flow_images_from_dataframe
Takes the dataframe and the path to a directory and generates batches of augmented/normalized data.
Description
Takes the dataframe and the path to a directory and generates batches of augmented/normalized data.
Usage
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
) Arguments
| Arguments | Description |
|---|---|
| dataframe | 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: - if class_mode is “categorical” (default value) it must include the 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. - if class_mode is “binary” or “sparse” it must include the given y_col column with class values as strings. - if class_mode is “other” it should contain the columns specified in y_col. - if class_mode is “input” or NULL no extra column is needed. |
| directory | character, path to the directory to read images from. If NULL, data in x_col column should be absolute paths. |
| x_col | character, column in dataframe that contains the filenames (or absolute paths if directory is NULL). |
| y_col | string or list, column/s in dataframe that has the target data. |
| generator | Image data generator to use for augmenting/normalizing image data. |
| target_size | Either NULL (default to original size) or integer vector (img_height, img_width). |
| color_mode | one of “grayscale”, “rgb”. Default: “rgb”. Whether the images will be converted to have 1 or 3 color channels. |
| classes | optional list of classes (e.g. c('dogs', 'cats'). Default: NULL If not provided, the list of classes will be automatically inferred from the 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 class_indices. |
| class_mode | 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 predict_generator()). |
| 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”. |
| 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. |
| drop_duplicates | (deprecated in TF >= 2.3) Boolean, whether to drop duplicate rows based on filename. The default value is TRUE. |
Details
Yields batches indefinitely, in an infinite loop.
Section
Yields
(x, y) where x is an array of image data and y is a array of corresponding labels. The generator loops indefinitely.
Note
This functions requires that pandas (Python module) is installed in the same environment as tensorflow and keras. If you are using r-tensorflow (the default environment) you can install pandas by running reticulate::virtualenv_install("pandas", envname = "r-tensorflow") or reticulate::conda_install("pandas", envname = "r-tensorflow") depending on the kind of environment you are using.
See Also
Other image preprocessing: fit_image_data_generator(), flow_images_from_data(), flow_images_from_directory(), image_load(), image_to_array()