Example #1
0
def apply_crop(input: torch.Tensor,
               params: Dict[str, torch.Tensor]) -> torch.Tensor:
    r"""Apply cropping by src bounding box and dst bounding box.
    Order: top-left, top-right, bottom-right and bottom-left. The coordinates must be in the x, y order.

    Args:
        input (torch.Tensor): Tensor to be transformed with shape (H, W), (C, H, W), (B, C, H, W).
        params (Dict[str, torch.Tensor]):
            - params['src']: The applied cropping src matrix :math: `(*, 4, 2)`.
            - params['dst']: The applied cropping dst matrix :math: `(*, 4, 2)`.
            - params['interpolation']: Integer tensor. NEAREST = 0, BILINEAR = 1.
            - params['align_corners']: Boolean tensor.

    Returns:
        torch.Tensor: The cropped input.
    """
    input = _transform_input(input)
    _validate_input_dtype(
        input, accepted_dtypes=[torch.float16, torch.float32, torch.float64])

    resample_mode: str = Resample.get(
        params['interpolation'].item()).name.lower()  # type: ignore
    align_corners: bool = cast(bool, params['align_corners'].item())

    return crop_by_boxes(input,
                         params['src'],
                         params['dst'],
                         resample_mode,
                         align_corners=align_corners)
Example #2
0
def apply_crop(input: torch.Tensor,
               params: Dict[str, torch.Tensor],
               return_transform: bool = False) -> UnionType:
    """
    Args:
        params (dict): A dict that must have {'src': torch.Tensor, 'dst': torch.Tensor}. Can be generated from
        kornia.augmentation.random_generator.random_crop_generator
        return_transform (bool): if ``True`` return the matrix describing the transformation applied to each
        input tensor.
    Returns:
        torch.Tensor: The grayscaled input
        torch.Tensor: The applied cropping matrix :math: `(*, 4, 2)` if return_transform flag
        is set to ``True``
    """
    input = _transform_input(input)
    _validate_input_dtype(
        input, accepted_dtypes=[torch.float16, torch.float32, torch.float64])

    return crop_by_boxes(
        input,
        params['src'],
        params['dst'],
        Resample.get(
            params['interpolation'].item()).name.lower(),  # type: ignore
        return_transform=return_transform)