albumentations.augmentations.geometric.distortion

Apply elastic deformation to images, masks, bounding boxes, and keypoints. This transformation introduces random elastic distortions to the input data. It's particularly useful for data augmentation in training deep learning models, especially for tasks like image segmentation or object detection where you want to maintain the relative positions of features while introducing realistic deformations. The transform works by generating random displacement fields and applying them to the input. These fields are smoothed using a Gaussian filter to create more natural-looking distortions. Targets: image, mask, bboxes, keypoints, volume, mask3d Image types: uint8, float32 Supported bboxes: hbb, obb

Parameters

Examples

>>> import albumentations as A
>>> transform = A.Compose([
...     A.ElasticTransform(alpha=1, sigma=50, p=0.5),
... ])
>>> 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 transform will maintain consistency across all targets (image, mask, bboxes, keypoints) by using the same displacement fields for all. - The 'approximate' parameter determines whether to use a precise or approximate method for generating displacement fields. The approximate method can be faster but may be less accurate for large sigma values. - Bounding boxes that end up outside the image after transformation will be removed. - Keypoints that end up outside the image after transformation will be removed.

Apply grid distortion to images, masks, bounding boxes, and keypoints. 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.

Parameters

Examples

>>> import albumentations as A
>>> transform = A.Compose([
...     A.GridDistortion(num_steps=5, distort_limit=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.

Apply optical distortion to images, masks, bounding boxes, and keypoints. Supports two distortion models: 1. Camera matrix model (original): Uses OpenCV's camera calibration model with k1=k2=k distortion coefficients 2. Fisheye model: Direct radial distortion: r_dist = r * (1 + gamma * r²)

Parameters

Examples

>>> import albumentations as A
>>> transform = A.Compose([
...     A.OpticalDistortion(distort_limit=0.1, 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 distortion is applied using OpenCV's initUndistortRectifyMap and remap functions. - The distortion coefficient (k) is randomly sampled from the distort_limit range. - Bounding boxes and keypoints are transformed along with the image to maintain consistency. - Fisheye model directly applies radial distortion

Apply Thin Plate Spline (TPS) transformation to create smooth, non-rigid deformations. Imagine the image printed on a thin metal plate that can be bent and warped smoothly: - Control points act like pins pushing or pulling the plate - The plate resists sharp bending, creating smooth deformations - The transformation maintains continuity (no tears or folds) - Areas between control points are interpolated naturally The transform works by: 1. Creating a regular grid of control points (like pins in the plate) 2. Randomly displacing these points (like pushing/pulling the pins) 3. Computing a smooth interpolation (like the plate bending) 4. Applying the resulting deformation to the image

Parameters

Examples

>>> import numpy as np
>>> import albumentations as A
>>> import cv2
>>>
>>> # Create sample data
>>> image = np.zeros((100, 100, 3), dtype=np.uint8)
>>> mask = np.zeros((100, 100), dtype=np.uint8)
>>> mask[25:75, 25:75] = 1  # Square mask
>>> bboxes = np.array([[10, 10, 40, 40]])  # Single box
>>> bbox_labels = [1]
>>> keypoints = np.array([[50, 50]])  # Single keypoint at center
>>> keypoint_labels = [0]
>>>
>>> # Set up transform with Compose to handle all targets
>>> transform = A.Compose([
...     A.ThinPlateSpline(scale_range=(0.2, 0.4), p=1.0)
... ], bbox_params=A.BboxParams(coord_format='pascal_voc', label_fields=['bbox_labels']),
...    keypoint_params=A.KeypointParams(coord_format='xy', label_fields=['keypoint_labels']))
>>>
>>> # Apply to all targets
>>> result = transform(
...     image=image,
...     mask=mask,
...     bboxes=bboxes,
...     bbox_labels=bbox_labels,
...     keypoints=keypoints,
...     keypoint_labels=keypoint_labels
... )
>>>
>>> # Access transformed results
>>> transformed_image = result['image']
>>> transformed_mask = result['mask']
>>> transformed_bboxes = result['bboxes']
>>> transformed_bbox_labels = result['bbox_labels']
>>> transformed_keypoints = result['keypoints']
>>> transformed_keypoint_labels = result['keypoint_labels']

Notes

- The transformation preserves smoothness and continuity - Stronger scale values may create more extreme deformations - Higher number of control points allows more local deformations - The same deformation is applied consistently to all targets

References

• [{'description': '"Principal Warps', 'source': 'Thin-Plate Splines and the Decomposition of Deformations" by F.L. Bookstein https://doi.org/10.1109/34.24792'}, {'description': 'Thin Plate Splines in Computer Vision', 'source': 'https://en.wikipedia.org/wiki/Thin_plate_spline'}, {'description': 'Similar implementation in Kornia', 'source': 'https://kornia.readthedocs.io/en/latest/augmentation.html#kornia.augmentation.RandomThinPlateSpline'}]