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main.py
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main.py
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import os
os.environ['OPENCV_IO_ENABLE_OPENEXR'] = '1'
import random
import numpy as np
import torch
import time
import cv2 as cv
from torch.utils.tensorboard import SummaryWriter
from network.arch_avatar import NerfRenderer, OccupancyNet, GeoTexAvatar
from network.arch_recon import ReconNetwork
from dataset.avatarcap_dataset import AvatarCapDataloader, to_cuda
import config
import utils.lr_schedule as lr_schedule
from utils.smpl_util import smpl_util
import utils.visualize_util as visualize_util
import utils.recon_util as recon_util
from utils.renderer import Renderer
import utils.obj_io as obj_io
from normal_fusion.normal_fusion import canonicalize_normal_map, merge_normal_images, merge_normal_images_cover
import datetime
now = datetime.datetime.now()
def train_avatar():
"""
Train the GeoTexAvatar Network for the subject with 3D scans.
"""
cfg = config.cfg
network = GeoTexAvatar().to(config.device)
network.train()
nerf_renderer = NerfRenderer(network)
occ_net = OccupancyNet(network)
optm = torch.optim.Adam([
{'params': network.cano_template.parameters(), 'lr': cfg['model']['cano_template_lr']},
{'params': network.warping_field.parameters(), 'lr': cfg['model']['warping_field_lr']}
])
lr_schedule_cano_template = lr_schedule.get_learning_rate_schedules('Step', Initial = cfg['model']['cano_template_lr'], Interval = 5000, Factor = 0.5)
lr_schedule_warping_field = lr_schedule.get_learning_rate_schedules('Step', Initial = cfg['model']['warping_field_lr'], Interval = 20000, Factor = 0.5)
image_loss_func = torch.nn.MSELoss()
geo_loss_func = torch.nn.L1Loss() if config.if_type == 'sdf' else torch.nn.BCELoss()
img_loss_weight = cfg['model']['img_loss_weight']
occ_loss_weight = cfg['model']['occ_loss_weight']
geo_offset_reg_loss_weight = cfg['model']['geo_offset_reg_loss_weight']
tex_offset_reg_loss_weight = cfg['model']['tex_offset_reg_loss_weight']
print('# Network: optimizable network parameter number: %d' % sum(p.numel() for p in network.parameters() if p.requires_grad))
os.makedirs(config.cfg['training']['net_ckpt_dir'], exist_ok = True)
log_dir = config.cfg['training']['net_ckpt_dir'] + '/' + now.strftime('%Y_%m_%d_%H_%M_%S')
os.makedirs(log_dir, exist_ok = True)
writer = SummaryWriter(log_dir)
if cfg['training']['net_ckpt'] is not None:
print('# Loading pretrained network from %s' % cfg['training']['net_ckpt'])
network.load_state_dict(torch.load(cfg['training']['net_ckpt'] + '/net.pt')['network'])
print('# Loading optimizer from %s' % cfg['training']['net_ckpt'])
optm.load_state_dict(torch.load(cfg['training']['net_ckpt'] + '/optm.pt')['optm'])
# initialize dataloader
batch_size = cfg['training'].get('batch_size', 1)
num_workers = cfg['training'].get('num_workers', 0)
print('# Batch size: %d' % batch_size)
print('# Workers num: %d' % num_workers)
loader = AvatarCapDataloader(cfg['training']['training_data_dir'], True, batch_size = batch_size, num_workers = num_workers)
batch_num = len(loader.dataset) // batch_size
# set auxiliary variables
smpl_util.set_cano_smpl_vertices(loader.dataset.cano_smpl_v)
start_epoch = cfg['training'].get('start_epoch', 0)
end_epoch = cfg['training'].get('end_epoch', 30)
for epoch_idx in range(start_epoch, end_epoch):
# update lr
lr_cano_template = lr_schedule_cano_template.get_learning_rate(epoch_idx * batch_num)
lr_cano_template = max(5e-4, lr_cano_template)
lr_warping_field = lr_schedule_warping_field.get_learning_rate(epoch_idx * batch_num)
if epoch_idx < 1:
lr_warping_field = 0. # firstly learn a plausible template with warping field fixed to zero
else:
lr_warping_field = max(5e-5, lr_warping_field)
optm.param_groups[0]['lr'] = lr_cano_template
optm.param_groups[1]['lr'] = lr_warping_field
epoch_losses = dict()
time_epoch_start = time.time()
for batch_idx, items in enumerate(loader):
items = to_cuda(items)
# forward
network.warping_field.precompute_conv(items)
occ_output = occ_net.query(items)
nerf_output = nerf_renderer.render(items)
# calculate losses
img_loss = image_loss_func(nerf_output['rgb_map'], items['rgb'])
if config.if_type == 'sdf':
items['cano_pts_ov'] = torch.clip(items['cano_pts_ov'], -config.sdf_thres, config.sdf_thres) / config.sdf_thres
else:
inside_flag = items['cano_pts_ov'] > 0.
