###
# Copyright (C) 2023, Computer Vision Lab, Seoul National University, https://cv.snu.ac.kr
# For permission requests, please contact robot0321@snu.ac.kr, esw0116@snu.ac.kr, namhj28@gmail.com, jarin.lee@gmail.com.
# All rights reserved.
###
import numpy as np


class GSParams: 
    def __init__(self):
        self.sh_degree = 3
        self.images = "images"
        self.resolution = -1
        self.white_background = False
        self.data_device = "cuda"
        self.eval = False
        self.use_depth = False

        self.iterations = 2990#3_000
        self.position_lr_init = 0.00016
        self.position_lr_final = 0.0000016
        self.position_lr_delay_mult = 0.01
        self.position_lr_max_steps = 2990#3_000
        self.feature_lr = 0.0025
        self.opacity_lr = 0.05
        self.scaling_lr = 0.005
        self.rotation_lr = 0.001
        self.percent_dense = 0.01
        self.lambda_dssim = 0.2
        self.densification_interval = 100
        self.opacity_reset_interval = 3001 # To prevent from saving right after reset opacity
        self.densify_from_iter = 500
        self.densify_until_iter = 15_000
        self.densify_grad_threshold = 0.0002

        self.convert_SHs_python = False
        self.compute_cov3D_python = False
        self.debug = False


class CameraParams:
    def __init__(self, H: int = 512, W: int = 512):
        self.H = H
        self.W = W
        self.focal = (5.8269e+02, 5.8269e+02)
        self.fov = (2*np.arctan(self.W / (2*self.focal[0])), 2*np.arctan(self.H / (2*self.focal[1])))
        self.K = np.array([
            [self.focal[0], 0., self.W/2],
            [0., self.focal[1], self.H/2],
            [0.,            0.,       1.],
        ]).astype(np.float32)