GridDistortion

Targets:

image

mask

bboxes

keypoints

Image Types:uint8, float32

Apply grid distortion by dividing the image into cells and warping each. Params: num_steps, distort_range, interpolation, normalized.

This transformation divides the image into a grid and randomly distorts each cell, creating localized warping effects. It's particularly useful for data augmentation in tasks like medical image analysis, OCR, and other domains where local geometric variations are meaningful.

Arguments

num_steps

int

Number of grid cells on each side of the image. Higher values create more granular distortions. Must be at least 1. Default: 5.

distort_range

tuple[float, float]

[-0.3,0.3]

Range of distortion, sampled per image. Higher absolute values create stronger distortions. Should be in [-1, 1]. Default: (-0.3, 0.3).

interpolation

0 | 1 | 2 | 3 | 4

OpenCV interpolation method used for image transformation. Options include cv2.INTER_LINEAR, cv2.INTER_CUBIC, etc. Default: cv2.INTER_LINEAR.

normalized

bool

true

If True, ensures that the distortion does not move pixels outside the image boundaries. This can result in less extreme distortions but guarantees that no information is lost. Default: True.

mask_interpolation

0 | 1 | 2 | 3 | 4

Flag that is used to specify the interpolation algorithm for mask. Should be one of: cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4. Default: cv2.INTER_NEAREST.

keypoint_remapping_method

direct | mask

mask

Method to use for keypoint remapping.

"mask": Uses mask-based remapping. Faster, especially for many keypoints, but may be less accurate for large distortions. Recommended for large images or many keypoints.
"direct": Uses inverse mapping. More accurate for large distortions but slower. Default: "mask"

p

float

0.5

Probability of applying the transform. Default: 0.5.

Examples

>>> import albumentations as A
>>> transform = A.Compose([
...     A.GridDistortion(num_steps=5, distort_range=(-0.3, 0.3), p=1.0),
... ])
>>> transformed = transform(image=image, mask=mask, bboxes=bboxes, keypoints=keypoints)
>>> transformed_image = transformed['image']
>>> transformed_mask = transformed['mask']
>>> transformed_bboxes = transformed['bboxes']
>>> transformed_keypoints = transformed['keypoints']

Notes

The same distortion is applied to all targets (image, mask, bboxes, keypoints) to maintain consistency.
When normalized=True, the distortion is adjusted to ensure all pixels remain within the image boundaries.

>>> import albumentations as A >>> transform = A.Compose([ ... A.GridDistortion(num_steps=5, distort_range=(-0.3, 0.3), p=1.0), ... ]) >>> transformed = transform(image=image, mask=mask, bboxes=bboxes, keypoints=keypoints) >>> transformed_image = transformed['image'] >>> transformed_mask = transformed['mask'] >>> transformed_bboxes = transformed['bboxes'] >>> transformed_keypoints = transformed['keypoints']