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C# create Object Detection sample using Onnx Model (dotnet#341)
* WIP: pushed sample for object detection * Removing object detection solution from master branch * Added a new sample i.e Scalable WEBAPI for Real time scenario in the repo. so added link for that sample in ReadME file in seperate table. * Working on Object Detection sample * pushed the code to interpret the scores of model and predict the objects in the image. * Created a sample object Detection using TinyYolo2 Onnx model * Adding the model file * Renamed the folder from TryOnnx to ObjectDetection. so this folder is created as copy. * added Build.Props file with version Ml.Net 1.0.0.-preview * Added ReadMe file and the images need for it. * Added the link for Object Detection sample in ReadMe page of launching page
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‎README.md

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@@ -148,13 +148,13 @@ The official ML.NET samples are divided in multiple categories depending on the
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<td>
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<h4>TensorFlow(ML.NET Scoring) &nbsp;&nbsp;&nbsp;<a href="samples/csharp/getting-started/DeepLearning_ImageClassification_TensorFlow">C#</a> &nbsp; &nbsp; <a href="samples/fsharp/getting-started/DeepLearning_ImageClassification_TensorFlow">F#</a> &nbsp;&nbsp;&nbsp;<img src="images/app-type-getting-started.png" alt="Getting started icon"></h4>
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<h4>TensorFlow(ML.NET Estimator) &nbsp;&nbsp;&nbsp;<a href="samples/csharp/getting-started/DeepLearning_TensorFlowEstimator">C#</a> &nbsp; &nbsp; <a href="samples/fsharp/getting-started/DeepLearning_TensorFlowEstimator">F#</a> &nbsp;&nbsp;&nbsp;<img src="images/app-type-getting-started.png" alt="Getting started icon"></h4>
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<h4>Object detection Coming soon &nbsp;&nbsp;&nbsp;<img src="images/app-type-getting-started.png" alt="Getting started icon"></h4>
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<h4>Object detection &nbsp;&nbsp;&nbsp;<a href="samples/csharp/getting-started\DeepLearning_ObjectDetection_Onnx">C#</a> &nbsp; &nbsp;<img src="images/app-type-getting-started.png" alt="Getting started icon"></h4>
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<h4>Style Transfer Coming soon &nbsp;&nbsp;&nbsp;<img src="images/app-type-e2e.png" alt="End-to-end app icon"></h4>
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<h4>ONNX with ML.NET - Coming soon &nbsp;&nbsp;&nbsp;<img src="images/app-type-getting-started.png" alt="Getting started icon"></h4>
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</td>
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</tr>
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</table>
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**Real Time ML.Net Samples:** The below samples are created for real time scenarios like Scalable WebAPI services, Datasets stored in Database etc.
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<table>

‎samples/csharp/getting-started/DeepLearning_ObjectDetection_Onnx/Directory.Build.props

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<Project>
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<PropertyGroup>
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<MicrosoftMLVersion>1.0.0-preview</MicrosoftMLVersion>
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</PropertyGroup>
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</Project>

‎samples/csharp/getting-started/DeepLearning_ObjectDetection_Onnx/ObjectDetection.sln

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Microsoft Visual Studio Solution File, Format Version 12.00
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# Visual Studio 15
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VisualStudioVersion = 15.0.28307.438
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MinimumVisualStudioVersion = 10.0.40219.1
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Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "ObjectDetection", "ObjectDetectionConsoleApp\ObjectDetection.csproj", "{17EF43C6-7F74-4491-B894-AD14209C2B47}"
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EndProject
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Global
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GlobalSection(SolutionConfigurationPlatforms) = preSolution
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Debug|Any CPU = Debug|Any CPU
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Release|Any CPU = Release|Any CPU
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EndGlobalSection
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GlobalSection(ProjectConfigurationPlatforms) = postSolution
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{17EF43C6-7F74-4491-B894-AD14209C2B47}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
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{17EF43C6-7F74-4491-B894-AD14209C2B47}.Debug|Any CPU.Build.0 = Debug|Any CPU
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{17EF43C6-7F74-4491-B894-AD14209C2B47}.Release|Any CPU.ActiveCfg = Release|Any CPU
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{17EF43C6-7F74-4491-B894-AD14209C2B47}.Release|Any CPU.Build.0 = Release|Any CPU
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EndGlobalSection
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GlobalSection(SolutionProperties) = preSolution
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HideSolutionNode = FALSE
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EndGlobalSection
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GlobalSection(ExtensibilityGlobals) = postSolution
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SolutionGuid = {BC2591CF-DDCB-45BA-9916-9E38D79561E9}
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EndGlobalSection
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EndGlobal

