ImageEstimatorsCatalog.ExtractPixels 方法
定义
重要
一些信息与预发行产品相关，相应产品在发行之前可能会进行重大修改。 对于此处提供的信息，Microsoft 不作任何明示或暗示的担保。
创建一个 ImagePixelExtractingEstimator，它将从列 inputColumnName 中指定的数据中提取像素值到新列： 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参数
- catalog
- TransformsCatalog
转换的目录。
- outputColumnName
- String
转换 inputColumnName生成的列的名称。
此列的数据类型将是 或 Byte 的Single已知大小向量，具体取决于 outputAsFloatArray。
- colorsToExtract
- ImagePixelExtractingEstimator.ColorBits
要从图像中提取的颜色。
- orderOfExtraction
- ImagePixelExtractingEstimator.ColorsOrder
从像素中提取颜色的顺序。
- interleavePixelColors
- Boolean
是交错像素颜色，表示按顺序保留像素 orderOfExtraction 颜色，还是将其保留在规划器窗体中：一种颜色的所有值表示所有像素，然后是另一种颜色的所有值，等等。
- offsetImage
- Single
将每个像素的颜色值偏移到此量。 应用于 之前 scaleImage的颜色值。
- scaleImage
- Single
按此量缩放每个像素的颜色值。 应用于 后 offsetImage的颜色值。
- outputAsFloatArray
- Boolean
将数组输出为 float 数组。 如果为 false，则输出为字节数组，并忽略 offsetImage 和 scaleImage。
返回
示例
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);
}
}
}
}