ImageEstimatorsCatalog.LoadRawImageBytes Método

Definición

Espacio de nombres:

Microsoft.ML

Ensamblado:

Microsoft.ML.ImageAnalytics.dll

Paquete:

Microsoft.ML.ImageAnalytics v3.0.1

Cree un ImageLoadingEstimatorobjeto , que carga los datos de la columna especificada en inputColumnName como una imagen de bytes sin procesar en una nueva columna: outputColumnName.

public static Microsoft.ML.Data.ImageLoadingEstimator LoadRawImageBytes (this Microsoft.ML.TransformsCatalog catalog, string outputColumnName, string imageFolder, string inputColumnName = default);

static member LoadRawImageBytes : Microsoft.ML.TransformsCatalog * string * string * string -> Microsoft.ML.Data.ImageLoadingEstimator

<Extension()>
Public Function LoadRawImageBytes (catalog As TransformsCatalog, outputColumnName As String, imageFolder As String, Optional inputColumnName As String = Nothing) As ImageLoadingEstimator

Parámetros

catalog

TransformsCatalog

Catálogo de la transformación.

outputColumnName

String

Nombre de la columna resultante de la transformación de inputColumnName. El tipo de datos de esta columna será VectorDataViewType.

imageFolder

String

Carpeta donde buscar imágenes.

inputColumnName

String

Nombre de la columna con rutas de acceso a las imágenes que se van a cargar. Este estimador funciona sobre los datos de texto.

Devoluciones

ImageLoadingEstimator

Ejemplos

using System;
using System.Collections.Generic;
using System.IO;
using System.IO.Compression;
using System.Linq;
using System.Net;
using System.Net.Http;
using System.Threading;
using System.Threading.Tasks;
using Microsoft.ML;
using Microsoft.ML.Data;
using static Microsoft.ML.DataOperationsCatalog;

namespace Samples.Dynamic
{
    public class ImageClassificationDefault
    {
        public static void Example()
        {
            // Set the path for input images.
            string assetsRelativePath = @"../../../assets";
            string assetsPath = GetAbsolutePath(assetsRelativePath);

            string imagesDownloadFolderPath = Path.Combine(assetsPath, "inputs",
                "images");

            //Download the image set and unzip, set the path to image folder.
            string finalImagesFolderName = DownloadImageSet(
                imagesDownloadFolderPath);

            string fullImagesetFolderPath = Path.Combine(
                imagesDownloadFolderPath, finalImagesFolderName);

            MLContext mlContext = new MLContext(seed: 1);
            mlContext.Log += MlContext_Log;

            // Load all the original images info
            IEnumerable<ImageData> images = LoadImagesFromDirectory(
                folder: fullImagesetFolderPath, useFolderNameAsLabel: true);

            // Shuffle images.
            IDataView shuffledFullImagesDataset = mlContext.Data.ShuffleRows(
                mlContext.Data.LoadFromEnumerable(images));

            // Apply transforms to the input dataset:
            // MapValueToKey : map 'string' type labels to keys
            // LoadImages : load raw images to "Image" column
            shuffledFullImagesDataset = mlContext.Transforms.Conversion
                    .MapValueToKey("Label", keyOrdinality: Microsoft.ML.Transforms
                    .ValueToKeyMappingEstimator.KeyOrdinality.ByValue)
                .Append(mlContext.Transforms.LoadRawImageBytes("Image",
                            fullImagesetFolderPath, "ImagePath"))
                .Fit(shuffledFullImagesDataset)
                .Transform(shuffledFullImagesDataset);

            // Split the data 90:10 into train and test sets.
            TrainTestData trainTestData = mlContext.Data.TrainTestSplit(
                shuffledFullImagesDataset, testFraction: 0.1, seed: 1);

            IDataView trainDataset = trainTestData.TrainSet;
            IDataView testDataset = trainTestData.TestSet;

            // Create the ImageClassification pipeline by just passing the 
            // input feature and label column name. 
            var pipeline = mlContext.MulticlassClassification.Trainers
                    .ImageClassification(featureColumnName: "Image")
                .Append(mlContext.Transforms.Conversion.MapKeyToValue(
                    outputColumnName: "PredictedLabel",
                    inputColumnName: "PredictedLabel"));

            Console.WriteLine("*** Training the image classification model " +
                "with DNN Transfer Learning on top of the selected " +
                "pre-trained model/architecture ***");

