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# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
import sys
import os
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
ROOT_PATH = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
import time
import json
import numpy as np
import cv2
import random
import torch
import torch.nn as nn
from tqdm import tqdm
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import matplotlib
from numpy.linalg import inv
from lib.options import BaseOptions
from lib.mesh_util import save_obj_mesh_with_color, reconstruction
from lib.data import EvalWPoseDataset, EvalDataset
from lib.model import HGPIFuNetwNML, HGPIFuMRNet
from lib.geometry import index
from PIL import Image
parser = BaseOptions()
def gen_mesh(res, net, cuda, data, save_path, thresh=0.5, use_octree=True, components=False):
image_tensor_global = data['img_512'].to(device=cuda)
image_tensor = data['img'].to(device=cuda)
calib_tensor = data['calib'].to(device=cuda)
net.filter_global(image_tensor_global)
net.filter_local(image_tensor[:,None])
try:
if net.netG.netF is not None:
image_tensor_global = torch.cat([image_tensor_global, net.netG.nmlF], 0)
if net.netG.netB is not None:
image_tensor_global = torch.cat([image_tensor_global, net.netG.nmlB], 0)
except:
pass
b_min = data['b_min']
b_max = data['b_max']
try:
save_img_path = save_path[:-4] + '.png'
save_img_list = []
for v in range(image_tensor_global.shape[0]):
save_img = (np.transpose(image_tensor_global[v].detach().cpu().numpy(), (1, 2, 0)) * 0.5 + 0.5)[:, :, ::-1] * 255.0
save_img_list.append(save_img)
save_img = np.concatenate(save_img_list, axis=1)
cv2.imwrite(save_img_path, save_img)
verts, faces, _, _ = reconstruction(
net, cuda, calib_tensor, res, b_min, b_max, thresh, use_octree=use_octree, num_samples=50000)
verts_tensor = torch.from_numpy(verts.T).unsqueeze(0).to(device=cuda).float()
# if 'calib_world' in data:
# calib_world = data['calib_world'].numpy()[0]
# verts = np.matmul(np.concatenate([verts, np.ones_like(verts[:,:1])],1), inv(calib_world).T)[:,:3]
color = np.zeros(verts.shape)
interval = 50000
for i in range(len(color) // interval + 1):
left = i * interval
if i == len(color) // interval:
right = -1
else:
right = (i + 1) * interval
net.calc_normal(verts_tensor[:, None, :, left:right], calib_tensor[:,None], calib_tensor)
nml = net.nmls.detach().cpu().numpy()[0] * 0.5 + 0.5
color[left:right] = nml.T
save_obj_mesh_with_color(save_path, verts, faces, color)
except Exception as e:
print(e)
def gen_mesh_imgColor(res, net, cuda, data, save_path, thresh=0.5, use_octree=True, components=False):
image_tensor_global = data['img_512'].to(device=cuda)
image_tensor = data['img'].to(device=cuda)
calib_tensor = data['calib'].to(device=cuda)
net.filter_global(image_tensor_global)
net.filter_local(image_tensor[:,None])
try:
if net.netG.netF is not None:
image_tensor_global = torch.cat([image_tensor_global, net.netG.nmlF], 0)
if net.netG.netB is not None:
image_tensor_global = torch.cat([image_tensor_global, net.netG.nmlB], 0)
except:
pass
b_min = data['b_min']
b_max = data['b_max']
try:
save_img_path = save_path[:-4] + '.png'
save_img_list = []
for v in range(image_tensor_global.shape[0]):
save_img = (np.transpose(image_tensor_global[v].detach().cpu().numpy(), (1, 2, 0)) * 0.5 + 0.5)[:, :, ::-1] * 255.0
save_img_list.append(save_img)
save_img = np.concatenate(save_img_list, axis=1)
