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helper3dg.py
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helper3dg.py
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#
# 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 [email protected]
#
import os
import torch
from random import randint
import random
import sys
import uuid
import time
import json
import numpy as np
import cv2
from tqdm import tqdm
import shutil
sys.path.append("./thirdparty/gaussian_splatting")
from utils.general_utils import safe_state
from argparse import ArgumentParser, Namespace
from arguments import ModelParams, PipelineParams, OptimizationParams, get_combined_args
def getparser():
parser = ArgumentParser(description="Training script parameters")
lp = ModelParams(parser)
op = OptimizationParams(parser) #we put more parameters in optimization params, just for convenience.
pp = PipelineParams(parser)
parser.add_argument('--ip', type=str, default="127.0.0.1")
parser.add_argument('--port', type=int, default=6029)
parser.add_argument('--debug_from', type=int, default=-2)
parser.add_argument('--detect_anomaly', action='store_true', default=False)
parser.add_argument("--save_iterations", nargs="+", type=int, default=[100, 5_000,7000,9_000, 12000,13_000,14_000, 15_000,17_000,18_000,20_000,23_000,25_000, 28_000,30_000])
parser.add_argument("--testing_iterations", nargs="+", type=int, default=[3000,4000,5_000,6_000,7_000,8_000,9_000,10_000,11_000,12000,12_500,13_000, 15_000,17_000,20_000,23_000,25_000, 28_000,30_000,30_500,31_000])
parser.add_argument("--test_iteration", default=-1, type=int)
parser.add_argument("--reload_iteration", default=None, type=str)
parser.add_argument("--quiet", action="store_true")
parser.add_argument("--checkpoint_iterations", nargs="+", type=int, default=[])
parser.add_argument("--start_checkpoint", type=str, default = None)
parser.add_argument("--densify", type=int, default=1, help="densify =1, we control points on N3d dataset")
parser.add_argument("--duration", type=int, default=5, help="5 debug , 50 used")
parser.add_argument("--basicfunction", type=str, default = "gaussian")
parser.add_argument("--rgbfunction", type=str, default = "rgbv1")
parser.add_argument("--rdpip", type=str, default = "v2")
parser.add_argument("--configpath", type=str, default = "None")
parser.add_argument("--exp_name",type=str,default=None)
parser.add_argument("--dataset",type=str,default=None)#表示是哪个dataset
parser.add_argument("--checkpoint",type=str,default=None)
parser.add_argument("--no_wandb", action="store_true")
parser.add_argument("--no_report",action="store_true")
parser.add_argument("--start_iteration",type=int,default=0)
args = parser.parse_args(sys.argv[1:])
args.save_iterations.append(args.iterations)
#config中的参数值会覆盖命令行提供的参数值!
# incase we provide config file not directly pass to the file
if os.path.exists(args.configpath) and args.configpath != "None":
print("overload config from " + args.configpath)
config = json.load(open(args.configpath))
for k in config.keys():
try:
value = getattr(args, k)
newvalue = config[k]
setattr(args, k, newvalue)
except:
print("failed set config: " + k)
print("finish load config from " + args.configpath)
else:
raise ValueError("config file not exist or not provided")
if args.model_path == "":
# print(args.dataset,args.exp_name)
args.model_path = os.path.join("log",os.path.join(args.dataset, args.exp_name ))
print("Optimizing " + args.model_path)
# Initialize system state (RNG)
safe_state(args.quiet)
torch.autograd.set_detect_anomaly(args.detect_anomaly)
if not os.path.exists(args.model_path):
os.makedirs(args.model_path)
return args, lp.extract(args), op.extract(args), pp.extract(args)
def getrenderparts(render_pkg):
return render_pkg["render"], render_pkg["viewspace_points"], render_pkg["visibility_filter"], render_pkg["radii"]
def gettestparse():
parser = ArgumentParser(description="Testing script parameters")
model = ModelParams(parser, sentinel=True)#并不保持Model的默认值，将默认值设为None
pipeline = PipelineParams(parser)
parser.add_argument("--skip_train", action="store_true")
parser.add_argument("--skip_test", action="store_true")
parser.add_argument("--skip_val", action="store_true")
