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colbylwilliamscolbylwilliams
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2 parents abe833f + 6276012 commit e047118

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‎samples/csharp/end-to-end-apps/ObjectDetection-Onnx/OnnxObjectDetection/BoundingBox.cs

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using System.Drawing;
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namespace OnnxObjectDetection
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{
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public class BoundingBoxDimensions
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{
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public float X { get; set; }
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public float Y { get; set; }
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public float Height { get; set; }
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public float Width { get; set; }
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}
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public class BoundingBox
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{
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public BoundingBoxDimensions Dimensions { get; set; }
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public string Label { get; set; }
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public float Confidence { get; set; }
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public RectangleF Rect
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{
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get { return new RectangleF(Dimensions.X, Dimensions.Y, Dimensions.Width, Dimensions.Height); }
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}
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public Color BoxColor { get; set; }
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public string Description => $"{Label} ({(Confidence * 100).ToString("0")}%)";
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private static readonly Color[] classColors = new Color[]
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{
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Color.Khaki, Color.Fuchsia, Color.Silver, Color.RoyalBlue,
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Color.Green, Color.DarkOrange, Color.Purple, Color.Gold,
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Color.Red, Color.Aquamarine, Color.Lime, Color.AliceBlue,
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Color.Sienna, Color.Orchid, Color.Tan, Color.LightPink,
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Color.Yellow, Color.HotPink, Color.OliveDrab, Color.SandyBrown,
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Color.DarkTurquoise
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};
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public static Color GetColor(int index) => index < classColors.Length ? classColors[index] : classColors[index % classColors.Length];
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}
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}

‎samples/csharp/end-to-end-apps/ObjectDetection-Onnx/OnnxObjectDetection/ML/DataModels/CustomVisionModel.cs

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using System;
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using System.IO;
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using System.IO.Compression;
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using System.Linq;
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namespace OnnxObjectDetection
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{
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public class CustomVisionModel : IOnnxModel
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{
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const string modelName = "model.onnx", labelsName = "labels.txt";
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private readonly string labelsPath;
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public string ModelPath { get; private set; }
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public string ModelInput { get; } = "data";
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public string ModelOutput { get; } = "model_outputs0";
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public string[] Labels { get; private set; }
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public (float,float)[] Anchors { get; } = { (0.573f,0.677f), (1.87f,2.06f), (3.34f,5.47f), (7.88f,3.53f), (9.77f,9.17f) };
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public CustomVisionModel(string modelPath)
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{
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var extractPath = Path.GetFullPath(modelPath.Replace(".zip", Path.DirectorySeparatorChar.ToString()));
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if (!Directory.Exists(extractPath))
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Directory.CreateDirectory(extractPath);
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ModelPath = Path.GetFullPath(Path.Combine(extractPath, modelName));
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labelsPath = Path.GetFullPath(Path.Combine(extractPath, labelsName));
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if (!File.Exists(ModelPath) || !File.Exists(labelsPath))
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ExtractArchive(modelPath);
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Labels = File.ReadAllLines(labelsPath);
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}
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void ExtractArchive(string modelPath)
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{
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using (ZipArchive archive = ZipFile.OpenRead(modelPath))
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{
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var modelEntry = archive.Entries.FirstOrDefault(e => e.Name.Equals(modelName, StringComparison.OrdinalIgnoreCase))
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?? throw new FormatException("The exported .zip archive is missing the model.onnx file");
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modelEntry.ExtractToFile(ModelPath);
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var labelsEntry = archive.Entries.FirstOrDefault(e => e.Name.Equals(labelsName, StringComparison.OrdinalIgnoreCase))
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?? throw new FormatException("The exported .zip archive is missing the labels.txt file");
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labelsEntry.ExtractToFile(labelsPath);
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}
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}
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}
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}

‎samples/csharp/end-to-end-apps/ObjectDetection-Onnx/OnnxObjectDetection/ML/DataModels/CustomVisionPrediction.cs