            // Train the model.
            // This involves calculating the bottleneck values, and then
            // training the final layerSample output is: 
            // [Source=ImageClassificationTrainer; ImageClassificationTrainer, Kind=Trace] Phase: Bottleneck Computation, Dataset used:      Train, Image Index:   1
            // [Source=ImageClassificationTrainer; ImageClassificationTrainer, Kind=Trace] Phase: Bottleneck Computation, Dataset used:      Train, Image Index:   2
            // ...
            // [Source=ImageClassificationTrainer; ImageClassificationTrainer, Kind=Trace] Phase: Training, Dataset used:      Train, Batch Processed Count:  18, Learning Rate:       0.01 Epoch:   0, Accuracy:        0.9, Cross-Entropy:  0.481340
            // ...
            // [Source=ImageClassificationTrainer; ImageClassificationTrainer, Kind=Trace] Phase: Training, Dataset used:      Train, Batch Processed Count:  18, Learning Rate: 0.004759203 Epoch:  25, Accuracy:          1, Cross-Entropy: 0.04848097
            // [Source=ImageClassificationTrainer; ImageClassificationTrainer, Kind=Trace] Phase: Training, Dataset used:      Train, Batch Processed Count:  18, Learning Rate: 0.004473651 Epoch:  26, Accuracy:          1, Cross-Entropy: 0.04930306
            var trainedModel = pipeline.Fit(trainDataset);

            Console.WriteLine("Training with transfer learning finished.");

            // Save the trained model.
            mlContext.Model.Save(trainedModel, shuffledFullImagesDataset.Schema,
                "model.zip");

            // Load the trained and saved model for prediction.
            ITransformer loadedModel;
            DataViewSchema schema;
            using (var file = File.OpenRead("model.zip"))
                loadedModel = mlContext.Model.Load(file, out schema);

            // Evaluate the model on the test dataset.
            // Sample output:
            // Making bulk predictions and evaluating model's quality...
            // Micro-accuracy: 0.925925925925926,macro-accuracy = 0.933333333333333
            EvaluateModel(mlContext, testDataset, loadedModel);

            // Predict on a single image class using an in-memory image.
            // Sample output:
            // Scores :  [0.8657553,0.006911285,1.46484E-05,0.1266835,0.0006352618], Predicted Label : daisy
            TrySinglePrediction(fullImagesetFolderPath, mlContext, loadedModel);

            Console.WriteLine("Prediction on a single image finished.");

            Console.WriteLine("Press any key to finish");
            Console.ReadKey();
        }

        private static void MlContext_Log(object sender, LoggingEventArgs e)
        {
            if (e.Message.StartsWith("[Source=ImageClassificationTrainer;"))
            {
                Console.WriteLine(e.Message);
            }
        }

        // Predict on a single image.
        private static void TrySinglePrediction(string imagesForPredictions,
            MLContext mlContext, ITransformer trainedModel)
        {
            // Create prediction function to try one prediction.
            var predictionEngine = mlContext.Model
                .CreatePredictionEngine<InMemoryImageData,
                ImagePrediction>(trainedModel);

            // Load test images.
            IEnumerable<InMemoryImageData> testImages =
                LoadInMemoryImagesFromDirectory(imagesForPredictions, false);

            // Create an in-memory image object from the first image in the test data.
            InMemoryImageData imageToPredict = new InMemoryImageData
            {
                Image = testImages.First().Image
            };

            // Predict on the single image.
            var prediction = predictionEngine.Predict(imageToPredict);

            Console.WriteLine($"Scores : [{string.Join(",", prediction.Score)}], " +
                $"Predicted Label : {prediction.PredictedLabel}");
        }

        // Evaluate the trained model on the passed test dataset.
        private static void EvaluateModel(MLContext mlContext,
            IDataView testDataset, ITransformer trainedModel)
        {
            Console.WriteLine("Making bulk predictions and evaluating model's " +
                "quality...");

            // Evaluate the model on the test data and get the evaluation metrics.
            IDataView predictions = trainedModel.Transform(testDataset);
            var metrics = mlContext.MulticlassClassification.Evaluate(predictions);

            Console.WriteLine($"Micro-accuracy: {metrics.MicroAccuracy}," +
                              $"macro-accuracy = {metrics.MacroAccuracy}");

