LegoColorIdentifier ML Sample

Author: vainolo

CSharp/ML/LegoColorIdentifier/Black.jpg

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+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFramework>net5.0</TargetFramework>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Microsoft.ML" Version="1.5.2" />
+    <PackageReference Include="Microsoft.ML.ImageAnalytics" Version="1.5.2" />
+    <PackageReference Include="Microsoft.ML.Vision" Version="1.5.2" />
+    <PackageReference Include="SciSharp.TensorFlow.Redist" Version="2.3.1" />
+  </ItemGroup>
+
+</Project>

CSharp/ML/LegoColorIdentifier/Blue.jpg

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+using System;
+using System.Collections.Generic;
+using System.IO;
+using System.Linq;
+using Microsoft.ML;
+using Microsoft.ML.Data;
+using Microsoft.ML.Vision;
+
+public class Program
+{
+    static readonly string inputDataDirectoryPath = Environment.CurrentDirectory + Path.DirectorySeparatorChar + "pieces";
+    static readonly string outputModelFilePath = Environment.CurrentDirectory + Path.DirectorySeparatorChar + "model.zip";
+    static MLContext mlContext = new MLContext(seed: 1);
+    static ITransformer mlModel;
+
+    public class ModelInput
+    {
+        public string Label { get; set; }
+        public string ImageSource { get; set; }
+    }
+
+    public class ModelOutput
+    {
+        public String PredictedLabel { get; set; }
+    }
+
+    static void TrainModel()
+    {
+        // Create the input dataset
+        var inputs = new List<ModelInput>();
+        foreach (var subDir in Directory.GetDirectories(inputDataDirectoryPath))
+        {
+            foreach (var file in Directory.GetFiles(subDir))
+            {
+                inputs.Add(new ModelInput() { Label = subDir.Split("\\").Last(), ImageSource = file });
+            }
+        }
+        var trainingDataView = mlContext.Data.LoadFromEnumerable<ModelInput>(inputs);
+        // Create training pipeline
+        var dataProcessPipeline = mlContext.Transforms.Conversion.MapValueToKey("Label", "Label")
+                                    .Append(mlContext.Transforms.LoadRawImageBytes("ImageSource_featurized", null, "ImageSource"))
+                                    .Append(mlContext.Transforms.CopyColumns("Features", "ImageSource_featurized"));
+        var trainer = mlContext.MulticlassClassification.Trainers.ImageClassification(new ImageClassificationTrainer.Options() { LabelColumnName = "Label", FeatureColumnName = "Features" })
+                                    .Append(mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel", "PredictedLabel"));
+        IEstimator<ITransformer> trainingPipeline = dataProcessPipeline.Append(trainer);
+        // Create the model
+        mlModel = trainingPipeline.Fit(trainingDataView);
+    }
+
+    static ModelOutput Classify(string filePath)
+    {
+        // Create input to classify
+        ModelInput input = new ModelInput() { ImageSource = filePath };
+        // Load model and predict
+        var predEngine = mlContext.Model.CreatePredictionEngine<ModelInput, ModelOutput>(mlModel);
+        return predEngine.Predict(input);
+    }
+
+    static void Main()
+    {
+        TrainModel();
+
+        var result = Classify(Environment.CurrentDirectory + Path.DirectorySeparatorChar + "Black.jpg");
+        Console.WriteLine($"Testing with black piece. Prediction: {result.PredictedLabel}.");
+        result = Classify(Environment.CurrentDirectory + Path.DirectorySeparatorChar + "Blue.jpg");
+        Console.WriteLine($"Testing with blue piece. Prediction: {result.PredictedLabel}.");
+        result = Classify(Environment.CurrentDirectory + Path.DirectorySeparatorChar + "Green.jpg");
+        Console.WriteLine($"Testing with green piece. Prediction: {result.PredictedLabel}.");
+        result = Classify(Environment.CurrentDirectory + Path.DirectorySeparatorChar + "Yellow.jpg");
+        Console.WriteLine($"Testing with yellow piece. Prediction: {result.PredictedLabel}.");
+    }
+}
+