items['cano_pts_ov'][inside_flag] = 1.
items['cano_pts_ov'][~inside_flag] = 0.
geo_loss = geo_loss_func(occ_output['cano_pts_ov'].squeeze(-1), items['cano_pts_ov'])
geo_offset_reg_loss = torch.linalg.norm(occ_output['nonrigid_offset'], dim = -1).mean()
tex_offset_reg_loss = torch.linalg.norm(nerf_output['nonrigid_offset'], dim = -1).mean()
total_loss = img_loss_weight * img_loss + occ_loss_weight * geo_loss + geo_offset_reg_loss_weight * geo_offset_reg_loss + tex_offset_reg_loss_weight * tex_offset_reg_loss
optm.zero_grad()
total_loss.backward()
optm.step()
batch_losses = dict()
batch_losses['tex_loss'] = img_loss.item()
batch_losses['geo_loss'] = geo_loss.item()
batch_losses['geo_offset_reg_loss'] = geo_offset_reg_loss.item()
batch_losses['tex_offset_reg_loss'] = tex_offset_reg_loss.item()
# record batch loss
log_info = 'epoch %d, batch %d, lr: %e, %e, ' % (epoch_idx, batch_idx, lr_cano_template, lr_warping_field)
for key in batch_losses.keys():
log_info = log_info + ('%s: %f, ' % (key, batch_losses[key]))
writer.add_scalar('%s/Batch' % key, batch_losses[key], epoch_idx * batch_num + batch_idx)
if key in epoch_losses:
epoch_losses[key] += batch_losses[key]
else:
epoch_losses[key] = batch_losses[key]
print(log_info)
with open(os.path.join(log_dir, 'loss.txt'), 'a') as fp:
fp.write(log_info + '\n')
print('\033[1;31m This epoch costs %f secs\033[0m' % (time.time() - time_epoch_start))
# record epoch loss
for key in epoch_losses.keys():
epoch_losses[key] /= batch_num
writer.add_scalar('%s/Epoch' % key, epoch_losses[key], epoch_idx)
# save the network every 'ckpt_interval' epochs
if epoch_idx % cfg['training']['ckpt_interval'] == 0:
model_folder = cfg['training']['net_ckpt_dir'] + '/epoch_%d' % epoch_idx
os.makedirs(model_folder, exist_ok = True)
torch.save({'network': network.state_dict()}, model_folder + '/net.pt')
torch.save({'optm': optm.state_dict()}, model_folder + '/optm.pt')
# save the latest network
latest_folder = cfg['training']['net_ckpt_dir'] + '/epoch_latest'
os.makedirs(latest_folder, exist_ok = True)
torch.save({'network': network.state_dict()}, latest_folder + '/net.pt')
torch.save({'optm': optm.state_dict()}, latest_folder + '/optm.pt')
writer.close()
if cfg['training'].get('finetune_tex', True): # finetune texture template using a single scan
finetune_texture_template(nerf_renderer, occ_net, loader)
def finetune_texture_template(nerf_renderer: NerfRenderer,
occ_net: OccupancyNet,
loader: AvatarCapDataloader):
"""
Finetune the texture template for more high-quality texture.
"""
print('# Starting finetuing the texture template...')
