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render_flow.py
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render_flow.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 torch.utils.data import DataLoader
from scene import Scene
import os
from tqdm import tqdm
from os import makedirs
from gaussian_renderer import render_flow
import torchvision
from utils.general_utils import safe_state
from utils import flow_viz
from argparse import ArgumentParser
from arguments import ModelParams, PipelineParams, get_combined_args
from gaussian_renderer import GaussianModel
import cv2 as cv
import numpy as np
def render_set(model_path, name, iteration, views, gaussians, pipeline, background, time_delta):
render_path = os.path.join(model_path, name, "ours_{}".format(iteration), "flow")
gts_path = os.path.join(model_path, name, "ours_{}".format(iteration), "gt")
makedirs(render_path, exist_ok=True)
makedirs(gts_path, exist_ok=True)
for idx, view in enumerate(tqdm(views, desc="Rendering progress")):
if type(view) is list:
view = view[0]
rendering = render_flow(view, gaussians, pipeline, background, time_delta=time_delta)["render"]
# print(view.world_view_transform)
tx, ty, tz = view.world_view_transform[3, :3]
# print(tx, ty, tz)
# print(rendering.shape)
# flow = rendering.permute(1, 2, 0) @ torch.from_numpy(np.array([[1, 0, -tx/tz], [0, 1, -ty/tz]])).float().T.cuda()
flow = rendering.permute(1, 2, 0) @ torch.from_numpy(np.array([[1, 0, 0], [0, 1, 0]])).float().T.cuda()
flow = flow.cpu().numpy()
# flow[..., 0] *= 1024
# flow[..., 1] *= 1386
# print(flow.mean(axis=(0, 1)), flow.std(axis=(0, 1)))
# for i in range(20):
# print(flow[40*i:40*(i+1)].mean(axis=(0, 1)))
gt = view.original_image[0:3, :, :]
# hsv = np.zeros((gt.shape[1], gt.shape[2], 3), dtype=np.uint8)
# hsv[..., 1] = 255
# mag, ang = cv.cartToPolar(flow[..., 0], flow[..., 1])
# hsv[..., 0] = ang*180/np.pi/2
# hsv[..., 2] = cv.normalize(mag, None, 0, 255, cv.NORM_MINMAX)
# bgr = cv.cvtColor(hsv, cv.COLOR_HSV2RGB)
bgr = flow_viz.flow_to_image(flow)
bgr = torch.from_numpy(bgr).permute(2, 0, 1).float()/255
torchvision.utils.save_image(bgr, 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"))
def render_sets(dataset : ModelParams, iteration : int, pipeline : PipelineParams, skip_train : bool, skip_test : bool):
with torch.no_grad():
gaussians = GaussianModel(dataset.sh_degree, dataset.approx_l)
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")
if not skip_train:
if scene.use_loader:
views = DataLoader(scene.getTrainCameras(), batch_size=1, shuffle=False, num_workers=16, collate_fn=list)
else:
views = scene.getTrainCameras()
render_set(dataset.model_path, "train", scene.loaded_iter, views, gaussians, pipeline, background, scene.time_delta)
if not skip_test:
render_set(dataset.model_path, "test", scene.loaded_iter, scene.getTestCameras(), gaussians, pipeline, background, scene.time_delta)
if __name__ == "__main__":
# Set up command line argument parser
parser = ArgumentParser(description="Testing script parameters")
model = ModelParams(parser, sentinel=True)
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")
args = get_combined_args(parser)
print("Rendering " + args.model_path)
# Initialize system state (RNG)
safe_state(args.quiet)
render_sets(model.extract(args), args.iteration, pipeline.extract(args), args.skip_train, args.skip_test)