Takes the dataframe and the path to a directory and generates batches of augmented/normalized data.
flow_images_from_dataframe( dataframe, directory = NULL, x_col = "filename", y_col = "class", 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", subset = NULL, interpolation = "nearest", drop_duplicates = NULL )
data.frame containing the filepaths relative to
directory (or absolute paths if directory is
NULL) of the images in a
character column. It should include other column/s depending on the
class_mode is "categorical" (default value) it must
y_col column with the class/es of each image. Values in
column can be character/list if a single class or list if multiple classes.
class_mode is "binary" or "sparse" it must include the given
y_col column with class values as strings.
class_mode is "other" it
should contain the columns specified in
class_mode is "input" or NULL no extra column is needed.
character, path to the directory to read images from.
NULL, data in
x_col column should be absolute paths.
character, column in dataframe that contains the filenames
(or absolute paths if directory is
string or list, column/s in dataframe that has the target data.
Image data generator to use for augmenting/normalizing image data.
NULL (default to original size) or integer vector
one of "grayscale", "rgb". Default: "rgb". Whether the images will be converted to have 1 or 3 color channels.
optional list of classes (e.g.
c('dogs', 'cats'). Default:
NULL If not provided, the list of classes will be automatically inferred
y_col, which will map to the label indices, will be alphanumeric).
The dictionary containing the mapping from class names to class indices
can be obtained via the attribute
one of "categorical", "binary", "sparse", "input", "other" or None. Default: "categorical". Mode for yielding the targets:
"binary": 1D array of binary labels,
"categorical": 2D array of one-hot encoded labels. Supports multi-label output.
"sparse": 1D array of integer labels,
"input": images identical to input images (mainly used to work with autoencoders),
"other": array of y_col data,
"multi_output": allow to train a multi-output model. Y is a list or a vector.
NULL, no targets are returned (the generator will only yield batches of
image data, which is useful to use in
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).
str (default: ''). Prefix to use for filenames of saved
pictures (only relevant if
save_to_dir is set).
one of "png", "jpeg" (only relevant if save_to_dir is set). Default: "png".
Subset of data (
validation_split is set in
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.
(deprecated in TF >= 2.3) Boolean, whether to drop
duplicate rows based on filename. The default value is
Yields batches indefinitely, in an infinite loop.
This functions requires that
pandas (Python module) is installed in the
same environment as
If you are using
r-tensorflow (the default environment) you can install
pandas by running
reticulate::virtualenv_install("pandas", envname = "r-tensorflow")
reticulate::conda_install("pandas", envname = "r-tensorflow") depending on
the kind of environment you are using.
(x, y) where
x is an array of image data and
y is a
array of corresponding labels. The generator loops indefinitely.