cfg = config.cfg
network = nerf_renderer.net
nerf_renderer.net.train()
# initial network
network_init = GeoTexAvatar().to(config.device)
network_init.load_state_dict(network.state_dict())
occ_net_init = OccupancyNet(network_init)
# freeze other nets
for name, params in network.named_parameters():
if not name.startswith('cano_template'):
params.requires_grad = False
optm = torch.optim.Adam([
{'params': network.cano_template.parameters(), 'lr': 5e-4}
])
lr_schedule_cano_template = lr_schedule.get_learning_rate_schedules('Constant', Value = 5e-4)
image_loss_func = torch.nn.MSELoss()
geo_loss_func = torch.nn.L1Loss()
print('# Network: optimizable network parameter number: %d' % sum(p.numel() for p in network.parameters() if p.requires_grad))
os.makedirs(config.cfg['training']['net_ckpt_dir'] + '/finetune_tex', exist_ok = True)
log_dir = config.cfg['training']['net_ckpt_dir'] + '/finetune_tex/' + now.strftime('%Y_%m_%d_%H_%M_%S')
os.makedirs(log_dir, exist_ok = True)
writer = SummaryWriter(log_dir)
# set auxiliary variables
smpl_util.set_cano_smpl_vertices(loader.dataset.cano_smpl_v)
finetune_data_idx = cfg['training'].get('finetune_tex_data_idx', 0)
print('# Scan %d is used for finetuning the texture template' % finetune_data_idx)
rel_idx = loader.dataset.data_indices.index(finetune_data_idx)
assert rel_idx >= 0
data_indices = list(range(loader.dataset.img_num_per_pose * rel_idx, loader.dataset.img_num_per_pose * (rel_idx+1)))
batch_num = len(data_indices)
start_epoch = 0
end_epoch = 1000
for epoch_idx in range(start_epoch, end_epoch):
# update lr
lr_cano_template = lr_schedule_cano_template.get_learning_rate(epoch_idx * batch_num)
optm.param_groups[0]['lr'] = lr_cano_template
epoch_losses = dict()
time_epoch_start = time.time()
random.shuffle(data_indices)
for batch_idx, index in enumerate(data_indices):
item = loader.dataset.__getitem__(index)
items = to_cuda(item, add_batch = True)
# forward
if epoch_idx == 0 and batch_idx == 0:
network.warping_field.precompute_conv(items)
nerf_output = nerf_renderer.render(items)
occ_output = occ_net.query(items)
with torch.no_grad():
if network_init.warping_field.pose_feat_map is None:
network_init.warping_field.precompute_conv(items)
occ_output_init = occ_net_init.query(items)
# calculate losses
img_loss = image_loss_func(nerf_output['rgb_map'], items['rgb'])
geo_loss = geo_loss_func(occ_output['cano_pts_ov'], occ_output_init['cano_pts_ov'])
total_loss = img_loss + 0.5 * geo_loss
optm.zero_grad()
total_loss.backward()
optm.step()
batch_losses = {
'tex_loss': img_loss.item()
, 'geo_loss': geo_loss.item()
}
# record batch loss
log_info = 'epoch %d, batch %d, lr: %e ' % (epoch_idx, batch_idx, lr_cano_template)
for key in batch_losses.keys():
log_info = log_info + ('%s: %f, ' % (key, batch_losses[key]))
writer.add_scalar('%s/Batch' % key, batch_losses[key], epoch_idx * batch_num + batch_idx)
if key in epoch_losses:
epoch_losses[key] += batch_losses[key]
else:
epoch_losses[key] = batch_losses[key]
print(log_info)
with open(os.path.join(log_dir, 'loss.txt'), 'a') as fp:
fp.write(log_info + '\n')
print('\033[1;31m This epoch costs %f secs\033[0m' % (time.time() - time_epoch_start))
# record epoch loss
for key in epoch_losses.keys():
epoch_losses[key] /= batch_num
writer.add_scalar('%s/Epoch' % key, epoch_losses[key], epoch_idx)
if epoch_idx % 20 == 0 and epoch_idx > 0:
model_folder = cfg['training']['net_ckpt_dir'] + '/finetune_tex/epoch_%d' % epoch_idx
os.makedirs(model_folder, exist_ok = True)
torch.save({'network': network.state_dict()}, model_folder + '/net.pt')
torch.save({'optm': optm.state_dict()}, model_folder + '/optm.pt')
writer.close()
def run_avatarcap(w_recon = False, save_avatar_mesh = False, save_final_mesh = False, w_nerf = False, frame_idx = None, view_idx = 0, interval = 1, integrate_manner = 'merge'):
"""
Test AvatarCap (the whole capture pipeline) or only the GeoTexAvatar module.
Reconstructed and animated results will be saved in cfg['testing]['output_dir'],
and "cano_avatar", "live_avatar" and "live_recon" folders contain rendered images of
animated canonical avatar, live avatar and final reconstructed mesh, respectively.
:param w_recon: True: test AvatarCap, False: test only GeoTexAvatar.
:param save_avatar_mesh: True: save animated results of GeoTexAvatar as ply file.
