ImageEstimatorsCatalog.ConvertToImage 方法

定義

命名空間:

Microsoft.ML

組件:

Microsoft.ML.ImageAnalytics.dll

套件:

Microsoft.ML.ImageAnalytics v2.0.0

建立 ， VectorToImageConvertingEstimator 從 中指定的 inputColumnName 資料行將資料建立至新資料行的映射： 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

參數

catalog

TransformsCatalog

轉換的目錄。

imageHeight

Int32

輸出影像的高度。

imageWidth

Int32

輸出影像的寬度。

outputColumnName

String

轉換 inputColumnName 所產生的資料行名稱。 此資料行的資料類型將會是 MLImage 。

inputColumnName

String

具有要轉換成影像之資料的資料行名稱。 此估算器會透過 、 Double 和 Byte 的已知大小向量 Single 運作。

colorsPresent

ImagePixelExtractingEstimator.ColorBits

指定目前輸入圖元向量中的 。 ImagePixelExtractingEstimator.ColorBits 色彩的順序是在 中 orderOfColors 指定。

orderOfColors

ImagePixelExtractingEstimator.ColorsOrder

輸入向量中呈現色彩的順序。

interleavedColors

Boolean

不論圖元是交錯的，這表示它們 orderOfColors 是否依序排列，或以平面形式分隔：所有圖元的一個色彩的所有值，然後針對另一個色彩的所有值等等。

scaleImage

Single

此值會在轉換成圖元之前，先依此值縮放。 套用至 之前的 offsetImage 向量值。

offsetImage

Single

將值轉換成圖元之前，會先減去位移。 套用至 之後的 scaleImage 向量值。

defaultAlpha

Int32

如果 colorsPresent 包含 Alpha ，則會覆寫 Alpha 色彩的預設值。

defaultRed

Int32

如果 colorsPresent 包含 Red ，則會覆寫紅色的預設值。

defaultGreen

Int32

如果 colorsPresent 包含 Green ，則會覆寫綠色的預設值。

defaultBlue

Int32

如果 colorsPresent 包含 ，則會覆寫藍色的 Blue 預設值。

傳回

VectorToImageConvertingEstimator

範例

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()
                };
        }
    }
}