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using Microsoft.ML.Data;
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namespace OnnxObjectDetection
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{
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public class CustomVisionPrediction : IOnnxObjectPrediction
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{
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[ColumnName("model_outputs0")]
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public float[] PredictedLabels { get; set; }
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}
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}

‎samples/csharp/end-to-end-apps/ObjectDetection-Onnx/OnnxObjectDetection/ML/DataModels/IOnnxModel.cs

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namespace OnnxObjectDetection
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{
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public interface IOnnxModel
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{
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string ModelPath { get; }
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// To check Model input and output parameter names, you can
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// use tools like Netron: https://github.com/lutzroeder/netron
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string ModelInput { get; }
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string ModelOutput { get; }
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string[] Labels { get; }
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(float, float)[] Anchors { get; }
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}
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public interface IOnnxObjectPrediction
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{
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float[] PredictedLabels { get; set; }
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}
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}

‎samples/csharp/end-to-end-apps/ObjectDetection-Onnx/OnnxObjectDetection/ML/DataModels/ImageInputData.cs

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33

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namespace OnnxObjectDetection
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{
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public struct ImageSettings
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{
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public const int imageHeight = 416;
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public const int imageWidth = 416;
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}
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public class ImageInputData
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{
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[ImageType(416, 416)]
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[ImageType(ImageSettings.imageHeight, ImageSettings.imageWidth)]
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public Bitmap Image { get; set; }
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}
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}

‎samples/csharp/end-to-end-apps/ObjectDetection-Onnx/OnnxObjectDetection/ML/DataModels/ImageObjectPrediction.cs

-10
This file was deleted.

‎samples/csharp/end-to-end-apps/ObjectDetection-Onnx/OnnxObjectDetection/ML/DataModels/TinyYoloModel.cs

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namespace OnnxObjectDetection
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{
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public class TinyYoloModel : IOnnxModel
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{
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public string ModelPath { get; private set; }
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public string ModelInput { get; } = "image";
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public string ModelOutput { get; } = "grid";
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public string[] Labels { get; } =
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{
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"aeroplane", "bicycle", "bird", "boat", "bottle",
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"bus", "car", "cat", "chair", "cow",
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"diningtable", "dog", "horse", "motorbike", "person",
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"pottedplant", "sheep", "sofa", "train", "tvmonitor"
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};
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public (float,float)[] Anchors { get; } = { (1.08f,1.19f), (3.42f,4.41f), (6.63f,11.38f), (9.42f,5.11f), (16.62f,10.52f) };
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public TinyYoloModel(string modelPath)
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{
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ModelPath = modelPath;
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}
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}
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}

‎samples/csharp/end-to-end-apps/ObjectDetection-Onnx/OnnxObjectDetection/ML/DataModels/TinyYoloPrediction.cs

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using Microsoft.ML.Data;
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namespace OnnxObjectDetection
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{
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public class TinyYoloPrediction : IOnnxObjectPrediction
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{
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[ColumnName("grid")]
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public float[] PredictedLabels { get; set; }
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}
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}

‎samples/csharp/end-to-end-apps/ObjectDetection-Onnx/OnnxObjectDetection/ML/OnnxModelConfigurator.cs

