RandomScale

Description

Randomly resize the input. Output image size is different from the input image size.

    Args:
        scale_limit (float or tuple[float, float]): scaling factor range. If scale_limit is a single float value, the
            range will be (-scale_limit, scale_limit). Note that the scale_limit will be biased by 1.
            If scale_limit is a tuple, like (low, high), sampling will be done from the range (1 + low, 1 + high).
            Default: (-0.1, 0.1).
        interpolation (OpenCV flag): 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 (OpenCV flag): 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.
        p (float): probability of applying the transform. Default: 0.5.

    Targets:
        image, mask, bboxes, keypoints

    Image types:
        uint8, float32

    Note:
        - The output image size is different from the input image size.
        - Scale factor is sampled independently per image side (width and height).
        - Bounding box coordinates are scaled accordingly.
        - Keypoint coordinates are scaled accordingly.

    Mathematical formulation:
        Let (W, H) be the original image dimensions and (W', H') be the output dimensions.
        The scale factor s is sampled from the range [1 + scale_limit[0], 1 + scale_limit[1]].
        Then, W' = W * s and H' = H * s.

    Example:
        >>> import numpy as np
        >>> import albumentations as A
        >>> image = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)
        >>> transform = A.RandomScale(scale_limit=0.1, p=1.0)
        >>> result = transform(image=image)
        >>> scaled_image = result['image']
        # scaled_image will have dimensions in the range [90, 110] x [90, 110]
        # (assuming the scale_limit of 0.1 results in a scaling factor between 0.9 and 1.1)

    

Parameters

• scale_limit: float | tuple[float, float] (default: (-0.1, 0.1))
• interpolation: Literal['cv2.INTER_NEAREST', 'cv2.INTER_LINEAR', 'cv2.INTER_CUBIC', 'cv2.INTER_AREA', 'cv2.INTER_LANCZOS4', 'cv2.INTER_BITS', 'cv2.INTER_NEAREST_EXACT', 'cv2.INTER_MAX'] (default: 1)
• mask_interpolation: Literal['cv2.INTER_NEAREST', 'cv2.INTER_LINEAR', 'cv2.INTER_CUBIC', 'cv2.INTER_AREA', 'cv2.INTER_LANCZOS4', 'cv2.INTER_BITS', 'cv2.INTER_NEAREST_EXACT', 'cv2.INTER_MAX'] (default: 0)
• p: float (default: 0.5)

Targets

• Image
• Mask
• BBoxes
• Keypoints