# MIT License

# Copyright (c) 2022 Intelligent Systems Lab Org

# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:

# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.

# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.

# File author: Shariq Farooq Bhat

import os

import numpy as np
import torch
from PIL import Image
from torch.utils.data import DataLoader, Dataset
from torchvision import transforms as T


class iBims(Dataset):
    def __init__(self, config):
        root_folder = config.ibims_root
        with open(os.path.join(root_folder, "imagelist.txt"), "r") as f:
            imglist = f.read().split()

        samples = []
        for basename in imglist:
            img_path = os.path.join(root_folder, "rgb", basename + ".png")
            depth_path = os.path.join(root_folder, "depth", basename + ".png")
            valid_mask_path = os.path.join(
                root_folder, "mask_invalid", basename + ".png"
            )
            transp_mask_path = os.path.join(
                root_folder, "mask_transp", basename + ".png"
            )

            samples.append((img_path, depth_path, valid_mask_path, transp_mask_path))

        self.samples = samples
        # self.normalize = T.Normalize(
        #     mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
        self.normalize = lambda x: x

    def __getitem__(self, idx):
        img_path, depth_path, valid_mask_path, transp_mask_path = self.samples[idx]

        img = np.asarray(Image.open(img_path), dtype=np.float32) / 255.0
        depth = (
            np.asarray(Image.open(depth_path), dtype=np.uint16).astype("float")
            * 50.0
            / 65535
        )

        mask_valid = np.asarray(Image.open(valid_mask_path))
        mask_transp = np.asarray(Image.open(transp_mask_path))

        # depth = depth * mask_valid * mask_transp
        depth = np.where(mask_valid * mask_transp, depth, -1)

        img = torch.from_numpy(img).permute(2, 0, 1)
        img = self.normalize(img)
        depth = torch.from_numpy(depth).unsqueeze(0)
        return dict(
            image=img,
            depth=depth,
            image_path=img_path,
            depth_path=depth_path,
            dataset="ibims",
        )

    def __len__(self):
        return len(self.samples)


def get_ibims_loader(config, batch_size=1, **kwargs):
    dataloader = DataLoader(iBims(config), batch_size=batch_size, **kwargs)
    return dataloader