cv2.imwrite(save_img_path, save_img)
verts, faces, _, _ = reconstruction(
net, cuda, calib_tensor, res, b_min, b_max, thresh, use_octree=use_octree, num_samples=100000)
verts_tensor = torch.from_numpy(verts.T).unsqueeze(0).to(device=cuda).float()
# if this returns error, projection must be defined somewhere else
xyz_tensor = net.projection(verts_tensor, calib_tensor[:1])
uv = xyz_tensor[:, :2, :]
color = index(image_tensor[:1], uv).detach().cpu().numpy()[0].T
color = color * 0.5 + 0.5
if 'calib_world' in data:
calib_world = data['calib_world'].numpy()[0]
verts = np.matmul(np.concatenate([verts, np.ones_like(verts[:,:1])],1), inv(calib_world).T)[:,:3]
save_obj_mesh_with_color(save_path, verts, faces, color)
except Exception as e:
print(e)
def recon(opt, use_rect=False):
# load checkpoints
state_dict_path = None
if opt.load_netMR_checkpoint_path is not None:
state_dict_path = opt.load_netMR_checkpoint_path
elif opt.resume_epoch < 0:
state_dict_path = '%s/%s_train_latest' % (opt.checkpoints_path, opt.name)
opt.resume_epoch = 0
else:
state_dict_path = '%s/%s_train_epoch_%d' % (opt.checkpoints_path, opt.name, opt.resume_epoch)
start_id = opt.start_id
end_id = opt.end_id
cuda = torch.device('cuda:%d' % opt.gpu_id if torch.cuda.is_available() else 'cpu')
state_dict = None
if state_dict_path is not None and os.path.exists(state_dict_path):
print('Resuming from ', state_dict_path)
state_dict = torch.load(state_dict_path, map_location=cuda)
print('Warning: opt is overwritten.')
dataroot = opt.dataroot
resolution = opt.resolution
results_path = opt.results_path
loadSize = opt.loadSize
opt = state_dict['opt']
opt.dataroot = dataroot
opt.resolution = resolution
opt.results_path = results_path
opt.loadSize = loadSize
else:
raise Exception('failed loading state dict!', state_dict_path)
# parser.print_options(opt)
if use_rect:
test_dataset = EvalDataset(opt)
else:
test_dataset = EvalWPoseDataset(opt)
print('test data size: ', len(test_dataset))
projection_mode = test_dataset.projection_mode
opt_netG = state_dict['opt_netG']
netG = HGPIFuNetwNML(opt_netG, projection_mode).to(device=cuda)
netMR = HGPIFuMRNet(opt, netG, projection_mode).to(device=cuda)
def set_eval():
netG.eval()
# load checkpoints
netMR.load_state_dict(state_dict['model_state_dict'])
os.makedirs(opt.checkpoints_path, exist_ok=True)
os.makedirs(opt.results_path, exist_ok=True)
os.makedirs('%s/%s/recon' % (opt.results_path, opt.name), exist_ok=True)
if start_id < 0:
start_id = 0
if end_id < 0:
end_id = len(test_dataset)
## test
with torch.no_grad():
set_eval()
print('generate mesh (test) ...')
for i in tqdm(range(start_id, end_id)):
if i >= len(test_dataset):
break
# for multi-person processing, set it to False
if True:
test_data = test_dataset[i]
save_path = '%s/%s/recon/result_%s_%d.obj' % (opt.results_path, opt.name, test_data['name'], opt.resolution)
print(save_path)
gen_mesh(opt.resolution, netMR, cuda, test_data, save_path, components=opt.use_compose)
else:
for j in range(test_dataset.get_n_person(i)):
test_dataset.person_id = j
test_data = test_dataset[i]
save_path = '%s/%s/recon/result_%s_%d.obj' % (opt.results_path, opt.name, test_data['name'], j)
gen_mesh(opt.resolution, netMR, cuda, test_data, save_path, components=opt.use_compose)
def reconWrapper(args=None, use_rect=False):
opt = parser.parse(args)
recon(opt, use_rect)
if __name__ == '__main__':
reconWrapper()
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