extractPixels: Machine Learning Extract Pixel Data Transform

Article
02/28/2023

Extracts the pixel values from an image.

Usage

  extractPixels(vars, useAlpha = FALSE, useRed = TRUE, useGreen = TRUE,
    useBlue = TRUE, interleaveARGB = FALSE, convert = TRUE, offset = NULL,
    scale = NULL)

Arguments

`vars`

A named list of character vectors of input variable names and the name of the output variable. Note that the input variables must be of the same type. For one-to-one mappings between input and output variables, a named character vector can be used.

`useAlpha`

Specifies whether to use alpha channel. The default value is FALSE.

`useRed`

Specifies whether to use red channel. The default value is TRUE.

`useGreen`

Specifies whether to use green channel. The default value is TRUE.

`useBlue`

Specifies whether to use blue channel. The default value is TRUE.

`interleaveARGB`

Whether to separate each channel or interleave in ARGB order. This might be important, for example, if you are training a convolutional neural network, since this would affect the shape of the kernel, stride etc.

`convert`

Whether to convert to floating point. The default value is FALSE.

`offset`

Specifies the offset (pre-scale). This requires convert = TRUE. The default value is NULL.

`scale`

Specifies the scale factor. This requires convert = TRUE. The default value is NULL.

Details

extractPixels extracts the pixel values from an image. The input variables are images of the same size, typically the output of a resizeImage transform. The output is pixel data in vector form that are typically used as features for a learner.

Value

A maml object defining the transform.

Author(s)

Microsoft Corporation Microsoft Technical Support

Examples


 train <- data.frame(Path = c(system.file("help/figures/RevolutionAnalyticslogo.png", package = "MicrosoftML")), Label = c(TRUE), stringsAsFactors = FALSE)

 # Loads the images from variable Path, resizes the images to 1x1 pixels and trains a neural net.
 model <- rxNeuralNet(
     Label ~ Features,
     data = train,
     mlTransforms = list(
         loadImage(vars = list(Features = "Path")),
         resizeImage(vars = "Features", width = 1, height = 1, resizing = "Aniso"),
         extractPixels(vars = "Features")
         ),
     mlTransformVars = "Path",
     numHiddenNodes = 1,
     numIterations = 1)

 # Featurizes the images from variable Path using the default model, and trains a linear model on the result.
 model <- rxFastLinear(
     Label ~ Features,
     data = train,
     mlTransforms = list(
         loadImage(vars = list(Features = "Path")),
         resizeImage(vars = "Features", width = 224, height = 224), # If dnnModel == "AlexNet", the image has to be resized to 227x227.
         extractPixels(vars = "Features"),
         featurizeImage(var = "Features")
         ),
     mlTransformVars = "Path")

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