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@@ -7,57 +7,40 @@ namespace OnnxObjectDetection
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{
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public class OnnxModelConfigurator
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{
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private readonly MLContext _mlContext;
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private readonly ITransformer _mlModel;
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private readonly MLContext mlContext;
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private readonly ITransformer mlModel;
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public OnnxModelConfigurator(string onnxModelFilePath)
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public OnnxModelConfigurator(IOnnxModel onnxModel)
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{
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_mlContext = new MLContext();
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mlContext = new MLContext();
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// Model creation and pipeline definition for images needs to run just once,
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// so calling it from the constructor:
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_mlModel = SetupMlNetModel(onnxModelFilePath);
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mlModel = SetupMlNetModel(onnxModel);
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}
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public struct ImageSettings
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private ITransformer SetupMlNetModel(IOnnxModel onnxModel)
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{
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public const int imageHeight = 416;
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public const int imageWidth = 416;
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}
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public struct TinyYoloModelSettings
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{
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// To check Tiny Yolo2 Model input and output parameter names,
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// you can use tools like Netron: https://github.com/lutzroeder/netron
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// Input tensor name
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public const string ModelInput = "image";
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// Output tensor name
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public const string ModelOutput = "grid";
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}
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public ITransformer SetupMlNetModel(string onnxModelFilePath)
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{
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var dataView = _mlContext.Data.LoadFromEnumerable(new List<ImageInputData>());
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var dataView = mlContext.Data.LoadFromEnumerable(new List<ImageInputData>());
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var pipeline = _mlContext.Transforms.ResizeImages(resizing: ImageResizingEstimator.ResizingKind.Fill, outputColumnName: "image", imageWidth: ImageSettings.imageWidth, imageHeight: ImageSettings.imageHeight, inputColumnName: nameof(ImageInputData.Image))
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.Append(_mlContext.Transforms.ExtractPixels(outputColumnName: "image"))
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.Append(_mlContext.Transforms.ApplyOnnxModel(modelFile: onnxModelFilePath, outputColumnNames: new[] { TinyYoloModelSettings.ModelOutput }, inputColumnNames: new[] { TinyYoloModelSettings.ModelInput }));
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var pipeline = mlContext.Transforms.ResizeImages(resizing: ImageResizingEstimator.ResizingKind.Fill, outputColumnName: onnxModel.ModelInput, imageWidth: ImageSettings.imageWidth, imageHeight: ImageSettings.imageHeight, inputColumnName: nameof(ImageInputData.Image))
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.Append(mlContext.Transforms.ExtractPixels(outputColumnName: onnxModel.ModelInput))
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.Append(mlContext.Transforms.ApplyOnnxModel(modelFile: onnxModel.ModelPath, outputColumnName: onnxModel.ModelOutput, inputColumnName: onnxModel.ModelInput));
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var mlNetModel = pipeline.Fit(dataView);
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return mlNetModel;
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}
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52-
public PredictionEngine<ImageInputData, ImageObjectPrediction> GetMlNetPredictionEngine()
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public PredictionEngine<ImageInputData, T> GetMlNetPredictionEngine<T>()
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where T : class, IOnnxObjectPrediction, new()
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{
54-
return _mlContext.Model.CreatePredictionEngine<ImageInputData, ImageObjectPrediction>(_mlModel);
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return mlContext.Model.CreatePredictionEngine<ImageInputData, T>(mlModel);
5538
}
5639

5740
public void SaveMLNetModel(string mlnetModelFilePath)
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{
5942
// Save/persist the model to a .ZIP file to be loaded by the PredictionEnginePool
60-
_mlContext.Model.Save(_mlModel, null, mlnetModelFilePath);
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mlContext.Model.Save(mlModel, null, mlnetModelFilePath);
6144
}
6245
}
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}

‎samples/csharp/end-to-end-apps/ObjectDetection-Onnx/OnnxObjectDetection/ML/OnnxModel/TinyYolo2_model.onnx renamed to ‎samples/csharp/end-to-end-apps/ObjectDetection-Onnx/OnnxObjectDetection/ML/OnnxModels/TinyYolo2_model.onnx

File renamed without changes.

‎samples/csharp/end-to-end-apps/ObjectDetection-Onnx/OnnxObjectDetection/OnnxObjectDetection.csproj

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</ItemGroup>
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<ItemGroup>
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<None Update="ML\OnnxModel\TinyYolo2_model.onnx">
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<None Update="ML\OnnxModels\TinyYolo2_model.onnx">
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<CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
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</None>
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</ItemGroup>

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