#!/usr/bin/env python
"""
render.py
"""
from __future__ import annotations

import json
import os
import struct
import sys
from contextlib import ExitStack
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any, Dict, List, Optional

import mediapy as media
import numpy as np
import torch
import tyro
from rich.console import Console
from rich.progress import (
    BarColumn,
    Progress,
    TaskProgressColumn,
    TextColumn,
    TimeRemainingColumn,
)
from torchtyping import TensorType
from typing_extensions import Literal, assert_never

from nerfstudio.cameras.camera_paths import (
    get_interpolated_camera_path,
    get_path_from_json,
    get_spiral_path,
)
from nerfstudio.cameras.cameras import Cameras, CameraType
from nerfstudio.data.scene_box import SceneBox
from nerfstudio.model_components import renderers
from nerfstudio.pipelines.base_pipeline import Pipeline
from nerfstudio.utils import colormaps, install_checks
from nerfstudio.utils.eval_utils import eval_setup
from nerfstudio.utils.rich_utils import ItersPerSecColumn

CONSOLE = Console(width=120)


def _render_trajectory_video(
    pipeline: Pipeline,
    cameras: Cameras,
    output_filename: Path,
    rendered_output_names: List[str],
    crop_data: Optional[CropData] = None,
    rendered_resolution_scaling_factor: float = 1.0,
    seconds: float = 5.0,
    output_format: Literal["images", "video"] = "video",
    camera_type: CameraType = CameraType.PERSPECTIVE,
    depth_near_plane: Optional[float] = None,
    depth_far_plane: Optional[float] = None,
    using_eval_images: bool = False,
) -> None:
    """Helper function to create a video from cameras.

    Args:
        pipeline: Pipeline to evaluate with.
        cameras: Cameras to render.
        output_filename: Name of the output file.
        rendered_output_names: List of outputs to visualise.
        crop_data: Crop data to apply to the rendered images.
        rendered_resolution_scaling_factor: Scaling factor to apply to the camera image resolution.
        seconds: Length of output video.
        output_format: How to save output data.
        camera_type: Camera projection format type.
        depth_near_plane: Closest depth to consider. If None, use min image value.
        depth_far_plane: Furthest depth to consider. If None, use max image value.
        using_eval_images: Whether the eval images are being used or not.
    """
    # pylint: disable=too-many-statements
    CONSOLE.print("[bold green]Creating trajectory " + output_format)
    cameras.rescale_output_resolution(rendered_resolution_scaling_factor)
    cameras = cameras.to(pipeline.device)
    fps = len(cameras) / seconds

    progress = Progress(
        TextColumn(":movie_camera: Rendering :movie_camera:"),
        BarColumn(),
        TaskProgressColumn(show_speed=True),
        ItersPerSecColumn(suffix="fps"),
        TimeRemainingColumn(elapsed_when_finished=True, compact=True),
    )
    if output_format == "images":
        output_image_dir = output_filename.parent / output_filename.stem
        output_image_dir.mkdir(parents=True, exist_ok=True)
    if output_format == "video":
        # make the folder if it doesn't exist
        output_filename.parent.mkdir(parents=True, exist_ok=True)
        # NOTE:
        # we could use ffmpeg_args "-movflags faststart" for progressive download,
        # which would force moov atom into known position before mdat,
        # but then we would have to move all of mdat to insert metadata atom
        # (unless we reserve enough space to overwrite with our uuid tag,
        # but we don't know how big the video file will be, so it's not certain!)

