Crop random part of 3D volume. Args: 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 Targets: volume, mask3d, keypoints Image types: uint8, float32 Note: 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

size

type:

tuple[int, int, int]

Default:

null

pad_if_needed

type:

True

False

Default:

false

fill

type:

tuple[float, ...] | float

Default:

0

fill_mask

type:

tuple[float, ...] | float

Default:

0