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1、前言

ML.NET 是面向 .NET 开发人员的开源跨平台机器学习框架，支持将自定义机器学习模型集成到 .NET 应用程序中。它包含一个 API，其中包含不同的 NuGet 包、名为模型生成器的 Visual Studio 扩展，以及作为 .NET 工具安装的命令行接口。

链接：ML.NET 概述 - ML.NET | Microsoft Learn

2、效果展示

3、实现

1)、环境依赖

借用Halcon做图像显示控件。

WPF应用程序。

2)、前台代码

 <Grid><Grid.ColumnDefinitions><ColumnDefinition /><ColumnDefinition /></Grid.ColumnDefinitions><Grid.RowDefinitions><RowDefinition Height="50" /><RowDefinition /></Grid.RowDefinitions><StackPanel Grid.ColumnSpan="2" Orientation="Horizontal"><ButtonClick="Button_Click"Content="选择图像"Tag="1" /><TextBoxx:Name="tbFiltScore"Width="50"Margin="15"VerticalAlignment="Center"BorderBrush="NavajoWhite"BorderThickness="0,0,0,1"Text="0.5"TextAlignment="Center" /><ButtonClick="Button_Click"Content="执行推理"Tag="2" /></StackPanel><halconDot:HSmartWindowControlWPF x:Name="ImgControl" Grid.Row="1" /><ListViewGrid.Row="1"Grid.RowSpan="2"Grid.Column="1"Background="DarkCyan"ItemsSource="{Binding DataInfos}"><ListView.ItemTemplate><DataTemplate DataType="local:ResultInfo"><StackPanel Orientation="Horizontal"><TextBlock Width="Auto" Text="{Binding Index}" /><TextBlock Width="100" Text="{Binding BoxLabel}" /><TextBlock Width="100" Text="{Binding Score, StringFormat={}{0:F6}}" /><TextBlock Width="100" Text="{Binding Box.XTop, StringFormat={}{0:F6}}" /><TextBlock Width="100" Text="{Binding Box.YTop, StringFormat={}{0:F6}}" /><TextBlock Width="100" Text="{Binding Box.XBottom, StringFormat={}{0:F6}}" /><TextBlock Width="100" Text="{Binding Box.YBottom, StringFormat={}{0:F6}}" /></StackPanel></DataTemplate></ListView.ItemTemplate></ListView></Grid>

3)、后台代码

1.推理处理

 public class DeepHelp{public List<ResultInfo> Test(string filename){// Create single instance of sample data from first line of dataset for model input.//var image = MLImage.CreateFromFile(@"D:\3项目文件\门锁项目\智能门锁图像\Image_20241119105244991.bmp");var image = MLImage.CreateFromFile(filename);SentimenModel.ModelInput sampleData = new SentimenModel.ModelInput(){Image = image,};// Make a single prediction on the sample data and print results.var predictionResult = SentimenModel.Predict(sampleData);Console.WriteLine("\n\nPredicted Boxes:\n");if (predictionResult.PredictedBoundingBoxes == null){Console.WriteLine("No Predicted Bounding Boxes");return null;}List<ResultInfo> boxes =predictionResult.PredictedBoundingBoxes.Chunk(4).Select(x => new BoxInfo { XTop = x[0], YTop = x[1], XBottom = x[2], YBottom = x[3] }).Zip(predictionResult.Score, (a, b) => new ResultInfo { Box = a, Score = b }).ToList();for (int i = 0; i < boxes.Count; i++){boxes[i].BoxLabel = predictionResult.PredictedLabel[i];boxes[i].Index = i + 1;}return boxes;//foreach (var item in boxes)//{//    Console.WriteLine($"XTop: {item.Box.XTop},YTop: {item.Box.YTop},XBottom: {item.Box.XBottom},YBottom: {item.Box.YBottom}, Score: {item.Score}");//}}}public class ResultInfo{public int Index { get; set; }public float Score { get; set; }public BoxInfo Box { get; set; }public string BoxLabel { get; set; }}public class BoxInfo{public float XTop { get; set; }public float YTop { get; set; }public float XBottom { get; set; }public float YBottom { get; set; }}

2.调用

   private async Task<List<ResultInfo>> ProcessAnayleData(string fileName){List<ResultInfo> boxes = await Task.Run(() => { return new DeepHelp().Test(fileName); });return boxes;}

3.显示

 private void DispResultInfo(List<ResultInfo> res){ImgControl.HalconWindow.SetDraw("margin");int i = 0;double minScore = Convert.ToDouble(tbFiltScore.Text.Trim());foreach (ResultInfo info in res){if (info.Score < minScore){continue;}HRegion hRegion = new HRegion();hRegion.GenRectangle1(info.Box.YTop, info.Box.XTop * 1.0, info.Box.YBottom, info.Box.XBottom);if (i >= colors.Length){i = 0;}string colorStr = colors[i++];ImgControl.HalconWindow.SetColor(colorStr);ImgControl.HalconWindow.DispRegion(hRegion);ImgControl.HalconWindow.DispText(info.Score.ToString("F3") + info.BoxLabel, "image", info.Box.YTop, info.Box.XTop * 1.0, colorStr, new HTuple(), new HTuple());//   break;}//DrawBoundingBox("/output", FileName,res);}