
{
Console.WriteLine("Usage: AIFotoONLUS.Console -d <directory> -c <output.csv> [-tn|--textnegative]");
return 1;
}
string? directory = null;
string? csvPath = null;
bool textNegative = false;
for (int i = 0; i < args.Length; i++)
{
var a = args[i];
if (a == "-d" || a == "--directory")
{
if (i + 1 < args.Length) directory = args[++i];
}
else if (a == "-c" || a == "--csv")
{
if (i + 1 < args.Length) csvPath = args[++i];
}
else if (a == "-tn" || a == "--textnegative")
{
textNegative = true;
}
}
if (string.IsNullOrEmpty(directory) || string.IsNullOrEmpty(csvPath))
{
Console.WriteLine("Missing required arguments.");
return 1;
}
if (!Directory.Exists(directory))
{
Console.WriteLine($"Directory not found: {directory}");
return 1;
}
var cfg = new ModelConfiguration
{
DetectionCfg = Path.Combine("models", "detection.cfg"),
DetectionWeights = Path.Combine("models", "detection.weights"),
RecognitionCfg = Path.Combine("models", "recognition.cfg"),
RecognitionWeights = Path.Combine("models", "recognition.weights"),
ConfidenceThreshold = 0.5,
NmsThreshold = 0.4
};
try
{
using var engine = new NumberRecognitionEngine(cfg);
var results = engine.ProcessDirectory(directory, textNegative).ToList();
using var sw = new StreamWriter(csvPath, false);
sw.WriteLine("filename,text");
foreach (var r in results)
{
sw.WriteLine($"{r.FileName},{r.Text}");
}
Console.WriteLine($"Results written to {csvPath}");
}
catch (Exception ex)
{
Console.WriteLine($"Error: {ex.Message}");
return 2;
}
return 0;
}
}
}

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src/AIFotoONLUS.Core/AIFotoONLUS.Core.csproj Normal file
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<Project Sdk="Microsoft.NET.Sdk">
<PropertyGroup>
<TargetFramework>net10.0</TargetFramework>
<Nullable>enable</Nullable>
<ImplicitUsings>enable</ImplicitUsings>
</PropertyGroup>
<ItemGroup>
<PackageReference Include="OpenCvSharp4" Version="4.13.0.20260214" />
<PackageReference Include="OpenCvSharp4.runtime.win" Version="4.13.0.20260214" />
</ItemGroup>
<ItemGroup>
</ItemGroup>
</Project>

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src/AIFotoONLUS.Core/DetectedRegion.cs Normal file
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using OpenCvSharp;
namespace AIFotoONLUS.Core
{
public record DetectedRegion(Rect BoundingBox, float Confidence, int ClassId, double CenterX);
public record RecognitionResult(string Text, Rect BoundingBox, double Confidence);
public record ImageResult(string FileName, string Text, string FilePath);
}

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src/AIFotoONLUS.Core/ModelConfiguration.cs Normal file
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using OpenCvSharp;
namespace AIFotoONLUS.Core
{
public class ModelConfiguration
{
public string DetectionCfg { get; set; } = "models/detection.cfg";
public string DetectionWeights { get; set; } = "models/detection.weights";
public string RecognitionCfg { get; set; } = "models/recognition.cfg";
public string RecognitionWeights { get; set; } = "models/recognition.weights";
public double ConfidenceThreshold { get; set; } = 0.5;
public double NmsThreshold { get; set; } = 0.4;
public Size DetectionInputSize { get; set; } = new Size(416, 416);
public Size RecognitionInputSize { get; set; } = new Size(140, 120);
public string[] NumberClasses { get; set; } = new[] { "0", "1", "2", "3", "4", "5", "6", "7", "8", "9" };
}
}

