RandomCropFromBorders

Description

Randomly crops the input from its borders without resizing.

    This transform randomly crops parts of the input (image, mask, bounding boxes, or keypoints)
    from each of its borders. The amount of cropping is specified as a fraction of the input's
    dimensions for each side independently.

    Args:
        crop_left (float): The maximum fraction of width to crop from the left side.
            Must be in the range [0.0, 1.0]. Default: 0.1
        crop_right (float): The maximum fraction of width to crop from the right side.
            Must be in the range [0.0, 1.0]. Default: 0.1
        crop_top (float): The maximum fraction of height to crop from the top.
            Must be in the range [0.0, 1.0]. Default: 0.1
        crop_bottom (float): The maximum fraction of height to crop from the bottom.
            Must be in the range [0.0, 1.0]. Default: 0.1
        p (float): Probability of applying the transform. Default: 1.0

    Targets:
        image, mask, bboxes, keypoints

    Image types:
        uint8, float32

    Note:
        - The actual amount of cropping for each side is randomly chosen between 0 and
          the specified maximum for each application of the transform.
        - The sum of crop_left and crop_right must not exceed 1.0, and the sum of
          crop_top and crop_bottom must not exceed 1.0. Otherwise, a ValueError will be raised.
        - This transform does not resize the input after cropping, so the output dimensions
          will be smaller than the input dimensions.
        - Bounding boxes that end up fully outside the cropped area will be removed.
        - Keypoints that end up outside the cropped area will be removed.

    Example:
        >>> import numpy as np
        >>> import albumentations as A
        >>> image = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)
        >>> transform = A.RandomCropFromBorders(
        ...     crop_left=0.1, crop_right=0.2, crop_top=0.2, crop_bottom=0.1, p=1.0
        ... )
        >>> result = transform(image=image)
        >>> transformed_image = result['image']
        # The resulting image will have random crops from each border, with the maximum
        # possible crops being 10% from the left, 20% from the right, 20% from the top,
        # and 10% from the bottom. The image size will be reduced accordingly.
    

Parameters

• crop_left: float (default: 0.1)
• crop_right: float (default: 0.1)
• crop_top: float (default: 0.1)
• crop_bottom: float (default: 0.1)
• p: float (default: 1)

Targets

• Image
• Mask
• BBoxes
• Keypoints