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OpenNest/OpenNest.Engine/ML/AnglePredictor.cs
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aj aec0523062 style: apply CSharpier formatting to all C# sources
Repo-wide sweep with the pinned CSharpier 1.3.0 tool. Whitespace and
line-wrapping only; OpenNest.Engine.Tests (109) and OpenNest.IO.Tests
pass after reformat, full solution builds 0 errors.

Added .csharpierignore so csproj/config XML keeps its existing layout
(CSharpier's XML wrapping churns attributes with zero benefit).

Formatting is now enforceable: dotnet csharpier check . passes.
2026-09-20 16:41:50 -04:00

123 lines
4.2 KiB
C#

using System;
using System.Collections.Generic;
using System.Diagnostics;
using System.IO;
using System.Linq;
using Microsoft.ML.OnnxRuntime;
using Microsoft.ML.OnnxRuntime.Tensors;
using OpenNest.Math;
namespace OpenNest.Engine.ML
{
public static class AnglePredictor
{
private static InferenceSession _session;
private static volatile bool _loadAttempted;
private static readonly object _lock = new();
public static List<double> PredictAngles(
PartFeatures features,
double sheetWidth,
double sheetHeight,
double threshold = 0.3
)
{
var session = GetSession();
if (session == null)
return null;
try
{
var input = new float[11];
input[0] = (float)features.Area;
input[1] = (float)features.Convexity;
input[2] = (float)features.AspectRatio;
input[3] = (float)features.BoundingBoxFill;
input[4] = (float)features.Circularity;
input[5] = (float)features.PerimeterToAreaRatio;
input[6] = features.VertexCount;
input[7] = (float)sheetWidth;
input[8] = (float)sheetHeight;
input[9] = (float)(sheetWidth / (sheetHeight > 0 ? sheetHeight : 1.0));
input[10] = (float)(features.Area / (sheetWidth * sheetHeight));
var tensor = new DenseTensor<float>(input, new[] { 1, 11 });
var inputs = new List<NamedOnnxValue>
{
NamedOnnxValue.CreateFromTensor("features", tensor),
};
using var results = session.Run(inputs);
var probabilities = results.First().AsEnumerable<float>().ToArray();
var angles = new List<(double angleDeg, float prob)>();
for (var i = 0; i < 36 && i < probabilities.Length; i++)
{
if (probabilities[i] >= threshold)
angles.Add((i * 5.0, probabilities[i]));
}
// Minimum 3 angles — take top by probability if fewer pass threshold.
if (angles.Count < 3)
{
angles = probabilities
.Select((p, i) => (angleDeg: i * 5.0, prob: p))
.OrderByDescending(x => x.prob)
.Take(3)
.ToList();
}
// Always include 0 and 90 as safety fallback.
var result = angles.Select(a => Angle.ToRadians(a.angleDeg)).ToList();
if (!result.Any(a => a.IsEqualTo(0)))
result.Add(0);
if (!result.Any(a => a.IsEqualTo(Angle.HalfPI)))
result.Add(Angle.HalfPI);
return result;
}
catch (Exception ex)
{
Debug.WriteLine($"[AnglePredictor] Inference failed: {ex.Message}");
return null;
}
}
private static InferenceSession GetSession()
{
if (_loadAttempted)
return _session;
lock (_lock)
{
if (_loadAttempted)
return _session;
_loadAttempted = true;
try
{
var dir = Path.GetDirectoryName(typeof(AnglePredictor).Assembly.Location);
var modelPath = Path.Combine(dir, "Models", "angle_predictor.onnx");
if (!File.Exists(modelPath))
{
Debug.WriteLine($"[AnglePredictor] Model not found: {modelPath}");
return null;
}
_session = new InferenceSession(modelPath);
Debug.WriteLine("[AnglePredictor] Model loaded successfully");
}
catch (Exception ex)
{
Debug.WriteLine($"[AnglePredictor] Failed to load model: {ex.Message}");
}
return _session;
}
}
}
}