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liuzhi
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@ -26,82 +26,77 @@ class Scene:
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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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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: 指定加载模型的迭代次数，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
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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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# 如果已有训练模型，则加载
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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:
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# 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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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:
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# 不是None且不是-1，则加载指定迭代次数的
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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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# 从COLMAP或Blender的输出结果中构建 场景信息（包括点云、训练用相机、测试用相机、场景归一化参数和点云文件路径）
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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!"
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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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# 如果没有加载模型，则将点云文件point3D.ply文件复制到input.ply文件
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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:
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# 测试相机添加到 camlist 中
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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:
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# 训练相机添加到 camlist 中
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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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# 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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# 随机打乱训练和测试相机列表
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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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# 根据resolution_scales加载不同分辨率的训练和测试相机（包含R、T、视场角）
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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，则直接读取对应（已经迭代出来的）场景
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self.gaussians.load_ply(os.path.join(self.model_path,
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"point_cloud",
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"iteration_" + str(self.loaded_iter),
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"point_cloud.ply"))
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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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# 不加载训练模型，则调用 GaussianModel.create_from_pcd 从稀疏点云 scene_info.point_cloud 中建立模型
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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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@ -96,8 +96,8 @@ def readColmapCameras(cam_extrinsics, cam_intrinsics, images_folder):
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if intr.model=="SIMPLE_PINHOLE" or intr.model=="SIMPLE_RADIAL":
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# 如果是简单针孔模型，只有一个焦距参数
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focal_length_x = intr.params[0]
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FovY = focal2fov(focal_length_x, height) # 计算垂直方向的视场角
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FovX = focal2fov(focal_length_x, width) # 计算水平方向的视场角
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FovY = focal2fov(focal_length_x, height) # 计算垂直方向的视场角: 2 * arctan(H / 2fy))
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FovX = focal2fov(focal_length_x, width) # 计算水平方向的视场角: 2 * arctan(W / 2fx)
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elif intr.model=="PINHOLE":
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# 如果是针孔模型，有两个焦距参数
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focal_length_x = intr.params[0]
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@ -115,7 +115,7 @@ def readColmapCameras(cam_extrinsics, cam_intrinsics, images_folder):
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continue
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image = Image.open(image_path) # PIL.Image读取的为RGB格式，OpenCV读取的为BGR格式
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# 创建相机信息类CameraInfo对象 (包含旋转矩阵、平移向量、视场角、图像数据、图片路径、图片名、宽度、高度)，并添加到列表cam_infos中
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# 创建相机信息类CameraInfo对象 (包含R、T、FovY、FovX、图像数据image、image_path、image_name、width、height)，并添加到列表cam_infos中
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cam_info = CameraInfo(uid=uid, R=R, T=T, FovY=FovY, FovX=FovX, image=image,
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image_path=image_path, image_name=image_name, width=width, height=height)
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cam_infos.append(cam_info)
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@ -155,7 +155,7 @@ def storePly(path, xyz, rgb):
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def readColmapSceneInfo(path, images, eval, llffhold=8):
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'''
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加载COLMAP的结果中的二进制相机外参文件imags.bin 和 内参文件cameras.bin
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path: GaussianModel中的源文件路径
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path: source_path
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images: 'images'
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eval: 是否为eval模式
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llffhold: 默认为8
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@ -181,20 +181,20 @@ def readColmapSceneInfo(path, images, eval, llffhold=8):
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# 根据图片名称排序，以保证顺序一致性
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cam_infos = sorted(cam_infos_unsorted.copy(), key = lambda x : (x.image_path.split('/')[-2], int(x.image_name)))
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# 根据是否为评估模式（eval），将相机分为训练集和测试集
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# 如果为评估模式，每llffhold张图片取一张作为测试集
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# 根据是否eval，将相机分为训练集和测试集
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if eval:
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# 若要评测，则每llffhold张图片取一张作为测试集
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train_cam_infos = [c for idx, c in enumerate(cam_infos) if idx % llffhold != 0]
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test_cam_infos = [c for idx, c in enumerate(cam_infos) if idx % llffhold == 0]