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src/AIFotoONLUS.Core/NumberRecognitionEngine.cs Normal file
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using OpenCvSharp;
using OpenCvSharp.Dnn;
using System;
using System.Collections.Concurrent;
using System.Collections.Generic;
using System.IO;
using System.Linq;
using System.Threading;
using System.Threading.Tasks;
namespace AIFotoONLUS.Core
{
/// <summary>
/// NumberRecognitionEngine: loads Darknet models via OpenCvSharp and
/// provides methods to detect text regions and recognize digits.
/// </summary>
public class NumberRecognitionEngine : IDisposable
{
private readonly Net _detectionNet;
private readonly Net _recognitionNet;
private readonly ModelConfiguration _cfg;
private bool _disposed;
public NumberRecognitionEngine(ModelConfiguration cfg)
{
_cfg = cfg ?? throw new ArgumentNullException(nameof(cfg));
if (!File.Exists(_cfg.DetectionCfg) || !File.Exists(_cfg.DetectionWeights))
throw new FileNotFoundException("Detection model files not found.", _cfg.DetectionCfg);
if (!File.Exists(_cfg.RecognitionCfg) || !File.Exists(_cfg.RecognitionWeights))
throw new FileNotFoundException("Recognition model files not found.", _cfg.RecognitionCfg);
_detectionNet = CvDnn.ReadNetFromDarknet(_cfg.DetectionCfg, _cfg.DetectionWeights);
_recognitionNet = CvDnn.ReadNetFromDarknet(_cfg.RecognitionCfg, _cfg.RecognitionWeights);
_detectionNet.SetPreferableBackend(Backend.OPENCV);
_detectionNet.SetPreferableTarget(Target.CPU);
_recognitionNet.SetPreferableBackend(Backend.OPENCV);
_recognitionNet.SetPreferableTarget(Target.CPU);
// Let OpenCV use multiple threads internally (use number of logical processors)
try
{
Cv2.SetNumThreads(Environment.ProcessorCount);
}
catch
{
// Ignore if not supported by OpenCvSharp build
}
}
public void Dispose()
{
if (_disposed) return;
_detectionNet?.Dispose();
_recognitionNet?.Dispose();
_disposed = true;
GC.SuppressFinalize(this);
}
private string[] GetOutputLayerNames(Net net) => net.GetUnconnectedOutLayersNames();
public IEnumerable<DetectedRegion> DetectTextRegions(Mat image)
{
if (image is null) throw new ArgumentNullException(nameof(image));
return DetectTextRegions(_detectionNet, image);
}
// Internal variant that accepts a Net instance so it can be used from parallel workers
private IEnumerable<DetectedRegion> DetectTextRegions(Net detectionNet, Mat image)
{
using var blob = CvDnn.BlobFromImage(image, 0.00392, _cfg.DetectionInputSize, new Scalar(0, 0, 0), true, false);
detectionNet.SetInput(blob);
var outNames = GetOutputLayerNames(detectionNet);
var outsList = new List<Mat>();
detectionNet.Forward(outsList, outNames);
Mat[] outs = outsList.ToArray();
var boxes = new List<Rect>();
var confidences = new List<float>();
var classIds = new List<int>();
var centerXList = new List<double>();
int imgW = image.Width;
int imgH = image.Height;
foreach (var outMat in outs)
{
for (int i = 0; i < outMat.Rows; i++)
{
float cx = outMat.At<float>(i, 0) * imgW;
float cy = outMat.At<float>(i, 1) * imgH;
float w = outMat.At<float>(i, 2) * imgW;
float h = outMat.At<float>(i, 3) * imgH;
float maxScore = 0f;
int bestClass = -1;
for (int c = 5; c < outMat.Cols; c++)
{
float score = outMat.At<float>(i, c);
if (score > maxScore)
{
maxScore = score;
bestClass = c - 5;
}
}
if (maxScore > _cfg.ConfidenceThreshold)
{
int x = (int)Math.Max(0, Math.Round(cx - w / 2));
int y = (int)Math.Max(0, Math.Round(cy - h / 2));
var rect = new Rect(x, y, (int)Math.Round(w), (int)Math.Round(h));
boxes.Add(rect);
confidences.Add(maxScore);
classIds.Add(bestClass);
centerXList.Add(cx);
}
}
}
if (boxes.Count == 0) return Enumerable.Empty<DetectedRegion>();
CvDnn.NMSBoxes(boxes, confidences, (float)_cfg.ConfidenceThreshold, (float)_cfg.NmsThreshold, out int[] indices);
var results = new List<DetectedRegion>();
