>>> 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 when possible
>>> transform = A.Compose([
...     A.Perspective(
...         scale=(0.05, 0.1),
...         keep_size=True,
...         fit_output=False,
...         border_mode=cv2.BORDER_CONSTANT,
...         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']      # Perspective-transformed image
>>> transformed_mask = transformed['mask']        # Perspective-transformed mask
>>> transformed_bboxes = transformed['bboxes']    # Perspective-transformed bounding boxes
>>> transformed_bbox_labels = transformed['bbox_labels']  # Labels for transformed bboxes
>>> transformed_keypoints = transformed['keypoints']  # Perspective-transformed keypoints
>>> transformed_keypoint_labels = transformed['keypoint_labels']  # Labels for transformed keypoints
Parameters

Bounding Box Parameters