# parser.add_argument("--configpath", type=str, default = "1")
parser.add_argument("--iteration",type=str,default="best") #调用哪个模型去测试
parser.add_argument("--require_segment",default=False,action="store_true") #调用哪个模型去测试
parser.add_argument("--quiet", action="store_true")
#读取cfg里面的值，具体做法是用目前parser非None的值（在命令行中提供的值）去替换cfg里的值。但不会对cfg新增值
args = get_combined_args(parser)
print("Rendering " + args.model_path)
# configpath
safe_state(args.quiet)
# multiview = True if args.valloader.endswith("mv") else False
#将parser的值覆盖成config的值，但对于parser中没有的值不会新加入
# if os.path.exists(args.configpath) and args.configpath != "None":
# print("overload config from " + args.configpath)
# config = json.load(open(args.configpath))
# for k in config.keys():
# try:
# value = getattr(args, k)
# newvalue = config[k]
# setattr(args, k, newvalue)
# except:
# print("failed set config: " + k)
# print("finish load config from " + args.configpath)
# print("args: " + str(args))
# # print(args,model.extract(args), pipeline.extract(args), multiview)
return args, model.extract(args), pipeline.extract(args)
def getcolmapsinglen3d(folder, offset):
folder = os.path.join(folder, "colmap_" + str(offset))
assert os.path.exists(folder)
dbfile = os.path.join(folder, "input.db")
inputimagefolder = os.path.join(folder, "input")
distortedmodel = os.path.join(folder, "distorted/sparse")
step2model = os.path.join(folder, "tmp")
if not os.path.exists(step2model):
os.makedirs(step2model)
manualinputfolder = os.path.join(folder, "manual")
if not os.path.exists(distortedmodel):
os.makedirs(distortedmodel)
featureextract = "colmap feature_extractor --database_path " + dbfile+ " --image_path " + inputimagefolder
exit_code = os.system(featureextract)
if exit_code != 0:
exit(exit_code)
featurematcher = "colmap exhaustive_matcher --database_path " + dbfile
exit_code = os.system(featurematcher)
if exit_code != 0:
exit(exit_code)
# threshold is from https://github.com/google-research/multinerf/blob/5b4d4f64608ec8077222c52fdf814d40acc10bc1/scripts/local_colmap_and_resize.sh#L62
triandmap = "colmap point_triangulator --database_path "+ dbfile + " --image_path "+ inputimagefolder + " --output_path " + distortedmodel \
+ " --input_path " + manualinputfolder + " --Mapper.ba_global_function_tolerance=0.000001"
exit_code = os.system(triandmap)
if exit_code != 0:
exit(exit_code)
print(triandmap)
img_undist_cmd = "colmap" + " image_undistorter --image_path " + inputimagefolder + " --input_path " + distortedmodel + " --output_path " + folder \
+ " --output_type COLMAP"
exit_code = os.system(img_undist_cmd)
if exit_code != 0:
exit(exit_code)
print(img_undist_cmd)
removeinput = "rm -r " + inputimagefolder
exit_code = os.system(removeinput)
if exit_code != 0:
exit(exit_code)
files = os.listdir(folder + "/sparse")
os.makedirs(folder + "/sparse/0", exist_ok=True)
for file in files:
if file == '0':
continue
source_file = os.path.join(folder, "sparse", file)
destination_file = os.path.join(folder, "sparse", "0", file)
shutil.move(source_file, destination_file)
def getcolmapsingleimundistort(folder, offset):
folder = os.path.join(folder, "colmap_" + str(offset))
assert os.path.exists(folder)
dbfile = os.path.join(folder, "input.db")
inputimagefolder = os.path.join(folder, "input")
distortedmodel = os.path.join(folder, "distorted/sparse")
step2model = os.path.join(folder, "tmp")
if not os.path.exists(step2model):
os.makedirs(step2model)
manualinputfolder = os.path.join(folder, "manual")
if not os.path.exists(distortedmodel):
os.makedirs(distortedmodel)
featureextract = "colmap feature_extractor SiftExtraction.max_image_size 6000 --database_path " + dbfile+ " --image_path " + inputimagefolder
exit_code = os.system(featureextract)
if exit_code != 0:
exit(exit_code)
featurematcher = "colmap exhaustive_matcher --database_path " + dbfile
exit_code = os.system(featurematcher)
if exit_code != 0:
exit(exit_code)
triandmap = "colmap point_triangulator --database_path "+ dbfile + " --image_path "+ inputimagefolder + " --output_path " + distortedmodel \
+ " --input_path " + manualinputfolder + " --Mapper.ba_global_function_tolerance=0.000001"
exit_code = os.system(triandmap)
if exit_code != 0:
exit(exit_code)
print(triandmap)
img_undist_cmd = "colmap" + " image_undistorter --image_path " + inputimagefolder + " --input_path " + distortedmodel + " --output_path " + folder \
