Augmentor

Allows building custom sequences of stochastic operations with fine-grained control, as shown in the README where users chain distortions, flips, and crops to generate diverse augmented images.

Supports parallel augmentation of images and their masks using the ground_truth() function, ensuring identical transformations for segmentation tasks, demonstrated with side-by-side examples in the documentation.

Provides generators for Keras and PyTorch via keras_generator() and torch_transform(), enabling on-the-fly augmentation during training without saving to disk, as outlined in the integration notebooks.

Includes unique operations like elastic distortions and perspective transforms that preserve image size without black padding, highlighted in the README with comparative visuals against other methods.