    with ExitStack() as stack:
        writer = None

        with progress:
            for camera_idx in progress.track(range(cameras.size), description=""):
                aabb_box = None
                if crop_data is not None:
                    bounding_box_min = crop_data.center - crop_data.scale / 2.0
                    bounding_box_max = crop_data.center + crop_data.scale / 2.0
                    aabb_box = SceneBox(torch.stack([bounding_box_min, bounding_box_max]).to(pipeline.device))
                camera_ray_bundle = cameras.generate_rays(camera_indices=camera_idx, aabb_box=aabb_box)

                if crop_data is not None:
                    with renderers.background_color_override_context(
                        crop_data.background_color.to(pipeline.device)
                    ), torch.no_grad():
                        outputs = pipeline.model.get_outputs_for_camera_ray_bundle(camera_ray_bundle)
                else:
                    with torch.no_grad():
                        outputs = pipeline.model.get_outputs_for_camera_ray_bundle(camera_ray_bundle)

                render_image = []
                depth_raw = None
                for rendered_output_name in rendered_output_names:
                    if rendered_output_name not in outputs:
                        CONSOLE.rule("Error", style="red")
                        CONSOLE.print(f"Could not find {rendered_output_name} in the model outputs", justify="center")
                        CONSOLE.print(
                            f"Please set --rendered_output_name to one of: {outputs.keys()}", justify="center"
                        )
                        sys.exit(1)
                    # apply colormap to depth outputs
                    if rendered_output_name == "depth":
                        depth_raw = outputs[rendered_output_name]
                        outputs[rendered_output_name] = colormaps.apply_depth_colormap(
                            outputs[rendered_output_name],
                            accumulation=outputs["accumulation"],
                            near_plane=depth_near_plane,
                            far_plane=depth_far_plane,
                        )
                    if rendered_output_name == "visibility_median_count":
                        # NOTE: we cap the number of visible images to 255 by using torch.uint8
                        outputs[rendered_output_name] = outputs[rendered_output_name].clamp(0, 255).to(torch.uint8)
                        if output_format == "video":
                            # rescaling to [0, 1] is needed to concatenate into a video output format (see below)
                            outputs[rendered_output_name] = outputs[rendered_output_name].float() / 255.0
                    output_image = outputs[rendered_output_name].cpu().numpy()
                    if output_image.shape[-1] == 1:
                        output_image = np.concatenate((output_image,) * 3, axis=-1)
                    render_image.append(output_image)

                if using_eval_images:
                    # if in the eval-images mode, then save the GT images as well
                    batch = pipeline.datamanager.eval_dataloader.input_dataset[camera_idx]
                    rgb_gt = batch["image"]
                    # resize to match the rendered image
                    h, w = output_image.shape[:2]
                    rgb_gt = media.resize_image(rgb_gt, (h, w))
                    render_image = [rgb_gt] + render_image
                    names = ["rgb_gt"] + rendered_output_names
                else:
                    names = rendered_output_names

                if output_format == "images":
                    # for images, we write outputs to separate folders
                    for name, im in zip(names, render_image):
                        folder = output_image_dir / name
                        folder.mkdir(parents=True, exist_ok=True)
                        media.write_image(folder / f"{camera_idx:05d}.png", im)
                    # save depth_raw, which doesn't have the colormap applied
                    if depth_raw is not None:
                        folder = output_image_dir / "depth_raw"
                        folder.mkdir(parents=True, exist_ok=True)
                        np.save(folder / f"{camera_idx:05d}.npy", depth_raw.cpu().numpy())
                if output_format == "video":
                    # for videos, we concatenate images horizontally
                    render_image = np.concatenate(render_image, axis=1)
                    if writer is None:
                        render_width = int(render_image.shape[1])
                        render_height = int(render_image.shape[0])
                        writer = stack.enter_context(
                            media.VideoWriter(
                                path=output_filename,
                                shape=(render_height, render_width),
                                fps=fps,
                            )
                        )
                    writer.add_image(render_image)

    if output_format == "video":
        if camera_type == CameraType.EQUIRECTANGULAR:
            insert_spherical_metadata_into_file(output_filename)


