imageseg (version 0.5.0)

dataAugmentation: Data augmentation: rotating and mirroring images, and adjusting colors

Description

Rotate and/or mirror images to create augmented training data. Optionally, apply a random shift in brightness, saturation and hue to a certain percentage of the images

Usage

dataAugmentation(
  images,
  subset = NULL,
  rotation_angles = 0,
  flip = FALSE,
  flop = FALSE,
  brightness_shift_lim = c(90, 110),
  saturation_shift_lim = c(95, 105),
  hue_shift_lim = c(80, 120),
  fraction_random_BSH = 0
)

Arguments

images

list. Output of loadImages. List with two items ($info: data frame with information about images, $img: tibble containing magick images)

subset

integer. Indices of images to process. Can be useful for only processing subsets of images (e.g. training images, not test/validation images).

rotation_angles

integer. Angles in which to rotate images using image_rotate)?

flip

logical. mirror along horizontal axis (turn images upside-down using image_flip)?

flop

mirror along vertical axis (switch left and right) using image_flop)?

brightness_shift_lim

numeric. Lower and upper limits for argument brightness in image_modulate

saturation_shift_lim

numeric. Lower and upper limits for argument saturation in image_modulate

hue_shift_lim

numeric. Lower and upper limits for argument hue in image_modulate

fraction_random_BSH

numeric. Fraction of images to apply random brightness / saturation / hue shifts to (between 0 and 1)

Value

A list with 2 elements: $info, a data frame with information about the images, and $img, a tibble with magick images

Details

For creating training data for canopy, rotation and mirroring in both axes is appropriate. For understory vegetation density, only flop images (don't flip), and don't apply a hue shift since recognition of the orange flysheet is color-critical.

Examples

# NOT RUN {
# Example 1: Canopy
wd_images_can <- system.file("images/canopy/resized",
                             package = "imageseg")

images_can <- loadImages(imageDir = wd_images_can)

images_can_aug <- dataAugmentation(images = images_can,
                                   rotation_angles = c(0, 90, 180, 270),
                                   flip = TRUE,
                                   flop = TRUE)
images_can_aug

# }