annotate.py

bboxes = [[0, 128, 300, 420], [366.7, 340, 270, 230]]
category_ids = [1, 2]

transform = A.Compose(
    [A.HorizontalFlip(p=0.5), A.Rotate()],
    bbox_params=A.BboxParams(format="coco", label_fields=["category_ids"]), # Configuring pipeline for annotation
)

# Passing annotation coordinates and categories with the image
transformed = transform(image=image, bboxes=bboxes, category_ids=category_ids)

augment.py

import albumentations as A
from PIL import Image
import numpy as np

# Create a pipline with 4 different transformations. 
transform = A.Compose(
    [
        A.RandomCrop(width=256, height=256),
        A.HorizontalFlip(p=0.5),
        A.RandomBrightnessContrast(brightness_limit=.5, contrast_limit=.3),
        A.Rotate(),
    ]
)

# Read the image and convert it to a numpy array
pillow_image = Image.open("image.jpg")
image = np.array(pillow_image)

# Apply transformation
transformed = transform(image=image)

# Access and show transformation
transformed_image = transformed["image"]
img = Image.fromarray(transformed_image)

img.show()