foreach (var idx in indices)
{
var b = boxes[idx];
double centerX = b.X + b.Width / 2.0;
results.Add(new DetectedRegion(b, confidences[idx], classIds[idx], centerX));
}
return results;
}
public string RecognizeDigits(Mat croppedImage)
{
if (croppedImage is null) throw new ArgumentNullException(nameof(croppedImage));
using var blob = CvDnn.BlobFromImage(croppedImage, 0.00392, _cfg.RecognitionInputSize, new Scalar(0, 0, 0), true, false);
_recognitionNet.SetInput(blob);
var outNames = GetOutputLayerNames(_recognitionNet);
var outsList = new List<Mat>();
_recognitionNet.Forward(outsList, outNames);
Mat[] outs = outsList.ToArray();
var boxes = new List<Rect>();
var confidences = new List<float>();
var classIds = new List<int>();
var centerXList = new List<double>();
int imgW = croppedImage.Width;
int imgH = croppedImage.Height;
foreach (var outMat in outs)
{
for (int i = 0; i < outMat.Rows; i++)
{
float cx = outMat.At<float>(i, 0) * imgW;
float cy = outMat.At<float>(i, 1) * imgH;
float w = outMat.At<float>(i, 2) * imgW;
float h = outMat.At<float>(i, 3) * imgH;
float maxScore = 0f;
int bestClass = -1;
for (int c = 5; c < outMat.Cols; c++)
{
float score = outMat.At<float>(i, c);
if (score > maxScore)
{
maxScore = score;
bestClass = c - 5;
}
}
if (maxScore > _cfg.ConfidenceThreshold)
{
int x = (int)Math.Max(0, Math.Round(cx - w / 2));
int y = (int)Math.Max(0, Math.Round(cy - h / 2));
boxes.Add(new Rect(x, y, (int)Math.Round(w), (int)Math.Round(h)));
confidences.Add(maxScore);
classIds.Add(bestClass);
centerXList.Add(cx);
}
}
}
if (classIds.Count == 0) return string.Empty;
CvDnn.NMSBoxes(boxes, confidences, (float)_cfg.ConfidenceThreshold, (float)_cfg.NmsThreshold, out int[] keep);
var ordered = keep.Select(i => new { Idx = i, Cx = centerXList[i], ClassId = classIds[i] })
.OrderBy(x => x.Cx)
.Select(x => _cfg.NumberClasses[x.ClassId]);
return string.Concat(ordered);
}
public ImageResult ProcessImage(string filePath)
{
if (!File.Exists(filePath)) throw new FileNotFoundException("Image not found", filePath);
using var image = Cv2.ImRead(filePath);
var regions = DetectTextRegions(image).ToArray();
var texts = new List<string>();
foreach (var r in regions)
{
using var crop = new Mat(image, r.BoundingBox);
var txt = RecognizeDigits(crop);
if (!string.IsNullOrEmpty(txt)) texts.Add(txt);
}
return new ImageResult(Path.GetFileName(filePath), string.Join(",", texts), filePath);
}
public IEnumerable<ImageResult> ProcessDirectory(string directoryPath, bool skipTextNegative = false)
{
// Simple wrapper over async implementation
return ProcessDirectoryAsync(directoryPath, skipTextNegative).GetAwaiter().GetResult();
}
public async Task<IEnumerable<ImageResult>> ProcessDirectoryAsync(string directoryPath, bool skipTextNegative = false, bool recursive = false, IProgress<ProcessingStats>? progress = null, IProgress<ImageResult>? resultProgress = null, CancellationToken cancellationToken = default)
{
if (!Directory.Exists(directoryPath)) throw new DirectoryNotFoundException(directoryPath);
var searchOption = recursive ? SearchOption.AllDirectories : SearchOption.TopDirectoryOnly;
var files = Directory.EnumerateFiles(directoryPath, "*.*", searchOption)
.Where(f => f.EndsWith(".jpg", StringComparison.OrdinalIgnoreCase) || f.EndsWith(".jpeg", StringComparison.OrdinalIgnoreCase))
.ToArray();
var bag = new ConcurrentBag<ImageResult>();
var dop = Environment.ProcessorCount;
// Create a ThreadLocal pair of nets to avoid reloading for every file while still avoiding concurrent use of the same Net
// Also keep a ConcurrentBag of created nets so we can dispose them safely from this thread
var netsBag = new ConcurrentBag<(Net detNet, Net recNet)>();
var threadLocalNets = new ThreadLocal<(Net detNet, Net recNet)>(() =>
{
var det = CvDnn.ReadNetFromDarknet(_cfg.DetectionCfg, _cfg.DetectionWeights);
