albumentations.augmentations.geometric.distortion

>>> import numpy as np
>>> import albumentations as A
>>> import cv2
>>>
>>> class CustomDistortion(A.BaseDistortion):
...     def __init__(self, distort_limit=0.3, *args, **kwargs):
...         super().__init__(*args, **kwargs)
...         self.distort_limit = distort_limit
...
...     def get_params_dependent_on_data(self, params, data):
...         height, width = params["shape"][:2]
...         # Create distortion maps - a simple radial distortion in this example
...         map_x = np.zeros((height, width), dtype=np.float32)
...         map_y = np.zeros((height, width), dtype=np.float32)
...
...         # Calculate distortion center
...         center_x = width / 2
...         center_y = height / 2
...
...         # Generate distortion maps
...         for y in range(height):
...             for x in range(width):
...                 # Distance from center
...                 dx = (x - center_x) / width
...                 dy = (y - center_y) / height
...                 r = np.sqrt(dx * dx + dy * dy)
...
...                 # Apply radial distortion
...                 factor = 1 + self.distort_limit * r
...                 map_x[y, x] = x + dx * factor
...                 map_y[y, x] = y + dy * factor
...
...         return {"map_x": map_x, "map_y": map_y}
>>>
>>> # Prepare sample data
>>> image = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)
>>> mask = np.random.randint(0, 2, (100, 100), dtype=np.uint8)
>>> bboxes = np.array([[10, 10, 50, 50], [40, 40, 80, 80]], dtype=np.float32)
>>> bbox_labels = [1, 2]
>>> keypoints = np.array([[20, 30], [60, 70]], dtype=np.float32)
>>> keypoint_labels = [0, 1]
>>>
>>> # Define transform with the custom distortion
>>> transform = A.Compose([
...     CustomDistortion(
...         distort_limit=0.2,
...         interpolation=cv2.INTER_LINEAR,
...         mask_interpolation=cv2.INTER_NEAREST,
...         keypoint_remapping_method="mask",
...         p=1.0
...     )
... ], bbox_params=A.BboxParams(format='pascal_voc', label_fields=['bbox_labels']),
...    keypoint_params=A.KeypointParams(format='xy', label_fields=['keypoint_labels']))
>>>
>>> # Apply the transform
>>> transformed = transform(
...     image=image,
...     mask=mask,
...     bboxes=bboxes,
...     bbox_labels=bbox_labels,
...     keypoints=keypoints,
...     keypoint_labels=keypoint_labels
... )
>>>
>>> # Get the transformed data
>>> transformed_image = transformed['image']
>>> transformed_mask = transformed['mask']
>>> transformed_bboxes = transformed['bboxes']
>>> transformed_keypoints = transformed['keypoints']

>>> 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']

>>> 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']

>>> 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']

>>> import numpy as np
>>> import albumentations as A
>>> image = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)
>>> transform = A.Compose([
...     A.PiecewiseAffine(scale=(0.03, 0.05), nb_rows=4, nb_cols=4, p=0.5),
... ])
>>> transformed = transform(image=image)
>>> transformed_image = transformed["image"]

>>> 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(format='pascal_voc', label_fields=['bbox_labels']),
...    keypoint_params=A.KeypointParams(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']