import glob
import os
from pathlib import Path
from typing import Dict, List, Optional, Union

import numpy as np
import torch
from natsort import natsorted

from .basedataset import GradSLAMDataset


class ScannetDataset(GradSLAMDataset):
    def __init__(
        self,
        config_dict,
        basedir,
        sequence,
        stride: Optional[int] = None,
        start: Optional[int] = 0,
        end: Optional[int] = -1,
        desired_height: Optional[int] = 968,
        desired_width: Optional[int] = 1296,
        load_embeddings: Optional[bool] = False,
        embedding_dir: Optional[str] = "embeddings",
        embedding_dim: Optional[int] = 512,
        **kwargs,
    ):
        self.input_folder = os.path.join(basedir, sequence)
        self.pose_path = None
        super().__init__(
            config_dict,
            stride=stride,
            start=start,
            end=end,
            desired_height=desired_height,
            desired_width=desired_width,
            load_embeddings=load_embeddings,
            embedding_dir=embedding_dir,
            embedding_dim=embedding_dim,
            **kwargs,
        )

    def get_filepaths(self):
        color_paths = natsorted(glob.glob(f"{self.input_folder}/color/*.jpg"))
        depth_paths = natsorted(glob.glob(f"{self.input_folder}/depth/*.png"))
        embedding_paths = None
        if self.load_embeddings:
            embedding_paths = natsorted(glob.glob(f"{self.input_folder}/{self.embedding_dir}/*.pt"))
        return color_paths, depth_paths, embedding_paths

    def load_poses(self):
        poses = []
        posefiles = natsorted(glob.glob(f"{self.input_folder}/pose/*.txt"))
        for posefile in posefiles:
            _pose = torch.from_numpy(np.loadtxt(posefile))
            poses.append(_pose)
        return poses

    def read_embedding_from_file(self, embedding_file_path):
        print(embedding_file_path)
        embedding = torch.load(embedding_file_path, map_location="cpu")
        return embedding.permute(0, 2, 3, 1)  # (1, H, W, embedding_dim)