def run_test():
if platform == 'linux':
os.environ['CUDA_VISIBLE_DEVICES'] = '2'
data_type = torch.float32
device = torch.device('cuda')
pose_size = 72
beta_size = 10
np.random.seed(9608)
model = SMPLModel(
device=device,
model_path='./model_lsp.pkl',
data_type=data_type,
)
dataset = Human36MDataset(model, max_item=100, calc_mesh=True)
# generate mesh, align with 14 point ground truth
case_num = 10
data = dataset[:case_num]
meshes = data['meshes']
input = data['lsp_joints']
target_2d = data['gt2d']
target_3d = data['gt3d']
transforms = map_3d_to_2d(input, target_2d, target_3d)
# Important: mesh should be centered at the origin!
deformed_meshes = transforms(meshes)
mesh_3d = deformed_meshes.detach().cpu().numpy()
# visualize(data['imagename'], mesh_3d[:,:,:2].astype(np.int),
# target_2d.detach().cpu().numpy().astype(np.int))
for i, mesh in enumerate(mesh_3d):
model.write_obj(mesh,
'_test_cache/real_mesh_{}.obj'.format(i)) # weird.
UV_position_map, UV_scatter, rgbs_backup = get_UV(mesh, 300)
# write colorized coordinates to ply
write_ply('_test_cache/colored_mesh_{}.ply'.format(i), mesh,
rgbs_backup)
out = np.concatenate((UV_position_map, UV_scatter), axis=1)
imsave('_test_cache/UV_position_map_{}.png'.format(i), out)
resampled_mesh = resample(UV_position_map)
model.write_obj(resampled_mesh,
'_test_cache/recon_mesh_{}.obj'.format(i))
def create_UV_maps(UV_label_root=None, uv_prefix='smpl_fbx_template'):
if platform == 'linux':
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
data_type = torch.float32
device=torch.device('cuda')
pose_size = 72
beta_size = 10
np.random.seed(9608)
model = SMPLModel(
device=device,
model_path='./model_lsp.pkl',
data_type=data_type,
)
dataset = Human36MWashedDataset(model, calc_mesh=True, root_dir='/home/wzeng/mydata/h3.6m/images_washed')
generator = UV_Map_Generator(
UV_height=256,
UV_pickle=uv_prefix+'.pickle'
)
# create root folder for UV labels
if UV_label_root is None:
UV_label_root=dataset.root_dir.replace('_washed',
'_UV_map_{}'.format(uv_prefix[:-9]))
if not os.path.isdir(UV_label_root):
os.makedirs(UV_label_root)
subs = [sub for sub in os.listdir(dataset.root_dir)
if os.path.isdir(dataset.root_dir + '/' + sub)]
for sub in subs:
os.makedirs(UV_label_root + '/' + sub)
else:
print('{} folder exists, process terminated...'.format(UV_label_root))
return
# generate mesh, align with 14 point ground truth
batch_size = 64
total_batch_num = dataset.length // batch_size + 1
_loop = tqdm(range(total_batch_num), ncols=80)
for batch_id in _loop:
data = dataset[batch_id * batch_size: (batch_id + 1) * batch_size]
meshes = data['meshes']
input = data['lsp_joints']
target_2d = data['gt2d']
target_3d = data['gt3d']
imagename = [UV_label_root + str for str in data['imagename']]
transforms = map_3d_to_2d(input, target_2d, target_3d)
# Important: mesh should be centered at the origin!
deformed_meshes = transforms(meshes)
mesh_3d = deformed_meshes.detach().cpu().numpy()
'''
test_folder = '_test_radvani'
if not os.path.isdir(test_folder):
os.makedirs(test_folder)
visualize(test_folder, data['imagename'], mesh_3d[:,:,:2].astype(np.int),
target_2d.detach().cpu().numpy().astype(np.int), dataset.root_dir)
'''
s=time()
for name, mesh in zip(imagename, mesh_3d):
UV_position_map, verts_backup = \
generator.get_UV_map(mesh)
imwrite(name, (UV_position_map * 255).astype(np.uint8))
# write colorized coordinates to ply
'''
def run_test():
if platform == 'linux':
os.environ['CUDA_VISIBLE_DEVICES'] = '2'
data_type = torch.float32
device = torch.device('cuda')
pose_size = 72
beta_size = 10
np.random.seed(9608)
model = SMPLModel(
device=device,
model_path='./model_lsp.pkl',
data_type=data_type,
)
dataset = Human36MDataset(model, max_item=100, calc_mesh=True)
# generate mesh, align with 14 point ground truth
case_num = 10
data = dataset[:case_num]
meshes = data['meshes']
input = data['lsp_joints']
target_2d = data['gt2d']
target_3d = data['gt3d']
transforms = map_3d_to_2d(input, target_2d, target_3d)
# Important: mesh should be centered at the origin!
deformed_meshes = transforms(meshes)
mesh_3d = deformed_meshes.detach().cpu().numpy()
file_prefix = 'smpl_fbx_template'
generator = UV_Map_Generator(UV_height=256,
UV_pickle=file_prefix + '.pickle')
test_folder = '_test_smpl_fbx'
if not os.path.isdir(test_folder):
os.makedirs(test_folder)
visualize(test_folder, data['imagename'], mesh_3d[:, :, :2].astype(np.int),
target_2d.detach().cpu().numpy().astype(np.int))
s = time()
for i, mesh in enumerate(mesh_3d):
model.write_obj(mesh, '{}/real_mesh_{}.obj'.format(test_folder,
i)) # weird.
UV_position_map, verts_backup = \
generator.get_UV_map(mesh)
# write colorized coordinates to ply
UV_scatter, _, _ = generator.render_point_cloud(verts=mesh)
generator.write_ply('{}/colored_mesh_{}.ply'.format(test_folder, i),
mesh)
out = np.concatenate((UV_position_map, UV_scatter), axis=1)
imsave('{}/UV_position_map_{}.png'.format(test_folder, i), out)
resampled_mesh = generator.resample(UV_position_map)
model.write_obj(resampled_mesh,
'{}/recon_mesh_{}.obj'.format(test_folder, i))
print('{} cases for {}s'.format(case_num, time() - s))