def __init__(self, opt, output_dir):
super().__init__(output_dir)
self.opt = opt
if self.opt.ip:
visualizer = VisdomVisualizer(env=self.opt.name,
ip=self.opt.ip,
port=self.opt.port)
else:
visualizer = None
self.visualizer = visualizer
self.model = None
def generate_orig_pose_novel_view_result(opt, src_path):
opt.src_path = src_path
# set imitator
viewer = Viewer(opt)
if opt.ip:
visualizer = VisdomVisualizer(env=opt.name, ip=opt.ip, port=opt.port)
else:
visualizer = None
if opt.post_tune:
adaptive_personalize(opt, viewer, visualizer)
viewer.personalize(opt.src_path, visualizer=visualizer)
print('\n\t\t\tPersonalization: completed...')
view_params = opt.view_params
params = parse_view_params(view_params)
length = 180
delta = 360 / length
logger = tqdm(range(length))
src_img_true_name = os.path.split(opt.src_path)[-1][:-4]
save_dir = os.path.join(opt.output_dir, src_img_true_name)
mkdir(os.path.join(save_dir, 'imgs'))
print('\n\t\t\tSynthesizing {} novel views'.format(length))
for i in logger:
params['R'][0] = 0
params['R'][1] = delta * i / 180.0 * np.pi
params['R'][2] = 0
preds = viewer.view(params['R'], params['t'], visualizer=None, name=str(i))
# pred_outs.append(preds)
save_img_name = '%s.%d.jpg' % (os.path.split(opt.src_path)[-1], delta * i)
cv2.imwrite('%s/imgs/%s' % (save_dir, save_img_name), tensor2cv2(preds))
"""
make video
"""
img_path_list = glob.glob("%s/imgs/*.jpg" % save_dir)
output_mp4_path = '%s/%s.mp4' % (save_dir, src_img_true_name)
make_video(output_mp4_path, img_path_list, save_frames_dir=None, fps=30)
clean(opt.output_dir)
clean(save_dir)
preds = preds.cpu().numpy()
filepath = os.path.join(output_dir,
'{}->{}.png'.format(src_name, tgt_name))
cv_utils.save_cv2_img(preds, filepath, normalize=True)
print('\n\t\t\tSaving results to {}'.format(filepath))
if __name__ == "__main__":
opt = TestOptions().parse()
# set imitator
swapper = Swapper(opt=opt)
if opt.ip:
visualizer = VisdomVisualizer(env=opt.name, ip=opt.ip, port=opt.port)
else:
visualizer = None
src_path = opt.src_path
tgt_path = opt.tgt_path
swapper.swap_setup(src_path, tgt_path)
if opt.post_tune:
print('\n\t\t\tPersonalization: meta cycle finetune...')
swapper.post_personalize(opt.output_dir,
visualizer=None,
verbose=False)
print('\n\t\t\tPersonalization: completed...')
# post tune
print('\n\t\t\tPersonalization: meta cycle finetune...')
loader = make_dataset(opt)
imitator.post_personalize(opt.output_dir,
loader,
visualizer=None,
verbose=False)
if __name__ == "__main__":
# meta imitator
test_opt = TestOptions().parse()
if test_opt.ip:
visualizer = VisdomVisualizer(env=test_opt.name,
ip=test_opt.ip,
port=test_opt.port)
else:
visualizer = None
# set imitator
imitator = Imitator(test_opt)
if test_opt.post_tune:
adaptive_personalize(test_opt, imitator, visualizer)
imitator.personalize(test_opt.src_path, visualizer=visualizer)
print('\n\t\t\tPersonalization: completed...')
if test_opt.save_res:
pred_output_dir = mkdir(os.path.join(test_opt.output_dir, 'imitators'))
import numpy as np
import os
import os.path as osp
import glob
from tqdm import tqdm
import time
from utils.visdom_visualizer import VisdomVisualizer
from utils.nmr import SMPLRenderer
from networks.hmr import HumanModelRecovery
os.environ['CUDA_DEVICES_ORDER'] = 'PCI_BUS_ID'
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
IMG_SIZE = 256
visualizer = VisdomVisualizer(env='visual',
ip='http://10.10.10.100',
port=31102)
def visual(model, out_dir):
global visualizer
render = SMPLRenderer(image_size=IMG_SIZE).cuda()
texs = render.debug_textures().cuda()[None]
with h5py.File(osp.join(out_dir, 'smpl_infos.h5'), 'r') as reader:
cams_crop = reader['cam_crop']
poses = reader['pose']
shapes = reader['shape']
frame_ids = reader['f_id']
params[name] = np.array(vals, dtype=np.float32)
params['R'] = params['R'] / 180 * np.pi
return params
if __name__ == "__main__":
opt = TestOptions().parse()
# set imitator
viewer = Viewer(opt=opt)
if opt.ip:
visualizer = VisdomVisualizer(env=opt.name, ip=opt.ip, port=opt.port)
else:
visualizer = None
if opt.post_tune:
adaptive_personalize(opt, viewer, visualizer)
viewer.personalize(opt.src_path, visualizer=visualizer)
print('\n\t\t\tPersonalization: completed...')
src_path = opt.src_path
view_params = opt.view_params
params = parse_view_params(view_params)
length = 16
delta = 360 / length