ImageEstimatorsCatalog.LoadImages Metodo

Definizione

Spazio dei nomi:

Microsoft.ML

Assembly:

Microsoft.ML.ImageAnalytics.dll

Pacchetto:

Microsoft.ML.ImageAnalytics v2.0.0

Creare un ImageLoadingEstimatoroggetto , che carica i dati dalla colonna specificata in inputColumnName come immagine in una nuova colonna: outputColumnName.

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

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

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

Parametri

catalog

TransformsCatalog

Catalogo della trasformazione.

outputColumnName

String

Nome della colonna risultante dalla trasformazione di inputColumnName. Il tipo di dati di questa colonna sarà MLImage.

imageFolder

String

Cartella in cui cercare le immagini.

inputColumnName

String

Nome della colonna con percorsi delle immagini da caricare. Questo strumento di stima opera sui dati di testo.

Restituisce

ImageLoadingEstimator

Esempio

using System;
using System.IO;
using Microsoft.ML;
using Microsoft.ML.Data;

namespace Samples.Dynamic
{
    public static class LoadImages
    {
        // Loads the images of the imagesFolder into an IDataView.
        public static void Example()
        {
            // Create a new ML context, for ML.NET operations. It can be used for
            // exception tracking and logging, as well as the source of randomness.
            var mlContext = new MLContext();

            // Downloading a few images, and an images.tsv file, which contains a
            // list of the files from the dotnet/machinelearning/test/data/images/.
            // If you inspect the fileSystem, after running this line, an "images"
            // folder will be created, containing 4 images, and a .tsv file
            // enumerating the images.
            var imagesDataFile = Microsoft.ML.SamplesUtils.DatasetUtils
                .GetSampleImages();

            // Preview of the content of the images.tsv file
            //
            // imagePath    imageType
            // tomato.bmp   tomato
            // banana.jpg   banana
            // hotdog.jpg   hotdog
            // tomato.jpg   tomato

            var data = mlContext.Data.CreateTextLoader(new TextLoader.Options()
            {
                Columns = new[]
                {
                        new TextLoader.Column("ImagePath", DataKind.String, 0),
                        new TextLoader.Column("Name", DataKind.String, 1),
                    }
            }).Load(imagesDataFile);

            var imagesFolder = Path.GetDirectoryName(imagesDataFile);
            // Image loading pipeline.
            var pipeline = mlContext.Transforms.LoadImages("ImageObject",
                imagesFolder, "ImagePath");

            var transformedData = pipeline.Fit(data).Transform(data);

            PrintColumns(transformedData);
            // Preview the transformedData.
            // ImagePath    Name         ImageObject
            // tomato.bmp   tomato       {Width=800, Height=534}
            // banana.jpg   banana       {Width=800, Height=288}
            // hotdog.jpg   hotdog       {Width=800, Height=391}
            // tomato.jpg   tomato       {Width=800, Height=534}
        }

        private static void PrintColumns(IDataView transformedData)
        {
            // The transformedData IDataView contains the loaded images now.
            Console.WriteLine("{0, -25} {1, -25} {2, -25}", "ImagePath", "Name",
                "ImageObject");

            using (var cursor = transformedData.GetRowCursor(transformedData
                .Schema))
            {
                // Note that it is best to get the getters and values *before*
                // iteration, so as to facilitate buffer sharing (if applicable),
                // and column-type validation once, rather than many times.
                ReadOnlyMemory<char> imagePath = default;
                ReadOnlyMemory<char> name = default;
                MLImage imageObject = null;

                var imagePathGetter = cursor.GetGetter<ReadOnlyMemory<char>>(cursor
                    .Schema["ImagePath"]);

                var nameGetter = cursor.GetGetter<ReadOnlyMemory<char>>(cursor
                    .Schema["Name"]);

                var imageObjectGetter = cursor.GetGetter<MLImage>(cursor.Schema[
                    "ImageObject"]);

                while (cursor.MoveNext())
                {
                    imagePathGetter(ref imagePath);
                    nameGetter(ref name);
                    imageObjectGetter(ref imageObject);

                    Console.WriteLine("{0, -25} {1, -25} {2, -25}",
                        imagePath, name,
                        $"Width={imageObject.Width}, Height={imageObject.Height}");
                }

                // Dispose the image.
                imageObject.Dispose();
            }
        }
    }
}