def insert_spherical_metadata_into_file(
    output_filename: Path,
) -> None:
    """Inserts spherical metadata into MP4 video file in-place.
    Args:
        output_filename: Name of the (input and) output file.
    """
    # NOTE:
    # because we didn't use faststart, the moov atom will be at the end;
    # to insert our metadata, we need to find (skip atoms until we get to) moov.
    # we should have 0x00000020 ftyp, then 0x00000008 free, then variable mdat.
    spherical_uuid = b"\xff\xcc\x82\x63\xf8\x55\x4a\x93\x88\x14\x58\x7a\x02\x52\x1f\xdd"
    spherical_metadata = bytes(
        """<rdf:SphericalVideo
xmlns:rdf='http://www.w3.org/1999/02/22-rdf-syntax-ns#'
xmlns:GSpherical='http://ns.google.com/videos/1.0/spherical/'>
<GSpherical:ProjectionType>equirectangular</GSpherical:ProjectionType>
<GSpherical:Spherical>True</GSpherical:Spherical>
<GSpherical:Stitched>True</GSpherical:Stitched>
<GSpherical:StitchingSoftware>nerfstudio</GSpherical:StitchingSoftware>
</rdf:SphericalVideo>""",
        "utf-8",
    )
    insert_size = len(spherical_metadata) + 8 + 16
    with open(output_filename, mode="r+b") as mp4file:
        try:
            # get file size
            mp4file_size = os.stat(output_filename).st_size

            # find moov container (probably after ftyp, free, mdat)
            while True:
                pos = mp4file.tell()
                size, tag = struct.unpack(">I4s", mp4file.read(8))
                if tag == b"moov":
                    break
                mp4file.seek(pos + size)
            # if moov isn't at end, bail
            if pos + size != mp4file_size:
                # TODO: to support faststart, rewrite all stco offsets
                raise Exception("moov container not at end of file")
            # go back and write inserted size
            mp4file.seek(pos)
            mp4file.write(struct.pack(">I", size + insert_size))
            # go inside moov
            mp4file.seek(pos + 8)
            # find trak container (probably after mvhd)
            while True:
                pos = mp4file.tell()
                size, tag = struct.unpack(">I4s", mp4file.read(8))
                if tag == b"trak":
                    break
                mp4file.seek(pos + size)
            # go back and write inserted size
            mp4file.seek(pos)
            mp4file.write(struct.pack(">I", size + insert_size))
            # we need to read everything from end of trak to end of file in order to insert
            # TODO: to support faststart, make more efficient (may load nearly all data)
            mp4file.seek(pos + size)
            rest_of_file = mp4file.read(mp4file_size - pos - size)
            # go to end of trak (again)
            mp4file.seek(pos + size)
            # insert our uuid atom with spherical metadata
            mp4file.write(struct.pack(">I4s16s", insert_size, b"uuid", spherical_uuid))
            mp4file.write(spherical_metadata)
            # write rest of file
            mp4file.write(rest_of_file)
        finally:
            mp4file.close()


@dataclass
class CropData:
    """Data for cropping an image."""

    background_color: TensorType[3] = torch.Tensor([0.0, 0.0, 0.0])
    """background color"""
    center: TensorType[3] = torch.Tensor([0.0, 0.0, 0.0])
    """center of the crop"""
    scale: TensorType[3] = torch.Tensor([2.0, 2.0, 2.0])
    """scale of the crop"""


def get_crop_from_json(camera_json: Dict[str, Any]) -> Optional[CropData]:
    """Load crop data from a camera path JSON

    args:
        camera_json: camera path data
    returns:
        Crop data
    """
    if "crop" not in camera_json or camera_json["crop"] is None:
        return None

    bg_color = camera_json["crop"]["crop_bg_color"]

    return CropData(
        background_color=torch.Tensor([bg_color["r"] / 255.0, bg_color["g"] / 255.0, bg_color["b"] / 255.0]),
        center=torch.Tensor(camera_json["crop"]["crop_center"]),
        scale=torch.Tensor(camera_json["crop"]["crop_scale"]),
    )