var rec = CvDnn.ReadNetFromDarknet(_cfg.RecognitionCfg, _cfg.RecognitionWeights);
det.SetPreferableBackend(Backend.OPENCV);
det.SetPreferableTarget(Target.CPU);
rec.SetPreferableBackend(Backend.OPENCV);
rec.SetPreferableTarget(Target.CPU);
netsBag.Add((det, rec));
return (det, rec);
});
var total = files.Length;
var processed = 0;
var sw = System.Diagnostics.Stopwatch.StartNew();
await Task.Run(() =>
{
try
{
Parallel.ForEach(files, new ParallelOptions { MaxDegreeOfParallelism = dop, CancellationToken = cancellationToken }, f =>
{
// Parallel will handle cancellation via the provided token; avoid rethrowing OperationCanceledException from workers
cancellationToken.ThrowIfCancellationRequested();
var filename = Path.GetFileName(f);
if (skipTextNegative && filename.StartsWith("tn_", StringComparison.OrdinalIgnoreCase))
return;
try
{
var nets = threadLocalNets.Value;
using var image = Cv2.ImRead(f);
var regions = DetectTextRegions(nets.detNet, image).ToArray();
var texts = new List<string>();
foreach (var r in regions)
{
using var crop = new Mat(image, r.BoundingBox);
var txt = RecognizeDigits(crop, nets.recNet);
if (!string.IsNullOrEmpty(txt)) texts.Add(txt);
}
var imgRes = new ImageResult(filename, string.Join(",", texts), f);
bag.Add(imgRes);
resultProgress?.Report(imgRes);
}
catch
{
// swallow per-file errors and report empty result
bag.Add(new ImageResult(filename, string.Empty, f));
}
finally
{
var proc = Interlocked.Increment(ref processed);
if (progress != null)
{
var elapsed = Math.Max(1, sw.ElapsedMilliseconds);
var ips = proc * 1000.0 / elapsed;
progress.Report(new ProcessingStats(total, proc, ips));
}
}
});
}
catch (OperationCanceledException)
{
// Cancellation requested — exit gracefully and return partial results
}
}, cancellationToken).ConfigureAwait(false);
// dispose created nets
while (netsBag.TryTake(out var pair))
{
try { pair.detNet.Dispose(); } catch { }
try { pair.recNet.Dispose(); } catch { }
}
threadLocalNets.Dispose();
return bag.OrderBy(b => b.FileName).ToList();
}
// Overload RecognizeDigits that accepts a Net for worker threads
private string RecognizeDigits(Mat croppedImage, Net recognitionNet)
{
if (croppedImage is null) throw new ArgumentNullException(nameof(croppedImage));
using var blob = CvDnn.BlobFromImage(croppedImage, 0.00392, _cfg.RecognitionInputSize, new Scalar(0, 0, 0), true, false);
recognitionNet.SetInput(blob);
var outNames = GetOutputLayerNames(recognitionNet);
var outsList = new List<Mat>();
recognitionNet.Forward(outsList, outNames);
Mat[] outs = outsList.ToArray();
var boxes = new List<Rect>();
var confidences = new List<float>();
var classIds = new List<int>();
var centerXList = new List<double>();
int imgW = croppedImage.Width;
int imgH = croppedImage.Height;
foreach (var outMat in outs)
{
for (int i = 0; i < outMat.Rows; i++)
{
float cx = outMat.At<float>(i, 0) * imgW;
float cy = outMat.At<float>(i, 1) * imgH;
float w = outMat.At<float>(i, 2) * imgW;
float h = outMat.At<float>(i, 3) * imgH;
float maxScore = 0f;
int bestClass = -1;
for (int c = 5; c < outMat.Cols; c++)
{
float score = outMat.At<float>(i, c);
if (score > maxScore)
{
maxScore = score;
bestClass = c - 5;
}
}
if (maxScore > _cfg.ConfidenceThreshold)
{
int x = (int)Math.Max(0, Math.Round(cx - w / 2));
int y = (int)Math.Max(0, Math.Round(cy - h / 2));
boxes.Add(new Rect(x, y, (int)Math.Round(w), (int)Math.Round(h)));
confidences.Add(maxScore);
classIds.Add(bestClass);
centerXList.Add(cx);
}
}
}
if (classIds.Count == 0) return string.Empty;
CvDnn.NMSBoxes(boxes, confidences, (float)_cfg.ConfidenceThreshold, (float)_cfg.NmsThreshold, out int[] keep);
var ordered = keep.Select(i => new { Idx = i, Cx = centerXList[i], ClassId = classIds[i] })
.OrderBy(x => x.Cx)
.Select(x => _cfg.NumberClasses[x.ClassId]);
return string.Concat(ordered);
}
}
}

