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render_video.py
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render_video.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 george.drettakis@inria.fr
#
import torch
from scene import VideoScene
import os
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
from gaussian_renderer import GaussianModel
from utils.loss_utils import get_pixel_loss
import numpy as np
import matplotlib.cm as cm
import time
import cv2
def weighted_percentile(x, w, ps, assume_sorted=False):
"""Compute the weighted percentile(s) of a single vector."""
x = x.reshape([-1])
w = w.reshape([-1])
if not assume_sorted:
sortidx = np.argsort(x)
x, w = x[sortidx], w[sortidx]
acc_w = np.cumsum(w)
return np.interp(np.array(ps) * (acc_w[-1] / 100), acc_w, x)
def visualize_cmap(value,
weight,
colormap,
lo=None,
hi=None,
percentile=99.,
curve_fn=lambda x: x,
modulus=None,
matte_background=True):
"""Visualize a 1D image and a 1D weighting according to some colormap.
Args:
value: A 1D image.
weight: A weight map, in [0, 1].
colormap: A colormap function.
lo: The lower bound to use when rendering, if None then use a percentile.
hi: The upper bound to use when rendering, if None then use a percentile.
percentile: What percentile of the value map to crop to when automatically
generating `lo` and `hi`. Depends on `weight` as well as `value'.
curve_fn: A curve function that gets applied to `value`, `lo`, and `hi`
before the rest of visualization. Good choices: x, 1/(x+eps), log(x+eps).
modulus: If not None, mod the normalized value by `modulus`. Use (0, 1]. If
`modulus` is not None, `lo`, `hi` and `percentile` will have no effect.
matte_background: If True, matte the image over a checkerboard.
Returns:
A colormap rendering.
"""
# Identify the values that bound the middle of `value' according to `weight`.
lo_auto, hi_auto = weighted_percentile(
value, weight, [50 - percentile / 2, 50 + percentile / 2])
# If `lo` or `hi` are None, use the automatically-computed bounds above.
eps = np.finfo(np.float32).eps
lo = lo or (lo_auto - eps)
hi = hi or (hi_auto + eps)
# Curve all values.
value, lo, hi = [curve_fn(x) for x in [value, lo, hi]]
# Wrap the values around if requested.
if modulus:
value = np.mod(value, modulus) / modulus
else:
# Otherwise, just scale to [0, 1].
value = np.nan_to_num(
np.clip((value - np.minimum(lo, hi)) / np.abs(hi - lo), 0, 1))
if colormap:
colorized = colormap(value)[:, :, :3]
else:
assert len(value.shape) == 3 and value.shape[-1] == 3
colorized = value
return colorized
depth_curve_fn = lambda x: -np.log(x + np.finfo(np.float32).eps)
def visualization(depth, save_path):
import matplotlib as mpl
import matplotlib.cm as cm
from PIL import Image
vmax = np.percentile(depth, 98)
vmin = depth.min()
# print(save_path, vmax, vmin)
normalizer = mpl.colors.Normalize(vmin=vmin, vmax=vmax)
mapper = cm.ScalarMappable(norm=normalizer, cmap='turbo')
colormapped_im = (mapper.to_rgba(depth)[:, :, :3] * 255).astype(np.uint8)
im = Image.fromarray(colormapped_im)
im.save(save_path)
return colormapped_im
def render_set(model_path, name, iteration, views, gaussians, pipeline, background):
render_path = os.path.join(model_path, name, "ours_{}".format(iteration), "renders")
depth_path = os.path.join(model_path, name, "ours_{}".format(iteration), "depth")
makedirs(render_path, exist_ok=True)
makedirs(depth_path, exist_ok=True)
fps = 30
size = (int(views[0].image_width), int(views[0].image_height))
print("size:", size)
fourcc = cv2.VideoWriter_fourcc(*'XVID')
final_video = cv2.VideoWriter(os.path.join(model_path, name, "ours_{}".format(iteration), 'render_video.mp4'), fourcc, fps, size)
final_video_depth = cv2.VideoWriter(os.path.join(model_path, name, "ours_{}".format(iteration), 'depth_video.mp4'), fourcc, fps, size)
for idx, view in enumerate(tqdm(views, desc="Rendering progress")):
render_pkg = render(view, gaussians, pipeline, background)
rendering = torch.clamp(render_pkg["render"], min=0., max=1.)
depth = render_pkg["rendered_depth"]
# print(idx, view.image_name, depth.min(), depth.max())
depth = (depth - depth.min()) / (depth.max() - depth.min())
# depth = ((depth - 3.7545) / (33.5699 - 3.7545)).clamp(0.0, 1.0)
# depth = ((depth - 3.2882) / (64.3477 - 3.2882)).clamp(0.0, 1.0)
torchvision.utils.save_image(rendering, os.path.join(render_path, '{0:05d}'.format(idx) + ".png"))
torchvision.utils.save_image(depth, os.path.join(depth_path, '{0:05d}'.format(idx) + ".png"))
# depth_est = (1 - depth * render_pkg["rendered_alpha"]).squeeze().cpu().numpy()
# depth_est = visualize_cmap(depth_est, np.ones_like(depth_est), cm.get_cmap('turbo'), curve_fn=depth_curve_fn).copy()
# depth_est = torch.as_tensor(depth_est).permute(2,0,1)
# torchvision.utils.save_image(depth_est, os.path.join(depth_path, 'color_{0:05d}'.format(idx) + ".png"))
color_depth = visualization(depth.detach().cpu().numpy()[0], os.path.join(depth_path, 'color_{0:05d}'.format(idx) + ".png"))
video_img = (rendering.permute(1, 2, 0).detach().cpu().numpy() * 255.).astype(np.uint8)[..., ::-1]
final_video.write(video_img)
color_depth = color_depth[..., ::-1]
final_video_depth.write(color_depth)
final_video.release()
final_video_depth.release()
def render_sets(dataset : ModelParams, iteration : int, pipeline : PipelineParams, skip_train : bool, skip_test : bool, skip_virtual : bool):
with torch.no_grad():
gaussians = GaussianModel(dataset.sh_degree)
scene = VideoScene(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")
render_set(dataset.model_path, "video", scene.loaded_iter, scene.getTestCameras(), gaussians, pipeline, background)
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("--skip_virtual", 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, args.skip_virtual)