RandomCrop

>>> import numpy as np
>>> import albumentations as A
>>> import cv2
>>>
>>> # 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]
>>>
>>> # Example 1: Basic random crop
>>> transform = A.Compose([
...     A.RandomCrop(height=64, width=64),
... ], bbox_params=A.BboxParams(coord_format='pascal_voc', label_fields=['bbox_labels']),
...    keypoint_params=A.KeypointParams(coord_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']  # Will be 64x64
>>> transformed_mask = transformed['mask']    # Will be 64x64
>>> transformed_bboxes = transformed['bboxes']  # Bounding boxes adjusted to the cropped area
>>> transformed_bbox_labels = transformed['bbox_labels']  # Labels for boxes that remain after cropping
>>> transformed_keypoints = transformed['keypoints']  # Keypoints adjusted to the cropped area
>>> transformed_keypoint_labels = transformed['keypoint_labels']  # Labels for keypoints that remain
>>>
>>> # Example 2: Random crop with padding when needed
>>> # This is useful when you want to crop to a size larger than some images
>>> transform_padded = A.Compose([
...     A.RandomCrop(
...         height=120,  # Larger than original image height
...         width=120,   # Larger than original image width
...         pad_if_needed=True,
...         border_mode=cv2.BORDER_CONSTANT,
...         fill=0,      # Black padding for image
...         fill_mask=0  # Zero padding for mask
...     ),
... ], bbox_params=A.BboxParams(coord_format='pascal_voc', label_fields=['bbox_labels']),
...    keypoint_params=A.KeypointParams(coord_format='xy', label_fields=['keypoint_labels']))
>>>
>>> # Apply the padded transform
>>> padded_transformed = transform_padded(
...     image=image,
...     mask=mask,
...     bboxes=bboxes,
...     bbox_labels=bbox_labels,
...     keypoints=keypoints,
...     keypoint_labels=keypoint_labels
... )
>>>
>>> # The result will be 120x120 with padding
>>> padded_image = padded_transformed['image']
>>> padded_mask = padded_transformed['mask']
>>> padded_bboxes = padded_transformed['bboxes']  # Coordinates adjusted to the new dimensions