# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.

from typing import List, Optional, Tuple

import torch
from pytorch3d import _C
from pytorch3d.ops.marching_cubes_data import EDGE_TO_VERTICES, FACE_TABLE, INDEX
from pytorch3d.transforms import Translate
from torch.autograd import Function


EPS = 0.00001


class Cube:
    def __init__(
        self,
        bfl_v: Tuple[int, int, int],
        volume: torch.Tensor,
        isolevel: float,
    ) -> None:
        """
        Initializes a cube given the bottom front left vertex coordinate
        and computes the cube configuration given vertex values and isolevel.

        Edge and vertex convention:

                    v4_______e4____________v5
                    /|                    /|
                   / |                   / |
                e7/  |                e5/  |
                 /___|______e6_________/   |
              v7|    |                 |v6 |e9
                |    |                 |   |
                |    |e8               |e10|
             e11|    |                 |   |
                |    |______e0_________|___|
                |   / v0(bfl_v)        |   |v1
                |  /                   |  /
                | /e3                  | /e1
                |/_____________________|/
                v3         e2          v2

        Args:
            bfl_vertex: a tuple of size 3 corresponding to the bottom front left vertex
                of the cube in (x, y, z) format
            volume: the 3D scalar data
            isolevel: the isosurface value used as a threshold for determining whether a point
                is inside/outside the volume
        """
        x, y, z = bfl_v
        self.x, self.y, self.z = x, y, z
        self.bfl_v = bfl_v
        self.verts = [
            [x + (v & 1), y + (v >> 1 & 1), z + (v >> 2 & 1)] for v in range(8)
        ]  # vertex position (x, y, z) for v0-v1-v4-v5-v3-v2-v7-v6

        # Calculates cube configuration index given values of the cube vertices
        self.cube_index = 0
        for i in range(8):
            v = self.verts[INDEX[i]]
            value = volume[v[2]][v[1]][v[0]]
            if value < isolevel:
                self.cube_index |= 1 << i

    def get_vpair_from_edge(self, edge: int, W: int, H: int) -> Tuple[int, int]:
        """
        Get a tuple of global vertex ID from a local edge ID
        Global vertex ID is calculated as (x + dx) + (y + dy) * W + (z + dz) * W * H

        Args:
            edge: local edge ID in the cube
            bfl_vertex: bottom-front-left coordinate of the cube

        Returns:
            a pair of global vertex ID
        """
        v1, v2 = EDGE_TO_VERTICES[edge]  # two end-points on the edge
        v1_id = self.verts[v1][0] + self.verts[v1][1] * W + self.verts[v1][2] * W * H
        v2_id = self.verts[v2][0] + self.verts[v2][1] * W + self.verts[v2][2] * W * H
        return (v1_id, v2_id)

    def vert_interp(
        self,
        isolevel: float,
        edge: int,
        vol: torch.Tensor,
    ) -> List:
        """
        Linearly interpolate a vertex where an isosurface cuts an edge
        between the two endpoint vertices, based on their values

        Args:
            isolevel: the isosurface value to use as the threshold to determine
                whether points are within a volume.
            edge: edge (ID) to interpolate
            cube: current cube vertices
            vol: 3D scalar field

        Returns:
            interpolated vertex: position of the interpolated vertex on the edge
        """
        v1, v2 = EDGE_TO_VERTICES[edge]
        p1, p2 = self.verts[v1], self.verts[v2]
        val1, val2 = (
            vol[p1[2]][p1[1]][p1[0]],
            vol[p2[2]][p2[1]][p2[0]],
        )
        point = None
        if abs(isolevel - val1) < EPS:
            point = p1
        elif abs(isolevel - val2) < EPS:
            point = p2
        elif abs(val1 - val2) < EPS:
            point = p1

        if point is None:
            mu = (isolevel - val1) / (val2 - val1)
            x1, y1, z1 = p1
            x2, y2, z2 = p2
            x = x1 + mu * (x2 - x1)
            y = y1 + mu * (y2 - y1)
            z = z1 + mu * (z2 - z1)
        else:
            x, y, z = point
        return [x, y, z]


def marching_cubes_naive(
    vol_batch: torch.Tensor,
    isolevel: Optional[float] = None,
    return_local_coords: bool = True,
) -> Tuple[List[torch.Tensor], List[torch.Tensor]]:
    """
    Runs the classic marching cubes algorithm, iterating over
    the coordinates of the volume and using a given isolevel
    for determining intersected edges of cubes.
    Returns vertices and faces of the obtained mesh.
    This operation is non-differentiable.

    Args:
        vol_batch: a Tensor of size (N, D, H, W) corresponding to
            a batch of 3D scalar fields
        isolevel: the isosurface value to use as the threshold to determine
            whether points are within a volume. If None, then the average of the
            maximum and minimum value of the scalar field will be used.
        return_local_coords: bool. If True the output vertices will be in local coordinates in
        the range [-1, 1] x [-1, 1] x [-1, 1]. If False they will be in the range
        [0, W-1] x [0, H-1] x [0, D-1]
    Returns:
        verts: [{V_0}, {V_1}, ...] List of N sets of vertices of shape (|V_i|, 3) in FloatTensor
        faces: [{F_0}, {F_1}, ...] List of N sets of faces of shape (|F_i|, 3) in LongTensors
    """
    batched_verts, batched_faces = [], []
    D, H, W = vol_batch.shape[1:]

