albumentations-team/albumentations 1.4.14

Albumentations 1.4.14 Release Notes

on GitHub

Grid-based Elastic deformation Albumentation implementation

This class applies elastic transformations using a grid-based approach.
The granularity and intensity of the distortions can be controlled using
the dimensions of the overlaying distortion grid and the magnitude parameter.
Larger grid sizes result in finer, less severe distortions.

Args:
    num_grid_xy (tuple[int, int]): Number of grid cells along the width and height.
        Specified as (grid_width, grid_height). Each value must be greater than 1.
    magnitude (int): Maximum pixel-wise displacement for distortion. Must be greater than 0.
    interpolation (int): Interpolation method to be used for the image transformation.
        Default: cv2.INTER_LINEAR
    mask_interpolation (int): Interpolation method to be used for mask transformation.
        Default: cv2.INTER_NEAREST
    p (float): Probability of applying the transform. Default: 1.0.

Targets:
    image, mask

Image types:
    uint8, float32

Example:
    >>> transform = GridElasticDeform(num_grid_xy=(4, 4), magnitude=10, p=1.0)
    >>> result = transform(image=image, mask=mask)
    >>> transformed_image, transformed_mask = result['image'], result['mask']

Note:
    This transformation is particularly useful for data augmentation in medical imaging
    and other domains where elastic deformations can simulate realistic variations.

Simulates shadows for the image by reducing the brightness of the image in shadow regions.

Args:
    shadow_roi (tuple): region of the image where shadows
        will appear (x_min, y_min, x_max, y_max). All values should be in range [0, 1].
    num_shadows_limit (tuple): Lower and upper limits for the possible number of shadows.
        Default: (1, 2).
    shadow_dimension (int): number of edges in the shadow polygons. Default: 5.
    shadow_intensity_range (tuple): Range for the shadow intensity.
        Should be two float values between 0 and 1. Default: (0.5, 0.5).
    p (float): probability of applying the transform. Default: 0.5.

Targets:
    image

Image types:
    uint8, float32

Reference:
    https://github.com/UjjwalSaxena/Automold--Road-Augmentation-Library