Class VFlipTransform

PATH/core/data/transforms/seg_aug_dev.py:81–132

Perform vertical flip.

class VFlipTransform(Transform):
    """
    Perform vertical flip.
    """

    def __init__(self, height: int):
        super().__init__()
        self._set_attributes(locals())

    def apply_image(self, img: np.ndarray) -> np.ndarray:
        """
        Flip the image(s).

        Args:
            img (ndarray): of shape HxW, HxWxC, or NxHxWxC. The array can be
                of type uint8 in range [0, 255], or floating point in range
                [0, 1] or [0, 255].
        Returns:
            ndarray: the flipped image(s).
        """
        tensor = torch.from_numpy(np.ascontiguousarray(img))
        if len(tensor.shape) == 2:
            # For dimension of HxW.
            tensor = tensor.flip((-2))
        elif len(tensor.shape) > 2:
            # For dimension of HxWxC, NxHxWxC.
            tensor = tensor.flip((-3))
        return tensor.numpy()

    def apply_coords(self, coords: np.ndarray) -> np.ndarray:
        """
        Flip the coordinates.

        Args:
            coords (ndarray): floating point array of shape Nx2. Each row is
                (x, y).
        Returns:
            ndarray: the flipped coordinates.

        Note:
            The inputs are floating point coordinates, not pixel indices.
            Therefore they are flipped by `(W - x, H - y)`, not
            `(W - 1 - x, H - 1 - y)`.
        """
        coords[:, 1] = self.height - coords[:, 1]
        return coords

    def inverse(self) -> Transform:
        """
        The inverse is to flip again
        """
        return self


class NoOpTransform(Transform):