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### | |
# Copyright (C) 2023, Computer Vision Lab, Seoul National University, https://cv.snu.ac.kr | |
# For permission requests, please contact [email protected], [email protected], [email protected], [email protected]. | |
# All rights reserved. | |
### | |
import os | |
import random | |
from arguments import GSParams | |
from utils.system import searchForMaxIteration | |
from scene.dataset_readers import readDataInfo | |
from scene.gaussian_model import GaussianModel | |
class Scene: | |
gaussians: GaussianModel | |
def __init__(self, traindata, gaussians: GaussianModel, opt: GSParams): | |
self.traindata = traindata | |
self.gaussians = gaussians | |
info = readDataInfo(traindata, opt.white_background) | |
random.shuffle(info.train_cameras) # Multi-res consistent random shuffling | |
self.cameras_extent = info.nerf_normalization["radius"] | |
print("Loading Training Cameras") | |
self.train_cameras = info.train_cameras | |
print("Loading Preset Cameras") | |
self.preset_cameras = {} | |
for campath in info.preset_cameras.keys(): | |
self.preset_cameras[campath] = info.preset_cameras[campath] | |
self.gaussians.create_from_pcd(info.point_cloud, self.cameras_extent) | |
self.gaussians.training_setup(opt) | |
def getTrainCameras(self): | |
return self.train_cameras | |
def getPresetCameras(self, preset): | |
assert preset in self.preset_cameras | |
return self.preset_cameras[preset] |