‎samples/csharp/getting-started/DeepLearning_ObjectDetection_Onnx/ObjectDetectionConsoleApp/DataStructures/ImageNetData.cs

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using Microsoft.ML.Data;
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using System;
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using System.Collections.Generic;
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using System.IO;
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using System.Linq;
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namespace ObjectDetection
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{
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public class ImageNetData
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{
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[LoadColumn(0)]
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public string ImagePath;
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[LoadColumn(1)]
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public string Label;
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public static IEnumerable<ImageNetData> ReadFromCsv(string file, string folder)
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{
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return File.ReadAllLines(file)
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.Select(x => x.Split('\t'))
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.Select(x => new ImageNetData { ImagePath = Path.Combine(folder, x[0]), Label = x[1] } );
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}
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}
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public class ImageNetDataProbability : ImageNetData
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{
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public string PredictedLabel;
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public float Probability { get; set; }
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}
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}

‎samples/csharp/getting-started/DeepLearning_ObjectDetection_Onnx/ObjectDetectionConsoleApp/DataStructures/ImageNetPrediction.cs

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using Microsoft.ML.Data;
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namespace ObjectDetection
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{
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public class ImageNetPrediction
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{
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[ColumnName(OnnxModelScorer.TinyYoloModelSettings.ModelOutput)]
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public float[] PredictedLabels;
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}
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}

‎samples/csharp/getting-started/DeepLearning_ObjectDetection_Onnx/ObjectDetectionConsoleApp/ObjectDetection.csproj

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<Project Sdk="Microsoft.NET.Sdk">
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<PropertyGroup>
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<OutputType>Exe</OutputType>
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<TargetFramework>netcoreapp2.2</TargetFramework>
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</PropertyGroup>
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<ItemGroup>
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<PackageReference Include="Microsoft.ML" Version="$(MicrosoftMLVersion)" />
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<PackageReference Include="Microsoft.ML.ImageAnalytics" Version="$(MicrosoftMLVersion)" />
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<PackageReference Include="Microsoft.ML.OnnxTransformer" Version="0.12.0-preview" />
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</ItemGroup>
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<ItemGroup>
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<None Update="assets\images\dog2.jpg">
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<CopyToOutputDirectory>Always</CopyToOutputDirectory>
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</None>
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<None Update="assets\images\Intersection-Counts.jpg">
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<CopyToOutputDirectory>Always</CopyToOutputDirectory>
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</None>
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<None Update="assets\images\tags.tsv">
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<CopyToOutputDirectory>Always</CopyToOutputDirectory>
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</None>
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</ItemGroup>
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<ItemGroup>
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<Folder Include="assets\Model\" />
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</ItemGroup>
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</Project>

‎samples/csharp/getting-started/DeepLearning_ObjectDetection_Onnx/ObjectDetectionConsoleApp/OnnxModelScorer.cs

