# 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
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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"""
Code to handle loading blender datasets.
"""

import os
from typing import Optional

import imageio
import numpy as np
import torch
from torchtyping import TensorType

from nerfactory.data.structs import DatasetInputs, SceneBounds
from nerfactory.utils.io import load_from_json


def load_blender_data(
    basedir: str,
    downscale_factor: int = 1,
    alpha_color: Optional[TensorType[3]] = None,
    split: str = "train",
    scale_factor: float = 1.0,
) -> DatasetInputs:
    """Processes the a blender dataset directory.
    Some of this code comes from https://github.com/yenchenlin/nerf-pytorch/blob/master/load_blender.py#L37.

    Args:
        data_directory (str): Location of data
        downscale_factor (int, optional): How much to downscale images. Defaults to 1.0.
        alpha_color (TensorType[3], optional): Sets transparent regions to specified color, otherwise black.
        split (str, optional): Which dataset split to generate.
        scale_factor (float): How much to scale the camera origins by.

    Returns:
        DatasetInputs
    """
    # pylint: disable=unused-argument
    # pylint: disable-msg=too-many-locals
    meta = load_from_json(os.path.join(basedir, f"transforms_{split}.json"))
    image_filenames = []
    poses = []
    for frame in meta["frames"]:
        fname = os.path.join(basedir, frame["file_path"].replace("./", "") + ".png")
        image_filenames.append(fname)
        poses.append(np.array(frame["transform_matrix"]))
    poses = np.array(poses).astype(np.float32)

    img_0 = imageio.imread(image_filenames[0])
    image_height, image_width = img_0.shape[:2]
    camera_angle_x = float(meta["camera_angle_x"])
    focal_length = 0.5 * image_width / np.tan(0.5 * camera_angle_x)

    cx = image_width / 2.0
    cy = image_height / 2.0
    camera_to_world = torch.from_numpy(poses[:, :3])  # camera to world transform
    num_cameras = len(image_filenames)
    num_intrinsics_params = 3
    intrinsics = torch.ones((num_cameras, num_intrinsics_params), dtype=torch.float32)
    intrinsics *= torch.tensor([cx, cy, focal_length])

    # in x,y,z order
    camera_to_world[..., 3] *= scale_factor
    scene_bounds = SceneBounds(aabb=torch.tensor([[-1.5, -1.5, -1.5], [1.5, 1.5, 1.5]], dtype=torch.float32))

    dataset_inputs = DatasetInputs(
        image_filenames=image_filenames,
        downscale_factor=downscale_factor,
        alpha_color=alpha_color,
        intrinsics=intrinsics * 1.0 / downscale_factor,  # downscaling the intrinsics here
        camera_to_world=camera_to_world,
        scene_bounds=scene_bounds,
    )

    return dataset_inputs