156 lines
6.5 KiB
Python
156 lines
6.5 KiB
Python
|
from keras_core.api_export import keras_core_export
|
||
|
from keras_core.utils.module_utils import tensorflow as tf
|
||
|
|
||
|
|
||
|
@keras_core_export(
|
||
|
[
|
||
|
"keras_core.utils.image_dataset_from_directory",
|
||
|
"keras_core.preprocessing.image_dataset_from_directory",
|
||
|
]
|
||
|
)
|
||
|
def image_dataset_from_directory(
|
||
|
directory,
|
||
|
labels="inferred",
|
||
|
label_mode="int",
|
||
|
class_names=None,
|
||
|
color_mode="rgb",
|
||
|
batch_size=32,
|
||
|
image_size=(256, 256),
|
||
|
shuffle=True,
|
||
|
seed=None,
|
||
|
validation_split=None,
|
||
|
subset=None,
|
||
|
interpolation="bilinear",
|
||
|
follow_links=False,
|
||
|
crop_to_aspect_ratio=False,
|
||
|
):
|
||
|
"""Generates a `tf.data.Dataset` from image files in a directory.
|
||
|
|
||
|
If your directory structure is:
|
||
|
|
||
|
```
|
||
|
main_directory/
|
||
|
...class_a/
|
||
|
......a_image_1.jpg
|
||
|
......a_image_2.jpg
|
||
|
...class_b/
|
||
|
......b_image_1.jpg
|
||
|
......b_image_2.jpg
|
||
|
```
|
||
|
|
||
|
Then calling `image_dataset_from_directory(main_directory,
|
||
|
labels='inferred')` will return a `tf.data.Dataset` that yields batches of
|
||
|
images from the subdirectories `class_a` and `class_b`, together with labels
|
||
|
0 and 1 (0 corresponding to `class_a` and 1 corresponding to `class_b`).
|
||
|
|
||
|
Supported image formats: `.jpeg`, `.jpg`, `.png`, `.bmp`, `.gif`.
|
||
|
Animated gifs are truncated to the first frame.
|
||
|
|
||
|
Args:
|
||
|
directory: Directory where the data is located.
|
||
|
If `labels` is `"inferred"`, it should contain
|
||
|
subdirectories, each containing images for a class.
|
||
|
Otherwise, the directory structure is ignored.
|
||
|
labels: Either `"inferred"`
|
||
|
(labels are generated from the directory structure),
|
||
|
`None` (no labels),
|
||
|
or a list/tuple of integer labels of the same size as the number of
|
||
|
image files found in the directory. Labels should be sorted
|
||
|
according to the alphanumeric order of the image file paths
|
||
|
(obtained via `os.walk(directory)` in Python).
|
||
|
label_mode: String describing the encoding of `labels`. Options are:
|
||
|
- `"int"`: means that the labels are encoded as integers
|
||
|
(e.g. for `sparse_categorical_crossentropy` loss).
|
||
|
- `"categorical"` means that the labels are
|
||
|
encoded as a categorical vector
|
||
|
(e.g. for `categorical_crossentropy` loss).
|
||
|
- `"binary"` means that the labels (there can be only 2)
|
||
|
are encoded as `float32` scalars with values 0 or 1
|
||
|
(e.g. for `binary_crossentropy`).
|
||
|
- `None` (no labels).
|
||
|
class_names: Only valid if `labels` is `"inferred"`.
|
||
|
This is the explicit list of class names
|
||
|
(must match names of subdirectories). Used to control the order
|
||
|
of the classes (otherwise alphanumerical order is used).
|
||
|
color_mode: One of `"grayscale"`, `"rgb"`, `"rgba"`.
|
||
|
Defaults to `"rgb"`. Whether the images will be converted to
|
||
|
have 1, 3, or 4 channels.
|
||
|
batch_size: Size of the batches of data. Defaults to 32.
|
||
|
If `None`, the data will not be batched
|
||
|
(the dataset will yield individual samples).
|
||
|
image_size: Size to resize images to after they are read from disk,
|
||
|
specified as `(height, width)`. Defaults to `(256, 256)`.