            Console.WriteLine("Predicting and Evaluation complete.");
        }

        //Load the Image Data from input directory.
        public static IEnumerable<ImageData> LoadImagesFromDirectory(string folder,
            bool useFolderNameAsLabel = true)
        {
            var files = Directory.GetFiles(folder, "*",
                searchOption: SearchOption.AllDirectories);
            foreach (var file in files)
            {
                if (Path.GetExtension(file) != ".jpg")
                    continue;

                var label = Path.GetFileName(file);
                if (useFolderNameAsLabel)
                    label = Directory.GetParent(file).Name;
                else
                {
                    for (int index = 0; index < label.Length; index++)
                    {
                        if (!char.IsLetter(label[index]))
                        {
                            label = label.Substring(0, index);
                            break;
                        }
                    }
                }

                yield return new ImageData()
                {
                    ImagePath = file,
                    Label = label
                };

            }
        }

        // Load In memory raw images from directory.
        public static IEnumerable<InMemoryImageData>
            LoadInMemoryImagesFromDirectory(string folder,
                bool useFolderNameAsLabel = true)
        {
            var files = Directory.GetFiles(folder, "*",
                searchOption: SearchOption.AllDirectories);
            foreach (var file in files)
            {
                if (Path.GetExtension(file) != ".jpg")
                    continue;

                var label = Path.GetFileName(file);
                if (useFolderNameAsLabel)
                    label = Directory.GetParent(file).Name;
                else
                {
                    for (int index = 0; index < label.Length; index++)
                    {
                        if (!char.IsLetter(label[index]))
                        {
                            label = label.Substring(0, index);
                            break;
                        }
                    }
                }

                yield return new InMemoryImageData()
                {
                    Image = File.ReadAllBytes(file),
                    Label = label
                };

            }
        }

        // Download and unzip the image dataset.
        public static string DownloadImageSet(string imagesDownloadFolder)
        {
            // get a set of images to teach the network about the new classes

            //SINGLE SMALL FLOWERS IMAGESET (200 files)
            string fileName = "flower_photos_small_set.zip";
            string url = $"https://aka.ms/mlnet-resources/datasets/flower_photos_small_set.zip";

            Download(url, imagesDownloadFolder, fileName).Wait();
            UnZip(Path.Combine(imagesDownloadFolder, fileName), imagesDownloadFolder);

            return Path.GetFileNameWithoutExtension(fileName);
        }

        // Download file to destination directory from input URL.
        public static async Task<bool> Download(string url, string destDir, string destFileName)
        {
            if (destFileName == null)
                destFileName = url.Split(Path.DirectorySeparatorChar).Last();

            Directory.CreateDirectory(destDir);

            string relativeFilePath = Path.Combine(destDir, destFileName);

            if (File.Exists(relativeFilePath))
            {
                Console.WriteLine($"{relativeFilePath} already exists.");
                return false;
            }

            Console.WriteLine($"Downloading {relativeFilePath}");

            using (HttpClient client = new HttpClient())
            {
                var response = await client.GetStreamAsync(new Uri($"{url}")).ConfigureAwait(false);

                using (var fs = new FileStream(relativeFilePath, FileMode.CreateNew))
                {
                    await response.CopyToAsync(fs);
                }
            }

            Console.WriteLine("");
            Console.WriteLine($"Downloaded {relativeFilePath}");

            return true;
        }

        // Unzip the file to destination folder.
        public static void UnZip(String gzArchiveName, String destFolder)
        {
            var flag = gzArchiveName.Split(Path.DirectorySeparatorChar)
                .Last()
                .Split('.')
                .First() + ".bin";

            if (File.Exists(Path.Combine(destFolder, flag))) return;

            Console.WriteLine($"Extracting.");
            ZipFile.ExtractToDirectory(gzArchiveName, destFolder);

            File.Create(Path.Combine(destFolder, flag));
            Console.WriteLine("");
            Console.WriteLine("Extracting is completed.");
        }

        // Get absolute path from relative path.
        public static string GetAbsolutePath(string relativePath)
        {
            FileInfo _dataRoot = new FileInfo(typeof(
                ImageClassificationDefault).Assembly.Location);

            string assemblyFolderPath = _dataRoot.Directory.FullName;

            string fullPath = Path.Combine(assemblyFolderPath, relativePath);

            return fullPath;
        }

        // InMemoryImageData class holding the raw image byte array and label.
        public class InMemoryImageData
        {
            [LoadColumn(0)]
            public byte[] Image;

            [LoadColumn(1)]
            public string Label;
        }

        // ImageData class holding the imagepath and label.
        public class ImageData
        {
            [LoadColumn(0)]
            public string ImagePath;

            [LoadColumn(1)]
            public string Label;
        }

        // ImagePrediction class holding the score and predicted label metrics.
        public class ImagePrediction
        {
            [ColumnName("Score")]
            public float[] Score;

            [ColumnName("PredictedLabel")]
            public string PredictedLabel;
        }
    }
}