:param save_final_mesh: True: save reconstructed results of AvatarCap as ply file.
:param w_nerf: True: get textured results, False: only geometry.
:param frame_idx: the frame indices that you want to reconstruct or animate.
:param view_idx: only useful for synthetic multi-view data, default: 0.
:param interval: frame interval for reconstruction or animation.
:param integrate_manner: the method of fusing image and avatar normals.
"""
cfg = config.cfg
os.makedirs(cfg['testing']['output_dir'], exist_ok = True)
os.makedirs(cfg['testing']['output_dir'] + '/cano_avatar', exist_ok = True)
os.makedirs(cfg['testing']['output_dir'] + '/live_avatar', exist_ok = True)
os.makedirs(cfg['testing']['output_dir'] + '/live_recon', exist_ok = True)
network = GeoTexAvatar().to(config.device)
network.eval()
occ_net = OccupancyNet(network)
nerf_renderer = NerfRenderer(network)
recon_net = ReconNetwork().to(config.device)
if cfg['testing']['net_ckpt'] is not None:
print('# Loading GeoTexAvatar network from %s' % cfg['testing']['net_ckpt'])
net_data = torch.load(cfg['testing']['net_ckpt'] + '/net.pt')
network.load_state_dict(net_data['network'])
net_ckpt_finetuned = cfg['testing'].get('net_ckpt_finetuned', None)
if net_ckpt_finetuned is not None:
print('# Loading finetuned GeoTexAvatar network from %s' % net_ckpt_finetuned)
net_data = torch.load(net_ckpt_finetuned + '/net.pt')
network_finetuned = GeoTexAvatar().to(config.device)
network_finetuned.eval()
network_finetuned.load_state_dict(net_data['network'])
nerf_renderer.net = network_finetuned
recon_net_ckpt = cfg['testing'].get('recon_net_ckpt', None)
if recon_net_ckpt is not None:
print('# Loading reconstruction network from %s' % recon_net_ckpt)
net_data = torch.load(recon_net_ckpt + '/recon_net.pt')
recon_net.load_state_dict(net_data['network'])
# init data loader
loader = AvatarCapDataloader(cfg['testing']['testing_data_dir'], False, batch_size = 1, num_workers = 0)
data_num = len(loader.dataset) // loader.dataset.img_num_per_pose
print('# Data num: %d' % data_num)
# init renderers
cam = loader.dataset.data_config['camera']
phong_renderer = Renderer(512, 512, shader_name = 'phong_geometry', bg_color = (1, 1, 1), window_name = 'Phong')
normal_renderer = Renderer(512, 512, shader_name = 'vertex_attribute', window_name = 'Normal')
position_renderer = Renderer(cam['img_width'], cam['img_height'], shader_name = 'position', window_name = 'Position')
front_mv, back_mv = None, None
# set auxiliary variables
smpl_util.set_cano_smpl_vertices(loader.dataset.cano_smpl_v)
cano_smpl_center = 0.5 * (smpl_util.cano_smpl_vertices.max(0)[0] + smpl_util.cano_smpl_vertices.min(0)[0]).cpu().numpy()
cano_smpl_center_for_render = cano_smpl_center.copy()
if frame_idx is None:
inferred_list = list(range(0, data_num, interval))
elif isinstance(frame_idx, int):
inferred_list = [frame_idx - loader.dataset.start_data_idx]
elif isinstance(frame_idx, list):
inferred_list = (np.array(frame_idx, np.int32) - loader.dataset.start_data_idx).tolist()
else:
raise TypeError('Invalid frame_idx!')
