RandomScale

>>> import numpy as np
>>> import albumentations as A
>>> import cv2
>>>
>>> # Create sample data for demonstration
>>> image = np.zeros((100, 100, 3), dtype=np.uint8)
>>> # Add some shapes to visualize scaling effects
>>> cv2.rectangle(image, (25, 25), (75, 75), (255, 0, 0), -1)  # Red square
>>> cv2.circle(image, (50, 50), 10, (0, 255, 0), -1)  # Green circle
>>>
>>> # Create a mask for segmentation
>>> mask = np.zeros((100, 100), dtype=np.uint8)
>>> mask[25:75, 25:75] = 1  # Mask covering the red square
>>>
>>> # Create bounding boxes and keypoints
>>> bboxes = np.array([[25, 25, 75, 75]])  # Box around the red square
>>> bbox_labels = [1]
>>> keypoints = np.array([[50, 50]])  # Center of circle
>>> keypoint_labels = [0]
>>>
>>> # Example 1: Uniform scaling with tuple scale_range
>>> transform = A.Compose([
...     A.RandomScale(
...         scale_range=(-0.3, 0.5),     # Uniform: scale between 0.7x and 1.5x
...         interpolation=cv2.INTER_LINEAR,
...         mask_interpolation=cv2.INTER_NEAREST,
...         area_for_downscale="image",  # Use INTER_AREA for image downscaling
...         p=1.0                         # Always apply
...     )
... ], bbox_params=A.BboxParams(coord_format='pascal_voc', label_fields=['bbox_labels']),
...    keypoint_params=A.KeypointParams(coord_format='xy', label_fields=['keypoint_labels']))
>>>
>>> # Apply the transform to all targets
>>> result = transform(
...     image=image,
...     mask=mask,
...     bboxes=bboxes,
...     bbox_labels=bbox_labels,
...     keypoints=keypoints,
...     keypoint_labels=keypoint_labels
... )
>>>
>>> # Get the transformed results
>>> scaled_image = result['image']        # Dimensions will be between 70-150 pixels
>>> scaled_mask = result['mask']          # Mask scaled proportionally to image
>>> scaled_bboxes = result['bboxes']      # Bounding boxes adjusted to new dimensions
>>> scaled_bbox_labels = result['bbox_labels']  # Labels remain unchanged
>>> scaled_keypoints = result['keypoints']      # Keypoints adjusted to new dimensions
>>> scaled_keypoint_labels = result['keypoint_labels']  # Labels remain unchanged
>>>
>>> # Example 2: Anisotropic scaling with dict scale_range
>>> transform2 = A.Compose([
...     A.RandomScale(
...         scale_range={"x": (-0.20, 0.30), "y": (-0.10, 0.15)},  # Independent axes
...         interpolation=cv2.INTER_LINEAR,
...         p=1.0
...     )
... ], bbox_params=A.BboxParams(coord_format='pascal_voc', label_fields=['bbox_labels']),
...    keypoint_params=A.KeypointParams(coord_format='xy', label_fields=['keypoint_labels']))
>>>
>>> result2 = transform2(
...     image=image,
...     mask=mask,
...     bboxes=bboxes,
...     bbox_labels=bbox_labels,
...     keypoints=keypoints,
...     keypoint_labels=keypoint_labels
... )
>>> # Width and height will scale independently