|
||
|
Since the pipeline processes batches of images that must all have
|
||
|
the same size, this must be provided.
|
||
|
shuffle: Whether to shuffle the data. Defaults to `True`.
|
||
|
If set to `False`, sorts the data in alphanumeric order.
|
||
|
seed: Optional random seed for shuffling and transformations.
|
||
|
validation_split: Optional float between 0 and 1,
|
||
|
fraction of data to reserve for validation.
|
||
|
subset: Subset of the data to return.
|
||
|
One of `"training"`, `"validation"`, or `"both"`.
|
||
|
Only used if `validation_split` is set.
|
||
|
When `subset="both"`, the utility returns a tuple of two datasets
|
||
|
(the training and validation datasets respectively).
|
||
|
interpolation: String, the interpolation method used when
|
||
|
resizing images. Defaults to `"bilinear"`.
|
||
|
Supports `"bilinear"`, `"nearest"`, `"bicubic"`, `"area"`,
|
||
|
`"lanczos3"`, `"lanczos5"`, `"gaussian"`, `"mitchellcubic"`.
|
||
|
follow_links: Whether to visit subdirectories pointed to by symlinks.
|
||
|
Defaults to `False`.
|
||
|
crop_to_aspect_ratio: If `True`, resize the images without aspect
|
||
|
ratio distortion. When the original aspect ratio differs from the
|
||
|
target aspect ratio, the output image will be cropped so as to
|
||
|
return the largest possible window in the image
|
||
|
(of size `image_size`) that matches the target aspect ratio. By
|
||
|
default (`crop_to_aspect_ratio=False`), aspect ratio may not be
|
||
|
preserved.
|
||
|
|
||
|
Returns:
|
||
|
|
||
|
A `tf.data.Dataset` object.
|
||
|
|
||
|
- If `label_mode` is `None`, it yields `float32` tensors of shape
|
||
|
`(batch_size, image_size[0], image_size[1], num_channels)`,
|
||
|
encoding images (see below for rules regarding `num_channels`).
|
||
|
- Otherwise, it yields a tuple `(images, labels)`, where `images` has
|
||
|
shape `(batch_size, image_size[0], image_size[1], num_channels)`,
|
||
|
and `labels` follows the format described below.
|
||
|
|
||
|
Rules regarding labels format:
|
||
|
|
||
|
- if `label_mode` is `"int"`, the labels are an `int32` tensor of shape
|
||
|
`(batch_size,)`.
|
||
|
- if `label_mode` is `"binary"`, the labels are a `float32` tensor of
|
||
|
1s and 0s of shape `(batch_size, 1)`.
|
||
|
- if `label_mode` is `"categorical"`, the labels are a `float32` tensor
|
||
|
of shape `(batch_size, num_classes)`, representing a one-hot
|
||
|
encoding of the class index.
|
||
|
|
||
|
Rules regarding number of channels in the yielded images:
|
||
|
|
||
|
- if `color_mode` is `"grayscale"`,
|
||
|
there's 1 channel in the image tensors.
|
||
|
- if `color_mode` is `"rgb"`,
|
||
|
there are 3 channels in the image tensors.
|
||
|
- if `color_mode` is `"rgba"`,
|
||
|
there are 4 channels in the image tensors.
|
||
|
"""
|
||
|
# TODO: long-term, port implementation.
|
||
|
return tf.keras.utils.image_dataset_from_directory(
|
||
|
directory,
|
||
|
labels=labels,
|
||
|
label_mode=label_mode,
|
||
|
class_names=class_names,
|
||
|
color_mode=color_mode,
|
||
|
batch_size=batch_size,
|
||
|
image_size=image_size,
|
||
|
shuffle=shuffle,
|
||
|
seed=seed,
|
||
|
validation_split=validation_split,
|
||
|
subset=subset,
|
||
|
interpolation=interpolation,
|
||
|
follow_links=follow_links,
|
||
|
crop_to_aspect_ratio=crop_to_aspect_ratio,
|
||
|
)
|