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using System;
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using System.Collections.Generic;
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using System.Linq;
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using Microsoft.ML;
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namespace ObjectDetection
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{
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class OnnxModelScorer
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{
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private readonly string imagesLocation;
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private readonly string imagesFolder;
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private readonly string modelLocation;
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private readonly MLContext mlContext;
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private IList<YoloBoundingBox> _boxes = new List<YoloBoundingBox>();
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private readonly YoloWinMlParser _parser = new YoloWinMlParser();
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public OnnxModelScorer(string imagesLocation, string imagesFolder, string modelLocation)
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{
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this.imagesLocation = imagesLocation;
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this.imagesFolder = imagesFolder;
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this.modelLocation = modelLocation;
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mlContext = new MLContext();
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}
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public struct ImageNetSettings
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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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// for checking TIny yolo2 Model input and output parameter names,
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//you can use tools like Netron,
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// which is installed by Visual Studio AI Tools
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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 void Score()
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{
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var model = LoadModel(imagesFolder, modelLocation);
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PredictDataUsingModel(imagesLocation, imagesFolder, model);
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}
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private PredictionEngine<ImageNetData, ImageNetPrediction> LoadModel(string imagesFolder, string modelLocation)
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{
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Console.WriteLine("Read model");
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Console.WriteLine($"Model location: {modelLocation}");
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Console.WriteLine($"Images folder: {imagesFolder}");
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Console.WriteLine($"Default parameters: image size=({ImageNetSettings.imageWidth},{ImageNetSettings.imageHeight})");
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var data = mlContext.Data.LoadFromTextFile<ImageNetData>(imagesLocation, hasHeader: true);
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var pipeline = mlContext.Transforms.LoadImages(outputColumnName: "image", imageFolder: imagesFolder, inputColumnName: nameof(ImageNetData.ImagePath))
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.Append(mlContext.Transforms.ResizeImages(outputColumnName: "image", imageWidth: ImageNetSettings.imageWidth, imageHeight: ImageNetSettings.imageHeight, inputColumnName: "image"))
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.Append(mlContext.Transforms.ExtractPixels(outputColumnName: "image"))
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.Append(mlContext.Transforms.ApplyOnnxModel(modelFile: modelLocation, outputColumnNames: new[] { TinyYoloModelSettings.ModelOutput }, inputColumnNames: new[] { TinyYoloModelSettings.ModelInput }));
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var model = pipeline.Fit(data);
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var predictionEngine = mlContext.Model.CreatePredictionEngine<ImageNetData, ImageNetPrediction>(model);
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return predictionEngine;
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}
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protected void PredictDataUsingModel(string imagesLocation,
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string imagesFolder,
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PredictionEngine<ImageNetData, ImageNetPrediction> model)
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{
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Console.WriteLine($"Tags file location: {imagesLocation}");
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Console.WriteLine("");
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Console.WriteLine("=====Identify the objects in the images=====");
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Console.WriteLine("");
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var testData = ImageNetData.ReadFromCsv(imagesLocation, imagesFolder);
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foreach (var sample in testData)
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{
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var probs = model.Predict(sample).PredictedLabels;
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IList<YoloBoundingBox> boundingBoxes = _parser.ParseOutputs(probs);
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var filteredBoxes = _parser.NonMaxSuppress(boundingBoxes, 5, .5F);
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Console.WriteLine(".....The objects in the image {0} are detected as below....", sample.Label);
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foreach (var box in filteredBoxes)
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{
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Console.WriteLine(box.Label + " and its Confidence score: " + box.Confidence);
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}
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Console.WriteLine("");
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}
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}
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}
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}
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‎samples/csharp/getting-started/DeepLearning_ObjectDetection_Onnx/ObjectDetectionConsoleApp/Program.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.Net;
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namespace ObjectDetection
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{
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class Program
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{
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public static void Main()
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{
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var assetsRelativePath = @"../../../assets";
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string assetsPath = GetAbsolutePath(assetsRelativePath);
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var modelFilePath = Path.Combine(assetsPath, "Model", "TinyYolo2_model.onnx");
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var imagesFolder = Path.Combine(assetsPath,"images");
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var tagsTsv = Path.Combine(assetsPath,"images", "tags.tsv");
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try
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{
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var modelScorer = new OnnxModelScorer(tagsTsv, imagesFolder, modelFilePath);
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modelScorer.Score();
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}
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catch (Exception ex)
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{
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Console.WriteLine(ex.Message);
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}
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Console.WriteLine("========= End of Process..Hit any Key ========");
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Console.ReadLine();
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}
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public static string GetAbsolutePath(string relativePath)
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{
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FileInfo _dataRoot = new FileInfo(typeof(Program).Assembly.Location);
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string assemblyFolderPath = _dataRoot.Directory.FullName;
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string fullPath = Path.Combine(assemblyFolderPath, relativePath);
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return fullPath;
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}
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}
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}
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‎samples/csharp/getting-started/DeepLearning_ObjectDetection_Onnx/ObjectDetectionConsoleApp/YoloParser/YoloBoundingBox.cs

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using System.Drawing;
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namespace ObjectDetection
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{
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class YoloBoundingBox
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{
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public string Label { get; set; }
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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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public float Confidence { get; set; }
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public RectangleF Rect
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{
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get { return new RectangleF(X, Y, Width, Height); }
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}
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}
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}

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