#  NeuralWarp  All rights reseved to Thales LAS and ENPC.
#
#  This code is freely available for academic use only and Provided “as is” without any warranty.
#
#  Modification are allowed for academic research provided that the following conditions are met :
#    * Redistributions of source code or any format must retain the above copyright notice and this list of conditions.
#    * Neither the name of Thales LAS and ENPC nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

# adapted from https://github.com/jzhangbs/DTUeval-python

import numpy as np
import open3d as o3d
import sklearn.neighbors as skln
from tqdm import tqdm
from scipy.io import loadmat
import multiprocessing as mp
import trimesh

def sample_single_tri(input_):
    n1, n2, v1, v2, tri_vert = input_
    c = np.mgrid[:n1 + 1, :n2 + 1]
    c += 0.5
    c[0] /= max(n1, 1e-7)
    c[1] /= max(n2, 1e-7)
    c = np.transpose(c, (1, 2, 0))
    k = c[c.sum(axis=-1) < 1]  # m2
    q = v1 * k[:, :1] + v2 * k[:, 1:] + tri_vert
    return q

def write_vis_pcd(file, points, colors):
    pcd = o3d.geometry.PointCloud()
    pcd.points = o3d.utility.Vector3dVector(points)
    pcd.colors = o3d.utility.Vector3dVector(colors)
    o3d.io.write_point_cloud(file, pcd)

def eval(in_file, scene, dataset_dir, eval_dir, suffix=""):
    data_mesh = o3d.io.read_triangle_mesh(str(in_file))

    data_mesh.remove_unreferenced_vertices()

    mp.freeze_support()

    # default dtu values
    max_dist = 20
    patch = 60
    thresh = 0.2  # downsample density

    pbar = tqdm(total=9)
    pbar.set_description('read data mesh')

    vertices = np.asarray(data_mesh.vertices)
    triangles = np.asarray(data_mesh.triangles)
    tri_vert = vertices[triangles]

    pbar.update(1)
    pbar.set_description('sample pcd from mesh')
    v1 = tri_vert[:, 1] - tri_vert[:, 0]
    v2 = tri_vert[:, 2] - tri_vert[:, 0]
    l1 = np.linalg.norm(v1, axis=-1, keepdims=True)
    l2 = np.linalg.norm(v2, axis=-1, keepdims=True)
    area2 = np.linalg.norm(np.cross(v1, v2), axis=-1, keepdims=True)
    non_zero_area = (area2 > 0)[:, 0]
    l1, l2, area2, v1, v2, tri_vert = [
        arr[non_zero_area] for arr in [l1, l2, area2, v1, v2, tri_vert]
    ]
    thr = thresh * np.sqrt(l1 * l2 / area2)
    n1 = np.floor(l1 / thr)
    n2 = np.floor(l2 / thr)

    with mp.Pool() as mp_pool:
        new_pts = mp_pool.map(sample_single_tri,
                              ((n1[i, 0], n2[i, 0], v1[i:i + 1], v2[i:i + 1], tri_vert[i:i + 1, 0]) for i in
                               range(len(n1))), chunksize=1024)

    new_pts = np.concatenate(new_pts, axis=0)
    data_pcd = np.concatenate([vertices, new_pts], axis=0)

    pbar.update(1)
    pbar.set_description('random shuffle pcd index')
    shuffle_rng = np.random.default_rng()
    shuffle_rng.shuffle(data_pcd, axis=0)

    pbar.update(1)
    pbar.set_description('downsample pcd')
    nn_engine = skln.NearestNeighbors(n_neighbors=1, radius=thresh, algorithm='kd_tree', n_jobs=-1)
    nn_engine.fit(data_pcd)
    rnn_idxs = nn_engine.radius_neighbors(data_pcd, radius=thresh, return_distance=False)
    mask = np.ones(data_pcd.shape[0], dtype=np.bool_)
    for curr, idxs in enumerate(rnn_idxs):
        if mask[curr]:
            mask[idxs] = 0
            mask[curr] = 1
    data_down = data_pcd[mask]

