ImageEstimatorsCatalog.ExtractPixels Metode

Definisi

Ruang nama:

Microsoft.ML

Rakitan:

Microsoft.ML.ImageAnalytics.dll

Paket:

Microsoft.ML.ImageAnalytics v2.0.0

Buat ImagePixelExtractingEstimator, yang mengekstrak nilai piksel dari data yang ditentukan dalam kolom: inputColumnName ke kolom baru: outputColumnName.

public static Microsoft.ML.Transforms.Image.ImagePixelExtractingEstimator ExtractPixels (this Microsoft.ML.TransformsCatalog catalog, string outputColumnName, string inputColumnName = default, Microsoft.ML.Transforms.Image.ImagePixelExtractingEstimator.ColorBits colorsToExtract = Microsoft.ML.Transforms.Image.ImagePixelExtractingEstimator+ColorBits.Rgb, Microsoft.ML.Transforms.Image.ImagePixelExtractingEstimator.ColorsOrder orderOfExtraction = Microsoft.ML.Transforms.Image.ImagePixelExtractingEstimator+ColorsOrder.ARGB, bool interleavePixelColors = false, float offsetImage = 0, float scaleImage = 1, bool outputAsFloatArray = true);

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

<Extension()>
Public Function ExtractPixels (catalog As TransformsCatalog, outputColumnName As String, Optional inputColumnName As String = Nothing, Optional colorsToExtract As ImagePixelExtractingEstimator.ColorBits = Microsoft.ML.Transforms.Image.ImagePixelExtractingEstimator+ColorBits.Rgb, Optional orderOfExtraction As ImagePixelExtractingEstimator.ColorsOrder = Microsoft.ML.Transforms.Image.ImagePixelExtractingEstimator+ColorsOrder.ARGB, Optional interleavePixelColors As Boolean = false, Optional offsetImage As Single = 0, Optional scaleImage As Single = 1, Optional outputAsFloatArray As Boolean = true) As ImagePixelExtractingEstimator

Parameter

catalog

TransformsCatalog

Katalog transformasi.

outputColumnName

String

Nama kolom yang dihasilkan dari transformasi inputColumnName. Jenis data kolom ini akan menjadi vektor berukuran dikenal atau SingleByte tergantung pada outputAsFloatArray.

inputColumnName

String

Nama kolom dengan gambar. Estimator ini beroperasi melalui MLImage.

colorsToExtract

ImagePixelExtractingEstimator.ColorBits

Warna untuk mengekstrak dari gambar.

orderOfExtraction

ImagePixelExtractingEstimator.ColorsOrder

Urutan untuk mengekstrak warna dari piksel.

interleavePixelColors

Boolean

Apakah akan mengintervensi warna piksel, yang berarti menyimpannya dalam orderOfExtraction urutan, atau membiarkannya dalam bentuk perencana: semua nilai untuk satu warna untuk semua piksel, lalu semua nilai untuk warna lain, dan sebagainya.

offsetImage

Single

Offset nilai warna setiap piksel dengan jumlah ini. Diterapkan ke nilai warna sebelum scaleImage.

scaleImage

Single

Skalakan nilai warna setiap piksel dengan jumlah ini. Diterapkan ke nilai warna setelah offsetImage.

outputAsFloatArray

Boolean

Array output sebagai array float. Jika false, output sebagai array byte dan mengabaikan offsetImage dan scaleImage.

Mengembalikan

ImagePixelExtractingEstimator

Contoh

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

namespace Samples.Dynamic
{
    public static class ExtractPixels
    {
        // Sample that loads the images from the file system, resizes them (
        // ExtractPixels requires a resizing operation), and extracts the values of
        // the pixels as a vector.
        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")
                .Append(mlContext.Transforms.ResizeImages("ImageObjectResized",
                    inputColumnName: "ImageObject", imageWidth: 100, imageHeight:
                    100))
                .Append(mlContext.Transforms.ExtractPixels("Pixels",
                    "ImageObjectResized"));

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

            // Preview the transformedData.
            PrintColumns(transformedData);

            // ImagePath    Name         ImageObject               ImageObjectResized        Pixels
            // tomato.bmp   tomato       {Width=800, Height=534}   {Width=100, Height=100}   255,255,255,255,255...
            // banana.jpg   banana       {Width=800, Height=288}   {Width=100, Height=100}   255,255,255,255,255...
            // hotdog.jpg   hotdog       {Width=800, Height=391}   {Width=100, Height=100}   255,255,255,255,255...
            // tomato.jpg   tomato       {Width=800, Height=534}   {Width=100, Height=100}   255,255,255,255,255...
        }

        private static void PrintColumns(IDataView transformedData)
        {
            Console.WriteLine("{0, -25} {1, -25} {2, -25} {3, -25} {4, -25}",
                "ImagePath", "Name", "ImageObject", "ImageObjectResized", "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.

