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render_360.py
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render_360.py
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#
# Copyright (C) 2022 Apple Inc. All rights reserved.
#
'''
Render 360 views of a Da-posed human.
Render 360 views of a posed human.
Examples:
python render_360.py --scene_dir ./data/seattle --use_cuda=no --white_bkg=yes --rays_per_batch=2048 --trajectory_resolution=40 --weights_path ./out/seattle_human/checkpoint.pth.tar --render_h=72 --render_w=128 --mode canonical_360 --can_posenc rotate
python render_360.py --scene_dir ./data/seattle --use_cuda=no --white_bkg=yes --rays_per_batch=2048 --trajectory_resolution=40 --weights_path ./out/seattle_human/checkpoint.pth.tar --render_h=72 --render_w=128 --mode posed_360 --can_posenc rotate
'''
import os
import argparse
import imageio
import torch
import numpy as np
from cameras.captures import ResizedPinholeCapture
from cameras.pinhole_camera import PinholeCamera
from models import human_nerf
from utils import render_utils, utils
from data_io import neuman_helper
from options import options
from utils.constant import CANONICAL_ZOOM_FACTOR, CANONICAL_CAMERA_DIST
def main_canonical_360(opt):
scene = neuman_helper.NeuManReader.read_scene(
opt.scene_dir,
tgt_size=opt.render_size,
normalize=opt.normalize,
bkg_range_scale=opt.bkg_range_scale,
human_range_scale=opt.human_range_scale
)
if opt.geo_threshold < 0:
can_bones = []
bones = []
for i in range(len(scene.captures)):
bones.append(np.linalg.norm(scene.smpls[i]['joints_3d'][3] - scene.smpls[i]['joints_3d'][0]))
can_bones.append(np.linalg.norm(scene.smpls[i]['static_joints_3d'][3] - scene.smpls[i]['static_joints_3d'][0]))
opt.geo_threshold = np.mean(bones)
net = human_nerf.HumanNeRF(opt)
weights = torch.load(opt.weights_path, map_location='cpu')
utils.safe_load_weights(net, weights['hybrid_model_state_dict'])
center, up = utils.smpl_verts_to_center_and_up(scene.static_vert[0])
render_poses = render_utils.default_360_path(center, up, CANONICAL_CAMERA_DIST, opt.trajectory_resolution)
for i, rp in enumerate(render_poses):
can_cap = ResizedPinholeCapture(
PinholeCamera(
scene.captures[0].pinhole_cam.width,
scene.captures[0].pinhole_cam.height,
CANONICAL_ZOOM_FACTOR * scene.captures[0].pinhole_cam.width,
CANONICAL_ZOOM_FACTOR * scene.captures[0].pinhole_cam.width,
scene.captures[0].pinhole_cam.width / 2.0,
scene.captures[0].pinhole_cam.height / 2.0,
),
rp,
tgt_size=scene.captures[0].pinhole_cam.shape
)
out = render_utils.render_smpl_nerf(
net,
can_cap,
scene.static_vert[0],
scene.faces,
Ts=None,
rays_per_batch=opt.rays_per_batch,
samples_per_ray=opt.samples_per_ray,
render_can=True,
return_mask=False,
return_depth=False,
interval_comp=opt.geo_threshold / np.mean(can_bones)
)
save_path = os.path.join('./demo', f'canonical_360/{os.path.basename(opt.scene_dir)}', f'out_{str(i).zfill(4)}.png')
if not os.path.isdir(os.path.dirname(save_path)):
os.makedirs(os.path.dirname(save_path))
imageio.imsave(save_path, out)
print(f'image saved: {save_path}')
def main_posed_360(opt):
scene = neuman_helper.NeuManReader.read_scene(
opt.scene_dir,
tgt_size=opt.render_size,
normalize=opt.normalize,
bkg_range_scale=opt.bkg_range_scale,
human_range_scale=opt.human_range_scale,
smpl_type='optimized'
)
if opt.geo_threshold < 0:
bones = []
for i in range(len(scene.captures)):
bones.append(np.linalg.norm(scene.smpls[i]['joints_3d'][3] - scene.smpls[i]['joints_3d'][0]))
opt.geo_threshold = np.mean(bones)
net = human_nerf.HumanNeRF(opt)
weights = torch.load(opt.weights_path, map_location='cpu')
utils.safe_load_weights(net, weights['hybrid_model_state_dict'])
cap_id = 0
center, up = utils.smpl_verts_to_center_and_up(scene.verts[cap_id])
dist = opt.geo_threshold * 36 # camera distance depends on the human size
render_poses = render_utils.default_360_path(center, up, dist, opt.trajectory_resolution)
for i, rp in enumerate(render_poses):
can_cap = ResizedPinholeCapture(
scene.captures[0].pinhole_cam,
rp,
tgt_size=scene.captures[0].size
)
out = render_utils.render_smpl_nerf(
net,
can_cap,
scene.verts[cap_id],
scene.faces,
scene.Ts[cap_id],
rays_per_batch=opt.rays_per_batch,
samples_per_ray=opt.samples_per_ray,
white_bkg=opt.white_bkg,
render_can=False,
geo_threshold=opt.geo_threshold
)
save_path = os.path.join('./demo', f'posed_360/{os.path.basename(opt.scene_dir)}', f'out_{str(i).zfill(4)}.png')
if not os.path.isdir(os.path.dirname(save_path)):
os.makedirs(os.path.dirname(save_path))
imageio.imsave(save_path, out)
print(f'image saved: {save_path}')
if __name__ == "__main__":
parser = argparse.ArgumentParser()
options.set_general_option(parser)
opt, _ = parser.parse_known_args()
options.set_nerf_option(parser)
options.set_pe_option(parser)
options.set_render_option(parser)
options.set_trajectory_option(parser)
parser.add_argument('--scene_dir', required=True, type=str, help='scene directory')
parser.add_argument('--image_dir', required=False, type=str, default=None, help='image directory')
parser.add_argument('--out_dir', default='./out', type=str, help='weights dir')
parser.add_argument('--offset_scale', default=1.0, type=float, help='scale the predicted offset')
parser.add_argument('--geo_threshold', default=-1, type=float, help='')
parser.add_argument('--normalize', default=True, type=options.str2bool, help='')
parser.add_argument('--bkg_range_scale', default=3, type=float, help='extend near/far range for background')
parser.add_argument('--human_range_scale', default=1.5, type=float, help='extend near/far range for human')
parser.add_argument('--mode', required=True, choices=['canonical_360', 'posed_360'], type=str, help='rendering mode')
parser.add_argument('--num_offset_nets', default=1, type=int, help='how many offset networks')
parser.add_argument('--offset_scale_type', default='linear', type=str, help='no/linear/tanh')
opt = parser.parse_args()
assert opt.geo_threshold == -1, 'please use auto geo_threshold'
if opt.render_h is None:
opt.render_size = None
else:
opt.render_size = (opt.render_h, opt.render_w)
options.print_opt(opt)
if opt.mode == 'canonical_360':
main_canonical_360(opt)
elif opt.mode == 'posed_360':
main_posed_360(opt)