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using System.IO;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;
namespace Samples.Dynamic
{
public static class DnnFeaturizeImage
{
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, which lists the images
// to operate on
//
// 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);
// Installing the Microsoft.ML.DNNImageFeaturizer packages copies the models in the
// `DnnImageModels` folder.
// Image loading pipeline.
var pipeline = mlContext.Transforms.LoadImages("ImageObject",
imagesFolder, "ImagePath")
.Append(mlContext.Transforms.ResizeImages("ImageObject", imageWidth:
224, imageHeight: 224))
.Append(mlContext.Transforms.ExtractPixels("Pixels", "ImageObject"))
.Append(mlContext.Transforms.DnnFeaturizeImage("FeaturizedImage",
m => m.ModelSelector.ResNet18(mlContext, m.OutputColumn, m
.InputColumn), "Pixels"));
var transformedData = pipeline.Fit(data).Transform(data);
var FeaturizedImageColumnsPerRow = transformedData.GetColumn<float[]>(
"FeaturizedImage").ToArray();
// Preview of FeaturizedImageColumnsPerRow for the first row,
// FeaturizedImageColumnsPerRow[0]
//
// 0.696136236
// 0.2661711
// 0.440882325
// 0.157903448
// 0.0339231342
// 0
// 0.0936501548
// 0.159010679
// 0.394427955
}
}
}
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