def __init__()

in loaders/video_dataset.py [0:0]

• 29 lines of code
• 6 McCabe index (conditional complexity)

    def __init__(self, path: str, meta_file: str = None):
        """
        Args:
            path: folder path of the 3D video
        """
        self.color_fmt = pjoin(path, "color_down", "frame_{:06d}.raw")
        if not os.path.isfile(self.color_fmt.format(0)):
            self.color_fmt = pjoin(path, "color_down", "frame_{:06d}.png")

        self.mask_fmt = pjoin(path, "mask", "mask_{:06d}_{:06d}.png")
        self.flow_fmt = pjoin(path, "flow", "flow_{:06d}_{:06d}.raw")

        if meta_file is not None:
            with open(meta_file, "rb") as f:
                meta = np.load(f)
                self.extrinsics = torch.tensor(meta["extrinsics"], dtype=_dtype)
                self.intrinsics = torch.tensor(meta["intrinsics"], dtype=_dtype)
            assert (
                self.extrinsics.shape[0] == self.intrinsics.shape[0]
            ), "#extrinsics({}) != #intrinsics({})".format(
                self.extrinsics.shape[0], self.intrinsics.shape[0]
            )

        flow_list_fn = pjoin(path, "flow_list.json")
        if os.path.isfile(flow_list_fn):
            with open(flow_list_fn, "r") as f:
                self.flow_indices = json.load(f)
        else:
            names = os.listdir(os.path.dirname(self.flow_fmt))
            self.flow_indices = [
                self.parse_index_pair(name)
                for name in names
                if os.path.splitext(name)[-1] == os.path.splitext(self.flow_fmt)[-1]
            ]
            self.flow_indices = sampling.to_in_range(self.flow_indices)
        self.flow_indices = list(sampling.SamplePairs.to_one_way(self.flow_indices))