>>> 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]
>>>
>>> # Define transform with parameters as tuples
>>> transform = A.Compose([
...     A.RandomResizedCrop(
...         size=(64, 64),
...         scale=(0.5, 0.9),  # Crop size will be 50-90% of original image
...         ratio=(0.75, 1.33),  # Aspect ratio will vary from 3:4 to 4:3
...         interpolation=cv2.INTER_LINEAR,
...         mask_interpolation=cv2.INTER_NEAREST,
...         area_for_downscale="image",  # Use INTER_AREA for image downscaling
...         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']       # Shape: (64, 64, 3)
>>> transformed_mask = transformed['mask']         # Shape: (64, 64)
>>> transformed_bboxes = transformed['bboxes']     # Bounding boxes adjusted to new crop and size
>>> transformed_bbox_labels = transformed['bbox_labels']  # Labels for the preserved bboxes
>>> transformed_keypoints = transformed['keypoints']      # Keypoints adjusted to new crop and size
>>> transformed_keypoint_labels = transformed['keypoint_labels']  # Labels for the preserved keypoints
Parameters

Bounding Box Parameters
