# Copyright (c) 2022-2024, The ORBIT Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause

"""Helper functions to project between pointcloud and depth images."""

from __future__ import annotations

import math
import numpy as np
import torch
import torch.nn.functional as F
from typing import Sequence
from typing_extensions import Literal

import omni.isaac.core.utils.stage as stage_utils
import warp as wp
from pxr import UsdGeom

import omni.isaac.orbit.utils.math as math_utils
from omni.isaac.orbit.utils.array import TensorData, convert_to_torch

"""
Depth <-> Pointcloud conversions.
"""


def transform_points(
    points: TensorData,
    position: Sequence[float] | None = None,
    orientation: Sequence[float] | None = None,
    device: torch.device | str | None = None,
) -> np.ndarray | torch.Tensor:
    r"""Transform input points in a given frame to a target frame.

    This function transform points from a source frame to a target frame. The transformation is defined by the
    position ``t`` and orientation ``R`` of the target frame in the source frame.

    .. math::
        p_{target} = R_{target} \times p_{source} + t_{target}

    If either the inputs `position` and `orientation` are None, the corresponding transformation is not applied.

    Args:
        points: a tensor of shape (p, 3) or (n, p, 3) comprising of 3d points in source frame.
        position: The position of source frame in target frame. Defaults to None.
        orientation: The orientation (w, x, y, z) of source frame in target frame.
            Defaults to None.
        device: The device for torch where the computation
            should be executed. Defaults to None, i.e. takes the device that matches the depth image.

    Returns:
        A tensor of shape (N, 3) comprising of 3D points in target frame.
        If the input is a numpy array, the output is a numpy array. Otherwise, it is a torch tensor.
    """
    # check if numpy
    is_numpy = isinstance(points, np.ndarray)
    # decide device
    if device is None and is_numpy:
        device = torch.device("cpu")
    # convert to torch
    points = convert_to_torch(points, dtype=torch.float32, device=device)
    # update the device with the device of the depth image
    # note: this is needed since warp does not provide the device directly
    device = points.device
    # apply rotation
    if orientation is not None:
        orientation = convert_to_torch(orientation, dtype=torch.float32, device=device)
    # apply translation
    if position is not None:
        position = convert_to_torch(position, dtype=torch.float32, device=device)
    # apply transformation
    points = math_utils.transform_points(points, position, orientation)

    # return everything according to input type
    if is_numpy:
        return points.detach().cpu().numpy()
    else:
        return points


def create_pointcloud_from_depth(
    intrinsic_matrix: np.ndarray | torch.Tensor | wp.array,
    depth: np.ndarray | torch.Tensor | wp.array,
    keep_invalid: bool = False,
    position: Sequence[float] | None = None,
    orientation: Sequence[float] | None = None,
    device: torch.device | str | None = None,
) -> np.ndarray | torch.Tensor:
    r"""Creates pointcloud from input depth image and camera intrinsic matrix.

    This function creates a pointcloud from a depth image and camera intrinsic matrix. The pointcloud is
    computed using the following equation:

    .. math::
        p_{camera} = K^{-1} \times [u, v, 1]^T \times d

    where :math:`K` is the camera intrinsic matrix, :math:`u` and :math:`v` are the pixel coordinates and
    :math:`d` is the depth value at the pixel.

    Additionally, the pointcloud can be transformed from the camera frame to a target frame by providing
    the position ``t`` and orientation ``R`` of the camera in the target frame:

    .. math::
        p_{target} = R_{target} \times p_{camera} + t_{target}

    Args:
        intrinsic_matrix: A (3, 3) array providing camera's calibration matrix.
        depth: An array of shape (H, W) with values encoding the depth measurement.
        keep_invalid: Whether to keep invalid points in the cloud or not. Invalid points
            correspond to pixels with depth values 0.0 or NaN. Defaults to False.
        position: The position of the camera in a target frame. Defaults to None.
        orientation: The orientation (w, x, y, z) of the camera in a target frame. Defaults to None.
        device: The device for torch where the computation should be executed.
            Defaults to None, i.e. takes the device that matches the depth image.

    Returns:
        An array/tensor of shape (N, 3) comprising of 3D coordinates of points.
        The returned datatype is torch if input depth is of type torch.tensor or wp.array. Otherwise, a np.ndarray
        is returned.
    """
    # We use PyTorch here for matrix multiplication since it is compiled with Intel MKL while numpy
    # by default uses OpenBLAS. With PyTorch (CPU), we could process a depth image of size (480, 640)
    # in 0.0051 secs, while with numpy it took 0.0292 secs.