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src/AIFotoONLUS.Core/ProcessingStats.cs Normal file
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namespace AIFotoONLUS.Core
{
public record ProcessingStats(int TotalFiles, int ProcessedFiles, double ImagesPerSecond);
}

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src/AIFotoONLUS.WPF/AIFotoONLUS.WPF.csproj Normal file
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<Project Sdk="Microsoft.NET.Sdk.WindowsDesktop">
<PropertyGroup>
<OutputType>WinExe</OutputType>
<TargetFramework>net10.0-windows</TargetFramework>
<UseWPF>true</UseWPF>
<UseWindowsForms>true</UseWindowsForms>
<Nullable>enable</Nullable>
<ImplicitUsings>enable</ImplicitUsings>
</PropertyGroup>
<ItemGroup>
<PackageReference Include="NLog" Version="6.1.0" />
</ItemGroup>
<ItemGroup>
<ProjectReference Include="..\AIFotoONLUS.Core\AIFotoONLUS.Core.csproj" />
</ItemGroup>
</Project>

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src/AIFotoONLUS.WPF/App.xaml Normal file
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<Application x:Class="AIFotoONLUS.WPF.App"
xmlns="http://schemas.microsoft.com/winfx/2006/xaml/presentation"
xmlns:x="http://schemas.microsoft.com/winfx/2006/xaml"
xmlns:converters="clr-namespace:AIFotoONLUS.WPF.Converters"
StartupUri="MainWindow.xaml">
<Application.Resources>
<ResourceDictionary>
<converters:InverseBoolConverter x:Key="InverseBoolConverter" />
</ResourceDictionary>
</Application.Resources>
</Application>

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src/AIFotoONLUS.WPF/App.xaml.cs Normal file
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using System.Windows;
namespace AIFotoONLUS.WPF
{
public partial class App : System.Windows.Application
{
}
}

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src/AIFotoONLUS.WPF/Converters/InverseBoolConverter.cs Normal file
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using System;
using System.Globalization;
using System.Windows.Data;
namespace AIFotoONLUS.WPF.Converters
{
public class InverseBoolConverter : IValueConverter
{
public object Convert(object value, Type targetType, object parameter, CultureInfo culture)
{
if (value is bool b) return !b;
return true;
}
public object ConvertBack(object value, Type targetType, object parameter, CultureInfo culture)
{
if (value is bool b) return !b;
return true;
}
}
}