@dataclass
class RenderTrajectory:
    """Load a checkpoint, render a trajectory, and save to a video file.
    The following trajectory options are available,
    filename: Load from trajectory created using viewer or blender vfx plugin.
    interpolate: Create trajectory by interpolating between eval dataset images.
    spiral: Create a spiral trajectory (can be hit or miss).
    """

    load_config: Path
    """Path to config YAML file."""
    rendered_output_names: List[str] = field(default_factory=lambda: ["rgb"])
    """Name of the renderer outputs to use. rgb, depth, etc. concatenates them along y axis"""
    traj: Literal["spiral", "filename", "interpolate", "eval-images"] = "spiral"
    """Trajectory type to render. Select between spiral-shaped trajectory, trajectory loaded from
    a viewer-generated file, interpolated camera paths from the eval dataset, of the eval cameras."""
    downscale_factor: int = 1
    """Scaling factor to apply to the camera image resolution."""
    camera_path_filename: Path = Path("camera_path.json")
    """Filename of the camera path to render."""
    output_path: Path = Path("renders/output.mp4")
    """Name of the output file."""
    seconds: float = 5.0
    """How long the video should be."""
    output_format: Literal["images", "video"] = "video"
    """How to save output data."""
    interpolation_steps: int = 10
    """Number of interpolation steps between eval dataset cameras."""
    eval_num_rays_per_chunk: Optional[int] = None
    """Specifies number of rays per chunk during eval."""
    depth_near_plane: Optional[float] = None
    """Specifies the near plane for depth rendering."""
    depth_far_plane: Optional[float] = None
    """Specifies the far plane for depth rendering."""

    def main(self) -> None:
        """Main function."""
        _, pipeline, _ = eval_setup(
            self.load_config,
            eval_num_rays_per_chunk=self.eval_num_rays_per_chunk,
            test_mode="test" if self.traj in ["spiral", "interpolate", "eval-images"] else "inference",
        )

        install_checks.check_ffmpeg_installed()

        seconds = self.seconds
        crop_data = None

        # TODO(ethan): use camera information from parsing args
        if self.traj == "spiral":
            camera_start = pipeline.datamanager.eval_dataloader.get_camera(image_idx=0).flatten()
            # TODO(ethan): pass in the up direction of the camera
            camera_type = CameraType.PERSPECTIVE
            camera_path = get_spiral_path(camera_start, steps=30, radius=0.1)
        elif self.traj == "filename":
            with open(self.camera_path_filename, "r", encoding="utf-8") as f:
                camera_path = json.load(f)
            seconds = camera_path["seconds"]
            if "camera_type" not in camera_path:
                camera_type = CameraType.PERSPECTIVE
            elif camera_path["camera_type"] == "fisheye":
                camera_type = CameraType.FISHEYE
            elif camera_path["camera_type"] == "equirectangular":
                camera_type = CameraType.EQUIRECTANGULAR
            else:
                camera_type = CameraType.PERSPECTIVE
            crop_data = get_crop_from_json(camera_path)
            camera_path = get_path_from_json(camera_path)
        elif self.traj == "interpolate":
            camera_type = CameraType.PERSPECTIVE
            camera_path = get_interpolated_camera_path(
                cameras=pipeline.datamanager.eval_dataloader.cameras, steps=self.interpolation_steps
            )
        elif self.traj == "eval-images":
            camera_path = pipeline.datamanager.eval_dataloader.cameras
            camera_type = pipeline.datamanager.eval_dataloader.cameras.camera_type[0]
            # TODO: support more than one camera type
        else:
            assert_never(self.traj)

        # check if visibility is a desired output. if not, disable it for fast rendering
        if "visibility" not in self.rendered_output_names:
            pipeline.model.visibility_field = None

        _render_trajectory_video(
            pipeline,
            camera_path,
            output_filename=self.output_path,
            rendered_output_names=self.rendered_output_names,
            rendered_resolution_scaling_factor=1.0 / self.downscale_factor,
            crop_data=crop_data,
            seconds=seconds,
            output_format=self.output_format,
            camera_type=camera_type,
            depth_near_plane=self.depth_near_plane,
            depth_far_plane=self.depth_far_plane,
            using_eval_images=self.traj == "eval-images",
        )


def entrypoint():
    """Entrypoint for use with pyproject scripts."""
    tyro.extras.set_accent_color("bright_yellow")
    tyro.cli(RenderTrajectory).main()


if __name__ == "__main__":
    entrypoint()

# For sphinx docs
get_parser_fn = lambda: tyro.extras.get_parser(RenderTrajectory)  # noqa