-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathimage_dataset_utils.py
More file actions
469 lines (416 loc) · 16.6 KB
/
image_dataset_utils.py
File metadata and controls
469 lines (416 loc) · 16.6 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
import numpy as np
from keras.src.api_export import keras_export
from keras.src.backend.config import standardize_data_format
from keras.src.utils import dataset_utils
from keras.src.utils import image_utils
from keras.src.utils.module_utils import tensorflow as tf
from dataset_utils import index_directory
ALLOWLIST_FORMATS = (".bmp", ".gif", ".jpeg", ".jpg", ".png")
@keras_export(
[
"keras.utils.image_dataset_from_directory",
"keras.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,
pad_to_aspect_ratio=False,
data_format=None,
verbose=True,
):
"""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"`.
Whether the images will be converted to
have 1, 3, or 4 channels. Defaults to `"rgb"`.
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)`.
Since the pipeline processes batches of images that must all have
the same size, this must be provided. Defaults to `(256, 256)`.
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.
Supports `"bilinear"`, `"nearest"`, `"bicubic"`, `"area"`,
`"lanczos3"`, `"lanczos5"`, `"gaussian"`, `"mitchellcubic"`.
Defaults to `"bilinear"`.
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.
pad_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 padded so as to
return the largest possible window in the image
(of size `image_size`) that matches the target aspect ratio. By
default (`pad_to_aspect_ratio=False`), aspect ratio may not be
preserved.
data_format: If None uses keras.config.image_data_format()
otherwise either 'channel_last' or 'channel_first'.
verbose: Whether to display number information on classes and
number of files found. Defaults to `True`.
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.
"""
if labels not in ("inferred", None):
if not isinstance(labels, (list, tuple)):
raise ValueError(
"`labels` argument should be a list/tuple of integer labels, "
"of the same size as the number of image files in the target "
"directory. If you wish to infer the labels from the "
"subdirectory "
'names in the target directory, pass `labels="inferred"`. '
"If you wish to get a dataset that only contains images "
f"(no labels), pass `labels=None`. Received: labels={labels}"
)
if class_names:
raise ValueError(
"You can only pass `class_names` if "
f'`labels="inferred"`. Received: labels={labels}, and '
f"class_names={class_names}"
)
if label_mode not in {"int", "categorical", "binary", None}:
raise ValueError(
'`label_mode` argument must be one of "int", '
'"categorical", "binary", '
f"or None. Received: label_mode={label_mode}"
)
if labels is None or label_mode is None:
labels = None
label_mode = None
if color_mode == "rgb":
num_channels = 3
elif color_mode == "rgba":
num_channels = 4
elif color_mode == "grayscale":
num_channels = 1
else:
raise ValueError(
'`color_mode` must be one of {"rgb", "rgba", "grayscale"}. '
f"Received: color_mode={color_mode}"
)
if isinstance(image_size, int):
image_size = (image_size, image_size)
elif not isinstance(image_size, (list, tuple)) or not len(image_size) == 2:
raise ValueError(
"Invalid `image_size` value. Expected a tuple of 2 integers. "
f"Received: image_size={image_size}"
)
interpolation = interpolation.lower()
supported_interpolations = (
"bilinear",
"nearest",
"bicubic",
"area",
"lanczos3",
"lanczos5",
"gaussian",
"mitchellcubic",
)
if interpolation not in supported_interpolations:
raise ValueError(
"Argument `interpolation` should be one of "
f"{supported_interpolations}. "
f"Received: interpolation={interpolation}"
)
dataset_utils.check_validation_split_arg(
validation_split, subset, shuffle, seed
)
if seed is None:
seed = np.random.randint(1e6)
image_paths, labels, class_names = index_directory(
directory,
labels,
formats=ALLOWLIST_FORMATS,
class_names=class_names,
shuffle=shuffle,
seed=seed,
follow_links=follow_links,
verbose=verbose,
)
if label_mode == "binary" and len(class_names) != 2:
raise ValueError(
'When passing `label_mode="binary"`, there must be exactly 2 '
f"class_names. Received: class_names={class_names}"
)
data_format = standardize_data_format(data_format=data_format)
if batch_size is not None:
shuffle_buffer_size = batch_size * 8
else:
shuffle_buffer_size = 1024
if subset == "both":
(
image_paths_train,
labels_train,
) = dataset_utils.get_training_or_validation_split(
image_paths, labels, validation_split, "training"
)
(
image_paths_val,
labels_val,
) = dataset_utils.get_training_or_validation_split(
image_paths, labels, validation_split, "validation"
)
if not image_paths_train:
raise ValueError(
f"No training images found in directory {directory}. "
f"Allowed formats: {ALLOWLIST_FORMATS}"
)
if not image_paths_val:
raise ValueError(
f"No validation images found in directory {directory}. "
f"Allowed formats: {ALLOWLIST_FORMATS}"
)
train_dataset = paths_and_labels_to_dataset(
image_paths=image_paths_train,
image_size=image_size,
num_channels=num_channels,
labels=labels_train,
label_mode=label_mode,
num_classes=len(class_names) if class_names else 0,
interpolation=interpolation,
crop_to_aspect_ratio=crop_to_aspect_ratio,
pad_to_aspect_ratio=pad_to_aspect_ratio,
data_format=data_format,
shuffle=shuffle,
shuffle_buffer_size=shuffle_buffer_size,
seed=seed,
)
val_dataset = paths_and_labels_to_dataset(
image_paths=image_paths_val,
image_size=image_size,
num_channels=num_channels,
labels=labels_val,
label_mode=label_mode,
num_classes=len(class_names) if class_names else 0,
interpolation=interpolation,
crop_to_aspect_ratio=crop_to_aspect_ratio,
pad_to_aspect_ratio=pad_to_aspect_ratio,
data_format=data_format,
shuffle=False,
)
if batch_size is not None:
train_dataset = train_dataset.batch(batch_size)
val_dataset = val_dataset.batch(batch_size)
train_dataset = train_dataset.prefetch(tf.data.AUTOTUNE)
val_dataset = val_dataset.prefetch(tf.data.AUTOTUNE)