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src/AIFotoONLUS.WPF/MainWindow.xaml Normal file
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<Window x:Class="AIFotoONLUS.WPF.MainWindow"
xmlns="http://schemas.microsoft.com/winfx/2006/xaml/presentation"
xmlns:x="http://schemas.microsoft.com/winfx/2006/xaml"
Title="AI Foto ONLUS - Demo" Height="460" Width="700">
<Grid Margin="10">
<Grid.RowDefinitions>
<RowDefinition Height="Auto"/>
<RowDefinition Height="Auto"/>
<RowDefinition Height="Auto"/>
<RowDefinition Height="*"/>
</Grid.RowDefinitions>
<StackPanel Orientation="Horizontal" Grid.Row="0" Margin="0,0,0,8">
<TextBlock VerticalAlignment="Center" Text="Images directory:" Margin="0,0,8,0"/>
<TextBox Text="{Binding ImagesDirectory, UpdateSourceTrigger=PropertyChanged}" Width="420" />
<Button Content="Browse" Margin="8,0,0,0" Width="75" Command="{Binding BrowseImagesCommand}"/>
</StackPanel>
<StackPanel Orientation="Horizontal" Grid.Row="1" Margin="0,0,0,8">
<TextBlock VerticalAlignment="Center" Text="Models directory:" Margin="0,0,8,0"/>
<TextBox Text="{Binding ModelsDirectory, UpdateSourceTrigger=PropertyChanged}" Width="340" />
<Button Content="Browse" Margin="8,0,0,0" Width="75" Command="{Binding BrowseModelsCommand}"/>
</StackPanel>
<StackPanel Orientation="Horizontal" Grid.Row="2" Margin="0,0,0,8">
<Button Content="Load Models" Width="100" Command="{Binding LoadModelsCommand}"/>
<Button Content="Process" Width="100" Margin="8,0,0,0" Command="{Binding ProcessCommand}" IsEnabled="{Binding IsProcessing, Converter={StaticResource InverseBoolConverter}}" />
<Button Content="Stop" Width="100" Margin="8,0,0,0" Command="{Binding CancelCommand}" IsEnabled="{Binding IsProcessing}" />
<TextBlock Text="{Binding Status}" Margin="12,4,0,0" VerticalAlignment="Center"/>
</StackPanel>
<Grid Grid.Row="3">
<Grid.ColumnDefinitions>
<ColumnDefinition Width="2*" />
<ColumnDefinition Width="3*" />
</Grid.ColumnDefinitions>
<StackPanel Grid.Column="0">
<DataGrid AutoGenerateColumns="False" IsReadOnly="True" ItemsSource="{Binding Results}" Height="300">
<DataGrid.Columns>
<DataGridTextColumn Header="Filename" Binding="{Binding FileName}" Width="*"/>
<DataGridTextColumn Header="Text" Binding="{Binding Text}" Width="2*"/>
</DataGrid.Columns>
</DataGrid>
<ProgressBar Minimum="0" Maximum="100" Value="{Binding ProgressValue, Mode=OneWay}" Height="20" Margin="0,8,0,0" />
<TextBlock Text="{Binding Status}" Margin="0,6,0,0" />
<StackPanel Orientation="Horizontal" Margin="0,6,0,0">
<TextBlock Text="Processed:" FontWeight="Bold"/>
<TextBlock Text=" " />
<TextBlock Text="{Binding ProcessedFiles}"/>
<TextBlock Text=" / " />
<TextBlock Text="{Binding TotalFiles}"/>
<TextBlock Text=" " />
<TextBlock Text="Imgs/sec:" FontWeight="Bold"/>
<TextBlock Text=" " />
<TextBlock Text="{Binding ImagesPerSecond, StringFormat=N2}"/>
</StackPanel>
</StackPanel>
<!-- Preview removed as requested -->
</Grid>
</Grid>
</Window>

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using AIFotoONLUS.WPF.ViewModels;
using System;
using System.Windows;
namespace AIFotoONLUS.WPF
{
public partial class MainWindow : Window
{
private MainViewModel _vm = new();
public MainWindow()
{
InitializeComponent();
DataContext = _vm;
}
protected override void OnClosed(EventArgs e)
{
base.OnClosed(e);
_vm.Dispose();
}
}
}

