resizeImage: Machine Learning Resize Image Transform

Άρθρο
02/28/2023

Resizes an image to a specified dimension using a specified resizing method.

Usage

  resizeImage(vars, width = 224, height = 224, resizingOption = "IsoCrop")

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.

`width`

Specifies the width of the scaled image in pixels. The default value is 224.

`height`

Specifies the height of the scaled image in pixels. The default value is 224.

`resizingOption`

Specified the resizing method to use. Note that all methods are using bilinear interpolation. The options are:

"IsoPad": The image is resized such that the aspect ratio is preserved. If needed, the image is padded with black to fit the new width or height.
"IsoCrop": The image is resized such that the aspect ratio is preserved. If needed, the image is cropped to fit the new width or height.
"Aniso": The image is stretched to the new width and height, without preserving the aspect ratio. The default value is "IsoPad".

Details

resizeImage resizes an image to the specified height and width using a specified resizing method. The input variables to this transform must be images, typically the result of the loadImage transform.

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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