124 lines
5.2 KiB
Python
124 lines
5.2 KiB
Python
#
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# Copyright (C) 2023, Inria
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# GRAPHDECO research group, https://team.inria.fr/graphdeco
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# All rights reserved.
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#
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# This software is free for non-commercial, research and evaluation use
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# under the terms of the LICENSE.md file.
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#
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# For inquiries contact george.drettakis@inria.fr
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#
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import imageio
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import numpy as np
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import torch
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from scene import Scene
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import os
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import cv2
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from tqdm import tqdm
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from os import makedirs
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from gaussian_renderer import render
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import torchvision
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from utils.general_utils import safe_state
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from argparse import ArgumentParser
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from arguments import ModelParams, PipelineParams, get_combined_args, ModelHiddenParams
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from gaussian_renderer import GaussianModel
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from time import time
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# import torch.multiprocessing as mp
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import threading
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import concurrent.futures
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def multithread_write(image_list, path):
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executor = concurrent.futures.ThreadPoolExecutor(max_workers=None)
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def write_image(image, count, path):
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try:
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torchvision.utils.save_image(image, os.path.join(path, '{0:05d}'.format(count) + ".png"))
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return count, True
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except:
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return count, False
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tasks = []
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for index, image in enumerate(image_list):
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tasks.append(executor.submit(write_image, image, index, path))
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executor.shutdown()
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for index, status in enumerate(tasks):
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if status == False:
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write_image(image_list[index], index, path)
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to8b = lambda x : (255*np.clip(x.cpu().numpy(),0,1)).astype(np.uint8)
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def render_set(model_path, name, iteration, views, gaussians, pipeline, background, cam_type):
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render_path = os.path.join(model_path, name, "ours_{}".format(iteration), "renders")
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gts_path = os.path.join(model_path, name, "ours_{}".format(iteration), "gt")
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makedirs(render_path, exist_ok=True)
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makedirs(gts_path, exist_ok=True)
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render_images = []
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gt_list = []
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render_list = []
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# breakpoint()
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print("point nums:",gaussians._xyz.shape[0])
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for idx, view in enumerate(tqdm(views, desc="Rendering progress")):
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if idx == 0:time1 = time()
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# breakpoint()
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rendering = render(view, gaussians, pipeline, background,cam_type=cam_type)["render"]
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# torchvision.utils.save_image(rendering, os.path.join(render_path, '{0:05d}'.format(idx) + ".png"))
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render_images.append(to8b(rendering).transpose(1,2,0))
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# print(to8b(rendering).shape)
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render_list.append(rendering)
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if name in ["train", "test"]:
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if cam_type != "PanopticSports":
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gt = view.original_image[0:3, :, :]
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else:
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gt = view['image'].cuda()
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# torchvision.utils.save_image(gt, os.path.join(gts_path, '{0:05d}'.format(idx) + ".png"))
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gt_list.append(gt)
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# if idx >= 10:
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# break
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time2=time()
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print("FPS:",(len(views)-1)/(time2-time1))
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# print("writing training images.")
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multithread_write(gt_list, gts_path)
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# print("writing rendering images.")
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multithread_write(render_list, render_path)
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imageio.mimwrite(os.path.join(model_path, name, "ours_{}".format(iteration), 'video_rgb.mp4'), render_images, fps=30)
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def render_sets(dataset : ModelParams, hyperparam, iteration : int, pipeline : PipelineParams, skip_train : bool, skip_test : bool, skip_video: bool):
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with torch.no_grad():
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gaussians = GaussianModel(dataset.sh_degree, hyperparam)
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scene = Scene(dataset, gaussians, load_iteration=iteration, shuffle=False)
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cam_type=scene.dataset_type
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bg_color = [1,1,1] if dataset.white_background else [0, 0, 0]
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background = torch.tensor(bg_color, dtype=torch.float32, device="cuda")
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if not skip_train:
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render_set(dataset.model_path, "train", scene.loaded_iter, scene.getTrainCameras(), gaussians, pipeline, background,cam_type)
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if not skip_test:
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render_set(dataset.model_path, "test", scene.loaded_iter, scene.getTestCameras(), gaussians, pipeline, background,cam_type)
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if not skip_video:
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render_set(dataset.model_path,"video",scene.loaded_iter,scene.getVideoCameras(),gaussians,pipeline,background,cam_type)
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if __name__ == "__main__":
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# Set up command line argument parser
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parser = ArgumentParser(description="Testing script parameters")
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model = ModelParams(parser, sentinel=True)
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pipeline = PipelineParams(parser)
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hyperparam = ModelHiddenParams(parser)
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parser.add_argument("--iteration", default=-1, type=int)
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parser.add_argument("--skip_train", action="store_true")
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parser.add_argument("--skip_test", action="store_true")
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parser.add_argument("--quiet", action="store_true")
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parser.add_argument("--skip_video", action="store_true")
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parser.add_argument("--configs", type=str)
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args = get_combined_args(parser)
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print("Rendering " , args.model_path)
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if args.configs:
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import mmcv
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from utils.params_utils import merge_hparams
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config = mmcv.Config.fromfile(args.configs)
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args = merge_hparams(args, config)
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# Initialize system state (RNG)
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safe_state(args.quiet)
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render_sets(model.extract(args), hyperparam.extract(args), args.iteration, pipeline.extract(args), args.skip_train, args.skip_test, args.skip_video) |