# Copyright 2022 The Plenoptix Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
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# distributed under the License is distributed on an "AS IS" BASIS,
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"""
Dataset input structures.
"""

from dataclasses import dataclass
from pathlib import Path
from typing import Dict, List, Optional, Union

import torch
from torchtyping import TensorType

from nerfactory.cameras.cameras import Cameras


@dataclass
class PointCloud:
    """Dataclass for a point cloud."""

    xyz: TensorType["num_points", 3] = None
    rgb: TensorType["num_points", 3] = None


@dataclass
class Semantics:
    """Dataclass for semantic labels."""

    stuff_filenames: List[Path]
    stuff_classes: List[str]
    thing_filenames: List[Path]
    thing_classes: List[str]
    stuff_colors: torch.Tensor
    thing_colors: torch.Tensor


@dataclass
class SceneBounds:
    """Data to represent the scene bounds.

    Attributes:
        aabb: axis-aligned bounding box.
            aabb[0] is the minimum (x,y,z) point.
            aabb[1] is the maximum (x,y,z) point.
    """

    aabb: TensorType[2, 3] = None

    def get_diagonal_length(self):
        """Returns the longest diagonal length."""
        diff = self.aabb[1] - self.aabb[0]
        length = torch.sqrt((diff**2).sum() + 1e-20)
        return length

    def get_center(self):
        """Returns the center of the box."""
        diff = self.aabb[1] - self.aabb[0]
        return self.aabb[0] + diff / 2.0

    def get_centered_and_scaled_scene_bounds(self, scale_factor: Union[float, torch.Tensor] = 1.0):
        """Returns a new box that has been shifted and rescaled to be centered
        about the origin."""
        return SceneBounds(aabb=(self.aabb - self.get_center()) * scale_factor)

    @staticmethod
    def get_normalized_positions(positions: TensorType[..., 3], aabb: TensorType[2, 3]):
        """Return normalized positions in range [0, 1] based on the aabb axis-aligned bounding box.

        Args:
            positions: the xyz positions
            aabb: the axis-aligned bounding box

        Returns:
            positions that are normalized into the range [0, 1].
        """
        aabb_lengths = aabb[1] - aabb[0]
        normalized_positions = (positions - aabb[0]) / aabb_lengths
        return normalized_positions

    def to_json(self) -> Dict:
        """Returns a json object from the Python object."""
        return {"type": "aabb", "min_point": self.aabb[0].tolist(), "max_point": self.aabb[1].tolist()}

    @staticmethod
    def from_json(json_: Dict) -> "SceneBounds":
        """Returns the an instance of SceneBounds from a json dictionary."""
        assert json_["type"] == "aabb"
        aabb = torch.tensor([json_[0], json_[1]])
        return SceneBounds(aabb=aabb)

    @staticmethod
    def from_camera_poses(poses: TensorType[..., 3, 4], scale_factor: float) -> "SceneBounds":
        """Returns the instance of SceneBounds that fully envelopes a set of poses"""
        xyzs = poses[..., :3, -1]
        aabb = torch.stack([torch.min(xyzs, dim=0)[0], torch.max(xyzs, dim=0)[0]])
        return SceneBounds(aabb=aabb * scale_factor)


@dataclass
class DatasetInputs:
    """Dataset inputs for the image dataset and the ray generator.

    Args:
        image_filenames: Filenames for the images.
        intrinsics: Tensor of per-image camera intrisics.
        camera_to_world: Tensor of per-image c2w matrices, in [R | t] format.
        ...
    """

    image_filenames: List[Path]
    cameras: Cameras
    downscale_factor: int = 1
    mask_filenames: Optional[List[Path]] = None
    depth_filenames: Optional[List[Path]] = None
    scene_bounds: SceneBounds = SceneBounds()
    semantics: Optional[Semantics] = None
    point_cloud: PointCloud = PointCloud()
    alpha_color: Optional[TensorType[3]] = None

    def as_dict(self) -> dict:
        """Returns the dataclass as a dictionary."""
        return vars(self)