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using System;
using System.IO;
namespace AIFotoONLUS.WPF
{
internal static class Preferences
{
private static readonly string PrefFile = Path.Combine(Environment.GetFolderPath(Environment.SpecialFolder.LocalApplicationData), "AIFotoONLUS", "prefs.txt");
public static void Save(string imagesDir, string modelsDir)
{
try
{
var dir = Path.GetDirectoryName(PrefFile);
if (!Directory.Exists(dir)) Directory.CreateDirectory(dir!);
File.WriteAllLines(PrefFile, new[] { imagesDir ?? string.Empty, modelsDir ?? string.Empty });
}
catch { }
}
public static (string imagesDir, string modelsDir) Load()
{
try
{
if (!File.Exists(PrefFile)) return (string.Empty, string.Empty);
var lines = File.ReadAllLines(PrefFile);
var img = lines.Length > 0 ? lines[0] : string.Empty;
var mdl = lines.Length > 1 ? lines[1] : string.Empty;
return (img, mdl);
}
catch { return (string.Empty, string.Empty); }
}
}
}

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using AIFotoONLUS.Core;
using System;
using System.Threading;
using System.Collections.ObjectModel;
using System.ComponentModel;
using System.IO;
using System.Linq;
using System.Threading.Tasks;
using System.Windows;
namespace AIFotoONLUS.WPF.ViewModels
{
public class MainViewModel : INotifyPropertyChanged, IDisposable
{
private ModelConfiguration _cfg = new();
private NumberRecognitionEngine? _engine;
private string _imagesDirectory = string.Empty;
private string _modelsDirectory = "models";
private string _status = string.Empty;
private double _progressValue;
private double _imagesPerSecond;
private int _totalFiles;
private int _processedFiles;
private bool _isProcessing;
private CancellationTokenSource? _cts;
public ObservableCollection<ImageResult> Results { get; } = new();
public string ImagesDirectory
{
get => _imagesDirectory;
set { _imagesDirectory = value; OnPropertyChanged(nameof(ImagesDirectory)); }
}
public string ModelsDirectory
{
get => _modelsDirectory;
set { _modelsDirectory = value; OnPropertyChanged(nameof(ModelsDirectory)); }
}
public string Status
{
get => _status;
private set { _status = value; OnPropertyChanged(nameof(Status)); }
}
public double ProgressValue
{
get => _progressValue;
private set { _progressValue = value; OnPropertyChanged(nameof(ProgressValue)); }
}
public double ImagesPerSecond
{
get => _imagesPerSecond;
private set { _imagesPerSecond = value; OnPropertyChanged(nameof(ImagesPerSecond)); }
}
public int TotalFiles
{
get => _totalFiles;
private set { _totalFiles = value; OnPropertyChanged(nameof(TotalFiles)); }
}
public int ProcessedFiles
{
get => _processedFiles;
private set { _processedFiles = value; OnPropertyChanged(nameof(ProcessedFiles)); }
}
public bool IsProcessing
{
get => _isProcessing;
private set { _isProcessing = value; OnPropertyChanged(nameof(IsProcessing)); ProcessCommand.RaiseCanExecuteChanged(); CancelCommand.RaiseCanExecuteChanged(); }
}
// No image preview required by user — selection/preview removed
public RelayCommand BrowseImagesCommand { get; }
public RelayCommand BrowseModelsCommand { get; }
public RelayCommand LoadModelsCommand { get; }
public RelayCommand ProcessCommand { get; }
public RelayCommand CancelCommand { get; }
public MainViewModel()
{
BrowseImagesCommand = new RelayCommand(_ => BrowseFolder(isModel: false));
BrowseModelsCommand = new RelayCommand(_ => BrowseFolder(isModel: true));
LoadModelsCommand = new RelayCommand(_ => LoadModels());
ProcessCommand = new RelayCommand(async _ => await ProcessAsync(), _ => !IsProcessing);
CancelCommand = new RelayCommand(_ => Cancel(), _ => IsProcessing);
// load prefs
var prefs = Preferences.Load();
