flow_images_from_directory: Generates batches of data from images in a directory (with...

In keras: R Interface to 'Keras'

Generates batches of data from images in a directory (with optional augmented/normalized data)

Description

Generates batches of data from images in a directory (with optional augmented/normalized data)

Usage

flow_images_from_directory(
  directory,
  generator = image_data_generator(),
  target_size = c(256, 256),
  color_mode = "rgb",
  classes = NULL,
  class_mode = "categorical",
  batch_size = 32,
  shuffle = TRUE,
  seed = NULL,
  save_to_dir = NULL,
  save_prefix = "",
  save_format = "png",
  follow_links = FALSE,
  subset = NULL,
  interpolation = "nearest"
)

Arguments

directory	path to the target directory. It should contain one subdirectory per class. Any PNG, JPG, BMP, PPM, or TIF images inside each of the subdirectories directory tree will be included in the generator. See this script for more details.
generator	Image data generator (default generator does no data augmentation/normalization transformations)
target_size	integer vector, default: c(256, 256). The dimensions to which all images found will be resized.
color_mode	one of "grayscale", "rbg". Default: "rgb". Whether the images will be converted to have 1 or 3 color channels.
classes	optional list of class subdirectories (e.g. c('dogs', 'cats')). Default: NULL, If not provided, the list of classes will be automatically inferred (and the order of the classes, which will map to the label indices, will be alphanumeric).
class_mode	one of "categorical", "binary", "sparse" or NULL. Default: "categorical". Determines the type of label arrays that are returned: "categorical" will be 2D one-hot encoded labels, "binary" will be 1D binary labels, "sparse" will be 1D integer labels. If NULL, no labels are returned (the generator will only yield batches of image data, which is useful to use predict_generator(), evaluate_generator(), etc.).
batch_size	int (default: 32).
shuffle	boolean (defaut: TRUE).
seed	int (default: NULL).
save_to_dir	NULL or str (default: NULL). This allows you to optionally specify a directory to which to save the augmented pictures being generated (useful for visualizing what you are doing).
save_prefix	str (default: ”). Prefix to use for filenames of saved pictures (only relevant if save_to_dir is set).
save_format	one of "png", "jpeg" (only relevant if save_to_dir is set). Default: "png".
follow_links	whether to follow symlinks inside class subdirectories (default: FALSE)
subset	Subset of data ("training" or "validation") if validation_split is set in image_data_generator().
interpolation	Interpolation method used to resample the image if the target size is different from that of the loaded image. Supported methods are "nearest", "bilinear", and "bicubic". If PIL version 1.1.3 or newer is installed, "lanczos" is also supported. If PIL version 3.4.0 or newer is installed, "box" and "hamming" are also supported. By default, "nearest" is used.

Details

Yields batches indefinitely, in an infinite loop.

Yields

⁠(x, y)⁠ where x is an array of image data and y is a array of corresponding labels. The generator loops indefinitely.

See Also

Other image preprocessing: fit_image_data_generator(), flow_images_from_data(), flow_images_from_dataframe(), image_load(), image_to_array()

keras documentation built on May 29, 2024, 3:20 a.m.