+ " --output_type COLMAP " # --blank_pixels 1
exit_code = os.system(img_undist_cmd)
if exit_code != 0:
exit(exit_code)
print(img_undist_cmd)
removeinput = "rm -r " + inputimagefolder
exit_code = os.system(removeinput)
if exit_code != 0:
exit(exit_code)
files = os.listdir(folder + "/sparse")
os.makedirs(folder + "/sparse/0", exist_ok=True)
#Copy each file from the source directory to the destination directory
for file in files:
if file == '0':
continue
source_file = os.path.join(folder, "sparse", file)
destination_file = os.path.join(folder, "sparse", "0", file)
shutil.move(source_file, destination_file)
def getcolmapsingleimdistort(folder, offset):
folder = os.path.join(folder, "colmap_" + str(offset))
assert os.path.exists(folder)
dbfile = os.path.join(folder, "input.db")
inputimagefolder = os.path.join(folder, "input")
distortedmodel = os.path.join(folder, "distorted/sparse")
step2model = os.path.join(folder, "tmp")
if not os.path.exists(step2model):
os.makedirs(step2model)
manualinputfolder = os.path.join(folder, "manual")
if not os.path.exists(distortedmodel):
os.makedirs(distortedmodel)
featureextract = "colmap feature_extractor SiftExtraction.max_image_size 6000 --database_path " + dbfile+ " --image_path " + inputimagefolder
exit_code = os.system(featureextract)
if exit_code != 0:
exit(exit_code)
featurematcher = "colmap exhaustive_matcher --database_path " + dbfile
exit_code = os.system(featurematcher)
if exit_code != 0:
exit(exit_code)
triandmap = "colmap point_triangulator --database_path "+ dbfile + " --image_path "+ inputimagefolder + " --output_path " + distortedmodel \
+ " --input_path " + manualinputfolder + " --Mapper.ba_global_function_tolerance=0.000001"
exit_code = os.system(triandmap)
if exit_code != 0:
exit(exit_code)
print(triandmap)
img_undist_cmd = "colmap" + " image_undistorter --image_path " + inputimagefolder + " --input_path " + distortedmodel + " --output_path " + folder \
+ " --output_type COLMAP " # --blank_pixels 1
exit_code = os.system(img_undist_cmd)
if exit_code != 0:
exit(exit_code)
print(img_undist_cmd)
removeinput = "rm -r " + inputimagefolder
exit_code = os.system(removeinput)
if exit_code != 0:
exit(exit_code)
files = os.listdir(folder + "/sparse")
os.makedirs(folder + "/sparse/0", exist_ok=True)
for file in files:
if file == '0':
continue
source_file = os.path.join(folder, "sparse", file)
destination_file = os.path.join(folder, "sparse", "0", file)
shutil.move(source_file, destination_file)
def getcolmapsingletechni(folder, offset):
folder = os.path.join(folder, "colmap_" + str(offset))
assert os.path.exists(folder)
dbfile = os.path.join(folder, "input.db")
inputimagefolder = os.path.join(folder, "input")
distortedmodel = os.path.join(folder, "distorted/sparse")
step2model = os.path.join(folder, "tmp")
if not os.path.exists(step2model):
os.makedirs(step2model)
manualinputfolder = os.path.join(folder, "manual")
if not os.path.exists(distortedmodel):
os.makedirs(distortedmodel)
featureextract = "colmap feature_extractor --database_path " + dbfile+ " --image_path " + inputimagefolder
exit_code = os.system(featureextract)
if exit_code != 0:
exit(exit_code)
featurematcher = "colmap exhaustive_matcher --database_path " + dbfile
exit_code = os.system(featurematcher)
if exit_code != 0:
exit(exit_code)
triandmap = "colmap point_triangulator --database_path "+ dbfile + " --image_path "+ inputimagefolder + " --output_path " + distortedmodel \
+ " --input_path " + manualinputfolder + " --Mapper.ba_global_function_tolerance=0.000001"
exit_code = os.system(triandmap)
if exit_code != 0:
exit(exit_code)
print(triandmap)
img_undist_cmd = "colmap" + " image_undistorter --image_path " + inputimagefolder + " --input_path " + distortedmodel + " --output_path " + folder \
+ " --output_type COLMAP " #
exit_code = os.system(img_undist_cmd)
if exit_code != 0:
exit(exit_code)
print(img_undist_cmd)
files = os.listdir(folder + "/sparse")
os.makedirs(folder + "/sparse/0", exist_ok=True)
for file in files:
if file == '0':
continue
source_file = os.path.join(folder, "sparse", file)
destination_file = os.path.join(folder, "sparse", "0", file)
shutil.move(source_file, destination_file)
return