if (!string.IsNullOrWhiteSpace(prefs.imagesDir)) ImagesDirectory = prefs.imagesDir;
if (!string.IsNullOrWhiteSpace(prefs.modelsDir)) ModelsDirectory = prefs.modelsDir;
}
private void BrowseFolder(bool isModel)
{
using var dlg = new System.Windows.Forms.FolderBrowserDialog();
var result = dlg.ShowDialog();
if (result == System.Windows.Forms.DialogResult.OK)
{
if (isModel) ModelsDirectory = dlg.SelectedPath; else ImagesDirectory = dlg.SelectedPath;
Preferences.Save(ImagesDirectory, ModelsDirectory);
}
}
private void LoadModels()
{
try
{
_engine?.Dispose();
_cfg = new ModelConfiguration
{
DetectionCfg = Path.Combine(ModelsDirectory, "detection.cfg"),
DetectionWeights = Path.Combine(ModelsDirectory, "detection.weights"),
RecognitionCfg = Path.Combine(ModelsDirectory, "recognition.cfg"),
RecognitionWeights = Path.Combine(ModelsDirectory, "recognition.weights"),
ConfidenceThreshold = 0.5,
NmsThreshold = 0.4
};
_engine = new NumberRecognitionEngine(_cfg);
Status = "Models loaded";
Preferences.Save(ImagesDirectory, ModelsDirectory);
}
catch (Exception ex)
{
System.Windows.MessageBox.Show(ex.Message, "Error loading models", MessageBoxButton.OK, MessageBoxImage.Error);
Status = "Error loading models";
}
}
private async Task ProcessAsync()
{
if (_engine == null)
{
System.Windows.MessageBox.Show("Load models first.", "Info", MessageBoxButton.OK, MessageBoxImage.Information);
return;
}
if (string.IsNullOrWhiteSpace(ImagesDirectory) || !Directory.Exists(ImagesDirectory))
{
System.Windows.MessageBox.Show("Select a valid directory.", "Info", MessageBoxButton.OK, MessageBoxImage.Information);
return;
}
Status = "Processing...";
Results.Clear();
ProgressValue = 0;
ImagesPerSecond = 0;
TotalFiles = 0;
ProcessedFiles = 0;
IsProcessing = true;
_cts = new CancellationTokenSource();
var progress = new Progress<ProcessingStats>(s =>
{
TotalFiles = s.TotalFiles;
ProcessedFiles = s.ProcessedFiles;
ImagesPerSecond = s.ImagesPerSecond;
ProgressValue = s.TotalFiles > 0 ? (double)s.ProcessedFiles / s.TotalFiles * 100.0 : 0;
});
try
{
var resultProgress = new Progress<ImageResult>(r => Results.Add(r));
await _engine.ProcessDirectoryAsync(ImagesDirectory, recursive: true, progress: progress, resultProgress: resultProgress, cancellationToken: _cts.Token);
Status = $"Done ({Results.Count})";
}
catch (Exception ex)
{
System.Windows.MessageBox.Show(ex.Message, "Processing error", MessageBoxButton.OK, MessageBoxImage.Error);
Status = "Error";
}
finally
{
IsProcessing = false;
_cts?.Dispose();
_cts = null;
}
}
private void Cancel()
{
if (!_isProcessing) return;
_cts?.Cancel();
Status = "Cancelling...";
}
public void Dispose()
{
_engine?.Dispose();
}
public event PropertyChangedEventHandler? PropertyChanged;
private void OnPropertyChanged(string name) => PropertyChanged?.Invoke(this, new PropertyChangedEventArgs(name));
}
}

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using System;
using System.Windows.Input;
namespace AIFotoONLUS.WPF.ViewModels
{
public class RelayCommand : ICommand
{
private readonly Action<object?> _execute;
private readonly Predicate<object?>? _canExecute;
public RelayCommand(Action<object?> execute, Predicate<object?>? canExecute = null)
{
_execute = execute ?? throw new ArgumentNullException(nameof(execute));
_canExecute = canExecute;
}
public event EventHandler? CanExecuteChanged;
public bool CanExecute(object? parameter) => _canExecute?.Invoke(parameter) ?? true;
public void Execute(object? parameter) => _execute(parameter);
public void RaiseCanExecuteChanged() => CanExecuteChanged?.Invoke(this, EventArgs.Empty);
}
}

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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.