>>> import numpy as np
>>> import albumentations as A
>>>
>>> # 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 crop fractions for each border
>>> transform = A.Compose([
...     A.RandomCropFromBorders(
...         crop_left=0.1,     # Max 10% crop from left
...         crop_right=0.2,    # Max 20% crop from right
...         crop_top=0.15,     # Max 15% crop from top
...         crop_bottom=0.05,  # Max 5% crop from bottom
...         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 transform
>>> result = transform(
...     image=image,
...     mask=mask,
...     bboxes=bboxes,
...     bbox_labels=bbox_labels,
...     keypoints=keypoints,
...     keypoint_labels=keypoint_labels
... )
>>>
>>> # Access transformed data
>>> transformed_image = result['image']  # Reduced size image with borders cropped
>>> transformed_mask = result['mask']    # Reduced size mask with borders cropped
>>> transformed_bboxes = result['bboxes']  # Bounding boxes adjusted to new dimensions
>>> transformed_bbox_labels = result['bbox_labels']  # Bounding box labels after crop
>>> transformed_keypoints = result['keypoints']  # Keypoints adjusted to new dimensions
>>> transformed_keypoint_labels = result['keypoint_labels']  # Keypoint labels after crop
>>>
>>> # The resulting output shapes will be smaller, with dimensions reduced by
>>> # the random crop amounts from each side (within the specified maximums)
>>> print(f"Original image shape: (100, 100, 3)")
>>> print(f"Transformed image shape: {transformed_image.shape}")  # e.g., (85, 75, 3)
Parameters