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else:
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# 如果不是评估模式，所有相机均为训练相机，测试相机列表为空
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# 不评测，则所有相机均为训练相机，测试相机列表为空
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train_cam_infos = cam_infos
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test_cam_infos = []
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# 计算场景归一化参数，这是为了处理不同尺寸和位置的场景，使模型训练更稳定
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nerf_normalization = getNerfppNorm(train_cam_infos)
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# 尝试读取COLMAP生成的稀疏点云数据，优先从PLY文件读取，如果不存在，则尝试从BIN或TXT文件转换并保存为PLY格式
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# 读取COLMAP生成的稀疏点云数据，优先从PLY文件读取，如果不存在，则尝试从BIN或TXT文件转换并保存为PLY格式
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ply_path = os.path.join(path, "sparse/0/points3D.ply")
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bin_path = os.path.join(path, "sparse/0/points3D.bin")
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txt_path = os.path.join(path, "sparse/0/points3D.txt")
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@ -206,10 +206,9 @@ def readColmapSceneInfo(path, images, eval, llffhold=8):
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xyz, rgb, _ = read_points3D_text(txt_path)
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storePly(ply_path, xyz, rgb) # 转换成ply文件
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# 读取PLY格式的稀疏点云
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try:
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pcd = fetchPly(ply_path) # points3D.ply读取COLMAP产生的稀疏点云
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pcd = fetchPly(ply_path)
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except:
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pcd = None
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@ -17,12 +17,17 @@ from utils.graphics_utils import fov2focal
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WARNED = False
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def loadCam(args, id, cam_info, resolution_scale):
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"""
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调整当前相机对应图像的分辨率，并根据当前相机的info创建相机（包含R、T、FovY、FovX、图像数据image、image_path、image_name、width、height）
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"""
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orig_w, orig_h = cam_info.image.size
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# 1. 计算下采样后的图像尺寸
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if args.resolution in [1, 2, 4, 8]:
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# 计算下采样后的图像尺寸 [1, 1/2, 1/4, 1/8]
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resolution = round(orig_w/(resolution_scale * args.resolution)), round(orig_h/(resolution_scale * args.resolution))
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else: # should be a type that converts to float
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else:
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if args.resolution == -1:
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# 如果用户没有指定分辨率，即默认为-1，则自动判断图片的宽度是>1.6K：如果大于，则自动进行下采样到1.6K时的采样倍率；如果小于，则采样倍率=1，即使用原图尺寸
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if orig_w > 1600:
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global WARNED
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if not WARNED:
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@ -33,19 +38,22 @@ def loadCam(args, id, cam_info, resolution_scale):
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else:
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global_down = 1
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else:
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# 如果用户指定了分辨率，则根据用户指定的分辨率计算采样倍率
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global_down = orig_w / args.resolution
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scale = float(global_down) * float(resolution_scale)
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resolution = (int(orig_w / scale), int(orig_h / scale))
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scale = float(global_down) * float(resolution_scale) # 缩放倍率
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resolution = (int(orig_w / scale), int(orig_h / scale)) # 下采样后的图像尺寸
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resized_image_rgb = PILtoTorch(cam_info.image, resolution) # 调整图片比例，归一化，并转换通道为torch上的 (C, H, W)
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# 2. 调整图片分辨率，归一化，并转换通道为torch上的 (C, H, W)
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resized_image_rgb = PILtoTorch(cam_info.image, resolution)
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gt_image = resized_image_rgb[:3, ...]
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loaded_mask = None
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if resized_image_rgb.shape[1] == 4:
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# 如果图片有alpha通道，则提取出来
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loaded_mask = resized_image_rgb[3:4, ...]
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# 3. 创建相机
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return Camera(colmap_id=cam_info.uid, R=cam_info.R, T=cam_info.T,
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FoVx=cam_info.FovX, FoVy=cam_info.FovY,
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image=gt_image, gt_alpha_mask=loaded_mask,
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@ -53,14 +61,14 @@ def loadCam(args, id, cam_info, resolution_scale):
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def cameraList_from_camInfos(cam_infos, resolution_scale, args):
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'''
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cam_infos: 训练或测试相机对象列表
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resolution_scale: 不同分辨率列表
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args: 高斯模型参数
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cam_infos: train或test相机info列表
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resolution_scale: 分辨率倍率
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args: 更新后的ModelParams()中的参数
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'''
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camera_list = []
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# 遍历每个camera_info（包含R、T、FovY、FovX、图像数据image、image_path、image_name、width、height）
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for id, c in enumerate(cam_infos):
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camera_list.append(loadCam(args, id, c, resolution_scale))
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camera_list.append( loadCam(args, id, c, resolution_scale) )
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return camera_list
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