item_transform_random_affine: Randomly apply an affine transformation on a dataset item

In torchvision: Models, Datasets and Transformations for Images

Randomly apply an affine transformation on a dataset item

Description

Draws a random rotation, translation, scale and shear inside the given ranges and applies the resulting affine transformation to the dataset item with item_transform_affine, keeping the image size unchanged. Image and target share the same draw, so that they stay aligned.

Usage

item_transform_random_affine(
  x,
  degrees,
  translate = NULL,
  scale = NULL,
  shear = NULL,
  interpolation = 0,
  fill = NULL,
  center = NULL
)

Arguments

x	A dataset item, typically an image_with_bounding_box, image_with_rotated_box or image_with_segmentation_mask object containing an image tensor and associated target data.
degrees	(numeric vector of length 2 or numeric): Range of degrees to select from. If degrees is a bare number instead of a numeric vector of length 2 like c(min, max), the range of degrees will be ⁠(-degrees, +degrees)⁠.
translate	(numeric vector of length 2, optional): Numeric vector of maximum absolute fraction for horizontal and vertical translations. For example translate = c(a, b), then horizontal shift is randomly sampled in the range ⁠-img_width * a < dx < img_width * a⁠ and vertical shift is randomly sampled in the range ⁠-img_height * b < dy < img_height * b⁠. Will not translate by default.
scale	(numeric vector of length 2, optional): Scaling factor interval, e.g. c(a, b), then scale is randomly sampled from the range ⁠a <= scale <= b⁠. Will keep original scale by default.
shear	(numeric vector or numeric, optional): Range of degrees to select from. If shear is a bare number, a shear parallel to the x axis in the range ⁠(-shear, +shear)⁠ will be applied. Else if shear is a numeric vector of length 2, a shear parallel to the x axis in the range ⁠(shear[1], shear[2])⁠ will be applied. Else if shear is a numeric vector of length 4, a x-axis shear in ⁠(shear[1], shear[2])⁠ and y-axis shear in ⁠(shear[3], shear[4])⁠ will be applied. Will not apply shear by default.
interpolation	(integer or character, optional): Interpolation mode. Supported values are 0 / "nearest" and 2 / "bilinear". Default is 0.
fill	Fill color for the area outside the transform. Default is NULL.
center	(numeric vector of length 2, optional): Optional center of rotation, c(x, y). Default is image center.

Details

The transformation is drawn again for every item, so that a dataset wrapped with this transform yields a different transformation on each access.

Value

A dataset item with the image and target transformed. Detection items are returned as image_with_rotated_box; segmentation items keep their class.

See Also

Other item_random_transforms: item_transform_random_crop(), item_transform_random_erasing(), item_transform_random_horizontal_flip(), item_transform_random_perspective(), item_transform_random_resize_crop(), item_transform_random_rotation(), item_transform_random_vertical_flip()

Examples

## Not run: 
url <- "https://upload.wikimedia.org/wikipedia/commons/b/b6/Felis_catus-cat_on_snow.jpg"
img <- base_loader(url) |> transform_to_tensor()

boxes <- torch_tensor(matrix(c(600, 200, 2880, 1860), ncol = 4), dtype = torch_float32())

before <- list(x = img, y = list(boxes = boxes, labels = "cat",
                                 image_height = img$shape[2], image_width = img$shape[3]))
class(before) <- c("image_with_bounding_box", "list")

after <- item_transform_random_affine(before, degrees = 30, translate = c(0.1, 0.1),
                                      scale = c(0.8, 1.2), shear = 10)

before_plot <- draw_bounding_boxes(before, colors = "blue", width = 10)
after_plot <- draw_bounding_boxes(after, colors = "red", width = 10)
tensor_image_browse(before_plot)
tensor_image_browse(after_plot)

## End(Not run)

torchvision documentation built on Oct. 14, 2026, 1:09 a.m.