    # each edge is represented with its two endpoints (represented with global id)
    for i in range(len(vol_batch)):
        vol = vol_batch[i]
        thresh = ((vol.max() + vol.min()) / 2).item() if isolevel is None else isolevel
        vpair_to_edge = {}  # maps from tuple of edge endpoints to edge_id
        edge_id_to_v = {}  # maps from edge ID to vertex position
        uniq_edge_id = {}  # unique edge IDs
        verts = []  # store vertex positions
        faces = []  # store face indices
        # enumerate each cell in the 3d grid
        for z in range(0, D - 1):
            for y in range(0, H - 1):
                for x in range(0, W - 1):
                    cube = Cube((x, y, z), vol, thresh)
                    edge_indices = FACE_TABLE[cube.cube_index]
                    # cube is entirely in/out of the surface
                    if len(edge_indices) == 0:
                        continue

                    # gather mesh vertices/faces by processing each cube
                    interp_points = [[0.0, 0.0, 0.0]] * 12
                    # triangle vertex IDs and positions
                    tri = []
                    ps = []
                    for i, edge in enumerate(edge_indices):
                        interp_points[edge] = cube.vert_interp(thresh, edge, vol)

                        # Bind interpolated vertex with a global edge_id, which
                        # is represented by a pair of vertex ids (v1_id, v2_id)
                        # corresponding to a local edge.
                        (v1_id, v2_id) = cube.get_vpair_from_edge(edge, W, H)
                        edge_id = vpair_to_edge.setdefault(
                            (v1_id, v2_id), len(vpair_to_edge)
                        )
                        tri.append(edge_id)
                        ps.append(interp_points[edge])
                        # when the isolevel are the same as the edge endpoints, the interploated
                        # vertices can share the same values, and lead to degenerate triangles.
                        if (
                            (i + 1) % 3 == 0
                            and ps[0] != ps[1]
                            and ps[1] != ps[2]
                            and ps[2] != ps[0]
                        ):
                            for j, edge_id in enumerate(tri):
                                edge_id_to_v[edge_id] = ps[j]
                                if edge_id not in uniq_edge_id:
                                    uniq_edge_id[edge_id] = len(verts)
                                    verts.append(edge_id_to_v[edge_id])
                            faces.append([uniq_edge_id[tri[j]] for j in range(3)])
                            tri = []
                            ps = []

        if len(faces) > 0 and len(verts) > 0:
            verts = torch.tensor(verts, dtype=vol.dtype)
            # Convert from world coordinates ([0, D-1], [0, H-1], [0, W-1]) to
            # local coordinates in the range [-1, 1]
            if return_local_coords:
                verts = (
                    Translate(x=+1.0, y=+1.0, z=+1.0, device=vol_batch.device)
                    .scale((vol_batch.new_tensor([W, H, D])[None] - 1) * 0.5)
                    .inverse()
                ).transform_points(verts[None])[0]
            batched_verts.append(verts)
            batched_faces.append(torch.tensor(faces, dtype=torch.int64))
        else:
            batched_verts.append([])
            batched_faces.append([])
    return batched_verts, batched_faces


########################################
# Marching Cubes Implementation in C++/Cuda
########################################
class _marching_cubes(Function):
    """
    Torch Function wrapper for marching_cubes implementation.
    This function is not differentiable. An autograd wrapper is used
    to ensure an error if user tries to get gradients.
    """

    @staticmethod
    def forward(ctx, vol, isolevel):
        verts, faces, ids = _C.marching_cubes(vol, isolevel)
        return verts, faces, ids

    @staticmethod
    def backward(ctx, grad_verts, grad_faces):
        raise ValueError("marching_cubes backward is not supported")


def marching_cubes(
    vol_batch: torch.Tensor,
    isolevel: Optional[float] = None,
    return_local_coords: bool = True,
) -> Tuple[List[torch.Tensor], List[torch.Tensor]]:
    """
    Run marching cubes over a volume scalar field with a designated isolevel.
    Returns vertices and faces of the obtained mesh.
    This operation is non-differentiable.

    Args:
        vol_batch: a Tensor of size (N, D, H, W) corresponding to
            a batch of 3D scalar fields
        isolevel: float used as threshold to determine if a point is inside/outside
            the volume.  If None, then the average of the maximum and minimum value
            of the scalar field is used.
        return_local_coords: bool. If True the output vertices will be in local coordinates in
            the range [-1, 1] x [-1, 1] x [-1, 1]. If False they will be in the range
            [0, W-1] x [0, H-1] x [0, D-1]

    Returns:
        verts: [{V_0}, {V_1}, ...] List of N sets of vertices of shape (|V_i|, 3) in FloatTensor
        faces: [{F_0}, {F_1}, ...] List of N sets of faces of shape (|F_i|, 3) in LongTensors
    """
    batched_verts, batched_faces = [], []
    D, H, W = vol_batch.shape[1:]
    for i in range(len(vol_batch)):
        vol = vol_batch[i]
        thresh = ((vol.max() + vol.min()) / 2).item() if isolevel is None else isolevel
        # pyre-fixme[16]: `_marching_cubes` has no attribute `apply`.
        verts, faces, ids = _marching_cubes.apply(vol, thresh)
        if len(faces) > 0 and len(verts) > 0:
            # Convert from world coordinates ([0, D-1], [0, H-1], [0, W-1]) to
            # local coordinates in the range [-1, 1]
            if return_local_coords:
                verts = (
                    Translate(x=+1.0, y=+1.0, z=+1.0, device=vol.device)
                    .scale((vol.new_tensor([W, H, D])[None] - 1) * 0.5)
                    .inverse()
                ).transform_points(verts[None])[0]
            # deduplication for cuda
            if vol.is_cuda:
                unique_ids, inverse_idx = torch.unique(ids, return_inverse=True)
                verts_ = verts.new_zeros(unique_ids.shape[0], 3)
                verts_[inverse_idx] = verts
                verts = verts_
                faces = inverse_idx[faces]
            batched_verts.append(verts)
            batched_faces.append(faces)
        else:
            batched_verts.append([])
            batched_faces.append([])
    return batched_verts, batched_faces
