__init__.py

#
# Copyright (C) 2023, Inria
# GRAPHDECO research group, https://team.inria.fr/graphdeco
# All rights reserved.
#
# This software is free for non-commercial, research and evaluation use
# under the terms of the LICENSE.md file.
#
# For inquiries contact  george.drettakis@inria.fr
#

import os
import random
import json
from utils.system_utils import searchForMaxIteration
from scene.dataset_readers import sceneLoadTypeCallbacks
from scene.gaussian_model import GaussianModel
from arguments import ModelParams
from utils.camera_utils import cameraList_from_camInfos, camera_to_JSON
from glob import glob


class Scene:
    gaussians: GaussianModel

    def __init__(
        self,
        args: ModelParams,
        gaussians: GaussianModel,
        load_iteration=None,
        shuffle=True,
        resolution_scales=[1.0],
        override_quantization=False
    ):
        """b
        :param path: Path to colmap scene main folder.
        """
        self.model_path = args.model_path
        self.loaded_iter = None
        self.gaussians = gaussians

        if load_iteration:
            if load_iteration == -1:
                self.loaded_iter = searchForMaxIteration(
                    os.path.join(self.model_path, "point_cloud")
                )
            else:
                self.loaded_iter = load_iteration
            print("Loading trained model at iteration {}".format(self.loaded_iter))

        self.train_cameras = {}
        self.test_cameras = {}

        if os.path.exists(os.path.join(args.source_path, "sparse")):
            scene_info = sceneLoadTypeCallbacks["Colmap"](
                args.source_path, args.images, args.eval
            )
        elif os.path.exists(os.path.join(args.source_path, "transforms_train.json")):
            print("Found transforms_train.json file, assuming Blender data set!")
            scene_info = sceneLoadTypeCallbacks["Blender"](
                args.source_path, args.white_background, args.eval
            )
        else:
            assert False, "Could not recognize scene type!"

        if not self.loaded_iter:
            with open(scene_info.ply_path, "rb") as src_file, open(
                os.path.join(self.model_path, "input.ply"), "wb"
            ) as dest_file:
                dest_file.write(src_file.read())
            json_cams = []
            camlist = []
            if scene_info.test_cameras:
                camlist.extend(scene_info.test_cameras)
            if scene_info.train_cameras:
                camlist.extend(scene_info.train_cameras)
            for id, cam in enumerate(camlist):
                json_cams.append(camera_to_JSON(id, cam))
            with open(os.path.join(self.model_path, "cameras.json"), "w") as file:
                json.dump(json_cams, file)

        if shuffle:
            random.shuffle(
                scene_info.train_cameras
            )  # Multi-res consistent random shuffling
            random.shuffle(
                scene_info.test_cameras
            )  # Multi-res consistent random shuffling

        self.cameras_extent = scene_info.nerf_normalization["radius"]

        for resolution_scale in resolution_scales:
            print("Loading Training Cameras")
            self.train_cameras[resolution_scale] = cameraList_from_camInfos(
                scene_info.train_cameras, resolution_scale, args
            )
            print("Loading Test Cameras")
            self.test_cameras[resolution_scale] = cameraList_from_camInfos(
                scene_info.test_cameras, resolution_scale, args
            )

        if self.loaded_iter:
            self.gaussians.load(
                glob(
                    os.path.join(
                        self.model_path,
                        "point_cloud",
                        "iteration_" + str(self.loaded_iter),
                        "point_cloud.*",
                    )
                )[0],
                override_quantization=override_quantization
            )
        else:
            raise Exception("no iteration to load was found")

    def save(self, iteration, format="ply"):
        point_cloud_path = os.path.join(
            self.model_path, "point_cloud/iteration_{}".format(iteration)
        )
        if format == "ply":
            self.gaussians.save_ply(os.path.join(point_cloud_path, "point_cloud.ply"))
        elif format == "npz":
            self.gaussians.save_npz(os.path.join(point_cloud_path, "point_cloud.npz"))
        else:
            raise Exception(f"format '{format}' not supported")

    def getTrainCameras(self, scale=1.0):
        return self.train_cameras[scale]

    def getTestCameras(self, scale=1.0):
        return self.test_cameras[scale]