Method get_data

monai/data/image_reader.py:1522–1560

Extract data array and metadata from loaded image and return them. This function must return two objects, the first is a numpy array of image data, the second is a dictionary of metadata. Args: img: a `NrrdImage` loaded from an image file or a list of im

(self, img: NrrdImage | list[NrrdImage])

        return img_ if len(filenames) > 1 else img_[0]

    def get_data(self, img: NrrdImage | list[NrrdImage]) -> tuple[np.ndarray, dict]:
        """
        Extract data array and metadata from loaded image and return them.
        This function must return two objects, the first is a numpy array of image data,
        the second is a dictionary of metadata.

        Args:
            img: a `NrrdImage` loaded from an image file or a list of image objects.

        """
        img_array: list[np.ndarray] = []
        compatible_meta: dict = {}

        for i in ensure_tuple(img):
            data = i.array.astype(self.dtype)
            img_array.append(data)
            header = dict(i.header)
            if self.index_order == "C":
                header = self._convert_f_to_c_order(header)
            header[MetaKeys.ORIGINAL_AFFINE] = self._get_affine(header)

            if self.affine_lps_to_ras:
                header = self._switch_lps_ras(header)
            if header.get(MetaKeys.SPACE, "left-posterior-superior") == "left-posterior-superior":
                header[MetaKeys.SPACE] = SpaceKeys.LPS  # assuming LPS if not specified

            header[MetaKeys.AFFINE] = header[MetaKeys.ORIGINAL_AFFINE].copy()
            header[MetaKeys.SPATIAL_SHAPE] = header["sizes"].copy()
            [header.pop(k) for k in ("sizes", "space origin", "space directions")]  # rm duplicated data in header

            if self.channel_dim is None:  # default to "no_channel" or -1
                header[MetaKeys.ORIGINAL_CHANNEL_DIM] = (
                    float("nan") if len(data.shape) == len(header[MetaKeys.SPATIAL_SHAPE]) else 0
                )
            else:
                header[MetaKeys.ORIGINAL_CHANNEL_DIM] = self.channel_dim
            _copy_compatible_dict(header, compatible_meta)

        return _stack_images(img_array, compatible_meta), compatible_meta

    def _get_affine(self, header: dict) -> np.ndarray:
        """