    # convert to numpy matrix
    is_numpy = isinstance(depth, np.ndarray)
    # decide device
    if device is None and is_numpy:
        device = torch.device("cpu")
    # convert depth to torch tensor
    depth = convert_to_torch(depth, dtype=torch.float32, device=device)
    # update the device with the device of the depth image
    # note: this is needed since warp does not provide the device directly
    device = depth.device
    # convert inputs to torch tensors
    intrinsic_matrix = convert_to_torch(intrinsic_matrix, dtype=torch.float32, device=device)
    if position is not None:
        position = convert_to_torch(position, dtype=torch.float32, device=device)
    if orientation is not None:
        orientation = convert_to_torch(orientation, dtype=torch.float32, device=device)
    # compute pointcloud
    depth_cloud = math_utils.unproject_depth(depth, intrinsic_matrix)
    # convert 3D points to world frame
    depth_cloud = math_utils.transform_points(depth_cloud, position, orientation)
    # keep only valid entries
    pts_idx_to_keep = torch.all(torch.logical_and(~torch.isnan(depth_cloud), ~torch.isinf(depth_cloud)), dim=1)
    depth_cloud = depth_cloud[pts_idx_to_keep, ...]

    # return everything according to input type
    if is_numpy:
        return depth_cloud.detach().cpu().numpy()
    else:
        return depth_cloud


def create_pointcloud_from_rgbd(
    intrinsic_matrix: torch.Tensor | np.ndarray | wp.array,
    depth: torch.Tensor | np.ndarray | wp.array,
    rgb: torch.Tensor | wp.array | np.ndarray | tuple[float, float, float] = None,
    normalize_rgb: bool = False,
    position: Sequence[float] | None = None,
    orientation: Sequence[float] | None = None,
    device: torch.device | str | None = None,
    num_channels: int = 3,
) -> tuple[torch.Tensor, torch.Tensor] | tuple[np.ndarray, np.ndarray]:
    """Creates pointcloud from input depth image and camera transformation matrix.

    This function provides the same functionality as :meth:`create_pointcloud_from_depth` but also allows
    to provide the RGB values for each point.

    The ``rgb`` attribute is used to resolve the corresponding point's color:

    - If a ``np.array``/``wp.array``/``torch.tensor`` of shape (H, W, 3), then the corresponding channels encode RGB values.
    - If a tuple, then the point cloud has a single color specified by the values (r, g, b).
    - If None, then default color is white, i.e. (0, 0, 0).

    If the input ``normalize_rgb`` is set to :obj:`True`, then the RGB values are normalized to be in the range [0, 1].

    Args:
        intrinsic_matrix: A (3, 3) array/tensor providing camera's calibration matrix.
        depth: An array/tensor of shape (H, W) with values encoding the depth measurement.
        rgb: Color for generated point cloud. Defaults to None.
        normalize_rgb: Whether to normalize input rgb. Defaults to False.
        position: The position of the camera in a target frame. Defaults to None.
        orientation: The orientation `(w, x, y, z)` of the camera in a target frame. Defaults to None.
        device: The device for torch where the computation should be executed. Defaults to None, in which case
            it takes the device that matches the depth image.
        num_channels: Number of channels in RGB pointcloud. Defaults to 3.

    Returns:
        A tuple of (N, 3) arrays or tensors containing the 3D coordinates of points and their RGB color respectively.
        The returned datatype is torch if input depth is of type torch.tensor or wp.array. Otherwise, a np.ndarray
        is returned.

    Raises:
        ValueError:  When rgb image is a numpy array but not of shape (H, W, 3) or (H, W, 4).
    """
    # check valid inputs
    if rgb is not None and not isinstance(rgb, tuple):
        if len(rgb.shape) == 3:
            if rgb.shape[2] not in [3, 4]:
                raise ValueError(f"Input rgb image of invalid shape: {rgb.shape} != (H, W, 3) or (H, W, 4).")
        else:
            raise ValueError(f"Input rgb image not three-dimensional. Received shape: {rgb.shape}.")
    if num_channels not in [3, 4]:
        raise ValueError(f"Invalid number of channels: {num_channels} != 3 or 4.")

