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RandomSizedCrop

Targets:
image
mask
bboxes
keypoints
volume
mask3d
Image Types:uint8, float32

Crop a random part of the input and rescale it to a specific size.

This transform first crops a random portion of the input and then resizes it to a specified size. The size of the random crop is controlled by the 'min_max_height' parameter.

Arguments
min_max_height
tuple[int, int]

Minimum and maximum height of the crop in pixels.

size
tuple[int, int]

Target size for the output image, i.e. (height, width) after crop and resize.

w2h_ratio
float
1

Aspect ratio (width/height) of crop. Default: 1.0

interpolation
0 | 6 | 1 | 2 | 3 | 4 | 5
1

Flag that is used to specify the interpolation algorithm. Should be one of: cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4. Default: cv2.INTER_LINEAR.

mask_interpolation
0 | 6 | 1 | 2 | 3 | 4 | 5
0

Flag that is used to specify the interpolation algorithm for mask. Should be one of: cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4. Default: cv2.INTER_NEAREST.

area_for_downscale
image | image_mask |

Controls automatic use of INTER_AREA interpolation for downscaling. Options:

  • None: No automatic interpolation selection, always use the specified interpolation method
  • "image": Use INTER_AREA when downscaling images, retain specified interpolation for upscaling and masks
  • "image_mask": Use INTER_AREA when downscaling both images and masks Default: None.
p
float
1

Probability of applying the transform. Default: 1.0

Examples
>>> 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.RandomSizedCrop(
...         min_max_height=(50, 80),
...         size=(64, 64),
...         w2h_ratio=1.0,
...         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(coord_format='pascal_voc', label_fields=['bbox_labels']),
...    keypoint_params=A.KeypointParams(coord_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
Notes
  • The crop size is randomly selected for each execution within the range specified by 'min_max_height'.
  • The aspect ratio of the crop is determined by the 'w2h_ratio' parameter.
  • After cropping, the result is resized to the specified 'size'.
  • Bounding boxes that end up fully outside the cropped area will be removed.
  • Keypoints that end up outside the cropped area will be removed.
  • This transform differs from RandomResizedCrop in that it allows more control over the crop size through the 'min_max_height' parameter, rather than using a scale parameter.
  • When area_for_downscale is set, INTER_AREA interpolation will be used automatically for downscaling (when the crop is larger than the target size), which provides better quality for size reduction.