Ten Python libraries for data augmentation

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_rotatinotallow=25, max_right_rotatinotallow=25) p.flip_left_right(probability=0.5) p.sample(100)

Albumentations

Albumentations Master supports various enhancement features such as random rotation , flip and brightness adjustment. He is one of my most commonly used enhancement libraries

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)

nlpaug

nlpaaug is a library specifically designed for text data enhancement. 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', actinotallow="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")

TAAE

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 used when using Keras and TensorFlow Built-in solution for image enhancement.

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)

Summary

These libraries cover a wide range of data augmentation techniques for image and text data, I hope it will be helpful to you.

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