    # check if input depth is numpy array
    is_numpy = isinstance(depth, np.ndarray)
    # decide device
    if device is None and is_numpy:
        device = torch.device("cpu")
    # convert depth to torch tensor
    if is_numpy:
        depth = torch.from_numpy(depth).to(device=device)
    # retrieve XYZ pointcloud
    points_xyz = create_pointcloud_from_depth(intrinsic_matrix, depth, True, position, orientation, device=device)

    # get image height and width
    im_height, im_width = depth.shape[:2]
    # total number of points
    num_points = im_height * im_width
    # extract color value
    if rgb is not None:
        if isinstance(rgb, (np.ndarray, torch.Tensor, wp.array)):
            # copy numpy array to preserve
            rgb = convert_to_torch(rgb, device=device, dtype=torch.float32)
            rgb = rgb[:, :, :3]
            # convert the matrix to (W, H, 3) from (H, W, 3) since depth processing
            # is done in the order (u, v) where u: (0, W-1) and v: (0 - H-1)
            points_rgb = rgb.permute(1, 0, 2).reshape(-1, 3)
        elif isinstance(rgb, (tuple, list)):
            # same color for all points
            points_rgb = torch.Tensor((rgb,) * num_points, device=device, dtype=torch.uint8)
        else:
            # default color is white
            points_rgb = torch.Tensor(((0, 0, 0),) * num_points, device=device, dtype=torch.uint8)
    else:
        points_rgb = torch.Tensor(((0, 0, 0),) * num_points, device=device, dtype=torch.uint8)
    # normalize color values
    if normalize_rgb:
        points_rgb = points_rgb.float() / 255

    # remove invalid points
    pts_idx_to_keep = torch.all(torch.logical_and(~torch.isnan(points_xyz), ~torch.isinf(points_xyz)), dim=1)
    points_rgb = points_rgb[pts_idx_to_keep, ...]
    points_xyz = points_xyz[pts_idx_to_keep, ...]

    # add additional channels if required
    if num_channels == 4:
        points_rgb = torch.nn.functional.pad(points_rgb, (0, 1), mode="constant", value=1.0)

    # return everything according to input type
    if is_numpy:
        return points_xyz.cpu().numpy(), points_rgb.cpu().numpy()
    else:
        return points_xyz, points_rgb


def convert_orientation_convention(
    orientation: torch.Tensor,
    origin: Literal["opengl", "ros", "world"] = "opengl",
    target: Literal["opengl", "ros", "world"] = "ros",
) -> torch.Tensor:
    r"""Converts a quaternion representing a rotation from one convention to another.

    In USD, the camera follows the ``"opengl"`` convention. Thus, it is always in **Y up** convention.
    This means that the camera is looking down the -Z axis with the +Y axis pointing up , and +X axis pointing right.
    However, in ROS, the camera is looking down the +Z axis with the +Y axis pointing down, and +X axis pointing right.
    Thus, the camera needs to be rotated by :math:`180^{\circ}` around the X axis to follow the ROS convention.

    .. math::

        T_{ROS} = \begin{bmatrix} 1 & 0 & 0 & 0 \\ 0 & -1 & 0 & 0 \\ 0 & 0 & -1 & 0 \\ 0 & 0 & 0 & 1 \end{bmatrix} T_{USD}

    On the other hand, the typical world coordinate system is with +X pointing forward, +Y pointing left,
    and +Z pointing up. The camera can also be set in this convention by rotating the camera by :math:`90^{\circ}`
    around the X axis and :math:`-90^{\circ}` around the Y axis.

    .. math::

        T_{WORLD} = \begin{bmatrix} 0 & 0 & -1 & 0 \\ -1 & 0 & 0 & 0 \\ 0 & 1 & 0 & 0 \\ 0 & 0 & 0 & 1 \end{bmatrix} T_{USD}

    Thus, based on their application, cameras follow different conventions for their orientation. This function
    converts a quaternion from one convention to another.

    Possible conventions are:

    - :obj:`"opengl"` - forward axis: -Z - up axis +Y - Offset is applied in the OpenGL (Usd.Camera) convention
    - :obj:`"ros"`    - forward axis: +Z - up axis -Y - Offset is applied in the ROS convention
    - :obj:`"world"`  - forward axis: +X - up axis +Z - Offset is applied in the World Frame convention

    Args:
        orientation: Quaternion of form `(w, x, y, z)` with shape (..., 4) in source convention
        origin: Convention to convert to. Defaults to "ros".
        target: Convention to convert from. Defaults to "opengl".

