Image augmentation for classification¶

We can divide the process of image augmentation into four steps:

1. Import albumentations and a library to read images from the disk (e.g., OpenCV).
2. Define an augmentation pipeline.
3. Read images from the disk.
4. Pass images to the augmentation pipeline and receive augmented images.

Step 1. Import the required libraries.¶

• Import Albumentations

import albumentations as A

• Import a library to read images from the disk. In this example, we will use OpenCV. It is an open-source computer vision library that supports many image formats. Albumentations has OpenCV as a dependency, so you already have OpenCV installed.

import cv2

Step 2. Define an augmentation pipeline.¶

To define an augmentation pipeline, you need to create an instance of the Compose class. As an argument to the Compose class, you need to pass a list of augmentations you want to apply. A call to Compose will return a transform function that will perform image augmentation.

Let's look at an example:

transform = A.Compose([
    A.RandomCrop(width=256, height=256),
    A.HorizontalFlip(p=0.5),
    A.RandomBrightnessContrast(p=0.2),
])

In the example, Compose receives a list with three augmentations: A.RandomCrop, A.HorizontalFlip, and A.RandomBrighntessContrast. You can find the full list of all available augmentations in the GitHub repository and in the API Docs. A demo playground that demonstrates how augmentations will transform the input image is available at https://explore.albumentations.ai.

To create an augmentation, you create an instance of the required augmentation class and pass augmentation parameters to it. A.RandomCrop receives two parameters, height and width. A.RandomCrop(width=256, height=256) means that A.RandomCrop will take an input image, extract a random patch with size 256 by 256 pixels from it and then pass the result to the next augmentation in the pipeline (in this case to A.HorizontalFlip).

A.HorizontalFlip in this example has one parameter named p. p is a special parameter that is supported by almost all augmentations. It controls the probability of applying the augmentation. p=0.5 means that with a probability of 50%, the transform will flip the image horizontally, and with a probability of 50%, the transform won't modify the input image.

A.RandomBrighntessContrast in the example also has one parameter, p. With a probability of 20%, this augmentation will change the brightness and contrast of the image received from A.HorizontalFlip. And with a probability of 80%, it will keep the received image unchanged.

A visualized version of the augmentation pipeline. You pass an image to it, the image goes through all transformations, and then you receive an augmented image from the pipeline.

Step 3. Read images from the disk.¶

To pass an image to the augmentation pipeline, you need to read it from the disk. The pipeline expects to receive an image in the form of a NumPy array. If it is a color image, it should have three channels in the following order: Red, Green, Blue (so a regular RGB image).

To read images from the disk, you can use OpenCV - a popular library for image processing. It supports a lot of input formats and is installed along with Albumentations since Albumentations utilizes that library under the hood for a lot of augmentations.

To import OpenCV

import cv2

To read an image with OpenCV

image = cv2.imread("/path/to/image.jpg")
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

    pip install pillow

from PIL import Image
import numpy as np

Step 4. Pass images to the augmentation pipeline and receive augmented images.¶

To pass an image to the augmentation pipeline you need to call the transform function created by a call to A.Compose at Step 2. In the image argument to that function, you need to pass an image that you want to augment.

transformed = transform(image=image)

transform will return a dictionary with a single key image. Value at that key will contain an augmented image.

transformed_image = transformed["image"]

To augment the next image, you need to call transform again and pass a new image as the image argument:

another_transformed_image = transform(image=another_image)["image"]

transform = A.Compose([
    A.RandomBrightnessContrast(brightness_limit=1, contrast_limit=1, p=1.0),
])
transformed_image_1 = transform(image=image)['image']
transformed_image_2 = transform(image=image)['image']
transformed_image_3 = transform(image=image)['image']

Examples¶

• Defining a simple augmentation pipeline for image augmentation
• Working with non-8-bit images
• Weather augmentations in Albumentations
• Showcase. Cool augmentation examples on diverse set of images from various real-world tasks.