ImageEstimatorsCatalog.ConvertToImage Metode

Definisi

Ruang nama:

Microsoft.ML

Rakitan:

Microsoft.ML.ImageAnalytics.dll

Paket:

Microsoft.ML.ImageAnalytics v2.0.0

Buat VectorToImageConvertingEstimator, yang membuat gambar dari data dari kolom yang ditentukan ke inputColumnName kolom baru: outputColumnName.

public static Microsoft.ML.Transforms.Image.VectorToImageConvertingEstimator ConvertToImage (this Microsoft.ML.TransformsCatalog catalog, int imageHeight, int imageWidth, string outputColumnName, string inputColumnName = default, Microsoft.ML.Transforms.Image.ImagePixelExtractingEstimator.ColorBits colorsPresent = Microsoft.ML.Transforms.Image.ImagePixelExtractingEstimator+ColorBits.Rgb, Microsoft.ML.Transforms.Image.ImagePixelExtractingEstimator.ColorsOrder orderOfColors = Microsoft.ML.Transforms.Image.ImagePixelExtractingEstimator+ColorsOrder.ARGB, bool interleavedColors = false, float scaleImage = 1, float offsetImage = 0, int defaultAlpha = 255, int defaultRed = 0, int defaultGreen = 0, int defaultBlue = 0);

static member ConvertToImage : Microsoft.ML.TransformsCatalog * int * int * string * string * Microsoft.ML.Transforms.Image.ImagePixelExtractingEstimator.ColorBits * Microsoft.ML.Transforms.Image.ImagePixelExtractingEstimator.ColorsOrder * bool * single * single * int * int * int * int -> Microsoft.ML.Transforms.Image.VectorToImageConvertingEstimator

<Extension()>
Public Function ConvertToImage (catalog As TransformsCatalog, imageHeight As Integer, imageWidth As Integer, outputColumnName As String, Optional inputColumnName As String = Nothing, Optional colorsPresent As ImagePixelExtractingEstimator.ColorBits = Microsoft.ML.Transforms.Image.ImagePixelExtractingEstimator+ColorBits.Rgb, Optional orderOfColors As ImagePixelExtractingEstimator.ColorsOrder = Microsoft.ML.Transforms.Image.ImagePixelExtractingEstimator+ColorsOrder.ARGB, Optional interleavedColors As Boolean = false, Optional scaleImage As Single = 1, Optional offsetImage As Single = 0, Optional defaultAlpha As Integer = 255, Optional defaultRed As Integer = 0, Optional defaultGreen As Integer = 0, Optional defaultBlue As Integer = 0) As VectorToImageConvertingEstimator

Parameter

catalog

TransformsCatalog

Katalog transformasi.

imageHeight

Int32

Tinggi gambar output.

imageWidth

Int32

Lebar gambar output.

outputColumnName

String

Nama kolom yang dihasilkan dari transformasi inputColumnName. Jenis data kolom ini adalah MLImage.

inputColumnName

String

Nama kolom dengan data yang akan dikonversi menjadi gambar. Estimator ini beroperasi atas vektor berukuran besar yang diketahui dari Single, Double dan Byte.

colorsPresent

ImagePixelExtractingEstimator.ColorBits

Menentukan mana yang ImagePixelExtractingEstimator.ColorBits ada saat ini vektor piksel input. Urutan warna ditentukan dalam orderOfColors.

orderOfColors

ImagePixelExtractingEstimator.ColorsOrder

Urutan warna disajikan dalam vektor input.

interleavedColors

Boolean

Apakah piksel diselingi, yang berarti apakah piksel berurutan orderOfColors , atau dipisahkan dalam bentuk planar: semua nilai untuk satu warna untuk semua piksel, lalu semua nilai untuk warna lain dan sebagainya.

scaleImage

Single

Nilai diskalakan oleh nilai ini sebelum dikonversi ke piksel. Diterapkan ke nilai vektor sebelum offsetImage.

offsetImage

Single

Offset dikurangi sebelum mengonversi nilai menjadi piksel. Diterapkan ke nilai vektor setelah scaleImage.

defaultAlpha

Int32

Nilai default untuk warna alfa, akan ditimpa jika colorsPresent berisi Alpha.

defaultRed

Int32

Nilai default untuk warna merah, akan diganti jika colorsPresent berisi Red.

defaultGreen

Int32

Nilai default untuk warna hijau, akan diganti jika colorsPresent berisi Green.

defaultBlue

Int32

Nilai default untuk warna biru, akan diganti jika colorsPresent berisi Blue.

Mengembalikan

VectorToImageConvertingEstimator

Contoh

using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;

namespace Samples.Dynamic
{
    public static class ConvertToImage
    {
        private const int imageHeight = 224;
        private const int imageWidth = 224;
        private const int numberOfChannels = 3;
        private const int inputSize = imageHeight * imageWidth * numberOfChannels;

        // Sample that shows how an input array (of doubles) can be used to interop
        // with image related estimators in ML.NET.
        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();

            // Create a list of training data points.
            var dataPoints = GenerateRandomDataPoints(4);

            // Convert the list of data points to an IDataView object, which is
            // consumable by ML.NET API.
            var data = mlContext.Data.LoadFromEnumerable(dataPoints);

            // Image loading pipeline.
            var pipeline = mlContext.Transforms.ConvertToImage(imageHeight,
                imageWidth, "Image", "Features")
                .Append(mlContext.Transforms.ExtractPixels("Pixels", "Image"));

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

            // Preview the transformedData.
            PrintColumns(transformedData);

            // Features                 Image                    Pixels
            // 185,209,196,142,52...    {Width=224, Height=224}  185,209,196,142,52...
            // 182,235,84,23,87...      {Width=224, Height=224}  182,235,84,23,87...
            // 192,214,247,22,38...     {Width=224, Height=224}  192,214,247,22,38...
            // 242,161,141,223,192...   {Width=224, Height=224}  242,161,141,223,192...
        }

        private static void PrintColumns(IDataView transformedData)
        {
            Console.WriteLine("{0, -25} {1, -25} {2, -25}", "Features", "Image",
                "Pixels");

            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.
                VBuffer<float> features = default;
                VBuffer<float> pixels = default;
                MLImage imageObject = null;

                var featuresGetter = cursor.GetGetter<VBuffer<float>>(cursor.Schema[
                    "Features"]);

                var pixelsGetter = cursor.GetGetter<VBuffer<float>>(cursor.Schema[
                    "Pixels"]);

                var imageGetter = cursor.GetGetter<MLImage>(cursor.Schema["Image"]);
                while (cursor.MoveNext())
                {

                    featuresGetter(ref features);
                    pixelsGetter(ref pixels);
                    imageGetter(ref imageObject);

                    Console.WriteLine("{0, -25} {1, -25} {2, -25}", string.Join(",",
                        features.DenseValues().Take(5)) + "...",
                        $"Width={imageObject.Width}, Height={imageObject.Height}",
                        string.Join(",", pixels.DenseValues().Take(5)) + "...");
                }

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

        private class DataPoint
        {
            [VectorType(inputSize)]
            public float[] Features { get; set; }
        }

        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
            int seed = 0)
        {
            var random = new Random(seed);

            for (int i = 0; i < count; i++)
                yield return new DataPoint
                {
                    Features = Enumerable.Repeat(0,
                    inputSize).Select(x => (float)random.Next(0, 256)).ToArray()
                };
        }
    }
}