for i in inferred_list:
item_idx = i * loader.dataset.img_num_per_pose + view_idx
item = loader.dataset.__getitem__(item_idx)
items = to_cuda(item, add_batch = True)
data_idx = items['data_idx']
"""
1. Generate geometric avatar in canonical space
"""
occ_volume = torch.zeros(cfg['testing']['vol_res'], dtype = torch.float32, device = config.device).reshape(-1)
with torch.no_grad():
network.warping_field.precompute_conv(items)
output = occ_net.query(items)
occ_volume[items['valid_pts_flag'][0]] = output['cano_pts_ov'][0, :, 0]
occ_volume[~items['valid_pts_flag'][0]] = loader.dataset.invalid_pts_ov
occ_volume = occ_volume.reshape(cfg['testing']['vol_res'])
bounds = items['cano_bounds'][0].cpu().numpy()
vertices, faces, normals = recon_util.recon_mesh(occ_volume, cfg['testing']['vol_res'], bounds, iso_value = config.iso_value)
front_avatar_normal, back_avatar_normal = visualize_util.render_cano_mesh(normal_renderer, vertices, normals, faces, cano_smpl_center_for_render)
# save results: canonical avatar
cano_img_front, cano_img_back = visualize_util.render_cano_mesh(phong_renderer, vertices, normals, faces, cano_smpl_center_for_render)
cano_img = np.concatenate([cano_img_front, cano_img_back], 1)
cano_img = (255 * cano_img).astype(np.uint8)
cv.imwrite('%s/%04d.jpg' % (cfg['testing']['output_dir'] + '/cano_avatar', data_idx), cano_img)
# store cano avatar mesh
cano_avatar_mesh = {'v': vertices.copy(),
'vn': normals.copy(),
'f': faces.copy()}
# skinning to live space
vertices_gpu = torch.from_numpy(vertices).to(torch.float32).to(config.device)
normals_gpu = torch.from_numpy(normals).to(torch.float32).to(config.device)
lbs = smpl_util.calculate_lbs(vertices_gpu[None, ...])
live_vertices, vert_mats = smpl_util.skinning(vertices_gpu[None, ...], lbs, items['cano2live_jnt_mats'], True)
live_vertices = live_vertices[0].cpu().numpy()
vert_mats = vert_mats[0]
live_normals = torch.einsum('vij,vj->vi', vert_mats[:, :3, :3], normals_gpu).cpu().numpy()
# store live avatar mesh
live_avatar_mesh = {'v': live_vertices.copy(),
'vn': live_normals.copy(),
'f': faces.copy()}
# save results: live avatar
if front_mv is None or back_mv is None:
front_mv = visualize_util.calc_front_mv(live_vertices, rot_x_angle = -0.15)
back_mv = visualize_util.calc_back_mv(live_vertices, rot_x_angle = -0.15)
live_img_front, live_img_back = visualize_util.render_live_mesh(phong_renderer, live_vertices, live_normals, faces, front_mv = front_mv, back_mv = back_mv)
live_img = np.concatenate([live_img_front, live_img_back], 1)
live_img = (255 * live_img).astype(np.uint8)
cv.imwrite('%s/%04d.jpg' % (cfg['testing']['output_dir'] + '/live_avatar', data_idx), live_img)
"""
2. Canonical normal fusion (if w_recon == True)
"""
if w_recon:
if loader.dataset.data_config['data_type'] == 'synthetic':
inferred_normal = cv.imread(loader.dataset.data_dir + '/imgs/%03d/normal_view_%03d.exr' % (data_idx, view_idx), cv.IMREAD_UNCHANGED) # synthetic multi-view data
elif loader.dataset.data_config['data_type'] == 'real':
inferred_normal = cv.imread(loader.dataset.data_dir + '/imgs/normal/normal_%04d.exr' % data_idx, cv.IMREAD_UNCHANGED) # monocular data
else:
raise ValueError('Invalid data type!')
front_image_normal, back_image_normal = canonicalize_normal_map(position_renderer, normal_renderer, vertices, live_vertices, faces, inferred_normal, vert_mats,
mv = items['w2c_RT'][0].cpu().numpy(), fx = cam['fx'], fy = cam['fy'], cx = cam['cx'], cy = cam['cy'],
cano_smpl_center = cano_smpl_center_for_render)
if integrate_manner == 'merge':
neck_vert = smpl_util.cano_smpl_vertices[3068].cpu().numpy() - cano_smpl_center_for_render
neck_y = int((1. - neck_vert[1]) / 2. * 512)
neck_x = int((neck_vert[0] - 1) / 2. * 512)
front_merged_normal = merge_normal_images(front_avatar_normal, front_image_normal, iter_num = 100, neck_xy = (neck_x, neck_y))
elif integrate_manner == 'cover':
front_merged_normal = merge_normal_images_cover(front_avatar_normal, front_image_normal)
else:
raise ValueError('Invalid integration manner!')
