mirror of
https://github.com/graphdeco-inria/gaussian-splatting
synced 2025-05-04 12:12:24 +00:00
119 lines
6.2 KiB
Python
119 lines
6.2 KiB
Python
#
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# Copyright (C) 2023, Inria
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# GRAPHDECO research group, https://team.inria.fr/graphdeco
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# All rights reserved.
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#
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# This software is free for non-commercial, research and evaluation use
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# under the terms of the LICENSE.md file.
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#
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# For inquiries contact george.drettakis@inria.fr
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#
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import os
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import random
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import json
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from utils.system_utils import searchForMaxIteration
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from scene.dataset_readers import sceneLoadTypeCallbacks
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from scene.gaussian_model import GaussianModel
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from arguments import ModelParams
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from utils.camera_utils import cameraList_from_camInfos, camera_to_JSON
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class Scene:
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"""
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Scene 类用于管理场景的3D模型，包括相机参数、点云数据和高斯模型的初始化和加载
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"""
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gaussians : GaussianModel
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def __init__(self, args : ModelParams, gaussians : GaussianModel, load_iteration=None, shuffle=True, resolution_scales=[1.0]):
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"""
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初始化3D场景对象
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args: 存储着与 GaussianMoedl 相关参数 的args，即包含scene/__init__.py/ModelParams()中的参数
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gaussians: 3D高斯模型对象，用于场景点的3D表示
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load_iteration: 训练时为 None，render时为 指定的iteration：如果是-1，则在输出文件夹下的point_cloud/文件夹下搜索迭代次数最大的模型；如果不是None且不是-1，则加载指定迭代次数的
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shuffle: 是否在训练前打乱相机列表（render.py中会设为False）
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resolution_scales: 分辨率比例列表，用于处理不同分辨率的相机
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"""
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self.model_path = args.model_path # 模型文件保存路径
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self.loaded_iter = None # 已加载的迭代次数
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self.gaussians = gaussians # 高斯模型对象
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# 1. 如果指定了加载模型的迭代次数，则赋给Scene.loaded_iter
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if load_iteration:
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if load_iteration == -1: # 是-1，则在输出文件夹下的point_cloud/文件夹下搜索迭代次数最大的模型，记录最大迭代次数
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self.loaded_iter = searchForMaxIteration(os.path.join(self.model_path, "point_cloud"))
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else: # 不是None且不是-1，则加载指定迭代次数的
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self.loaded_iter = load_iteration
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print("Loading trained model at iteration {}".format(self.loaded_iter))
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self.train_cameras = {} # 用于训练的相机
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self.test_cameras = {} # 用于测试的相机
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# 2. 从COLMAP或Blender的输出结果中构建 scene_info（包含 点云、train相机info、test相机info、场景归一化参数、点云文件路径）
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if os.path.exists(os.path.join(args.source_path, "sparse")):
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scene_info = sceneLoadTypeCallbacks["Colmap"](args.source_path, args.images, args.eval)
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elif os.path.exists(os.path.join(args.source_path, "transforms_train.json")):
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print("Found transforms_train.json file, assuming Blender data set!")
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scene_info = sceneLoadTypeCallbacks["Blender"](args.source_path, args.white_background, args.eval)
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else:
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assert False, "Could not recognize scene type! {}".format(os.path.join(args.source_path, "sparse"))
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if not self.loaded_iter:
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# 未加载已训练模型，则：
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# (1) 将点云文件point3D.ply文件复制到input.ply文件
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with open(scene_info.ply_path, 'rb') as src_file, open(os.path.join(self.model_path, "input.ply") , 'wb') as dest_file:
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dest_file.write(src_file.read())
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# (2) 将相机参数写入cameras.json文件
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json_cams = []
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camlist = []
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if scene_info.test_cameras: # 添加test相机到 camlist 中
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camlist.extend(scene_info.test_cameras)
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if scene_info.train_cameras: # 添加train相机到 camlist 中
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camlist.extend(scene_info.train_cameras)
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# 遍历 camlist 中的所有训练和测试相机,使用 camera_to_JSON 函数将每个相机转换为 JSON 格式,并添加到 json_cams 列表中，并将 json_cams 写入 cameras.json 文件中
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for id, cam in enumerate(camlist):
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json_cams.append(camera_to_JSON(id, cam))
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with open(os.path.join(self.model_path, "cameras.json"), 'w') as file:
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json.dump(json_cams, file)
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if shuffle:
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# 3. 随机打乱train、test相机info
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random.shuffle(scene_info.train_cameras) # Multi-res consistent random shuffling
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random.shuffle(scene_info.test_cameras) # Multi-res consistent random shuffling
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self.cameras_extent = scene_info.nerf_normalization["radius"]
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# 4. 调整图片分辨率，并根据train、test相机info(包含R、T、FovY、FovX、图像数据image、image_path、image_name、width、height)创建相机
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for resolution_scale in resolution_scales:
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print("Loading Training Cameras")
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self.train_cameras[resolution_scale] = cameraList_from_camInfos(scene_info.train_cameras, resolution_scale, args)
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print("Loading Test Cameras")
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self.test_cameras[resolution_scale] = cameraList_from_camInfos(scene_info.test_cameras, resolution_scale, args)
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# 5. 加载高斯体
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if self.loaded_iter:
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# 如果加载已训练模型，则直接读取其已训练的高斯体
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self.gaussians.load_ply(os.path.join(self.model_path, "point_cloud", "iteration_" + str(self.loaded_iter), "point_cloud.ply"))
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else:
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# 不加载，则从稀疏点云中初始建立高斯体
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self.gaussians.create_from_pcd(scene_info.point_cloud, self.cameras_extent)
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def save(self, iteration):
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"""
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保存当前迭代下的3D高斯模型点云。
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iteration: 当前的迭代次数
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"""
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point_cloud_path = os.path.join(self.model_path, "point_cloud/iteration_{}".format(iteration))
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self.gaussians.save_ply(os.path.join(point_cloud_path, "point_cloud.ply"))
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def getTrainCameras(self, scale=1.0):
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"""
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获取指定分辨率比例的训练相机列表
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scale: 分辨率比例
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return: 指定分辨率比例的训练相机列表
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"""
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return self.train_cameras[scale]
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def getTestCameras(self, scale=1.0):
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return self.test_cameras[scale] |