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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 torch
from scene import Scene, DeformModel, SpecModel
import os
from tqdm import tqdm
from os import makedirs
from gaussian_renderer import render
import torchvision
from utils.general_utils import safe_state, safe_normalize, reflect, flip_align_view, rotation_matrix_from_vectors
from utils.pose_utils import pose_spherical, render_wander_path
from utils.rigid_utils import from_homogenous, to_homogenous
from argparse import ArgumentParser
from arguments import ModelParams, PipelineParams, get_combined_args, OptimizationParams
from gaussian_renderer import GaussianModel
import imageio
import numpy as np
from PIL import Image
def render_set(model_path, opt, load2gpu_on_the_fly, is_6dof, name, iteration, views, gaussians, pipeline, background, deform, specdecoder):
render_path = os.path.join(model_path, name, "ours_{}".format(iteration), "renders")
gts_path = os.path.join(model_path, name, "ours_{}".format(iteration), "gt")
# depth_path = os.path.join(model_path, name, "ours_{}".format(iteration), "depth")
# diffuse_path = os.path.join(model_path, name, "ours_{}".format(iteration), "diffuse")
# specular_path = os.path.join(model_path, name, "ours_{}".format(iteration), "specular")
# specular_tint_path = os.path.join(model_path, name, "ours_{}".format(iteration), "specular_tint")
# new_normal_path = os.path.join(model_path, name, "ours_{}".format(iteration), "new_normal")
makedirs(render_path, exist_ok=True)
makedirs(gts_path, exist_ok=True)
# makedirs(depth_path, exist_ok=True)
# makedirs(diffuse_path, exist_ok=True)
# makedirs(specular_path, exist_ok=True)
# makedirs(specular_tint_path, exist_ok=True)
# makedirs(new_normal_path, exist_ok=True)
for idx, view in enumerate(tqdm(views, desc="Rendering progress")):
if load2gpu_on_the_fly:
view.load2device()
fid = view.fid
xyz = gaussians.get_xyz
time_input = fid.unsqueeze(0).expand(xyz.shape[0], -1)
d_xyz, d_rotation, d_scaling = deform.step(xyz.detach(), time_input)
view_pos = view.camera_center.repeat(gaussians.get_opacity.shape[0], 1)
wo = safe_normalize(view_pos - gaussians.get_xyz.detach())
dir_pp_normalized = -wo
normal = gaussians.get_normal(dir_pp_normalized=dir_pp_normalized)
deform_wo = safe_normalize(view_pos - (gaussians.get_xyz.detach() + d_xyz.detach()))
deform_dir_pp_normalized = -deform_wo
deform_normal = gaussians.get_deformnormal(d_rotation, d_scaling, dir_pp_normalized=deform_dir_pp_normalized)
rotation_matrix = rotation_matrix_from_vectors(normal, deform_normal)
deform_deltanormal = torch.matmul(rotation_matrix, gaussians.get_delta_normal.unsqueeze(-1)).squeeze(-1)
#norm_deform_deltanormal = safe_normalize(deform_deltanormal)
new_normal = safe_normalize(deform_normal + deform_deltanormal)
reflvec = safe_normalize(reflect(deform_wo, new_normal))
spat = torch.cat([gaussians.get_xyz.detach() + d_xyz.detach(), gaussians.get_roughness * torch.abs(gaussians.get_scaling.detach() + d_scaling.detach()), gaussians.get_rotation.detach() + d_rotation.detach()], dim=-1)
spec_color = specdecoder.step(spat, reflvec)
results = render(view, gaussians, opt, pipeline, background, d_xyz, d_rotation, d_scaling, spec_color, new_normal, iteration, is_6dof)
rendering = results["render"]
# depth = results["depth"]
# diffuse = results["diffuse"]
# specular = results["specular_color"]
# specular_tint = results["specular_tint"]
#new_render_normal = results["new_normal"]
gt = view.original_image[0:3, :, :]
torchvision.utils.save_image(rendering, os.path.join(render_path, '{0:05d}'.format(idx) + ".png"))
torchvision.utils.save_image(gt, os.path.join(gts_path, '{0:05d}'.format(idx) + ".png"))
# torchvision.utils.save_image(depth, os.path.join(depth_path, '{0:05d}'.format(idx) + ".png"))
# torchvision.utils.save_image(diffuse, os.path.join(diffuse_path, '{0:05d}'.format(idx) + ".png"))
# torchvision.utils.save_image(specular, os.path.join(specular_path, '{0:05d}'.format(idx) + ".png"))
# torchvision.utils.save_image(specular_tint, os.path.join(specular_tint_path, '{0:05d}'.format(idx) + ".png"))
#torchvision.utils.save_image(new_render_normal, os.path.join(new_normal_path, '{0:05d}'.format(idx) + ".png"))
def render_sets(dataset: ModelParams, opt: OptimizationParams, iteration: int, pipeline: PipelineParams, skip_train: bool, skip_test: bool,
mode: str):
with torch.no_grad():
gaussians = GaussianModel(dataset.sh_degree)
scene = Scene(dataset, gaussians, load_iteration=iteration, shuffle=False)
deform = DeformModel(dataset.is_blender, dataset.is_6dof)
deform.load_weights(dataset.model_path, iteration)
specdecoder = SpecModel()
specdecoder.load_weights(dataset.model_path, iteration)
bg_color = [1, 1, 1] if dataset.white_background else [0, 0, 0]
background = torch.tensor(bg_color, dtype=torch.float32, device="cuda")
if mode == "render":
render_func = render_set
if not skip_train:
render_func(dataset.model_path, opt, dataset.load2gpu_on_the_fly, dataset.is_6dof, "train", scene.loaded_iter,
scene.getTrainCameras(), gaussians, pipeline,
background, deform, specdecoder)
if not skip_test:
render_func(dataset.model_path, opt, dataset.load2gpu_on_the_fly, dataset.is_6dof, "test", scene.loaded_iter,
scene.getTestCameras(), gaussians, pipeline,
background, deform, specdecoder)
if __name__ == "__main__":
# Set up command line argument parser
parser = ArgumentParser(description="Testing script parameters")
model = ModelParams(parser, sentinel=True)
op = OptimizationParams(parser)
pipeline = PipelineParams(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("--mode", default='render', choices=['render', 'time', 'view', 'all', 'pose', 'original'])
args = get_combined_args(parser)
print("Rendering " + args.model_path)
# Initialize system state (RNG)
safe_state(args.quiet)
render_sets(model.extract(args), op.extract(args), args.iteration, pipeline.extract(args), args.skip_train, args.skip_test, args.mode)