# suppose that the performer is facing the camera, i.e., the visible regions are almost in the front of the body
back_merged_normal = back_avatar_normal
items['front_normal'] = torch.from_numpy(front_merged_normal.transpose(2, 0, 1)).to(torch.float32).to(config.device)[None]
items['back_normal'] = torch.from_numpy(back_merged_normal.transpose(2, 0, 1)).to(torch.float32).to(config.device)[None]
"""
3. Reconstruction network (if w_recon == True)
"""
occ_volume = occ_volume.reshape(-1).fill_(0.)
with torch.no_grad():
output = recon_net.infer(items)
occ_volume[items['valid_pts_flag'][0]] = output[0]
occ_volume[~items['valid_pts_flag'][0]] = loader.dataset.invalid_pts_ov
vertices, faces, normals = recon_util.recon_mesh(occ_volume, cfg['testing']['vol_res'],
items['cano_bounds'][0].cpu().numpy())
vertices = torch.from_numpy(vertices).to(torch.float32).to(config.device)
normals = torch.from_numpy(normals).to(vertices)
# skinning to live space
lbs = smpl_util.calculate_lbs(vertices[None, ...])
live_vertices = smpl_util.skinning(vertices[None, ...], lbs, items['cano2live_jnt_mats'])[0].cpu().numpy()
live_normals = smpl_util.skinning_normal(normals[None, ...], lbs, items['cano2live_jnt_mats'])[0].cpu().numpy()
# store live recon mesh
live_recon_mesh = {'v': live_vertices.copy(),
'vn': live_normals.copy(),
'f': faces.copy()}
"""
4. Evaluate vertex colors in canonical space (if w_nerf == True)
"""
if w_nerf: # generate vertex color from the texture template
with torch.no_grad():
# integrate on the avatar surface
vertices_avatar = torch.from_numpy(cano_avatar_mesh['v']).to(torch.float32).to(config.device)
normals_avatar = torch.from_numpy(cano_avatar_mesh['vn']).to(torch.float32).to(config.device)
items['ray_o'] = (vertices_avatar + normals_avatar)[None]
items['ray_d'] = -normals_avatar[None]
items['depth'] = torch.ones((1, vertices_avatar.shape[0])).to(vertices_avatar)
items['near'] = items['depth'] - 0.05
items['far'] = items['depth'] + 0.05
items['occupancy'] = items['depth'].clone()
nerf_renderer.net.warping_field.precompute_conv(items)
nerf_output = nerf_renderer.render(items, pts_space = 'cano', near_dist = 0.02, far_dist = 0.05)
color_avatar = nerf_output['rgb_map'][0][:, [2, 1, 0]]
live_avatar_mesh['vc'] = color_avatar.cpu().numpy()
if w_recon:
# nn for recon vertices
from pytorch3d.ops import knn_points, knn_gather
_, indices, _ = knn_points(vertices[None], vertices_avatar[None])
color_recon = knn_gather(color_avatar[None], indices)[0, :, 0]
live_recon_mesh['vc'] = color_recon.cpu().numpy()
else:
live_avatar_mesh['vc'] = None
if w_recon:
live_recon_mesh['vc'] = None
if save_avatar_mesh:
obj_io.save_mesh_as_ply('%s/%04d_avatar.ply' % (cfg['testing']['output_dir'], data_idx),
live_avatar_mesh['v'], live_avatar_mesh['f'], live_avatar_mesh['vn'], live_avatar_mesh['vc'])
if w_recon:
if save_final_mesh:
obj_io.save_mesh_as_ply('%s/%04d_recon.ply' % (cfg['testing']['output_dir'], data_idx),
live_vertices, faces, live_normals, live_recon_mesh['vc'])
# save results: reconstructed mesh
live_img_front, live_img_back = visualize_util.render_live_mesh(phong_renderer, live_vertices, live_normals, faces, front_mv = front_mv, back_mv = back_mv)
live_img = np.concatenate([live_img_front, live_img_back], 1)
live_img = (255*live_img).astype(np.uint8)
cv.imwrite('%s/%04d.jpg' % (cfg['testing']['output_dir'] + '/live_recon', data_idx), live_img)
if __name__ == '__main__':
torch.manual_seed(31359)
np.random.seed(31359)
from argparse import ArgumentParser
arg_parser = ArgumentParser()
arg_parser.add_argument('-c', '--config_path', type = str, help = 'Configuration file path.')
arg_parser.add_argument('-m', '--mode', type = str, default = 'test', choices = ['train', 'test'], help = 'Train or test.')
args = arg_parser.parse_args()
config.cfg = config.load_config(args.config_path)
if args.mode == 'train':
train_avatar()
else:
run_avatarcap(w_recon = True,
save_avatar_mesh = False,
save_final_mesh = False,
w_nerf = False,
frame_idx = None,
view_idx = 0,
interval = 1)