ImageEstimatorsCatalog.LoadImages Yöntem

Tanım

Ad Alanı:

Microsoft.ML

Bütünleştirilmiş Kod:

Microsoft.ML.ImageAnalytics.dll

Paket:

Microsoft.ML.ImageAnalytics v2.0.0

ImageLoadingEstimatoriçinde görüntü olarak belirtilen inputColumnName sütundaki verileri yeni bir sütuna yükleyen bir oluşturun: 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

Parametreler

catalog

TransformsCatalog

Dönüşümün kataloğu.

outputColumnName

String

dönüştürmesinden kaynaklanan sütunun inputColumnNameadı. Bu sütunun veri türü olacaktır MLImage.

imageFolder

String

Görüntülerin aranacağı klasör.

inputColumnName

String

Yüklenemiyor görüntülerin yollarını içeren sütunun adı. Bu tahmin aracı metin verileri üzerinde çalışır.

Döndürülenler

ImageLoadingEstimator

Örnekler

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();
            }
        }
    }
}