能帮我解释一下这个构造multiview 相机视角的代码在做什么吗？
class MultiviewIterableDataset(IterableDataset):
    def __init__(self, cfg: Any) -> None:
        super().__init__()
        self.cfg: MultiviewsDataModuleConfig = cfg

        assert self.cfg.batch_size == 1
        scale = self.cfg.train_downsample_resolution

        camera_dict = json.load(
            open(os.path.join(self.cfg.dataroot, "transforms.json"), "r")
        )
        assert camera_dict["camera_model"] == "OPENCV"

        frames = camera_dict["frames"]
        frames = frames[:: self.cfg.train_data_interval]
        frames_proj = []
        frames_c2w = []
        frames_position = []
        frames_direction = []
        frames_img = []

        self.frame_w = frames[0]["w"] // scale
        self.frame_h = frames[0]["h"] // scale
        threestudio.info("Loading frames...")
        self.n_frames = len(frames)

        c2w_list = []
        for frame in tqdm(frames):
            extrinsic: Float[Tensor, "4 4"] = torch.as_tensor(
                frame["transform_matrix"], dtype=torch.float32
            )
            c2w = extrinsic
            c2w_list.append(c2w)
        c2w_list = torch.stack(c2w_list, dim=0)

        if self.cfg.camera_layout == "around":
            c2w_list[:, :3, 3] -= torch.mean(c2w_list[:, :3, 3], dim=0).unsqueeze(0)
        elif self.cfg.camera_layout == "front":
            assert self.cfg.camera_distance > 0
            c2w_list[:, :3, 3] -= torch.mean(c2w_list[:, :3, 3], dim=0).unsqueeze(0)
            z_vector = torch.zeros(c2w_list.shape[0], 3, 1)
            z_vector[:, 2, :] = -1
            rot_z_vector = c2w_list[:, :3, :3] @ z_vector
            rot_z_vector = torch.mean(rot_z_vector, dim=0).unsqueeze(0)
            c2w_list[:, :3, 3] -= rot_z_vector[:, :, 0] * self.cfg.camera_distance
        else:
            raise ValueError(
                f"Unknown camera layout {self.cfg.camera_layout}. Now support only around and front."
            )

        for idx, frame in tqdm(enumerate(frames)):
            intrinsic: Float[Tensor, "4 4"] = torch.eye(4)
            intrinsic[0, 0] = frame["fl_x"] / scale
            intrinsic[1, 1] = frame["fl_y"] / scale
            intrinsic[0, 2] = frame["cx"] / scale
            intrinsic[1, 2] = frame["cy"] / scale

            frame_path = os.path.join(self.cfg.dataroot, frame["file_path"])
            img = cv2.imread(frame_path)[:, :, ::-1].copy()
            img = cv2.resize(img, (self.frame_w, self.frame_h))
            img: Float[Tensor, "H W 3"] = torch.FloatTensor(img) / 255
            frames_img.append(img)

            direction: Float[Tensor, "H W 3"] = get_ray_directions(
                self.frame_h,
                self.frame_w,
                (intrinsic[0, 0], intrinsic[1, 1]),
                (intrinsic[0, 2], intrinsic[1, 2]),
                use_pixel_centers=False,
            )

            c2w = c2w_list[idx]
            camera_position: Float[Tensor, "3"] = c2w[:3, 3:].reshape(-1)

            near = 0.1
            far = 1000.0
            proj = convert_proj(intrinsic, self.frame_h, self.frame_w, near, far)
            proj: Float[Tensor, "4 4"] = torch.FloatTensor(proj)
            frames_proj.append(proj)
            frames_c2w.append(c2w)
            frames_position.append(camera_position)
            frames_direction.append(direction)
        threestudio.info("Loaded frames.")

        self.frames_proj: Float[Tensor, "B 4 4"] = torch.stack(frames_proj, dim=0)
        self.frames_c2w: Float[Tensor, "B 4 4"] = torch.stack(frames_c2w, dim=0)
        self.frames_position: Float[Tensor, "B 3"] = torch.stack(frames_position, dim=0)
        self.frames_direction: Float[Tensor, "B H W 3"] = torch.stack(
            frames_direction, dim=0
        )
        self.frames_img: Float[Tensor, "B H W 3"] = torch.stack(frames_img, dim=0)

        self.rays_o, self.rays_d = get_rays(
            self.frames_direction, self.frames_c2w, keepdim=True
        )
        self.mvp_mtx: Float[Tensor, "B 4 4"] = get_mvp_matrix(
            self.frames_c2w, self.frames_proj
        )
        self.light_positions: Float[Tensor, "B 3"] = torch.zeros_like(
            self.frames_position
        )