RandomCrop3D

Crop random part of 3D volume.

Supported Targets

keypoints

Arguments

Name	Type	Description
size	tuple[int, int, int]	Desired output size of the crop in format (depth, height, width)
pad_if_needed	bool	Whether to pad if the volume is smaller than desired crop size. Default: False
fill	tuple[float, float] \| float	Padding value for image if pad_if_needed is True. Default: 0
fill_mask	tuple[float, float] \| float	Padding value for mask if pad_if_needed is True. Default: 0
p	float	probability of applying the transform. Default: 1.0

Image Types

uint8, float32

Notes

If you want to perform random cropping only in the XY plane while preserving all slices along the Z axis, consider using RandomCrop instead. RandomCrop will apply the same XY crop to each slice independently, maintaining the full depth of the volume.

Examples

>>> import numpy as np
>>> import albumentations as A
>>>
>>> # Prepare sample data
>>> volume = np.random.randint(0, 256, (20, 200, 200), dtype=np.uint8)  # (D, H, W)
>>> mask3d = np.random.randint(0, 2, (20, 200, 200), dtype=np.uint8)    # (D, H, W)
>>> keypoints = np.array([[100, 100, 10], [150, 150, 15]], dtype=np.float32)  # (x, y, z)
>>> keypoint_labels = [1, 2]  # Labels for each keypoint
>>>
>>> # Create the transform with random crop and padding if needed
>>> transform = A.Compose([
...     A.RandomCrop3D(
...         size=(16, 128, 128),        # Output size (depth, height, width)
...         pad_if_needed=True,         # Pad if input is smaller than crop size
...         fill=0,                     # Fill value for volume padding
...         fill_mask=1,                # Fill value for mask padding
...         p=1.0
...     )
... ], keypoint_params=A.KeypointParams(format='xyz', label_fields=['keypoint_labels']))
>>>
>>> # Apply the transform
>>> transformed = transform(
...     volume=volume,
...     mask3d=mask3d,
...     keypoints=keypoints,
...     keypoint_labels=keypoint_labels
... )
>>>
>>> # Get the transformed data
>>> cropped_volume = transformed["volume"]           # Shape: (16, 128, 128)
>>> cropped_mask3d = transformed["mask3d"]           # Shape: (16, 128, 128)
>>> cropped_keypoints = transformed["keypoints"]     # Keypoints shifted relative to random crop
>>> cropped_keypoint_labels = transformed["keypoint_labels"]  # Labels remain unchanged