                ReadOnlyMemory<char> imagePath = default;
                ReadOnlyMemory<char> name = default;
                MLImage imageObject = null;
                MLImage resizedImageObject = null;
                VBuffer<float> pixels = default;

                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"]);

                var resizedImageGetter = cursor.GetGetter<MLImage>(cursor.Schema[
                    "ImageObjectResized"]);

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

                while (cursor.MoveNext())
                {

                    imagePathGetter(ref imagePath);
                    nameGetter(ref name);
                    imageObjectGetter(ref imageObject);
                    resizedImageGetter(ref resizedImageObject);
                    pixelsGetter(ref pixels);

                    Console.WriteLine("{0, -25} {1, -25} {2, -25} {3, -25} " +
                        "{4, -25}", imagePath, name,
                        $"Width={imageObject.Width}, Height={imageObject.Height}",
                        $"Width={resizedImageObject.Width}, Height={resizedImageObject.Height}",
                        string.Join(",", pixels.DenseValues().Take(5)) + "...");
                }

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

using System;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Transforms.Image;

namespace Samples.Dynamic
{
    public static class ApplyOnnxModelWithInMemoryImages
    {
        // Example of applying ONNX transform on in-memory images.
        public static void Example()
        {
            // Download the squeeznet image model from ONNX model zoo, version 1.2
            // https://github.com/onnx/models/tree/master/vision/classification/squeezenet or use
            // Microsoft.ML.Onnx.TestModels nuget.
            // It's a multiclass classifier. It consumes an input "data_0" and
            // produces an output "softmaxout_1".
            var modelPath = @"squeezenet\00000001\model.onnx";

            // Create ML pipeline to score the data using OnnxScoringEstimator
            var mlContext = new MLContext();

            // Create in-memory data points. Its Image/Scores field is the
            // input /output of the used ONNX model.
            var dataPoints = new ImageDataPoint[]
            {
                new ImageDataPoint(red: 255, green: 0, blue: 0), // Red color
                new ImageDataPoint(red: 0, green: 128, blue: 0)  // Green color
            };

            // Convert training data to IDataView, the general data type used in
            // ML.NET.
            var dataView = mlContext.Data.LoadFromEnumerable(dataPoints);

            // Create a ML.NET pipeline which contains two steps. First,
            // ExtractPixle is used to convert the 224x224 image to a 3x224x224
            // float tensor. Then the float tensor is fed into a ONNX model with an
            // input called "data_0" and an output called "softmaxout_1". Note that
            // "data_0" and "softmaxout_1" are model input and output names stored
            // in the used ONNX model file. Users may need to inspect their own
            // models to get the right input and output column names.
            // Map column "Image" to column "data_0"
            // Map column "data_0" to column "softmaxout_1"
            var pipeline = mlContext.Transforms.ExtractPixels("data_0", "Image")
                .Append(mlContext.Transforms.ApplyOnnxModel("softmaxout_1",
                "data_0", modelPath));

            var model = pipeline.Fit(dataView);
            var onnx = model.Transform(dataView);

            // Convert IDataView back to IEnumerable<ImageDataPoint> so that user
            // can inspect the output, column "softmaxout_1", of the ONNX transform.
            // Note that Column "softmaxout_1" would be stored in ImageDataPont
            //.Scores because the added attributed [ColumnName("softmaxout_1")]
            // tells that ImageDataPont.Scores is equivalent to column
            // "softmaxout_1".
            var transformedDataPoints = mlContext.Data.CreateEnumerable<
                ImageDataPoint>(onnx, false).ToList();

            // The scores are probabilities of all possible classes, so they should
            // all be positive.
            foreach (var dataPoint in transformedDataPoints)
            {
                var firstClassProb = dataPoint.Scores.First();
                var lastClassProb = dataPoint.Scores.Last();
                Console.WriteLine("The probability of being the first class is " +
                    (firstClassProb * 100) + "%.");

                Console.WriteLine($"The probability of being the last class is " +
                    (lastClassProb * 100) + "%.");
            }

            // Expected output:
            //  The probability of being the first class is 0.002542659%.
            //  The probability of being the last class is 0.0292684%.
            //  The probability of being the first class is 0.02258059%.
            //  The probability of being the last class is 0.394428%.
        }

        // This class is used in Example() to describe data points which will be
        // consumed by ML.NET pipeline.
        private class ImageDataPoint
        {
            // Height of Image.
            private const int height = 224;

            // Width of Image.
            private const int width = 224;

            // Image will be consumed by ONNX image multiclass classification model.
            [ImageType(height, width)]
            public MLImage Image { get; set; }

            // Expected output of ONNX model. It contains probabilities of all
            // classes. Note that the ColumnName below should match the output name
            // in the used ONNX model file.
            [ColumnName("softmaxout_1")]
            public float[] Scores { get; set; }

            public ImageDataPoint()
            {
                Image = null;
            }

            public ImageDataPoint(byte red, byte green, byte blue)
            {
                byte[] imageData = new byte[width * height * 4]; // 4 for the red, green, blue and alpha colors
                for (int i = 0; i < imageData.Length; i += 4)
                {
                    // Fill the buffer with the Bgra32 format
                    imageData[i] = blue;
                    imageData[i + 1] = green;
                    imageData[i + 2] = red;
                    imageData[i + 3] = 255;
                }

                Image = MLImage.CreateFromPixels(width, height, MLPixelFormat.Bgra32, imageData);
            }
        }
    }
}