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augment

Apply identical random transformations to multiple images

Syntax

augI = augment(augmenter,I)

Description

example

augI = augment(augmenter,I) augments image I using a random transformation from the set of image preprocessing options defined by image data augmenter, augmenter. If I consists of multiple images, then augment applies an identical transformation to all images.

Examples

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Create an image augmenter that rotates images by a random angle. To use a custom range of valid rotation angles, you can specify a function handle when you create the augmenter. This example specifies a function called myrange (defined at the end of the example) that selects an angle from within two disjoint intervals.

imageAugmenter = imageDataAugmenter('RandRotation',@myrange);

Read multiple images into the workspace, and display the images.

img1 = imread('peppers.png');
img2 = imread('corn.tif',2);
inImg = imtile({img1,img2});
imshow(inImg)

Augment the images with identical augmentations. The randomly selected rotation angle is returned in a temporary variable, angle.

outCellArray = augment(imageAugmenter,{img1,img2});
angle = 8.1158

View the augmented images.

outImg = imtile(outCellArray);
imshow(outImg);

Supporting Function

This example defines the myrange function that first randomly selects one of two intervals (-10, 10) and (170, 190) with equal probability. Within the selected interval, the function returns a single random number from a uniform distribution.

function angle = myrange()
    if randi([0 1],1)
        a = -10;
        b = 10;
    else
        a = 170;
        b = 190;
    end
    angle = a + (b-a).*rand(1)
end

Input Arguments

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Augmentation options, specified as an imageDataAugmenter object.

Images to augment, specified as one of the following.

  • Numeric array, representing a single grayscale or color image.

  • Cell array of numeric and categorical images. Images can be different sizes and types.

Output Arguments

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Augmented images, returned as a numeric array or cell array of numeric and categorical images, consistent with the format of the input images I.

Tips

  • You can use the augment function to preview the transformations applied to sample images.

  • To perform image augmentation during training, create an augmentedImageDatastore and specify preprocessing options by using the 'DataAugmentation' name-value pair with an imageDataAugmenter. The augmented image datastore automatically applies random transformations to the training data.

Introduced in R2018b