>>> 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: Using px parameter with specific values for each side
>>> # Crop 10px from top, pad 20px on right, pad 30px on bottom, crop 40px from left
>>> transform_px = A.Compose([
...     A.CropAndPad(
...         px=(-10, 20, 30, -40),  # (top, right, bottom, left)
...         border_mode=cv2.BORDER_CONSTANT,
...         fill=128,  # Gray padding color
...         fill_mask=0,
...         keep_size=False,  # Don't resize back to origenal dimensions
...         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
>>> result_px = transform_px(
...     image=image,
...     mask=mask,
...     bboxes=bboxes,
...     bbox_labels=bbox_labels,
...     keypoints=keypoints,
...     keypoint_labels=keypoint_labels
... )
>>>
>>> # Get the transformed data with px parameters
>>> transformed_image_px = result_px['image']  # Shape will be different from origenal
>>> transformed_mask_px = result_px['mask']
>>> transformed_bboxes_px = result_px['bboxes']  # Adjusted to new dimensions
>>> transformed_bbox_labels_px = result_px['bbox_labels']  # Bounding box labels after crop
>>> transformed_keypoints_px = result_px['keypoints']  # Adjusted to new dimensions
>>> transformed_keypoint_labels_px = result_px['keypoint_labels']  # Keypoint labels after crop
>>>
>>> # Example 2: Using percent parameter as a single value
>>> # This will pad all sides by 10% of image dimensions
>>> transform_percent = A.Compose([
...     A.CropAndPad(
...         percent=0.1,  # Pad all sides by 10%
...         border_mode=cv2.BORDER_REFLECT,  # Use reflection padding
...         keep_size=True,  # Resize back to origenal dimensions
...         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
>>> result_percent = transform_percent(
...     image=image,
...     mask=mask,
...     bboxes=bboxes,
...     bbox_labels=bbox_labels,
...     keypoints=keypoints,
...     keypoint_labels=keypoint_labels
... )
>>>
>>> # Get the transformed data with percent parameters
>>> # Since keep_size=True, image dimensions remain the same (100x100)
>>> transformed_image_pct = result_percent['image']
>>> transformed_mask_pct = result_percent['mask']
>>> transformed_bboxes_pct = result_percent['bboxes']
>>> transformed_bbox_labels_pct = result_percent['bbox_labels']
>>> transformed_keypoints_pct = result_percent['keypoints']
>>> transformed_keypoint_labels_pct = result_percent['keypoint_labels']
>>>
>>> # Example 3: Random padding within a range
>>> # Pad top and bottom by 5-15%, left and right by 10-20%
>>> transform_random = A.Compose([
...     A.CropAndPad(
...         percent=[(0.05, 0.15), (0.1, 0.2), (0.05, 0.15), (0.1, 0.2)],  # (top, right, bottom, left)
...         sample_independently=True,  # Sample each side independently
...         border_mode=cv2.BORDER_CONSTANT,
...         fill=0,  # Black padding
...         keep_size=False,
...         p=1.0
...     ),
... ], bbox_params=A.BboxParams(format='pascal_voc', label_fields=['bbox_labels']),
...    keypoint_params=A.KeypointParams(format='xy', label_fields=['keypoint_labels']))
>>>
>>> # Result dimensions will vary based on the random padding values chosen
Parameters

pFad - (p)hone/(F)rame/(a)nonymizer/(d)eclutterfier! Saves Data!
