SmallestMaxSize

Description

Rescale an image so that minimum side is equal to max_size, keeping the aspect ratio of the initial image.

    Args:
        max_size (int, list of int): Maximum size of smallest side of the image after the transformation. When using a
            list, max size will be randomly selected from the values in the list.
        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: 1.

    Targets:
        image, mask, bboxes, keypoints

    Image types:
        uint8, float32

    Note:
        - If the smallest side of the image is already equal to max_size, the image will not be resized.
        - This transform will not crop the image. The resulting image may be larger than max_size in both dimensions.
        - For non-square images, the larger side will be scaled proportionally to maintain the aspect ratio.
        - Bounding boxes and keypoints are scaled accordingly.

    Mathematical Details:
        1. Let (W, H) be the original width and height of the image.
        2. The scaling factor s is calculated as:
           s = max_size / min(W, H)
        3. The new dimensions (W', H') are:
           W' = W * s
           H' = H * s
        4. The image is resized to (W', H') using the specified interpolation method.
        5. Bounding boxes and keypoints are scaled by the same factor s.

    Example:
        >>> import numpy as np
        >>> import albumentations as A
        >>> image = np.random.randint(0, 256, (100, 150, 3), dtype=np.uint8)
        >>> transform = A.SmallestMaxSize(max_size=120, p=1.0)
        >>> result = transform(image=image)
        >>> resized_image = result['image']
        # resized_image will have shape (120, 180, 3), as the smallest side (100)
        # is scaled to 120, and the larger side is scaled proportionally
    

Parameters

• max_size: int | list[int] (default: 1024)
• 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: Annotated[int, annotation=NoneType required=True description='Interpolation', AfterValidator(func=<function check_valid_interpolation at 0x7c132ac54220>)] | None (default: 0)
• p: float (default: 1)

Targets

• Image
• Mask
• Keypoints
• BBoxes