Data augmentation is a key technology in the field of artificial intelligence and machine learning. It involves creating variations on existing datasets to improve model performance and generalization. Python is a popular AI and ML language that provides several powerful data augmentation libraries. In this article, we will introduce ten Python libraries for data augmentation and provide code snippets and explanations for each library.
Augmentor
Augmentor is a general-purpose Python library for image enhancement. It allows you to easily apply a range of operations to your images, such as rotation, flipping, and color manipulation. Here is a simple example of how to use Augmentor for image enhancement:
import Augmentor
p = Augmentor.Pipeline("path/to/your/images")
p.rotate(probability=0.7, max_left_rotation=25, max_right_rotation=25)
p.flip_left_right(probability=0.5)
p.sample(100)
Albumentations
Albumentations Master supports various enhancements such as random rotation, flipping and brightness adjustment. He is my most commonly used enhancement library
import albumentations as A
transform = A.Compose([
A.RandomRotate90(),
A.HorizontalFlip(),
A.RandomBrightnessContrast(),
])
augmented_image = transform(image=image)["image"]
Imgaug
Imgaug is a library for enhancing images and videos. It provides a wide range of enhancements, including geometric transformations and color space modifications. Here is an example using Imgaug:
import imgaug.augmenters as iaa
augmenter = iaa.Sequential([
iaa.Fliplr(0.5),
iaa.Sometimes(0.5, iaa.GaussianBlur(sigma=(0, 2.0))),
iaa.ContrastNormalization((0.5, 2.0)),
])
augmented_image = augmenter.augment_image(image)
npaug
nlpaaug is a library designed specifically for text data augmentation. It provides various techniques for generating text variations, such as synonym substitution and character-level substitution.
import nlpaug.augmenter.word as naw
aug = naw.ContextualWordEmbsAug(model_path='bert-base-uncased', action="insert")
augmented_text = aug.augment("This is a sample text.")
imgaugment
imgauge is a lightweight library focused on image enhancement. It's easy to use and offers operations like rotation, flipping, and color adjustment.
from imgaug import augmenters as iaa
seq = iaa.Sequential([
iaa.Fliplr(0.5),
iaa.Sometimes(0.5, iaa.GaussianBlur(sigma=(0, 2.0))),
iaa.ContrastNormalization((0.5, 2.0)),
])
augmented_image = seq(image=image)
TextAttack
TextAttack is a Python library for enhancing and attacking natural language processing (NLP) models. It provides various transformations to generate adversarial examples for NLP tasks. Here's how to use it:
from textattack.augmentation import WordNetAugmenter
augmenter = WordNetAugmenter()
augmented_text = augmenter.augment("The quick brown fox")
TOO
The Text Augmentation and Adversarial Examples (TAAE) library is another tool for text enhancement. It includes techniques such as synonym substitution and sentence shuffling.
from taae import SynonymAugmenter
augmenter = SynonymAugmenter()
augmented_text = augmenter.augment("This is a test sentence.")
Audiomentations
Audiomentations focuses on audio data enhancement. It is an essential library for tasks involving sound processing.
import audiomentations as A
augmenter = A.Compose([
A.PitchShift(),
A.TimeStretch(),
A.AddBackgroundNoise(),
])
augmented_audio = augmenter(samples=audio_data, sample_rate=sample_rate)
ImageDataAugmentor
ImageDataAugmentor is designed for image data augmentation and works well with popular deep learning frameworks. Here's how to use it with TensorFlow:
from ImageDataAugmentor.image_data_augmentor import *
import tensorflow as tf
datagen = ImageDataAugmentor(
augment=augmentor,
preprocess_input=None,
)
train_generator = datagen.flow_from_directory("data/train", batch_size=32, class_mode="binary")
Keras ImageDataGenerator
Keras provides the ImageDataGenerator class, which is a built-in solution for image augmentation when using Keras and TensorFlow.
from tensorflow.keras.preprocessing.image import ImageDataGenerator
datagen = ImageDataGenerator(
rotation_range=40,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
fill_mode="nearest",
)
augmented_images = datagen.flow_from_directory("data/train", batch_size=32)
Summarize
These libraries cover a wide range of data augmentation techniques for image and text data and we hope they will be helpful to you.
https://avoid.overfit.cn/post/ed54d70833db468cbb18d111b65c99cf
Author:Everything Programming