albumentations-team/albumentations 1.4.21

Albumentations 1.4.21 Release Notes

on GitHub

• Support Our Work
• Transforms
• Core
• Benchmark
• Speedups

Support Our Work

1. Love the library? You can contribute to its development by becoming a sponsor for the library. Your support is invaluable, and every contribution makes a difference.
2. Haven't starred our repo yet? Show your support with a ⭐! It's just only one mouse click away.
3. Got ideas or facing issues? We'd love to hear from you. Share your thoughts in our issues or join the conversation on our Discord server

Transforms

Auto padding in crops

Added option to pad the image if crop size is larger than the crop size

Old way

[
A.PadIfNeeded(min_height=1024, min_width=1024, p=1),
A.RandomCrop(height=1204, width=1024, p=1)
]

New way:

A.RandomCrop(height=1204, width=1024, p=1, pad_if_needed=True)

Works for:

• RandomCrop
• CenterCrop
• Crop

You may also use it to pad image to a desired size.

Core

Random state

Now random state for the pipeline does not depend on the global random state

Before

random.seed(seed)
np.random.seed(seed)

transform = A.Compose(...) 

Now

transform = A.Compose(seed=seed, ...)

or

transform = A.Compose(...)
transform.set_random_seed(seed)

Saving used parameters

Now you can get exact parameters that were used in the pipeline on a given sample with

transform = A.Compose(save_applied_params=True, ...)

result = transform(image=image, bboxes=bboxes, mask=mask, keypoints=keypoints)

print(result["applied_transforms"])

Benchmark

Moved benchmark to a separate repo

https://github.com/albumentations-team/benchmark/

Current result for uint8 images:

Speedups

• Speedup in PlankianJitter in uint8 mode
• Replaced cv2.addWeighted with wsum from simsimd package