Method get_data

monai/data/image_reader.py:626–701

Extract data array and metadata from loaded image and return them. This function returns two objects, first is numpy array of image data, second is dict of metadata. It constructs `affine`, `original_affine`, and `spatial_shape` and stores them in meta dict. For dico

(self, data)

        return stack_array, stack_metadata

    def get_data(self, data) -> tuple[np.ndarray, dict]:
        """
        Extract data array and metadata from loaded image and return them.
        This function returns two objects, first is numpy array of image data, second is dict of metadata.
        It constructs `affine`, `original_affine`, and `spatial_shape` and stores them in meta dict.
        For dicom series within the input, all slices will be stacked first,
        When loading a list of files (dicom file, or stacked dicom series), they are stacked together at a new
        dimension as the first dimension, and the metadata of the first image is used to represent the output metadata.

        To use this function, all pydicom dataset objects (if not segmentation data) should contain:
        `pixel_array`, `PixelSpacing`, `ImagePositionPatient` and `ImageOrientationPatient`.

        For segmentation data, we assume that the input is not a dicom series, and the object should contain
        `SegmentSequence` in order to identify it.
        In addition, tags (5200, 9229) and (5200, 9230) are required to achieve
        `PixelSpacing`, `ImageOrientationPatient` and `ImagePositionPatient`.

        Args:
            data: a pydicom dataset object, or a list of pydicom dataset objects, or a list of list of
                pydicom dataset objects.

        """

        dicom_data = []
        # combine dicom series if exists
        if self.has_series is True:
            # a list, all objects within a list belong to one dicom series
            if not isinstance(data[0], list):
                # input is a dir, self.filenames is a list of list of filenames
                dicom_data.append(self._combine_dicom_series(data, self.filenames[0]))  # type: ignore
            # a list of list, each inner list represents a dicom series
            else:
                for i, series in enumerate(data):
                    dicom_data.append(self._combine_dicom_series(series, self.filenames[i]))  # type: ignore
        else:
            # a single pydicom dataset object
            if not isinstance(data, list):
                data = [data]
            for i, d in enumerate(data):
                if hasattr(d, "SegmentSequence"):
                    data_array, metadata = self._get_seg_data(d, self.filenames[i])
                else:
                    data_array = self._get_array_data(d, self.filenames[i])
                    metadata = self._get_meta_dict(d)
                    metadata[MetaKeys.SPATIAL_SHAPE] = data_array.shape
                dicom_data.append((data_array, metadata))

        img_array: list[NdarrayOrCupy] = []
        compatible_meta: dict = {}

        for data_array, metadata in ensure_tuple(dicom_data):
            if self.swap_ij:
                data_array = cp.swapaxes(data_array, 0, 1) if self.to_gpu else np.swapaxes(data_array, 0, 1)
            img_array.append(cp.ascontiguousarray(data_array) if self.to_gpu else np.ascontiguousarray(data_array))
            affine = self._get_affine(metadata, self.affine_lps_to_ras)
            metadata[MetaKeys.SPACE] = SpaceKeys.RAS if self.affine_lps_to_ras else SpaceKeys.LPS
            if self.swap_ij:
                affine = affine @ np.array([[0, 1, 0, 0], [1, 0, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]])