>>> 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 different parameter types
>>> transform = A.Compose([
...     A.Affine(
...         # Tuple for scale (will be used for both x and y)
...         scale=(0.8, 1.2),
...         # Dictionary with tuples for different x/y translations
...         translate_percent={"x": (-0.2, 0.2), "y": (-0.1, 0.1)},
...         # Tuple for rotation range
...         rotate=(-30, 30),
...         # Dictionary with tuples for different x/y shearing
...         shear={"x": (-10, 10), "y": (-5, 5)},
...         # Interpolation methods
...         interpolation=cv2.INTER_LINEAR,
...         mask_interpolation=cv2.INTER_NEAREST,
...         # Other parameters
...         fit_output=False,
...         keep_ratio=True,
...         rotate_method="largest_box",
...         balanced_scale=True,
...         border_mode=cv2.BORDER_CONSTANT,
...         fill=0,
...         fill_mask=0,
...         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']      # Image with affine transforms applied
>>> transformed_mask = transformed['mask']        # Mask with affine transforms applied
>>> transformed_bboxes = transformed['bboxes']    # Bounding boxes with affine transforms applied
>>> transformed_bbox_labels = transformed['bbox_labels']  # Labels for transformed bboxes
>>> transformed_keypoints = transformed['keypoints']  # Keypoints with affine transforms applied
>>> transformed_keypoint_labels = transformed['keypoint_labels']  # Labels for transformed keypoints
>>>
>>> # Simpler example with only essential parameters
>>> simple_transform = A.Compose([
...     A.Affine(
...         scale=1.1,  # Single scalar value for scale
...         rotate=15,  # Single scalar value for rotation (degrees)
...         translate_px=30,  # Single scalar value for translation (pixels)
...         p=1.0
...     ),
... ])
>>> simple_result = simple_transform(image=image)
>>> simple_transformed = simple_result['image']
Parameters

Bounding Box Parameters