    pbar.update(1)
    pbar.set_description('masking data pcd')
    obs_mask_file = loadmat(f'{dataset_dir}/ObsMask/ObsMask{scene}_10.mat')
    ObsMask, BB, Res = [obs_mask_file[attr] for attr in ['ObsMask', 'BB', 'Res']]
    BB = BB.astype(np.float32)

    inbound = ((data_down >= BB[:1] - patch) & (data_down < BB[1:] + patch * 2)).sum(axis=-1) == 3
    data_in = data_down[inbound]

    data_grid = np.around((data_in - BB[:1]) / Res).astype(np.int32)
    grid_inbound = ((data_grid >= 0) & (data_grid < np.expand_dims(ObsMask.shape, 0))).sum(axis=-1) == 3
    data_grid_in = data_grid[grid_inbound]
    in_obs = ObsMask[data_grid_in[:, 0], data_grid_in[:, 1], data_grid_in[:, 2]].astype(np.bool_)
    data_in_obs = data_in[grid_inbound][in_obs]

    pbar.update(1)
    pbar.set_description('read STL pcd')
    stl_pcd = o3d.io.read_point_cloud(f'{dataset_dir}/Points/stl/stl{scene:03}_total.ply')
    stl = np.asarray(stl_pcd.points)

    pbar.update(1)
    pbar.set_description('compute data2stl')
    nn_engine.fit(stl)
    dist_d2s, idx_d2s = nn_engine.kneighbors(data_in_obs, n_neighbors=1, return_distance=True)

    mean_d2s = dist_d2s[dist_d2s < max_dist].mean()

    pbar.update(1)
    pbar.set_description('compute stl2data')
    ground_plane = loadmat(f'{dataset_dir}/ObsMask/Plane{scene}.mat')['P']

    stl_hom = np.concatenate([stl, np.ones_like(stl[:, :1])], -1)
    above = (ground_plane.reshape((1, 4)) * stl_hom).sum(-1) > 0
    stl_above = stl[above]

    nn_engine.fit(data_in)
    dist_s2d, idx_s2d = nn_engine.kneighbors(stl_above, n_neighbors=1, return_distance=True)
    mean_s2d = dist_s2d[dist_s2d < max_dist].mean()

    pbar.update(1)
    pbar.set_description('visualize error')
    vis_dist = 1
    R = np.array([[1, 0, 0]], dtype=np.float64)
    G = np.array([[0, 1, 0]], dtype=np.float64)
    B = np.array([[0, 0, 1]], dtype=np.float64)
    W = np.array([[1, 1, 1]], dtype=np.float64)
    data_color = np.tile(B, (data_down.shape[0], 1))
    data_alpha = dist_d2s.clip(max=vis_dist) / vis_dist
    data_color[np.where(inbound)[0][grid_inbound][in_obs]] = R * data_alpha + W * (1 - data_alpha)
    data_color[np.where(inbound)[0][grid_inbound][in_obs][dist_d2s[:, 0] >= max_dist]] = G
    write_vis_pcd(f'{eval_dir}/vis_{scene:03}_d2s{suffix}.ply', data_down, data_color)
    stl_color = np.tile(B, (stl.shape[0], 1))
    stl_alpha = dist_s2d.clip(max=vis_dist) / vis_dist
    stl_color[np.where(above)[0]] = R * stl_alpha + W * (1 - stl_alpha)
    stl_color[np.where(above)[0][dist_s2d[:, 0] >= max_dist]] = G
    write_vis_pcd(f'{eval_dir}/vis_{scene:03}_s2d{suffix}.ply', stl, stl_color)

    pbar.update(1)
    pbar.close()
    over_all = (mean_d2s + mean_s2d) / 2

    print("**************************************************")
    print(f"mean_d2s: {mean_d2s}")
    print(f"mean_s2d: {mean_s2d}")
    print(f"over_all: {over_all}")
    
    with open(f'{eval_dir}/result{suffix}.txt', 'w') as f:
        f.write(f'{mean_d2s} {mean_s2d} {over_all}')
