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Lib Comparison
FAQ
showcasepytorch classificationpytorch semantic segmentationexample bboxesexample keypointsmigrating from torchvision to albumentationsexampleexample bboxes2example chromatic aberrationexample d4example documentsexample domain adaptationexample gridshuffleexample hfhubexample kaggle saltexample mosaicexample multi targetexample OverlayElementsexample textimageexample weather transformsexample xymaskingreplayserialization
API Reference
import albumentations as A
import cv2
from matplotlib import pyplot as plt
def visualize(image):
    plt.figure(figsize=(10, 5))
    plt.axis("off")
    plt.imshow(image)

Load the image from the disk 🔗

img_path = "../images/alina_rossoshanska.jpeg"
img = cv2.imread(img_path, cv2.IMREAD_COLOR_RGB)

Visualize the original image 🔗

visualize(img)

png

Red-blue mode 🔗

transform = A.Compose(
    [A.ChromaticAberration(mode="red_blue", primary_distortion_limit=0.5, secondary_distortion_limit=0.1, p=1)],
    seed=137,
    strict=True,
)
 
plt.figure(figsize=(15, 10))
 
num_images = 12
 
# Loop through the list of images and plot them with subplot
for i in range(num_images):
    transformed_image = transform(image=img)["image"]
    plt.subplot(4, 3, i + 1)
    plt.imshow(transformed_image)
    plt.axis("off")
 
plt.tight_layout()
plt.show()

png