    Returns:
        Quaternion of form `(w, x, y, z)` with shape (..., 4) in target convention
    """
    if target == origin:
        return orientation.clone()

    # -- unify input type
    if origin == "ros":
        # convert from ros to opengl convention
        rotm = math_utils.matrix_from_quat(orientation)
        rotm[:, :, 2] = -rotm[:, :, 2]
        rotm[:, :, 1] = -rotm[:, :, 1]
        # convert to opengl convention
        quat_gl = math_utils.quat_from_matrix(rotm)
    elif origin == "world":
        # convert from world (x forward and z up) to opengl convention
        rotm = math_utils.matrix_from_quat(orientation)
        rotm = torch.matmul(
            rotm,
            math_utils.matrix_from_euler(
                torch.tensor([math.pi / 2, -math.pi / 2, 0], device=orientation.device), "XYZ"
            ),
        )
        # convert to isaac-sim convention
        quat_gl = math_utils.quat_from_matrix(rotm)
    else:
        quat_gl = orientation

    # -- convert to target convention
    if target == "ros":
        # convert from opengl to ros convention
        rotm = math_utils.matrix_from_quat(quat_gl)
        rotm[:, :, 2] = -rotm[:, :, 2]
        rotm[:, :, 1] = -rotm[:, :, 1]
        return math_utils.quat_from_matrix(rotm)
    elif target == "world":
        # convert from opengl to world (x forward and z up) convention
        rotm = math_utils.matrix_from_quat(quat_gl)
        rotm = torch.matmul(
            rotm,
            math_utils.matrix_from_euler(
                torch.tensor([math.pi / 2, -math.pi / 2, 0], device=orientation.device), "XYZ"
            ).T,
        )
        return math_utils.quat_from_matrix(rotm)
    else:
        return quat_gl.clone()


# @torch.jit.script
def create_rotation_matrix_from_view(
    eyes: torch.Tensor,
    targets: torch.Tensor,
    device: str = "cpu",
) -> torch.Tensor:
    """
    This function takes a vector ''eyes'' which specifies the location
    of the camera in world coordinates and the vector ''targets'' which
    indicate the position of the object.
    The output is a rotation matrix representing the transformation
    from world coordinates -> view coordinates.

        The inputs camera_position and targets can each be a
        - 3 element tuple/list
        - torch tensor of shape (1, 3)
        - torch tensor of shape (N, 3)

    Args:
        eyes: position of the camera in world coordinates
        targets: position of the object in world coordinates

    The vectors are broadcast against each other so they all have shape (N, 3).

    Returns:
        R: (N, 3, 3) batched rotation matrices

    Reference:
    Based on PyTorch3D (https://github.com/facebookresearch/pytorch3d/blob/eaf0709d6af0025fe94d1ee7cec454bc3054826a/pytorch3d/renderer/cameras.py#L1635-L1685)
    """
    up_axis_token = stage_utils.get_stage_up_axis()
    if up_axis_token == UsdGeom.Tokens.y:
        up_axis = torch.tensor((0, 1, 0), device=device, dtype=torch.float32).repeat(eyes.shape[0], 1)
    elif up_axis_token == UsdGeom.Tokens.z:
        up_axis = torch.tensor((0, 0, 1), device=device, dtype=torch.float32).repeat(eyes.shape[0], 1)
    else:
        raise ValueError(f"Invalid up axis: {up_axis_token}")

    # get rotation matrix in opengl format (-Z forward, +Y up)
    z_axis = -F.normalize(targets - eyes, eps=1e-5)
    x_axis = F.normalize(torch.cross(up_axis, z_axis, dim=1), eps=1e-5)
    y_axis = F.normalize(torch.cross(z_axis, x_axis, dim=1), eps=1e-5)
    is_close = torch.isclose(x_axis, torch.tensor(0.0), atol=5e-3).all(dim=1, keepdim=True)
    if is_close.any():
        replacement = F.normalize(torch.cross(y_axis, z_axis, dim=1), eps=1e-5)
        x_axis = torch.where(is_close, replacement, x_axis)
    R = torch.cat((x_axis[:, None, :], y_axis[:, None, :], z_axis[:, None, :]), dim=1)
    return R.transpose(1, 2)
