def get_data_info()

in a2d2/a2d2_dataset.py [0:0]

• 20 lines of code
• 2 McCabe index (conditional complexity)

    def get_data_info(self, index):
        """Get data info according to the given index.

        Args:
            index (int): Index of the sample data to get.

        Returns:
            dict: Data information that will be passed to the data \
                preprocessing pipelines. It includes the following keys:

                - sample_idx (str): Sample index.
                - pts_filename (str): Filename of point clouds.
                - img_prefix (str | None): Prefix of image files.
                - img_info (dict): Image info.
                - lidar2img (list[np.ndarray], optional): Transformations \
                    from lidar to different cameras.
                - ann_info (dict): Annotation info.
        """
        info = self.data_infos[index]
        sample_idx = info['image']['image_idx']
        img_filename = self.prefix + os.path.join(self.data_root,
                                    info['image']['image_path'])

        # TODO: consider use torch.Tensor only
        rect = info['calib']['R0_rect'].astype(np.float32)
        Trv2c = info['calib']['Tr_velo_to_cam'].astype(np.float32)
        P2 = info['calib']['P2'].astype(np.float32)
        lidar2img = P2 @ rect @ Trv2c

        pts_filename = self._get_pts_filename(sample_idx)
        input_dict = dict(
            sample_idx=sample_idx,
            pts_filename=pts_filename,
            img_prefix=None,
            img_info=dict(filename=img_filename),
            lidar2img=lidar2img)

        if not self.test_mode:
            annos = self.get_ann_info(index)
            input_dict['ann_info'] = annos

        return input_dict