mmcv.image.imresize_to_multiple

mmcv.image.imresize_to_multiple(img: numpy.ndarray, divisor: Union[int, Tuple[int, int]], size: Optional[Union[int, Tuple[int, int]]] = None, scale_factor: Optional[Union[float, int, Tuple[float, float], Tuple[int, int]]] = None, keep_ratio: bool = False, return_scale: bool = False, interpolation: str = 'bilinear', out: Optional[numpy.ndarray] = None, backend: Optional[str] = None) → Union[Tuple[numpy.ndarray, float, float], numpy.ndarray]

Resize image according to a given size or scale factor and then rounds up the the resized or rescaled image size to the nearest value that can be divided by the divisor.

Parameters

• img (ndarray) – The input image.

• divisor (int | tuple) – Resized image size will be a multiple of divisor. If divisor is a tuple, divisor should be (w_divisor, h_divisor).

• size (None | int | tuple[int]) – Target size (w, h). Default: None.

• scale_factor (None | float | int | tuple[float] | tuple[int]) – Multiplier for spatial size. Should match input size if it is a tuple and the 2D style is (w_scale_factor, h_scale_factor). Default: None.

• keep_ratio (bool) – Whether to keep the aspect ratio when resizing the image. Default: False.

• return_scale (bool) – Whether to return w_scale and h_scale.

• interpolation (str) – Interpolation method, accepted values are “nearest”, “bilinear”, “bicubic”, “area”, “lanczos” for ‘cv2’ backend, “nearest”, “bilinear” for ‘pillow’ backend.

• out (ndarray) – The output destination.

• backend (str | None) – The image resize backend type. Options are cv2, pillow, None. If backend is None, the global imread_backend specified by mmcv.use_backend() will be used. Default: None.

Returns

(resized_img, w_scale, h_scale) or resized_img.

Return type

tuple | ndarray