# Users may need to reference `class_names`.
train_dataset.class_names = class_names
val_dataset.class_names = class_names
# Include file paths for images as attribute.
train_dataset.file_paths = image_paths_train
val_dataset.file_paths = image_paths_val
dataset = [train_dataset, val_dataset]
else:
image_paths, labels = dataset_utils.get_training_or_validation_split(
image_paths, labels, validation_split, subset
)
if not image_paths:
raise ValueError(
f"No images found in directory {directory}. "
f"Allowed formats: {ALLOWLIST_FORMATS}"
)
dataset = paths_and_labels_to_dataset(
image_paths=image_paths,
image_size=image_size,
num_channels=num_channels,
labels=labels,
label_mode=label_mode,
num_classes=len(class_names) if class_names else 0,
interpolation=interpolation,
crop_to_aspect_ratio=crop_to_aspect_ratio,
pad_to_aspect_ratio=pad_to_aspect_ratio,
data_format=data_format,
shuffle=shuffle,
shuffle_buffer_size=shuffle_buffer_size,
seed=seed,
)
if batch_size is not None:
dataset = dataset.batch(batch_size)
dataset = dataset.prefetch(tf.data.AUTOTUNE)
# Users may need to reference `class_names`.
dataset.class_names = class_names
# Include file paths for images as attribute.
dataset.file_paths = image_paths
return dataset
def paths_and_labels_to_dataset(
image_paths,
image_size,
num_channels,
labels,
label_mode,
num_classes,
interpolation,
data_format,
crop_to_aspect_ratio=False,
pad_to_aspect_ratio=False,
shuffle=False,
shuffle_buffer_size=None,
seed=None,
):
"""Constructs a dataset of images and labels."""
path_ds = tf.data.Dataset.from_tensor_slices(image_paths)
if label_mode:
label_ds = dataset_utils.labels_to_dataset(
labels, label_mode, num_classes
)
ds = tf.data.Dataset.zip((path_ds, label_ds))
else:
ds = path_ds
if shuffle:
ds = ds.shuffle(buffer_size=shuffle_buffer_size or 1024, seed=seed)
args = (
image_size,
num_channels,
interpolation,
data_format,
crop_to_aspect_ratio,
pad_to_aspect_ratio,
)
temp = load_image(image_paths[0], *args)
if label_mode:
ds = ds.map(
lambda x, y: (load_image(x, *args), y),
num_parallel_calls=tf.data.AUTOTUNE,
)
else:
ds = ds.map(
lambda x: load_image(x, *args), num_parallel_calls=tf.data.AUTOTUNE
)
return ds
def load_image(
path,
image_size,
num_channels,
interpolation,
data_format,
crop_to_aspect_ratio=False,
pad_to_aspect_ratio=False,
):
"""Load an image from a path and resize it."""
img = tf.io.read_file(path)
img = tf.image.decode_image(
img, channels=num_channels, expand_animations=False
)
if pad_to_aspect_ratio and crop_to_aspect_ratio:
raise ValueError(
"Only one of `pad_to_aspect_ratio`, `crop_to_aspect_ratio`"
" can be set to `True`."
)
if crop_to_aspect_ratio:
from keras.src.backend import tensorflow as tf_backend
if data_format == "channels_first":
img = tf.transpose(img, (2, 0, 1))
img = image_utils.smart_resize(
img,
image_size,
interpolation=interpolation,
data_format=data_format,
backend_module=tf_backend,
)
elif pad_to_aspect_ratio:
img = tf.image.resize_with_pad(
img, image_size[0], image_size[1], method=interpolation
)
if data_format == "channels_first":
img = tf.transpose(img, (2, 0, 1))
else:
img = tf.image.resize(img, image_size, method=interpolation)
if data_format == "channels_first":
img = tf.transpose(img, (2, 0, 1))
if data_format == "channels_last":
img.set_shape((image_size[0], image_size[1], num_channels))
else:
img.set_shape((num_channels, image_size[0], image_size[1]))
return img