ImageEstimatorsCatalog.ConvertToImage Método

Definición

Espacio de nombres:

Microsoft.ML

Ensamblado:

Microsoft.ML.ImageAnalytics.dll

Paquete:

Microsoft.ML.ImageAnalytics v2.0.0

Cree un VectorToImageConvertingEstimatorobjeto , que crea una imagen a partir de los datos de la columna especificada en inputColumnName en una nueva columna: 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

Parámetros

catalog

TransformsCatalog

Catálogo de la transformación.

imageHeight

Int32

Alto de las imágenes de salida.

imageWidth

Int32

Ancho de las imágenes de salida.

outputColumnName

String

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

inputColumnName

String

Nombre de la columna con datos que se van a convertir en imagen. Este estimador funciona sobre el vector de tamaño conocido de Singley DoubleByte.

colorsPresent

ImagePixelExtractingEstimator.ColorBits

Especifica cuáles ImagePixelExtractingEstimator.ColorBits están presentes en los vectores de píxeles de entrada. El orden de los colores se especifica en orderOfColors.

orderOfColors

ImagePixelExtractingEstimator.ColorsOrder

Orden en el que se presentan los colores en el vector de entrada.

interleavedColors

Boolean

Si los píxeles están intercalados, lo que significa si están en orderOfColors orden o separados en la forma planar: todos los valores de un color para todos los píxeles, después todos los valores de otro color, etc.

scaleImage

Single

Los valores se escalan mediante este valor antes de convertirse en píxeles. Se aplica al valor vectorial antes de offsetImage.

offsetImage

Single

El desplazamiento se resta antes de convertir los valores en píxeles. Se aplica al valor vectorial después scaleImagede .

defaultAlpha

Int32

El valor predeterminado para el color alfa se invalidaría si colorsPresent contiene Alpha.

defaultRed

Int32

El valor predeterminado para el color rojo se invalidaría si colorsPresent contiene Red.

defaultGreen

Int32

El valor predeterminado para el color verde se invalidaría si colorsPresent contiene Green.

defaultBlue

Int32

El valor predeterminado para el color azul se invalidaría si colorsPresent contiene Blue.

Devoluciones

VectorToImageConvertingEstimator

Ejemplos

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()
                };
        }
    }
}