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using System;
using System.IO;
using Microsoft.ML;
using Microsoft.ML.Data;
namespace Samples.Dynamic
{
public static class ConvertToGrayscale
{
// Sample that loads images from the file system, and converts them to
// grayscale.
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.ConvertToGrayscale("Grayscale",
"ImageObject"));
var transformedData = pipeline.Fit(data).Transform(data);
PrintColumns(transformedData);
// ImagePath Name ImageObject Grayscale
// tomato.bmp tomato {Width=800, Height=534} {Width=800, Height=534}
// banana.jpg banana {Width=800, Height=288} {Width=800, Height=288}
// hotdog.jpg hotdog {Width=800, Height=391} {Width=800, Height=391}
// tomato.jpg tomato {Width=800, Height=534} {Width=800, Height=534}
}
private static void PrintColumns(IDataView transformedData)
{
Console.WriteLine("{0, -25} {1, -25} {2, -25} {3, -25}", "ImagePath",
"Name", "ImageObject", "Grayscale");
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 grayscaleImageObject = null;
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 grayscaleGetter = cursor.GetGetter<MLImage>(cursor.Schema[
"Grayscale"]);
while (cursor.MoveNext())
{
imagePathGetter(ref imagePath);
nameGetter(ref name);
imageObjectGetter(ref imageObject);
grayscaleGetter(ref grayscaleImageObject);
Console.WriteLine("{0, -25} {1, -25} {2, -25} {3, -25}",
imagePath, name, $"Width={imageObject.Width}, Height={imageObject.Height}",
$"Width={grayscaleImageObject.Width}, Height={grayscaleImageObject.Height}");
}
// Dispose the image.
imageObject.Dispose();
grayscaleImageObject.Dispose();
}
}
}
}
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