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render.py
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render.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, get_combined_args, ModelHiddenParams
from gaussian_renderer import GaussianModel
from time import time
import threading
import concurrent.futures
def multithread_write(image_list, path):
executor = concurrent.futures.ThreadPoolExecutor(max_workers=None)
def write_image(image, count, path):
try:
torchvision.utils.save_image(image, os.path.join(path, '{0:05d}'.format(count) + ".png"))
return count, True
except:
return count, False
tasks = []
for index, image in enumerate(image_list):
tasks.append(executor.submit(write_image, image, index, path))
executor.shutdown()
for index, status in enumerate(tasks):
if status == False:
write_image(image_list[index], index, path)
to8b = lambda x : (255*np.clip(x.cpu().numpy(),0,1)).astype(np.uint8)
def render_set(model_path, name, iteration, views, gaussians, pipeline, background, cam_type):
render_path = os.path.join(model_path, name, "ours_{}".format(iteration), "renders")
gts_path = os.path.join(model_path, name, "ours_{}".format(iteration), "gt")
makedirs(render_path, exist_ok=True)
makedirs(gts_path, exist_ok=True)
render_images = []
gt_list = []
render_list = []
print("point nums:",gaussians._xyz.shape[0])
for idx, view in enumerate(tqdm(views, desc="Rendering progress")):
if idx == 0:time1 = time()
rendering = render(view, gaussians, pipeline, background,cam_type=cam_type)["render"]
render_images.append(to8b(rendering).transpose(1,2,0))
render_list.append(rendering)
if name in ["train", "test"]:
if cam_type != "PanopticSports":
gt = view.original_image[0:3, :, :]
else:
gt = view['image'].cuda()
gt_list.append(gt)
time2=time()
print("FPS:",(len(views)-1)/(time2-time1))
multithread_write(gt_list, gts_path)
multithread_write(render_list, render_path)
imageio.mimwrite(os.path.join(model_path, name, "ours_{}".format(iteration), 'video_rgb.mp4'), render_images, fps=30)
def render_sets(dataset : ModelParams, hyperparam, iteration : int, pipeline : PipelineParams, skip_train : bool, skip_test : bool, skip_video: bool):
with torch.no_grad():
gaussians = GaussianModel(dataset.sh_degree, hyperparam)
scene = Scene(dataset, gaussians, load_iteration=iteration, shuffle=False)
cam_type=scene.dataset_type
bg_color = [1,1,1] if dataset.white_background else [0, 0, 0]
background = torch.tensor(bg_color, dtype=torch.float32, device="cuda")
if not skip_train:
render_set(dataset.model_path, "train", scene.loaded_iter, scene.getTrainCameras(), gaussians, pipeline, background,cam_type)
if not skip_test:
render_set(dataset.model_path, "test", scene.loaded_iter, scene.getTestCameras(), gaussians, pipeline, background,cam_type)
if not skip_video:
render_set(dataset.model_path,"video",scene.loaded_iter,scene.getVideoCameras(),gaussians,pipeline,background,cam_type)
if __name__ == "__main__":
# Set up command line argument parser
parser = ArgumentParser(description="Testing script parameters")
model = ModelParams(parser, sentinel=True)
pipeline = PipelineParams(parser)
hyperparam = ModelHiddenParams(parser)
parser.add_argument("--iteration", default=-1, type=int)
parser.add_argument("--skip_train", action="store_true")
parser.add_argument("--skip_test", action="store_true")
parser.add_argument("--quiet", action="store_true")
parser.add_argument("--skip_video", action="store_true")
parser.add_argument("--configs", 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)
args = merge_hparams(args, config)
# Initialize system state (RNG)
safe_state(args.quiet)
render_sets(model.extract(args), hyperparam.extract(args), args.iteration, pipeline.extract(args), args.skip_train, args.skip_test, args.skip_video)