def apply_image()

in fvcore/transforms/transform.py [0:0]

• 22 lines of code
• 9 McCabe index (conditional complexity)

    def apply_image(self, img: np.ndarray, interp: str = None) -> np.ndarray:
        """
        Resize the image(s).

        Args:
            img (ndarray): of shape NxHxWxC, or HxWxC or HxW. The array can be
                of type uint8 in range [0, 255], or floating point in range
                [0, 1] or [0, 255].
            interp (str): interpolation methods. Options includes `nearest`, `linear`
                (3D-only), `bilinear`, `bicubic` (4D-only), and `area`.
                Details can be found in:
                https://pytorch.org/docs/stable/nn.functional.html

        Returns:
            ndarray: resized image(s).
        """
        if len(img.shape) == 4:
            h, w = img.shape[1:3]
        elif len(img.shape) in (2, 3):
            h, w = img.shape[:2]
        else:
            raise ("Unsupported input with shape of {}".format(img.shape))
        assert (
            self.h == h and self.w == w
        ), "Input size mismatch h w {}:{} -> {}:{}".format(self.h, self.w, h, w)
        interp_method = interp if interp is not None else self.interp
        # Option of align_corners is only supported for linear, bilinear,
        # and bicubic.
        if interp_method in ["linear", "bilinear", "bicubic"]:
            align_corners = False
        else:
            align_corners = None

        # note: this is quite slow for int8 images because torch does not
        # support it https://github.com/pytorch/pytorch/issues/5580
        float_tensor = torch.nn.functional.interpolate(
            to_float_tensor(img),
            size=(self.new_h, self.new_w),
            mode=interp_method,
            align_corners=align_corners,
        )
        return to_numpy(float_tensor, img.shape, img.dtype)