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"""
Modified from https://github.com/kujason/ip_basic.git
Uses kornia tensor operations as pytorch alternatives for cv2 operations.

@inproceedings{ku2018defense,
  title={In Defense of Classical Image Processing: Fast Depth Completion on the CPU},
  author={Ku, Jason and Harakeh, Ali and Waslander, Steven L},
  booktitle={2018 15th Conference on Computer and Robot Vision (CRV)},
  pages={16--22},
  year={2018},

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def ray_sphere_intersection_rough(
    rays_o: torch.Tensor, rays_d: torch.Tensor, *,
    r=1.0, keepdim=True, t_min_cons: float=0.0, t_max_cons: float=None) -> Tuple[torch.Tensor, torch.Tensor]:
    """
    NOTE: Modified from https://github.com/Totoro97/NeuS
    rays_o: camera center's coordinate
    rays_d: camera rays' directions. already normalized.
    """
    dir_scale = rays_d.norm(dim=-1, keepdim=keepdim).clamp_min_(1e-10)
    # NOTE: (minus) the length of the line projected from [the line from camera to sphere center] to [the line of camera rays]

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import numpy as np
import scipy
from scipy.spatial.transform import Rotation as R
np.set_printoptions(precision=4,suppress=True)

rot = R.random().as_matrix()
trans = np.random.uniform(size=(3,))
c2w = np.eye(4)
c2w[:3,:3] = rot
c2w[:3,3] = trans

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/*
 * Copyright (c) 2020-2022, NVIDIA CORPORATION.  All rights reserved.
 *
 * Redistribution and use in source and binary forms, with or without modification, are permitted
 * provided that the following conditions are met:
 *     * Redistributions of source code must retain the above copyright notice, this list of
 *       conditions and the following disclaimer.
 *     * Redistributions in binary form must reproduce the above copyright notice, this list of
 *       conditions and the following disclaimer in the documentation and/or other materials
 *       provided with the distribution.

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import torch
import torch.nn as nn
from torch import autograd
from torch.optim import Adam
import torch.nn.functional as F

import tinycudann as tcnn

class SDF(nn.Module):
    def __init__(self, hash=True, n_levels=12, log2_hashmap_size=15, base_resolution=16, smoothstep=False) -> None:

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'''
camera extrinsics visualization tools
modified from https://github.com/opencv/opencv/blob/master/samples/python/camera_calibration_show_extrinsics.py
'''

from utils.print_fn import log

import numpy as np
import cv2 as cv

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if __name__ == "__main__":
    from scipy.spatial.transform import Rotation as R
    from scipy.interpolate import interp1d

    # test = 'easy'
    test = 'hard'

    _intr3x3 = torch.tensor([[500,0,1000],[0,500,500],[0,0,1]])
    cam_intrinsic = torch.eye(4, dtype=torch.double)
    cam_intrinsic[:3, :3] = _intr3x3

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# NOTE: 给定一个点，找到其在一个样条函数上的最近点（或者说，投影点）

import numpy as np, matplotlib.pyplot as pp, mpl_toolkits.mplot3d as mp
import scipy.interpolate as si, scipy.optimize as so, scipy.spatial.distance as ssd

data = (1,2,3,4,5,6,7,8),(1,2.5,4,5.5,6.5,8.5,10,12.5),(1,2.5,4.5,6.5,8,8.5,10,12.5)
p = 6.5,9.5,9

# Fit a spline to the data - s is the amount of smoothing, tck is the parameters of the resulting spline
(tck, uu) = si.splprep(data, s=0)