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render_for_eval.py
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render_for_eval.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 imageio
import numpy as np
import torch
from scene import Scene
import os
import cv2
from tqdm import tqdm
from os import makedirs
from gaussian_renderer import render
import torchvision
from utils.general_utils import safe_state
from argparse import ArgumentParser
from arguments import ModelParams, PipelineParams,OptimizationParams, get_combined_args, ModelHiddenParams
from gaussian_renderer import GaussianModel
from time import time
to8b = lambda x : (255*np.clip(x.cpu().numpy(),0,1)).astype(np.uint8)
def render_set( iteration, views, gaussians, pipeline, background,multiview_video,time_fix=-1,front=False,back=False,side=False,side2=False,id=None,savedir=None):
render_images = []
gt_list = []
render_list = []
print(len(views))
if multiview_video:
for idx in tqdm(range (100)):
view = views[idx]
if idx == 0:time1 = time()
if time_fix!=-1:
ww=torch.tensor([time_fix/16]).unsqueeze(0)
rendering = render(view['cur_cam'], gaussians, pipeline, background, time=ww, stage='fine')["render"]
render_images.append(to8b(rendering).transpose(1,2,0))
render_list.append(rendering)
save_path=savedir+'/'+id+'/'
os.makedirs(save_path,exist_ok=True)
save_path+=str(time_fix)+'.mp4'
else:
for idx in tqdm(range (16)):
view = views[idx]
if idx == 0:time1 = time()
ww = torch.tensor([idx / 16]).unsqueeze(0)
if front:
rendering = render(view['front_cam'], gaussians, pipeline, background, time=ww, stage='fine')["render"]
save_path=savedir+'/'+id+'/front/'
os.makedirs(save_path,exist_ok=True)
save_path+='front.mp4'
if back:
rendering = render(view['back_cam'], gaussians, pipeline, background, time=ww, stage='fine')["render"]
save_path=savedir+'/'+id+'/back/'
os.makedirs(save_path,exist_ok=True)
save_path+='back.mp4'
if side:
rendering = render(view['side_cam'], gaussians, pipeline, background, time=ww, stage='fine')["render"]
save_path=savedir+'/'+id+'/side/'
os.makedirs(save_path,exist_ok=True)
save_path+='side.mp4'
if side2:
rendering = render(view['side_cam2'], gaussians, pipeline, background, time=ww, stage='fine')["render"]
save_path=savedir+'/'+id+'/side2/'
os.makedirs(save_path,exist_ok=True)
save_path+='side.mp4'
render_images.append(to8b(rendering).transpose(1,2,0))
render_list.append(rendering)
time2=time()
print("FPS:",(len(views)-1)/(time2-time1))
print('Len', len(render_images))
imageio.mimwrite(save_path, render_images, fps=8, quality=8)
print(save_path)
def render_sets(dataset : ModelParams, hyperparam, opt,iteration : int, pipeline : PipelineParams,id=None ,savedir=None):
with torch.no_grad():
gaussians = GaussianModel(dataset.sh_degree, hyperparam)
scene = Scene(dataset, gaussians, load_iteration=iteration, shuffle=False)
bg_color = [1,1,1] if dataset.white_background else [0, 0, 0]
background = torch.tensor(bg_color, dtype=torch.float32, device="cuda")
for i in range(16):
render_set(scene.loaded_iter,scene.getVideoCameras(),gaussians,pipeline,background,
multiview_video=True,
front=False,back=False,side=False,
time_fix=i,
id=id,savedir=savedir)
render_set(scene.loaded_iter,scene.getVideoCameras(),gaussians,pipeline,background,
multiview_video=False,
front=True,back=False,side=False,
time_fix=-1,
id=id,savedir=savedir)
render_set(scene.loaded_iter,scene.getVideoCameras(),gaussians,pipeline,background,
multiview_video=False,
front=False,back=True,side=False,
time_fix=-1,
id=id
,savedir=savedir)
render_set(scene.loaded_iter,scene.getVideoCameras(),gaussians,pipeline,background,
multiview_video=False,
front=False,back=False,side=True,
time_fix=-1,
id=id,savedir=savedir)
render_set(scene.loaded_iter,scene.getVideoCameras(),gaussians,pipeline,background,
multiview_video=False,
front=False,back=False,side=False,side2=True,
time_fix=-1,
id=id,savedir=savedir)
if __name__ == "__main__":
# Set up command line argument parser
parser = ArgumentParser(description="Testing script parameters")
model = ModelParams(parser)
op = OptimizationParams(parser)
pipeline = PipelineParams(parser)
hyperparam = ModelHiddenParams(parser)
# model_path_list=['/data/users/yyy/4dgen_exp/output/2023-11-17/abl_nozero123_11:42:05','/data/users/yyy/4dgen_exp/output/2023-11-18/1wpoint_00:13:03',
# '/data/users/yyy/4dgen_exp/output/2023-11-16/11.16newbaseline_3_12:08:29','/data/users/yyy/4dgen_exp/output/2023-11-17/no_recon_23:39:40',
# '/data/users/yyy/4dgen_exp/output/2023-11-17/abl_nopts_05:37:18'
# ]
parser.add_argument("--iteration", default=-1, type=int)
parser.add_argument("--configs", default='arguments/i2v.py',type=str)
parser.add_argument("--id", default='wosds',type=str)
# parser.add_argument("--filename", default='name',type=str)
parser.add_argument("--savedir", default='./expdata',type=str)
args = get_combined_args(parser)
print("Rendering " , args.model_path)
if args.configs:
import mmcv
from utils.params_utils import merge_hparams
config = mmcv.Config.fromfile(args.configs)
options={'ModelParams.name':args.id}
#print('ModelParams.name:',config.ModelParams.name)
config.merge_from_dict(options)
# print('ModelParams.name:',config.ModelParams.name)
args = merge_hparams(args, config)
print('args:',args.name)
# Initialize system state (RNG)
safe_state(args.quiet)
#id_list=['wosds','1wpoint','wosmooth','worecon','wopts']
render_sets(model.extract(args), hyperparam.extract(args), op.extract(args),args.iteration, pipeline.extract(args), id=args.name,savedir=args.savedir)